Electrical and Electronics Engineering publications abstract of: 05-2017 sorted by title, page: 14

» Protocol Design and Game Theoretic Solutions for Device-to-Device Radio Resource Allocation
Abstract:
Device-to-device (D2D) communication has been proposed to improve the resource efficiency and lighten the heavy load of the base station (BS) in Long-Term Evolution (LTE)-Advanced systems. In a D2D-enabled LTE-A system, the resource efficiency is primarily determined by D2D/cellular mode selection and resource allocation. However, the D2D channel quality, which is the key factor for the BS to allocate resources, cannot be learned directly by the BS, owing to the peculiarity of D2D communication. Rational user equipment (UE) will take advantage of the peculiarity to report their experienced D2D quality untruthfully for their selfish interests and, consequently, degrade system efficiency. This so-called unknown channel quality (UCQ) problem imposes a fatal impact on the resource efficiency and will limit the practicality of D2D communication. To overcome the UCQ problem, we propose to use game theory to analyze the peculiarity of D2D communication. In this paper, two practical D2D resource allocating protocols were investigated, and the system efficiencies were analyzed to show the potential performance degradation when the UCQ problem is not addressed. To circumvent the performance degradation caused by the UCQ problem, a contract-based mechanism and the corresponding algorithms were proposed to eliminate the UE's incentive of reporting untruthfully. Numerical and simulation results validated the feasibility and the effectiveness of our approach.
Autors: Shih-Tang Su;Bo-Yuan Huang;Chih-Yu Wang;Che-Wei Yeh;Hung-Yu Wei;
Appeared in: IEEE Transactions on Vehicular Technology
Publication date: May 2017, volume: 66, issue:5, pages: 4271 - 4286
Publisher: IEEE
 
» Pt-AlGaN/GaN Hydrogen Sensor With Water-Blocking PMMA Layer
Abstract:
One of the biggest issues with GaN-based hydrogen sensors is their sensitivity to humidity in the ambient. We demonstrate that encapsulation of Pt-AlGaN/GaN Schottky diode with poly(methyl methacrylate) (PMMA) provides effective mitigation of the effects of water. Without PMMA encapsulation, the absolute current signal for detection of 500 ppm of H2 was decreased by a factor of 8 in the presence of water. By sharp contrast, encapsulated diodes show no decrease in response in the presence of water. The relative current changes are of the order % when 500 ppm H2 is introduced to the surface of bare or PMMA encapsulated diodes in the absence of water or to encapsulated diode in the presence of water. Detection limits of ~ 100 ppm H2 (0.01% by volume) were obtained with standard forward bias detection mode at 1.3 V.
Autors: Sunwoo Jung;Kwang Hyeon Baik;Fan Ren;Stephen J. Pearton;Soohwan Jang;
Appeared in: IEEE Electron Device Letters
Publication date: May 2017, volume: 38, issue:5, pages: 657 - 660
Publisher: IEEE
 
» Purchase Bidding Strategy for a Retailer With Flexible Demands in Day-Ahead Electricity Market
Abstract:
The paper aims to determine the day-ahead market bidding strategies for retailers with flexible demands to maximize the short-term profit. It proposes a short-term planning framework to forecast the load under dynamic tariffs and construct biding curves. Stochastic programming is applied to manage the uncertainties of spot price, regulating price, consumption behaviors, and responsiveness to dynamic tariffs. A case study based on data from Sweden is carried out. It demonstrates that a real-time selling price can affect the aggregate load of a residential consumer group and lead to load shift toward low-price periods. The optimal bidding curves for specific trading periods are illustrated. Through comparing the bidding strategies under different risk factors, the case study shows that a risk-averse retailer tends to adopt the strategies with larger imbalances. The benefit lies in the reduction of low-profit risk. However, the aversion to risk can only be kept in a certain level. A larger imbalance may lead to a quick reduction of profit in all scenarios.
Autors: Meng Song;Mikael Amelin;
Appeared in: IEEE Transactions on Power Systems
Publication date: May 2017, volume: 32, issue:3, pages: 1839 - 1850
Publisher: IEEE
 
» QoE Enhanced Mobile Data Offloading With Balking
Abstract:
In this letter, we analyze the delayed mobile data offloading scheme by modeling it as an alternating regenerative process. We use a novel way for deriving transmission delay and offloading efficiency, by analyzing the stochastic process characterizing the virtual waiting time. Furthermore, we propose a delayed offloading scheme with balking to enhance quality of experience. The balking scheme is characterized based on 1) current WLAN status; 2) number of packets queued for transmission; and 3) packet deadline. The proposed scheme achieves reduction in mean transmission delay without sacrificing much of the offloading efficiency. The performance of the scheme is mathematically analyzed using matrix-geometric techniques and validated through simulation.
Autors: Anusree Ajith;T. G. Venkatesh;
Appeared in: IEEE Communications Letters
Publication date: May 2017, volume: 21, issue:5, pages: 1143 - 1146
Publisher: IEEE
 
» QoS-Constrained Relay Control for Full-Duplex Relaying With SWIPT
Abstract:
This study investigates relay control for simultaneous wireless information and power transfer in full-duplex relay networks under Nakagami- fading channels. Unlike previous work, harvest-transmit (HT) and general harvest-transmit-store (HTS) models are respectively considered to maximize average throughput subject to quality of service (QoS) constraints. The end-to-end outage probability of the network in an HT model is presented in an exact integral-form. To prevent outage performance degradation in an HT model, time switching (TS) is designed to maximize average throughput subject to QoS constraints of minimizing outage probability and maintaining a target outage probability, respectively. The optimal TS factors subject to QoS constraints are presented for an HT model. In general, in an HTS model, energy scheduling is performed across different transmission blocks and TS is performed within each block. Compared with the block-based HTS model without TS, the proposed general HTS model can greatly improve outage performance via greedy search (GS). By modeling the relay’s energy levels as a Markov chain with a two-stage state transition, the outage probability for the GS implementation of the general HTS model is derived. To demonstrate the practical significance of QoS-constrained relay control, numerical results are presented showing that the proposed relay control achieves substantial improvement of outage performance and successful rate.
Autors: Hongwu Liu;Kyeong Jin Kim;Kyung Sup Kwak;H. Vincent Poor;
Appeared in: IEEE Transactions on Wireless Communications
Publication date: May 2017, volume: 16, issue:5, pages: 2936 - 2949
Publisher: IEEE
 
» QoS-Driven Resource Allocation and EE-Balancing for Multiuser Two-Way Amplify-and-Forward Relay Networks
Abstract:
In this paper, we study the problem of energy-efficient resource allocation in multiuser two-way amplify-and-forward (AF) relay networks with the aim of maximizing the energy efficiency (EE), while ensuring the quality-of-service (QoS) requirements and balancing the EE of the user links. We formulate an EE-balancing optimization problem that maximizes the ratio of the spectral efficiency (SE) over the total power dissipation subject to QoS and a limited transmit power constraints. The problem which maximizes the EE by jointly optimizing the subcarrier pairing, power allocation, and subcarrier allocation, turns out to be a non-convex fractional mixed-integer nonlinear programming problem, which has an intractable complexity in general. We apply a concave lower bound on the achievable sum rate and a series of convex transformations to make the problem convex one and propose an iterative algorithm for iteratively tightening the lower bound and finding the optimal solution through dual decomposition approach. In addition, a low-complexity suboptimal algorithm is investigated. We then characterize the impact of various network parameters on the attainable EE and SE of the network employing both EE maximization and SE maximization algorithms when the network is designed from the EE perspective. Simulation results demonstrate the effectiveness of the proposed algorithms.
Autors: Keshav Singh;Ankit Gupta;Tharmalingam Ratnarajah;
Appeared in: IEEE Transactions on Wireless Communications
Publication date: May 2017, volume: 16, issue:5, pages: 3189 - 3204
Publisher: IEEE
 
» Quadrature Spatial Modulation for 5G Outdoor Millimeter–Wave Communications: Capacity Analysis
Abstract:
Capacity analysis for millimeter–wave (mmWave) quadrature spatial modulation (QSM) multiple-input multiple-output (MIMO) system is presented in this paper. QSM is a new MIMO technique proposed to enhance the performance of conventional spatial modulation (SM) while retaining almost all its inherent advantages. Furthermore, mmWave utilizes a wide-bandwidth spectrum and is a very promising candidate for future wireless systems. Detailed and novel analysis of the mutual information and the achievable capacity for mmWave–QSM system using a 3-D statistical channel model for outdoor mmWave communications are presented in this paper. Monte Carlo simulation results are provided to corroborate derived formulas. Obtained results reveal that the 3-D mmWave channel model can be closely approximated by a log–normal fading channel. The conditions under which capacity can be achieved are derived and discussed. It is shown that the capacity of QSM system can be achieved, by carefully designing the constellation symbols for each specific channel model.
Autors: Abdelhamid Younis;Nagla Abuzgaia;Raed Mesleh;Harald Haas;
Appeared in: IEEE Transactions on Wireless Communications
Publication date: May 2017, volume: 16, issue:5, pages: 2882 - 2890
Publisher: IEEE
 
» Quantification of External Heat Load on HEV Integrated IPMs Using the Air-Gap Shear Stress
Abstract:
State-of-the-art design methods for electric machines use published correlations of either power or torque with electromagnetic and current loading, available for different cooling approaches. However, the applicability of these correlations for electric machines used within hybrid electric vehicles, with their different degrees of integration into the overall system, is limited. Using an integrated starter generator and an electric rear axle drive as example case applications, we study the influences of the thermal environment within the hybrid electric vehicle application, i.e., heat sinks and sources external to the machine on the thermal design of the machines. We quantify these influences by scaling factors for the air-gap shear stress of the machine.
Autors: Christian Paar;Annette Muetze;
Appeared in: IEEE Transactions on Industry Applications
Publication date: May 2017, volume: 53, issue:3, pages: 1909 - 1919
Publisher: IEEE
 
» Quantifying Nonlinear Contributions to Cortical Responses Evoked by Continuous Wrist Manipulation
Abstract:
Cortical responses to continuous stimuli as recorded using either magneto- or electroencephalography (EEG) have shown power at harmonics of the stimulated frequency, indicating nonlinear behavior. Even though the selection of analysis techniques depends on the linearity of the system under study, the importance of nonlinear contributions to cortical responses has not been formally addressed. The goal of this paper is to quantify the nonlinear contributions to the cortical response obtained from continuous sensory stimulation. EEG was used to record the cortical response evoked by continuous movement of the wrist joint of healthy subjects applied with a robotic manipulator. Multisine stimulus signals (i.e., the sum of several sinusoids) elicit a periodic cortical response and allow to assess the nonlinear contributions to the response. Wrist dynamics (relation between joint angle and torque) were successfully linearized, explaining 99% of the response. In contrast, the cortical response revealed a highly nonlinear relation; where most power (%) occurred at non-stimulated frequencies. Moreover, only 10% of the response could be explained using a nonparametric linear model. These results indicate that the recorded evoked cortical responses are governed by nonlinearities and that linear methods do not suffice when describing the relation between mechanical stimulus and cortical response.
Autors: Martijn P. Vlaar;Teodoro Solis-Escalante;Alistair N. Vardy;Frans C. T. van der Helm;Alfred C. Schouten;
Appeared in: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Publication date: May 2017, volume: 25, issue:5, pages: 481 - 491
Publisher: IEEE
 
» Quantization Design for Distributed Optimization
Abstract:
We consider the problem of solving a distributed optimization problem using a distributed computing platform, where the communication in the network is limited: each node can only communicate with its neighbors and the channel has a limited data-rate. A common technique to address the latter limitation is to apply quantization to the exchanged information. We propose two distributed optimization algorithms with an iteratively refining quantization design based on the inexact proximal gradient method and its accelerated variant. We show that if the parameters of the quantizers, i.e., the number of bits and the initial quantization intervals, satisfy certain conditions, then the quantization error is bounded by a linearly decreasing function and the convergence of the distributed algorithms is guaranteed. Furthermore, we prove that after imposing the quantization scheme, the distributed algorithms still exhibit a linear convergence rate, and show complexity upper-bounds on the number of iterations to achieve a given accuracy. Finally, we demonstrate the performance of the proposed algorithms and the theoretical findings for solving a distributed optimal control problem.
Autors: Ye Pu;Melanie N. Zeilinger;Colin N. Jones;
Appeared in: IEEE Transactions on Automatic Control
Publication date: May 2017, volume: 62, issue:5, pages: 2107 - 2120
Publisher: IEEE
 
» R-VCANet: A New Deep-Learning-Based Hyperspectral Image Classification Method
Abstract:
Deep-learning-based methods have displayed promising performance for hyperspectral image (HSI) classification, due to their capacity of extracting deep features from HSI. However, these methods usually require a large number of training samples. It is quite difficult for deep-learning model to provide representative feature expression for HSI data when the number of samples are limited. In this paper, a novel simplified deep-learning model, rolling guidance filter (RGF) and vertex component analysis network (R-VCANet), is proposed, which achieves higher accuracy when the number of training samples is not abundant. In R-VCANet, the inherent properties of HSI data, spatial information and spectral characteristics, are utilized to construct the network. And by this means the obtained model could generate more powerful feature expression with less samples. First, spectral and spatial information are combined via the RGF, which could explore the contextual structure features and remove small details from HSI. More importantly, we have designed a new network called vertex component analysis network for deep features extraction from the smoothed HSI. Experiments on three popular datasets indicate that the proposed R-VCANet based method reveals better performance than some state-of-the-art methods, especially when the training samples available are not abundant.
Autors: Bin Pan;Zhenwei Shi;Xia Xu;
Appeared in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication date: May 2017, volume: 10, issue:5, pages: 1975 - 1986
Publisher: IEEE
 
» Radio Environment Map-Aided Doppler Shift Estimation in LTE Railway
Abstract:
Due to the high mobility of high-speed trains (HSTs), Doppler shift estimation has been a big challenge for HSTs. In this paper, we consider an orthogonal frequency-division multiplexing (OFDM) system based on the long-term evolution (LTE) railway standard and design the novel Doppler shift estimation algorithm. By exploiting features of HSTs, i.e., regular and repetitive routes and timetables, resulting in a predictable Doppler shift curve, a radio environment map (REM) including the Doppler shift information can be constructed via field tests. Based on REM, a maximum a posteriori estimator (MAPE) is proposed to provide an accurate estimation of Doppler shift. It uses the estimation from REM (REME) as a priori knowledge and exploits the cyclic prefix (CP) structure of OFDM to provide a maximum a posteriori estimation. The Cramer–Rao lower bounds (CRLBs) are derived. The performance of MAPE is evaluated via simulations and compared to that of REME, the classical CP-based estimator, and other existing methods. It is shown that MAPE significantly outperforms the existing methods in terms of both estimation mean square error (MSE) and bit error rate.
Autors: Zhanwei Hou;Yiqing Zhou;Lin Tian;Jinglin Shi;Yonghui Li;Branka Vucetic;
Appeared in: IEEE Transactions on Vehicular Technology
Publication date: May 2017, volume: 66, issue:5, pages: 4462 - 4467
Publisher: IEEE
 
» Radio Frequency Interference Mitigation in High-Frequency Surface Wave Radar Based on CEMD
Abstract:
Radio frequency interference (RFI) is a common interference source in high-frequency surface wave (HFSW) radar. Its existence degrades the performance of HFSW radar greatly and makes it necessary to find an effective method to mitigate the interferences. There are two kinds of RFI in the experimental data. One is transient RFI, which is usually suppressed by temporal processing. The other is nontransient RFI, which is suppressed by adaptive beamforming methods. However, the temporal processing techniques suffer performance loss in nontransient cases, whereas the adaptive beamforming methods need spatially structuring, which is difficult to meet within the coherent integration time (CIT) of a few minutes. The fact that the experimental data are usually interfered by two kinds of RFI over a CIT motivates us to find a unified method for interference mitigation. In this letter, a new method based on complex empirical mode decomposition (CEMD) is proposed. CEMD is a local decomposition algorithm that can decompose the echoes and the RFI including transient and nontransient RFI into different intrinsic mode functions (IMFs). Then, the IMFs that correspond to RFI are processed. Experimental results indicate that the proposed method can effectively mitigate both kinds of RFI, and improve the signal-to-noise ratio without losing echoes.
Autors: Zezong Chen;Fei Xie;Chen Zhao;Chao He;
Appeared in: IEEE Geoscience and Remote Sensing Letters
Publication date: May 2017, volume: 14, issue:5, pages: 764 - 768
Publisher: IEEE
 
» Radiotherapy Planning Using an Improved Search Strategy in Particle Swarm Optimization
Abstract:
Objective: Evolutionary stochastic global optimization algorithms are widely used in large-scale, nonconvex problems. However, enhancing the search efficiency and repeatability of these techniques often requires well-customized approaches. This study investigates one such approach. Methods: We use particle swarm optimization (PSO) algorithm to solve a 4D radiation therapy (RT) inverse planning problem, where the key idea is to use respiratory motion as an additional degree of freedom in lung cancer RT. The primary goal is to administer a lethal dose to the tumor target while sparing surrounding healthy tissue. Our optimization iteratively adjusts radiation fluence-weights for all beam apertures across all respiratory phases. We implement three PSO-based approaches: conventionally used unconstrained, hard-constrained, and our proposed virtual search. As proof of concept, five lung cancer patient cases are optimized over ten runs using each PSO approach. For comparison, a dynamically penalized likelihood (DPL) algorithm—a popular RT optimization technique is also implemented and used. Results: The proposed technique significantly improves the robustness to random initialization while requiring fewer iteration cycles to converge across all cases. DPL manages to find the global optimum in 2 out of 5 RT cases over significantly more iterations. Conclusion: The proposed virtual search approach boosts the swarm search efficiency, and consequently, improves the optimization convergence rate and robustness for PSO. Significance: RT planning is a large-scale, nonconvex optimization problem, where finding optimal solutions in a clinically practical time is critical. Our proposed approach can potentially improve the optimization efficiency in similar time-sensitive problems.
Autors: Arezoo Modiri;Xuejun Gu;Aaron M. Hagan;Amit Sawant;
Appeared in: IEEE Transactions on Biomedical Engineering
Publication date: May 2017, volume: 64, issue:5, pages: 980 - 989
Publisher: IEEE
 
» Railway Traffic Conflict Detection via a State Transition Prediction Approach
Abstract:
Conflict detection and resolution is one of the most important tasks in daily railway traffic management, although it is still difficult to solve all its aspects. In fact, the aspect of conflict detection has not been amply studied. In this paper, an approach of traffic state prediction and conflict detection, based on proper state transition maps (STMaps) and corresponding relation matrices, is proposed. First, the traffic state sequences, which mainly concern infrastructure status and train movement information, are studied. These state sequences are expressed as segment and route state vectors and kept in corresponding state-domain tables (SDTables). The empirical state transitions are then applied to detect irregular states in a dynamic traffic environment. Furthermore, the structural constraints of infrastructure topology and route compatibilities are represented in matrices to aid the calculation and prediction of potential conflicting situations. Scenarios such as train delay and infrastructure failure are designed to test the proposed approach. The test results show that irregular states can be efficiently detected and potential conflicts can be further identified, and the detailed conflict information is also approachable.
Autors: Taomei Zhu;José Manuel Mera Sánchez de Pedro;
Appeared in: IEEE Transactions on Intelligent Transportation Systems
Publication date: May 2017, volume: 18, issue:5, pages: 1268 - 1278
Publisher: IEEE
 
» Rakeness-Based Design of Low-Complexity Compressed Sensing
Abstract:
Compressed Sensing (CS) can be introduced in the processing chain of a sensor node as a mean to globally reduce its operating cost, while maximizing the quality of the acquired signal. We exploit CS as a simple early-digital compression stage that performs a multiplication of the signal by a matrix. The operating costs (e.g., the consumed power) of such an encoding stage depend on the number of rows of the matrix, but also on the value and position of the rows’ coefficients. Our novel design flow yields optimized sparse matrices with very few rows. It is a non-trivial extension of the rakeness-based approach to CS and yields an extremely lightweight stage implemented by a very small number of possibly signed sums with an excellent compression performance. By means of a general signal model we explore different corners of the design space and show that, for example, our method is capable of compressing the signal by a factor larger than 2.5 while not considering 30% of the original samples (so that they may not be acquired at all, leaving the analog front-end and ADC stages inactive) and by processing each of the considered samples with not more than three signed sums.
Autors: Mauro Mangia;Fabio Pareschi;Valerio Cambareri;Riccardo Rovatti;Gianluca Setti;
Appeared in: IEEE Transactions on Circuits and Systems I: Regular Papers
Publication date: May 2017, volume: 64, issue:5, pages: 1201 - 1213
Publisher: IEEE
 
» RAMSYS: Resource-Aware Asynchronous Data Transfer with Multicore SYStems
Abstract:
High-speed data transfer is vital to data-intensive computing that often requires moving large data volumes efficiently within a local data center and among geographically dispersed facilities. Effective utilization of the abundant resources in modern multicore environments for data transfer remains a persistent challenge, particularly, for Non-Uniform Memory Access (NUMA) systems wherein the locality of data accessing is an important factor. This requires rethinking how to exploit parallel access to data and to optimize the storage and network I/Os. We address this challenge and present a novel design of asynchronous processing and resource-aware task scheduling in the context of high-throughput data replication. Our software allocates multiple sets of threads to different stages of the processing pipeline, including storage I/O and network communication, based on their capacities. Threads belonging to each stage follow an asynchronous model, and attain high performance via multiple locality-aware and peer-aware mechanisms, such as task grouping, buffer sharing, affinity control and communication protocols. Our design also integrates high performance features to enhance the scalability of data transfer in several scenarios, e.g., file-level sorting, block-level asynchrony, and thread-level pipelining. Our experiments confirm the advantages of our software under different types of workloads and dynamic environments with contention for shared resources, including a 28-160 percent increase in bandwidth for transferring large files, 1.7-66 times speed-up for small files, and up to 108 percent larger throughput for mixed workloads compared with three state of the art alternatives, GridFTP , BBCP and Aspera.
Autors: Tan Li;Yufei Ren;Dantong Yu;Shudong Jin;
Appeared in: IEEE Transactions on Parallel and Distributed Systems
Publication date: May 2017, volume: 28, issue:5, pages: 1430 - 1444
Publisher: IEEE
 
» Random Access Protocols for Massive MIMO
Abstract:
5G wireless networks are expected to support new services with stringent requirements on data rates, latency and reliability. One novel feature is the ability to serve a dense crowd of devices, calling for radically new ways of accessing the network. This is the case in machine-type communications, but also in urban environments and hotspots. In those use cases, the high number of devices and the relatively short channel coherence interval do not allow per-device allocation of orthogonal pilot sequences. This article addresses the need for random access by the devices to pilot sequences used for channel estimation, and shows that Massive MIMO is a main enabler to achieve fast access with high data rates, and delay-tolerant access with different data rate levels. Three pilot access protocols along with data transmission protocols are described, fulfilling different requirements of 5G services.
Autors: Elisabeth de Carvalho;Emil Bjornson;Jesper H. Sorensen;Petar Popovski;Erik G. Larsson;
Appeared in: IEEE Communications Magazine
Publication date: May 2017, volume: 55, issue:5, pages: 216 - 222
Publisher: IEEE
 
» Rapid Detection of pM Concentration of Insulin Using Microwave Whispering Gallery Mode
Abstract:
In this paper, we are going to investigate insulin, a bio-molecule responsible for the onset of diabetes, which is generally diagnosed using the conventional ELISA method. Its picomolar concentration in blood and short life time was a challenge in developing a sensor sensitive to such small volumes. This paper presents an experimental study of the dielectric properties of hepes buffer solution with varying concentration of insulin, a peptide hormone at microwave frequency range. A composite whispering gallery electric mode () microwave dielectric resonator with sapphire and polycarbonate disposable sample holding disc has been used at 17.59 GHz. From observations, imaginary part of permittivity has been found very sensitive to changes in insulin concentration ranging from to . Studies show that the present method has the potential to detect and sense ultralow concentration of bioanalyte with regression coefficient 0.943 and sensitivity of 0.039 for 10 pM change in insulin concentration.
Autors: Ritika Verma;K. S. Daya;
Appeared in: IEEE Sensors Journal
Publication date: May 2017, volume: 17, issue:9, pages: 2758 - 2765
Publisher: IEEE
 
» Rapid Nondestructive-Testing Technique for In-Line Quality Control of Li-Ion Batteries
Abstract:
Quality control in the production of automotive Li-ion cells is essential for both safety and economic reasons. At present, as part of the production process, it is common practice to store Li-ion cells for up to two weeks to analyze self-discharge performance and to subject sample cells to months of cycling to assess lifetime performance. This paper presents a new state-of-the-art nondestructive testing technique for automotive scale, Li-ion batteries. Importantly, the test can discriminate between viable and nonviable cells in less than one minute. This is significantly quicker than many industrially applied techniques. The proposed method, developed in partnership with three independent original equipment manufacturer automotive Li-ion cell manufacturers, uses empirical data gathered off-line for benchmarking cell response followed by a unique targeting process to reduce the test time to a level compatible with industrial manufacturing processes. The technique used is a targeted form of electrochemical impedance spectroscopy (EIS) using a commercially available potentiostat with EIS capability. The novel aspect of the research is the treatment of off-line empirical data, the construction of an empirical library database, and the development of a reliable and robust in-line test procedure. For reasons of commercial sensitivity, no knowledge of the underlying chemistry of the cells is available for use. This demonstrates the functionality of the proposed method across a range of different cell technologies and its applicability to multiple battery technologies.
Autors: Simon M. Lambert;Matthew Armstrong;Pierrot S. Attidekou;Paul A. Christensen;James D. Widmer;Chen Wang;Keith Scott;
Appeared in: IEEE Transactions on Industrial Electronics
Publication date: May 2017, volume: 64, issue:5, pages: 4017 - 4026
Publisher: IEEE
 
» Ray Tracing and Modal Methods for Modeling Radio Propagation in Tunnels With Rough Walls
Abstract:
At the ultrahigh frequencies common to portable radios, tunnels such as mine entries are often modeled by hollow dielectric waveguides. The roughness condition of the tunnel walls has an influence on radio propagation, and therefore should be taken into account when an accurate power prediction is needed. This paper investigates how wall roughness affects radio propagation in tunnels, and presents a unified ray tracing and modal method for modeling radio propagation in tunnels with rough walls. First, general analytical formulas for modeling the influence of the wall roughness are derived, based on the modal method and the ray tracing method, respectively. Second, the equivalence of the ray tracing and modal methods in the presence of wall roughnesses is mathematically proved, by showing that the ray tracing-based analytical formula can converge to the modal-based formula through the Poisson summation formula. The derivation and findings are verified by simulation results based on ray tracing and modal methods.
Autors: Chenming Zhou;
Appeared in: IEEE Transactions on Antennas and Propagation
Publication date: May 2017, volume: 65, issue:5, pages: 2624 - 2634
Publisher: IEEE
 
» Ray-Tracing-Assisted Fingerprinting Based on Channel Impulse Response Measurement for Indoor Positioning
Abstract:
Position fingerprinting (FP), in which a common position signature is based on the received signal strength (RSS), is one of the most efficient indoor positioning methods. Another position signature, known as the channel impulse response (CIR), is regarded as a linear temporal filter, which characterizes the multipath channel of the operating environment. We implement a channel sounder based on an orthogonal frequency-division multiplexing system to collect off-line/online CIR measurements and develop a ray-tracing (RT) channel predictor to capture the main characteristics of the channel for the off-line predicted database. We are the first to utilize RT as a channel predictor to assist indoor FP using CIR measurements. We utilize coarse localization to classify the reference points (RPs) based on the access point with the strongest RSS. We propose an RT-assisted FP (RAFP) method, in which we estimate a position by fusing the measured and predicted signatures to find the RPs with the highest correlation values between the online measurement and the off-line measured and simulated CIR databases. Experimental results show that the RAFP—positioning with a hybrid of the predicted and measured CIR—reduces the FP localization error by 25%. By incorporating simulated CIRs, the RAFP has the advantages in reducing human labor for off-line measurement collection and in using less number of CIR measurements to maintain a satisfactory performance. The results encourage a further development to reduce the cost by replacing the sounding system with the wireless network interface cards for a scalable deployment.
Autors: Po-Hsuan Tseng;Yao-Chia Chan;Yi-Jie Lin;Ding-Bing Lin;Nan Wu;Tsai-Mao Wang;
Appeared in: IEEE Transactions on Instrumentation and Measurement
Publication date: May 2017, volume: 66, issue:5, pages: 1032 - 1045
Publisher: IEEE
 
» Reagent Addition Control for Stibium Rougher Flotation Based on Sensitive Froth Image Features
Abstract:
The reagent addition control level of stibium rougher flotation has a significant impact on the performance of cleaner and scavenger flotation. Currently, due to the complexity of the froth flotation process, the addition rates of reagents, which are controlled by operators, are often not regulated properly in time. Therefore, it is necessary to study a control strategy for the reagent addition rates. This paper proposes a sensitive froth image feature (FIF)-based control strategy that involves an estimator of the feed grade, a preset controller, and a feedback controller. Because the addition rates of reagents should be regulated according to the feed grade, an estimator model for feed grade is built based on the probabilistic support vector regress method. Then, the addition rates of reagents are preset according to the feed grade type based on the operational pattern method. To overcome the steady-state disturbances, a feedback dosage controller is developed to further regulate the reagent addition rates based on the interval type-II fuzzy control system. In the proposed control strategy, the selection of FIF, the inference of feed grade, and the process of uncertainty in sample data are described in detail. Finally, industrial experiments show the effectiveness of the proposed method.
Autors: Yongfang Xie;Jia Wu;Degang Xu;Chunhua Yang;Weihua Gui;
Appeared in: IEEE Transactions on Industrial Electronics
Publication date: May 2017, volume: 64, issue:5, pages: 4199 - 4206
Publisher: IEEE
 
» Real-Time Classification of Healthy and Apnea Subjects Using ECG Signals With Variational Mode Decomposition
Abstract:
This paper introduces a novel method for the classification of healthy and apnea subjects using variational mode decomposition. The proposed method distinguishes the apnea and normal subjects with the help of an electrocardiogram (ECG) signal. Polysomnogram is the gold standard used for the identification of apnea subjects. This process is complex, expensive, and time-consuming. In this paper, both online and off-line-based feature extraction and classification methods are explored. The proper extraction of suitable features from the signal is done by applying variational mode decomposition. Two features are extracted from the variational mode functions, namely, energy and RR interval of ECG signal. These features are fed in to a support vector machine classifier, where they are classified as healthy and apnea. The accuracy obtained for both online and off-line processes are 97.5% and 95%, respectively.
Autors: A. Smruthy;M. Suchetha;
Appeared in: IEEE Sensors Journal
Publication date: May 2017, volume: 17, issue:10, pages: 3092 - 3099
Publisher: IEEE
 
» Real-Time Optimization of Automatic Control Systems With Application to BLDC Motor Test Rig
Abstract:
Driven by the increasing demands on production quality, system performance, and the reliability and safety issues of process industry, this paper proposes an integrated process monitoring and control design technique for industrial control systems. The proposed approach is an alternative realization of Youla parameterization which allows the performance of the controlled systems to be improved without modifying or replacing the predesigned control systems, while the closed-loop stability is guaranteed. In addition, a residual signal is available for the fault detection and isolation purpose. The effectiveness and performance of the proposed approach are demonstrated on a brushless direct current motor test rig.
Autors: Hao Luo;Minjia Krueger;Tim Koenings;Steven X. Ding;Shane Dominic;Xu Yang;
Appeared in: IEEE Transactions on Industrial Electronics
Publication date: May 2017, volume: 64, issue:5, pages: 4306 - 4314
Publisher: IEEE
 
» Real-Time Traffic Light Recognition Based on Smartphone Platforms
Abstract:
Traffic light recognition is of great significance for driver assistance or autonomous driving. In this paper, a traffic light recognition system based on smartphone platforms is proposed. First, an ellipsoid geometry threshold model in Hue Saturation Lightness color space is built to extract interesting color regions. These regions are further screened with a postprocessing step to obtain candidate regions that satisfy both color and brightness conditions. Second, a new kernel function is proposed to effectively combine two heterogeneous features, histograms of oriented gradients and local binary pattern, which is used to describe the candidate regions of traffic light. A kernel extreme learning machine (K-ELM) is designed to validate these candidate regions and simultaneously recognize the phase and type of traffic lights. Furthermore, a spatial-temporal analysis framework based on a finite-state machine is introduced to enhance the reliability of the recognition of the phase and type of traffic light. Finally, a prototype of the proposed system is implemented on a Samsung Note 3 smartphone. To achieve a real-time computational performance of the proposed K-ELM, a CPU-GPU fusion-based approach is adopted to accelerate the execution. The experimental results on different road environments show that the proposed system can recognize traffic lights accurately and rapidly.
Autors: Wei Liu;Shuang Li;Jin Lv;Bing Yu;Ting Zhou;Huai Yuan;Hong Zhao;
Appeared in: IEEE Transactions on Circuits and Systems for Video Technology
Publication date: May 2017, volume: 27, issue:5, pages: 1118 - 1131
Publisher: IEEE
 
» Real-Time Two-Dimensional Imaging of Solid Contaminants in Gas Pipelines Using an Electrical Capacitance Tomography System
Abstract:
In this paper, an electrical capacitance tomography (ECT) system for real-time measurement of solid contaminants in gas pipelines is presented. It consists of a ring of eight electrodes evenly distributed in the circular cross section of the probe. The speed-up enhancement is achieved using a field programmable gate array (FPGA) for the post-processing part of the system to accelerate the intensive matrix multiplications which are required in the image reconstruction algorithm. Experimental results on field-collected solid contaminants demonstrated the capability of the system to build in real-time two-dimensional cross-sectional images of the contaminants while giving an estimated measurement of their concentration. This allows the flow regime of the contaminants in the pipeline to be identified. Results also show that using Altera's Stratix V FPGA, 305 KLEs are required to achieve image reconstruction throughput up to 3233 frames/s for image size of 64 × 64 pixels. Simulation results were also conducted using finite-element method solver to assess the ECT probe for various image reconstruction algorithms (i.e., Linear back projection, Landweber, and modified Landweber algorithms). The results indicate a good matching with the experimental results.
Autors: Mahmoud Meribout;Imran M. Saied;
Appeared in: IEEE Transactions on Industrial Electronics
Publication date: May 2017, volume: 64, issue:5, pages: 3989 - 3996
Publisher: IEEE
 
» Recent Advances in the Capture and Display of Macroscopic and Microscopic 3-D Scenes by Integral Imaging
Abstract:
The capture and display of images of 3-D scenes under incoherent and polychromatic illumination is currently a hot topic of research, due to its broad applications in bioimaging, industrial procedures, military and surveillance, and even in the entertainment industry. In this context, Integral Imaging (InI) is a very competitive technology due to its capacity for recording with a single exposure the spatial-angular information of light-rays emitted by the 3-D scene. From this information, it is possible to calculate and display a collection of horizontal and vertical perspectives with high depth of field. It is also possible to calculate the irradiance of the original scene at different depths, even when these planes are partially occluded or even immersed in a scattering medium. In this paper, we describe the fundaments of InI and the main contributions to its development. We also focus our attention on the recent advances of the InI technique. Specifically, the application of InI concept to microscopy is analyzed and the achievements in resolution and depth of field are explained. In a different context, we also present the recent advances in the capture of large scenes. The progresses in the algorithms for the calculation of displayable 3-D images and in the implementation of setups for the 3-D displays are reviewed.
Autors: Manuel Martínez-Corral;Adrián Dorado;Juan Carlos Barreiro;Genaro Saavedra;Bahram Javidi;
Appeared in: Proceedings of the IEEE
Publication date: May 2017, volume: 105, issue:5, pages: 825 - 836
Publisher: IEEE
 
» Reciprocity Calibration for Massive MIMO: Proposal, Modeling, and Validation
Abstract:
This paper presents a mutual coupling-based calibration method for time-division-duplex massive MIMO systems, which enables downlink precoding based on uplink channel estimates. The entire calibration procedure is carried out solely at the base station (BS) side by sounding all BS antenna pairs. An expectation-maximization (EM) algorithm is derived, which processes the measured channels in order to estimate calibration coefficients. The EM algorithm outperforms the current state-of-the-art narrow-band calibration schemes in a mean squared error and sum-rate capacity sense. Like its predecessors, the EM algorithm is general in the sense that it is not only suitable to calibrate a co-located massive MIMO BS, but also very suitable for calibrating multiple BSs in distributed MIMO systems. The proposed method is validated with experimental evidence obtained from a massive MIMO testbed. In addition, we address the estimated narrow-band calibration coefficients as a stochastic process across frequency, and study the subspace of this process based on measurement data. With the insights of this study, we propose an estimator which exploits the structure of the process in order to reduce the calibration error across frequency. A model for the calibration error is also proposed based on the asymptotic properties of the estimator, and is validated with measurement results.
Autors: Joao Vieira;Fredrik Rusek;Ove Edfors;Steffen Malkowsky;Liang Liu;Fredrik Tufvesson;
Appeared in: IEEE Transactions on Wireless Communications
Publication date: May 2017, volume: 16, issue:5, pages: 3042 - 3056
Publisher: IEEE
 
» Recognition - A Critical Responsibility [President's Message]
Abstract:
Presents the President’s message for this issue of the publication.
Autors: Tomy Sebastian;
Appeared in: IEEE Industry Applications Magazine
Publication date: May 2017, volume: 23, issue:3, pages: 4 - 72
Publisher: IEEE
 
» Recognizing and Presenting the Storytelling Video Structure With Deep Multimodal Networks
Abstract:
In this paper, we propose a novel scene detection algorithm which employs semantic, visual, textual, and audio cues. We also show how the hierarchical decomposition of the storytelling video structure can improve retrieval results presentation with semantically and aesthetically effective thumbnails. Our method is built upon two advancements of the state of the art: first is semantic feature extraction which builds video-specific concept detectors; and second is multimodal feature embedding learning that maps the feature vector of a shot to a space in which the Euclidean distance has task specific semantic properties. The proposed method is able to decompose the video in annotated temporal segments which allow us for a query specific thumbnail extraction. Extensive experiments are performed on different data sets to demonstrate the effectiveness of our algorithm. An in-depth discussion on how to deal with the subjectivity of the task is conducted and a strategy to overcome the problem is suggested.
Autors: Lorenzo Baraldi;Costantino Grana;Rita Cucchiara;
Appeared in: IEEE Transactions on Multimedia
Publication date: May 2017, volume: 19, issue:5, pages: 955 - 968
Publisher: IEEE
 
» Recognizing the Gradual Changes in sEMG Characteristics Based on Incremental Learning of Wavelet Neural Network Ensemble
Abstract:
Most myoelectric prosthetic hands use a fixed pattern recognition model to identify the user's hand motion commands. Since surface electromyogram (sEMG) characteristics vary with time, it is difficult to employ the fixed pattern recognition model in identifying hand motion commands stably for a long period of time. In order to adapt to the gradual changes in sEMG characteristics, we utilized incremental learning based on the wavelet neural network (WNN) ensemble, and used negative correlation learning (NCL) to train it. To verify the effect of the proposed method, a group of subjects executed six hand motions in a continual experiment for more than 2 h. Compared with the fixed pattern recognition model, the classification accuracy rate of incremental learning with nonintegration becomes substantially improved. In addition, the results of the WNN ensemble with the fixed-size mode are more stable than those of the WNN ensemble with the growth mode. The experimental results demonstrate that our method can recognize the gradual changes in sEMG characteristics stably. Using the proposed method, the average accuracy rate is found to be 92.17%, even after a long period of time. Moreover, since the update time is short, the proposed method can be successfully applied in myoelectric prosthetic hands.
Autors: Feng Duan;Lili Dai;
Appeared in: IEEE Transactions on Industrial Electronics
Publication date: May 2017, volume: 64, issue:5, pages: 4276 - 4286
Publisher: IEEE
 
» Reconfiguring the Frequency and Directive Behavior of a Printed V–Shaped Structure
Abstract:
In this communication, a frequency and pattern reconfigurable V–shaped printed antenna is presented. The proposed structure operates at two different frequency bands. The variation in the frequency of operation is achieved by individually extending the length of each of the two antenna arms. The directional behavior of the proposed antenna structure is improved through the incorporation of a triangular parasitic element within the empty space between the two antenna arms. More specifically, the antenna is able to steer its gain pattern to either the left or right directions based on which arm of the V–shaped printed structure is fed. A prototype structure is fabricated and tested to validate the proposed approach in achieving directional behavior over two distinct frequency bands.
Autors: Y. Tawk;J. Costantine;C. G. Christodoulou;
Appeared in: IEEE Transactions on Antennas and Propagation
Publication date: May 2017, volume: 65, issue:5, pages: 2655 - 2660
Publisher: IEEE
 
» Reconstruction Guarantee Analysis of Basis Pursuit for Binary Measurement Matrices in Compressed Sensing
Abstract:
Recently, binary 0-1 measurement matrices, especially those from coding theory, were introduced to compressed sensing. Dimakis et al. found that the linear programming (LP) decoding of LDPC codes is very similar to the LP reconstruction of compressed sensing, and they further showed that the sparse binary parity-check matrices of good LDPC codes can be used as provably good measurement matrices for compressed sensing under basis pursuit (BP). Moreover, Khajehnejad et al. made use of girth to certify the good performances of sparse binary measurement matrices. In this paper, we examine the performance of binary measurement matrices with uniform column weight and arbitrary girth under BP. For a fixed measurement matrix, we first introduce a performance indicator called minimum BP weight, and show that any -sparse signals could be exactly recovered by BP if and only if . Then, lower bounds of are studied. Borrowing ideas from the tree bound for the LDPC codes, we obtain several explicit lower bounds of , which improve on the previous results in some cases. These lower bounds also imply explicit , and sparse approximation guarantees, and fur- her confirm that large girth has positive impacts on the performance of binary measurement matrices under BP.
Autors: Xin-Ji Liu;Shu-Tao Xia;Fang-Wei Fu;
Appeared in: IEEE Transactions on Information Theory
Publication date: May 2017, volume: 63, issue:5, pages: 2922 - 2932
Publisher: IEEE
 
» Reconstruction of Subsurface Velocities From Satellite Observations Using Iterative Self-Organizing Maps
Abstract:
A new method based on modified self-organizing maps is presented for the reconstruction of deep ocean current velocities from surface information provided by satellites. This method takes advantage of local correlations in the data-space to improve the accuracy of the reconstructed deep velocities. No assumptions regarding the structure of the water column, nor the underlying dynamics of the flow field, are made. Using satellite observations of surface velocity, sea-surface height and sea-surface temperature, as well as observations of the deep current velocity from autonomous Argo floats to train the map, we are able to reconstruct realistic high-resolution velocity fields at a depth of 1000 m. Validation reveals promising results, with a speed root mean squared error of ~2.8 cm., more than a factor of two smaller than competing methods, and direction errors consistently smaller than 30°. Finally, we discuss the merits and shortcomings of this methodology.
Autors: Christopher Chapman;Anastase Alexandre Charantonis;
Appeared in: IEEE Geoscience and Remote Sensing Letters
Publication date: May 2017, volume: 14, issue:5, pages: 617 - 620
Publisher: IEEE
 
» Recover Corrupted Data in Sensor Networks: A Matrix Completion Solution
Abstract:
Affected by hardware and wireless conditions in WSNs, raw sensory data usually have notable data loss and corruption. Existing studies mainly consider the interpolation of random missing data in the absence of the data corruption. There is also no strategy to handle the successive missing data. To address these problems, this paper proposes a novel approach based on matrix completion (MC) to recover the successive missing and corrupted data. By analyzing a large set of weather data collected from 196 sensors in Zhu Zhou, China, we verify that weather data have the features of low-rank, temporal stability, and spatial correlation. Moreover, from simulations on the real weather data, we also discover that successive data corruption not only seriously affects the accuracy of missing and corrupted data recovery but even pollutes the normal data when applying the matrix completion in a traditional way. Motivated by these observations, we propose a novel Principal Component Analysis (PCA)-based scheme to efficiently identify the existence of data corruption. We further propose a two-phase MC-based data recovery scheme, named MC-Two-Phase, which applies the matrix completion technique to fully exploit the inherent features of environmental data to recover the data matrix due to either data missing or corruption. Finally, the extensive simulations with real-world sensory data demonstrate that the proposed MC-Two-Phase approach can achieve very high recovery accuracy in the presence of successively missing and corrupted data.
Autors: Kun Xie;Xueping Ning;Xin Wang;Dongliang Xie;Jiannong Cao;Gaogang Xie;Jigang Wen;
Appeared in: IEEE Transactions on Mobile Computing
Publication date: May 2017, volume: 16, issue:5, pages: 1434 - 1448
Publisher: IEEE
 
» Reduced Switching Connectivity for Large Scale Antenna Selection
Abstract:
In this paper, we explore reduced-connectivity radio frequency (RF) switching networks for reducing the analog hardware complexity and switching power losses in antenna selection (AS) systems. In particular, we analyze different hardware architectures for implementing the RF switching matrices required in AS designs with a reduced number of RF chains. We explicitly show that fully-flexible switching matrices, which facilitate the selection of any possible subset of antennas and attain the maximum theoretical sum rates of AS, present numerous drawbacks such as the introduction of significant insertion losses, particularly pronounced in massive multiple-input multiple-output (MIMO) systems. Since these disadvantages make fully-flexible switching suboptimal in the energy efficiency sense, we further consider partially-connected switching networks as an alternative switching architecture with reduced hardware complexity, which we characterize in this work. In this context, we also analyze the impact of reduced switching connectivity on the analog hardware and digital signal processing of AS schemes that rely on received signal power information. Overall, the analytical and simulation results shown in this paper demonstrate that partially-connected switching maximizes the energy efficiency of massive MIMO systems for a reduced number of RF chains, while fully-flexible switching offers sub-optimal energy efficiency benefits due to its significant switching power losses.
Autors: Adrian Garcia-Rodriguez;Christos Masouros;Pawel Rulikowski;
Appeared in: IEEE Transactions on Communications
Publication date: May 2017, volume: 65, issue:5, pages: 2250 - 2263
Publisher: IEEE
 
» Reducing the Computational Complexity of Multicasting in Large-Scale Antenna Systems
Abstract:
In this paper, we study the physical layer multicasting to multiple co-channel groups in large-scale antenna systems. The users within each group are interested in a common message and different groups have distinct messages. In particular, we aim at designing the precoding vectors solving the so-called quality of service (QoS) and weighted max-min fairness (MMF) problems, assuming that the channel state information is available at the base station (BS). To solve both problems, the baseline approach exploits the semidefinite relaxation (SDR) technique. Considering a BS with antennas, the SDR complexity is more than , which prevents its application in large-scale antenna systems. To overcome this issue, we present two new classes of algorithms that, not only have significantly lower computational complexity than existing solutions, but also largely outperform the SDR-based methods. Moreover, we present a novel duality between transformed versions of the QoS and the weighted MMF problems. The duality explicitly determines the solution to the weighted MMF problem given the solution to the QoS problem, and vice versa. Numerical results are used to validate the effectiveness of the proposed solutions and to make comparisons with existing alternatives under different operating conditions.
Autors: Meysam Sadeghi;Luca Sanguinetti;Romain Couillet;Chau Yuen;
Appeared in: IEEE Transactions on Wireless Communications
Publication date: May 2017, volume: 16, issue:5, pages: 2963 - 2975
Publisher: IEEE
 
» Reducing the costs of FTTH networks by optimized splitter and OLT card deployment
Abstract:
In this paper, a problem of rational splitter and OLT card installation in fiber-to-the-home (FTTH) networks is considered. We assume that the most tedious part of FTTH network deployment, i.e., trenching and cable installation, is completed, and the operator can start to connect customers to the network. The connecting process still requires expenditures for both passive (splitters) and active (ONUs, OLT cards) equipment. In this paper, we define the problem of rational splitter and OLT card deployment and present the multi-state optimization (MuSO) approach to handle it. MuSO minimizes the expected total cost of deployment using a stochastic model; thus, it optimizes under stochastic behavior. Finally, we compare MuSO to other rational methods, numerically validating its efficiency. We also investigate the impact of various factors—e.g., the take-up rates assumed when designing a network, delays in providing service to newly acquired customers, optimization time limits—on the final FTTH network deployment cost. The presented methodology not only can be used to minimize the deployment cost of FTTH networks, but also to assess the impact of uncertainty on network designs returned by automated methods.
Autors: M. Żotkiewicz;M. Mycek;
Appeared in: IEEE/OSA Journal of Optical Communications and Networking
Publication date: May 2017, volume: 9, issue:5, pages: 412 - 422
Publisher: IEEE
 
» ReflectFX: In-Band Full-Duplex Wireless Communication by Means of Reflected Power
Abstract:
In this paper, we introduce a new full-duplex wireless communication system, named ReflectFX, that relies on backscatter modulation. This paper offers a new concept for two-way wireless communication: rather than avoiding self-interference as in half-duplex, or combatting self-interference as in conventional full-duplex, nodes will re-use the received interfering radio-carrier waves to transfer information. The electromagnetic waves are modulated and reflected by the same antenna that receives them. We consider two nodes, a base-station and an end-user, that wish to exchange data over a wireless Rayleigh fading channel with additive white Gaussian noise. With ReflectFX, the end-user receives a self-interference free signal. We improve the transmission range of ReflectFX by adding negative resistance to the end-user load. We derive an expression for the overall achievable throughput and ergodic capacity of ReflectFX. Simulation results show that ReflectFX outperforms both conventional full-duplex and half-duplex.
Autors: Besma Smida;Seiran Khaledian;
Appeared in: IEEE Transactions on Communications
Publication date: May 2017, volume: 65, issue:5, pages: 2207 - 2219
Publisher: IEEE
 
» Reflections on a Perfect Match: Volunteers and Mentors
Abstract:
Discusses the need for MMTS society volunteers and mentors.
Autors: Wayne Shiroma;
Appeared in: IEEE Microwave Magazine
Publication date: May 2017, volume: 18, issue:3, pages: 88 - 94
Publisher: IEEE
 
» Refractive Index Modulation by Crystallization in Sapphire-Derived Fiber
Abstract:
We have proposed and demonstrated a new refractive index (RI) modulation method based on crystallization in sapphire-derived fibers (SDFs), which are special fibers with a high concentration of alumina in the silica fiber core. Reheating and cooling an SDF with an arc discharge can generate mullite particles in the fiber core. Such crystallization can achieve a maximum RI modulation of ~0.015. Using the point-by-point arc discharge method, crystallization-based long-period gratings (LPGs) can be inscribed in the core of the SDF. Due to the highly modulated RI at the inscribed points, only three to four arc discharge points are required to achieve a strong resonant dip of up to 16 dB in the transmission spectrum of the SDF. The obtained results show that RI modulation by crystallization in SDFs has great potential for the fabrication of functional fiber components that can be applied in high-temperature and -pressure environments.
Autors: Lin Hong;Fufei Pang;Huanhuan Liu;Jin Xu;Zhenyi Chen;Ziwen Zhao;Tingyun Wang;
Appeared in: IEEE Photonics Technology Letters
Publication date: May 2017, volume: 29, issue:9, pages: 723 - 726
Publisher: IEEE
 
» Regularized Dual Averaging Image Reconstruction for Full-Wave Ultrasound Computed Tomography
Abstract:
Ultrasound computed tomography (USCT) holds great promise for breast cancer screening. Waveform inversion-based image reconstruction methods account for higher order diffraction effects and can produce high-resolution USCT images, but are computationally demanding. Recently, a source encoding technique has been combined with stochastic gradient descent (SGD) to greatly reduce image reconstruction times. However, this method bundles the stochastic data fidelity term with the deterministic regularization term. This limitation can be overcome by replacing SGD with a structured optimization method, such as the regularized dual averaging method, that exploits knowledge of the composition of the cost function. In this paper, the dual averaging method is combined with source encoding techniques to improve the effectiveness of regularization while maintaining the reduced reconstruction times afforded by source encoding. It is demonstrated that each iteration can be decomposed into a gradient descent step based on the data fidelity term and a proximal update step corresponding to the regularization term. Furthermore, the regularization term is never explicitly differentiated, allowing nonsmooth regularization penalties to be naturally incorporated. The wave equation is solved by the use of a time-domain method. The effectiveness of this approach is demonstrated through computer simulation and experimental studies. The results suggest that the dual averaging method can produce images with less noise and comparable resolution to those obtained by the use of SGD.
Autors: Thomas P. Matthews;Kun Wang;Cuiping Li;Neb Duric;Mark A. Anastasio;
Appeared in: IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control
Publication date: May 2017, volume: 64, issue:5, pages: 811 - 825
Publisher: IEEE
 
» Reinforcement Learning-Based Plug-in Electric Vehicle Charging With Forecasted Price
Abstract:
This paper proposes a novel demand response method that aims at reducing the long-term cost of charging the battery of an individual plug-in electric vehicle (PEV). The problem is cast as a daily decision-making problem for choosing the amount of energy to be charged in the PEV battery within a day. We model the problem as a Markov decision process (MDP) with unknown transition probabilities. A batch reinforcement-learning (RL) algorithm is proposed for learning an optimum cost-reducing charging policy from a batch of transition samples and making cost-reducing charging decisions in new situations. In order to capture the day-to-day differences of electricity charging costs, the method makes use of actual electricity prices for the current day and predicted electricity prices for the following day. A Bayesian neural network is employed for predicting the electricity prices. For constructing the RL training dataset, we use historical prices. A linear-programming-based method is developed for creating a dataset of optimal charging decisions. Different charging scenarios are simulated for each day of the historical time frame using the set of past electricity prices. Simulation results using real-world pricing data demonstrate cost savings of 10%–50% for the PEV owner when using the proposed charging method.
Autors: Adriana Chiş;Jarmo Lundén;Visa Koivunen;
Appeared in: IEEE Transactions on Vehicular Technology
Publication date: May 2017, volume: 66, issue:5, pages: 3674 - 3684
Publisher: IEEE
 
» Relationship Between Mirrored Aperture Synthesis Radiometers and Aperture Synthesis Radiometers
Abstract:
The mirrored aperture synthesis radiometer (MASR) has been proposed as a new technique for high-resolution observation. Compared with ASRs, MASRs have the advantage of lower system complexity. However, the relationship between MASRs and ASRs has not been studied. In order to thoroughly study MASRs, it is necessary to establish the relationship between MASRs and ASRs. In this letter, the array factor of 1-D MASRs in the discrete cosine transform domain (DCT-domain) is defined. The reconstructed image for a 1-D MASR is a symmetric convolution of the observed brightness temperature distribution and the defined array factor in the DCT domain. Since a symmetric convolution can be turned into a linear convolution, the relationship between 1-D MASRs and 1-D ASRs can be established. A 1-D MASR is equivalent to a 1-D ASR that has a mirrored window. Additionally, for the equivalent 1-D ASR, its observed scene is a symmetrically extended real scene. This established relationship is validated by the simulation results. In practical applications, a MASR can be understood as an ASR.
Autors: Yufang Li;Qingxia Li;Li Feng;
Appeared in: IEEE Geoscience and Remote Sensing Letters
Publication date: May 2017, volume: 14, issue:5, pages: 631 - 635
Publisher: IEEE
 
» Relationships Between Subclasses of Integral Input-to-State Stability
Abstract:
A certain “qualitative equivalence” has been recently demonstrated between integral input-to-state stability (iISS) and a nonlinear -gain property. Furthermore, it has been observed that the properties of strong iISS and nonlinear -gain are preserved when multiple systems satisfying the respective property are connected in cascade. This technical note clarifies the relationships between various input-to-state stability and (non)linear -gain properties.
Autors: Christopher M. Kellett;Peter M. Dower;Hiroshi Ito;
Appeared in: IEEE Transactions on Automatic Control
Publication date: May 2017, volume: 62, issue:5, pages: 2476 - 2482
Publisher: IEEE
 
» Reliability Analysis of LPCVD SiN Gate Dielectric for AlGaN/GaN MIS-HEMTs
Abstract:
In this paper, we investigate Low Pressure Chemical Vapor Deposition (LPCVD) SiN as a gate isolation material for AlGaN/gallium nitride (GaN) MIS-high electron mobility transistor power transistors. We compared the dielectric failure by forward-biased constant-current stress time-dependent dielectric breakdown measurements and statistical Weibull analysis. Several 4” AlGaN/GaN-on-Si wafers have been processed with different gate isolations and processes. Our investigation includes the dependence of the dielectric breakdown on the process flow (influence of dry etch), the thickness of the dielectric (12–20 nm), the area scaling, and the gate electrode, where we also consider the recently presented poly-silicon electrode. Additionally, we show the influence of the current density through the gate on the charge-to-breakdown characteristics as well as the influence of the temperature on the breakdown behavior. Using the poly-silicon electrode and 20 nm LPCVD SiN as gate isolation, we achieved a charge-to-breakdown of at for . A 20-years lifetime (100 ppm, ) extrapolation for a scaled area of () leads to a positive gate voltage of .
Autors: Simon A. Jauss;Kazim Hallaceli;Sebastian Mansfeld;Stephan Schwaiger;Walter Daves;Oliver Ambacher;
Appeared in: IEEE Transactions on Electron Devices
Publication date: May 2017, volume: 64, issue:5, pages: 2298 - 2305
Publisher: IEEE
 
» Reliability Assessment of InAlN/GaN HFETs With Lifetime $8.9times 10^{mathrm {6}}$ h
Abstract:
Based on the three-temperature 30 V DC stress tests, the reliability of InAlN/GaN heterostructure field-effect transistors (HFETs) on SiC substrate was assessed for the first time. Using a failure criterion defined as 20% reduction in zero-gate-voltage drain current (), the activation energy was estimated to be 1.94 eV, and the median time to failure was estimated to be h at junction temperature of 150°. Moreover, the high temperature material storage indicates that the lifetime of InAlN/GaN HFETs can be further prolonged by the optimization of device process, such as introducing LPCVD SiN or ALD Al2O3 as gate dielectric layer.
Autors: Yuangang Wang;Yuanjie Lv;Xubo Song;Lei Chi;Jiayun Yin;Xingye Zhou;Yulong Fang;Xin Tan;Hongyu Guo;Hao Peng;Guodong Gu;Zhihong Feng;Shujun Cai;
Appeared in: IEEE Electron Device Letters
Publication date: May 2017, volume: 38, issue:5, pages: 604 - 606
Publisher: IEEE
 
» Reliability of Universal Decoding Based on Vector-Quantized Codewords
Abstract:
Motivated by applications of biometric identification and content identification systems, we consider the problem of random coding for channels, where each codeword undergoes vector quantization, and where the decoder bases its decision only on the compressed codewords and the channel output, which is, in turn, the channel’s response to the transmission of an original codeword, before compression. For memoryless sources and memoryless channels with finite alphabets, we propose a new universal decoder and analyze its error exponent, which improves on an earlier result by Dasarathy and Draper (2011), who used the classic maximum mutual information universal decoder. We show that our universal decoder provides the same error exponent as that of the optimal, maximum likelihood decoder, at least as long as all single-letter transition probabilities of the channel are positive.
Autors: Neri Merhav;
Appeared in: IEEE Transactions on Information Theory
Publication date: May 2017, volume: 63, issue:5, pages: 2696 - 2709
Publisher: IEEE
 
» Reliable Detection of Rotor Winding Asymmetries in Wound Rotor Induction Motors via Integral Current Analysis
Abstract:
Current analysis has been widely employed in academy and industry for the diagnosis of rotor damages in cage induction motors. The conventional approach based on the fast Fourier transform analysis of steady-state current [motor current signature analysis (MCSA)] has been recently complemented with the development of alternative techniques that rely on the time-frequency analysis of transient quantities of the machine. These techniques may bring important advantages that are related to the avoidance of eventual false indications provided by the classical MCSA. Moreover, their application is also suitable for variable speed conditions. However, the application of current-based methodologies to wound rotor induction motors (WRIM) has been much less studied and, hence, their validation in field WRIM is scarce. This work proposes the application of an integral methodology based on the analysis of both stationary and transient currents for the diagnosis of winding asymmetries in WRIM. The method, based on up to five different fault evidences, is validated in laboratory motors and it is subsequently applied to a large field motor (1500 kW) that was showing signs of abnormal rotor functioning. The results prove that the method is of interest for the field since it helps to ratify without ambiguity the existence of eventual asymmetries in the rotor windings, with no interference with the machine operation. However, due to the complex constructive nature of the rotor winding as well as the presence of auxiliary systems (slip rings, brushes, contactors, etc.), once the fault presence is detected, it may be interesting the utilization of complementary tools to accurately locate the root cause of the asymmetry.
Autors: Jose Antonino-Daviu;Alfredo Quijano-López;Vicente Climente-Alarcon;Carlos Garín-Abellán;
Appeared in: IEEE Transactions on Industry Applications
Publication date: May 2017, volume: 53, issue:3, pages: 2040 - 2048
Publisher: IEEE
 
» Removal of ICI and IBI in Wireless Heterogeneous Networks With Timing Misalignment
Abstract:
Since a small cell base station (SBS) of an orthogonal frequency division multiple access heterogeneous network (HetNet) does not intend to align arriving macrocell user signals, inter-carrier interference (ICI) and inter-block interference (IBI) inevitably arise in the uplink demodulation at the SBS. This letter proposes a novel precoding scheme based on interference alignment to remove the uplink ICI and IBI in a HetNet completely. The proposed scheme is able to remove the ceiling effect on the small cell uplink rate and increase the degrees of freedom of the system. Simulation results demonstrate the superiority of the scheme.
Autors: Hong Wang;Rongfang Song;Shu-Hung Leung;
Appeared in: IEEE Communications Letters
Publication date: May 2017, volume: 21, issue:5, pages: 1195 - 1198
Publisher: IEEE
 
» Removal of Optically Thick Clouds From High-Resolution Satellite Imagery Using Dictionary Group Learning and Interdictionary Nonlocal Joint Sparse Coding
Abstract:
In this paper, we propose a method for cloud removal in a cloud-contaminated high-resolution (HR) optical satellite image with two kinds of auxiliary images of different types: a low-resolution (LR) optical satellite composite image and a synthetic aperture radar (SAR) image. In the proposed method, we assume that cloud-contaminated and cloud-free regions have been detected accurately, then dictionary group learning (DGL) is used to establish structure correspondences between HR, LR, and SAR data from cloud-free patches, while interdictionary nonlocal joint sparse coding (INJSC) is used to estimate the universal representation coefficients of patches contaminated by clouds, and finally, cloud-contaminated HR patches can be reconstructed with their universal coefficients and the HR dictionary learned from DGL process. In this way, the missing information in the cloud-contaminated HR image can be reconstructed patch by patch. The proposed method is tested on a series of experiments on both simulated and real data. Experimental results show that both DGL and INJSC are beneficial to better reconstructing the missing information. This method is also compared against our previous work on the same topic, which adopted dictionary pair learning (DPL) and sparse coding (SC) to recover the missing information and achieved state-of-the-art performance at that time. The comparison shows that the method proposed in this paper significantly outperforms the previous one.
Autors: Ying Li;Wenbo Li;Chunhua Shen;
Appeared in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication date: May 2017, volume: 10, issue:5, pages: 1870 - 1882
Publisher: IEEE
 
» Renewable Energy Pricing Driven Scheduling in Distributed Smart Community Systems
Abstract:
A smart community is a distributed system consisting of a set of smart homes which utilize the smart home scheduling techniques to enable customers to automatically schedule their energy loads targeting various purposes such as electricity bill reduction. Smart home scheduling is usually implemented in a decentralized fashion inside a smart community, where customers compete for the community level renewable energy due to their relatively low prices. Typically there exists an aggregator as a community wide electricity policy maker aiming to minimize the total electricity bill among all customers. This paper develops a new renewable energy aware pricing scheme to achieve this target. We establish the proof that under certain assumptions the optimal solution of decentralized smart home scheduling is equivalent to that of the centralized technique, reaching the theoretical lower bound of the community wide total electricity bill. In addition, an advanced cross entropy optimization technique is proposed to compute the pricing scheme of renewable energy, which is then integrated in smart home scheduling. The simulation results demonstrate that our pricing scheme facilitates the reduction of both the community wide electricity bill and individual electricity bills compared to the uniform pricing. In particular, the community wide electricity bill can be reduced to only 0.06 percent above the theoretic lower bound.
Autors: Yang Liu;Shiyan Hu;
Appeared in: IEEE Transactions on Parallel and Distributed Systems
Publication date: May 2017, volume: 28, issue:5, pages: 1445 - 1456
Publisher: IEEE
 
» Repetitive ${ {X}}$ -Band Relativistic Traveling Wave Oscillator
Abstract:
This paper presents the results of an experimental study of a relativistic traveling wave oscillator with a tubular electron beam of enlarged cross section. Repetitively pulsed (30 Hz) generation of 10.1-GHz, 80-ns microwave pulses in 1-s batches is realized. The microwave pulse power a level of quasi-steady-state oscillation was 220 ± 44 MW. The driving electron beam (370-keV, 2.4-kA, 107-ns pulsewidth) was transported along the interaction space by a quasi-constant (few seconds in duration) external magnetic field with an induction of 0.6 T. The power efficiency of the generator is 25% ± 5%. The energy in a single microwave pulse is about 15 J as measured with an aperture calorimeter.
Autors: Eugene M. Totmeninov;Pavel V. Vykhodtsev;Alexei S. Stepchenko;Aleksei I. Klimov;
Appeared in: IEEE Transactions on Electron Devices
Publication date: May 2017, volume: 64, issue:5, pages: 2398 - 2402
Publisher: IEEE
 
» Replacing the Grid Interface Transformer in Wind Energy Conversion System With Solid-State Transformer
Abstract:
In wind energy conversion systems, the fundamental frequency step-up transformer acts as a key interface between the wind turbine and the grid. Recently, there have been efforts to replace this transformer by an advanced power-electronics-based solid-state transformer (SST). This paper proposes a configuration that combines the doubly fed induction generator-based wind turbine and SST operation. The main objective of the proposed configuration is to interface the turbine with the grid while providing enhanced operation and performance. In this paper, SST controls the active power to/from the rotor side converter, thus, eliminating the grid side converter. The proposed system meets the recent grid code requirements of wind turbine operation under fault conditions. Additionally, it has the ability to supply reactive power to the grid when the wind generation is not up to its rated value. A detailed simulation study is conducted to validate the performance of the proposed configuration.
Autors: Imran Syed;Vinod Khadkikar;
Appeared in: IEEE Transactions on Power Systems
Publication date: May 2017, volume: 32, issue:3, pages: 2152 - 2160
Publisher: IEEE
 
» Requirements, Design Challenges, and Review of Routing and MAC Protocols for CR-Based Smart Grid Systems
Abstract:
Cognitive radio technology can facilitate communication in smart grid applications through dynamic spectrum access. However, traditional routing and MAC protocols adopted for cognitive radio networks may not be beneficial in CR-based smart grid environments due to large data sizes and variable link quality among different functional blocks of smart grids. The interference and fading in wireless links necessitate efficient routing for reliable low-latency data delivery of smart grid applications. This low-latency data delivery must be achieved while protecting the legitimate primary users. Besides efficient routing, MAC layer protocols should be enhanced to achieve successful data delivery with simultaneous spectrum sensing and duty cycling for energy-efficient operation. In this article, we evaluate the requirements and key design challenges for routing and MAC protocols in the CR-based smart grid. We also provide a review of research carried out to date for routing and MAC protocols for the CR-based smart grid.
Autors: Athar Ali Khan;Mubashir Husain Rehmani;Martin Reisslein;
Appeared in: IEEE Communications Magazine
Publication date: May 2017, volume: 55, issue:5, pages: 206 - 215
Publisher: IEEE
 
» Research on the Cooperative Train Control Strategy to Reduce Energy Consumption
Abstract:
Based on the mature energy-saving strategy of a single train, the optimization of multiple trains' trajectories is studied. A cooperative control model is formulated with the utilization of the regenerative energy considered, which is used to calculate the total energy consumption of an electric subway system under various energy-saving control strategies. Taking the cooperative operation of two trains within the same section of an electrical system as an example, the front one adopt the optimal driving strategy with four modes of movement, which are the maximum traction, cruising, coasting, and the maximum braking by sequence. The latter is controlled by the strategy with four movement modes (mentioned earlier) and five movement modes (namely, the maximum traction, cruising/coasting, the maximum traction, coasting, and the maximum braking), respectively. The minimum energy consumption under different departure headway is calculated by using a heuristic algorithm. It turned out that the optimal energy-saving control strategy can be obtained with the departure headway given, and an energy-saving control strategy corresponding to the minimum energy consumption can be worked out too. The proposed energy-saving strategy can reduce energy consumption by 19.2% at the most.
Autors: Jianqiang Liu;Huailong Guo;Yingxue Yu;
Appeared in: IEEE Transactions on Intelligent Transportation Systems
Publication date: May 2017, volume: 18, issue:5, pages: 1134 - 1142
Publisher: IEEE
 
» Research on the Impact of DFIG Virtual Inertia Control on Power System Small-Signal Stability Considering the Phase-Locked Loop
Abstract:
Doubly fed induction generator (DFIG) wind turbines with virtual inertia control are coupled to power system in dynamic characteristics, and the control input of virtual inertia control is directly affected by the tracking ability of phase-locked loop (PLL). Thus, it is urgent to study the impact of DFIG wind turbines with virtual inertia control on power system small-signal stability considering the effects of PLL. First, based on DFIG operation characteristic and control strategy, a small-signal model of interconnected system with DFIG integration considering PLL and virtual inertial control is established. Second, the attenuation time constants of DFIG state variables are calculated, and according to the attenuation speeds of different state variables and the coupling between them, it is found out that PLL and virtual inertia are the main factors that affect the coupling between DFIG and synchronous generators. And then, considering that both PLL and virtual inertia control will affect the oscillation modes of synchronous generators, analytical method is used to reveal system small-signal stability under the joint effects of the two factors quantitatively. Analysis results show that, for DFIG wind turbines with virtual inertial control, PLL affects system damping mainly by affecting the participation of virtual inertia in the system. The smaller the PI parameters of PLL are, the smaller the participation factor of virtual inertia control state variables in the interarea oscillation mode is, and the bigger the electromechanical oscillation mode damping ratio is. Simulation results verify the reasonableness of the established model and the possibility that virtual inertia control may cause system small-signal stability to deteriorate in multimachine system.
Autors: Jing Ma;Yang Qiu;Yinan Li;Weibo Zhang;Zhanxiang Song;James S. Thorp;
Appeared in: IEEE Transactions on Power Systems
Publication date: May 2017, volume: 32, issue:3, pages: 2094 - 2105
Publisher: IEEE
 
» Research Papers: Writing Tips and Top-Tier Targets
Abstract:
As a part of each graduate program study, graduate students need to conduct research in a specific field and, most importantly, publish the results of their work in journals and conference publications. While this might sound easy to some graduate students, it may be the most challenging task for many, specifically those in the early years of their studies. Particularly, students have questions regarding transferring the results of their work from a "report" into a research paper, writing high-quality research papers, and how to choose among the various journals and conference publications in their fields. Very often, many students have good results but they have a difficult time publishing them.
Autors: Mahdi Nikdast;
Appeared in: IEEE Potentials
Publication date: May 2017, volume: 36, issue:3, pages: 26 - 29
Publisher: IEEE
 
» Residual Stress and Pop-Out Simulation for TSVs and Contacts in Via-Middle Process
Abstract:
In the via-middle process of 3-D integrated circuit manufacturing, through-silicon via (TSV) annealing causes mechanical stress not only to its surrounding structures, including liner and landing pad, but also the contacts nearby. This process may result in a noticeable pop-out in TSVs and/or contacts, thus complicating the subsequent chemical mechanical polishing (CMP). In addition, residual stress may cause delamination or crack. In this paper, we conducted detailed simulations of the residual stress and pop-out mechanisms for TSVs and neighboring contacts. Our primary focus was on the interplay of TSV-induced and contact-induced stresses and their combined impact on pop-out height. In addition, we conducted a sensitivity analysis of key parameters, including distance, plasticity, annealing temperature, and the distribution of neighboring contacts. This paper showed that these parameters notably affect the stress and the pop-out of TSVs and contacts. This in turn is expected to complicate the subsequent CMP steps. Finally, we applied the linear superposition method to predict stress and validated its accuracy by comparing the results with finite element analysis simulation. The results of the comparison demonstrated that the superposition method was accurate. Therefore, it could be used to predict the stress for full-chip design.
Autors: Can Rao;Tongqing Wang;Yarui Peng;Jie Cheng;Yuhong Liu;Sung Kyu Lim;Xinchun Lu;
Appeared in: IEEE Transactions on Semiconductor Manufacturing
Publication date: May 2017, volume: 30, issue:2, pages: 143 - 154
Publisher: IEEE
 
» Resource Allocation and Multicast Routing in Elastic Optical Networks
Abstract:
In this paper, we formulate an integer linear programming (ILP) to perform multicast routing and spectrum assignment (MRSA) in elastic optical networks, which serves jointly a set of multicast requests. In this formulation, all physical layer restrictions including modulation level assignment, maximum number of multicast capable nodes (MCNs), and maximum splitting degree (MSD) of MCNs, are considered. In addition, we modify the proposed joint ILP to serve multicast requests one-by-one, which is referred to as a separate ILP. Furthermore, we present three heuristic algorithms for MRSA, namely distance-based MRSA (DMRSA), congestion-based MRSA (CMRSA), and mixed CMRSA/DMRSA, which are applicable in both static and dynamic operation scenarios. In CMRSA and DMRSA, the link length and the amount of occupied spectrum are considered as the cost function of multicast routing, respectively; and in mixed CMRSA/DMRSA, a combination of normalized link length and normalized occupied spectrum is considered as the cost function. The comparison of ILPs and heuristic algorithms in static operation reveals that the joint ILP, as the benchmark, gives the optimum solution while has the most computational complexity. Furthermore, the separate ILP has lower complexity at the cost of consuming slightly more spectrum. Unless the DMRSA method, which has the worst performance, the gap between the other two heuristic algorithms and the ILPs is negligible. Furthermore, simulation results of dynamic operation scenarios reveal that mixed CMRSA/DMRSA outperforms other two heuristics algorithms in terms of blocking probability.
Autors: Mehrdad Moharrami;Ahmad Fallahpour;Hamzeh Beyranvand;Jawad A. Salehi;
Appeared in: IEEE Transactions on Communications
Publication date: May 2017, volume: 65, issue:5, pages: 2101 - 2113
Publisher: IEEE
 
» Retention-Aware DRAM Assembly and Repair for Future FGR Memories
Abstract:
Refresh operations consume substantial energy and bandwidth in high-density dynamic random-access memory (DRAM) memory. The trend of increasing refresh overhead limits the scalability of DRAM memory that refreshes all cells at the same rate, because the refresh rate setting depends on the worst-case weak cell manufactured in unstable process technology. To cope with this issue, fine-grained refresh (FGR) is proposed to eliminate the unnecessary refresh operations caused by minor weak cells. Even JEDEC’s DDR4 DRAM specification announces the support of FGR, which is likely to evolve and become a standard in future DRAM. Unfortunately, according to our key observation, the effectiveness of FGR is greatly confined by the procedure of refresh-oblivious device integration because all memory devices within a module have to be controlled and refreshed in a lockstep way after the step of assembly. In this paper, we are the first to propose a holistic FGR-oriented DRAM optimization framework, retention-aware DRAM assembly and repair (RADAR), to enhance the effectiveness of FGR in DRAM modules. RADAR includes two novel techniques applicable at the stage of DRAM assembly. The first one is retention-aware device clustering that integrates the “compatible” devices to achieve low refresh rate through a preassembly testing and retention-aware matching method. The second technique, Microfix, exploits the hierarchical DRAM array structure and its redundancy to fix critical weak DRAM rows through fine-grained row and subarray remapping. With this optimization architecture, RADAR, the refresh overhead of DRAM dual in-line memory modules can be dramatically reduced as implied in the experiments.
Autors: Ying Wang;Yin-He Han;Cheng Wang;Huawei Li;Xiaowei Li;
Appeared in: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Publication date: May 2017, volume: 36, issue:5, pages: 705 - 718
Publisher: IEEE
 
» Revolutionizing Wearables for 5G: 5G Technologies: Recent Developments and Future Perspectives for Wearable Devices and Antennas
Abstract:
Wearable devices present an increasingly attractive solution for numerous applications in sectors ranging from the military to medicine to consumer electronics. They will also play an integral role in the imminent fifth-generation (5G) networks, which are expected to operate with higher bit rates and lower outage probabilities in smaller microcells and picocells covering broader areas than fourth-generation (4G) or older technologies. In addition, beam reconfigurability and beamforming are expected to facilitate spectral and energy efficiencies at both the mobile device and base station levels. In overcoming the limitations of current 4G systems, the new features and capabilities envisioned for 5G networks will radically change applications in transportation, health care, smart homes, and wireless robots, among many others [1], [2].
Autors: Nur Farahiyah Mohamad Aun;Ping Jack Soh;Azremi Abdullah Al-Hadi;Mohd Faizal Jamlos;Guy A.E. Vandenbosch;Dominique Schreurs;
Appeared in: IEEE Microwave Magazine
Publication date: May 2017, volume: 18, issue:3, pages: 108 - 124
Publisher: IEEE
 
» Riemannian Geometry Applied to Detection of Respiratory States From EEG Signals: The Basis for a Brain–Ventilator Interface
Abstract:
Goal: During mechanical ventilation, patient-ventilator disharmony is frequently observed and may result in increased breathing effort, compromising the patient's comfort and recovery. This circumstance requires clinical intervention and becomes challenging when verbal communication is difficult. In this study, we propose a brain–computer interface (BCI) to automatically and noninvasively detect patient-ventilator disharmony from electroencephalographic (EEG) signals: a brain–ventilator interface (BVI). Methods: Our framework exploits the cortical activation provoked by the inspiratory compensation when the subject and the ventilator are desynchronized. Use of a one-class approach and Riemannian geometry of EEG covariance matrices allows effective classification of respiratory states. The BVI is validated on nine healthy subjects that performed different respiratory tasks that mimic a patient-ventilator disharmony. Results: Classification performances, in terms of areas under receiver operating characteristic curves, are significantly improved using EEG signals compared to detection based on air flow. Reduction in the number of electrodes that can achieve discrimination can be often desirable (e.g., for portable BCI systems). By using an iterative channel selection technique, the common highest order ranking, we find that a reduced set of electrodes () can slightly improve for an intrasubject configuration, and it still provides fairly good performances for a general intersubject setting. Conclusion: Results support the discriminant capacity of our approach to identify anomalous respiratory states, by learning from a training set containing only normal respiratory epochs. Significance: The proposed framework opens the door to BVIs for monitoring pati- nt's breathing comfort and adapting ventilator parameters to patient respiratory needs.
Autors: X. Navarro-Sune;A. L. Hudson;F. De Vico Fallani;J. Martinerie;A. Witon;P. Pouget;M. Raux;T. Similowski;M. Chavez;
Appeared in: IEEE Transactions on Biomedical Engineering
Publication date: May 2017, volume: 64, issue:5, pages: 1138 - 1148
Publisher: IEEE
 
» Right Ventricular Strain, Torsion, and Dyssynchrony in Healthy Subjects Using 3D Spiral Cine DENSE Magnetic Resonance Imaging
Abstract:
Mechanics of the left ventricle (LV) are important indicators of cardiac function. The role of right ventricular (RV) mechanics is largely unknown due to the technical limitations of imaging its thin wall and complex geometry and motion. By combining 3D Displacement Encoding with Stimulated Echoes (DENSE) with a post-processing pipeline that includes a local coordinate system, it is possible to quantify RV strain, torsion, and synchrony. In this study, we sought to characterize RV mechanics in 50 healthy individuals and compare these values to their LV counterparts. For each cardiac frame, 3D displacements were fit to continuous and differentiable radial basis functions, allowing for the computation of the 3D Cartesian Lagrangian strain tensor at any myocardial point. The geometry of the RV was extracted via a surface fit to manually delineated endocardial contours. Throughout the RV, a local coordinate system was used to transform from a Cartesian strain tensor to a polar strain tensor. It was then possible to compute peak RV torsion as well as peak longitudinal and circumferential strain. A comparable analysis was performed for the LV. Dyssynchrony was computed from the standard deviation of regional activation times. Global circumferential strain was comparable between the RV and LV (−18.0% for both) while longitudinal strain was greater in the RV (−18.1% vs. −15.7%). RV torsion was comparable to LV torsion (6.2 vs. 7.1 degrees, respectively). Regional activation times indicated that the RV contracted later but more synchronously than the LV. 3D spiral cine DENSE combined with a post–processing pipeline that includes a local coordinate system can resolve both the complex geometry and 3D motion of the RV.
Autors: Jonathan D. Suever;Gregory J. Wehner;Linyuan Jing;David K. Powell;Sean M. Hamlet;Jonathan D. Grabau;Dimitri Mojsejenko;Kristin N. Andres;Christopher M. Haggerty;Brandon K. Fornwalt;
Appeared in: IEEE Transactions on Medical Imaging
Publication date: May 2017, volume: 36, issue:5, pages: 1076 - 1085
Publisher: IEEE
 
» Road Structure Refined CNN for Road Extraction in Aerial Image
Abstract:
In this letter, we propose a road structure refined convolutional neural network (RSRCNN) approach for road extraction in aerial images. In order to obtain structured output of road extraction, both deconvolutional and fusion layers are designed in the architecture of RSRCNN. For training RSRCNN, a new loss function is proposed to incorporate the geometric information of road structure in cross-entropy loss, thus called road-structure-based loss function. Experimental results demonstrate that the trained RSRCNN model is able to advance the state-of-the-art road extraction for aerial images, in terms of precision, recall, F-score, and accuracy.
Autors: Yanan Wei;Zulin Wang;Mai Xu;
Appeared in: IEEE Geoscience and Remote Sensing Letters
Publication date: May 2017, volume: 14, issue:5, pages: 709 - 713
Publisher: IEEE
 
» Robust Channel Phase Error Calibration Algorithm for Multichannel High-Resolution and Wide-Swath SAR Imaging
Abstract:
High-resolution and wide-swath synthetic aperture radar (SAR) imaging can be achieved by the azimuth multichannel system. The minimum variance distortionless response (MVDR) beamformer can be utilized to suppress azimuth ambiguities. However, the presence of channel phase errors significantly deteriorates the performance of the azimuth multichannel SAR system. Instead of employing subspace techniques, this letter proposes a robust channel phase error calibration algorithm via maximizing the MVDR beamformer output power. Compared with the conventional subspace-based calibration methods, there is no redundancy of channels required to estimate the subspaces in the proposed algorithm. Also, the proposed algorithm is relatively robust, because it avoids the subspace swap phenomenon, which probably takes place at low signal-to-noise ratios for the subspace techniques. Moreover, the proposed method has the advantage of estimating the channel phase errors without covariance matrix decomposition, which reduces the computation load. The simulation experiments and the real data processing validate the effectiveness of the proposed calibration method.
Autors: Linjian Zhang;Yesheng Gao;Xingzhao Liu;
Appeared in: IEEE Geoscience and Remote Sensing Letters
Publication date: May 2017, volume: 14, issue:5, pages: 649 - 653
Publisher: IEEE
 
» Robust Collaborative Spectrum Sensing Using PHY-Layer Fingerprints in Mobile Cognitive Radio Networks
Abstract:
Collaborative spectrum sensing has been proposed to significantly improve the performance of spectrum sensing in cognitive radio networks (CRNs). However, a serious attack, called a primary user emulation attack (PUEA), could decrease the performance of collaborative spectrum sensing. In this letter, according to mobile CRNs, we propose a novel robust collaborative spectrum sensing method using physical-layer fingerprints power in a multipath Rayleigh fading channel. Moreover, a fingerprints-power-belief-based noncentral detection algorithm is designed to defend against PUEAs. Simulation results show that our proposed method can rapidly detect the presence of a primary user under PUEAs with good performance.
Autors: Ning Gao;Xiaojun Jing;Hai Huang;Junsheng Mu;
Appeared in: IEEE Communications Letters
Publication date: May 2017, volume: 21, issue:5, pages: 1063 - 1066
Publisher: IEEE
 
» Robust Detection of Cyber Attacks on State Estimators Using Phasor Measurements
Abstract:
This letter proposes a statistical consistency check-based imperfect false data injection attacks detector that is more effective than the conventional residual-based methods. It is shown that the proposed detector could detect attacks with high probability by using a limited number of secure PMU measurements even if the probability of the false alarm is low. Numerical results validate its effectiveness and practicability.
Autors: Junbo Zhao;Gexiang Zhang;Rabih A. Jabr;
Appeared in: IEEE Transactions on Power Systems
Publication date: May 2017, volume: 32, issue:3, pages: 2468 - 2470
Publisher: IEEE
 
» Robust Estimation and Protection of Locked Charge in Grinding Mills
Abstract:
The most critical equipment in mining concentrator plants is the grinding mill. One very important charge condition that can produce a catastrophic failure during the starting of grinding mills is the compacting of the load inside the mill, especially after long downtimes. This condition is usually named frozen charge. The load remains locked to the mill shell and after a half-turn drops damaging the mill body and bearings. Therefore, the mill protection system must give an early detection of this condition and abort the start before the compacted mill charge falls hitting the bottom of the mill shell. This paper presents the development of a robust estimation and protection algorithm of locked charge condition based on drive and process signals. The proposed algorithm includes a locked charge model that permits to define torque, root mean square (rms) current, and power thresholds for the existing starting charge conditions, and compares the locked charge signals to the online estimation of drive variables. This comparison is used to detect the beginning of charge cascading and the threshold values are used to command the termination of the start-up. Proposed algorithm was evaluated using field records of an 8.2 MW semiautogenous mill drive confirming its ability to handle startups with different charge conditions.
Autors: Pablo Castro Palavicino;M. Aníbal Valenzuela;
Appeared in: IEEE Transactions on Industry Applications
Publication date: May 2017, volume: 53, issue:3, pages: 2608 - 2620
Publisher: IEEE
 
» Robust Finite-Time Tracking of Stewart Platform: A Super-Twisting Like Observer-Based Forward Kinematics Solution
Abstract:
The paper presents the robust finite-time tracking of Stewart platform using super-twisting sliding mode observer based forward kinematics solution. The forward kinematics problem—finding the states from the leg length measurements—in Stewart platform gives nonunique solution for any given leg lengths, and due to this it poses difficulties in the control design. The super-twisting observer is designed from the available leg length measurements which is the output of the system to obtain the position and orientation of movable platform and their respective derivatives. The finite-time convergence of this observer and the stability of the closed loop system are presented in detail. It is shown that using this leg length measurements, the states of the observer converge to actual state in finite-time and hence, it provides a solution to the forward kinematics problem. Using these estimated states, a robust finite-time tracking control is designed for the Stewart platform by considering all the uncertainties and parameter variations. However, the proposed method for forward kinematics solution can also be incorporated with any other control strategies. Simulation results are given to demonstrate the performance of the proposed observer for the Stewart platform.
Autors: P. R. Kumar;Abhisek K. Behera;Bijnan Bandyopadhyay;
Appeared in: IEEE Transactions on Industrial Electronics
Publication date: May 2017, volume: 64, issue:5, pages: 3776 - 3785
Publisher: IEEE
 
» Robust Generalized Low-Rank Decomposition of Multimatrices for Image Recovery
Abstract:
Low-rank approximation has been successfully used for dimensionality reduction, image noise removal, and image restoration. In existing work, input images are often reshaped to a matrix of vectors before low-rank decomposition. It has been observed that this procedure will destroy the inherent two-dimensional correlation within images. To address this issue, the generalized low-rank approximation of matrices (GLRAM) method has been recently developed, which is able to perform low-rank decomposition of multiple matrices directly without the need for vector reshaping. In this paper, we propose a new robust generalized low-rank matrices decomposition method, which further extends the existing GLRAM method by incorporating rank minimization into the decomposition process. Specifically, our method aims to minimize the sum of nuclear norms and -norms. We develop a new optimization method, called alternating direction matrices tri-factorization method , to solve the minimization problem. We mathematically prove the convergence of the proposed algorithm. Our extensive experimental results demonstrate that our method significantly outperforms existing GLRAM methods.
Autors: Hengyou Wang;Yigang Cen;Zhihai He;Ruizhen Zhao;Yi Cen;Fengzhen Zhang;
Appeared in: IEEE Transactions on Multimedia
Publication date: May 2017, volume: 19, issue:5, pages: 969 - 983
Publisher: IEEE
 
» Robust Infrared Maritime Target Detection Based on Visual Attention and Spatiotemporal Filtering
Abstract:
It has always been a great challenge to efficiently detect small infrared targets from complex image backgrounds without any prior knowledge. This is especially true when both strong and weak targets appear in the same image or when the weak targets come up on image borders. The main contribution of this paper is to design a robust infrared maritime target detection method, in which a visual attention and pipeline-filtering model is proposed by integrating a revised visual attention model (VAM) and the antivibration pipeline-filtering algorithm. The revised VAM, a single-frame target detection strategy, will first compute a saliency map (SM) from a specific modality, which is automatically selected according to image background smoothness. Then, an automatic strategy for extracting suspected targets from an SM is also proposed here, which highlights targets and suppresses background clutters in SMs through local saliency singularity evaluation. Moreover, contrary to the original VAM, we adopt border saliency preservation in center-surround difference so that robust detection can be guaranteed for targets on image borders. Finally, to eliminate the interference of sea glints and confirm real targets, we adopt the antivibration pipeline-filtering algorithm, a multiframe-based clutter removal method. Compared with the original VAM and two other existing target detection algorithms, experimental results have proven that our strategy can detect infrared maritime targets much better under different environmental conditions. This research can significantly improve the success rate and efficiency of searching maritime targets in different weathers using infrared imager, especially in heavy sea fog and strong ocean waves.
Autors: Lili Dong;Bin Wang;Ming Zhao;Wenhai Xu;
Appeared in: IEEE Transactions on Geoscience and Remote Sensing
Publication date: May 2017, volume: 55, issue:5, pages: 3037 - 3050
Publisher: IEEE
 
» Robust Matrix Discriminative Analysis for Feature Extraction From Hyperspectral Images
Abstract:
Linear discriminative analysis (LDA) is an effective feature extraction method for hyperspectral image (HSI) classification. Most of the existing LDA-related methods are based on spectral features, ignoring spatial information. Recently, a matrix discriminative analysis (MDA) model has been proposed to incorporate the spatial information into the LDA. However, due to sensor interferers, calibration errors, and other issues, HSIs can be noisy. These corrupted data easily degrade the performance of the MDA. In this paper, a robust MDA (RMDA) model is proposed to address this important issue. Specifically, based on the prior knowledge that the pixels in a small spatial neighborhood of the HSI lie in a low-rank subspace, a denoising model is first employed to recover the intrinsic components from the noisy HSI. Then, the MDA model is used to extract discriminative spatial–spectral features from the recovered components. Besides, different HSIs exhibit different spatial contextual structures, and even a single HSI may contain both large and small homogeneous regions simultaneously. To sufficiently describe these multiscale spatial structures, a multiscale RMDA model is further proposed. Experiments have been conducted using three widely used HSIs, and the obtained results show that the proposed method allows for a significant improvement in the classification performance when compared to other LDA-based methods.
Autors: Renlong Hang;Qingshan Liu;Yubao Sun;Xiaotong Yuan;Hucheng Pei;Javier Plaza;Antonio Plaza;
Appeared in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication date: May 2017, volume: 10, issue:5, pages: 2002 - 2011
Publisher: IEEE
 
» Robust MMSE Beamforming for Multiantenna Relay Networks
Abstract:
In this paper, we propose a robust minimum mean square error (MMSE)-based beamforming technique for multiantenna relay broadcast channels, where a multiantenna base station transmits signal to single antenna users with the help of a multiantenna relay. The signal transmission from the base station to the single antenna users is completed in two time slots, where the relay receives the signal from the base station in the first time slot, and it then forwards the received signal to different users based on amplify and forward (AF) protocol. We propose a robust beamforming technique for a sum-power minimization problem with an imperfect channel state information (CSI) between the relay and the users. This robust scheme is developed based on the worst-case optimization framework and the Nemirovski Lemma by incorporating uncertainties in the CSI. The original optimization problem is divided into three subproblems due to joint nonconvexity in terms of beamforming vectors at the base station, the relay amplification matrix, and receiver coefficients. These subproblems are formulated into a convex optimization framework by exploiting the Nemirovski Lemma, and an iterative algorithm is developed by alternatively optimizing each of them with channel uncertainties. In addition, we provide an optimization framework to evaluate the achievable worst-case mean square error (MSE) of each user for a given set of design parameters. Simulation results are provided to validate the convergence of the proposed algorithm.
Autors: Kanapathippillai Cumanan;Zhiguo Ding;Yogachandran Rahulamathavan;Mehdi M. Molu;Hsiao-Hwa Chen;
Appeared in: IEEE Transactions on Vehicular Technology
Publication date: May 2017, volume: 66, issue:5, pages: 3900 - 3912
Publisher: IEEE
 
» Robust Object Tracking via Locality Sensitive Histograms
Abstract:
This paper presents a novel locality sensitive histogram (LSH) algorithm for visual tracking. Unlike the conventional image histogram that counts the frequency of occurrence of each intensity value by adding ones to the corresponding bin, an LSH is computed at each pixel location, and a floating-point value is added to the corresponding bin for each occurrence of an intensity value. The floating-point value exponentially reduces with respect to the distance to the pixel location where the histogram is computed. An efficient algorithm is proposed that enables the LSHs to be computed in time linear in the image size and the number of bins. In addition, this efficient algorithm can be extended to exploit color images. A robust tracking framework based on the LSHs is proposed, which consists of two main components: a new feature for tracking that is robust to illumination change and a novel multiregion tracking algorithm that runs in real time even with hundreds of regions. Extensive experiments demonstrate that the proposed tracking framework outperforms the state-of-the-art methods in challenging scenarios, especially when the illumination changes dramatically. Evaluation using the latest benchmark shows that our algorithm is the top performer.
Autors: Shengfeng He;Rynson W. H. Lau;Qingxiong Yang;Jiang Wang;Ming-Hsuan Yang;
Appeared in: IEEE Transactions on Circuits and Systems for Video Technology
Publication date: May 2017, volume: 27, issue:5, pages: 1006 - 1017
Publisher: IEEE
 
» Robust Operation of Distribution Networks Coupled With Urban Transportation Infrastructures
Abstract:
We study the energy dispatch of power distribution networks (PDNs) coupled with urban transportation networks. The electricity demand at each charging/swapping facility is influenced by the arrival rates and charging requests of electric vehicles, which further depends on the spatial distribution of traffic flows over the entire transportation system. We consider the impact of the road congestion on route choices of vehicles from a system-level perspective. The traffic flow pattern in steady state is characterized by the Wardrop user equilibrium. We consider the PDN load perturbation caused by the traffic demand uncertainty, and propose a robust dispatch method that maintains the feasibility of an alternating current power flow constraints. By applying the convex relaxation to nonlinear branch power flow equations, the proposed model yields a two-stage robust convex optimization problem with an implicit uncertainty set. Moreover, a decomposition framework is proposed, in which the first phase determines the uncertainty set of electricity demand by solving two traffic assignment problems associated with the extreme scenarios, and the second phase solves a two-stage robust second-order cone program following a delayed constraint generation framework. Several issues regarding the scalability and conservatism are elaborated. Case studies corroborate the applicability and efficiency of the proposed method.
Autors: Wei Wei;Shengwei Mei;Lei Wu;Jianhui Wang;Yujuan Fang;
Appeared in: IEEE Transactions on Power Systems
Publication date: May 2017, volume: 32, issue:3, pages: 2118 - 2130
Publisher: IEEE
 
» Robust Registration of Multimodal Remote Sensing Images Based on Structural Similarity
Abstract:
Automatic registration of multimodal remote sensing data [e.g., optical, light detection and ranging (LiDAR), and synthetic aperture radar (SAR)] is a challenging task due to the significant nonlinear radiometric differences between these data. To address this problem, this paper proposes a novel feature descriptor named the histogram of orientated phase congruency (HOPC), which is based on the structural properties of images. Furthermore, a similarity metric named HOPCncc is defined, which uses the normalized correlation coefficient (NCC) of the HOPC descriptors for multimodal registration. In the definition of the proposed similarity metric, we first extend the phase congruency model to generate its orientation representation and use the extended model to build HOPCncc. Then, a fast template matching scheme for this metric is designed to detect the control points between images. The proposed HOPCncc aims to capture the structural similarity between images and has been tested with a variety of optical, LiDAR, SAR, and map data. The results show that HOPCncc is robust against complex nonlinear radiometric differences and outperforms the state-of-the-art similarities metrics (i.e., NCC and mutual information) in matching performance. Moreover, a robust registration method is also proposed in this paper based on HOPCncc, which is evaluated using six pairs of multimodal remote sensing images. The experimental results demonstrate the effectiveness of the proposed method for multimodal image registration.
Autors: Yuanxin Ye;Jie Shan;Lorenzo Bruzzone;Li Shen;
Appeared in: IEEE Transactions on Geoscience and Remote Sensing
Publication date: May 2017, volume: 55, issue:5, pages: 2941 - 2958
Publisher: IEEE
 
» Robust Resource Allocation for MIMO Wireless Powered Communication Networks Based on a Non-Linear EH Model
Abstract:
In this paper, we consider a multiple-input multiple-output wireless powered communication network, where multiple users harvest energy from a dedicated power station in order to be able to transmit their information signals to an information receiving station. Employing a practical non-linear energy harvesting (EH) model, we propose a joint time allocation and power control scheme, which takes into account the uncertainty regarding the channel state information (CSI) and provides robustness against imperfect CSI knowledge. In particular, we formulate two non-convex optimization problems for different objectives, namely system sum throughput maximization and the maximization of the minimum individual throughput across all wireless powered users. To overcome the non-convexity, we apply several transformations along with a one-dimensional search to obtain an efficient resource allocation algorithm. Numerical results reveal that a significant performance gain can be achieved when the resource allocation is designed based on the adopted non-linear EH model instead of the conventional linear EH model. Besides, unlike a non-robust baseline scheme designed for perfect CSI, the proposed resource allocation schemes are shown to be robust against imperfect CSI knowledge.
Autors: Elena Boshkovska;Derrick Wing Kwan Ng;Nikola Zlatanov;Alexander Koelpin;Robert Schober;
Appeared in: IEEE Transactions on Communications
Publication date: May 2017, volume: 65, issue:5, pages: 1984 - 1999
Publisher: IEEE
 
» Robust Tracking Control of Networked Control Systems With Communication Constraints and External Disturbance
Abstract:
This paper addresses the problem of robust tracking control of networked control systems with communication constraints (network-induced delay, packet dropouts, and packet disorder) and external disturbance. A novel networked predictive control algorithm based on k-order adaptive discrete-time sliding-mode control (k -ADSMC) is proposed. The ADSMC, which can self-adapting adjust sliding-mode parameters and ensure faster convergence, is designed based on the error and error difference of tracking output. Furthermore, k -ADSMC is designed, which uses the estimated disturbance as weight factors and synthesizes the sliding- mode values of all k times, in order to improve the robust of tracking control. Finally, the stability of the system is proved, and a dc motor servo system is utilized to verify the effectiveness of the proposed method.
Autors: Meng Li;Yong Chen;
Appeared in: IEEE Transactions on Industrial Electronics
Publication date: May 2017, volume: 64, issue:5, pages: 4037 - 4047
Publisher: IEEE
 
» Robust Visual Tracking With Multitask Joint Dictionary Learning
Abstract:
Dictionary learning for sparse representation has been increasingly applied to object tracking, however, the existing methods only utilize one modality of the object to learn a single dictionary. In this paper, we propose a robust tracking method based on multitask joint dictionary learning. Through extracting different features of the target, multiple linear sparse representations are obtained. Each sparse representation can be learned by a corresponding dictionary. Instead of separately learning the multiple dictionaries, we adopt a multitask learning approach to learn the multiple linear sparse representations, which provide additional useful information to the classification problem. Because different tasks may favor different sparse representation coefficients, yet the joint sparsity may enforce the robustness in coefficient estimation. During tracking, a classifier is constructed based on a joint linear representation, and the candidate with the smallest joint decision error is selected to be the tracked object. In addition, reliable tracking results and augmented training samples are accumulated into two sets to update the dictionaries for classification, which helps our tracker adapt to the fast time-varying object appearance. Both qualitative and quantitative evaluations on CVPR2013 visual tracking benchmark demonstrate that our method performs favorably against state-of-the-art trackers.
Autors: Heng Fan;Jinhai Xiang;
Appeared in: IEEE Transactions on Circuits and Systems for Video Technology
Publication date: May 2017, volume: 27, issue:5, pages: 1018 - 1030
Publisher: IEEE
 
» Robustness Analysis of a Memristive Crossbar PUF Against Modeling Attacks
Abstract:
In the greater context of computer security, hardware security issues such as integrated circuit counterfeiting, cloning, reverse engineering and piracy have emerged as critical issues due in part to an increasingly globalized supply chain. To help combat hardware security vulnerabilities, a wide range of security primitives have emerged in recent years. A popular example is physical unclonable functions (PUFs) that leverage process variations to provide unique signatures or fingerprints that can be used for authentication or secret key generation. Nanoelectronic technologies, such as the memristor technologies considered here, provide an excellent opportunity to engineer dense, energy-efficient PUF circuits with desirable statistical properties. Here, we specifically focus on the design considerations of a memristive crossbar based PUF that generates response bits as a function of variable memristor switching time. In addition to describing the operation of the crossbar PUF, we also consider its resilience to two specific machine learning attacks, specifically through the use of linear regression and support vector machines. Two circuit design modifications for the crossbar PUF are provided to improve the resilience to machine learning attacks: XORing of response bits and internal column swapping. We show that the design modifications lead to a reduction in the likelihood of successful attack to about 50% (near ideal) even given 5000 iterations for the attack itself. We also provide power estimates and performance considerations for the crossbar PUF based on three specific memristive material stacks: hafnium-oxide, tantalum-oxide, and titanium-oxide.
Autors: Mesbah Uddin;Md. Badruddoja Majumder;Garrett S. Rose;
Appeared in: IEEE Transactions on Nanotechnology
Publication date: May 2017, volume: 16, issue:3, pages: 396 - 405
Publisher: IEEE
 
» Robustness of Electricity and Chilled Water Supply Systems Subject to Change Technical and Economic
Abstract:
This paper presents a methodology to evaluate the economic robustness of thermal systems designed to supply air conditioning and electricity for shopping malls in an Brazilian northeastern state. The procedure uses a hybrid method to minimize the Net Present Value (NPV) of the life costs. The optimization considers technical and economic aspects such as curves of demand, tariff variation along the day, equipment efficiencies and costs. It was studied the robustness of the systems in relation to variations of fuel tariff, electricity tariff, engine price, dollar fluctuation and demand profile.
Autors: Lucas Ademar Freitas;Fabio Santana Magnani;Eric Monroe Hornsby;
Appeared in: IEEE Latin America Transactions
Publication date: May 2017, volume: 15, issue:5, pages: 908 - 915
Publisher: IEEE
 
» Rotated Nonuniform Subgrids in the FDTD Method With Application to a Hemispherical Antenna Array
Abstract:
The use of subgrids in the finite-difference time-domain method to facilitate the analysis of multiscale problems is now well established. However, many of the proposed algorithms are restricted to cases where the subgrid and the main grid share the same Cartesian coordinate system and where the ratio of the cell sizes in the two grids has a constant integer ratio. More recently, it has been shown that subgrids, based on Cartesian grids which are rotated with respect to the main grid, can be effectively used, but the cell size ratio was still kept constant. In this contribution, the method is further generalized in order to allow nonuniform subgrids to be used. This greatly increases the range of structures which can be efficiently analyzed. The effectiveness of the method is demonstrated by application to a 31-element hemispherical array of broadband cavity backed slot antenna elements.
Autors: Chris J. Railton;
Appeared in: IEEE Transactions on Antennas and Propagation
Publication date: May 2017, volume: 65, issue:5, pages: 2460 - 2466
Publisher: IEEE
 
» Rule-Based Data-Driven Analytics for Wide-Area Fault Detection Using Synchrophasor Data
Abstract:
Synchrophasor technology, also known as wide-area monitoring system technology, utilizes phasor measurement unit (PMU) to monitor real-time system data, which can provide unique insights into the operation of a power grid. In this paper, a rule-based data-driven analytics method for wide-area fault detection in a power system using synchrophasor data is proposed. As a data-driven approach, this method relies on rules created using PMU measurement data, and does not require knowledge of the power system's topology and model. It can detect fault location (bus and line) and fault type for a particular fault event. Three common types of short circuit faults in a power grid, single-line-to-ground, line-to-line, and three-phase faults, can be identified using the proposed method. Fault thresholds used in rules are determined based on theoretical values and recorded PMU data during fault events in Bonneville power administration (BPA)'s large power grid. The proposed method is validated by comparing with the recorded field data for fault events provided by BPA. It is found that it can effectively detect most faults with a great accuracy. It has been developed into a software program, and can be readily used by utility companies.
Autors: Xiaodong Liang;Scott A. Wallace;Duc Nguyen;
Appeared in: IEEE Transactions on Industry Applications
Publication date: May 2017, volume: 53, issue:3, pages: 1789 - 1798
Publisher: IEEE
 
» Safety and Standards ? Closely Interlinked [Standards News]
Abstract:
Examines the relationship between safety and standards development. Standards may be divided into two distinct categories, safety and performance. In the first instance, safety standards provide rules, recommendations, and mitigation techniques to eliminate or minimize risks associated with the identified hazards. Legal requirements are normally issued in the form of standards. In the second circumstance, performance standards define the performance of a product or the practices under a certain set of conditions for evaluation and application between products of various suppliers.
Autors: Daleep Mohla;
Appeared in: IEEE Industry Applications Magazine
Publication date: May 2017, volume: 23, issue:3, pages: 71 - 72
Publisher: IEEE
 
» Safety in Vehicle Platooning: A Systematic Literature Review
Abstract:
Vehicle platooning has been studied for several decades, with objectives such as improved traffic throughput on existing infrastructure or reduced energy consumption. All the time, it has been apparent that safety is an important issue. However, there are no comprehensive analyses of what is needed to achieve safety in platooning, but only scattered pieces of information. This paper investigates, through a systematic literature review, what is known about safety for platooning, including what analysis methods have been used, what hazards and failures have been identified, and solution elements that have been proposed to improve safety. Based on this, a gap analysis is performed to identify outstanding questions that need to be addressed in future research. These include dealing with a business ecosystem of actors that cooperate and compete around platooning, refining safety analysis methods to make them suitable for systems-of-systems, dealing with variability in vehicles, and finding solutions to various human factors issues.
Autors: Jakob Axelsson;
Appeared in: IEEE Transactions on Intelligent Transportation Systems
Publication date: May 2017, volume: 18, issue:5, pages: 1033 - 1045
Publisher: IEEE
 
» Sampled Observability and State Estimation of Linear Discrete Ensembles
Abstract:
We consider the problem of reconstructing the initial states of a finite group of structurally identical linear systems in the situation that output measurements of the individual systems are received at discrete time steps and in an anonymized manner: While we do know all output measurements of the individual systems in the group, we do not know which output measurement corresponds to which system. This state estimation problem addresses the essence of state estimation problems for populations, in which the output measurements of the individual systems are given only as statistics. We adopt a measure theoretical approach in which the group is modelled by an LTI system describing the structure of the individual systems and an initial state which is expressed by a discrete measure. In this framework we derive a geometric characterization for the state estimation to admit a unique solution, which combined with a result on the observability of linear systems under irregular sampling, yields a sufficient condition for the sampled observability of discrete ensembles. As a supplement to our theoretical findings, we provide illustrations by means of simulation examples. Furthermore we consider the practical state estimation problem under noisy output measurements.
Autors: Shen Zeng;Hideaki Ishii;Frank Allgöwer;
Appeared in: IEEE Transactions on Automatic Control
Publication date: May 2017, volume: 62, issue:5, pages: 2406 - 2418
Publisher: IEEE
 
» Sampled-Data Stabilization of Nonlinear Dynamics With Input Delays Through Immersion and Invariance
Abstract:
In this technical note, we show that Immersion and Invariance is a natural framework for the design of sampled-data stabilizing controllers for input-delayed systems. Assuming the existence of a continuous-time feedback in the delay free case, Immersion and Invariance stabilizability of the equivalent sampled-data dynamics is proven. The proof is constructive for the stabilizing controller. Two simulated examples illustrate the performances.
Autors: Salvatore Monaco;Dorothée Normand-Cyrot;Mattia Mattioni;
Appeared in: IEEE Transactions on Automatic Control
Publication date: May 2017, volume: 62, issue:5, pages: 2561 - 2567
Publisher: IEEE
 
» SAR Image Content Retrieval Based on Fuzzy Similarity and Relevance Feedback
Abstract:
This paper presents a new content-based synthetic aperture radar (SAR) image retrieval method to search out SAR image patches, which consists of two essential parts: an initial retrieval and later refined results. To obtain the proper initial retrievals, we develop a similarity measure named region-based fuzzy matching (RFM) to evaluate the similarities between SAR image patches. First, to reduce the negative influence of speckle noise, we segment the SAR image patches into brightness-texture regions at the superpixel level rather than the pixel level. Second, a multiscale edge detector is utilized to resolve the multiscale property of the SAR image patches, and then the edge regions of the SAR image patches are defined by those edge features. Third, to overcome the segmented uncertainty and the blurry boundaries, the obtained regions are described by fuzzy features. Finally, the RFM similarity between two SAR image patches is converted into the linear summation of the resemblance between different fuzzy feature sets. After we obtain the initial retrieval results, the multiple relevance feedback (MRF) scheme is proposed to refine the original ranked list. In this scheme, different relevance feedback approaches are carried out respectively, and then their results are fused to improve the initial retrieval. In addition, a new kernel function based on the RFM measure is developed for MRF. The encouraging experimental results counted on a manually constructed ground truth SAR image patch dataset demonstrate that our retrieval method is effective for SAR images compared with some existing approaches proposed in the remote sensing community
Autors: Xu Tang;Licheng Jiao;William J. Emery;
Appeared in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication date: May 2017, volume: 10, issue:5, pages: 1824 - 1842
Publisher: IEEE
 
» SAR Image Denoising via Sparse Representation in Shearlet Domain Based on Continuous Cycle Spinning
Abstract:
How to suppress speckle noise effectively has become one of the key problems in remote sensing image processing. This problem also restricts the development of key technology severely, especially in military applications and so on. To overcome the shortcoming that the optimal solution of image denoising based on sparse representation does not have one-to-one mapping of the original signal space, in this paper, we propose a novel synthetic aperture radar (SAR) image denoising via sparse representation in Shearlet domain based on continuous cycle spinning. First, the Shearlet transform is applied to the noised SAR image. Second, a new optimal denoising model is constructed using the sparse representation model based on the cycle spinning theory. Finally, the alternate iteration algorithm is used to solve the optimal denoising model to obtain the denoised image. The experimental results show that the proposed method not only effectively suppresses the speckle noise and improves the peak signal-to-noise ratio of denoising SAR image, but also obviously improves the visual effect of the SAR image, especially by enhancing the texture of the SAR image.
Autors: Shuaiqi Liu;Ming Liu;Peifei Li;Jie Zhao;Zhihui Zhu;Xuehu Wang;
Appeared in: IEEE Transactions on Geoscience and Remote Sensing
Publication date: May 2017, volume: 55, issue:5, pages: 2985 - 2992
Publisher: IEEE
 
» Satellite-Based Nowcasting of Extreme Rainfall Events Over Western Himalayan Region
Abstract:
Western Himalayan (WH) region is considered to be one of the most vulnerable spots for flash flooding-related natural disasters in the world. The confluence of moist air advected from Arabian sea with complex terrain has produced a series of extreme rainfall triggered disasters in recent past. However, the causal events leading to these enhanced episodes of precipitation lack clarity and thus flash flood forecasting still remains a big challenge. In this paper, we address the problem by studying cloud development over this region and its relationship with the underlying topography. Our results demonstrate that WH region is mostly inhabited by low-medium level clouds and governed by warm rain processes. Satellite-based analysis shows that in comparison to cloud top temperature, cloud top cooling rate (CTCR) is a better indicator for extreme rain producing events over this region. A model for nowcasting of extreme orographic rain events has thus been developed using the spatial characteristics of CTCR to predict potential locations for orographically induced severe precipitation events. The heavy rainfall nowcasts produced by this methodology, when compared with global precipitation fields, show very encouraging results. The probability of positive identification of a heavy rainfall event is 82.8%, with a false alarm rate of 29.7% and average lead time of 2–3 h. The insights provided by this study will give an impetus to the flash flood advance warning over WH region bringing about a significant beneficial societal impact.
Autors: Bipasha Paul Shukla;C. M. Kishtawal;Pradip K. Pal;
Appeared in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication date: May 2017, volume: 10, issue:5, pages: 1681 - 1686
Publisher: IEEE
 
» Scalable Design of Heterogeneous Networks
Abstract:
A systematic approach to the analysis and design of a class of large dynamical systems is presented. The approach allows decentralized control laws to be designed independently using only local subsystem models. Design can be conducted using standard techniques, including loopshaping based on Nyquist and Popov plots, methods, and -synthesis procedures. The approach is applied to a range of network models, including those for consensus, congestion control, electrical power systems, and distributed optimization algorithms subject to delays.
Autors: Richard Pates;Glenn Vinnicombe;
Appeared in: IEEE Transactions on Automatic Control
Publication date: May 2017, volume: 62, issue:5, pages: 2318 - 2333
Publisher: IEEE
 
» Scanning the Issue
Abstract:
Safety in Vehicle 1 Platooning: A Systematic Literature Review
Autors: Petros Ioannou;
Appeared in: IEEE Transactions on Intelligent Transportation Systems
Publication date: May 2017, volume: 18, issue:5, pages: 1029 - 1032
Publisher: IEEE
 
» Scattering Property Analysis of Supraglacial Debris Using Target Decomposition on Polarimetric SAR Imagery
Abstract:
Supraglacial debris is widely distributed in the ablation zones of the glaciers in mountain valleys, and it influences glacier melting considerably. Polarimetric Synthetic Aperture Radar (SAR) presents promising results in terms of glacier classification and monitoring, but the scattering mechanism of debris has been unclear until now. In this paper, we attempted to verify the main scattering components of debris in the L- and C-bands polarimetric SAR images. A newly developed target decomposition method that is specially designed for debris is used to quantitatively analyze the scattering component. The method combines the X-Bragg surface scattering, double bounce, and completely random volume scattering models. The results from the target decomposition agree well with the scattering property analysis from the phase difference and entropy-alpha methods. The Keqikaer glacier, which is in the southern Tianshan Mountains, is selected as the study area. Phased-Array L-band SAR (PALSAR) images from the Advanced Land Observing Satellite (ALOS), PALSAR-2 images from ALOS-2, and RADARSAT-2 polarimetric SAR images are employed. The results show that in the C-band, surface scattering is dominant in debris, and it accounts for approximately 70% of the total power; in the L-band, volume scattering increases to a larger portion (approximately 40%), but remains slightly weaker than surface scattering (approximately 56%).
Autors: Lei Huang;Bang-sen Tian;Zhen Li;Jian-min Zhou;
Appeared in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication date: May 2017, volume: 10, issue:5, pages: 1843 - 1852
Publisher: IEEE
 
» Scenario Reduction With Submodular Optimization
Abstract:
Stochastic programming methods have been proven to deal effectively with the uncertainty and variability of renewable generation resources. However, the quality of the solution that they provide (as measured by cost and reliability metrics) depends on the accuracy and the number of scenarios used to model this uncertainty and variability. Scenario reduction techniques are used to manage the computational burden by selecting representative scenarios. The common drawback of existing scenario reduction techniques is that the number of representative scenarios is a user-defined parameter. We propose a scenario reduction algorithm based on submodular function optimization to endogenously optimize the number of scenarios as well as rank these scenarios. This algorithm is compared, both qualitatively and quantitatively, with the state-of-the-art fast forward selection algorithm.
Autors: Yishen Wang;Yuzong Liu;Daniel S. Kirschen;
Appeared in: IEEE Transactions on Power Systems
Publication date: May 2017, volume: 32, issue:3, pages: 2479 - 2480
Publisher: IEEE
 
» Schottky Barrier in Organic Transistors
Abstract:
Organic FETs (OFETs) are essential devices in future flexible electronics. Yet, a crucial issue associated with electronic contact is still unsolved and our fundamental understanding remains very limited. Unlike many other previous reports talking about the contact resistance, in this paper, we specifically discuss its major root: the Schottky barrier, by comparison of the conventional metal-silicon contacts, and the unconventional metal–organic contacts, where the special features in OFETs are underlined. We not only examine the basics of the Schottky barrier but also the extrinsic effects as well as the characterization methods. The key factors in device fabrication are also reviewed in order to minimize the detrimental impacts of the Schottky barrier for obtaining optimum device performance.
Autors: Yong Xu;Huabin Sun;Yong-Young Noh;
Appeared in: IEEE Transactions on Electron Devices
Publication date: May 2017, volume: 64, issue:5, pages: 1932 - 1943
Publisher: IEEE
 
» Screening Tests for Lasso Problems
Abstract:
This paper is a survey of dictionary screening for the lasso problem. The lasso problem seeks a sparse linear combination of the columns of a dictionary to best match a given target vector. This sparse representation has proven useful in a variety of subsequent processing and decision tasks. For a given target vector, dictionary screening quickly identifies a subset of dictionary columns that will receive zero weight in a solution of the corresponding lasso problem. These columns can be removed from the dictionary prior to solving the lasso problem without impacting the optimality of the solution obtained. This has two potential advantages: it reduces the size of the dictionary, allowing the lasso problem to be solved with less resources, and it may speed up obtaining a solution. Using a geometrically intuitive framework, we provide basic insights for understanding useful lasso screening tests and their limitations. We also provide illustrative numerical studies on several datasets.
Autors: Zhen James Xiang;Yun Wang;Peter J. Ramadge;
Appeared in: IEEE Transactions on Pattern Analysis and Machine Intelligence
Publication date: May 2017, volume: 39, issue:5, pages: 1008 - 1027
Publisher: IEEE
 
» SDBD: A Hierarchical Region-of-Interest Detection Approach in Large-Scale Remote Sensing Image
Abstract:
Region-of-interest (ROI) detection techniques are of great importance in the analysis of remote sensing images, especially in target detection, since the size of the image to be dealt with grows substantially with the improvement of spatial resolution. Most of current studies are not aiming at the specific type of object area detection, and the processed images are rather small compared to the size of the raw data acquired by high-resolution satellite. In this letter, a hierarchical task-driven ROI detection method, based on saliency and density, is proposed to address the detection of the potential object areas in large-scale remote sensing images. The proposed saliency and density-based detection method (SDBD) integrates bottom–up and top–down strategies, where the saliency-based multilevel histogram contrast is presented in the bottom–up phase to obtain the preliminary regions, while the centroid density distribution index (CDDI) is defined in the top–down scheme to refine the previous results. Specifically, superpixel segmentation is introduced in this letter to narrow down the ROI candidates. SDBD is capable of extracting ROI of different objects by adjusting the threshold of CDDI. The experiments are conducted on two data sets to extract ROIs of storage tanks and residence. Experimental results demonstrate that the proposed method is effective in identifying ROI in large-scale data.
Autors: Tong Li;Junping Zhang;Xiaochen Lu;Ye Zhang;
Appeared in: IEEE Geoscience and Remote Sensing Letters
Publication date: May 2017, volume: 14, issue:5, pages: 699 - 703
Publisher: IEEE
 
» SDN-Based Anchor Scheduling Scheme for Localization in Heterogeneous WSNs
Abstract:
In this letter, the anchor scheduling scheme for localization in heterogeneous wireless sensor networks is studied. In order to minimize the number of actively participating anchors to prolong the network lifetime, we propose a centralized anchor scheduling scheme on the basis of the software-defined networking (SDN) paradigm. First, an expression evaluating the connectivity degree of an agent is derived and used to judge if this agent has desired number of connected anchors for its localization. Then, the state of each anchor is determined by the SDN controller through a flow table via sensor OpenFlow. Simulations show that the proposed anchor scheduling scheme reduces the number of active anchors and prolongs the network lifetime. It can also be shown that this scheme ensures the desired number of anchors for the localization, and can tradeoff the localization accuracy for energy by ensuring a better balance of energy consumption among minimum number of active anchors.
Autors: Yaping Zhu;Feng Yan;Yueyue Zhang;Rui Zhang;Lianfeng Shen;
Appeared in: IEEE Communications Letters
Publication date: May 2017, volume: 21, issue:5, pages: 1127 - 1130
Publisher: IEEE
 

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