Electrical and Electronics Engineering publications abstract of: 11-2017 sorted by title, page: 15

» RAPT: Rare Class Prediction in Absence of True Labels
Abstract:
Many real-world problems involve learning models for rare classes in situations where there are no gold standard labels for training samples but imperfect labels are available for all instances. In this paper, we present RAPT, a three step predictive modeling framework for classifying rare class in such problem settings. The first step of the proposed framework learns a classifier that jointly optimizes precision and recall by only using imperfectly labeled training samples. We also show that, under certain assumptions on the imperfect labels, the quality of this classifier is almost as good as the one constructed using perfect labels. The second and third steps of the framework make use of the fact that imperfect labels are available for all instances to further improve the precision and recall of the rare class. We evaluate the RAPT framework on two real-world applications of mapping forest fires and urban extent from earth observing satellite data. The experimental results indicate that RAPT can be used to identify forest fires and urban areas with high precision and recall by using imperfect labels, even though obtaining expert annotated samples on a global scale is infeasible in these applications.
Autors: Varun Mithal;Guruprasad Nayak;Ankush Khandelwal;Vipin Kumar;Nikunj C. Oza;Ramakrishna Nemani;
Appeared in: IEEE Transactions on Knowledge and Data Engineering
Publication date: Nov 2017, volume: 29, issue:11, pages: 2484 - 2497
Publisher: IEEE
 
» RAR: Real-Time Acoustic Ranging in Underwater Sensor Networks
Abstract:
As a core component of underwater localization, accurate acoustic ranging is quite challenging because of the harsh characteristics of underwater environments (e.g., refraction and reflection). To enhance ranging accuracy, ranging schemes employing a ray tracing model and a sound speed profile have been proposed; however, the existing schemes require numerous ray tracing iterations to perform ranging estimation within a certain range of accuracy. This renders the solutions that they generate impractical for real-time localization. In this letter, we propose a novel ranging scheme called real-time acoustic ranging (RAR). To reduce computational overhead while maintaining reliable accuracy, RAR exploits the property of ray pattern locality, whereby spatially proximate rays exhibit similar patterns. The results revealed that RAR can ensure a better tradeoff between accuracy and computational overhead than a state-of-the-art solution.
Autors: Yonghun Kim;Youngtae Noh;Kiseon Kim;
Appeared in: IEEE Communications Letters
Publication date: Nov 2017, volume: 21, issue:11, pages: 2328 - 2331
Publisher: IEEE
 
» Rate-Splitting for Max-Min Fair Multigroup Multicast Beamforming in Overloaded Systems
Abstract:
In this paper, we consider the problem of achieving max-min fairness amongst multiple co-channel multicast groups through transmit beamforming. We explicitly focus on overloaded scenarios in which the number of transmitting antennas is insufficient to neutralize all inter-group interference. Such scenarios are becoming increasingly relevant in the light of growing low-latency content delivery demands, and also commonly appear in multibeam satellite systems. We derive performance limits of classical beamforming strategies using degrees of freedom (DoF) analysis unveiling their limitations; for example, rates saturate in overloaded scenarios due to inter-group interference. To tackle interference, we propose a strategy based on degraded beamforming and successive interference cancellation. While the degraded strategy resolves the rate-saturation issue, this comes at a price of sacrificing all spatial multiplexing gains. This motivates the development of a unifying strategy that combines the benefits of the two previous strategies. We propose a beamforming strategy based on rate-splitting (RS), which divides the messages intended to each group into a degraded part and a designated part, and transmits a superposition of both degraded and designated beamformed streams. The superiority of the proposed strategy is demonstrated through DoF analysis. Finally, we solve the RS beamforming design problem and demonstrate significant performance gains through simulations.
Autors: Hamdi Joudeh;Bruno Clerckx;
Appeared in: IEEE Transactions on Wireless Communications
Publication date: Nov 2017, volume: 16, issue:11, pages: 7276 - 7289
Publisher: IEEE
 
» Rates of DNA Sequence Profiles for Practical Values of Read Lengths
Abstract:
A recent study by one of the authors has demonstrated the importance of profile vectors in DNA-based data storage. We provide exact values and lower bounds on the number of profile vectors for finite values of alphabet size , read length , and word length . Consequently, we demonstrate that for and , the number of profile vectors is at least with very close to 1. In addition to enumeration results, we provide a set of efficient encoding and decoding algorithms for certain families of profile vectors.
Autors: Zuling Chang;Johan Chrisnata;Martianus Frederic Ezerman;Han Mao Kiah;
Appeared in: IEEE Transactions on Information Theory
Publication date: Nov 2017, volume: 63, issue:11, pages: 7166 - 7177
Publisher: IEEE
 
» Raw Signal Simulator for SAR With Trajectory Deviation Based on Spatial Spectrum Analysis
Abstract:
This paper presents a whole new method for simulating a raw signal of synthetic aperture radar (SAR) with trajectory deviation. In this paper, a SAR raw signal is viewed as a spatial function in terms of radar location, at a fixed range frequency component of the radar signal. It is disclosed and verified that the SAR raw signals is band limited from the perspective of spatial spectrum analysis. Based on this finding, SAR raw signals under condition of trajectory deviation are obtained by interpolating the SAR raw signal along a set of ideally linear trajectories. The unprecedent performance achieved by the new method mainly includes the following. First, it provides a way to update existing simulators designed for ideally linear trajectory, giving them the capability to take trajectory deviation into consideration. Second, it is an efficient SAR raw signal simulator whose validity limit is greatly expanded compared with existing efficient simulators, as many constraints are eliminated without sacrificing computational efficiency or phase accuracy. Third, it is a uniform framework applicable for multiple SAR acquisition modes. Finally, the computational complexity of Monte Carlo experiment under various trajectory deviations is dramatically reduced, at the expense of storage memory requirement. Theoretical analyses are demonstrated by experimental results.
Autors: Yongcai Liu;Wei Wang;Xiaoyi Pan;Zhaoyu Gu;Guoyu Wang;
Appeared in: IEEE Transactions on Geoscience and Remote Sensing
Publication date: Nov 2017, volume: 55, issue:11, pages: 6651 - 6665
Publisher: IEEE
 
» RE@40: Midlife Crisis or Graceful Maturity?
Abstract:
The RE@40 seminar offered a diagnosis of the state of RE as it enters its 40s. At 40, RE has grown up a bit and should have a clear sense of who it is as it moves deeper into its most productive years. Of course, many 40-somethings also begin to experience a midlife crisis and suddenly change direction, perhaps not attending well to their current responsibilities. Where does RE sit at this juncture?
Autors: Sarah Gregory;
Appeared in: IEEE Software
Publication date: Nov 2017, volume: 34, issue:6, pages: 14 - 17
Publisher: IEEE
 
» Real Time Multiphase State Estimation in Weakly Meshed Distribution Networks With Distributed Generation
Abstract:
Recent research has presented a load scaling based real time state estimation (SE) approach for radial and multiphase distribution networks; the approach makes use of successive weighted least squares estimation and power flow to adjust forecasted load values so that they conform with the available real time measurements. This paper extends the load scaling based SE approach along two directions, while maintaining performance that is commensurate with real time computing requirements. First, the approach is extended to allow for meshes that are practically available in some distribution systems; this is achieved by embedding the power flow equations of the nodes that are part of the loops within the load scaling formulation. Second, a sensitivity-based technique is introduced to handle isolated line-to-ground voltage magnitude measurements in radial feeders; this technique is compatible with load scaling and is timely as many utilities have increased the monitoring of voltage measurements at pilot nodes to guard against voltage problems that are associated with distributed generation. The efficiency of the proposed estimator is demonstrated by comparison with the three-phase voltage based normal equations approach that handles both voltage magnitudes and meshes.
Autors: Izudin Džafić;Rabih A. Jabr;
Appeared in: IEEE Transactions on Power Systems
Publication date: Nov 2017, volume: 32, issue:6, pages: 4560 - 4569
Publisher: IEEE
 
» Real-Time In Vivo Intraocular Pressure Monitoring Using an Optomechanical Implant and an Artificial Neural Network
Abstract:
Optimized glaucoma therapy requires frequent monitoring and timely lowering of elevated intraocular pressure (IOP). A recently developed microscale IOP-monitoring implant, when illuminated with broadband light, reflects a pressure-dependent optical spectrum that is captured and converted to measure IOP. However, its accuracy is limited by background noise and the difficulty of modeling non-linear shifts of the spectra with respect to pressure changes. Using an end-to-end calibration system to train an artificial neural network (ANN) for signal demodulation we improved the speed and accuracy of pressure measurements obtained with an optically probed IOP-monitoring implant and make it suitable for real-time in vivo IOP monitoring. The ANN converts captured optical spectra into corresponding IOP levels. We achieved an IOP-measurement accuracy of ±0.1 mmHg at a measurement rate of 100 Hz, which represents a ten-fold improvement from previously reported values. This technique allowed real-time tracking of artificially induced sub-1 s transient IOP elevations and minor fluctuations induced by the respiratory motion of the rabbits during in vivo monitoring. All in vivo sensor readings paralleled those obtained concurrently using a commercial tonometer and showed consistency within ±2 mmHg. Real-time processing is highly useful for IOP monitoring in clinical settings and home environments, and improves the overall practicality of the optical IOP-monitoring approach.
Autors: Kun Ho Kim;Jeong Oen Lee;Juan Du;David Sretavan;Hyuck Choo;
Appeared in: IEEE Sensors Journal
Publication date: Nov 2017, volume: 17, issue:22, pages: 7394 - 7404
Publisher: IEEE
 
» Real-Time Cooperative Communication for Automation Over Wireless
Abstract:
High-performance industrial automation systems rely on tens of simultaneously active sensors and actuators and have stringent communication latency and reliability requirements. Current wireless technologies, such as Wi-Fi, Bluetooth, and LTE are unable to meet these requirements, forcing the use of wired communication in industrial control systems. This paper introduces a wireless communication protocol that capitalizes on multiuser diversity and cooperative communication to achieve the ultra-reliability with a low-latency constraint. Our protocol is analyzed using the communication-theoretic delay-limited-capacity framework and compared with baseline schemes that primarily exploit frequency diversity. For a scenario inspired by an industrial printing application with 30 nodes in the control loop, 20-B messages transmitted between pairs of nodes and a cycle time of 2 ms, an idealized protocol can achieve a cycle failure probability (probability that any packet in a cycle is not successfully delivered) lower than 10−9 with nominal SNR below 5 dB in a 20-MHz wide channel.
Autors: Vasuki Narasimha Swamy;Sahaana Suri;Paul Rigge;Matthew Weiner;Gireeja Ranade;Anant Sahai;Borivoje Nikolić;
Appeared in: IEEE Transactions on Wireless Communications
Publication date: Nov 2017, volume: 16, issue:11, pages: 7168 - 7183
Publisher: IEEE
 
» Real-Time Demonstration of Augmented-Spectral-Efficiency DMT Transmitter Using a Single IFFT
Abstract:
Increasing the power and spectral efficiency in intensity-modulated direct-detection short-haul fiber-optic links enables higher data rates in power- and bandwidth-limited optical communication systems. Augmented spectral efficiency discrete multi-tone (ASE-DMT) can improve the spectral efficiency of pulse-amplitude-modulated DMT while maintaining its power advantage over dc-biased DMT, whose transmitter requires only one inverse fast Fourier transform (IFFT) with Hermitian symmetric inputs. Although the ASE-DMT transmitter requires multiple IFFTs, we show how these can be mapped onto a single IFFT, by using both the real and imaginary outputs of the IFFT and by extracting some signals from within the IFFT's structure. Using only one IFFT, we first demonstrate a real-time PAM4-encoded optical ASE-DMT transmitter with a net data rate of 18.4 Gb/s. When implemented in a FPGA, using a single IFFT saves 30% of logic resources, compared with a four-IFFT ASE-DMT transmitter. Finally, a 1550-nm directly modulated laser is used to evaluate its optical transmission performance with offline signal processing in the receiver. Without using any optical amplifiers, the ASE-DMT signal can be successfully transmitted over 10-km standard single-mode fiber (SSMF), but fails over 20-km SSMF due to the influence of fiber dispersion and laser chirp.
Autors: Qibing Wang;Binhuang Song;Bill Corcoran;Leimeng Zhuang;Arthur James Lowery;
Appeared in: Journal of Lightwave Technology
Publication date: Nov 2017, volume: 35, issue:21, pages: 4796 - 4803
Publisher: IEEE
 
» Real-Time Epileptic Seizure Detection Using EEG
Abstract:
This paper proposes a novel patient-specific real-time automatic epileptic seizure onset detection, using both scalp and intracranial electroencephalogram (EEG). The proposed technique obtains harmonic multiresolution and self-similarity-based fractal features from EEG for robust seizure onset detection. A fast wavelet decomposition method, known as harmonic wavelet packet transform (HWPT), is computed based on Fourier transform to achieve higher frequency resolutions without recursive calculations. Similarly, fractal dimension (FD) estimates are obtained to capture self-similar repetitive patterns in the EEG signal. Both FD and HWPT energy features across all EEG channels at each epoch are organized following the spatial information due to electrode placement on the skull. The final feature vector combines feature configurations of each epoch within the specified moving window to reflect the temporal information of EEG. Finally, relevance vector machine is used to classify the feature vectors due to its efficiency in classifying sparse, yet high-dimensional data sets. The algorithm is evaluated using two publicly available long-term scalp EEG (data set A) and short-term intracranial and scalp EEG (data set B) databases. The proposed algorithm is effective in seizure onset detection with 96% sensitivity, 0.1 per hour median false detection rate, and 1.89 s average detection latency, respectively. Results obtained from analyzing the short-term data offer 99.8% classification accuracy. These results demonstrate that the proposed method is effective with both short- and long-term EEG signal analyzes recorded with either scalp or intracranial modes, respectively. Finally, the use of less computationally intensive feature extraction techniques enables faster seizure onset detection when compared with similar techniques in the literature, indicating potential usage in real-time applications.
Autors: Lasitha S. Vidyaratne;Khan M. Iftekharuddin;
Appeared in: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Publication date: Nov 2017, volume: 25, issue:11, pages: 2146 - 2156
Publisher: IEEE
 
» Real-Time Feedback Impacts on Eco-Driving Behavior and Influential Variables in Fuel Consumption in a Lisbon Urban Bus Operator
Abstract:
The main purposes of this research were, in a first stage, to assess the impacts of real-time feedback on the driving behavior of bus drivers, considering vehicle age and type and drivers experience and, in a second stage, to identify the main influential variables in fuel consumption. Data was collected with an on-board device used by a Portuguese urban bus transport operator. Significant increases in the performance of undesirable events were observed without real-time feedback, followed by decreases with the restart of real-time feedback. Higher increases (between 6% and 170%) were observed when driving Mini vehicles in comparison with other bus types, particularly in extreme accelerations, excess rpm, extreme braking, and hard starts. Furthermore, a General Linear Model was applied to assess the most influential variables on fuel consumption. Vehicle type and age are the most influential variables on fuel consumption, with minibuses presenting higher increases when compared with standard buses. Increases up to 3% were observed leading to an extra 3769 liters of fuel consumed when feedback was not provided. Decreases in fuel consumption between 0.3% and 2% were observed with real-time feedback, avoiding the consumption of 4280 L of fuel. The results obtained in this paper provide bus companies with insightful information for the development of future operational strategies and training programs.
Autors: Catarina Rolim;Patrícia Baptista;Gonçalo Duarte;Tiago Farias;João Pereira;
Appeared in: IEEE Transactions on Intelligent Transportation Systems
Publication date: Nov 2017, volume: 18, issue:11, pages: 3061 - 3071
Publisher: IEEE
 
» Real-Time Low-Power FPGA Architecture for Stereo Vision
Abstract:
Stereo vision is a well-known technique used to extract depth information from two or more images. In the last years, many efforts have been made to achieve high quality and efficient results. Although different techniques have been proposed to develop a working system. In this brief, we implemented a previous proposed algorithm for DNA sequence alignment, in order to align images on a field programmable gate array architecture. The design is embedded in a full processing pipeline for the use on low budget boards. The main goal of the project is to develop a real-time device with low power consumption. Such requirements should allow the use in critical and battery dependent applications. The written code is well parametrized and the synthesis is supported for different image resolution and disparity levels. Thanks to an easy refactoring, it is possible to migrate the architecture to every FPGA present on the market. The design reaches the processing ability of pixels, 64 disparity levels, 30 FPS with a power usage on chip of only 0.17 W.
Autors: Luca Puglia;Mario Vigliar;Giancarlo Raiconi;
Appeared in: IEEE Transactions on Circuits and Systems II: Express Briefs
Publication date: Nov 2017, volume: 64, issue:11, pages: 1307 - 1311
Publisher: IEEE
 
» Real-Time Nonlinear Model Predictive Control of a Battery–Supercapacitor Hybrid Energy Storage System in Electric Vehicles
Abstract:
A nonlinear model predictive control (NMPC) method has been presented as the energy management strategy of a battery–supercapacitor (SC) hybrid energy storage system (H-ESS) in a Toyota Rav4EV. For the first time, the NMPC has been shown to be real-time implementable for these fast systems. The performance of the proposed controller has been demonstrated against a linear model predictive control (LMPC) and a rule-based control (RBC) strategy. The NMPC shows to outperform the RBC even with no prior knowledge of the future trip available. The NMPC also shows performance improvement over the LMPC by compensating for the error accompanied by linearization in LMPCs. Hardware-in-the-loop (HiL) testing has been performed to demonstrate the NMPC capability for real-time implementation in a battery–SC H-ESS. Upon carefully choosing the prediction horizon and control horizon size, as well as the maximum number of iterations, the turn-around time for the control update is shown to fall far below the necessary sampling time of 10 ms in vehicle control.
Autors: Parisa Golchoubian;Nasser L. Azad;
Appeared in: IEEE Transactions on Vehicular Technology
Publication date: Nov 2017, volume: 66, issue:11, pages: 9678 - 9688
Publisher: IEEE
 
» Real-Time Observation of Molecularly Thin Lubricant Films on Head Sliders Using Rotating-Compensator-Based Ellipsometric Microscopy
Abstract:
Lubricant transfer or pickup from a disk to a head slider is a crucial issue in designing the head–disk interfaces of hard disk drives (HDDs). A method based on ellipsometric microscopy is presented for observing thin lubricant film on a head slider in real time. The use of rotating-compensator ellipsometry (RCE) results in one order of magnitude higher temporal resolution (0.6 frames/s) and several times higher thickness resolution (0.2 nm) compared to null-ellipsometry-based microscopy, which was recently proposed. RCE-based ellipsometric microscopy will be useful in clarifying the mechanism of lubricant pickup for higher density HDDs.
Autors: K. Fukuzawa;K. Miyata;C. Yamashita;S. Itoh;H. Zhang;
Appeared in: IEEE Transactions on Magnetics
Publication date: Nov 2017, volume: 53, issue:11, pages: 1 - 4
Publisher: IEEE
 
» Realistic and Scalable Benchmarking Cloud File Systems: Practices and Lessons from AliCloud
Abstract:
The past decade has witnessed the rapid boom of cloud computing. Many public cloud infrastructures have been implemented and serve millions of tenants. Cloud file systems, which take charge of petabyte-scale data storage, play a crucial role in the performance of cloud infrastructures. Typical cloud file systems, including GFS, HDFS and Ceph, have attracted notable research efforts for performance evaluation and optimization. However, due to the heterogeneity and complexity of I/O workload characteristics in cloud environments, it is still challenging to conduct an accurate and efficient performance evaluation. To address this problem, we collected a two-week I/O workload trace from a 2,500-node production cluster in AliCloud, which is one of the largest cloud providers in Asia. Using the AliCloud trace, we characterized the I/O workload and data distribution, and compared two cloud services in multiple perspectives, including the request arrival pattern, request size, data population and so on. A list of observations and implications were derived and applied to help design a cloud file system benchmarking suite, called Porcupine. Porcupine aims to deploy a scalable and efficient performance evaluation on cloud file systems using realistic I/O workloads. We conducted a group of validation experiments, which demonstrated that Porcupine can achieve high accuracy and scalability. This paper provides our experiences and lessons in generating I/O workloads and deploying performance tests on cloud file systems, which we believe will be insightful to the cloud computing community in general.
Autors: Zujie Ren;Weisong Shi;Jian Wan;Feng Cao;Jiangbin Lin;
Appeared in: IEEE Transactions on Parallel and Distributed Systems
Publication date: Nov 2017, volume: 28, issue:11, pages: 3272 - 3285
Publisher: IEEE
 
» Realization and Discretization of Asymptotically Stable Homogeneous Systems
Abstract:
Sufficient conditions for the existence and convergence to zero of numeric approximations to solutions of asymptotically stable homogeneous systems are obtained for the explicit and implicit Euler integration schemes. It is shown that the explicit Euler method has certain drawbacks for the global approximation of homogeneous systems with nonzero degrees, whereas the implicit Euler scheme ensures convergence of the approximating solutions to zero. Properties of absolute and relative errors of the respective discretizations are investigated.
Autors: Denis Efimov;Andrey Polyakov;Arie Levant;Wilfrid Perruquetti;
Appeared in: IEEE Transactions on Automatic Control
Publication date: Nov 2017, volume: 62, issue:11, pages: 5962 - 5969
Publisher: IEEE
 
» Recent Advances in Healthcare Software: Toward Context-Aware and Smart Solutions
Abstract:
This theme issue presents some of the most recent advances in and applications of software for context-aware and smart healthcare, so as to provide a view of the state of the technology.
Autors: Agusti Solanas;Jens H. Weber;Ayse Basar Bener;Frank van der Linden;Rafael Capilla;
Appeared in: IEEE Software
Publication date: Nov 2017, volume: 34, issue:6, pages: 36 - 40
Publisher: IEEE
 
» Recent Advances in Synthetic Aperture Radar Remote Sensing—Systems, Data Processing, and Applications
Abstract:
This letter closes a special stream consisting of selected papers from the fifth Asia–Pacific Conference on Synthetic Aperture Radar in 2015 (APSAR 2015). The latest research results and outcomes from APSAR 2015, particularly on the synthetic aperture radar (SAR) systems/subsystems design, data processing techniques, and various SAR applications in remote sensing, are summarized and presented. All these results represent the recent advances in SAR remote sensing. Hopefully, this letter can provide some references for SAR researchers/engineers and stimulate the future development of SAR technology for remote sensing.
Autors: Hongbo Sun;Masanobu Shimada;Feng Xu;
Appeared in: IEEE Geoscience and Remote Sensing Letters
Publication date: Nov 2017, volume: 14, issue:11, pages: 2013 - 2016
Publisher: IEEE
 
» Reconsideration of Dielectric Breakdown Mechanism of Gate Dielectrics on Basis of Dominant Carrier Change Model
Abstract:
Dielectric breakdown mechanism of gate dielectrics has been reconsidered with SiO2 films of various thicknesses ranging from 3.6 to 9.5 nm. Careful time-dependent dielectric breakdown (TDDB) measurements have realized the detection of anomalous lifetime ( lowering than the expectation from generally accepted power-law (PL) model at relatively low-voltage region in 6- and 6.5-nm-thick films. Analysis from the viewpoint of the hole and electron fluence to breakdown ( and has demonstrated the dominant carrier change(DCC) from hole to electron with the lowering of the stress voltage. These results strongly support the DCC model. The DCC model provides stress voltage-independent defect generation efficiencies of electron and of hole at the stress voltage region where each carrier dominantes the breakdown, while the PL model provides the stress voltage-dependent efficiency. The DCC model is also capable to explain the empirically reported linear electric field () dependence of in a wide range of stress voltage which supports the thermochemical model (E-model). Therefore, it is expected that the DCC model can also be employed in the lifetime prediction of gate dielectrics thicker than ~5 nm, where the E-model is generally employed in the lifetime insurance of actual market products. The DCC model is expected to give longer predicted TDDB lifetimes at the actual device operating conditions in most devices than the E-model and other models and, hence, more aggressive usage of gate dielectric films c- n be realized.
Autors: Kenji Okada;Masayuki Kamei;Shigeyuki Ohno;
Appeared in: IEEE Transactions on Electron Devices
Publication date: Nov 2017, volume: 64, issue:11, pages: 4386 - 4392
Publisher: IEEE
 
» Recursive Autoencoders-Based Unsupervised Feature Learning for Hyperspectral Image Classification
Abstract:
For hyperspectral image (HSI) classification, it is very important to learn effective features for the discrimination purpose. Meanwhile, the ability to combine spectral and spatial information together in a deep level is also important for feature learning. In this letter, we propose an unsupervised feature learning method for HSI classification, which is based on recursive autoencoders (RAE) network. RAE utilizes the spatial and spectral information and produces high-level features from the original data. It learns features from the neighborhood of the investigated pixel to represent the whole local homogeneous area of the image. In addition, to obtain more accurate representation of the investigated pixel, a weighting scheme is adopted based on the neighboring pixels, where the weights are determined by the spectral similarity between the neighboring pixels and the investigated pixel. The effectiveness of our method is evaluated by the experiments on two hyperspectral data sets, and the results show that our proposed method has a better performance.
Autors: Xiangrong Zhang;Yanjie Liang;Chen Li;Ning Huyan;Licheng Jiao;Huiyu Zhou;
Appeared in: IEEE Geoscience and Remote Sensing Letters
Publication date: Nov 2017, volume: 14, issue:11, pages: 1928 - 1932
Publisher: IEEE
 
» Recursive Robust Regulator for Discrete-Time Markovian Jump Linear Systems
Abstract:
In this paper, we propose a robust regulator for discrete-time Markovian jump linear systems (DMJLS) subject to structured parameter uncertainties. States of Markov chain are considered known at each instant and the transition probabilities matrix is time-varying. The framework proposed to deal with this problem is developed through a regularization approach based on robust least-squares and penalty functions. Recursive solutions in terms of coupled Riccati equations, which do not depend on any auxiliary parameter to be tuned, are provided. These equations resemble the recursive solutions of DMJLS standard regulators when the system is not subject to uncertainties. Conditions for convergence and stability of this robust regulator are established. Numerical examples present comparative studies considering the proposed robust regulator, the standard linear quadratic regulator, and an -controller for DMJLS.
Autors: João P. Cerri;Marco H. Terra;
Appeared in: IEEE Transactions on Automatic Control
Publication date: Nov 2017, volume: 62, issue:11, pages: 6004 - 6011
Publisher: IEEE
 
» Reducing Synchronization Overhead with Computation Replication in Parallel Agent-Based Road Traffic Simulation
Abstract:
Road traffic simulation is a useful tool for studying road traffic and evaluating solutions to traffic problems. Large-scale agent-based road traffic simulation is computationally intensive, which triggers the need for conducting parallel simulation. This paper deals with the synchronization problem in parallel agent-based road traffic simulation to reduce the overall simulation execution time. We aim to reduce synchronization operations by introducing some redundant computation to the simulation. There is a trade-off between the benefit of reduced synchronization operations and the overhead of redundant computation. The challenge is to minimize the total overhead of redundant computation and synchronization. First, to determine the amount of redundant computation, we proposed a way to define extended layers of partitions in the road network. The sizes of extended layers are determined by the behavior of agents and the topology of road networks. Second, due to the dynamic nature of road traffic, a heuristic was proposed to adjust the amount of redundant computation according to traffic conditions during simulation run-time to minimize the overall simulation execution time. The efficiency of the proposed method was investigated in a parallel agent-based road traffic simulator using real-world network and trip data. Results have shown that the method can reduce synchronization overhead and improve the overall performance of the parallel simulation significantly.
Autors: Yadong Xu;Vaisagh Viswanathan;Wentong Cai;
Appeared in: IEEE Transactions on Parallel and Distributed Systems
Publication date: Nov 2017, volume: 28, issue:11, pages: 3286 - 3297
Publisher: IEEE
 
» Reduction of Air Traffic Complexity Using Trajectory-Based Operations and Validation of Novel Complexity Indicators
Abstract:
Airspace capacity is limited primarily by the saturation of air traffic controller’s capacity, whose workload increases as air traffic complexity increases. Workload can be reduced through task automation by advanced controller tools. Automation and the development of novel controller tools is therefore one of the key aspects of future concepts of operations in European and American air traffic management systems. Implementation of trajectory-based operations (TBOs) has been proposed as a way to reduce workload, but few studies have examined how TBO affects air traffic complexity. This paper compares air traffic complexity experienced by ten air traffic controllers in a real-time simulation environment involving conventional operations and TBO. Analysis of subjective complexity scores collected in real time showed that TBO significantly reduced complexity when at least 70% of aircraft were flying according to TBO and when the airspace was occupied simultaneously by more than 15 aircraft. Subjective complexity scores were tested for correlation with 20 commonly used complexity indicators, and six indicators were used to generate a predictive linear model that performed well in conventional operations but less well under TBO. Therefore, we defined and experimentally validated two of seven novel TBO-specific complexity indicators. A second correlation model combining these two novel indicators with four already in use generated much better predictions of complexity than the first model.
Autors: Tomislav Radišić;Doris Novak;Biljana Juričić;
Appeared in: IEEE Transactions on Intelligent Transportation Systems
Publication date: Nov 2017, volume: 18, issue:11, pages: 3038 - 3048
Publisher: IEEE
 
» Reduction of Distributions: Definitions, Properties, and Applications
Abstract:
In this work, a notion of reduction of distributions is proposed as a technical tool for improving the complexity of decomposability verification and supporting parallel verification of decomposability, by exploiting the rich structures of distributions. We provide some results that reduce the search space of candidate reductions, as a first step toward efficiently computing optimal reductions. It is then shown that a distribution has a reduction if and only if a particular candidate reduction is indeed a reduction. We then provide a sound substitution-based proof technique that can be used for (automatic) reduction verification. Techniques for refuting candidate reductions are also provided. We then explain an application of the decomposability verification problem in the lower bound proofs for the problem of supervisor decomposition and the problem of existence of a decentralized supervisor. Finally, some other applications of the notion of reduction of distributions are also shown.
Autors: Liyong Lin;Simon Ware;Rong Su;W. Murray Wonham;
Appeared in: IEEE Transactions on Automatic Control
Publication date: Nov 2017, volume: 62, issue:11, pages: 5755 - 5768
Publisher: IEEE
 
» Reduction of Eddy-Current Loss in Flux-Switching Permanent-Magnet Machines Using Rotor Magnetic Flux Barriers
Abstract:
This paper investigates magnet eddy-current (EC) loss of a five-phase flux-switching permanent-magnet (FSPM) machine. Using magnetic flux barriers in the two proposed rotor topologies, it is possible to reduce some space harmonics in the air-gap flux density, thus reducing magnet EC loss. A 1-D magnetic permeability model is adopted to account for the reduction of the main sub-harmonics as well. Then, the magnet EC loss of both the existing machine and two proposed machines is compared by using time-stepped finite-element analysis. It is shown that the 55.2% and 49.6% EC loss reduction can be achieved by adopting proposed I and proposed II topologies, respectively. The temperature of proposed machine is lower than that of the existing one, and the temperature of both winding and magnet in the new topologies was reduced by 24°. Therefore, the proposed machine model can provide a direct guidance for dependable design and performance optimization of FSPM machines.
Autors: Jianhua Luo;Wenxiang Zhao;Jinghua Ji;Junqiang Zheng;Ying Zhang;Zhijian Ling;Jingfeng Mao;
Appeared in: IEEE Transactions on Magnetics
Publication date: Nov 2017, volume: 53, issue:11, pages: 1 - 5
Publisher: IEEE
 
» Reduction of Iron Loss on Laminated Electrical Steel Sheet Cores by Means of Secondary Current Heating Method
Abstract:
This paper presents the newly proposed heating method named “secondary current heating method” to reduce the iron loss on laminated electrical steel sheet cores for high-efficiency electrical motors. It is well known that the magnetic properties of electrical steel sheets, which are used for electrical motor cores, are deteriorated by the residual stress during manufacturing of the electrical motors, and then the iron loss of the laminated electrical steel sheet cores of the electrical motors increases inevitably. The newly proposed secondary current heating method can improve the magnetic properties on laminated electrical steel sheet cores and reduce the iron loss of them in the very short time in comparison with conventional methods. In this paper, the electromagnetic and heating characteristics of the specimens by the proposed method were confirmed, and then it was applied into the laminated cores to reduce iron loss.
Autors: Yuji Tsuchida;Naoyuki Yoshino;Masato Enokizono;
Appeared in: IEEE Transactions on Magnetics
Publication date: Nov 2017, volume: 53, issue:11, pages: 1 - 4
Publisher: IEEE
 
» Reflectionless Adaptive RF Filters: Bandpass, Bandstop, and Cascade Designs
Abstract:
A class of frequency-reconfigurable input-reflectionless/absorptive RF/microwave filters is presented. They consist of tunable complementary-duplexer architectures that are composed of a main and an auxiliary channel with opposite filtering transfer functions. By loading the auxiliary channel with a reference-impedance resistor and by taking the output node of the main channel as the output terminal of the overall circuit, a filtering network of the same type of the main channel with theoretically perfect input-reflectionless behavior at all frequencies can be realized. This technique can be applied to design spectrally agile completely input-reflectionless filters with any kind of transfer function, such as low-pass, high-pass, and single/multiband bandpass/bandstop filters. The theoretical analysis of the first-order absorptive bandpass/bandstop filtering sections based on a coupling-matrix formulation is detailed. Furthermore, the synthesis of high-selectivity reflectionless filters either by cascading multiple first-order cells or using high-order channels in a single complementary duplexer is also described. For practical-demonstration purposes, frequency-tunable lumped-element and microstrip prototypes are manufactured and characterized. They correspond to first- and second-order bandpass/bandstop filters. In addition, their in-series cascade connection is used to implement a bandpass filter with spectrally controllable passband and out-of-band notches.
Autors: Dimitra Psychogiou;Roberto Gómez-García;
Appeared in: IEEE Transactions on Microwave Theory and Techniques
Publication date: Nov 2017, volume: 65, issue:11, pages: 4593 - 4605
Publisher: IEEE
 
» Refraction Correction of Airborne LiDAR Bathymetry Based on Sea Surface Profile and Ray Tracing
Abstract:
Water depth can be measured using airborne LiDAR bathymetry (ALB). However, when the green laser beam passes through the air–water interface, the sea surface slope greatly affects the laser propagation path, significantly influencing the accuracy of the measured seafloor topography. To reduce its influence, a refraction correction method at the air–water interface based on the sea surface profile and ray tracing is proposed. First, the 3-D sea surface profile is fit based on the least-squares criteria and the wave spectrum, using the laser point data reflected by the sea surface. Then, on the basis of the sea surface slope, the geolocation biases of the laser points are corrected by tracing every laser transmission path at the air–water interface. The developed method is used to correct the ALB data collected in the South China Sea, and verified by the topography data captured by a ship-borne multibeam echo sounder. Before the refraction correction, the mean absolute error (MAE) is 14.2 cm, and the root-mean-square error (RMSE) is 17.5 cm. After the refraction correction, the MAE and RMSE decrease to 7.2 and 8.3 cm, respectively. The developed method can effectively improve the bathymetric accuracy of the ALB data.
Autors: Fanlin Yang;Dianpeng Su;Yue Ma;Chengkai Feng;Anxiu Yang;Mingwei Wang;
Appeared in: IEEE Transactions on Geoscience and Remote Sensing
Publication date: Nov 2017, volume: 55, issue:11, pages: 6141 - 6149
Publisher: IEEE
 
» Regional Carbon Predictions in a Temperate Forest Using Satellite Lidar
Abstract:
Large uncertainties in terrestrial carbon stocks and sequestration predictions result from insufficient regional data characterizing forest structure. This study uses satellite waveform lidar from ICESat to estimate regional forest structure in central New England, where each lidar waveform estimates fine-scale forest heterogeneity. ICESat is a global sampling satellite, but does not provide wall-to-wall coverage. Comprehensive, wall-to-wall ecosystem state characterization is achieved through spatial extrapolation using the random forest machine-learning algorithm. This forest description allows for effective initialization of individual-based terrestrial biosphere models making regional carbon flux predictions. Within 42/43.5 N and 73/71.5 W, aboveground carbon was estimated at 92.47 TgC or 45.66 MgC ha−1, and net carbon fluxes were estimated at 4.27 TgC yr−1 or 2.11 MgC ha−1 yr−1. This carbon sequestration potential was valued at 47% of fossil fuel emissions in eight central New England counties. In preparation for new lidar and hyperspectral satellites, linking satellite data and terrestrial biosphere models are crucial in improving estimates of carbon sequestration potential counteracting anthropogenic sources of carbon.
Autors: Alexander S. Antonarakis;Alejandro Guizar Coutiño;
Appeared in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication date: Nov 2017, volume: 10, issue:11, pages: 4954 - 4960
Publisher: IEEE
 
» Regret Benefit Ratio Link Scheduler for Wireless Backhaul With Directional Antennas
Abstract:
With huge bandwidth available in the mmWave band, wireless backhaul at mmWave frequencies can be a promising backhaul solution for small cells densely deployed underlying the homogeneous macrocells. With multiple links under such mmWave wireless network, it is desired to have a scheduling mechanism that can effectively improve the capacity of network with Quality of Service (QoS) considered. In this paper, we propose the regret benefit ratio scheduler (RBRS) that can maximize the number of links with their QoS requirements satisfied. Our proposed indicator, called regret benefit ratio (RBR), allows us to simultaneously maximize the QoS benefit and minimize contention among links under directional antennas. We design RBRS for a time slot based centralized control mmWave network in which we utilize RBR to find suitable concurrent transmission links for every single time slot. Furthermore, we also propose a distributed scheme under Carrier-sense multiple access with collision avoidance (CSMA/CA), which implements the RBR by prioritizing Medium Access Control (MAC) contention window to provide better concurrent transmission support while achieving the QoS-aware capability. Simulations in the 73-GHz band are conducted to demonstrate the superior performance of our algorithm under different criteria.
Autors: Yun Zhu;Jiade Li;Qiuyuan Huang;Dapeng Wu;
Appeared in: IEEE Transactions on Vehicular Technology
Publication date: Nov 2017, volume: 66, issue:11, pages: 10220 - 10232
Publisher: IEEE
 
» Regulation of High-Efficiency Region in Permanent Magnet Machines According to a Given Driving Cycle
Abstract:
This paper proposes a principle to regulate the high-efficiency region of permanent magnet (PM) machines according to a given driving cycle. The key of this method is to establish the relationship between the highest efficiency point and the other around four points (top, bottom, left, and right points). Then, the loss (e.g., copper loss, iron loss, and PM eddy current loss) combination of these points is obtained. By optimizing the loss matching of these points, the high-efficiency region can be adjusted to be suitable area for a given driving cycle. In order to verify the effectiveness of proposed principle, a 12-slot 10-pole surface-mounted PM machine based on finite-element method is built, and several loss regulation methods are demonstrated. The results shows that the high-efficiency region can move from high-torque area to low-torque area according to the demand.
Autors: Qian Chen;Xun Fan;Guohai Liu;Liang Xu;Meimei Xu;
Appeared in: IEEE Transactions on Magnetics
Publication date: Nov 2017, volume: 53, issue:11, pages: 1 - 5
Publisher: IEEE
 
» Reinforcement Learning-Based Variable Speed Limit Control Strategy to Reduce Traffic Congestion at Freeway Recurrent Bottlenecks
Abstract:
The primary objective of this paper was to incorporate the reinforcement learning technique in variable speed limit (VSL) control strategies to reduce system travel time at freeway bottlenecks. A Q-learning (QL)-based VSL control strategy was proposed. The controller included two components: a QL-based offline agent and an online VSL controller. The VSL controller was trained to learn the optimal speed limits for various traffic states to achieve a long-term goal of system optimization. The control effects of the VSL were evaluated using a modified cell transmission model for a freeway recurrent bottleneck. A new parameter was introduced in the cell transmission model to account for the overspeed of drivers in unsaturated traffic conditions. Two scenarios that considered both stable and fluctuating traffic demands were evaluated. The effects of the proposed strategy were compared with those of the feedback-based VSL strategy. The results showed that the proposed QL-based VSL strategy outperformed the feedback-based VSL strategy. More specifically, the proposed VSL control strategy reduced the system travel time by 49.34% in the stable demand scenario and 21.84% in the fluctuating demand scenario.
Autors: Zhibin Li;Pan Liu;Chengcheng Xu;Hui Duan;Wei Wang;
Appeared in: IEEE Transactions on Intelligent Transportation Systems
Publication date: Nov 2017, volume: 18, issue:11, pages: 3204 - 3217
Publisher: IEEE
 
» Relation Between the Frequency of Short-Pulse Electrical Stimulation of Afferent Nerve Fibers and Evoked Muscle Force
Abstract:
Objective: Functional electrical stimulation (FES) is conventionally performed by the stimulation of motor axons causing the muscle fibers innervated by these axons to contract. An alternative strategy that may evoke contractions with more natural motor unit behavior is to stimulate afferent fibers (primarily type Ia) to excite the motor neurons at the spinal level. The aim of the study was to investigate the range of forces that can be evoked in this way and the degree to which the torque can be controlled. Methods: We stimulated the tibial nerve of ten healthy participants at amplitudes at which the highest H-reflex with minimal M-wave was present. The evoked plantar flexion torque was recorded following short stimulation pulses (0.4 ms) with frequencies ranging from 20 to 200 Hz. Results: Across all subjects, the median highest evocable torque was 38.3% (quartiles: 16.9–51.0) of the maximum voluntary contraction torque (MVC). The average torque variability (standard deviation) was 1.7 +/- 0.7% MVC. For most subjects, the relation between stimulation frequency and evoked torque was well characterized by sigmoidal curves (median root mean square error: 6.4% MVC). The plateau of this sigmoid curve (indicating the range of frequencies over which torque amplitude could be modulated) was reached at 56.0 (quartiles: 29.4–81.9) Hz. Conclusion: Using the proposed method for FES, substantial evoked torques that could be controlled by stimulation frequency were achieved. Significance: Stimulation of afferent fibers could be a useful and fatigue-resistant strategy for several applications of FES.
Autors: Jakob Dideriksen;Kasper Leerskov;Magdalena Czyzewska;Rune Rasmussen;
Appeared in: IEEE Transactions on Biomedical Engineering
Publication date: Nov 2017, volume: 64, issue:11, pages: 2737 - 2745
Publisher: IEEE
 
» Relaxation-Free and Inertial Switching in Synthetic Antiferromagnets Subject to Super-Resonant Excitation
Abstract:
Applications of magnetic memory devices greatly benefit from ultra-fast, low-power switching. In this paper, we propose a method for how this can be achieved efficiently in a nano-sized synthetic antiferromagnet by using perpendicular-to-the-plane picosecond-range magnetic-field pulses. Our detailed micromagnetic simulations, supported by analytical results, yield the parameter space where inertial switching and relaxation-free switching can be achieved in the system. We furthermore discuss the advantages of dynamic switching in synthetic antiferromagnets and, specifically, their relatively low-power switching as compared with that in single ferromagnetic particles. Finally, we show how the excitation of spin waves in the system can be used to significantly reduce the post-switching spin oscillations for practical device geometries.
Autors: B. C. Koop;T. Descamps;E. Holmgren;V. Korenivski;
Appeared in: IEEE Transactions on Magnetics
Publication date: Nov 2017, volume: 53, issue:11, pages: 1 - 5
Publisher: IEEE
 
» Reliability Characteristics and Mechanisms of HRL’s T3 GaN Technology
Abstract:
HRL’s T3 GaN MMIC technology is evaluated using dc reliability experiments, including a voltage step-stress test, a temperature step-stress test, and a 3-temperature life test. The drain voltage step-stress test revealed three distinct regions of operation through gate leakage characteristic changes: burn-in stabilization up to 5 V, followed by stable operation up to 20 V, then voltage-dependent degradation up to catastrophic failure around 30 V. As a result, a de-rated recommended safe-operating limit of Vds = 12 V was established. The temperature step-stress test resulted in clear Arrhenius temperature-dependent degradation of the device on-resistance above 250 °C. Similarly, a recommended safe-operating limit of 150 °C was established for the technology. Finally, the 3-temperature life test resulted in clear temperature group separation and an activation energy of 2.52 eV, with a mean time-to-failure of 4 million hours at 150 °C with only a +0.2 ohm.mm increase in device on-resistance. Physical failure analysis of degraded parts showed the mechanism was a defect formation in the epitaxial barrier layer at the drain edge of the gate. Finite-element electrical simulations duplicated this degradation mechanism by modeling the defect as a void, which depleted the 2DEG and therefore increased device on-resistance.
Autors: Shawn D. Burnham;Ross Bowen;Joe Tai;David Brown;Robert Grabar;Dayward Santos;Jesus Magadia;Isaac Khalaf;Miroslav Micovic;
Appeared in: IEEE Transactions on Semiconductor Manufacturing
Publication date: Nov 2017, volume: 30, issue:4, pages: 480 - 485
Publisher: IEEE
 
» Reliability-Enhanced Hybrid CMOS/MTJ Logic Circuit Architecture
Abstract:
Benefitting from its non-volatility, nearly infinite endurance, good scalability, and great CMOS compatibility, magnetic tunnel junction (MTJ) embedded in conventional CMOS logic circuits has been proposed as one potentially powerful solution to introduce non-volatility in today’s programmable logic circuits, which is envisioned to achieve low power and high area efficiency. However, the recently proposed hybrid CMOS/MTJ logic circuits and prototypes based on such a logic-in-memory architecture suffers from a severe reliability issue, which is the precise transformation from MTJ resistance to electric signals due to its limited tunnel magnetoresistance ratio (TMR150%), i.e., the requirement of nearly zero sensing error for logic applications. In this paper, to overcome this sensing reliability issue, a novel sense amplifier (SA) with high sensing margin is proposed for such hybrid CMOS/MTJ logic circuit architecture. By using a commercial CMOS 40 nm design kit and a physics-based MTJ compact model, hybrid CMOS/MTJ transient and Monte Carlo statistic simulations have been conducted to demonstrate the functionality of the proposed SA and evaluate its performance, respectively.
Autors: Deming Zhang;Lang Zeng;Youguang Zhang;Jacques Olivier Klein;Weisheng Zhao;
Appeared in: IEEE Transactions on Magnetics
Publication date: Nov 2017, volume: 53, issue:11, pages: 1 - 5
Publisher: IEEE
 
» Reliability-Oriented Decoding Strategy for LDPC Codes-Based D-JSCC System
Abstract:
In this letter, a reliability oriented decoding strategy (RODS) for low density parity check codes-based distributed joint source and channel coding system is proposed. During each local decoding iteration, the reliability of each variable node is evaluated based on the sign of received log-likelihood ratios (LLRs) and its oscillation situation. Then, by employing reliability-oriented updating operation, the negative influence of those inaccurate messages is suppressed. Moreover, extra operations are performed on a group of most unreliable source nodes so that more accurate posteriori LLRs can be obtained. During global iteration, offset operation is performed to reduce error propagation. The simulation results illustrate that the proposed RODS outperforms other decoding algorithms at the aspects of error correction performance and convergence speed, especially when the correlation between distributed sources is low.
Autors: Yibo Lyu;Shaohua Hong;Lin Wang;Zixiang Xiong;
Appeared in: IEEE Communications Letters
Publication date: Nov 2017, volume: 21, issue:11, pages: 2364 - 2367
Publisher: IEEE
 
» Remarks on Stability of Time-Varying Linear Systems
Abstract:
The relationships between attractivity and asymptotic stability are fleshed out for homogeneous linear ordinary differential equations with time-varying coefficients.
Autors: Andrew D. Lewis;
Appeared in: IEEE Transactions on Automatic Control
Publication date: Nov 2017, volume: 62, issue:11, pages: 6039 - 6043
Publisher: IEEE
 
» Replacing a 16,500-hp Adjustable Frequency Drive System: Best Practices to Maximize Uptime and Minimize Execution Risks
Abstract:
The application and installation of high-power adjustable frequency drives (AFDs) in the oil and gas industry require a significant allocation of resources and a large toolbox in the evaluation and deployment of tailing pumps as capital equipment. This article describes the successful installation of a 16,500-hp, medium-voltage AFD replacing an existing tailing pump AFD in an effort to increase process availability. This case study was written to provide a road map of key performance indicators, AFD design characteristics, life-cycle phases, and target measures used to support the goals and objectives of efforts in engineering, operations, and maintenance planning.
Autors: Nilesh Patel;Donald Wilson;Giovanni Vignolo;Ashok Mangukia;
Appeared in: IEEE Industry Applications Magazine
Publication date: Nov 2017, volume: 23, issue:6, pages: 39 - 53
Publisher: IEEE
 
» Research in the age of numerical simulation
Abstract:
Most creative intellectual activity is directed toward abstracting some universal aspect ("truth") from human experience. This is true whether the intellectual activity is in the domain of "arts," "literature," or "science." A liberal arts education brings an appreciation for the universality of intellectual activity and the realization that what we do in "science" differs not so greatly from our colleagues' endeavors in the humanities. We are both in pursuit of the same thing-"universal truth," although the form of that "truth" differs. Great art abstracts as an image "truth" in human experience or the world around us. Great literature abstracts "truth" in human experience into a narrative of human activity. Great poetry minimizes that narrative to artful language. Great music abstracts "truth" in human activity in many ways, e.g., psychological drama in the case of opera (Verdi, Othello; Schoenberg, Erwartung; Bartok, Bluebeard's Castle) or, in the case of abstract music, evokes the human experience through "association," much as abstract art.
Autors: Steven A. Boggs;
Appeared in: IEEE Electrical Insulation Magazine
Publication date: Nov 2017, volume: 33, issue:6, pages: 8 - 16
Publisher: IEEE
 
» Research of Magnetocaloric Effect For Ni-Mn-In-Co Heusler Alloys by the Direct Methods in Magnetic Fields Up to 14 T
Abstract:
This paper is devoted to the study of magnetocaloric effect (MCE) in Ni-Mn-In-Co Heusler alloys by direct methods in high magnetic fields. Ni-Mn-In-Co Heusler alloys demonstrate the inverse MCE in the magnetostructural transition area. The adiabatic temperature change value () is determined by direct extraction method in magnetic fields up to 10 T. It is shown that the value of increases with decrease in the difference between the temperature of magnetostructural transition and the Curie point temperatures of the compounds. The amount of isothermal heat emission (sorption) for Ni43Mn37.8In12.2Co7 alloy is determined as a result of magnetization in magnetic field up to 10 T in the magnetostructural transition area. The obtained results are discussed from the viewpoint of magnetic and lattice subsystems influences on the total entropy change ().
Autors: Elvina T. Dilmieva;Yurii S. KoshkidKo;Alexander P. Kamantsev;Victor V. Koledov;Aleksey V. Mashirov;Vladimir G. Shavrov;Vladimir V. Khovaylo;Mariya V. Lyange;J. Cwik;Lorena Gonzalez-Legarreta;Hernado Blanca Grande;
Appeared in: IEEE Transactions on Magnetics
Publication date: Nov 2017, volume: 53, issue:11, pages: 1 - 5
Publisher: IEEE
 
» Research on a PM Slotless Linear Generator Based on Magnet Field Analysis Model for Wave Energy Conversion
Abstract:
In this paper, a permanent magnet (PM) slotless tubular linear generator based on magnet field analysis model is presented for wave energy conversion. To improve air flux density and efficiency of the generator, two improved Halbach PM Arrays (T-PM structure and claw-PM structure) applied in the slotless linear generator is proposed and compared with Halbach PM Arrays. Combined with axisymmetric dimensional finite-element method (FEM), no-load electromagnetic performance is analyzed. Finally, due to test and analysis model results of prototype concordant with the FEM results, the generator model and the analysis method are correct.
Autors: Jing Zhang;Haitao Yu;Minqiang Hu;Lei Huang;Tao Xia;
Appeared in: IEEE Transactions on Magnetics
Publication date: Nov 2017, volume: 53, issue:11, pages: 1 - 4
Publisher: IEEE
 
» Resilient Sampled-Data Control for Markovian Jump Systems With an Adaptive Fault-Tolerant Mechanism
Abstract:
This brief investigates the problem of passivity-based resilient sampled-data control for Markovian jump systems subject to actuator faults via an adaptive fault-tolerant mechanism. By constructing a proper Lyapunov function, a set of sufficient conditions is obtained in terms of linear matrix inequalities (LMIs), which ensures that the closed-loop system is stochastically passive. In order to reflect the imprecision in controller, the additive gain variations is considered. Then, the resilient sampled-data control parameters can be determined by solving the obtained LMIs. Finally, an illustrative example is presented to show the validity and applicability of the proposed design technique.
Autors: R. Sakthivel;Hamid Reza Karimi;Maya Joby;Srimanta Santra;
Appeared in: IEEE Transactions on Circuits and Systems II: Express Briefs
Publication date: Nov 2017, volume: 64, issue:11, pages: 1312 - 1316
Publisher: IEEE
 
» Resistive Switching Characteristics of Al2O3 Film for Transparent Nonvolatile Memory
Abstract:
Transparent resistive memory requires a transparent electrode and thin storage layer. In this letter, we highlight the importance of a dielectric and metal multilayer electrode for transparency with good flexible characteristics also. In particular, we utilized the stable properties of resistive memory obtained from an inserted thin layer near the oxide layer. The optimized thickness of the whole structure was calculated by MATLAB simulation, which followed the model of the optical transfer matrix theory. The transparent resistive memory has stable resistive switching behaviors.
Autors: Myeongcheol Kim;Kyung Cheol Choi;
Appeared in: IEEE Transactions on Nanotechnology
Publication date: Nov 2017, volume: 16, issue:6, pages: 1129 - 1131
Publisher: IEEE
 
» Resistive Switching Characteristics of Flexible TiO2 Thin Film Fabricated by Deep Ultraviolet Photochemical Solution Method
Abstract:
A novel ultraviolet photochemical method was used to prepare TiO2 resistive-switching films. Amorphous TiO2 films were formed on flexible indium-tin oxide (ITO) coated polyethylene terephthalate (PET) substrates by deep ultraviolet irradiation at 150 °C. A Pt/TiO2/ITO/PET device was then fabricated to investigate bipolar resistive switching of the films for potential application in non-volatile memories. The ratio of on-state to off-state currents was measured, and a good value of 1000 was obtained. The retention and switch-cycling characteristics of the device were investigated for different bending radii. The resistive switching behavior of the flexible device remained stable after 600 cycles of electrical switching and 1000 cycles of bending.
Autors: Yuanqing Chen;Lingwei Li;Xiaoru Yin;Aditya Yerramilli;Yuxia Shen;Yang Song;Weibai Bian;Na Li;Zhao Zhao;Wenwen Qu;N. David Theodore;T. L. Alford;
Appeared in: IEEE Electron Device Letters
Publication date: Nov 2017, volume: 38, issue:11, pages: 1528 - 1531
Publisher: IEEE
 
» Resource Allocation for Energy Harvesting-Powered D2D Communication Underlaying Cellular Networks
Abstract:
Energy harvesting (EH)-powered wireless communications have attracted great attention from both industry and academia. However, the available energy, which relies on EH efficiency, will become another nonnegligible factor when we do research on EH-powered wireless communications. This paper studies the resource allocation problems in terms of spectrum and energy under EH-powered Device-to-Device (D2D) Communication underlaying Cellular Network (EH-DCCN). In this network, D2D pairs powered by EH are allowed to reuse the spectrum resources occupied by Cellular Users (CUs). To investigate the resource allocation problems, a sum-rate maximization problem of the whole cellular network with consideration of Quality of Service (QoS) and available energy constraints is formulated. The maximization problem is a nonconcave mixed-integer nonlinear programming (MINLP) problem, which has been proved to be NP-hard. To solve the problem, we first relax it with a concave lower bound on the original problem and then obtain the theoretical performance of the lower bound by Outer Approximation Algorithm. Moreover, a heuristic algorithm, an Energy-aware Space Matching approach (ESM), is proposed to acquire a suboptimal solution with low computational complexity. Finally, numerical simulation results indicate our considered resource allocation strategy is more effective than the strategy only based on channel state information under the EH-DCCN. Moreover, the performance in aspects of the sum rate and the matching probability shows that the ESM can approximately obtain the theoretical performance of the lower bound on the original problem under the scenarios with higher ratio of CU and EH-powered D2D numbers.
Autors: Ying Luo;Peilin Hong;Ruolin Su;Kaiping Xue;
Appeared in: IEEE Transactions on Vehicular Technology
Publication date: Nov 2017, volume: 66, issue:11, pages: 10486 - 10498
Publisher: IEEE
 
» Response to Comments on “Microstrip T-Junction Power Divider With Exponentially Tapered Transmission Lines”
Abstract:
The authors recognize that the equation for the cutoff wavelength in [1] is incorrect. It is unfortunate as it was directly used in the derivations in [2]. However, the authors would like to emphasize that by using the correct equation for , the rest of the analysis in [2] is valid for the magnitude of the S-parameters, as was demonstrated in the comment by Sinha and Chatterjee [3]. In fact, this correction greatly improves the agreement between the theoretical, simulated, and measured responses. Using the corrected expression for , updated results for Prototype 2 are shown in Fig. 1.
Autors: C. Justin R. Smith;Hjalti H. Sigmarsson;
Appeared in: IEEE Microwave and Wireless Components Letters
Publication date: Nov 2017, volume: 27, issue:11, pages: 1039 - 1039
Publisher: IEEE
 
» Reusability of the Output of Map-Matching Algorithms Across Space and Time Through Machine Learning
Abstract:
A map-matching algorithm outputs a vector per GPS point, projecting the moving object on one of the segments of the transportation network. Although developing more sophisticated map-matching algorithms for vehicle and pedestrian navigation systems have been the focus of research in this field, reusability of the historical information already provided by map-matching algorithms has not been addressed yet. In other words, although researchers have been attempting to improve the accuracy of the aforementioned vector to correctly project GPS points on the transportation network, no research has exploited the spatial-temporal pattern in the arrangement of these projection vectors. This pattern, if properly detected, can be used as a rough surrogate for map-matching algorithms, in addition to other applications that require better positional accuracy for moving objects in smart cities. This paper detects and validates the spatial-temporal pattern in projection vectors produced by map-matching algorithms via machine learning. Projection vectors showed a strong spatial-temporal pattern in Chicago, IL, USA, which was captured best via a local nonlinear regressor, K-nearest neighbors, and helped double the positional accuracy of unseen GPS points. While a global nonlinear regressor, multilayer Perceptron was able to slightly improve the positional accuracy of GPS points, the linear least squares had an exacerbating effect on the positional accuracy.
Autors: Mahdi Hashemi;
Appeared in: IEEE Transactions on Intelligent Transportation Systems
Publication date: Nov 2017, volume: 18, issue:11, pages: 3017 - 3026
Publisher: IEEE
 
» Reverse Backprojection Algorithm for the Accurate Generation of SAR Raw Data of Natural Scenes
Abstract:
Future synthetic aperture radar (SAR) mission concepts often rely on locally nonlinear (e.g., high orbits and bistatic) surveys or acquisition schemes. The simulation of the raw data of natural scenes as acquired by future systems appears as one powerful tool in order to understand the particularities of these systems and assess the impact of system and propagation errors on their performance. We put forward, in this letter, a new formulation of the reverse backprojection algorithm for the accurate simulation of raw data of natural surfaces. In particular, the algorithm is perfectly suited to accommodate any kind (1-D/2-D) of temporal and spatial variation, e.g., in observation geometry, acquisition strategy, or atmospheric propagation. The algorithm is analyzed with respect to its SAR image formation sibling, and tested under different simulation scenarios. We expect the reverse backprojection algorithm to play a relevant role in the simulation of future geosynchronous and multistatic SAR missions.
Autors: Dexin Li;Marc Rodriguez-Cassola;Pau Prats-Iraola;Manqing Wu;Alberto Moreira;
Appeared in: IEEE Geoscience and Remote Sensing Letters
Publication date: Nov 2017, volume: 14, issue:11, pages: 2072 - 2076
Publisher: IEEE
 
» Reversible Permeabilization of Cancer Cells by High Sub-Microsecond Magnetic Field
Abstract:
Exposure of cells to pulsed electric fields (PEFs) induces a phenomenon known as electroporation, which leads to increase of membrane permeability. Electroporation is applied in biotechnology, food processing, and medicine, including cancer treatment. Recently, a contactless method based on pulsed magnetic fields (PMFs) for the permeabilization of biological cells has been proposed; however, the permeabilization mechanism of the PMF method is still hypothetical. In this paper, we have shown that it is possible to reversibly permeabilize Sp2/0 myeloma cells by sub-microsecond (450 ns) PMF in the range of 0–3.3 T. The PMF methodology was also combined with PEF treatment to evaluate additive effects. The 1.35 kV/cm (PEF) and 3.3 T, 50 pulses, 0.25 Hz (PMF) protocols were applied. The cells were treated in the presence of fluorescence dye YO-PRO-1 and influx into the cells was evaluated by cytometry. Cell viability after the treatment was evaluated by CellEvent Caspase-3/7 assays. A significant (P < 0.05) additive effect of the two pulsed power methodologies was detected, resulting in up to 12% increase of membrane permeabilization. The PMF method is an emerging technique and the results of the study can be used for the development of new effective protocols, while the determined additive effects with PEF are promising in the field of electrochemotherapy.
Autors: Vitalij Novickij;Irutė Girkontaitė;Auksė Zinkevičienė;Jurgita Švedienė;Eglė Lastauskienė;Algimantas Paškevičius;Svetlana Markovskaja;Jurij Novickij;
Appeared in: IEEE Transactions on Magnetics
Publication date: Nov 2017, volume: 53, issue:11, pages: 1 - 4
Publisher: IEEE
 
» Revision of Clausius–Clapeyron Relation for the First-Order Phase Transition in Ni–Mn–In Heusler Alloys
Abstract:
The derivation is presented of the Clausius–Clapeyron relation (CCR) in the second order of expansion of free energy potential on the change of temperature and magnetic field on the example of Ni–Mn–In Heusler alloys with the first-order metamagnetostructural phase transition (FOMMSPT), which can be treated as thermoelastic structural transition: austenite—martensite merging with metamagnetic transition: ferromagnet–antiferromagnet. It is shown that the second-order CCR describes satisfactory the nonlinear shift in the characteristic temperatures of the FOMMSPT in magnetic fields up to 25 T. It qualitatively and quantitatively explains the observed nonlinear dependence of the characteristic temperatures and the hysteresis of FOMMSPT including its elimination in some compositions of the Ni–Mn–In Heusler alloys under sufficiently high magnetic field.
Autors: A. V. Mashirov;A. P. Kamantsev;A. V. Koshelev;E. A. Ovchenkov;E. T. Dilmieva;A. S. Los;A. M. Aliev;V. V. Koledov;V. G. Shavrov;
Appeared in: IEEE Transactions on Magnetics
Publication date: Nov 2017, volume: 53, issue:11, pages: 1 - 4
Publisher: IEEE
 
» Revisiting and Optimizing the Design of the Timer-Based Distributed Selection Scheme for Tackling Imperfect Power Control
Abstract:
Opportunistic selection improves the performance of a multi-node wireless system by exploiting multi-user or spatial diversity. In it, the nodes are sorted in the descending order of their metrics, which captures the utility of a node to the system if selected, and the best node with the highest metric is selected. We analyze the effect of imperfect power control on the conventional timer with power control scheme, which selects the best node in a distributed manner, and quantify the extent by which it reduces the probability of selecting the best node and increases the probability of selecting a non-best node. We then redesign it to ameliorate the impact of imperfect power control. Our systematic approach eschews several ad hoc assumptions implicit in the design of the conventional timer scheme, and jointly optimizes its various parameters to maximize the probability of selecting the best node in the presence of imperfect power control. We present several structural insights, including asymptotic ones, about the optimal scheme, which also enable it to be determined with much lower computational complexity. Our benchmarking results show that it is scalable and outperforms the conventional schemes.
Autors: Vikas Kumar Dewangan;Neelesh B. Mehta;
Appeared in: IEEE Transactions on Wireless Communications
Publication date: Nov 2017, volume: 16, issue:11, pages: 7646 - 7657
Publisher: IEEE
 
» Riemannian Alternative Matrix Completion for Image-Based Flame Recognition
Abstract:
The flame image has important significance in combustion state recognition and judgment, which can be used effectively for control of energy consumption and exhaust emissions. Due to the harsh industrial environments, flame images are usually corrupted by transmission errors or coding issues, which makes the combustion state analysis very challenging. This paper proposes a novel flame combustion state analysis framework, which provides new insight into two crucial issues: corrupted flame image recovery and combustion state recognition. First, we propose Riemannian alternative optimization (RAO) with fast convergence and the global optimization ability to recover the corrupted flame image. More specifically, RAO constructs a low-rank factorization model and exploits the geometric nature of the flame image to perform the optimization on Riemannian manifolds. Second, we use Fisher discriminant analysis to exploit discriminative features of the recovered flame image and provide well-separated classes of the combustion state for recognition. The experiments show that the proposed framework recovers the corrupted flame image efficiently and achieves satisfying performance of combustion state recognition.
Autors: Zhichao Wang;Min Liu;Mingyu Dong;Lian Wu;
Appeared in: IEEE Transactions on Circuits and Systems for Video Technology
Publication date: Nov 2017, volume: 27, issue:11, pages: 2490 - 2503
Publisher: IEEE
 
» Risk-Limiting Unit Commitment in Smart Grid With Intelligent Periphery
Abstract:
This paper proposes the risk-limiting unit commitment (RLUC) as the operational method to address the uncertainties in the smart grid with intelligent periphery (GRIP). Three key requirements are identified for the RLUC in GRIP. The first one requires the RLUC to be modeled as a multistage multiperiod unit commitment problem considering power trades, operational constraints, and operational risks. The second one requires the RLUC considering the conditional prediction to achieve a globally optimal solution. It is addressed by using conditional probability in a scenario-based form. The last one requires the risk index in the RLUC to be both valid and computationally friendly, and it is tackled by the utilization of a coherent risk index and the mathematical proof of a risk chain theorem. Finally, the comprehensive RLUC in GRIP satisfying all the three requirements is solved by an equivalent transformation into a mixed integer piecewise linear programming problem. Case studies on a nine-bus system, a realistic provincial power system, and a regional power grid in China demonstrate the advantages of the proposed RLUC in GRIP.
Autors: Chaoyi Peng;Yunhe Hou;Nanpeng Yu;Weisheng Wang;
Appeared in: IEEE Transactions on Power Systems
Publication date: Nov 2017, volume: 32, issue:6, pages: 4696 - 4707
Publisher: IEEE
 
» Road Recognition From Remote Sensing Imagery Using Incremental Learning
Abstract:
Roads, as important artificial objects, are the main body of modern traffic system, providing many conveniences for human civilization. With the development of Intelligent Transportation Systems (ITS), the road structure is changing frequently. Road recognition is to identify the road type from remote sensing imagery, and road types depend largely on the characteristics of roads. Thus, how to extract road features and further making road classification efficient have become a popular and challenging research topic. In this paper, we propose a road recognition method for remote sensing imagery using incremental learning. In principle, our method includes the following steps: 1) the non-road remote sensing imagery is first filtered by using support vector machine; 2) the road network is obtained from the road remote sensing imagery by computing multiple saliency features; 3) the road features are extracted from road network and background environment; and 4) the roads are recognized as three road types according to the classification results of incremental learning algorithm. The experimental results show that our method has higher road recognition rate as well as less recognition time than the other popular algorithms.
Autors: Jing Zhang;Lu Chen;Chao Wang;Li Zhuo;Qi Tian;Xi Liang;
Appeared in: IEEE Transactions on Intelligent Transportation Systems
Publication date: Nov 2017, volume: 18, issue:11, pages: 2993 - 3005
Publisher: IEEE
 
» Robust $H_{infty }$ Observer-Based Control of Fractional-Order Systems With Gain Parametrization
Abstract:
This paper investigates the robust observer-based control (OBC) for linear time-invariant disturbed uncertain fractional-order systems (DU-FOS). First, the existence conditions for robust OBC are given. Then, based on the -norm analysis using the generalized Kalman–Yakubovich–Popov lemma for FOS, and following the fractional derivative order , new sufficient linear matrix inequalities (LMIs) conditions are obtained to ensure the stability of the estimation errors and the stabilization of the DU-FOS simultaneously. All observer matrices gains and control laws can be computed by solving a unique LMI condition in one step. Numerical simulation is given to illustrate the validity of the proposed method.
Autors: Yassine Boukal;Mohamed Darouach;Michel Zasadzinski;Nour-Eddine Radhy;
Appeared in: IEEE Transactions on Automatic Control
Publication date: Nov 2017, volume: 62, issue:11, pages: 5710 - 5723
Publisher: IEEE
 
» Robust AN-Aided Secure Precoding for an AF MIMO Untrusted Relay System
Abstract:
Robust artificial noise (AN) aided secure precoding for an amplify-and-forward multiple-input multiple-output untrusted relay system is studied, where the relay is untrusted and willing to help forwarding multiple data streams from the source to destination. We consider that the available channel state information is imperfect and modeled by the worst case model. Our objective is to maximize the worst case secrecy rate under the robust transmit power constraints at the source and relay, by jointly designing the signal and AN precoding matrices at the source and the precoding matrix at the relay. The robust secure precoding problem is nonconvex and hard to solve. To overcome this difficulty, we propose the weighted minimum mean square error based method, where the sign-definiteness lemma is used to eliminate the channel uncertainties and an effective iterative optimization algorithm is developed. Simulation results are provided to demonstrate the effectiveness of the proposed scheme.
Autors: Quanzhong Li;Liang Yang;Qi Zhang;Jiayin Qin;
Appeared in: IEEE Transactions on Vehicular Technology
Publication date: Nov 2017, volume: 66, issue:11, pages: 10572 - 10576
Publisher: IEEE
 
» Robust Control of Magnetic Levitation Systems Considering Disturbance Force by LSM Propulsion Systems
Abstract:
In this paper, the robust control method is proposed for air-gap positioning of magnetic levitation systems considering levitation disturbance forces caused by propulsion systems. Even though the disturbance effect occurs inevitably by propulsion systems, it is very difficult or impossible to be measured by sensors in real time. In order to maintain the constant air-gap position according to the reference command in the propulsion state of the vehicle, robust control for electromagnetic suspension against levitation disturbance force is highly required. The disturbance force caused by propulsion systems is predicted by the finite-element method analysis of the magnetic flux distribution. Based on the analyzed result, the robust and optimal levitation controller is designed by the convex optimization method for the proposed proportional integral derivative controller with the inner feedback compensator stabilizing the nonlinear plant. The proposed controller has the formulation of the conventional full-state feedback optimal controller based on state-output matching for the unmeasured state. The effectiveness of the proposed controller is verified by simulation and finite-element method analysis.
Autors: Chang-Hyun Kim;
Appeared in: IEEE Transactions on Magnetics
Publication date: Nov 2017, volume: 53, issue:11, pages: 1 - 5
Publisher: IEEE
 
» Robust Degradation Analysis With Non-Gaussian Measurement Errors
Abstract:
Degradation analysis is an effective way to infer the health status and lifetime of products. Due to variability in the measurement, degradation observations are often subject to measurement errors. Existing studies generally assume Gaussian measurement errors, which may be deficient when there are outliers in the observations. To make a robust inference, we propose a Wiener degradation model with measurement errors modeled by Student's t-distribution. The t-distribution is a useful extension to the Gaussian distribution that provides a parametric approach to robust statistics. Nevertheless, the resulting likelihood function involves multiple integrals, which makes direct maximization difficult. Therefore, we propose an expectation-maximization algorithm, where the variational Bayes technique is introduced to derive an approximate conditional distribution in the E-step. The effectiveness of the proposed model is validated through Monte Carlo simulations. The applicability of the robust method is illustrated through applications to the degradation data of lithium-ion batteries and hard disk drives.
Autors: Qingqing Zhai;Zhi-Sheng Ye;
Appeared in: IEEE Transactions on Instrumentation and Measurement
Publication date: Nov 2017, volume: 66, issue:11, pages: 2803 - 2812
Publisher: IEEE
 
» Robust Design of a Supersonic Natural Laminar Flow Wing-Body
Abstract:
The robust design of a natural laminar flow wingbody for a supersonic business jet is here described. The pursued goal is to obtain a wing shape whose performance is influenced as least as possible by geometrical uncertainties. The starting point is a supersonic business jet wing-body that was already optimized for natural laminar flow using a deterministic objective function formulation. The definition of the optimization goal is based on special risk functions, namely Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR), that are widely used in financial engineering community and that offer interesting advantages with respect to more classical approaches based on expectation or variance risk functions. VaR and CVaR are used in conjunction with two different stochastic optimization algorithms, namely the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and the Surrogate-based Local Optimization (SBLO). These risk functions are computed using a very coarse sample set and their confidence intervals are computed using the bootstrap computational statistics technique. The results illustrate the feasibility of such a robust optimization approach for the application to industrial class robust design optimization problems in aerospace.
Autors: Domenico Quagliarella;Emiliano Iuliano;
Appeared in: IEEE Computational Intelligence Magazine
Publication date: Nov 2017, volume: 12, issue:4, pages: 14 - 27
Publisher: IEEE
 
» Robust Disjunctive-Codiagnosability of Discrete-Event Systems Against Permanent Loss of Observations
Abstract:
Recently, the so-called robust diagnosability of DESs against permanent loss of observations (RDPLO) has been introduced. In this regard, the language generated by the system is said to be robustly diagnosable if it is possible to detect the failure occurrence, within a bounded delay, even when some sensors permanently fail to communicate the occurrence of the events to the diagnoser. In this technical note, we extend the definition of RDPLO to the decentralized case, considering the disjunctive architecture, leading to the definition of robust disjunctive-codiagnosability against permanent loss of observations (RDCPLO). The technical note also addresses the issue of online implementation, and we propose an efficient scheme to carry out online robust decentralized diagnosis against permanent loss of observations. Other contributions of the technical note are the development of algorithms for the verification of the RDCPLO, and the computation of the delay bound for robust decentralized diagnosis.
Autors: Jean H. A. Tomola;Felipe G. Cabral;Lilian K. Carvalho;Marcos V. Moreira;
Appeared in: IEEE Transactions on Automatic Control
Publication date: Nov 2017, volume: 62, issue:11, pages: 5808 - 5815
Publisher: IEEE
 
» Robust Dual Clustering with Adaptive Manifold Regularization
Abstract:
In recent years, various data clustering algorithms have been proposed in the data mining and engineering communities. However, there are still drawbacks in traditional clustering methods which are worth to be further investigated, such as clustering for the high dimensional data, learning an ideal affinity matrix which optimally reveals the global data structure, discovering the intrinsic geometrical and discriminative properties of the data space, and reducing the noises influence brings by the complex data input. In this paper, we propose a novel clustering algorithm called robust dual clustering with adaptive manifold regularization (RDC), which simultaneously performs dual matrix factorization tasks with the target of an identical cluster indicator in both of the original and projected feature spaces, respectively. Among which, the -norm is used instead of the conventional -norm to measure the loss, which helps to improve the model robustness by relieving the influences by the noises and outliers. In order to better consider the intrinsic geometrical and discriminative data structure, we incorporate the manifold regularization term on the cluster indicator by using a particularly learned affinity matrix which is more suitable for the clustering task. Moreover, a novel augmented lagrangian method (ALM) based procedure is designed to effectively and efficiently seek the optimal solution of the proposed RDC optimization. Numerous experiments on the representative data sets demonstrate the superior performance of the proposed method compares to the existing clustering algorithms.
Autors: Nengwen Zhao;Lefei Zhang;Bo Du;Qian Zhang;Jane You;Dacheng Tao;
Appeared in: IEEE Transactions on Knowledge and Data Engineering
Publication date: Nov 2017, volume: 29, issue:11, pages: 2498 - 2509
Publisher: IEEE
 
» Robust Event-Triggered MPC With Guaranteed Asymptotic Bound and Average Sampling Rate
Abstract:
We propose a robust event-triggered model predictive control (MPC) scheme for linear time-invariant discrete-time systems subject to bounded additive stochastic disturbances and hard constraints on the input and state. For given probability distributions of the disturbances acting on the system, we design event conditions such that the average frequency of communication between the controller and the actuator in the closed-loop system attains a given value. We employ Tube MPC methods to guarantee robust constraint satisfaction and a robust asymptotic bound on the system state. Moreover, we show that instead of a given periodically updated Tube MPC scheme, an appropriate event-triggered MPC scheme can be applied, with the same guarantees on constraints and region of attraction, but with a reduced number of average communications.
Autors: Florian David Brunner;W. P. M. H. Heemels;Frank Allgöwer;
Appeared in: IEEE Transactions on Automatic Control
Publication date: Nov 2017, volume: 62, issue:11, pages: 5694 - 5709
Publisher: IEEE
 
» Robust Granger Analysis in Lp Norm Space for Directed EEG Network Analysis
Abstract:
Granger analysis (GA) is widely used to construct directed brain networks based on various physiological recordings, such as functional magnetic resonance imaging, and electroencephalogram (EEG). However, in real applications, EEGs are inevitably contaminated by unexpected artifacts that may distort the networks because of the L2 norm structure utilized in GAs when estimating directed links. Compared with the L2 norm, the Lp () norm can compress outlier effects. In this paper, an extended GA is constructed by applying the Lp () norm strategy to estimate robust causalities under outlier conditions, and a feasible iteration procedure is utilized to solve the new GA model. A quantitative evaluation using a predefined simulation network demonstrates smaller bias errors and higher linkage consistence for the Lp (, 0.8, 0.6, 0.4, 0.2) -GAs compared with both the Lasso- and L2-GAs under various simulated outlier conditions. Applications in resting-state EEGs that contain ocular artifacts also show that the proposed GA can effectively compress the ocular outlier influence and recover the reliable networks. The proposed Lp-GA may be helpful in capturing the reliable network structure when EEGs are contaminated with artifacts in related studies.
Autors: Peiyang Li;Xiaoye Huang;Fali Li;Xurui Wang;Weiwei Zhou;Huan Liu;Teng Ma;Tao Zhang;Daqing Guo;Dezhong Yao;Peng Xu;
Appeared in: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Publication date: Nov 2017, volume: 25, issue:11, pages: 1959 - 1969
Publisher: IEEE
 
» Robust Minimum Volume Simplex Analysis for Hyperspectral Unmixing
Abstract:
Most blind hyperspectral unmixing methods exploit convex geometry properties of hyperspectral data. The minimum volume simplex analysis (MVSA) is one of such methods, which, as many others, estimates the minimum volume (MV) simplex where the measured vectors live. MVSA was conceived to circumvent the matrix factorization step often implemented by MV-based algorithms and also to cope with outliers, which compromise the results produced by MV algorithms. Inspired by the recently proposed robust MV enclosing simplex (RMVES) algorithm, we herein introduce the robust MVSA (RMVSA), which is a version of MVSA robust to noise. As in RMVES, the robustness is achieved by employing chance constraints, which control the volume of the resulting simplex. RMVSA differs, however, substantially from RMVES in the way optimization is carried out. In this paper, we develop a linearization relaxation of the nonlinear chance constraints, which can greatly lighten the computational complex of chance constraint problems. The effectiveness of RMVSA is illustrated by comparing its performance with the state of the art.
Autors: Shaoquan Zhang;Alexander Agathos;Jun Li;
Appeared in: IEEE Transactions on Geoscience and Remote Sensing
Publication date: Nov 2017, volume: 55, issue:11, pages: 6431 - 6439
Publisher: IEEE
 
» Robust Online Algorithms for Peak-Minimizing EV Charging Under Multistage Uncertainty
Abstract:
In this paper, we study how to utilize forecasts to design online electrical vehicle (EV) charging algorithms that can attain strong performance guarantees. We consider the scenario of an aggregator serving a large number of EVs together with its background load, using both its own renewable energy (for free) and the energy procured from the external grid. The goal of the aggregator is to minimize its peak procurement from the grid, subject to the constraint that each EV has to be fully charged before its deadline. Further, the aggregator can predict the future demand and the renewable energy supply with some levels of uncertainty. We show that such prediction can be very effective in reducing the competitive ratios of online control algorithms, and even allow online algorithms to achieve close-to-offline-optimal peak. Specifically, we first propose a 2-level increasing precision model (2-IPM), to model forecasts with different levels of accuracy. We then develop a powerful computational approach that can compute the optimal competitive ratio under 2-IPM over any online algorithm, and also online algorithms that can achieve the optimal competitive ratio. Simulation results show that, even with up to 20% day-ahead prediction errors, our online algorithms still achieve competitive ratios fairly close to 1, which are much better than the classic results in the literature with a competitive ratio of . The second contribution of this paper is that we solve a dilemma for online algorithm design, e.g., an online algorithm with good competitive ratio may exhibit poor average-case performance. We propose a new Algorithm-Robustification procedure that can convert an online algorithm with good average-case performance to one with both the optimal competitive ratio and good average-case performance. We demonstrate via trace-based simulations the superior performance - f the robustified version of a well-known heuristic algorithm based on model predictive control.
Autors: Shizhen Zhao;Xiaojun Lin;Minghua Chen;
Appeared in: IEEE Transactions on Automatic Control
Publication date: Nov 2017, volume: 62, issue:11, pages: 5739 - 5754
Publisher: IEEE
 
» Robust Online Multi-Task Learning with Correlative and Personalized Structures
Abstract:
Multi-Task Learning (MTL) can enhance a classifier’s generalization performance by learning multiple related tasks simultaneously. Conventional MTL works under the offline or batch setting, and suffers from expensive training cost and poor scalability. To address such inefficiency issues, online learning techniques have been applied to solve MTL problems. However, most existing algorithms of online MTL constrain task relatedness into a presumed structure via a single weight matrix, which is a strict restriction that does not always hold in practice. In this paper, we propose a robust online MTL framework that overcomes this restriction by decomposing the weight matrix into two components: The first one captures the low-rank common structure among tasks via a nuclear norm and the second one identifies the personalized patterns of outlier tasks via a group lasso. Theoretical analysis shows the proposed algorithm can achieve a sub-linear regret with respect to the best linear model in hindsight. Even though the above framework achieves good performance, the nuclear norm that simply adds all nonzero singular values together may not be a good low-rank approximation. To improve the results, we use a log-determinant function as a non-convex rank approximation. The gradient scheme is applied to optimize log-determinant function and can obtain a closed-form solution for this refined problem. Experimental results on a number of real-world applications verify the efficacy of our method.
Autors: Peng Yang;Peilin Zhao;Xin Gao;
Appeared in: IEEE Transactions on Knowledge and Data Engineering
Publication date: Nov 2017, volume: 29, issue:11, pages: 2510 - 2521
Publisher: IEEE
 
» Robust Secure Beamforming for Wireless Powered Full-Duplex Systems With Self-Energy Recycling
Abstract:
In this paper, we study a multiuser wireless powered communication system, where an energy-constrained full-duplex information transmitter (IT), powered by wireless energy from a dedicated energy transmitter (ET), intends to send confidential information to the information receiver (IR) in the presence of multiple idle users that could be the potential eavesdroppers. In the practical scenario of imperfect channel state information and assuming that the idle users need to harvest energy from the ET, we aim to maximize the worst-case secrecy rate at the IR by jointly optimizing the transmit covariance matrix at the ET as well as the information beamforming and artificial noise covariance at the IT, subject to their individual transmit power constraints and the minimum required power transferred to the idle users. We employ the semidefinite relaxation (SDR) and extended S-procedure approaches to transform the original nonconvex optimization problem into convex problem, which can be efficiently solved by solving a sequence of semidefinite programs. Furthermore, we show that the SDR is tight since there always exists a rank-one optimal solution. For performance comparison, two heuristic schemes for ease of implementation are also developed. Numerical results are presented to show the effectiveness of our proposed schemes.
Autors: Wei Wu;Baoyun Wang;Yong Zeng;Haiyang Zhang;Zhenxing Yang;Zhixiang Deng;
Appeared in: IEEE Transactions on Vehicular Technology
Publication date: Nov 2017, volume: 66, issue:11, pages: 10055 - 10069
Publisher: IEEE
 
» Robust Semisupervised Classification for PolSAR Image With Noisy Labels
Abstract:
The robustness of the supervised polarimetric synthetic aperture radar (PolSAR) image classification is severely affected by two main aspects, namely, the quantity and quality of the labeled training pixels. Specifically, limited manually labeled pixels with respect to the large scale of PolSAR image have limited the performance of the automatic classification methods, while manually labeled training pixels shall be unfaithful with the speckle and impure cell for their low qualities. In order to address the above two fundamental problems, we propose a robust semisupervised probability graphic-based classification framework. First, a semisupervised learning scheme is implemented to simultaneously exploit both labeled and unlabeled pixels for information compensation. Moreover, structural relationship among neighboring pixels inducing from the prior information is further benefit to reduce the influence of limited labeled pixels. Second, a robust classification loss function is added in the process of training classifier to enhance the robustness to the noisy labeled pixels. Third, unfaithful limited labeled data can be settled with a hybrid generative/discriminative classification framework, where labeled and unlabeled pixels are simultaneously exploited for learning high-level feature for the low-quality pixels. The effectiveness of the proposed framework on the specific aspect is validated in experiments on real PolSAR data sets, which reveal the superiority in both visual performance and classification accuracy compared with the state-of-the-art methods. Totally speaking, our model has improved the classification accuracy by at least 20% on data set Flevoland, 10% on Oberpfaffenhofen, and 5% on Weihe River than the compared ones.
Autors: Biao Hou;Qian Wu;Zaidao Wen;Licheng Jiao;
Appeared in: IEEE Transactions on Geoscience and Remote Sensing
Publication date: Nov 2017, volume: 55, issue:11, pages: 6440 - 6455
Publisher: IEEE
 
» Robust Synchronization Waveform Design for Massive IoT
Abstract:
Machine-type communication (MTC) is the key technology to support data transfer among devices (sensors and actuators) in Internet of Things (IoT). However, MTC, especially when applied to massive low-power IoT (mIoT), poses some unique and serious challenges due to the low-cost and low-power nature of an mIoT device. One of the most challenging issues is providing a robust way for an mIoT device to acquire the network under a large frequency offset/error (due to the use of a low-cost crystal oscillator) and a low operating SNR (due to the extended coverage). We address the issues in the existing mIoT system acquisition, particularly the initial synchronization waveform detection, and derive a new synchronization waveform that is more robust in an mIoT environment. The mathematical approach provides a useful analytical insight into the design of the synchronization signal waveform for the 5G mIoT system.
Autors: Jingjing Zhang;Mao Wang;Min Hua;Wenjie Yang;Xiaohu You;
Appeared in: IEEE Transactions on Wireless Communications
Publication date: Nov 2017, volume: 16, issue:11, pages: 7551 - 7559
Publisher: IEEE
 
» Robust Ultraminiature Capsule Antenna for Ingestible and Implantable Applications
Abstract:
Progress in implantable and ingestible wireless biotelemetry requires versatile and efficient antennas to communicate reliably from a body. We propose an ultraminiature 434 MHz antenna immune to impedance detuning caused by varying electromagnetic properties of the surrounding biological environment. It is designed for a standard input impedance of 50 . The antenna is synthesized and miniaturized using a hybrid analytical–numerical approach, and then optimized to conform to the inner surface of a 17 mm long biocompatible encapsulation (7 mm diameter). The substrate is 50 thick. The capsule antenna is analyzed both in simplified and anatomically realistic heterogeneous phantoms. It remains matched at common implantation sites and through the whole gastrointestinal tract. Enhanced robustness allows using the antenna for a wide range of in-body applications. Computed reflection coefficients and radiation performance both show good agreement with measurements. The far field is characterized with the direct illumination technique using an analog fiber optic link. The realized gain (measured max. value −19.6 dBi) exceeds the counterparts by about 3 dBi. The proposed antenna contributes to the further development of a new generation of miniature in-body devices that involve complex and dense integration of sensors, logic, and power source.
Autors: Denys Nikolayev;Maxim Zhadobov;Laurent Le Coq;Pavel Karban;Ronan Sauleau;
Appeared in: IEEE Transactions on Antennas and Propagation
Publication date: Nov 2017, volume: 65, issue:11, pages: 6107 - 6119
Publisher: IEEE
 
» Rotational Magnetization Lag-Angle Plots Using the Anisotropic Stoner–Wohlfarth Model
Abstract:
A numerical implementation of the classical Stoner–Wohlfarth (SW) model to simulate the magnetization angle of an assembly of SW particles with an effective axis of anisotropy under a rotating applied field is presented. Using an angular distribution for the angle each SW particle is making with the medium’s reference axis, the proposed model successfully simulated lag-angle plots exhibiting the same rotational magnetization behavior measured for different ellipsoidally magnetizable media in the literature. The developed algorithm provides a simple tool for rotational-energy-loss calculations, preserves the physical intuition of the classical SW model, and is computationally faster compared to the Preisach–Stoner–Wohlfarth models. The effect of the angular distribution parameters on the switching transition angle and the algorithm’s potential for modeling additional anisotropies through using different angular distributions are discussed.
Autors: Hatem ElBidweihy;
Appeared in: IEEE Transactions on Magnetics
Publication date: Nov 2017, volume: 53, issue:11, pages: 1 - 6
Publisher: IEEE
 
» Rough Surface and Volume Scattering of Soil Surfaces, Ocean Surfaces, Snow, and Vegetation Based on Numerical Maxwell Model of 3-D Simulations
Abstract:
In this paper, we give an overview and an update on the recent progress of our research group in numerical model of Maxwell equations in three dimensions (NMM3D) on random rough surfaces and discrete random media and their applications in active and passive microwave remote sensing. The random rough surface models were applied to soil surfaces and ocean surfaces. The discrete random media models were applied to snow and vegetation. For rough surface scattering, we use the surface integral equations of Poggio–Miller–Chang–Harrington–Wu–Tsai that are solved by the method of moments using the Rao–Wilton–Glisson basis functions. The sparse matrix canonical grid method is used to accelerate the matrix column multiplications. In modeling the rough surfaces, we use the exponential correlation functions for soil surfaces and the Durden–Vesecky ocean spectrum for ocean surfaces. In scattering by terrestrial snow and snow on sea ice, we use the volume integral equations formulated with the dyadic half-space Green's function. The microstructure of snow is modeled by the bicontinuous media. In scattering by vegetation, we use the discrete scatterers of cylinder. The NMM3D formulation is based on the Foldy–Lax multiple scattering equations in conjunction with the body of revolution for a single scatterer. For rough surface scattering, simulations results are compared with advanced integral equation model, small slope approximation, small perturbation method, and two scale model. For volume scattering by snow, results are compared with the bicontinuous dense media radiative transfer. For scattering by vegetation, results are compared with distorted Born approximation and radiative transfer equation. Comparisons are also made with experiments.
Autors: Leung Tsang;Tien-Hao Liao;Shurun Tan;Huanting Huang;Tai Qiao;Kung-Hau Ding;
Appeared in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication date: Nov 2017, volume: 10, issue:11, pages: 4703 - 4720
Publisher: IEEE
 
» Round-Trip Delay Modeling for Smart Body Area Networks
Abstract:
This letter presents the first round-trip delay study of the recently proposed smart body area networks (SmartBANs) to address the challenge of achieving ultra-low round-trip delay for ubiquitous e-health applications. Based on the SmartBAN medium access control (MAC) protocol, an embedded Markov chain is developed for queuing and delay analysis of downlink packets broadcasted by the central hub to the attached nodes. The uplink delay caused by a time division multiple access mechanism is formulated by an M/D/1 queue with a vacation model. Finally, the round-trip delay, as the combination of both uplink and downlink delays, is derived. Based on our proposed model, we highlight a tradeoff between uplink and downlink delays under varying downlink transmission durations, providing an understanding of how the predefined MAC timing parameters impact the round-trip delay. The accuracy of our models is validated by extensive simulations.
Autors: Lihua Ruan;Maluge P. I. Dias;Ye Feng;Elaine Wong;
Appeared in: IEEE Communications Letters
Publication date: Nov 2017, volume: 21, issue:11, pages: 2528 - 2531
Publisher: IEEE
 
» Routability-Driven TSV-Aware Floorplanning Methodology for Fixed-Outline 3-D ICs
Abstract:
Although 3-D floorplanning has been studied widely, routability which is a very important issue in modern integrated circuit (IC) designs is rarely discussed. Floorplanning in 3-D ICs is much difficult than that in 2-D ICs because of large difference in sizes between modules and through silicon vias (TSVs), which are key components in 3-D ICs. And the locations of TSVs have great impact on wirelength and routability in resulting floorplans. Hence, this paper proposes a TSV-aware 3-D floorplanning methodology which can consider wirelength and routability at the same time under the fixed-outline constraint. Unlike most of previous works which completely apply the simulated annealing algorithm, our methodology mainly apply deterministic algorithms to resolve the problem. Thus, our approach is more efficient and flexible than previous works. Experimental results have demonstrated that the proposed methodology can significantly reduce routing congestion in 3-D ICs with a slight increase in wirelength.
Autors: Jai-Ming Lin;Jung-An Yang;
Appeared in: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Publication date: Nov 2017, volume: 36, issue:11, pages: 1856 - 1868
Publisher: IEEE
 
» RSD: Rate-Based Sync Deferment for Personal Cloud Storage Services
Abstract:
Cloud storage services, such as Dropbox, Google Drive, and OneDrive, to cite a few, are becoming an increasingly “vital” tool in our everyday life. Unluckily, these services can incur large network overhead in different usage scenarios. To reduce it, these systems utilize several techniques, such as source-based deduplication, chunking, delta compression, and so on. One of these techniques is sync deferment, which relies on the packing of updates to intentionally defer the synchronization process for some time, and increase the volume of useful data per overhead byte. The scientific literature has shown this technique to be very helpful, though there are still some limitations on current solutions. To resolve them, we present here a new adaptive sync deferment method, which is comparable to the current state of the art in terms of network overhead, but is also able to minimize the file synchronization time up to 12 times.
Autors: Raúl Sáiz-Laudó;Marc Sánchez-Artigas;Pedro García-López;
Appeared in: IEEE Communications Letters
Publication date: Nov 2017, volume: 21, issue:11, pages: 2384 - 2387
Publisher: IEEE
 
» Rural Building Detection in High-Resolution Imagery Based on a Two-Stage CNN Model
Abstract:
High-level feature extraction and hierarchical feature representation of image objects with a convolutional neural network (CNN) can overcome the limitations of the traditional building detection models using middle/low-level features extracted from a complex background. Aiming at the drawbacks of manual village location, high cost, and the limited accuracy of building detection in the existing rural building detection models, a two-stage CNN model is proposed in this letter to detect rural buildings in high-resolution imagery. Simulating the hierarchical processing mechanism of human vision, the proposed model is constructed with two CNNs, whose architectures can automatically locate villages and efficiently detect buildings, respectively. This two-stage CNN model effectively reduces the complexity of the background and improves the efficiency of rural building detection. The experiments showed that the proposed model could automatically locate all the villages in the two study areas, achieving a building detection accuracy of 88%. Compared with the existing models, the proposed model was proved to be effective in detecting buildings in rural areas with a complex background.
Autors: Li Sun;Yuqi Tang;Liangpei Zhang;
Appeared in: IEEE Geoscience and Remote Sensing Letters
Publication date: Nov 2017, volume: 14, issue:11, pages: 1998 - 2002
Publisher: IEEE
 
» RWW 2018 Student Paper Contest [RWW]
Abstract:
Presents information on the RWW 2018 Student Paper Contest.
Autors: Holger Maune;
Appeared in: IEEE Microwave Magazine
Publication date: Nov 2017, volume: 18, issue:7, pages: 22 - 22
Publisher: IEEE
 
» RWW 2018 Technical Program Chair's Greeting [RWW]
Abstract:
Presents the Chair's opening message for the RWW 2018 Technical Program.
Autors: Robert Caverly;
Appeared in: IEEE Microwave Magazine
Publication date: Nov 2017, volume: 18, issue:7, pages: 14 - 16
Publisher: IEEE
 
» S-Shaped ${I}$ – ${V}$ Characteristics of Organic Solar Cells: Solving Mazhari’s Lumped-Parameter Equivalent Circuit Model
Abstract:
We explain how to obtain closed-form analytic solutions from the set of equations that describe the three-diode lumped-parameter equivalent circuit model proposed by Mazhari [1] to portray the undesirable S-shape often observed in – characteristics of illuminated organic solar cells (OSCs), and occasionally seen in other types of solar cells. This allows quick extraction of the model’s parameter values by directly fitting the resulting closed-form solution to the cell’s measured – data. Such mathematical simplification of the extraction procedure facilitates individually studying the effect of each parameter on the illuminated OSC – characteristics, and thus on its power generation capacity. We illustrate application of the direct extraction procedure to measured – characteristics of an experimental OSC, which exhibits the illumination intensity-dependent S-shapes. The usefulness of the analytic solution to assess the effect of the model parameters is further corroborated by graphically illustrating the progression of a series of hypothetical synthetic – characteristics generated by this analytic solution using gradually changing the parameter values. Analysis of the results, in this case, indicates that activation of the diode that represents recombination is the key factor responsible for the emergence of the illuminated – curve’s S-shape.
Autors: Beatriz Romero;Gonzalo del Pozo;Belén Arredondo;Diego Martín-Martín;María P. Ruiz Gordoa;Andrew Pickering;Ana Pérez-Rodríguez;Esther Barrena;Francisco J. García-Sánchez;
Appeared in: IEEE Transactions on Electron Devices
Publication date: Nov 2017, volume: 64, issue:11, pages: 4622 - 4627
Publisher: IEEE
 
» Safe Optimization of Highway Traffic With Robust Model Predictive Control-Based Cooperative Adaptive Cruise Control
Abstract:
Road traffic crashes have been the leading cause of death among young people. Most of these accidents occur when the driver becomes distracted due to fatigue or external factors. Vehicle platooning systems, as cooperative adaptive cruise control, are one of the results of efforts devoted to the development of technologies for decreasing the number of road crashes and fatalities. Previous studies have suggested that such systems improve up to 273% highway traffic throughput and over 15% of fuel consumption if the clearance between vehicles in this class of roads can be reduced to 2 m. In this paper, we propose an approach that guarantees a minimum safety distance between vehicles taking into account the overall system delays and braking capacity of each vehicle. An -norm robust model predictive controller has been developed to guarantee the minimum safety distance is not violated due to uncertainties on the preceding vehicle behavior. A formulation for a lower bound clearance of vehicles inside a platoon is also proposed. Simulation results show the performance of the approach compared to a nominal controller when the system is subject to both modeled and unmodeled disturbances.
Autors: Carlos Massera Filho;Marco H. Terra;Denis F. Wolf;
Appeared in: IEEE Transactions on Intelligent Transportation Systems
Publication date: Nov 2017, volume: 18, issue:11, pages: 3193 - 3203
Publisher: IEEE
 
» Sampling and Distortion Tradeoffs for Indirect Source Retrieval
Abstract:
Consider a continuous signal that cannot be observed directly. Instead, one has access to multiple corrupted versions of the signal. The available corrupted signals are correlated because they carry information about the common remote signal. The goal is to reconstruct the original signal from the data collected from its corrupted versions. Known as the indirect or remote reconstruction problem, it has been mainly studied in the literature from an information theoretic perspective. A variant of this problem for a class of Gaussian signals, known as the “Gaussian CEO problem,” has received particular attention; for example, it has been shown that the problem of recovering the remote signal is equivalent with the problem of recovering the set of corrupted signals (separation principle). The information theoretic formulation of the remote reconstruction problem assumes that the corrupted signals are uniformly sampled and the focus is on optimal compression of the samples. On the other hand, in this paper, we revisit this problem from a sampling perspective. More specifically, assuming restrictions on the sampling rate from each corrupted signal, we look at the problem of finding the best sampling locations for each signal to minimize the total reconstruction distortion of the remote signal. In finding the sampling locations, one can take advantage of the correlation among the corrupted signals. The statistical model of the original signal and its corrupted versions adopted in this paper are similar to the one considered for the Gaussian CEO problem; i.e., we restrict to a class of Gaussian signals. Our main contribution is a fundamental lower bound on the reconstruction distortion for any arbitrary nonuniform sampling strategy. This lower bound is valid for any sampling rate. Furthermore, it is tight and matches the optimal reconstruction distortion in low and high sampling rates. Moreover, it is shown that- in the low sampling rate region, it is optimal to use a certain nonuniform sampling scheme on all the signals. On the other hand, in the high sampling rate region, it is optimal to uniformly sample all the signals. We also consider the problem of finding the optimal sampling locations to recover the set of corrupted signals, rather than the remote signal. Unlike the information theoretic formulation of the problem in which these two problems were equivalent, we show that they are not equivalent in our setting. Finally, another contribution of this paper is a new reverse majorization inequality that might be of independent interest.
Autors: Elaheh Mohammadi;Alireza Fallah;Farokh Marvasti;
Appeared in: IEEE Transactions on Information Theory
Publication date: Nov 2017, volume: 63, issue:11, pages: 6833 - 6848
Publisher: IEEE
 
» Sampling-Based Path Planning for UAV Collision Avoidance
Abstract:
The ability to avoid collisions with moving obstacles, such as commercial aircraft is critical to the safe operation of unmanned aerial vehicles (UAVs) and other air traffic. This paper presents the design and implementation of sampling-based path planning methods for a UAV to avoid collision with commercial aircraft and other moving obstacles. In detail, the authors develop and demonstrate a method based on the closed-loop rapidly-exploring random tree algorithm and three variations of it. The variations are: 1) simplification of trajectory generation strategy; 2) utilization of intermediate waypoints; 3) collision prediction using reachable set. The methods were validated in software-in-the-loop simulations, hardware-in-the-loop simulations, and real flight experiments. It is shown that the algorithms are able to generate collision free paths in real time for the different types of UAVs among moving obstacles of different numbers, approaching angles, and speeds.
Autors: Yucong Lin;Srikanth Saripalli;
Appeared in: IEEE Transactions on Intelligent Transportation Systems
Publication date: Nov 2017, volume: 18, issue:11, pages: 3179 - 3192
Publisher: IEEE
 
» SAR Target Discrimination Based on BOW Model With Sample-Reweighted Category-Specific and Shared Dictionary Learning
Abstract:
To improve the synthetic aperture radar (SAR) target discrimination performance under complex scenes, this letter presents a new SAR target discrimination method based on the bag-of-words model. The method contains three main stages. In the local feature extraction stage, the SAR-SIFT feature is extracted. In the feature coding stage, we improve the existing category-specific and shared dictionary learning (CSDL) and propose the sample-reweighted CSDL (SR-CSDL). The local features are sparsely coded using the codebook learned from SR-CSDL. In the feature pooling stage, spatial pyramid matching with max pooling is used to aggregate the local coding coefficients to generate the global feature for each chip image. Experimental results using the miniSAR data verify the effectiveness of the proposed method.
Autors: Yinghua Wang;Hongwei Liu;
Appeared in: IEEE Geoscience and Remote Sensing Letters
Publication date: Nov 2017, volume: 14, issue:11, pages: 2097 - 2101
Publisher: IEEE
 
» SAR-Based Vessel Velocity Estimation From Partially Imaged Kelvin Pattern
Abstract:
Spaceborne synthetic aperture radar (SAR) can be considered an operational asset for maritime monitoring applications. Well-assessed approaches exist for ship detection, validated in several maritime surveillance systems. However, measuring vessel velocity from detected single-channel SAR images of ships is in general difficult. This letter contributes to this problem by investigating the possibility of retrieving vessel velocity by wake analysis. An original method for velocity estimation is developed for calm sea (Beaufort scale 1–2) and applied over seven X-band SAR images, gathered by COSMO-SkyMed mission over the Gulf of Naples, Italy. The algorithm exploits the well-known relation between the wavelength of the waves composing the Kelvin pattern and the ship velocity. But the proposed approach extends the applicability of the existing wake-based techniques since it foresees evaluation of the wavelength along a generic direction in the Kelvin angle. Promising results have been achieved, which are in good agreement with those of more assessed techniques for ship velocity estimation in SAR images.
Autors: Alessandro Panico;Maria Daniela Graziano;Alfredo Renga;
Appeared in: IEEE Geoscience and Remote Sensing Letters
Publication date: Nov 2017, volume: 14, issue:11, pages: 2067 - 2071
Publisher: IEEE
 
» Scalable and Unified Digit-Serial Processor Array Architecture for Multiplication and Inversion Over GF( $2^{m}$ )
Abstract:
This paper proposes a scalable and unified digit-serial structure, with low space complexity to perform multiplication and inversion operations in , based on the bit serial multiplication algorithm and the previously modified extended Euclidean inversion algorithm. In this structure, the multiplier and inverter shares the data-path and thus saves more area resources and power than the case of using separate data-path for each operation. Also, this structure is suitable for fixed size processor that only reuse the core and does not require to modulate the core size when the field size is modified. This structure is extracted by applying a nonlinear methodology that gives the designer more flexibility to control the processing element workload. Implementation results for of the proposed scalable and unified digit-serial design and previously reported efficient designs show that the proposed scalable structure achieves a significant reduction in area ranging from 64.3% to 85.5% and also achieves a significant saving in energy ranging from 21.9% to 92.5% over them, but it has lower throughput compared with them. This makes the proposed design more suitable for constrained implementations of cryptographic primitives in ultra-low power devices, such as wireless sensor nodes and radio frequency identification devices.
Autors: Atef Ibrahim;Fayez Gebali;
Appeared in: IEEE Transactions on Circuits and Systems I: Regular Papers
Publication date: Nov 2017, volume: 64, issue:11, pages: 2894 - 2906
Publisher: IEEE
 
» Scalable Mammogram Retrieval Using Composite Anchor Graph Hashing With Iterative Quantization
Abstract:
Content-based image retrieval (CBIR) shows great significance in clinical decision-making, which explores the visual content of medical images rather than keywords, tags, or descriptions. It provides doctors an image-guided approach to explore relevant cases that could offer doctors instructive reference. Mammogram screening has been known to be widely used in the early stage diagnosis of breast cancer and could reduce its morbidity and mortality. In this paper, we aim to develop a scalable CBIR method for a large repository of mammogram. To this end, we extend the original Anchor Graph Hashing (AGH) and propose a new unsupervised hashing algorithm, named as composite AGH with iterative quantization (C-AGH-ITQ), which compresses mammographic regions of interest (ROIs) into compact binary codes and enables real-time searching in Hamming space. Multimodal features and different distance metrics are integrated, performing upon a composite Anchor Graph. To improve the effectiveness of the hash code, quantization error is further iteratively minimized by introducing an orthogonal rotation matrix. We evaluate the presented C-AGH-ITQ algorithm on a data set of 11 533 mammographic ROIs obtained from the Digital Database for Screening Mammography. Our method obtains more than 84% retrieval precision and 93% classification accuracy (using NN prediction), which demonstrates that hash codes produced by C-AGH-ITQ well capture the visual similarities between mammographic images. In addition, since C-AGH-ITQ ensures linear complexity of the training procedure and constant time for query, our system is readily applicable to large-scale mammogram databases and has the potential to provide abundant clinical cases as reference.
Autors: Jingjing Liu;Shaoting Zhang;Wei Liu;Cheng Deng;Yuanjie Zheng;Dimitris N. Metaxas;
Appeared in: IEEE Transactions on Circuits and Systems for Video Technology
Publication date: Nov 2017, volume: 27, issue:11, pages: 2450 - 2460
Publisher: IEEE
 
» Scalable Planning for Energy Storage in Energy and Reserve Markets
Abstract:
Energy storage can facilitate the integration of renewable energy resources by providing arbitrage and ancillary services. Jointly optimizing energy and ancillary services in a centralized electricity market reduces the system's operating cost and enhances the profitability of energy storage systems. However, achieving these objectives requires that storage be located and sized properly. We use a bilevel formulation to optimize the location and size of energy storage systems, which perform energy arbitrage and provide regulation services. Our model also ensures the profitability of investments in energy storage by enforcing a rate of return constraint. Computational tractability is achieved through the implementation of a primal decomposition and a subgradient-based cutting-plane method. We test the proposed approach on a 240-bus model of the Western Electricity Coordinating Council system and analyze the effects of different storage technologies, rate of return requirements, and regulation market policies on energy storage participation on the optimal storage investment decisions. We also demonstrate that the proposed approach outperforms exact methods in terms of solution quality and computational performance.
Autors: Bolun Xu;Yishen Wang;Yury Dvorkin;Ricardo Fernández-Blanco;Cesar A. Silva-Monroy;Jean-Paul Watson;Daniel S. Kirschen;
Appeared in: IEEE Transactions on Power Systems
Publication date: Nov 2017, volume: 32, issue:6, pages: 4515 - 4527
Publisher: IEEE
 
» Scaling Agile
Abstract:
Scaling agile allows tailoring and blending agile and lean practices to address actual industry needs for critical systems. This article explores the state of the practice with frameworks such as the Scaled Agile Framework (SAFe).
Autors: Christof Ebert;Maria Paasivaara;
Appeared in: IEEE Software
Publication date: Nov 2017, volume: 34, issue:6, pages: 98 - 103
Publisher: IEEE
 
» Scaling Electrochemical Battery Models for Time-Accelerated and Size-Scaled Experiments on Test-Benches
Abstract:
This paper presents a dimensional-analysis supported scaling procedure applied to a mathematical model of electrochemical batteries. The main objective of this research is to allow for laboratory size-scaled and time-compressed experimental analysis of processes involving large physical magnitudes and evolving over long time spans. These situations are of interest when considering the sizing of battery packs and other components of energy systems, particularly smart grids, and further systems where battery storage is relevant, like hybrid vehicles and other standalone systems, as well as deciding management strategies on them. Voltage-, current- and time-scaled models preserving the dynamic evolution of a group of relevant physical magnitudes are presented. These models have been validated through simulation and physical experiments on a test-bench designed and constructed on purpose. The physical implementation of the scaled models is not possible in the cases where some of the scaled model parameters cannot be met using real batteries. But, as the mathematical construction of the scaled models is always possible, this problem can be circumvented with a Hardware-in-the-loop approach: the scaled battery is numerically emulated on a programmable and controllable power source/sink system, which is run in real-time embedded in the test-bench representing the whole system under study.
Autors: Javier M. Cabello;Xavier Roboam;Sergio Junco;Eric Bru;Fabien Lacressonniere;
Appeared in: IEEE Transactions on Power Systems
Publication date: Nov 2017, volume: 32, issue:6, pages: 4233 - 4240
Publisher: IEEE
 
» Scaling Study of Spin-Hall-Assisted Spin Transfer Torque Driven Magnetization Switching in the Presence of Dzyaloshinskii–Moriya Interaction
Abstract:
Spin-hall-assisted spin transfer torque (SHA-STT) can achieve high-speed, magnetic-field-free, and high-reliable magnetization switching in a three-terminal device consisting of magnetic tunnel junctions (MTJ) above a heavy-metal. Nowadays, the development of perpendicular magnetic anisotropy drives the continuous scaling of the MTJ. In addition, an asymmetric exchange interaction called Dzyaloshinskii–Moriya interaction (DMI) inevitably exists at the heavy metal/ferromagnet interface and has a considerable influence on the magnetization dynamics. Considering these factors, in this work, we study the scaling performance of the SHA-STT driven magnetization dynamics in the presence of DMI. Simulation results demonstrate that, for nonzero DMI, the magnetization switching is activated by domain nucleation, whose mechanism is strongly dependent on the MTJ size and DMI magnitude. The critical SHE current density for magnetization switching decreases with the enlarged MTJ or enhanced DMI. In the presence of DMI, the switching time decreases with the scaling of the MTJ. Moreover, compared with the case of zero DMI, the switching speed is improved or deteriorated for the weak or strong DMI, respectively. Our work demonstrates that the MTJ size and DMI magnitude should be optimized in order to achieve a good tradeoff among a set of performance metrics of the SHA-STT devices.
Autors: Yuqian Gao;Zhaohao Wang;Xiaoyang Lin;Wang Kang;Youguang Zhang;Weisheng Zhao;
Appeared in: IEEE Transactions on Nanotechnology
Publication date: Nov 2017, volume: 16, issue:6, pages: 1138 - 1142
Publisher: IEEE
 
» Scanning Enhanced Low-Profile Broadband Phased Array With Radiator-Sharing Approach and Defected Ground Structures
Abstract:
In this paper, a scanning enhanced low-profile broadband phased array is presented. This array is based on the radiator-sharing approach and the defected ground structures (DGSs). In this phased array, the radiator-sharing approach has been implemented by using metasurface antenna, and the scanning range has been remarkably enhanced by reducing element spacing. An attractive behavior in broadband impedance matching has still been obtained, in spite that closely spaced feedings are adopted. Moreover, the scanning performance is considerably improved by applying meander-line slots cut from the ground plane, forming DGS. In order to validate this proposed design, a nine-element linear phased array with DGSs has been fabricated and measured. Based on the uniform magnitude and the progressive phase, the capability of wide-angle scanning can be achieved over the measured bandwidth of 23.1% (4.6–5.8 GHz). At the center frequency of 5.2 GHz, this array scans up to 50° and the realized gain varies from 14.76 to 11.85 dBi.
Autors: Li Gu;Yan-Wen Zhao;Qiang-Ming Cai;Zhi-Peng Zhang;Bi-Hui Xu;Zai-Ping Nie;
Appeared in: IEEE Transactions on Antennas and Propagation
Publication date: Nov 2017, volume: 65, issue:11, pages: 5846 - 5854
Publisher: IEEE
 
» Scanning Spreading Resistance Microscopy for Doping Profile in Saddle-Fin Devices
Abstract:
Scanning capacitance microscopy (SCM) and scanning spreading resistance microscopy (SSRM) are used to investigate the doping profile of saddle-fin (S-fin) devices in a 30-nm dynamic-random-access-memory (DRAM) technology. Due to the limited resolution of SCM, SCM cannot provide a clear doping profile of an S-fin array device. In the meantime, SSRM and focused ion beam milling during sample preparation provide an opportunity to obtain a 2-D and scanning line doping profile. The common-source region between two adjacent buried word lines is treated with an additional phosphorus (P) implantation with energy and dosage modification to have a doping profile modification in the array devices of DRAM product. With condition of the medium energy and high dosage in this additional P implantation, the row hammer effect of 30-nm DRAM could be minimized by the localized shielding effect from the electric field by a depletion effect. The junction profile of the additional P implantation is about 10 nm deeper than that of the control sample, as verified by SSRM and technology computer-aided design simulation. The experimental results of the doping profile can be used to support a mechanism of improvement of row hammer. The SSRM methodology proposed in this study could be used to optimize the doping profiles in DRAMs for future scaling technology.
Autors: Chia-Ming Yang;Chen-Kang Wei;Hsiu-Pin Chen;Jian-Shing Luo;Yu Jing Chang;Tieh-Chiang Wu;Chao-Sung Lai;
Appeared in: IEEE Transactions on Nanotechnology
Publication date: Nov 2017, volume: 16, issue:6, pages: 999 - 1003
Publisher: IEEE
 
» Scanning the Issue
Abstract:
Cyber Threats Facing Autonomous and Connected Vehicles: Future Challenges
Autors: Petros Ioannou;
Appeared in: IEEE Transactions on Intelligent Transportation Systems
Publication date: Nov 2017, volume: 18, issue:11, pages: 2893 - 2897
Publisher: IEEE
 
» Scheduling of Electric Vehicle Charging via Multi-Server Fair Queueing
Abstract:
Charging electric vehicles (EVs) at home is attractive to EV users. However, when the penetration level of EVs becomes high, a distribution grid suffers from problems such as under-voltage and transformer overloading. EV users also experience a fairness problem, i.e., the limited capacity is unfairly shared among EVs. To solve these problems, a physical fair-queueing framework is established for EV charging. In this framework, a distribution sub-grid is first mapped to a multi-server queueing system, and then a fluid-model based queueing scheme called physical multi-server generalized processor sharing (pMGPS) is designed. pMGPS ensures perfect fairness but cannot be used practically due to its nature of fluid model. To this end, a packetized scheme called physical start-time fair queueing (pSTFQ) is developed to schedule tasks of EV charging. The fairness performance of the pSTFQ scheduling scheme is characterized by the ratio of energy difference between pSTFQ and pMGPS. This critical performance metric is studied through theoretical analysis and is also evaluated via simulations. Performance results show that the pSTFQ scheduling scheme achieves an energy difference ratio of less than 4 percent in various scenarios without causing under-voltage and transformer overloading problems.
Autors: Xudong Wang;Yibo Pi;Aimin Tang;
Appeared in: IEEE Transactions on Parallel and Distributed Systems
Publication date: Nov 2017, volume: 28, issue:11, pages: 3298 - 3312
Publisher: IEEE
 
» Schottky Barrier FET Biosensor for Dual Polarity Detection: A Simulation Study
Abstract:
In this letter, we present the first tunable polarity biosensor which exhibits a high sensitivity detection capability for both negatively and positively charged biomolecules. The concept is enabled by the use of a reconfigurable transistor in which two individually gated Schottky junctions permit a selective transport of carriers through the junctions. Using TCAD simulations, we show that a threshold voltage shift of ~0.6 V and a current sensitivity are obtained. We further demonstrate through simulations that the proposed structure enhances the sensitivity (~3 times) of the state-of-the-art Schottky FET biosensors which suffer from severely reduced transconductance due to contact passivation. With a high sensitivity and operation versatility, the proposed device holds a great promise for more compact and multiplexed sensing capabilities than the existing FET biosensors.
Autors: Sumeet Kalra;Mamidala Jagadesh Kumar;Anuj Dhawan;
Appeared in: IEEE Electron Device Letters
Publication date: Nov 2017, volume: 38, issue:11, pages: 1594 - 1597
Publisher: IEEE
 
» Sea Ice Classification Using Cryosat-2 Altimeter Data by Optimal Classifier–Feature Assembly
Abstract:
Sea ice type is one of the most sensitive variables in Arctic ice monitoring and detailed information about it is essential for ice situation evaluation, vessel navigation, and climate prediction. Many machine-learning methods including deep learning can be employed for ice-type detection, and most classifiers tend to prefer different feature combinations. In order to find the optimal classifier–feature assembly (OCF) for sea ice classification, it is necessary to assess their performance differences. The objective of this letter is to make a recommendation for the OCF for sea ice classification using Cryosat-2 (CS-2) data. Six classifiers including convolutional neural network (CNN), Bayesian, nearest-neighbor (KNN), support vector machine (SVM), random forest (RF), and back propagation neural network (BPNN) were studied. CS-2 altimeter data of November 2015 and May 2016 in the whole Arctic were used. The overall accuracy was estimated using multivalidation to evaluate the performances of individual classifiers with different feature combinations. Overall, RF achieved a mean accuracy of 89.15%, followed by Bayesian, SVM, and BPNN (~86%), outperforming the worst (CNN and KNN) by 7%. Trailing-edge width (TeW) and leading-edge width (LeW) were the most important features, and feature combination of TeW, LeW, Sigma0, maximum of the returned power waveform (MAX), and pulse peakiness (PP) was the best choice. RF with feature combination of TeW, LeW, Sigma0, MAX, and PP was finally selected as the OCF for sea ice classification and the results that demonstrated this method achieved a mean accuracy of 91.45%, which outperformed the other state-of-art methods by 9%.
Autors: Xiaoyi Shen;Jie Zhang;Xi Zhang;Junmin Meng;Changqing Ke;
Appeared in: IEEE Geoscience and Remote Sensing Letters
Publication date: Nov 2017, volume: 14, issue:11, pages: 1948 - 1952
Publisher: IEEE
 
» Second-Order Sliding Mode Based P-Q Coordinated Modulation of DFIGs Against Interarea Oscillations
Abstract:
This letter presents an active and reactive power coordinated damping controller for doubly fed induction generator (DFIG) using second-order sliding mode technique. Through simultaneously modulating active and reactive power of DFIG, the proposed controller has an enhanced damping performance. Its advantages include faster damping speed and robustness to modeling uncertainties and parameter variations. Simulation results have verified its superior performance over existing schemes.
Autors: Kai Liao;Yan Xu;Zhengyou He;Zhao Yang Dong;
Appeared in: IEEE Transactions on Power Systems
Publication date: Nov 2017, volume: 32, issue:6, pages: 4978 - 4980
Publisher: IEEE
 
» Secret Key Generation With Limited Interaction
Abstract:
A basic two-terminal secret key generation model is considered, where the interactive communication rate between the terminals may be limited, and in particular may not be enough to achieve the maximum key rate. We first prove a multi-letter characterization of the key-communication rate region (where the number of auxiliary random variables depends on the number of rounds of the communication), and then provide an equivalent but simpler characterization in terms of concave envelopes in the case of unlimited number of rounds. Two extreme cases are given special attention. First, in the regime of very low communication rates, the key bits per interaction bit (KBIB) is expressed with a new “symmetric strong data processing constant”, which has a concave envelope characterization analogous to that of the conventional strong data processing constant. The symmetric strong data processing constant can be upper bounded by the supremum of the maximal correlation coefficient over a set of distributions, which allows us to determine the KBIB for binary symmetric sources, and conclude, in particular, that the interactive scheme is not more efficient than the one-way scheme at least in the low communication-rate regime. Second, a new characterization of the minimum interaction rate needed for achieving the maximum key rate (MIMK) is given, and we resolve a conjecture by Tyagi regarding the MIMK for (possibly nonsymmetric) binary sources. We also propose a new conjecture for binary symmetric sources that the interactive scheme is not more efficient than the one-way scheme at any communication rate.
Autors: Jingbo Liu;Paul Cuff;Sergio Verdú;
Appeared in: IEEE Transactions on Information Theory
Publication date: Nov 2017, volume: 63, issue:11, pages: 7358 - 7381
Publisher: IEEE
 

Publication archives by date

  2017:   January     February     March     April     May     June     July     August     September     October     November     December    

  2016:   January     February     March     April     May     June     July     August     September     October     November     December    

  2015:   January     February     March     April     May     June     July     August     September     October     November     December    

  2014:   January     February     March     April     May     June     July     August     September     October     November     December    

  2013:   January     February     March     April     May     June     July     August     September     October     November     December    

  2012:   January     February     March     April     May     June     July     August     September     October     November     December    

  2011:   January     February     March     April     May     June     July     August     September     October     November     December    

  2010:   January     February     March     April     May     June     July     August     September     October     November     December    

  2009:   January     February     March     April     May     June     July     August     September     October     November     December    

 
0-C     D-L     M-R     S-Z