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FedGiA: An Efficient Hybrid Algorithm for Federated Learning

IEEE TRANSACTIONS ON signal processing 2023年 71卷 1493-1508页

作者： Zhou, Shenglong Li, Geoffrey Ye Beijing Jiaotong Univ Sch Math & Stat Beijing 100044 Peoples R China Imperial Coll London Dept Elect & Elect Engn ITP Lab London SW7 2AZ England

Federated learning has shown its advances recently but is still facing many challenges, such as how algorithms save communication resources and reduce computational costs, and whether they converge. To address these critical issues, we propose a hybrid federated learning algorithm (FedGiA) that combines the gradient descent and the inexact alternating direction method of multipliers. The proposed algorithm is more communication- and computation-efficient than several state-of-the-art algorithms theoretically and numerically. Moreover, it also converges globally under mild conditions.

关键词： signal processing algorithms Convergence Convex functions Servers Radio frequency Federated learning Optimization gradient descent inexact ADMM communication-efficiency computational efficiency global convergence

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Sleep signal Analysis for Early Detection of Alzheimer's Disease and Related Dementia (ADRD)

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2023年第5期27卷 2264-2275页

作者： Khosroazad, Somayeh Abedi, Ali Hayes, Marie J. Univ Maine Elect & Comp Engn Orono ME 04469 USA

Objective: Alzheimer's Disease and Related Dementia (ADRD) is growing at alarming rates, putting research and development of diagnostic methods at the forefront of the biomedical research community. Sleep disorder has been proposed as an early sign of Mild Cognitive Impairment (MCI) in Alzheimer's disease. Although several clinical studies have been conducted to assess sleep and association with early MCI, reliable and efficient algorithms to detect MCI in home-based sleep studies are needed in order to address both healthcare costs and patient discomfort in hospital/lab-based sleep studies. Methods: In this paper, an innovative MCI detection method is proposed using an overnight recording of movements associated with sleep combined with advanced signal processing and artificial intelligence. A new diagnostic parameter is introduced which is extracted from the correlation between high frequency, sleep-related movements and respiratory changes during sleep. The newly defined parameter, Time-Lag (TL), is proposed as a distinguishing criterion that indicates movement stimulation of brainstem respiratory regulation that may modulate hypoxemia risk during sleep and serve as an effective parameter for early detection of MCI in ADRD. By implementing Neural Networks (NN) and Kernel algorithms with choosing TL as the principle component in MCI detection, high sensitivity (86.75% for NN and 65% for Kernel method), specificity (89.25% and 100%), and accuracy (88% and 82.5%) have been achieved.

关键词： Sleep Diseases Alzheimer's disease Feature extraction signal processing algorithms Electroencephalography Sleep apnea Alzheimer's Disease kernel estimation mild cognitive impairment neural network respiratory changes sleep movements

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Robust Low Sidelobe Synthesis for Arbitrary Arrays Based on Gradient Ascent With Min-Oracle

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2023年第1期71卷 450-462页

作者： Shen, Zhubin Li, Jianfeng Wu, Qihui Nanjing Univ Aeronaut & Astronaut Key Lab Dynam Cognit Syst Electromagnet Spectrum S Minist Ind & Informat Technol Nanjing 211106 Peoples R China

This article studies the robust pattern synthesis of a narrow main beam and low sidelobes of arbitrary arrays, which represents a classical but challenging problem for array antennas. In addition, pattern synthesis for a given sidelobe envelope is discussed. The synthesis problem is formulated as a nonconvex-nonconcave minimax problem with nonconvex constraints, which avoids the design of the optimal desired pattern and ensures the robustness of pattern synthesis for arbitrary arrays by nonconvex constraints. An algorithm named gradient ascent with min-oracle (GAmin) is proposed to address the considered minimax problem. Furthermore, the majorization-minimization algorithmic framework is used to reduce computational complexity. It is shown that for arbitrary linear and planar arrays, the proposed GAmin-based pattern synthesis method can perform well for pattern synthesis both without and with a given sidelobe envelope. A variety of numerical examples are provided to demonstrate the superior performance of the proposed method.

关键词： signal processing algorithms Linear antenna arrays Array signal processing Upper bound Transmission line matrix methods Robustness Computational complexity Arbitrary arrays array pattern synthesis gradient ascent with min-oracle (GAmin) minimax problem

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Digital Time-Domain Predistortion of Linear Periodically Time-Varying Effects and Its Application to a 100-GS/s Time-Interleaved CMOS DAC

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2023年第12期70卷 5098-5109页

作者： Widmann, Daniel Groezing, Markus Berroth, Manfred Univ Stuttgart Inst Elect & Opt Commun Engn D-70569 Stuttgart Germany

In high-speed data transmission systems, CMOS digital-to-analog converters (DACs) at very high sampling rates are essential components allowing predistortion of the transmitted signal. Next to compensation of bandwidth limitations of the DAC and assembly, predistortion is also able to compensate other effects originating from time interleaving or from artifacts of the DAC circuit concept. Especially, linear, periodically time-varying (LPTV) effects are one source of impairments in the output signal causing spurious components and their compensation is of particular importance for time-interleaved systems. In this work, a universal predistortion concept including system identification based on the system's reactions to unit impulses at all corresponding positions in a period is presented and applied to a 28-nm CMOS DAC with time interleaving by an analog multiplexer at sampling rates up to 100 GS/s. Measurements of this DAC reveal LPTV effects with a period of 32 affecting the analog output signal that may be attributed to time interleaving as well as architecture. A theoretical problem description is given and sources of LPTV distortions are identified. Starting from the theoretical description, a universal N:1 predistortion method in time domain is deduced based on a simple system identification method. Measurements of single-tone signals reveal a signal-to-noise and distortion ratio improvement up to 13.5 dB and around 7 dB for a broadband, two-level pulse-amplitude modulated signal at 100 GS/s compared to a linear, time-invariant filter. The proposed predistortion concept is a universal method to compensate for any N:1 LPTV artifacts with significant reduction of LPTV distortions and can be translated to a common transversal filter structure.

关键词： Analog-digital integrated circuits arbitrary waveform generator CMOS integrated circuits digital-analog conversion digital-to-analog converter digital filters mixed-signal integrated circuits predistortion pulse-amplitude modulation signal processing algorithms time-varying channels time-varying systems transmitters

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Imaging With Equivariant Deep Learning: From unrolled network design to fully unsupervised learning

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IEEE signal processing MAGAZINE 2023年第1期40卷 134-147页

作者： Chen, Dongdong Davies, Mike Ehrhardt, Matthias J. Schonlieb, Carola-Bibiane Sherry, Ferdia Tachella, Julian Univ Edinburgh Computat Sensing & Machine Learning Edinburgh EH8 9YL Midlothian Scotland Univ Edinburgh Signal & Image Proc Edinburgh EH9 3JL Midlothian Scotland European Assoc Signal Proc Sesimbra Portugal Royal Acad Engn London England Univ Bath Bath BA2 7AY Avon England Univ Cambridge Cambridge CB3 0WA England Univ Cambridge Dept Appl Math & Theoret Phys Cambridge CB3 0WA England Ecole Normale Super Lyon CNRS F-69364 Lyon France

From early image processing to modern computational imaging, successful models and algorithms have relied on a fundamental property of natural signals: symmetry. Here symmetry refers to the invariance property of signal sets to transformations, such as translation, rotation, or scaling. Symmetry can also be incorporated into deep neural networks (DNNs) in the form of equivariance, allowing for more data-efficient learning. While there have been important advances in the design of end-to-end equivariant networks for image classification in recent years, computational imaging introduces unique challenges for equivariant network solutions since we typically only observe the image through some noisy ill-conditioned forward operator that itself may not be equivariant. We review the emerging field of equivariant imaging (EI) and show how it can provide improved generalization and new imaging opportunities. Along the way, we show the interplay between the acquisition physics and group actions and links to iterative reconstruction, blind compressed sensing, and self-supervised learning.

关键词： Deep learning Neural networks Imaging signal processing algorithms Self-supervised learning Noise measurement Iterative methods

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An Unassisted Super-Resolution Satellite Navigation Receiver Using GPS L5 signals

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第6期59卷 7666-7680页

作者： Luo, Yiran Hsu, Li-Ta Tsang, Chin Lok El-Sheimy, Naser Univ Calgary Dept Geomat Engn Calgary T2N 1N4 AB Canada Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hong Kong Peoples R China

Many existing works use external devices or information, e.g., real-world maps, reference receivers, dead reckoning (DR) sensors, and multiantennas, to compensate for signal dynamics and mitigate multipath interference in global navigation satellite system (GNSS) signal reception. This article proposes a standalone global positioning system L5 receiver using the fractional Fourier transform (FRFT) in an over-one-second superlong coherent integration (S-LCI) correlating process. Then, a code phase DR method is presented in the baseband processing by leveraging the super-resolution average Doppler rate and instantaneous Doppler frequency from the S-LCI-based FRFT to enhance the code discriminating process. A software-defined radio prototype of the unassisted super-resolution receiver (U-SRR) based on the S-LCI, FRFT, and code phase DR is developed where the pilot-channel L5 signal serves for positioning. The transmitting time splicing comes from the respective frame synchronization and code phase tracking in the data and pilot channels. Real-world experiments verify the proposed U-SRR in an extremely dense urban area. Compared to the legacy L1 coarse/acquisition (C/A) signals from the u-blox ZED-F9P receiver, the code-based positioning precision is increased by 86.8% with 100% measurement availability.

关键词： Codes Global navigation satellite system Baseband Receivers Doppler effect Global Positioning System signal processing algorithms Code phase dead reckoning (DR) cross-ambiguity function (CAF) fractional Fourier transform (FRFT) global positioning system (GPS) L5 pilot channel multipath mitigation superlong coherent integration (S-LCI) super-resolution (SR) signal processing

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Multi-TDOA Estimation and Source Direct Position Determination Based on Parallel Factor Analysis

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IEEE INTERNET OF THINGS JOURNAL 2023年第8期10卷 7405-7415页

作者： Li, Jianfeng Li, Yingying Jiang, Hang Zhang, Xiaofei Wu, Qihui Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210016 Peoples R China

In this article, source localization exploiting time difference of arrival (TDOA) information is discussed, and a parallel factor (PARAFAC) analysis-based method for multi-TDOA estimation and direct position determination (DPD) is proposed. First, signals from the radiation source are received by multiple antennas through synchronous sensing. Then, the data from multiple antennas is fused by extracting the time-delay matrix to construct a PARAFAC model. Thereafter, the time-delay matrix is obtained by fast iterative decomposition using the trilinear alternating least square (TALS) algorithm. In the case of no multipath or weak multipath, where the typical scenario is the antennas being deployed on the airborne platform, the multi-TDOA estimation can be obtained simultaneously according to the time-delay matrix to improve the processing speed. In the case of multipath, such as the antennas being located on the ground, the DPD cost function directly related to the source position can be established based on the time-delay matrix, and the source position estimation can be achieved through grid search. Compared with the other DPD methods, the proposed DPD method has better positioning performance and lower complexity. The effectiveness and superiority of the proposed methods are verified by both simulations and actual scenario tests.

关键词： Estimation Location awareness Delay effects signal processing algorithms Matrix decomposition Cost function Antennas Direct position determination (DPD) multipath propagation parallel factor (PARAFAC) time-delay difference estimation

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Unique Key-Based Secured Transmission Protocol for Rolling Shutter Visible Light Communications

IEEE SENSORS LETTERS

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IEEE SENSORS LETTERS 2024年第12期8卷 1页

作者： Pandey, Puneet Joshi, Sandeep Birla Inst Technol & Sci Pilani Dept Elect & Elect Engn Pilani 333031 India

In this letter, we propose a novel secure transmission protocol for rolling shutter visible light communication (RS-VLC) that leverages unique cryptographic key generation through linear feedback shift registers and bad pixel (BP) mapping. This method ensures data integrity and confidentiality by dynamically encoding data based on device-specific BP maps. Our results demonstrate a substantial improvement in bit error rate performance while reducing the complexity of key generation, encryption, and decryption to the order of N, where N is the number of bits in the key. The lower computational complexity of the proposed protocol makes it suitable for low-power internet-of-things devices and mobile applications. We also provide a comparative analysis with demonstrating the enhanced security and lightweight nature of the proposed approach.

关键词： Protocols Visible light communication Transmitters signal to noise ratio Image sensors Bit error rate signal processing algorithms Polynomials Cameras Sensor phenomena and characterization Sensor applications camera cryptography pixel security visible light communication (VLC)

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Distributed Inference Over Linear Models Using Alternating Gaussian Belief Propagation

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IEEE INTERNET OF THINGS JOURNAL 2023年第22期10卷 19949-19963页

作者： Cosovic, Mirsad Miskovic, Dragisa Delalic, Muhamed Raca, Darijo Vukobratovic, Dejan Univ Sarajevo Fac Elect Engn Sarajevo 71000 Bosnia & Herceg Inst Artificial Intelligence Res & Dev Serbia Novi Sad 21000 Serbia Univ Novi Sad Fac Tech Sci Novi Sad 21000 Serbia

We consider the problem of maximum-likelihood estimation in linear models represented by factor graphs and solved via the Gaussian belief propagation algorithm. Motivated by massive Internet of Things (IoT) networks and edge computing, we set the above problem in a clustered scenario, where the factor graph is divided into clusters and assigned for processing in a distributed fashion across a number of edge computing nodes. For these scenarios, we show that an alternating Gaussian belief propagation (AGBP) algorithm that alternates between inter- and intracluster iterations, demonstrates superior performance in terms of convergence properties compared to the existing solutions in the literature. We present a comprehensive framework and introduce appropriate metrics to analyze the AGBP algorithm across a wide range of linear models characterized by symmetric and nonsymmetric, square, and rectangular matrices. We extend the analysis to the case of dynamic linear models by introducing the dynamic arrival of new data over time. Using a combination of analytical and extensive numerical results, we show the efficiency and scalability of the AGBP algorithm, making it a suitable solution for large-scale inference in massive IoT networks.

关键词： Heuristic algorithms Inference algorithms Maximum likelihood estimation Internet of Things Estimation signal processing algorithms Computational modeling Distributed systems factor graphs Gaussian belief propagation (GBP) Internet of Things (IoT) networks linear models

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Faulty-Feeder Detection for Single Phase-to-Ground Faults in Distribution Networks Based on Waveform Encoding and Waveform Segmentation

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IEEE TRANSACTIONS ON SMART GRID 2023年第5期14卷 4100-4115页

作者： Yuan, Jiawei Jiao, Zaibin Xi An Jiao Tong Univ Sch Elect Engn Xian 710049 Peoples R China

Faulty feeder detection helps ensure the stability and safety of power grids after single-phase-to-ground (SPG) faults occur in distribution networks. The existing detection techniques identify the faulty feeder by extracting representative fault features, while they fail to show reliable detection performance due to variable fault conditions and complex fault transients. To address these drawbacks, this paper proposes a method based on waveform encoding and waveform segmentation. Since the waveforms have complete fault features in fault signals, it is suitable to recognize the signals on the waveform scale, rather than extracting and fusing several fault features. Firstly, raw sampled zero-sequence voltage (ZSV) and zero-sequence current (ZSC) are processed by using the proposed encoding method, and the ZSV-ZSC image can be generated quickly. Secondly, to learn and understand the waveforms of ZSV and ZSC, a two-path fully convolutional network (FCN) is established to make pixel-wise prediction on the ZSV-ZSC image. Finally, the fault degree of each feeder can be estimated based on the segmented waveform in the ZSV-ZSC image. The performance evaluation is implemented in the NVIDIA Jetson Xavier embedded platform, and the experimental results demonstrate that the proposed method can identify the faulty feeder with high accuracy within 28 ms.

关键词： Feature extraction Transient analysis Image segmentation Image coding Fault diagnosis Encoding signal processing algorithms Faulty-feeder detection NVIDIA Jetson Xavier waveform encoding waveform segmentation waveform understanding

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