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  • 1
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    IEEE PES T&D Conference & Exposition (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-21
    Print ISSN: 1540-7977
    Electronic ISSN: 1558-4216
    Topics: Electrical Engineering, Measurement and Control Technology
    Published by Institute of Electrical and Electronics Engineers (IEEE)
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  • 2
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    IEEE Fellows: Congratulations to the Class of 2018 [Awards] (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-21
    Print ISSN: 1540-7977
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    Topics: Electrical Engineering, Measurement and Control Technology
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  • 3
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    PowerAfrica 2018 (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-21
    Print ISSN: 1540-7977
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    Topics: Electrical Engineering, Measurement and Control Technology
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  • 4
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    PES Meetings: For More Information, www.ieee-pes.org [Calendar] (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-21
    Print ISSN: 1540-7977
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    Topics: Electrical Engineering, Measurement and Control Technology
    Published by Institute of Electrical and Electronics Engineers (IEEE)
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  • 5
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    2018 PES General Meeting (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-21
    Print ISSN: 1540-7977
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    Topics: Electrical Engineering, Measurement and Control Technology
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  • 6
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    From AlphaGo to Power System AI: What Engineers Can Learn from Solving the Most Complex Board Game (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-21
    Description: Since its early development in the 1950s, artificial intelligence (AI) has been studied to mimic the function of human brains in solving science and engineering problems. Despite alternating bust and boom periods, AI has consistently progressed with a number of development milestones such as evolutionary algorithms, neural networks, and deep learning. For many AI test applications, the game of Go has been long considered one of the hardest problems and the most difficult among all major board games for AI to solve or mimic the way that the human brain functions.
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    Topics: Electrical Engineering, Measurement and Control Technology
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  • 7
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    IEEE PES Resource Center (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-21
    Print ISSN: 1540-7977
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    Topics: Electrical Engineering, Measurement and Control Technology
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  • 8
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    Is IT Ready for Them?: Future Energy & Distribution Management Systems [In My View] (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-21
    Print ISSN: 1540-7977
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    Topics: Electrical Engineering, Measurement and Control Technology
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  • 9
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    Direct Power Control of DFIG System Without Phase-Locked Loop Under Unbalanced and Harmonically Distorted Voltage (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-21
    Description: This paper proposes an improved direct power control (DPC) strategy for the rotor-side converter (RSC) and grid-side converter (GSC) of the doubly fed induction generator under unbalanced and distorted voltage conditions. The proposed scheme is implemented in a synchronous reference frame with the fixed angular speed, so that the phase-locked loop can be removed and relevant potential instability issues can be avoided. Besides, the sequential separations and complex calculations of the power compensating items can be eliminated with the direct-resonant method based on a dual-frequency frequency adaptive vector PI. Thus, the negative and harmonic components can be directly eliminated, achieving balanced and sinusoidal current injected into the power grid. Meanwhile, the control strategy can achieve self-adaptive to the grid frequency variation in the practical situation with the proposed frequency estimation scheme. Finally, the experimental results validate the availability of the proposed DPC strategy.
    Print ISSN: 0885-8969
    Electronic ISSN: 1558-0059
    Topics: Electrical Engineering, Measurement and Control Technology
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  • 10
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    Analytical 2-D Model of Slotted Brushless Machines With Cubic Spoke-Type Permanent Magnets (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-21
    Description: Brushless machines with interior permanent magnets (PMs) are mostly made with PM geometries inconsistent with the polar coordinates of the rotor cross section. Therefore, without any subdomain transformation, the analytical approaches cannot precisely model the interior PM machines in a single coordinate. This paper presents an accurate two-dimensional (2-D) analytical model for electromagnetic analysis of slotted brushless machines with rectangular spokePMs assisted by hub magnets. The slotting effect is modeled via subdomain technique. However, the spoke PM is not consistent enough with polar coordinates to be approximated by one subdomain. In order to map the spoke PMs in polar coordinates, herein, the rectangular geometry is approximated by a combination of arc shaped subdomains. On this basis, the 2-D analytic model is developed for M-fraction spoke PM. To verify the accuracy of the proposed 2-D approach, analytic results are compared with numeric results of finite elements method. It is revealed that by dividing the spoke PM into arc subdomains, the 2-D model achieves higher accuracy in calculation of electromagnetic quantities such as PM and armature reaction fields, electromagnetic torque, and back electromotive force.
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    Topics: Electrical Engineering, Measurement and Control Technology
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  • 11
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    Design of Computational Experiment for Performance Optimization of a Switched Reluctance Generator in Wind Systems (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-21
    Description: This paper presents as a new contribution a proposal to optimize the performance of a switched reluctance generator (SRG) in a variable wind energy conversion system. An approach based on the design of computational experiment is applied to determinate the optimal firing angles and the optimal dc link voltage that guarantee the best system behavior for each rotor speed. A third-order response surface model based on space-filling designs is applied to build a multiobjective function considering efficiency improvement and torque ripple reduction. An interior point method is applied to find the minimum of the surface, optimizing the process. Direct power control is used to obtain the maximum power as a function of the rotor speed, employing the optimal parameters. Hysteresis and single-pulse current control are applied for low- and high-speed operation, respectively. Simulation and experimental results have shown that the proposed approach returned a good compromise between SRG high efficiency and low torque ripple. Moreover, the presented technique reduces computational effort and provides a clear massive data simulation framework.
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  • 12
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    Erratum to “Mitigation of Turn-to-Turn Faults in Fault Tolerant Permanent Magnet Synchronous Motors” (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-21
    Print ISSN: 0885-8969
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    Topics: Electrical Engineering, Measurement and Control Technology
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  • 13
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    Blind Source Separation Algorithms Using Hyperbolic and Givens Rotations for High-Order QAM Constellations (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-21
    Description: This paper addresses the problem of blind demixing of instantaneous mixtures in a multiple-input multiple-output communication system. The main objective is to present efficient blind source separation (BSS) algorithms dedicated to moderate or high-order quadratic-amplitude modulation (QAM) constellations. Four new iterative batch, BSS algorithms are presented dealing with the multimodulus (MM) and alphabet matched (AM) criteria. For the optimization of these cost functions, iterative methods of Givens and hyperbolic rotations are used. A prewhitening operation is also utilized to reduce the complexity of design problem. It is noticed that the designed algorithms using Givens rotations give satisfactory performance only for a large number of samples. However, for a small number of samples, the algorithms designed by combining both Givens and hyperbolic rotations compensate for the ill-whitening that occurs in this case and thus improves the performance. Two algorithms dealing with the MM criterion are presented for moderate-order QAM signals such as 16-QAM. The other two dealing with the AM criterion are presented for high-order QAM signals. These methods are finally compared with the state-of-the-art batch BSS algorithms in terms of signal-to-interference and noise ratio, symbol error rate, and convergence rate. Simulation results show that the proposed methods outperform the contemporary batch BSS algorithms.
    Print ISSN: 1053-587X
    Electronic ISSN: 1941-0476
    Topics: Electrical Engineering, Measurement and Control Technology
    Published by Institute of Electrical and Electronics Engineers (IEEE) on behalf of The IEEE Signal Processing Society.
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  • 14
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    Remote Sensing Image Classification With Large-Scale Gaussian Processes (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-01-31
    Description: Current remote sensing image classification problems have to deal with an unprecedented amount of heterogeneous and complex data sources. Upcoming missions will soon provide large data streams that will make land cover/use classification difficult. Machine-learning classifiers can help at this, and many methods are currently available. A popular kernel classifier is the Gaussian process classifier (GPC), since it approaches the classification problem with a solid probabilistic treatment, thus yielding confidence intervals for the predictions as well as very competitive results to the state-of-the-art neural networks and support vector machines. However, its computational cost is prohibitive for large-scale applications, and constitutes the main obstacle precluding wide adoption. This paper tackles this problem by introducing two novel efficient methodologies for GP classification. We first include the standard random Fourier features approximation into GPC, which largely decreases its computational cost and permits large-scale remote sensing image classification. In addition, we propose a model which avoids randomly sampling a number of Fourier frequencies and alternatively learns the optimal ones within a variational Bayes approach. The performance of the proposed methods is illustrated in complex problems of cloud detection from multispectral imagery and infrared sounding data. Excellent empirical results support the proposal in both computational cost and accuracy.
    Print ISSN: 0196-2892
    Electronic ISSN: 1558-0644
    Topics: Architecture, Civil Engineering, Surveying , Geography
    Published by Institute of Electrical and Electronics Engineers (IEEE)
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  • 15
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    A Theoretical Framework for Change Detection Based on a Compound Multiclass Statistical Model of the Difference Image (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-01-31
    Description: The change detection (CD) problem is very important in the remote sensing domain. The advent of a new generation of multispectral (MS) sensors has given rise to new challenges in the development of automatic CD techniques. In particular, typical approaches to CD are not able to well model and properly exploit the increased radiometric resolution characterizing new data as this results in a higher sensitivity to the number of natural classes that can be statistically modeled in the images. In this paper, we introduce a theoretical framework for the description of the statistical distribution of the difference image as a compound model where each class is determined by temporally correlated class transitions in the bitemporal images. The potential of the proposed framework is demonstrated on the very common problem of binary CD based on setting a threshold on the magnitude of the difference image. Here, under some simplifying assumptions, a multiclass distribution of the magnitude feature is derived and an unsupervised method based on the expectation–maximization algorithm and Bayes decision is proposed. Its effectiveness is demonstrated on a large variety of data sets from different MS sensors. In particular, experimental tests confirm that: 1) the fitting of the magnitude distribution significantly improves if compared with already existing models and 2) the overall CD error is close to the optimal value.
    Print ISSN: 0196-2892
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    Topics: Architecture, Civil Engineering, Surveying , Geography
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  • 16
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    Multilabel Remote Sensing Image Retrieval Using a Semisupervised Graph-Theoretic Method (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-01-31
    Description: Conventional supervised content-based remote sensing (RS) image retrieval systems require a large number of already annotated images to train a classifier for obtaining high retrieval accuracy. Most systems assume that each training image is annotated by a single label associated to the most significant semantic content of the image. However, this assumption does not fit well with the complexity of RS images, where an image might have multiple land-cover classes (i.e., multilabels). Moreover, annotating images with multilabels is costly and time consuming. To address these issues, in this paper, we introduce a semisupervised graph-theoretic method in the framework of multilabel RS image retrieval problems. The proposed method is based on four main steps. The first step segments each image in the archive and extracts the features of each region. The second step constructs an image neighborhood graph and uses a correlated label propagation algorithm to automatically assign a set of labels to each image in the archive by exploiting only a small number of training images annotated with multilabels. The third step associates class labels with image regions by a novel region labeling strategy, whereas the final step retrieves the images similar to a given query image by a subgraph matching strategy. Experiments carried out on an archive of aerial images show the effectiveness of the proposed method when compared with the state-of-the-art RS content-based image retrieval methods.
    Print ISSN: 0196-2892
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    Topics: Architecture, Civil Engineering, Surveying , Geography
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  • 17
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    Insertion Loss-Aware Routing Analysis and Optimization for a Fat-Tree-Based Optical Network-on-Chip (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-21
    Description: Fat-tree-based optical network-on-chip (FONoC) is an emerging architecture that enables next-generation computing platforms to achieve ultimate performance and energy efficiency. However, the architecture suffers from high insertion loss, which degrades energy efficiency and signal reliability severely. Focusing primarily on microring resonator (MR) drops, we analyze the relationship between the insertion loss caused by MR drops and the routing paths in the FONoCs. Our approach involves developing a simplified graph model named a drop-characterized fat-tree graph with vertex indexing. We propose three types of routing algorithms: 1) insertion loss-minimized deterministic routing; 2) minimized loss path-prioritized adaptive routing; and 3) insertion loss-constrained adaptive routing. Furthermore, we present the associated optical router architectures and additional insertion loss optimization by minimizing the number of waveguide crossings. Based on our simulation results for the latency, throughput, energy efficiency, and MR activation power, we discuss the tradeoffs and suggest appropriate optimization techniques to be adopted according to the priorities of the design goals.
    Print ISSN: 0278-0070
    Electronic ISSN: 1937-4151
    Topics: Electrical Engineering, Measurement and Control Technology
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  • 18
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    Detection and Diagnosis of Single Faults in Quantum Circuits (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-21
    Description: Detection and isolation of faults is a crucial step in the physical realization of quantum circuits. Even though quantum gates and circuits compute reversible functions, the standard techniques of automatic test pattern generation (ATPG) for classical reversible circuits are not directly applicable to quantum circuits. For faulty quantum circuits under the widely accepted single fault assumption, we show that their behavior can be fully characterized by the (single) faulty gate and the corresponding fault model. This allows us to efficiently determine test input states as well as measurement strategy for fault detection and diagnosis. Building on top of these, we design randomized algorithms which are able to detect every nontrivial single-gate fault with minimal probability of error. We also describe similar algorithms for fault diagnosis. We evaluate our algorithms by the number of output samples that needs to be collected and the probability of error. Both of these can be related to the eigenvalues of the operators corresponding to the circuit gates. We experimentally compare all our strategies with the state-of-the-art ATPG techniques for quantum circuits under the “single missing faulty gate” model and demonstrate that significant improvement is possible if we can exploit the quantum nature of circuits.
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    Topics: Electrical Engineering, Measurement and Control Technology
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  • 19
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    Combating Coordinated Pricing Cyberattack and Energy Theft in Smart Home Cyber-Physical Systems (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-21
    Description: The information exchange between the utility company and the smart community is crucial to the smart home cyber-physical systems. Yet the interaction between the two parties is vulnerable to many potential cyberattacks, among which the most striking ones are pricing cyberattacks and energy theft. Coordinated cyberattacks have emerged as an advanced attacking scheme with both pricing attack and energy theft applied in the cooperative manner, which can induce significant impact to smart home systems even if each attack is applied with only moderate strength. Such attacks cannot be effectively detected since the existing techniques are designed for detecting either pricing attack or energy theft without considering the impact due to coordinated attacks. This paper aims at developing the detection framework for coordinated cyberattacks considering coordinated impacts of various attacking strategies using an advanced continuous state partially observable Markov decision process. Handling coordinated attacks induces drastic increase in time complexity, which motivates us to propose innovative cross entropy state sampling and Fourier belief state approximation for the solving of developed detection framework. Our simulation results demonstrate that the coordinated cyberattack can reduce his/her electricity bill by 32.65%. In addition, the proposed detection technique can better capture coordinated attacks than the conventional detection technique, resulting in 10.31% increase in the hacker’s bill.
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  • 20
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    Efficient and Adaptive Error Recovery in a Micro-Electrode-Dot-Array Digital Microfluidic Biochip (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-21
    Description: A digital microfluidic biochip (DMFB) is an attractive technology platform for automating laboratory procedures in biochemistry. In recent years, DMFBs based on a micro-electrode-dot-array (MEDA) architecture have been proposed. MEDA biochips can provide advantages of better capability of droplet manipulation and real-time sensing ability. However, errors are likely to occur due to defects, chip degradation, and the lack of precision inherent in biochemical experiments. Therefore, an efficient error-recovery strategy is essential to ensure the correctness of assays executed on MEDA biochips. By exploiting MEDA-specific advances in droplet sensing, we present a novel error-recovery technique to dynamically reconfigure the biochip using real-time data provided by on-chip sensors. Local recovery strategies based on probabilistic-timed-automata are presented for various types of errors. An online synthesis technique and a control flow are also proposed to connect local-recovery procedures with global error recovery for the complete bioassay. Moreover, an integer linear programming-based method is also proposed to select the optimal local-recovery time for each operation. Laboratory experiments using a fabricated MEDA chip are used to characterize the outcomes of key droplet operations. The PRISM model checker and three benchmarks are used for an extensive set of simulations. Our results highlight the effectiveness of the proposed error-recovery strategy.
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  • 21
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    Scheduling and Fluid Routing for Flow-Based Microfluidic Laboratories-on-a-Chip (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-21
    Description: Microfluidic laboratories-on-a-chip (LoCs) are replacing the conventional biochemical analyzers and are able to integrate the necessary functions for biochemical analysis on-chip. There are several types of LoCs, each having its advantages and limitations. In this paper we are interested in flow-based LoCs, in which a continuous flow of liquid is manipulated using integrated microvalves. By combining several microvalves, more complex units, such as micropumps, switches, mixers, and multiplexers, can be built. We consider that the architecture of the LoC is given, and we are interested in synthesizing an implementation, consisting of the binding of operations in the application to the functional units of the architecture, the scheduling of operations and the routing and scheduling of the fluid flows, such that the application completion time is minimized. To solve this problem, we propose a list scheduling-based application mapping (LSAM) framework and evaluate it by using real-life as well as synthetic benchmarks. When biochemical applications contain fluids that may adsorb on the substrate on which they are transported, the solution is to use rinsing operations for contamination avoidance. Hence, we also propose a rinsing heuristic, which has been integrated in the LSAM framework.
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  • 22
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    How Preserving Circuit Design Hierarchy During FPGA Packing Leads to Better Performance (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-21
    Description: Generating a configuration for a field-programmable gate array (FPGA) starting from a high level description of a design is a time consuming task. The resulting configuration should have a high quality so that the FPGA resources are used in an efficient way while being able to run at high clock frequencies and having a low power consumption. In this paper, we present MultiPart, a new hierarchical packing algorithm that obtains better quality and faster runtimes when compared to the frequently used AAPack packer in VPR. MultiPart combines the benefits of partitioning-based and seed-based packing approaches. It tries to preserve the design hierarchy during packing. This results in a gain of 32% in total wirelength and a gain of 10% in critical path delay. The partitioning-based methodology allows us to exploit multithreading, leading to $9.3 {times }$ faster packing runtimes on a CPU with 10 cores. We also gain in the total routing runtime because MultiPart reduces congestion problems on a higher level. The subcircuits in the partitioned circuit are clustered with a seed-based packer. This allows MultiPart to deal with the constraints of complex heterogeneous architectures. In short, MultiPart targets heterogeneous commercial FPGAs with a lower runtime while increasing the quality of the configuration. The source code of MultiPart is available in our FPGA CAD framework on Github.
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  • 23
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    Design and Performance of a Segmented Stator Permanent Magnet Alternator for Aerospace (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-21
    Description: In this paper, a permanent magnet alternator which presently relies on magnetic slot wedges is redesigned to give lower iron loss. The new segmented stator design is used as the basis for a validated loss study—comparing bonded lamination stacks with those held together with a punched notch. Segmentation is shown to improve the production process, reduce losses, and remove the need for wedges, while not significantly altering the acoustic performance. In the first series of prototypes, however, where notching is used to secure lamination packs, the process is shown to have adversely affected material properties of the nickel iron laminations and overall measured losses increased. In the second prototype, with thinner laminations and glued stacks, iron loss predictions and measurement agree and are less than the present slot wedge design.
    Print ISSN: 0885-8969
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  • 24
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    A Multiplier-Free Generator for Polyphase Complete Complementary Codes (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-01-31
    Description: Complementary sets of sequences (CSS) and complete complementary codes (CCC) have found numerous applications in wireless communications and radar sensing owing to their perfect aperiodic correlation properties. In this paper, we first present a new algorithm for generating polyphase CSS and CCC based on paraunitary (PU) matrices which uses equivalent forms of unimodular unitary matrices. Then, we propose a multiplier-free implementation of this generator based on multiplexers and read-only memories (ROMs). Our proposed algorithm generalizes the previous PU generator for complementary pairs by Budišin and Spasojević. Some previous algorithms for CSS and CCC can also be derived from our CCC generator as special cases. In addition, we give the enumeration formula and show that the number of generated sequences is significantly higher compared to previous works.
    Print ISSN: 1053-587X
    Electronic ISSN: 1941-0476
    Topics: Electrical Engineering, Measurement and Control Technology
    Published by Institute of Electrical and Electronics Engineers (IEEE) on behalf of The IEEE Signal Processing Society.
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  • 25
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    On the Restricted Isometry of the Columnwise Khatri–Rao Product (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-01-31
    Description: The columnwise Khatri–Rao product of two matrices is an important matrix type, reprising its role as a structured sensing matrix in many fundamental linear inverse problems. Robust signal recovery in such inverse problems is often contingent on proving the restricted isometry property (RIP) of a certain system matrix expressible as a Khatri–Rao product of two matrices. In this paper, we analyze the RIP of a generic columnwise Khatri–Rao product matrix by deriving two upper bounds for its $k$ th order restricted isometry constant ( $k$ -RIC) for different values of $k$ . The first RIC bound is computed in terms of the individual RICs of the real-valued input matrices participating in the Khatri–Rao product. The second RIC bound is probabilistic and is specified in terms of the input matrix dimensions. We show that the Khatri–Rao product of a pair of $m times n$ sized random matrices comprising independent and identically distributed sub-Gaussian entries satisfies $k$ -RIP with arbitrarily high probability, provided $m$ exceeds $mathcal {O}(sqrt{k}log ^{3/2}{n})$ . This is a substantially milder condition compared to $mathcal {O}(klog {n})$ rows needed to guarantee $k$ -RIP of the input sub-Gaussian random matrices participating in the Khat- i–Rao product. Our RIC bounds confirm that the Khatri–Rao product exhibits stronger restricted isometry compared to its constituent matrices for the same RIP order. The proposed RIC bounds are potentially useful in obtaining improved performance guarantees in several sparse signal recovery and tensor decomposition problems.
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    Topics: Electrical Engineering, Measurement and Control Technology
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  • 26
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    Fast Algorithms for Designing Unimodular Waveform(s) With Good Correlation Properties (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-01-31
    Description: We develop new fast and efficient algorithms for designing single or multiple unimodular waveforms with good auto- and cross-correlation or weighted correlation properties, which are highly desired in radar and communication systems. The waveform design is based on the minimization of the integrated sidelobe level (ISL) and weighted ISL (WISL) of waveforms. As the corresponding problems can quickly grow to a large scale with increasing the code length and the number of waveforms, the main issue turns to be the development of fast large-scale optimization techniques. The difficulty is also that the corresponding optimization problems are nonconvex, but the required accuracy is high. Therefore, we formulate the ISL and WISL minimization problems as nonconvex quartic optimization problems in frequency domain, and then simplify them into quadratic problems via majorization-minimization technique, which is one of the basic techniques for addressing large-scale and/or nonconvex optimization problems. While designing our fast algorithms, we explore and use the inherent algebraic structures in objective functions to rewrite them into quartic forms, and in the case of WISL minimization, to derive additionally an alternative quartic form that allows us to apply the quartic-quadratic transformation. Our algorithms are applicable to large-scale unimodular waveform design problems as they are proved to have lower or comparable computational burden (analyzed theoretically) and faster convergence speed (confirmed by comprehensive simulations) than the state-of-the-art algorithms. In addition, the waveforms designed by our algorithms demonstrate better correlation properties compared to their counterparts.
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    Online Anomaly Detection With Minimax Optimal Density Estimation in Nonstationary Environments (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-01-31
    Description: We introduce a truly online anomaly detection algorithm that sequentially processes data to detect anomalies in time series. In anomaly detection, while the anomalous data are arbitrary, the normal data have similarities and generally conforms to a particular model. However, the particular model that generates the normal data is generally unknown (even nonstationary) and needs to be learned sequentially. Therefore, a two stage approach is needed, where in the first stage, we construct a probability density function to model the normal data in the time series. Then, in the second stage, we threshold the density estimation of the newly observed data to detect anomalies. We approach this problem from an information theoretic perspective and propose minimax optimal schemes for both stages to create an optimal anomaly detection algorithm in a strong deterministic sense. To this end, for the first stage, we introduce a completely online density estimation algorithm that is minimax optimal with respect to the log-loss and achieves Merhav's lower bound for general nonstationary exponential-family of distributions without any assumptions on the observation sequence. For the second stage, we propose a threshold selection scheme that is minimax optimal (with logarithmic performance bounds) against the best threshold chosen in hindsight with respect to the surrogate logistic loss. Apart from the regret bounds, through synthetic and real life experiments, we demonstrate substantial performance gains with respect to the state-of-the-art density estimation based anomaly detection algorithms in the literature.
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    Topics: Electrical Engineering, Measurement and Control Technology
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    Lower Bound on the Localization Error in Infinite Networks With Random Sensor Locations (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-01-31
    Description: We present novel lower bounds on the mean square error (MSE) of the location estimation of an emitting source via a network where the sensors are deployed randomly. The sensor locations are modeled as a homogenous Poisson point process. In contrast to previous bounds that are a function of the specific locations of all the sensors, we present Cramér–Rao bound (CRB) type bounds on the expected mean square error; that is, we first derive the CRB on the MSE as a function of the sensors’ location, and then take expectation with respect to the distribution of the sensors’ location. Thus, these bounds are not a function of a particular sensor configuration, but rather of the sensor statistics. Hence, these novel bounds can be evaluated prior to sensor deployment and provide insights into design issues such as the necessary sensor density, the effect of the channel model, the effect of the signal power, and others. The derived bounds are simple to evaluate and provide a good prediction of the actual network performance. Numerical results show that the novel bounds give a good approximation even in other deployment scenarios such as a square or a triangular grid.
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    Topics: Electrical Engineering, Measurement and Control Technology
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    Optimal Filter Design for Signal Processing on Random Graphs: Accelerated Consensus (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-01-31
    Description: In graph signal processing, filters arise from polynomials in shift matrices that respect the graph structure, such as the graph adjacency matrix or the graph Laplacian matrix. Hence, filter design for graph signal processing benefits from knowledge of the spectral decomposition of these matrices. Often, stochastic influences affect the network structure and, consequently, the shift matrix empirical spectral distribution. Although the joint distribution of the shift matrix eigenvalues is typically inaccessible, deterministic functions that asymptotically approximate the matrix empirical spectral distribution can be found for suitable random graph models using tools from random matrix theory. We employ this information regarding the density of eigenvalues to develop criteria for optimal graph filter design. In particular, we consider filter design for distributed average consensus and related problems, leading to improvements in short-term error minimization or in asymptotic convergence rate.
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    Topics: Electrical Engineering, Measurement and Control Technology
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    Tensor Similarity in Two Modes (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-01-31
    Description: Multiway datasets are widespread in signal processing and play an important role in blind signal separation, array processing, and biomedical signal processing, among others. One key strength of tensors is that their decompositions are unique under mild conditions, which allows the recovery of features or source signals. In several applications, such as classification, we wish to compare factor matrices of the decompositions. Though this is possible by first computing the tensor decompositions and subsequently comparing the factors, these decompositions are often computationally expensive. In this paper, we present a similarity method that indicates whether the factors in two modes are essentially equal without explicitly computing them. Essential equality conditions, which ensure the theoretical validity of our approach, are provided for various underlying tensor decompositions. The developed algorithm provides a computationally efficient way to compare factors. The method is illustrated in a context of emitter movement detection and fluorescence data analysis.
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    Signal Recovery From Unlabeled Samples (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-01-31
    Description: In this paper, we study the recovery of a signal from a set of noisy linear projections (measurements), when such projections are unlabeled, that is, the correspondence between the measurements and the set of projection vectors (i.e., the rows of the measurement matrix) is not known a priori . We consider a special case of unlabeled sensing referred to as unlabeled ordered sampling (UOS) where the ordering of the measurements is preserved. We identify a natural duality between this problem and classical compressed sensing (CS), where we show that the unknown support (location of nonzero elements) of a sparse signal in CS corresponds to the unknown indices of the measurements in UOS. While in CS, it is possible to recover a sparse signal from an underdetermined set of linear equations (less equations than the signal dimension), successful recovery in UOS requires taking more samples than the dimension of the signal. Motivated by this duality, we develop a restricted isometry property (RIP) similar to that in CS. We also design a low-complexity alternating minimization algorithm that achieves a stable signal recovery under the established RIP. We analyze our proposed algorithm for different signal dimensions and number of measurements theoretically and investigate its performance empirically via numerical simulations. The results are reminiscent of a phase transition occurring in CS.
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    Relaxed Biquadratic Optimization for Joint Filter-Signal Design in Signal-Dependent STAP (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-01-31
    Description: We investigate an alternative solution method to the joint signal-beamformer optimization problem considered in a recent publication devoted to this topic. First, we directly demonstrate that the problem, which minimizes the received noise, interference, and clutter power under a minimum variance distortionless response constraint, is generally nonconvex and we provide insight into the nature of the nonconvexity. Second, we employ the theory of biquadratic optimization and semidefinite relaxations to produce a relaxed version of the problem, which we show to be convex . The optimality conditions of this relaxed problem are examined and a variety of potential solutions are found, both analytically and numerically. These solutions are then compared to existing alternating minimization schemes.
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    Pattern Synthesis for Arbitrary Arrays via Weight Vector Orthogonal Decomposition (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-01-31
    Description: This paper presents a new scheme based on Weight vector ORthogonal Decomposition (WORD) to control the array response at a given direction and a novel WORD-based approach to pattern synthesis for arbitrary arrays. The central concept of the proposed methods stems from the adaptive array theory. More precisely, it is found that the inverse of the noise-plus-interference covariance matrix in adaptive beamforming can be regarded as a linear combination of two orthogonal projection matrices, and, accordingly, the optimal weight vector is a linear combination of two orthogonal vectors. With such an observation, the WORD scheme is developed to design the desired weight vector. It is shown that the array response at a given direction can be precisely adjusted to an arbitrary level, by simply determining appropriate combination coefficients for those two orthogonal vectors. Furthermore, a closed-form expression of the weight vector can be achieved by introducing a new cost function that measures pattern variation. By employing the WORD scheme successively, a novel approach to pattern synthesis for arbitrary arrays is devised. At each implementation step of this approach, the array pattern is adjusted in a point-by-point manner by successively modifying the weight vector. As such, both the sidelobe and mainlobe regions can be flexibly synthesized. Numerical examples are provided to demonstrate the effectiveness and flexibility of the WORD scheme in array response control at a single direction as well as pattern synthesis.
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    A SPR Glucose Sensor Based on Immobilized Glucose Oxidases and Silica Mesocellular Foams (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-24
    Description: An improved glucose-sensitive membrane (GSM) was prepared by immobilizing glucose oxidases (GODs) onto silica mesocellular foams and trapping them in a polyacrylamide gel. This gel was then coated on a gold/glass sheet to realize surface plasmon resonance (SPR) sensors. A series of sensing experiments was conducted to obtain the optimized parameters with the improved GSM. The experimental results showed that the improved SPR glucose sensor has a sensitivity of 0.0135 degree/(mg/dL) and a linear range of 0–160 mg/dL. This linear range is twice that obtained with the GSM by immobilizing GODs on SiO 2 nanoparticles.
    Print ISSN: 1530-437X
    Electronic ISSN: 1558-1748
    Topics: Electrical Engineering, Measurement and Control Technology
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    Fabrication of a Room Temperature Ammonia Gas Sensor Based on Polyaniline With N-Doped Graphene Quantum Dots (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-24
    Description: In this paper, we have investigated a resistive ammonia (NH 3 ) gas sensor fabricated with a combination of polyaniline (PANI) as a conductive polymer and different weight percentages of nitrogen-doped graphene quantum dots (N-GQDs). We used two different metals of silver (Ag) and aluminium (Al) as electrodes to the sensing films. It has been found that Ag contact showed an ohmic behaviour and Al electrode exhibited a Schottky junction. This paper presented here shows that the sensor with Ag electrode has much higher response to NH 3 than that of the sensor with Al electrode. The sensor of (50wt%)N-GQDs/PANI with Ag contact exhibited the best response of 110.92 towards 1500 ppm NH 3 at room temperature. However, a much response of 86.91 was measured for the sensor with Al electrode. Fourier transform infrared spectroscopy of the synthesized structure was investigated to analyze the differences between pure PANI and N-GQDs/PANI. Further, field emissions scanning electron microscopy was used to characterize sensing samples.
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    Molecular Imprinted Polymer Based Electrode for Sensing Catechin (+C) in Green Tea (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-24
    Description: Green tea is believed to be a healthy beverage due to a number of therapeutic benefits. Catechin, one of its constituents, is an important antioxidant and possesses free radical scavenging abilities. This paper demonstrates a low cost solution related to the sensing of catechin (+C) using the principle of molecular imprinted polymer technique. Here the electrode was synthesized using the co-polymer of acrylonitrile and ethylene glycol dimethacrylate and was subsequently imprinted with catechin. The material was extensively characterized using Fourier transform infra-red spectroscope and field emission scanning electron microscope, respectively. Cyclic voltammetry and differential pulse voltammetry using the three electrode system were employed for determining the electrochemical characteristics of the proposed electrode. It exhibited a linear range from 5 to 100 $mu text{M}$ with the limit of detection of 37 nm (S/N = 3). On studying the analytical characteristics, the electrode was found to be repeatable, reproducible, and offered a good selectivity. Our sensing device was subjected to green tea samples in order to study their catechin content. A partial least square regression model was developed for correlating the response with that of the high performance liquid chromatography data and it resulted in a prediction accuracy of about 92%.
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    FPGA Implementation of Steinhart–Hart Equation for Accurate Thermistor Linearization (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-24
    Description: This paper presents fixed point operation-based Field Programmable Gate Arrays (FPGA) implementation of Steinhart–Hart Equation (SHHE) for thermistor linearization. FPGA implementation issues of SHHE are presented and their solutions are proposed and experimentally validated in a LabVIEW TM environment. Experimental temperature calibration, performed using a M/S Fluke drywell calibrator, revealed a lowest nonlinearity of 0.11% for an industrial grade thermistor in the input temperature range from −20 °C to 120 °C. Therefore, the main contribution of this work is to demonstrate the lowest nonlinearity for a wider temperature range. This work is expected to be very useful to instrumentation engineers as it employs a time-tested technique, for thermistor linearization in FPGA, leading to the lowest nonlinearity for a larger input temperature range.
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    Sub-Network Kernels for Measuring Similarity of Brain Connectivity Networks in Disease Diagnosis (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-24
    Description: As a simple representation of interactions among distributed brain regions, brain networks have been widely applied to automated diagnosis of brain diseases, such as Alzheimer’s disease (AD) and its early stage, i.e. , mild cognitive impairment (MCI). In brain network analysis, a challenging task is how to measure the similarity between a pair of networks. Although many graph kernels ( i.e. , kernels defined on graphs) have been proposed for measuring the topological similarity of a pair of brain networks, most of them are defined using general graphs, thus ignoring the uniqueness of each node in brain networks. That is, each node in a brain network denotes a particular brain region, which is a specific characteristics of brain networks. Accordingly, in this paper, we construct a novel sub-network kernel for measuring the similarity between a pair of brain networks and then apply it to brain disease classification. Different from current graph kernels, our proposed sub-network kernel not only takes into account the inherent characteristic of brain networks, but also captures multi-level (from local to global) topological properties of nodes in brain networks, which are essential for defining the similarity measure of brain networks. To validate the efficacy of our method, we perform extensive experiments on subjects with baseline functional magnetic resonance imaging data obtained from the Alzheimer’s disease neuroimaging initiative database. Experimental results demonstrate that the proposed method outperforms several state-of-the-art graph-based methods in MCI classification.
    Print ISSN: 1057-7149
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    Deep Visual Attention Prediction (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-24
    Description: In this paper, we aim to predict human eye fixation with view-free scenes based on an end-to-end deep learning architecture. Although convolutional neural networks (CNNs) have made substantial improvement on human attention prediction, it is still needed to improve the CNN-based attention models by efficiently leveraging multi-scale features. Our visual attention network is proposed to capture hierarchical saliency information from deep, coarse layers with global saliency information to shallow, fine layers with local saliency response. Our model is based on a skip-layer network structure, which predicts human attention from multiple convolutional layers with various reception fields. Final saliency prediction is achieved via the cooperation of those global and local predictions. Our model is learned in a deep supervision manner, where supervision is directly fed into multi-level layers, instead of previous approaches of providing supervision only at the output layer and propagating this supervision back to earlier layers. Our model thus incorporates multi-level saliency predictions within a single network, which significantly decreases the redundancy of previous approaches of learning multiple network streams with different input scales. Extensive experimental analysis on various challenging benchmark data sets demonstrate our method yields the state-of-the-art performance with competitive inference time. 1 1 Our source code is available at https://github.com/wenguanwang/deepattention .
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    Peer-Reviewed Compilation: Manuscripts from Compumag 2017 (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-24
    Print ISSN: 0018-9464
    Electronic ISSN: 1941-0069
    Topics: Physics
    Published by Institute of Electrical and Electronics Engineers (IEEE) on behalf of The IEEE Magnetics Society.
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    Hookworm Detection in Wireless Capsule Endoscopy Images With Deep Learning (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-24
    Description: As one of the most common human helminths, hookworm is a leading cause of maternal and child morbidity, which seriously threatens human health. Recently, wireless capsule endoscopy (WCE) has been applied to automatic hookworm detection. Unfortunately, it remains a challenging task. In recent years, deep convolutional neural network (CNN) has demonstrated impressive performance in various image and video analysis tasks. In this paper, a novel deep hookworm detection framework is proposed for WCE images, which simultaneously models visual appearances and tubular patterns of hookworms. This is the first deep learning framework specifically designed for hookworm detection in WCE images. Two CNN networks, namely edge extraction network and hookworm classification network, are seamlessly integrated in the proposed framework, which avoid the edge feature caching and speed up the classification. Two edge pooling layers are introduced to integrate the tubular regions induced from edge extraction network and the feature maps from hookworm classification network, leading to enhanced feature maps emphasizing the tubular regions. Experiments have been conducted on one of the largest WCE datasets with $440K$ WCE images, which demonstrate the effectiveness of the proposed hookworm detection framework. It significantly outperforms the state-of-the-art approaches. The high sensitivity and accuracy of the proposed method in detecting hookworms shows its potential for clinical application.
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    Reformulating Level Sets as Deep Recurrent Neural Network Approach to Semantic Segmentation (2018)
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    Publication Date: 2018-02-24
    Description: Variational Level Set (LS) has been a widely used method in medical segmentation. However, it is limited when dealing with multi-instance objects in the real world. In addition, its segmentation results are quite sensitive to initial settings and highly depend on the number of iterations. To address these issues and boost the classic variational LS methods to a new level of the learnable deep learning approaches, we propose a novel definition of contour evolution named Recurrent Level Set (RLS) 1 to employ Gated Recurrent Unit under the energy minimization of a variational LS functional. The curve deformation process in RLS is formed as a hidden state evolution procedure and updated by minimizing an energy functional composed of fitting forces and contour length. By sharing the convolutional features in a fully end-to-end trainable framework , we extend RLS to Contextual RLS (CRLS) to address semantic segmentation in the wild. The experimental results have shown that our proposed RLS improves both computational time and segmentation accuracy against the classic variational LS-based method whereas the fully end-to-end system CRLS achieves competitive performance compared to the state-of-the-art semantic segmentation approaches. 1 Source codes will be publicly available.
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    Dictionary-Based Tensor Canonical Polyadic Decomposition (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-24
    Description: To ensure interpretability of extracted sources in tensor decomposition, we introduce in this paper a dictionary-based tensor canonical polyadic decomposition, which enforces one factor to belong exactly to a known dictionary. A new formulation of sparse coding is proposed, which enables high-dimensional tensors dictionary-based canonical polyadic decomposition. The benefits of using a dictionary in tensor decomposition models are explored both in terms of parameter identifiability and estimation accuracy. Performances of the proposed algorithms are evaluated on the decomposition of simulated data and the unmixing of hyperspectral images.
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    Accelerated Distributed Dual Averaging Over Evolving Networks of Growing Connectivity (2018)
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    Publication Date: 2018-02-24
    Description: We consider the problem of accelerating distributed optimization in multi-agent networks by sequentially adding edges. Specifically, we extend the distributed dual averaging (DDA) subgradient algorithm to evolving networks of growing connectivity and analyze the corresponding improvement in convergence rate. It is known that the convergence rate of DDA is influenced by the algebraic connectivity of the underlying network, where better connectivity leads to faster convergence. However, the impact of the network topology design on the convergence rate of DDA has not been fully understood. In this paper, we begin by designing network topologies via edge selection and scheduling in an offline manner. For edge selection, we determine the best set of candidate edges that achieves the optimal tradeoff between the growth of network connectivity and the usage of network resources. The dynamics of network evolution is then incurred by edge scheduling. Furthermore, we provide a tractable approach to analyze the improvement in the convergence rate of DDA induced by the growth of network connectivity. Our analysis reveals the connection between network topology design and the convergence rate of DDA, and provides quantitative evaluation of DDA acceleration for distributed optimization that is absent in the existing analysis. Lastly, numerical experiments show that DDA can be significantly accelerated using a sequence of well-designed networks, and our theoretical predictions are well matched to its empirical convergence behavior.
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    Front Cover (2018)
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    Publication Date: 2018-02-24
    Description: Presents the front cover for this issue of the publication.
    Print ISSN: 0733-8724
    Electronic ISSN: 1558-2213
    Topics: Electrical Engineering, Measurement and Control Technology
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    Sparse Activity Detection for Massive Connectivity (2018)
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    Publication Date: 2018-02-24
    Description: This paper considers the massive connectivity application in which a large number of devices communicate with a base-station (BS) in a sporadic fashion. Device activity detection and channel estimation are central problems in such a scenario. Due to the large number of potential devices, the devices need to be assigned non-orthogonal signature sequences. The main objective of this paper is to show that by using random signature sequences and by exploiting sparsity in the user activity pattern, the joint user detection and channel estimation problem can be formulated as a compressed sensing single measurement vector (SMV) or multiple measurement vector (MMV) problem depending on whether the BS has a single antenna or multiple antennas and efficiently solved using an approximate message passing (AMP) algorithm. This paper proposes an AMP algorithm design that exploits the statistics of the wireless channel and provides an analytical characterization of the probabilities of false alarm and missed detection via state evolution. We consider two cases depending on whether or not the large-scale component of the channel fading is known at the BS and design the minimum mean squared error denoiser for AMP according to the channel statistics. Simulation results demonstrate the substantial advantage of exploiting the channel statistics in AMP design; however, knowing the large-scale fading component does not appear to offer tangible benefits. For the multiple-antenna case, we employ two different AMP algorithms, namely the AMP with vector denoiser and the parallel AMP-MMV, and quantify the benefit of deploying multiple antennas.
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    Online Distributed Learning Over Networks in RKH Spaces Using Random Fourier Features (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-24
    Description: We present a novel diffusion scheme for online kernel-based learning over networks. So far, a major drawback of any online learning algorithm, operating in a reproducing kernel Hilbert space (RKHS), is the need for updating a growing number of parameters as time iterations evolve. Besides complexity, this leads to an increased need of communication resources in a distributed setting. In contrast, we propose to approximate the solution as a fixed-size vector (of larger dimension than the input space) using the previously introduced framework of random Fourier features. This paves the way to use standard linear combine-then-adapt techniques. To the best of our knowledge, this is the first time that a complete protocol for distributed online learning in RKHS is presented. Conditions for asymptotic convergence and boundness of the networkwise regret are also provided. The simulated tests illustrate the performance of the proposed scheme.
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    The Power of Side-Information in Subgraph Detection (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-24
    Description: In this paper, we tackle the problem of hidden community detection. We consider belief propagation (BP) applied to the problem of detecting a hidden Erdős-Rényi (ER) graph embedded in a larger and sparser ER graph, in the presence of side-information. We derive two related algorithms based on BP to perform subgraph detection in the presence of two kinds of side-information. The first variant of side-information consists of a set of nodes, called cues, known to be from the subgraph. The second variant of side-information consists of a set of nodes that are cues with a given probability. It was shown in past works that BP without side-information fails to detect the subgraph correctly when a so-called effective signal-to-noise ratio parameter falls below a threshold. In contrast, in the presence of nontrivial side-information, we show that the BP algorithm achieves asymptotically zero error for any value of a suitably defined phase-transition parameter. We validate our results on synthetic datasets and a few real world networks.
    Print ISSN: 1053-587X
    Electronic ISSN: 1941-0476
    Topics: Electrical Engineering, Measurement and Control Technology
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    Table of Contents (2018)
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    Publication Date: 2018-02-24
    Description: Presents the table of contents for this issue of the publication.
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    Topics: Electrical Engineering, Measurement and Control Technology
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  • 50
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    Journal of Lightwave Technology (2018)
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    Publication Date: 2018-02-24
    Description: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
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    Topics: Electrical Engineering, Measurement and Control Technology
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    Realization and Application of Large-Scale Fast Optical Circuit Switch for Data Center Networking (2018)
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    Publication Date: 2018-02-24
    Description: Applying optical switching to data center networks can greatly expand network bandwidth and reduce electrical power consumption, both of which are needed to meet the explosive traffic increase. The optical switch offers large bandwidth switching capability, and so eliminates the multistage switch network architecture needed with electrical switching. Furthermore, the single stage architecture of the optical switch greatly simplifies operating costs, which include cabling, while substantially reducing the number of transponders needed. Data center networks place very different demands on optical systems than communication networks. Grasping the right direction to proceed is of paramount importance. To realize the full potential of optical switches, such as scalability and cost effectiveness, we analyze the role of large-scale optical circuit switches and discuss the realization technologies that combine the two dimensions of space and wavelength. Our recent advances in large port-count optical switches are presented.
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    Sliceable Transponders: Pre-Programmed OAM, Control, and Management (2018)
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    Publication Date: 2018-02-24
    Description: Sliceable transponders (S-BVTs) are expected to increase flexibility of elastic optical networks by providing multiple independent optical flows that can be routed based on operators needs and traffic. The support of several transmission modes (e.g., modulation format, coding, baud rate) also enables to adapt the transmission to the physical layer characteristics. Thus, a proper control of the S-BVT configuration is mandatory, as well as the monitoring of optical flows and the management of monitoring information. NETCONF, based on YANG modeling language, is emerging as a software defined networking protocol supporting configuration messages as well as messages to exchange monitoring information within the management system. This paper presents control and management of sliceable transponders through NETCONF and YANG. Three operation, administration, and maintenance (OAM) procedures are presented to guarantee the maintenance of services in the presence of faults or physical layer degradations: centralized , hierarchical , and a scheme based on preprogrammed OAM. A YANG model enabling the preprogrammed OAM is proposed. Simulations show that hierarchical and preprogrammed OAM provide faster reaction time to degradations.
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    Blank page (2018)
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    Publication Date: 2018-02-24
    Description: This page or pages intentionally left blank.
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    Cognitive Assurance Architecture for Optical Network Fault Management (2018)
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    Publication Date: 2018-02-24
    Description: In face of staggering traffic growth driven by cloud-based platforms, modern optical networks—forming the backbone of such connectivity—are faced with increasing requirements in terms of operational reliability. The challenge is that of cognition-driven learning and fault management workflows, cost-effectively assuring the next-generation networks. Machine learning, an artificial intelligence tool, can be conceived as an extremely promising instrument to address network assurance via dynamic data-driven operation, as opposed to static pre-engineered solutions. In this paper, we propose and demonstrate a cognitive fault detection architecture for intelligent network assurance. We introduce the concept of cognitive fault management, elaborate on its integration in transport software defined network controller, and demonstrate its operation based on real-world fault examples. Our framework both detects and identifies significant faults, and outperforms conventional fixed threshold-triggered operations, both in terms of detection accuracy and proactive reaction time.
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    Demonstration of Single-Mode Multicore Fiber Transport Network With Crosstalk-Aware In-Service Optical Path Control (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-24
    Description: Multicore fiber (MCF) transmission is considered as one of the promising technologies for breaking the capacity limit of traditional single mode fibers. Managing the crosstalk (XT) and configuring optical paths adaptively based on the XT as well as achieving longer distance and larger capacity transmission are important, because intercore XT could be the main limiting factor for MCF transmission. In a real MCF network, the intercore XT in a particular core is likely to change continuously as the optical paths in the adjacent cores are dynamically assigned to match the dynamic nature of the data traffic. If we configure the optical paths while ignoring the intercore XT value, the Q-factors may become excessive. Therefore, monitoring the intercore XT value continuously and configuring optical path parameters adaptively and flexibly are essential. To address these challenges, we develop an MCF transport network testbed and demonstrate an XT-aware traffic engineering scenario. With the help of a software-defined network controller, the modulation format and optical path route are adaptively changed based on the monitored XT values by using programmable devices such as a real-time transponder and a reconfigurable optical add–drop multiplexer.
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    CS Connection (2018)
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    Publication Date: 2018-02-28
    Description: Information of interest to Computer Society members. This article includes two errata. One erratum for “The New Science Wars” (H. Berghel, vol. 50, no. 11, 2017, pp. 72-76; doi: 10.1109/MC.2017.4041367). The second erratum is for the Table of Contents for the January 2018 issue (vol. 51, no. 1; doi: 10.1109/MC.2018.1151011).
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    Computer WRITE FOR US (2018)
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    Publication Date: 2018-02-28
    Description: Advertisement, IEEE.
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    Topics: Computer Science
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    IEEE Computer Society Information (2018)
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    Publication Date: 2018-02-28
    Description: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
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    Topics: Computer Science
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    Looking for the BEST Tech Job for You? (2018)
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    Publication Date: 2018-02-28
    Description: Advertisement, IEEE.
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    Shape Parameter Estimator of the Generalized Gaussian Distribution Based on the MoLC (2018)
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    Publication Date: 2018-02-28
    Description: A novel estimator for the shape parameter of the generalized Gaussian distribution (GGD) is proposed based on the method of logarithmic cumulants. First, the expression of the log-cumulant for the GGD is theoretically derived. As a result, a simple equation for the estimation of the shape parameter is obtained. The processing procedure of the new estimator for practical applications is also provided. Numerical experiments are used to verify the excellent estimation performances of the proposed estimator for both large and small samples. Moreover, experiments using a measured CARABAS-II data set also validate the superiority of the proposed estimator.
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    A Massively Parallel Deep Rule-Based Ensemble Classifier for Remote Sensing Scenes (2018)
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    Publication Date: 2018-02-28
    Description: In this letter, we propose a new approach for remote sensing scene classification by creating an ensemble of the recently introduced massively parallel deep (fuzzy) rule-based (DRB) classifiers trained with different levels of spatial information separately. Each DRB classifier consists of a massively parallel set of human-interpretable, transparent zero-order fuzzy IF…THEN… rules with a prototype-based nature. The DRB classifier can self-organize “from scratch” and self-evolve its structure. By employing the pretrained deep convolution neural network as the feature descriptor, the proposed DRB ensemble is able to exhibit human-level performance through a transparent and parallelizable training process. Numerical examples using benchmark data set demonstrate the superior accuracy of the proposed approach together with human-interpretable fuzzy rules autonomously generated by the DRB classifier.
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    Knowledge-Aided Ocean Clutter Suppression Method for Sky-Wave Over-the-Horizon Radar (2018)
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    Publication Date: 2018-02-28
    Description: In a sky-wave radar, the strong ocean clutter may cover up the echo signal of slow-speed targets. This letter proposed a knowledge-aided ocean clutter suppression method for the sky-wave radar. The proposed method uses the radar carrier frequency and the pulse repetition interval as prior knowledge to reconstruct the prior ocean clutter. This reconstructed clutter is combined with the ionosphere phase perturbation model. The resulting prior clutter is included in the optimal filter design. The simulation results show that the output signal-to-clutter plus noise ratio of this proposed method is 2.507 dB larger than the methods proposed by others.
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    An Automatic Evaluation Platform for Feature Matching Algorithms Based on an Orbital Optical Pushbroom Stereo Imaging System (2018)
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    Publication Date: 2018-02-28
    Description: Thanks to the high robustness of feature-based matching algorithms, they can be applied in remote sensing (RS) applications with complex image changes. However, evaluating feature matching algorithms on RS images is still challenging, because creating ground truth matching data sets of RS images costs a lot of both computing time and human operator time. Therefore, in this letter, we present an evaluation platform for simulating RS ground truth data sets of feature-points correspondences based on a customizable orbital optical pushbroom stereo imaging system. With the help of the proposed platform, evaluating feature matching algorithms could be fully automatic and customized. The performance of three state-of-the-art feature matching algorithms based on local transformation constraint is evaluated and discussed on the proposed platform with comprehensive experiments. The evaluation results of the platform are also compared with the manual evaluation results of 10 pairs of real RS stereo images. The evaluation results show that the proposed platform indeed offers an efficient way for evaluating RS feature matching algorithms.
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    High-Resolution Seismic Time–Frequency Analysis Using the Synchrosqueezing Generalized S-Transform (2018)
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    Publication Date: 2018-02-28
    Description: In this letter, a new method is introduced for a seismic time–frequency (TF) analysis. The proposed method is called synchrosqueezing generalized S-transform (SSGST), which belongs to a postprocessing procedure of the GST. The frequency-dependent Gaussian window used in the standard S-transform may be not suitable for real applications. In order to overcome this limitation, the frequency-dependent Gaussian window is replaced by a parameterized function containing three parameters. These three parameters result in flexibility in the variation of TF resolution. Then, the synchrosqueezing transform is employed to squeeze the TF coefficients of the GST to achieve an energy-concentrated TF representation. Synthetic examples and field data show that the SSGST achieves a high resolution and has the potential in highlighting geological structures with high precision.
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    Ship Classification in SAR Images Improved by AIS Knowledge Transfer (2018)
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    Publication Date: 2018-02-28
    Description: A major bottleneck in limiting the application of the existing methods of ship classification in synthetic aperture radar (SAR) images is the inadequate amount of labeled data available for training a classifier. However, generating ground truth involves expensive and time-consuming ground campaigns or is costly, since a high number of SAR image acquisition will be necessary. In contrast, an automatic identification system (AIS), which is an automatic tracking system used for monitoring maritime ships, can provide plenty of labeled ship samples that is relatively easier to be obtained. Inspired by these facts, this letter proposes to improve ship classification in SAR images by transferring AIS knowledge. We propose an improved multiclass adaptive support vector machine, combined with the naive geometric features (NGFs), to achieve transfer learning between the AIS domain and the SAR image domain. The experiments prove that the traditional method can be significantly improved by AIS information transfer, especially when only a few training samples in the SAR domain are available. In addition, it also shows that after feature selection, the performance of the proposed method can be close to that of the state of the art, even if by only using simpler NGFs and few training samples.
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    Evaluating IMERG V04 Final Run for Monitoring Three Heavy Rain Events Over Mainland China in 2016 (2018)
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    Publication Date: 2018-02-28
    Description: Predicting and monitoring the spatiotemporal characteristics of heavy rain events are important to hazard preparedness, mitigation efforts, and local water resource management. Using three data sets, namely, the daily rain product from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) version 04 Final Run, the daily output from European Centre for Medium-Range Weather Forecasts reanalysis data Interim version (ERA-Interim), and the high-quality gauge-satellite merged precipitation product, the spatiotemporal patterns of three heavy rain events are investigated for the first time over China in 2016, with the objective of assessing the capability of IMERG product for monitoring heavy rain events. It is found that the daily IMERG Final Run can better capture the spatial and temporal characteristics of heavy rain compared with that from ERA-Interim, but it significantly overestimates the amounts of the heaviest rainfalls by 11%–85% over the example regions. The comparison of regional averaged precipitation demonstrates that time series of precipitation retrieved by the IMERG algorithm agree well with that from gauge-satellite merged data set, with differences less than 10 mm on most days over each region. The statistic metrics demonstrate that the IMERG Final Run has a strong potential for detecting heavy rain events but with a relatively large error. This letter may provide useful feedback and insights for further improving the precipitation retrieving algorithm and the application of such data sets.
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    Focusing of SAR With Curved Trajectory Based on Improved Hyperbolic Range Equation (2018)
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    Publication Date: 2018-02-28
    Description: For a synthetic aperture radar (SAR) system with curved trajectory, which is different from the conventional SAR, the downward velocity and the acceleration result in highly complicated range history, making it hard to achieve a focused target response. The traditional SAR imaging algorithms are not accurate enough to compensate the phase errors introduced from the highly complicated range history. In this letter, considering the impact of complex range history, an improved hyperbolic range equation is proposed to access the 2-D spectrum for curved trajectory SAR imaging. Based on the derived spectrum, frequency-domain imaging algorithm can be performed to focus targets. By analyzing the phase error and comparing with other current range models, the proposed range model is proved to be precise enough to deal with the complex motion model happened in curved trajectory SAR imaging. Simulation experiments are implemented to evaluate the imaging performance of the proposed approach.
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    Multisensor Earth Observation Image Classification Based on a Multimodal Latent Dirichlet Allocation Model (2018)
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    Publication Date: 2018-02-28
    Description: Many previous researches have already shown the advantages of multisensor land-cover classification. Here, we propose an innovative land-cover classification approach based on learning a joint latent model of synthetic aperture radar (SAR) and multispectral satellite images using multimodal latent Dirichlet allocation (mmLDA), a probabilistic generative model. It has already been successfully applied to various other problems dealing with multimodal data. For our experiments, we chose overlapping SAR and multispectral images of two regions of interest. The images were tiled into patches and their local primitive features were extracted. Then each image patch is represented by SAR and multispectral bag-of-words (BoW) models. The BoW values are both fed to the mmLDA, resulting in a joint latent data model. A qualitative and quantitative validation of the topics based on ground-truth data demonstrate that the land-cover categories of the regions are correctly classified, outperforming the topics obtained using individual single modality data.
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    Deep Recurrent Neural Networks for Winter Vegetation Quality Mapping via Multitemporal SAR Sentinel-1 (2018)
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    Publication Date: 2018-02-28
    Description: Mapping winter vegetation quality is a challenging problem in remote sensing. This is due to cloud coverage in winter periods, leading to a more intensive use of radar rather than optical images. The aim of this letter is to provide a better understanding of the capabilities of Sentinel-1 radar images for winter vegetation quality mapping through the use of deep learning techniques. Analysis is carried out on a multitemporal Sentinel-1 data over an area around Charentes-Maritimes, France. This data set was processed in order to produce an intensity radar data stack from October 2016 to February 2017. Two deep recurrent neural network (RNN)-based classifiers were employed. Our work revealed that the results of the proposed RNN models clearly outperformed classical machine learning approaches (support vector machine and random forest).
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    Introducing IEEE Collabratec (2018)
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    IEEE Open Access (2018)
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    IEEE Geoscience and Remote Sensing Letters information for authors (2018)
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    Fully Convolutional Networks for Semantic Segmentation of Very High Resolution Remotely Sensed Images Combined With DSM (2018)
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    Publication Date: 2018-02-28
    Description: Recently, approaches based on fully convolutional networks (FCN) have achieved state-of-the-art performance in the semantic segmentation of very high resolution (VHR) remotely sensed images. One central issue in this method is the loss of detailed information due to downsampling operations in FCN. To solve this problem, we introduce the maximum fusion strategy that effectively combines semantic information from deep layers and detailed information from shallow layers. Furthermore, this letter develops a powerful backend to enhance the result of FCN by leveraging the digital surface model, which provides height information for VHR images. The proposed semantic segmentation scheme has achieved an overall accuracy of 90.6% on the ISPRS Vaihingen benchmark.
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    Active Learning Improved by Neighborhoods and Superpixels for Hyperspectral Image Classification (2018)
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    Publication Date: 2018-02-28
    Description: Active learning (AL) is a promising solution to hyperspectral image classification with very few initial labeled samples. Although previous AL heuristics have exhibited encouraging results, some challenges are still open. On the one hand, traditional AL heuristics measured uncertainty only in feature domain (i.e., spectral or spectral–spatial features) with a pixelwise manner, which ignores the spatial uncertainty. On the other hand, traditional batch-mode AL methods rarely considered spatial homogeneity, since they selected a batch of samples from the candidates, which will induce redundancy unavoidably. To overcome these issues, we first propose an enhanced uncertainty measure considering the neighborhood information. We then propose to use simple linear iterative clustering for generating superpixels, where the selected batch samples are constrained to be from different superpixels, which improves the diversity of the selected samples. The experimental results with two popular hyperspectral data sets indicate that the proposed methods can significantly improve the classification accuracy compared with the traditional methods.
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    IEEE Geoscience and Remote Sensing Letters Institutional Listings (2018)
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    Theoretical Modeling of 4.3 μm Mid-Infrared Lasing in Dy3+-Doped Chalcogenide Fiber Lasers (2018)
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    Publication Date: 2018-02-28
    Description: We theoretically investigate a Dy 3+ -doped chalcogenide fiber lasers operating at 4.3 μ m based on the rate equations and propagation equations. The two main pump bands for 1319 and 1707 nm corresponding to the 6 H 15/2 → 6 H 9/2 and 6 H 15/2 → 6 H 13/2 transitions are discussed in detail. The predicted maximum slope efficiencies are determined to be ∼7.8% and ∼15.1% for the two pumping wavelength, respectively. Besides, the variety of laser performance has been systematically analyzed when the pump configurations, fiber length, fiber loss are varied. This numerical analysis might be useful to explore the 4.3 μ m lasing operation for the mid-infrared chalcogenide fiber lasers in future.
    Electronic ISSN: 1943-0655
    Topics: Electrical Engineering, Measurement and Control Technology , Physics
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    Features of Thermally Induced Depolarization in Magneto-Active Media With Negative Optical Anisotropy Parameter (2018)
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    Publication Date: 2018-02-28
    Description: The dependence of thermally induced depolarization on crystallographic axis orientation in crystal materials with negative optical anisotropy parameter in the presence of Faraday rotation has been analyzed. The orientation at which thermally induced depolarization is minimal has been found. The contributions to thermally induced depolarization of linear birefringence caused by photoelastic effect, of the temperature dependence of Verdet constant, and of the temperature dependence of optical anisotropy parameter are assessed. Three magneto-optical materials with negative optical anisotropy parameter have been compared with a currently widely used TGG crystal from the viewpoint of their prospective application in Faraday devices for powerful laser radiation. It has been demonstrated that these magneto-optical materials are undoubtedly superior to a TGG crystal.
    Print ISSN: 0018-9197
    Electronic ISSN: 1558-1713
    Topics: Electrical Engineering, Measurement and Control Technology
    Published by Institute of Electrical and Electronics Engineers (IEEE) on behalf of The IEEE Photonics Society.
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    IEEE COMPUTER SOCIETY: Be at the Center of It All (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-28
    Description: Advertisement, IEEE.
    Print ISSN: 0018-9162
    Electronic ISSN: 1558-0814
    Topics: Computer Science
    Published by Institute of Electrical and Electronics Engineers (IEEE) on behalf of The IEEE Computer Society.
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    Front Cover (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-28
    Description: As systems increase in complexity, so too must the design principles used by software engineers. Also in this issue: The New Killer App for Security: Software Inventory (p. 60) and A Future with Quantum Machine Learning (p. 68).
    Print ISSN: 0018-9162
    Electronic ISSN: 1558-0814
    Topics: Computer Science
    Published by Institute of Electrical and Electronics Engineers (IEEE) on behalf of The IEEE Computer Society.
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    Table of Contents (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-28
    Description: Presents the table of contents for this issue of the publication.
    Print ISSN: 0018-9162
    Electronic ISSN: 1558-0814
    Topics: Computer Science
    Published by Institute of Electrical and Electronics Engineers (IEEE) on behalf of The IEEE Computer Society.
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  • 81
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    Masthead (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-28
    Description: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
    Print ISSN: 0018-9162
    Electronic ISSN: 1558-0814
    Topics: Computer Science
    Published by Institute of Electrical and Electronics Engineers (IEEE) on behalf of The IEEE Computer Society.
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  • 82
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    Highlights Society Magazines (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-28
    Description: A summary of articles recently published in IEEE Computer Society magazines.
    Print ISSN: 0018-9162
    Electronic ISSN: 1558-0814
    Topics: Computer Science
    Published by Institute of Electrical and Electronics Engineers (IEEE) on behalf of The IEEE Computer Society.
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  • 83
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    50 & 25 Years Ago (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-28
    Description: A summary of articles published in Computer 50 and 25 years ago. Plus a comic strip by Ergun Akleman, “Computing through Time.”
    Print ISSN: 0018-9162
    Electronic ISSN: 1558-0814
    Topics: Computer Science
    Published by Institute of Electrical and Electronics Engineers (IEEE) on behalf of The IEEE Computer Society.
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  • 84
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    Software Engineering (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-28
    Description: This theme issue includes three articles that explore various aspects of engineering complex systems that involve software. As these systems increase in complexity, so too must the design principles employed.
    Print ISSN: 0018-9162
    Electronic ISSN: 1558-0814
    Topics: Computer Science
    Published by Institute of Electrical and Electronics Engineers (IEEE) on behalf of The IEEE Computer Society.
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    Towards Energy-Efficient Wireless Networking in the Big Data Era: A Survey (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-28
    Description: With the proliferation of wireless devices, wireless networks in various forms have become global information infrastructure and an important part of our daily life, which, at the same time, incur fast escalations of