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

» Plane Wave Scattering by a Conducting Cylinder Located Near an Interface Between Two Dielectric Half-Spaces: A Perturbation Method
Abstract:
A new and simple method is presented for transverse electric and transverse magnetic plane waves scattering by a perfectly conducting cylinder located near two-media interface. A particular formulation of the surface equivalence principle is employed to form a set of coupled integral equations by considering the interface as a scatterer. In this method, the infinite planar interface is represented by a single equivalent magnetic current which is expressed in terms of the difference in current with cylinder present and the exact solution with cylinder removed. Data for the induced current, near and far field are compared to the results available in the literature for verification.
Autors: Cengiz Ozzaim;
Appeared in: IEEE Transactions on Antennas and Propagation
Publication date: May 2017, volume: 65, issue:5, pages: 2754 - 2758
Publisher: IEEE
 
» Planning of Renewable Generation in Distribution Systems Considering Daily Operating Periods
Abstract:
This paper presents an optimization approach for planning of renewable generation in energy distribution systems. The considered generations are based on wind turbines and photovoltaic panels. The objective is to determine the optimal placement of the renewable distributed generation aiming at minimizing the investment and operational costs in a long term planning. The proposed approach represents the stochastic behavior of the renewable resources by handling historical data to determine the wind and photovoltaic capacity factors. The method for obtaining such factors considers the daily load periods, which allows representing the availability of the energetic resources in different periods. The optimization method is based on the bioinspired metaheuristic known as artificial immune system. Two systems of the literature and practical wind and solar data from Brazil are used to assess the proposed approach.
Autors: Leonardo Willer Oliveira;Thaisy Cristina Jose Maria;
Appeared in: IEEE Latin America Transactions
Publication date: May 2017, volume: 15, issue:5, pages: 901 - 907
Publisher: IEEE
 
» Platform and Across-Swath Comparison of Vorticity Spectra From QuikSCAT, ASCAT-A, OSCAT, and ASCAT-B Scatterometers
Abstract:
In the last few years, there has been tremendous improvement in the calibration of ocean surface vector winds from scatterometers and polarimetric radiometers. This is the first detailed investigation of across-swath consistency in scatterometer-derived (i.e., QSCAT, ASCAT-A, OSCAT, and ASCAT-B) vorticity (curl of the ocean surface vector winds). Spatial derivatives of the winds fields are very important for atmospheric boundary-layer processes, upper ocean forcing, and deep ocean forcing. Improvements in wind calibration imply improvements in derivatives of these winds; however, it does not imply consistency. This study demonstrates near consistency in across-swath vorticity and near consistency between platforms.
Autors: Heather M. Holbach;Mark A. Bourassa;
Appeared in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication date: May 2017, volume: 10, issue:5, pages: 2205 - 2213
Publisher: IEEE
 
» PlenoPatch: Patch-Based Plenoptic Image Manipulation
Abstract:
Patch-based image synthesis methods have been successfully applied for various editing tasks on still images, videos and stereo pairs. In this work we extend patch-based synthesis to plenoptic images captured by consumer-level lenselet-based devices for interactive, efficient light field editing. In our method the light field is represented as a set of images captured from different viewpoints. We decompose the central view into different depth layers, and present it to the user for specifying the editing goals. Given an editing task, our method performs patch-based image synthesis on all affected layers of the central view, and then propagates the edits to all other views. Interaction is done through a conventional 2D image editing user interface that is familiar to novice users. Our method correctly handles object boundary occlusion with semi-transparency, thus can generate more realistic results than previous methods. We demonstrate compelling results on a wide range of applications such as hole-filling, object reshuffling and resizing, changing object depth, light field upscaling and parallax magnification.
Autors: Fang-Lue Zhang;Jue Wang;Eli Shechtman;Zi-Ye Zhou;Jia-Xin Shi;Shi-Min Hu;
Appeared in: IEEE Transactions on Visualization and Computer Graphics
Publication date: May 2017, volume: 23, issue:5, pages: 1561 - 1573
Publisher: IEEE
 
» Plug-In Electric Vehicles Parking Lot Equilibria With Energy and Reserve Markets
Abstract:
This paper proposes a comprehensive model for the interactions of the plug-in electric vehicles (PEVs) involved parties. An aggregator with mixed resources is assumed to be the interface between the parking lot (PL) and the upstream energy and reserve markets. On the other hand, the interactions of the PEV owners and the PL are also modeled as they impose restrictions to the PL's behavior. Therefore, a bilevel problem is constructed where in the upper level the objective of the aggregator is to maximize its profit through its interactions, and in the lower level the PL maximizes its own profit limited to the preferences of PEVs. The objectives of the upper and lower levels are contradictory; hence, an equilibrium point should be found to solve the problem. In this regard, the duality theorem is employed to convert the bilevel model to a mathematical program with equilibrium constraints. The model is implemented on the IEEE 37-bus network with added distributed generations. Various cases are thoroughly investigated and conclusions are duly drawn.
Autors: Nilufar Neyestani;Maziar Yazdani Damavandi;Miadreza Shafie-khah;Anastasios G. Bakirtzis;João P. S. Catalão;
Appeared in: IEEE Transactions on Power Systems
Publication date: May 2017, volume: 32, issue:3, pages: 2001 - 2016
Publisher: IEEE
 
» Plug-in Free Web-Based 3-D Interactive Laboratory for Control Engineering Education
Abstract:
This paper introduces the design and implementation of a plug-in free online 3-D interactive laboratory based on networked control system laboratory (NCSLab) framework. The system relying only on HTML5 provides full supports for control engineering experimentation. The users are allowed to design their own control algorithms and apply them to the remote test rigs. Using the web-based interface, multiple widgets such as real-time charts, virtual gauges, and live images are available to customize the monitoring interfaces. To enhance the sense of immersion, 3-D animations which are synchronized with the remote experimental processes are also provided. The users can watch and interact with the remote experiments through the 3-D replicas. Various HTML5 based toolkits are integrated seamlessly under the NCSLab framework. NCSLab provides visualized services for the whole process of control experimentation including remote monitoring, tuning, configuration, and control algorithm implementation. As the network delay could disturb the 3-D representation, a communication scheme using web protocols is also implemented. The feedback from teaching shows the general acceptance and effectiveness of NCSLab is notably high. As most existing online laboratories adopt either native applications or plug-ins, the methodologies and technologies used in NCSLab could be insightful for other online laboratories toward web-based cross-platform systems.
Autors: Wenshan Hu;Zhongcheng Lei;Hong Zhou;Guo-Ping Liu;Qijun Deng;Dongguo Zhou;Zhi-Wei Liu;
Appeared in: IEEE Transactions on Industrial Electronics
Publication date: May 2017, volume: 64, issue:5, pages: 3808 - 3818
Publisher: IEEE
 
» PMCC: Fast and Accurate System-Level Power Modeling for Processors on Heterogeneous SoC
Abstract:
Accurate estimation of power at the system level is essential for system-on-chip (SoC) architects. The integration of heterogeneous processors like CPUs and emerging coarse-grained reconfigurable architectures (CGRAs) in SoCs significantly complicates the power-estimation process. This brief presents an accurate and efficient system-level power modeling framework, power modeling with a customized calibration, for processors on heterogeneous SoCs. Quantitative criteria are developed to classify the computing resources of heterogeneous SoCs, including instruction-driven processing architectures and CGRAs-based architectures, into two categories automatically. A novel power-modeling technique featuring a genetic algorithm and backpropagation neural network (GA-BPNN) is introduced to address CGRA-alike architectures, which cannot be properly handled by the traditional linear regression-based power calibration method. Experimental results show that the power estimation error for CGRAs using GA-BPNN is less than 5% with three orders faster speed compared with gate-level estimations. In the meanwhile, accuracy is improved on most benchmarks compared with the linear model. The average improvement in accuracy is 81% and ranges between 29% and 99%.
Autors: Chenchen Deng;Leibo Liu;Yang Liu;Shouyi Yin;Shaojun Wei;
Appeared in: IEEE Transactions on Circuits and Systems II: Express Briefs
Publication date: May 2017, volume: 64, issue:5, pages: 540 - 544
Publisher: IEEE
 
» Polar Applications of Spaceborne Scatterometers
Abstract:
Wind scatterometers were originally developed for observation of near-surface winds over the ocean. They retrieve wind indirectly by measuring the normalized radar cross section () of the surface, and estimating the wind via a geophysical model function relating to the vector wind. The measurements have proven to be remarkably capable in studies of the polar regions where they can map snow cover; detect the freeze/thaw state of forest, tundra, and ice; map and classify sea ice; and track icebergs. Further, a long time series of scatterometer observations is available to support climate studies. In addition to fundamental scientific research, scatterometer data are operationally used for sea-ice mapping to support navigation. Scatterometers are, thus, invaluable tools for monitoring the polar regions. In this paper, a brief review of some of the polar applications of spaceborne wind scatterometer data is provided. The paper considers both C-band and Ku-band scatterometers, and the relative merits of fan-beam and pencil-beam scatterometers in polar remote sensing are discussed.
Autors: David G. Long;
Appeared in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication date: May 2017, volume: 10, issue:5, pages: 2307 - 2320
Publisher: IEEE
 
» Polarization-Dependent Optical Sensor Based on Reduced Graphene Oxide
Abstract:
We report a polarization-dependent optical sensor based on reduced graphene oxide (rGO) and demonstrate its applications in detecting refractive indices. The s-polarized absorption of the rGO under the conditions of total internal reflection is unique over the range of 400–1100 nm and its amplitude is sensitive to the media that is in contact with the rGO. We have conducted detailed calculations and experiments to optimize the performance of the sensor. The sensitivity of the optical sensor is R/RIU and its resolution is RIU.
Autors: Guohua Liu;Jun Yu;Lixia Xie;Zheng Dou;Wei Zhang;Zhao Yue;
Appeared in: IEEE Photonics Technology Letters
Publication date: May 2017, volume: 29, issue:9, pages: 767 - 770
Publisher: IEEE
 
» Polarization-Independent Backscattering Enhancement of Cylinders Based on Conformal Gradient Metasurfaces
Abstract:
The electromagnetic backscattering enhancement of both elliptic and circular conducting cylinders is investigated in this paper through the design of conformal and polarization-independent gradient metasurfaces. The presented metasurface designs employ varying phase gradient along the circumferential direction of the involved cylinder so that effective retroreflection can be achieved through redirecting the scattering dispersed by the conducting cylinder back to the direction from which the plane electromagnetic wave is coming. Supported by a grounded thin substrate with a relatively high dielectric constant, a modified loop array with compact unit cell is used to implement the nonuniformly or uniformly sampled phase gradient of the metasurface. It is observed that the metasurface-coated elliptic and circular cylinders can generate backscattering very close to that by corresponding flat conducting plates with their main planes perpendicular to the incident wave vector, for both transverse magnetic (TM) and transverse electric (TE) polarizations. Compared with the conducting cylinders without coating, the backscattering is thus effectively enhanced by the conformal gradient metasurfaces. Good agreement between simulated and measured backscattering results validates the observations.
Autors: Yuping Shang;Zhongxiang Shen;
Appeared in: IEEE Transactions on Antennas and Propagation
Publication date: May 2017, volume: 65, issue:5, pages: 2386 - 2396
Publisher: IEEE
 
» Polling and Prediction in the 2016 Presidential Election
Abstract:
In the wake of experts' failure to predict the outcome of the 2016 presidential election, a rigorous analysis of what went right and wrong is needed to improve future polling. Despite claims that "data is dead," low-tech factors such as sampling errors and inaccurate likely-voter models were probably most responsible.
Autors: Nicholas A. Valentino;John Leslie King;Walter W. Hill;
Appeared in: Computer
Publication date: May 2017, volume: 50, issue:5, pages: 110 - 115
Publisher: IEEE
 
» Portable Luminescence Based Fiber Optic Probe for REE Detection and Quantification
Abstract:
A novel luminescence-based sensor was developed for the rapid, insitu detection and quantification of rare earth elements for potential applications in waste recovery. The device has the capability to detect /L (part-per billion) concentrations of several rare earths in aqueous solution within 1 min, and it is portable given a physical dimension of less than 1/2 m3. Whereas, conventional table-top devices used for this type of analysis are bulky and have high costs, in addition to the typical two week long processing times for laboratory analyses of rare earths. The rapid return of results that this portable and rugged device provides can save the end user the cost of inaction during recovery or mining operations, potentially allowing for “in line” monitoring or rapid field sampling. Luminescence sensitizers were used to lower the limit of detection of the rare earths in comparison with direct excitation, by approximately an order of magnitude. Examples of luminescence sensitizers tested include M(CN)2 (where M = Ag, Au, Cu) and 2,2 bipyridine.
Autors: John C. Ahern;Zsolt L. Poole;John Baltrus;Paul R. Ohodnicki;
Appeared in: IEEE Sensors Journal
Publication date: May 2017, volume: 17, issue:9, pages: 2644 - 2648
Publisher: IEEE
 
» Position Sensorless Control of Switched Reluctance Motor Drives Based on Numerical Method
Abstract:
In this paper, a new position sensorless control method for switched reluctance motor drives is proposed. Rotor position is initially calculated based on the flux linkage-position–phase current characteristics by numerical method. Then, a third-order-phase-locked loop considering the acceleration variation is designed to undermine the impact of current sampling noise and numerical residual error on the estimated rotor position. A new start-up sequence is proposed. Simulation and experimental results show that the proposed position sensorless control method has achieved sufficient accuracy in terms of position and speed estimation.
Autors: Fei Peng;Jin Ye;Ali Emadi;Yunkai Huang;
Appeared in: IEEE Transactions on Industry Applications
Publication date: May 2017, volume: 53, issue:3, pages: 2159 - 2168
Publisher: IEEE
 
» Postearthquake Landslides Mapping From Landsat-8 Data for the 2015 Nepal Earthquake Using a Pixel-Based Change Detection Method
Abstract:
The 2015 Nepal earthquake and its aftershocks not only caused huge damage with severe loss of life and property, also induced many geohazards with the major type of landslide which should bring continuous threats to the affected region. To gain a better understanding of the landslides induced by this earthquake, we proposed a pixel-based change detection method for postearthquake landslide mapping by using bitemporal Landsat-8 remote sensing data [May 29, 2014 (pre-earthquake) and June 1, 2015 (postearthquake)]. Two river valleys (Trishuli river valley and Sun Koshi river valley) that contain important economic arteries linking Nepal and China were selected as the study areas. Validation of the mapping results with postearthquake high-resolution images from Google Earth shows that the pixel-based landslide mapping method is able to identify landslides with relatively high accuracy, and it also approves the applicability of Landsat-8 satellite for landslide mapping with its multispectral information. The spatial distribution analysis indicates that both river valleys are substantially affected by landslides, and the situation is even more serious in the high mountain areas. Landslides are generally found in areas of high elevation and large surface slopes, with mean values above 1600 m and 30°, respectively. These findings suggest that these areas suffer greatly from these geohazards, and the threat will continue for the next few years.
Autors: Wei Zhao;Ainong Li;Xi Nan;Zhengjian Zhang;Guangbin Lei;
Appeared in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication date: May 2017, volume: 10, issue:5, pages: 1758 - 1768
Publisher: IEEE
 
» Pothole in the Dark: Perceiving Pothole Profiles with Participatory Urban Vehicles
Abstract:
Accessing to timely and accurate road condition information, especially about dangerous potholes is of great importance to the public and the government. In this paper, we propose a novel scheme, called , which utilizes smartphones placed in normal vehicles to sense and estimate the profiles of potholes on urban surface roads. In particular, a -enabled smartphone can actively learn the knowledge about the suspension system of the host vehicle without any human intervention and adopts a one degree-of-freedom (DOF) vibration model to infer the depth and length of pothole while the vehicle is hitting the pothole. Furthermore, shows the potential to derive more accurate results by aggregating individual estimates. In essence, is light-weighted and robust to various conditions such as poor light, bad weather, and different vehicle types. We have implemented a prototype system to prove the practical feasibility of . The results of extensive experiments based on real trace demonstrate the efficacy of the design. On average, can achieve low depth and length estimation error rates of 13 and 16 percent, respectively.
Autors: Guangtao Xue;Hongzi Zhu;Zhenxian Hu;Jiadi Yu;Yanmin Zhu;Yuan Luo;
Appeared in: IEEE Transactions on Mobile Computing
Publication date: May 2017, volume: 16, issue:5, pages: 1408 - 1419
Publisher: IEEE
 
» POTORI: a passive optical top-of-rack interconnect architecture for data centers
Abstract:
Several optical interconnect architectures inside data centers (DCs) have been proposed to efficiently handle the rapidly growing traffic demand. However, not many works have tackled the interconnects at top-of-rack (ToR), which have a large impact on the performance of the data center networks (DCNs) and can introduce serious scalability limitations due to their high cost and power consumption. In this paper, we propose a passive optical ToR interconnect architecture (POTORI) to replace the conventional electronic packet switch (EPS) in the access tier of DCNs. In the data plane, POTORI relies on a passive optical coupler to interconnect the servers within the rack and interfaces toward the aggregation/core tiers. The POTORI control plane is based on a centralized rack controller responsible for managing the communications among the servers in the rack. We propose a cycle-based medium access control (MAC) protocol to efficiently manage the exchange of control messages and the data transmission inside the rack. We also introduce and evaluate a dynamic bandwidth allocation algorithm for POTORI, namely largest first (LF). Extensive simulation results show that, with the use of fast tunable optical transceivers, POTORI and the proposed LF strategy are able to achieve an average packet delay below 10 μs under realistic DC traffic scenarios, outperforming conventional EPSs. On the other hand, with slower tunable optical transceivers, a careful configuration of the network parameters (e.g., maximum cycle time of the MAC protocol) is necessary to obtain a good network performance in terms of the average packet delay.
Autors: Yuxin Cheng;Matteo Fiorani;Rui Lin;Lena Wosinska;Jiajia Chen;
Appeared in: IEEE/OSA Journal of Optical Communications and Networking
Publication date: May 2017, volume: 9, issue:5, pages: 401 - 411
Publisher: IEEE
 
» Power Management Comparison for a Dual-Motor-Propulsion System Used in a Battery Electric Bus
Abstract:
The efficiency performance of multi-motor-driven system highly depends on the power management. Three aspects of contribution have been made in this study. 1) A predictive power management for a DMPS is developed. To improve the performance of the predictive power management, an adaptive velocity predictor is proposed and the coefficients of proposed predictor can update its parameters according to identified driving patterns. Simulation results show that the new velocity predictor have best prediction performance compared with traditional predictors. 2) A neural network based power management is proposed. According to the optimization results of dynamic programming, radial-basis-function neural network is trained. The input dimensions and the number of hidden layer neurons of the neural network are optimized. 3) The performance of proposed control strategies are compared with three different drive cycles including MANHATTAN cycle, Japanese 1015 cycle, and UDDSHDV cycle. Simulation results indicate that compared with original control strategy, the predictive control strategy and neural network based control strategy show better efficiency performance. The neural network based strategy is verified by hardware-in-loop experiment and experiment results indicate that the control performance in real hardware shows similar property with simulation results.
Autors: Chengning Zhang;Shuo Zhang;Guangwei Han;Haipeng Liu;
Appeared in: IEEE Transactions on Industrial Electronics
Publication date: May 2017, volume: 64, issue:5, pages: 3873 - 3882
Publisher: IEEE
 
» Power Quality Enhancement for a Grid Connected Wind Turbine Energy System
Abstract:
A comprehensive control of a wind turbine system connected to an industrial plant is discussed in this paper, where an algorithm has been developed allowing a control structure that utilizes a four-leg inverter connected to the grid side to inject the available energy, as well as to work as an active power filter mitigating load current disturbances and enhancing power quality. A four-wire system is considered with three-phase and single-phase linear and nonlinear loads. During the connection of the wind turbine, the utility-side controller is designed to compensate the disturbances caused in presence of reactive, nonlinear, and/or unbalanced single- and intra-phase loads, in addition to providing active and reactive power as required. When there is no wind power available, the controller is intended to improve the power quality using the dc-link capacitor with the power converter attached to the grid. The main difference of the proposed methodology with respect to others in the literature is that the proposed control structure is based on the conservative power theory decompositions. This choice provides decoupled power and current references for the inverter control, offering very flexible, selective, and powerful functionalities. Real-time software benchmarking has been conducted in order to evaluate the performance of the proposed control algorithm for full real-time implementation. The control methodology is implemented and validated in hardware-in-the-loop based on the real time simulator “Opal-RT” and a TMSF28335 DSP microcontroller. The results corroborated our power quality enhancement control and allowed to exclude passive filters, contributing to a more compact, flexible, and reliable electronic implementation of a smart-grid based control.
Autors: Abdullah S. Bubshait;Ali Mortezaei;Marcelo Godoy Simões;Tiago Davi Curi Busarello;
Appeared in: IEEE Transactions on Industry Applications
Publication date: May 2017, volume: 53, issue:3, pages: 2495 - 2505
Publisher: IEEE
 
» Power Spectral Density of Magnetization Dynamics Driven by a Jump-Noise Process
Abstract:
Random magnetization dynamics driven by a jump-noise process is reduced to stochastic magnetic energy dynamics on specific graphs using an averaging technique. An approach to analyzing stochastic energy dynamics on graphs is presented and applied to the calculation of power spectral density of random magnetization dynamics. An eigenvalue technique for computing the power spectral density under specific cases is also presented and illustrated by numerical results.
Autors: A. Lee;G. Bertotti;C. Serpico;I. Mayergoyz;
Appeared in: IEEE Transactions on Magnetics
Publication date: May 2017, volume: 53, issue:5, pages: 1 - 5
Publisher: IEEE
 
» Power System State and Transmission Line Conductor Temperature Estimation
Abstract:
This paper presents a state estimation technique for three-phase power systems where not only bus voltage phasors but also the temperature of transmission line conductors is considered as states. Transmission line admittance parameters depending on line conductor and ambient temperature are approximated from the pre-computed data based on polynomial interpolations. Weather environment and heat-balance equations have been also included in the measurement functions in order to estimate the temperature of conductors. The technique for segmenting transmission lines is also applied for handling temperature variations along the lines. The estimation problem is then formulated as a constrained nonlinear optimization based on the weighted least-squares criterion. A solution is obtained by applying a predictor–corrector interior point algorithm. Simulation results on some three-phase power systems indicate that the proposed method yields estimations with a better accuracy.
Autors: Chawasak Rakpenthai;Sermsak Uatrongjit;
Appeared in: IEEE Transactions on Power Systems
Publication date: May 2017, volume: 32, issue:3, pages: 1818 - 1827
Publisher: IEEE
 
» Power-Constrained Secrecy Rate Maximization for Joint Relay and Jammer Selection Assisted Wireless Networks
Abstract:
In this paper, we examine the physical layer security for cooperative wireless networks with multiple intermediate nodes, where the decode-and-forward protocol is considered. We propose a new joint relay and jammer selection (JRJS) scheme for protecting wireless communications against eavesdropping, where an intermediate node is selected as the relay for the sake of forwarding the source signal to the destination and meanwhile, the remaining intermediate nodes are employed to act as friendly jammers, which broadcast the artificial noise for disturbing the eavesdropper. We further investigate the power allocation among the source, relay and friendly jammers for maximizing the secrecy rate of proposed JRJS scheme, and derive a closed-form sub-optimal solution. Specifically, all the intermediate nodes, which successfully decode the source signal, are considered as relay candidates. For each candidate, we derive the sub-optimal closed-form power allocation solution and obtain the secrecy rate result of the corresponding JRJS scheme. Then, the candidate, which is capable of achieving the highest secrecy rate, is selected as the relay. Two assumptions about the channel state information (CSI), namely the full CSI (FCSI) and partial CSI (PCSI), are considered. Simulation results show that the proposed JRJS scheme outperforms the conventional pure relay selection, pure jamming, and generalized singular-value-decomposition-based beamforming schemes in terms of secrecy rate. Additionally, the proposed FCSI-based power allocation and PCSI-based power allocation schemes both achieve higher secrecy rates than the equal power allocation scheme.
Autors: Haiyan Guo;Zhen Yang;Linghua Zhang;Jia Zhu;Yulong Zou;
Appeared in: IEEE Transactions on Communications
Publication date: May 2017, volume: 65, issue:5, pages: 2180 - 2193
Publisher: IEEE
 
» Practical Opportunistic Data Collection in Wireless Sensor Networks with Mobile Sinks
Abstract:
Wireless Sensor Networks with Mobile Sinks (WSN-MSs) are considered a viable alternative to the heavy cost of deployment of traditional wireless sensing infrastructures at scale. However, current state-of-the-art approaches perform poorly in practice due to their requirement of mobility prediction and specific assumptions on network topology. In this paper, we focus on low-delay and high-throughput opportunistic data collection in WSN-MSs with general network topologies and arbitrary numbers of mobile sinks. We first propose a novel routing metric, Contact-Aware ETX (CA-ETX), to estimate the packet transmission delay caused by both packet retransmissions and intermittent connectivity. By implementing CA-ETX in the defacto TinyOS routing standard CTP and the IETF IPv6 routing protocol RPL, we demonstrate that CA-ETX can work seamlessly with ETX. This means that current ETX-based routing protocols for static WSNs can be easily extended to WSN-MSs with minimal modification by using CA-ETX. Further, by combing CA-ETX with the dynamic backpressure routing, we present a throughput-optimal scheme Opportunistic Backpressure Collection (OBC). Both CA-ETX and OBC are lightweight, easy to implement, and require no mobility prediction. Through test-bed experiments and extensive simulations, we show that the proposed schemes significantly outperform current approaches in terms of packet transmission delay, communication overhead, storage overheads, reliability, and scalability.
Autors: Shusen Yang;Usman Adeel;Yad Tahir;Julie A. McCann;
Appeared in: IEEE Transactions on Mobile Computing
Publication date: May 2017, volume: 16, issue:5, pages: 1420 - 1433
Publisher: IEEE
 
» Precise Position Synchronous Control for Multi-Axis Servo Systems
Abstract:
This paper presents a general solution of precise position synchronous control for multi-axis servo systems. The control strategy to achieve high-precision motion is summarized in two main points: an adaptive-fuzzy friction compensator is adopted in the independent control loop of each axis to compensate the nonlinear friction, and then a method which combines global sliding mode control with two adjacent axes cross-coupling technology is proposed to minimize not only single-axis position error but also synchronous errors of all motion axes. At first, the adaptive fuzzy algorithm including dynamic model of the system is utilized to design a friction compensation controller. Next, to improve robustness of the multi-axis motion system against variation of motor parameters and external disturbances, global sliding mode control is introduced. In addition, the multi-axis synchronous control based on cross-coupling technology is elaborately designed via proportional-differential control law. The performance of the proposed control system is investigated through extensive simulations based on a popular motion platform. Furthermore, experimental study shows that the results successfully demonstrate the effectiveness of the proposed position synchronous control method for a general four-axis servo system.
Autors: Guoliang Zhong;Zhizhong Shao;Hua Deng;Junli Ren;
Appeared in: IEEE Transactions on Industrial Electronics
Publication date: May 2017, volume: 64, issue:5, pages: 3707 - 3717
Publisher: IEEE
 
» Preconditioned Data Sparsification for Big Data With Applications to PCA and K-Means
Abstract:
We analyze a compression scheme for large data sets that randomly keeps a small percentage of the components of each data sample. The benefit is that the output is a sparse matrix, and therefore, subsequent processing, such as principal component analysis (PCA) or K-means, is significantly faster, especially in a distributed-data setting. Furthermore, the sampling is single-pass and applicable to streaming data. The sampling mechanism is a variant of previous methods proposed in the literature combined with a randomized preconditioning to smooth the data. We provide guarantees for PCA in terms of the covariance matrix, and guarantees for K-means in terms of the error in the center estimators at a given step. We present numerical evidence to show both that our bounds are nearly tight and that our algorithms provide a real benefit when applied to standard test data sets, as well as providing certain benefits over related sampling approaches.
Autors: Farhad Pourkamali-Anaraki;Stephen Becker;
Appeared in: IEEE Transactions on Information Theory
Publication date: May 2017, volume: 63, issue:5, pages: 2954 - 2974
Publisher: IEEE
 
» Predicting Image Memorability Through Adaptive Transfer Learning From External Sources
Abstract:
Remembering images is an innate human capability. Camera images are captured by different people under varying environmental conditions, which leads to highly diverse image memorability scores. However, the factors that make an image more or less memorable are unclear, and it remains unknown how we can more accurately predict image memorability by using such factors. In this paper, we propose a novel framework called multiview transfer learning from external sources (MTLES) to predict image memorability. In this framework, we simultaneously leverage different types of visual feature sets and multiple types of predefined image attributes derived from external sources. In particular, to enhance representation ability of visual features, we construct connections between visual feature sets and higher level image attributes by transferring attribute knowledge from external sources. MTLES integrates weak learning through external sources, transfer learning, and multiview consistency loss with different types of feature sets into a joint framework. To better solve this joint optimization problem, we further develop an alternating iterative algorithm to deal with it. Experiments performed on the publicly available LaMem dataset demonstrate the effectiveness of the proposed scheme.
Autors: Peiguang Jing;Yuting Su;Liqiang Nie;Huimin Gu;
Appeared in: IEEE Transactions on Multimedia
Publication date: May 2017, volume: 19, issue:5, pages: 1050 - 1062
Publisher: IEEE
 
» Preflight Spectral Calibration of the Orbiting Carbon Observatory 2
Abstract:
This paper describes the preflight spectral calibration methods and results for the Orbiting Carbon Observatory 2 (OCO-2), following the approach developed for the first OCO. The instrument line shape (ILS) function and dispersion parameters were determined through laser-based spectroscopic measurements, and then further optimized by comparing solar spectra recorded simultaneously on the ground by the OCO-2 flight instrument and a collocated high-resolution Fourier transform spectrometer (FTS). The resulting ILS profiles and dispersion parameters, when applied to the FTS solar data, showed agreement between the spectra recorded by the spectrometers and FTS to approximately 0.2% RMS, satisfying the preflight spectral calibration accuracy requirement of <;0.25% RMS. Specific changes to the OCO-2 instrument and calibration process, compared to the original OCO, include stray-light protection; improved laser setup; increased spectral sampling; enhanced data screening, and incremental improvements in the ILS, dispersion, and FTS optimization analyses.
Autors: Richard A. M. Lee;Christopher W. O’Dell;Debra Wunch;Coleen M. Roehl;Gregory B. Osterman;Jean-Francois Blavier;Robert Rosenberg;Lars Chapsky;Christian Frankenberg;Sarah L. Hunyadi-Lay;Brendan M. Fisher;David M. Rider;David Crisp;Randy Pollock;
Appeared in: IEEE Transactions on Geoscience and Remote Sensing
Publication date: May 2017, volume: 55, issue:5, pages: 2499 - 2508
Publisher: IEEE
 
» Preserving Privacy with Probabilistic Indistinguishability in Weighted Social Networks
Abstract:
The increasing popularity of social networks has inspired recent research to explore social graphs for marketing and data mining. As social networks often contain sensitive information about individuals, preserving privacy when publishing social graphs becomes an important issue. In this paper, we consider the identity disclosure problem in releasing weighted social graphs. We identify weighted 1*-neighborhood attacks, which assume that an attacker has knowledge about not only a target's one-hop neighbors and connections between them (1-neighborhood graph), but also related node degrees and edge weights. With this information, an attacker may re-identify a target with high confidence, even if any node's 1-neighborhood graph is isomorphic with other nodes’ graphs. To counter this attack while preserving high utility of the published graph, we define a key privacy property, probabilistic indistinguishability, and propose a heuristic indistinguishable group anonymization (HIGA) scheme to anonymize a weighted social graph with such a property. Extensive experiments on both real and synthetic data sets illustrate the effectiveness and efficiency of the proposed scheme.
Autors: Qin Liu;Guojun Wang;Feng Li;Shuhui Yang;Jie Wu;
Appeared in: IEEE Transactions on Parallel and Distributed Systems
Publication date: May 2017, volume: 28, issue:5, pages: 1417 - 1429
Publisher: IEEE
 
» Preserving Synchronization Accuracy From the Plug-in of NonSynchronized Nodes in a Wireless Sensor Network
Abstract:
The synchronization accuracy of the nodes of a wireless sensor network (WSN) can be perturbed by the plug-in of nonsynchronized nodes (NSNs). In the case of peer-to-peer synchronization algorithms, the reference time of the WSN is established on the basis of the clock time of all nodes. Therefore, each NSN changes the reference time to synchronize all nodes with the new reference time interval needs. In this time interval, the synchronization accuracy can degrade, i.e., the delay among node clocks overcomes the admissible range. In the case of only one or many NSNs, it was assessed in previous papers that by filtering the message of each NSN, the synchronization accuracy of the already synchronized nodes (ASNs) is preserved. However, the spatial distribution of the NSNs can fool the ASNs, foiling the effect of the message filtering. This paper presents a procedure that overcomes this inconvenience. The new fully distributed and consensus-based procedure iteratively filters the messages of communicating NSNs that would increase the time delay over the admissible range. As a consequence, the synchronization accuracy is preserved whatever the spatial distribution of ASNs and NSNs. Numerical and experimental tests are performed to validate the proposed procedure.
Autors: Francesco Lamonaca;Domenico Luca Carnì;Maria Riccio;Domenico Grimaldi;Gregorio Andria;
Appeared in: IEEE Transactions on Instrumentation and Measurement
Publication date: May 2017, volume: 66, issue:5, pages: 1058 - 1066
Publisher: IEEE
 
» Printed Motes for IoT Wireless Networks: State of the Art, Challenges, and Outlooks
Abstract:
Although wireless sensor networks (WSNs) have been an active field of research for many years, the modules incorporated by WSN nodes have been mainly manufactured utilizing conventional fabrication techniques that are mostly subtractive, requiring significant amounts of materials and increased chemical waste. The new era of the Internet of Things (IoT) will see the fabrication of numerous small form factor devices for wireless sensing for a plurality of applications, including security, health, and environmental monitoring. The large volume of these devices will require new directions in terms of manufacturing cost and energy efficiency, which will be achieved with redesigned, energy-aware modules. This paper presents the state of the art of printed passives, sensors, energy harvesting modules, actives, and communication front ends, and summarizes the challenges of implementing modules that feature low power consumptions without compromising the low fabrication cost. The plethora of the modules presented herein will facilitate the implementation of low cost, additively manufactured, energy-aware IoT nodes that can be fabricated in large volumes with green processes.
Autors: Jimmy G. D. Hester;John Kimionis;Manos M. Tentzeris;
Appeared in: IEEE Transactions on Microwave Theory and Techniques
Publication date: May 2017, volume: 65, issue:5, pages: 1819 - 1830
Publisher: IEEE
 
» Printed Organic Circuits for Reading Ferroelectric Rewritable Memory Capacitors
Abstract:
We demonstrate an inkjet-printed organic thin-film transistor (OTFT) circuit for reading ferroelectric (FE) nonvolatile rewritable memories. With the large difference in polarization charge between FE memory states, we implement a single-OTFT gain stage with latch and show that a gain of −2.8 is sufficient to distinguish memory states. This paper evaluates the effect of device variations on the yield of this readout circuit.
Autors: Tse Nga Ng;David E. Schwartz;Ping Mei;Sivkheng Kor;Janos Veres;Per Bröms;Christer Karlsson;
Appeared in: IEEE Transactions on Electron Devices
Publication date: May 2017, volume: 64, issue:5, pages: 1981 - 1984
Publisher: IEEE
 
» Privacy Preserving Cloth Try-On Using Mobile Augmented Reality
Abstract:
Virtual try-on applications make it possible for buyers to watch themselves wearing different garments without physically trying on them. The prevailing approach for virtual try-on has been based on virtual fitting rooms, in which several cameras are used to identify the skeleton and posture of a user in order to render a garment on the user's image. Although this approach has been implemented successfully using different techniques, the privacy of users can be compromised as some users might be reluctant to stand in front of cameras in a fitting room. This paper proposes an alternative approach that allows a customer to watch a three-dimensional (3D) model of her/him wearing garments on a personal mobile device using augmented reality (AR). Among 3D human models that are automatically generated, a model selection technique is proposed that makes it possible to find the right size model representing the anthropometric features of the user. This approach is accompanied by body customization and face generation modules to generate a realistic representation. Several quantitative experiments as well as user studies were performed to evaluate the accuracy, efficiency, usefulness, and privacy of the proposed technique.
Autors: Yoones A. Sekhavat;
Appeared in: IEEE Transactions on Multimedia
Publication date: May 2017, volume: 19, issue:5, pages: 1041 - 1049
Publisher: IEEE
 
» Proactive Eavesdropping via Cognitive Jamming in Fading Channels
Abstract:
To enhance the national security, there is a growing need for authorized parties to legitimately monitor suspicious communication links for preventing intended crimes and terror attacks. In this paper, we propose a new wireless information surveillance paradigm by investigating a scenario, where a legitimate monitor aims to intercept a suspicious wireless link over fading channels. The legitimate monitor can successfully eavesdrop (decode) the information of the suspicious link at each fading state only when its achievable data rate is no smaller than that at the suspicious receiver. We propose a new approach, namely, proactive eavesdropping via cognitive jamming, in which the legitimate monitor purposely jams the receiver in a full-duplex mode so as to change the suspicious communication (e.g., to a smaller data rate) for overhearing more efficiently. By assuming perfect self-interference cancelation (SIC) and global channel state information (CSI) at the legitimate monitor, we characterize the fundamental information-theoretic limits of proactive eavesdropping. We consider both delay-sensitive and delay-tolerant applications for the suspicious communication, under which the legitimate monitor maximizes the eavesdropping non-outage probability (for event-based monitoring) and the relative eavesdropping rate (for content analysis), respectively, by optimizing the jamming power allocation over different fading states subject to an average power constraint. Numerical results show that the proposed proactive eavesdropping via cognitive jamming approach greatly outperforms other benchmark schemes. Furthermore, by extending to a more practical scenario with residual SI and local CSI, we design an efficient online cognitive jamming scheme inspired by the optimal cognitive jamming with perfect SIC and global CSI.
Autors: Jie Xu;Lingjie Duan;Rui Zhang;
Appeared in: IEEE Transactions on Wireless Communications
Publication date: May 2017, volume: 16, issue:5, pages: 2790 - 2806
Publisher: IEEE
 
» Probabilistic Equalization With a Smoothing Expectation Propagation Approach
Abstract:
In this paper, we face the soft equalization of channels with inter-symbol interference for large constellation sizes, . In this scenario, the optimal BCJR solution and most of their approximations are intractable, as the number of states they track grows fast with . We present a probabilistic equalizer to approximate the posterior distributions of the transmitted symbols using the expectation propagation (EP) algorithm. The solution is presented as a recursive sliding window approach to ensure that the computational complexity is linear with the length of the frame. The estimations can be further improved with a forward–backward approach. This novel soft equalizer, denoted as smoothing EP (SEP), is also tested as a turbo equalizer, with a low-density parity-check (LDPC) channel decoder. The extensive results reported reveal remarkably good behavior of the SEP. In low dimensional cases, the bit error rate (BER) curves after decoding are closer than 1 dB from those of the BJCR, robust to the channel response. For large , the SEP exhibits gains in the range of 3–5 dB compared to the linear minimum mean square error algorithm.
Autors: Irene Santos;Juan José Murillo-Fuentes;Eva Arias-de-Reyna;Pablo M. Olmos;
Appeared in: IEEE Transactions on Wireless Communications
Publication date: May 2017, volume: 16, issue:5, pages: 2950 - 2962
Publisher: IEEE
 
» Probabilistic Forecasting of Photovoltaic Generation: An Efficient Statistical Approach
Abstract:
A novel efficient probabilistic forecasting approach is proposed to accurately quantify the variability and uncertainty of the power production from photovoltaic (PV) systems. Distinguished from most existing models, a linear programming-based prediction interval construction model for PV power generation is established based on an extreme learning machine and quantile regression, featuring high reliability and computational efficiency. The proposed approach is validated through the numerical studies on PV data from Denmark.
Autors: Can Wan;Jin Lin;Yonghua Song;Zhao Xu;Guangya Yang;
Appeared in: IEEE Transactions on Power Systems
Publication date: May 2017, volume: 32, issue:3, pages: 2471 - 2472
Publisher: IEEE
 
» Probabilistic Small-Cell Caching: Performance Analysis and Optimization
Abstract:
Small-cell caching utilizes the embedded storage of small-cell base stations (SBSs) to store popular contents for the sake of reducing duplicated content transmissions in networks and for offloading the data traffic from macrocell base stations to SBSs. In this paper, we study a probabilistic small-cell caching strategy, where each SBS caches a subset of contents with a specific caching probability. We consider two kinds of network architectures: 1) The SBSs are always active, which is referred to as the always-on architecture; and 2) the SBSs are activated on demand by mobile users (MUs), which is referred to as the dynamic on–off architecture. We focus our attention on the probability that MUs can successfully download content from the storage of SBSs. First, we derive theoretical results of this successful download probability (SDP) using stochastic geometry theory. Then, we investigate the impact of the SBS parameters, such as the transmission power and deployment intensity on the SDP. Furthermore, we optimize the caching probabilities by maximizing the SDP based on our stochastic geometry analysis. The intrinsic amalgamation of optimization theory and stochastic geometry based analysis leads to our optimal caching strategy, characterized by the resultant closed-form expressions. Our results show that in the always-on architecture, the optimal caching probabilities solely depend on the content request probabilities, while in the dynamic on–off architecture, they also relate to the MU-to-SBS intensity ratio. Interestingly, in both architectures, the optimal caching probabilities are linear functions of the square root of the content request probabilities. Monte-Carlo simulations validate our theoretical analysis and show that the proposed schemes relying on the optimal caching probabilities are capable of achieving substantial SDP improvement, compared with the benchmark schemes.
Autors: Youjia Chen;Ming Ding;Jun Li;Zihuai Lin;Guoqiang Mao;Lajos Hanzo;
Appeared in: IEEE Transactions on Vehicular Technology
Publication date: May 2017, volume: 66, issue:5, pages: 4341 - 4354
Publisher: IEEE
 
» Procedure to Match the Dynamic Response of MPPT and Droop-Controlled Microinverters
Abstract:
Due to the absence of communication needs and great reliability, the droop-control technique is a great choice for controlling of inverters that are subjected to load sharing or to work in an islanded mode. On the other hand, current-controlled inverters are often used in grid-connected systems due to their fast response to power the implementation of maximum power point tracking (MPPT) algorithms to maximize the power extracted from these systems. However, the application of such algorithms in grid-connected droop-controlled systems is hampered by differences in the dynamic responses of the respective techniques. In this context, this study presents the development of a strategy that enables a push–pull converter controlled by MPPT and a low-power plug and play grid-connected inverter governed by droop control to operate stably even under variations in solar radiation. The goal is achieved based on the following two approaches: designing the dc-link capacitor properly and using a control loop in order to adapt the droop curves in accordance with the available input power. Theoretical analysis and experimental results have proven the viability of the approach.
Autors: Ruben Barros Godoy;Douglas Buytendorp Bizarro;Elvey Tessaro de Andrade;Jurandir de Oliveira Soares;Pedro Eugênio Marcondes Justino Ribeiro;Leonardo A. Carniato;Marcio L. M. Kimpara;João O. P. Pinto;Kamal Al-Haddad;Carlos Alberto Canesin
Appeared in: IEEE Transactions on Industry Applications
Publication date: May 2017, volume: 53, issue:3, pages: 2358 - 2368
Publisher: IEEE
 
» Process Monitoring for Multimodal Processes With Mode-Reachability Constraints
Abstract:
For increased efficiency and profitability, many processes have multiple modes of operation. Switching between different operating modes is performed according to the standard operating procedures. These procedures are set by considering safety and operating limitations of various subsystems and equipment, and thus put restrictions on the switching of the process modes. In this paper, a hidden Markov model based monitoring method is proposed that can not only handle the multimodality of process data but can also capture the mode switching restrictions. A two-step Viterbi algorithm is proposed for effective mode detection in the event of faults, and a reconstruction-based fault isolation algorithm is developed to build the contribution plots. Application examples demonstrate the effectiveness of the proposed monitoring method.
Autors: Muhammad Shahzad Afzal;Wen Tan;Tongwen Chen;
Appeared in: IEEE Transactions on Industrial Electronics
Publication date: May 2017, volume: 64, issue:5, pages: 4325 - 4335
Publisher: IEEE
 
» Process Variation Analysis and Optimization of a FinFET-Based VCO
Abstract:
Fin-type field-effect transistors (FinFETs) are promising substitutes for bulk CMOS for nanoscale technologies. In this paper, the viability of a mixed-signal design for FinFET-based technologies using a nanoscale current-starved voltage controlled oscillator (VCO) is investigated. Design issues are analyzed and a comparison between a CMOS VCO and a FinFET-based VCO is presented. The figures-of-merit used for comparison are center frequency and frequency–voltage (–) characteristics under process variation. Models are developed for the – characteristics of both the CMOS and FinFET VCOs. In addition, width quantization-aware modeling has been performed for the FinFET-based VCO using a polynomial metamodel, which can be used for further optimization. The quantization aware modeling is highly accurate as evident from a correlation coefficient of 0.999 and root mean square error of 6.2 MHz. The FinFET VCO has faster oscillation frequency with 2.6% variability as opposed to 19.7% for the CMOS VCO. To the best of the authors’ knowledge, this is the first paper that examines FinFET technology with respect to process variation in mixed signal designs at the circuit level, and presents a quantitative as well as qualitative comparison between CMOS and FinFET technologies.
Autors: Venkata P. Yanambaka;Saraju P. Mohanty;Elias Kougianos;Dhruva Ghai;Garima Ghai;
Appeared in: IEEE Transactions on Semiconductor Manufacturing
Publication date: May 2017, volume: 30, issue:2, pages: 126 - 134
Publisher: IEEE
 
» Processing Sliding Mosaic Mode Data With Modified Full-Aperture Imaging Algorithm Integrating Scalloping Correction
Abstract:
Modified full-aperture imaging algorithm for sliding Mosaic mode synthetic aperture radar (SAR) is presented in this paper, which includes scalloping correction and spikes suppression. The full-aperture imaging algorithm is introduced into Mosaic mode and validated by real C-band airborne SAR imaging experiments. The main idea is to substitute zeros between bursts with linear-predicted data extrapolated from adjacent bursts to suppress the spikes caused by multibursts processing. Furthermore, scalloping correction for sliding Mosaic mode is integrated with this algorithm. It is innovational to correct the azimuth beam pattern weighting altered by radar antenna rotation in azimuth with deramping preprocessing operation. Finally, experiments performed by the C-band airborne SAR system with a maximum bandwidth of 200 MHz validate the effectiveness of the approach.
Autors: Ning Li;Robert Wang;Yunkai Deng;Tuan Zhao;Wei Wang;Heng Zhang;
Appeared in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication date: May 2017, volume: 10, issue:5, pages: 1804 - 1812
Publisher: IEEE
 
» Profile: enviro power - This company is bringing microcogeneration to the U.S. [Resources_Startups]
Abstract:
Cogeneration, or combined heat and power (CHP), is the simultaneous production of electricity and useful heat. The global market for cogeneration equipment could hit US $43.8 billion by 2020, according to market research firm Global Industry Analysts. Cogeneration is often associated with large power plants in North America, and few there have modified the technology for small-scale commercial use. And nobody is selling a microcogeneration unit in the United States that is affordable enough for the average single-family house.
Autors: Theresa Sullivan Barger;
Appeared in: IEEE Spectrum
Publication date: May 2017, volume: 54, issue:5, pages: 22 - 22
Publisher: IEEE
 
» Progress Overview of Capturing Method for Integral 3-D Imaging Displays
Abstract:
An integral 3-D technique provides a 3-D spatial image viewable from varying positions without the use of special light sources or viewing glasses. Therefore, this technique shows promise for diverse applications in various fields, including 3-D television broadcasting, advertising, and medical diagnostics. However, there are problems in capturing and displaying large amounts of information in realizing practical integral imaging devices. This paper overviews integral 3-D capturing methods and analyzes integral 3-D imaging technology at its capturing and displaying stages. To overcome the resolution problem, it also introduces our recent work for capturing high-resolution integral imaging information. The introduced device consists of a multiple-lens array and a complementary metal–oxide–semiconductor image sensor with a circuit patterned using multiple exposures. This device can capture depth-controlled spatial information by introducing additional optics. Two types of optics for depth control are applied to the capturing device: one functions as a convex lens to control and compress a relatively large object space and the other functions as an afocal lens array that controls a relatively small object space without any distortion in the depthwise direction. Experimental results of spatial information capturing and 3-D image displays confirm that the method produces 3-D images having an appropriate motion parallax. The presented method is scalable; thus, this technique offers possibilities for developing advanced high-resolution integral 3-D imaging devices.
Autors: Jun Arai;Eisuke Nakasu;Takayuki Yamashita;Hitoshi Hiura;Masato Miura;Tomohiro Nakamura;Ryohei Funatsu;
Appeared in: Proceedings of the IEEE
Publication date: May 2017, volume: 105, issue:5, pages: 837 - 849
Publisher: IEEE
 
» Promoting Device-to-Device Communication in Cellular Networks by Contract-based Incentive Mechanisms
Abstract:
Recently, device-to-device (D2D) communication has emerged to offer opportunities for high-rate wireless transmission locally in cellular networks. Such proximity-based communication technology, with advantages in high data transmission rate, spectrum efficiency, and energy efficiency over traditional technologies, has attracted tremendous research interest. Since D2D communication highly relies on user participation, providing incentives is critical to enable D2D communication with desired quality of service (QoS). Existing popular approaches for designing incentive mechanisms (e.g., auction) may cause large overhead of both communication and computation, and thus is inefficient for D2D communication. In this article, we introduce contract theory to provide effective and distinctive incentive mechanisms, which can also reduce computation and communication costs and thus has great potential for practical implementation. To evaluate the performance of the proposed contract-based approach, we also provide a case study that shows the advantages of contract theory in designing efficient incentives and handling uncertainty.
Autors: Yichao Chen;Shibo He;Fen Hou;Zhiguo Shi;Jiming Chen;
Appeared in: IEEE Network
Publication date: May 2017, volume: 31, issue:3, pages: 14 - 20
Publisher: IEEE
 
» Properties of an Aloha-Like Stability Region
Abstract:
A well-known inner bound on the stability region of the finite-user slotted Aloha protocol is the set of all arrival rates for which there exists some choice of the contention probabilities such that the associated worst case service rate for each user exceeds the user’s arrival rate, denoted . Although testing membership in of a given arrival rate can be posed as a convex program, it is nonetheless of interest to understand the properties of this set. In this paper, we develop new results of this nature, including, 1) an equivalence between membership in and the existence of a positive root of a given polynomial, 2) a method to construct a vector of contention probabilities to stabilize any stabilizable arrival rate vector, 3) the volume of , 4) explicit polyhedral, spherical, and ellipsoid inner and outer bounds on , and 5) characterization of the generalized convexity properties of a natural “excess rate” function associated with , including the convexity of the set of contention probabilities that stabilize a given arrival rate vector.
Autors: Nan Xie;John MacLaren Walsh;Steven Weber;
Appeared in: IEEE Transactions on Information Theory
Publication date: May 2017, volume: 63, issue:5, pages: 3181 - 3208
Publisher: IEEE
 
» Proportional Fairness-Based Beamforming and Signal Splitting for MISO-SWIPT Systems
Abstract:
The problem of proportional fairness-based beamforming and power splitting is investigated for simultaneous wireless information and power transferring systems. Due to the non-convexity of the formulated problem, a suboptimal solution is proposed based on the classical zero-forcing beamforming (ZFBF) and maximal ratio transmission techniques. To overcome the limitation of the ZFBF technique, a successive approximation-based algorithm is proposed. Simulation results verify the effectiveness of the proposed algorithms.
Autors: Yanjie Dong;Xin Ge;Jahangir Hossain;Julian Cheng;Victor C. M. Leung;
Appeared in: IEEE Communications Letters
Publication date: May 2017, volume: 21, issue:5, pages: 1135 - 1138
Publisher: IEEE
 
» 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-TTWD: Robust Device-Free Through-The-Wall Detection of Moving Human With WiFi
Abstract:
Due to rapid developments of smart devices and mobile applications, there is an urgent need for a new human-in-the-loop architecture with better system efficiency and user experience. Compared with conventional device-based human–computer interactive (HCI) methods, device-free technology with WiFi provides a new HCI method and is promising for providing better user-perceived quality-of-experience. Being essential for device-free applications, device-free human detection has gained increasing interest, of which through-the-wall (TTW) human detection is of great challenge. Existing TTW detection systems either rely on massive deployment of transceivers or require specialized WiFi monitors, making them inapplicable for real-world applications. Recently, more and more researchers have tapped into the physical layer for more robust and reliable human detection, ever since channel state information (CSI) can be exported with commodity devices. Despite great progress achieved, there have been few works studying TTW detection. In this paper, we propose a novel scheme for robust device-free TTW detection (R-TTWD) of a moving human with commodity devices. Different from the time dimension-based features exploited in the previous works, R-TTWD takes advantage of the correlated changes over different subcarriers and extracts the first-order difference of eigenvector of CSI across different subcarriers for TTW human detection. Instead of direct feature extraction, we first perform a PCA-based filtering on the preprocessed data, since a simple low-pass filtering is insufficient for noise removal. Furthermore, the detection results across different transmit–receive antenna pairs are fused with a majority-vote-based scheme for more robust and accurate detection. We prototype R-TTWD on commodity WiFi devices and evaluate its performance both in different environments and over long test period, validating the robustness of R-TTWD with both detec- ion rates for moving human and human absence over 99% regardless of different wall materials, dynamic moving speeds, and so on.
Autors: Hai Zhu;Fu Xiao;Lijuan Sun;Ruchuan Wang;Panlong Yang;
Appeared in: IEEE Journal on Selected Areas in Communications
Publication date: May 2017, volume: 35, issue:5, pages: 1090 - 1103
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
 
» Radar Remote Sensing of Agricultural Canopies: A Review
Abstract:
Observations from spaceborne radar contain considerable information about vegetation dynamics. The ability to extract this information could lead to improved soil moisture retrievals and the increased capacity to monitor vegetation phenology and water stress using radar data. The purpose of this review paper is to provide an overview of the current state of knowledge with respect to backscatter from vegetated (agricultural) landscapes and to identify opportunities and challenges in this domain. Much of our understanding of vegetation backscatter from agricultural canopies stems from SAR studies to perform field-scale classification and monitoring. Hence, SAR applications, theory, and applications are considered here too. An overview will be provided of the knowledge generated from ground-based and airborne experimental campaigns that contributed to the development of crop classification, crop monitoring, and soil moisture monitoring applications. A description of the current vegetation modeling approaches will be given. A review of current applications of spaceborne radar will be used to illustrate the current state of the art in terms of data utilization. Finally, emerging applications, opportunities and challenges will be identified and discussed. Improved representation of vegetation phenology and water dynamics will be identified as essential to improve soil moisture retrievals, crop monitoring, and for the development of emerging drought/water stress applications.
Autors: Susan C. Steele-Dunne;Heather McNairn;Alejandro Monsivais-Huertero;Jasmeet Judge;Pang-Wei Liu;Kostas Papathanassiou;
Appeared in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication date: May 2017, volume: 10, issue:5, pages: 2249 - 2273
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 Keystrokes Using WiFi Devices
Abstract:
Keystroke privacy is critical for ensuring the security of computer systems and the privacy of human users as what is being typed could be passwords or privacy sensitive information. In this paper, we show for the first time that WiFi signals can also be exploited to recognize keystrokes. The intuition is that while typing a certain key, the hands and fingers of a user move in a unique formation and direction and thus generate a unique pattern in the time-series of channel state information (CSI) values, which we call CSI-waveform for that key. In this paper, we propose a WiFi signal-based keystroke recognition system called WiKey. WiKey consists of two commercial off-the-shelf WiFi devices, a sender (such as a router) and a receiver (such as a laptop). The sender continuously emits signals and the receiver continuously receives signals. When a human subject types on a keyboard, WiKey recognizes the typed keys based on how the CSI values at the WiFi signal receiver end. We implemented the WiKey system using a TP-Link TL-WR1043ND WiFi router and a Lenovo X200 laptop. WiKey achieves over 97.5% detection rate for detecting the keystroke and 96.4% recognition accuracy for classifying single keys. In real-world experiments, WiKey can recognize keystrokes in a continuously typed sentence with an accuracy of 93.5%. WiKey can also recognize complete words inside a sentence with over 85% accuracy.
Autors: Kamran Ali;Alex X. Liu;Wei Wang;Muhammad Shahzad;
Appeared in: IEEE Journal on Selected Areas in Communications
Publication date: May 2017, volume: 35, issue:5, pages: 1175 - 1190
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
 

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