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Primal-Dual Fixed Point algorithms Based on Adapted Metric for Distributed Optimization With Bounded Communication Delays

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第4期70卷 2212-2227页

作者： Su, Yanxu Wang, Qingling Sun, Changyin Anhui Univ Sch Artificial Intelligence Hefei 230601 Peoples R China Minist Educ Engn Res Ctr Autonomous Unmanned Syst Technol Hefei 230601 Peoples R China Southeast Univ Sch Automat Nanjing 210096 Peoples R China

This article concentrates on distributed optimization over networks with communication delays. Each subsystem in the network performs its local updates by using the information received from its neighbors, be it possibly outdated. The communication delays with respect to different neighbors are assumed to be arbitrary but bounded. The objective function consists of a twice differentiable coupling term and an aggregated private term. The private function of each subsystem is the sum of two possibly nonsmooth terms, one of which is composed of a linear mapping. We propose a primal-dual fixed point algorithm framework based on the adapted metric for two scenarios where the coupling among subsystems is only enacted by the global objective function and enforced both by the global objective function and the linear mapping. The adapted metric method utilizes an adequate quadratic approximation of the global objective function as the updating step-size to exploit the second-order information. Under some mild assumptions, the convergence of the proposed algorithms is rigorously analyzed based on the quasi-Fej & eacute;r monotonicity. The numerical simulation verifies the correctness and effectiveness of the proposed algorithms.

关键词： Delays Optimization Couplings Measurement Convergence Linear programming Vectors Synchronization Symmetric matrices signal processing algorithms Adapted metric methods asynchronous algorithms distributed optimization primal-dual fixed point (PDFP) algorithms

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Modern FDA-LFM Radar System and Efficient Range-Angle-Doppler processing Algorithm

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第2期61卷 3577-3588页

作者： Kim, Geon U. Kim, Jeong Phill Chung Ang Univ Dept Elect & Elect Engn Seoul South Korea

A radar system is proposed that combines frequency diverse array (FDA) and linear frequency modulation (LFM) signals to provide efficient and high-range resolution characteristics. The combined signal could be generated by using a multichannel direct digital synthesizer. Previous studies of the FDA-LFM system based on the delay line are reviewed. Some errors in the signal expressions are corrected and the associated characteristics are revised. For the proposed FDA-LFM radar system, the signal formulations and the signal processing algorithm for range, angle, and Doppler are detailed. The range-time information can initially be extracted by applying the short-time Fourier transform to the time-domain data. In addition, the target angle is extracted from the obtained range-time data through the coordinate transformation. The Doppler information is then extracted by applying the fast Fourier transform to the successive range-angle data. Numerical simulations are performed and the obtained results fully validate the proposed FDA-LFM radar configuration and the systematic and efficient signal processing algorithm. Further Monte Carlo simulations are performed to analyze the estimation accuracy of the proposed radar system.

关键词： signal resolution signal processing algorithms Radar signal processing Frequency diversity Chirp modulation Systematics Frequency modulation Doppler radar Time-frequency analysis Synthesizers Beat frequency coordinate transform direct digital synthesizer (DDS) Doppler processing frequency diverse array (FDA) linear frequency modulation (LFM) range-angle processing short-time Fourier transform (STFT)

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USSD: Unsupervised Sleep Spindle Detector

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IEEE ACCESS 2025年 13卷 18644-18659页

作者： Ramirez, Edgardo Estevez, Pablo A. Adams, Martin D. Perez, Claudio A. Garrido Gonzalez, Marcelo Peirano, Patricio Univ Chile Dept Elect Engn Santiago 8330015 Chile Millennium Inst Intelligent Healthcare Engn Santiago 1025000 Chile Ctr Intervent Med Precis & Adv Cellular Therapy IMPACT Santiago 1025000 Chile Univ Chile Inst Nutr & Food Technol Sleep Lab Santiago 8330015 Chile Univ Diego Portales UDP Fac Educ Santiago 8370109 Chile

Sleep spindles (SSs) appear in electroencephalogram (EEG) recordings during sleep stage N2, and they are usually detected through visual inspection by an expert. Labeling SSs in large datasets is time-consuming and depends on the expert criteria. In this work, we propose an unsupervised SS detector based on dictionary learning called the Unsupervised Sleep Spindle Detector (USSD). The proposed detector learns prototype SSs of different lengths (called atoms). An unsupervised adaptive threshold method based on the distribution of the automatically detected SS lengths is developed, which allows the adaptation of the USSD algorithm to different datasets in an unsupervised way. For each detection, the USSD estimates the probability of being an SS. The USSD performances on the labeled MASS-SS2 and INTA-UCH datasets yield F1-scores of $0.72 \pm 0.02$ and $0.72 \pm 0.04$ , respectively. The USSD outperforms the A7 and LUNA detectors, which are traditional unsupervised models. Next, we fine-tune the resulting USSD model with 20% of the labeled MASS-SS2 and INTA-UCH datasets, achieving F1 scores of $0.78 \pm 0.06$ and $0.75 \pm 0.05$ , respectively. In addition, the SSs detected by USSD on the unlabeled CAP dataset are used to pre-train a supervised deep learning method, which after fine-tuning with 20% of the MODA dataset, reaches an F1-score of $0.81 \pm 0.02$ .

关键词： Sleep Electroencephalography signal processing algorithms Brain modeling Detectors Dictionaries Matching pursuit algorithms Machine learning Recording Filtering algorithms EEG sleep spindle unsupervised learning dictionary learning

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Cognitive Radar Subpulses Waveform Design via Online Greedy Search

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 1122-1137页

作者： Wang, Yi Yu, Xianxiang Yang, Jing Cui, Guolong Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Peoples R China

This paper proposes a cognitive radar subpulse waveform design (CRSWD) approach including a smart acquirement process of some a prior information and multi-sequences optimization algorithm against multiple mainlobe interrupted sampling repeater jamming (ISRJ). Specifically, the radar first interacts with the environment on a pulse-by-pulse basis to quest for radar survival window (RSW) dynamically. Hence, a novel RSW searching method incorporating the greedy strategy is devised, where a reasonable value function is defined for measuring the anti-jamming and detection capabilities. Based on the estimated RSW information, orthogonal shielding and probing subpulses are strategically positioned for confusing the jammer and then detecting targets, respectively. To this respect, a non-convex optimization problem based on the peak to-sidelobe level (PSL) criterion and peak-to-average ratio (PAR) constraints is formulated for designing orthogonal probing and shielding waveforms with optimized RSW knowledge. A fast iterative methodology based on block coordinate descent (BCD) and majorize-minimization (MM) framework is proposed with the convergence performance ensured. Numerical simulations demonstrate that the proposed framework can effectively acquire jamming-resistant RSW in the presence of multiple targets and ISRJ with different parameters and achieve reliable detection outperforming some counterparts. Experiments are conducted to further verify its engineering feasibility.

关键词： Jamming Radar Optimization Resource management Repeaters Cognitive radar Training Noise Vectors signal processing algorithms Cognitive radar (CR) interrupted sampling repeater jamming (ISRJ) radar survival window (RSW) greedy strategy majorize-minimization (MM) waveform design

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Distributed Solvers for Network Linear Equations With Scalarized Compression

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第4期70卷 2644-2651页

作者： Wang, Lei Ren, Zihao Yuan, Deming Shi, Guodong Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Zhejiang Peoples R China Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China Univ Sydney Australia Ctr Robot Sydney NSW 2006 Australia

Distributed computing is fundamental to multiagent systems, with solving distributed linear equations as a typical example. In this article, we study distributed solvers for network linear equations over a network with node-to-node communication messages compressed as scalar values. Our key idea lies in a dimension compression scheme that includes a dimension-compressing vector and a data unfolding step. The compression vector applies to individual node states as an inner product to generate a real-valued message for node communication. In the unfolding step, such scalar message is then plotted along the subspace generated by the compression vector for the local computations. We first present a compressed consensus flow that relies only on such scalarized communication, and show that linear convergence can be achieved with well excited signals for the compression vector. We then employ such a compressed consensus flow as a fundamental consensus subroutine to develop distributed continuous-time and discrete-time solvers for network linear equations, and prove their linear convergence properties under scalar node communications. With scalar communications, a direct benefit would be the reduced node-to-node communication channel burden for distributed computing. Numerical examples are presented to illustrate the effectiveness of the established theoretical results.

关键词： Vectors Convergence signal processing algorithms Heuristic algorithms Electronic mail Consensus algorithm Communication channels Distributed algorithms Computational efficiency Australia Compression consensus scalar communication network linear equation

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Radar-PIM: Developing IoT Processors Utilizing processing-in-Memory Architecture for Ultrawideband-Radar-Based Respiration Detection

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IEEE INTERNET OF THINGS JOURNAL 2025年第1期12卷 515-530页

作者： Lee, Kyeongwon Jeon, Sangmin Lee, Kangju Lee, Woojoo Pedram, Massoud Chung Ang Univ Dept Intelligent Semicond Engn Seoul 06974 South Korea Univ Southern Calif Dept Elect & Comp Engn Los Angeles CA 90007 USA

The adoption of ultrawideband (UWB) radar technology in IoT and healthcare applications for respiration detection is rapidly expanding, opening up a wide array of potential use cases. Despite its burgeoning utility, the integration of UWB radar-based respiration detection in IoT end node devices faces significant challenges due to the memory-intensive nature of these tasks, which strain the capabilities of IoT processors. This article introduces a streamlined UWB radar-based respiration detection application designed for operation on IoT processors, emphasizing that when executed on conventional IoT processors, the limited processing power still results in significant data loss, underscoring the need for enhanced processing solutions. To address these challenges, we propose the adoption of processing-in-memory (PIM) technology and unveil the novel Radar-PIM architecture. This architecture is meticulously engineered to boost the efficiency of respiration detection while ensuring seamless integration with existing embedded processor frameworks. This article extensively describes the Radar-PIM architecture and its operational mechanisms. We further demonstrate its superior performance by implementing and empirically testing a Radar-PIM processor prototype. Next, we present an optimization strategy tailored for designing energy-efficient Radar-PIM processors, specifically adapted for diverse UWB radar-based respiration detection applications. For instance, a Radar-PIM processor prototype, optimized for a particular application, achieved approximately 42% energy savings compared to its unoptimized counterpart and delivered performance nearly three times greater than that of a multicore processor with equivalent power consumption. This demonstrates the transformative potential of our proposed solution in enhancing the capabilities of radar-based respiration detection systems for IoT end nodes.

关键词： Program processors Internet of Things Ultra wideband radar Computer architecture signal processing algorithms Random access memory Prototypes IoT processor impulse radio (IR)-ultrawideband (UWB) radar low-power designs respiration detection RISC-V

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High-Precision UWB TDOA Localization Algorithm Based on UKF-FNN-CHAN-RIC

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Wang, Ershen Wang, Yongkang Zhang, Shuning Xu, Song Chen, Yunhao Wang, Yanwen Yu, Tengli Lei, Hong Shenyang Aerosp Univ Coll Elect Informat Engn Shenyang 110136 Peoples R China Southern Univ Sci & Technol Sch Automat & Intelligent Mfg Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Guangdong Prov Key Lab Fully Actuated Syst Control Shenzhen 518055 Peoples R China Yunnan Key Lab Unmanned Autonomous Syst Kunming 650500 Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Peoples R China Shenyang Aerosp Univ Coll Aeronaut & Astronaut Shenyang 110136 Peoples R China Shenyang Aircraft Design & Res Inst Shenyang 110035 Peoples R China

Ultrawideband (UWB) technology has garnered significant attention due to its extensive applications in indoor positioning. However, its performance is susceptible to interference from environmental factors. In response to the positioning accuracy issues in time difference of arrival (TDOA) scenarios, this article proposes an integrated TDOA-UKF-FNN-Chan-RIC algorithm that incorporates the unscented Kalman filter (UKF), feedforward neural networks (FNNs), Chan's algorithm, and redundant information correction (RIC) to effectively enhance the positioning accuracy and robustness of UWB systems. Initially, by investigating the redundant information within the UWB solution model in TDOA scenarios, the inter-relationship between redundant information and measurement vectors is revealed. Subsequently, a gradient descent error correction algorithm for redundant information suitable for TDOA scenarios is proposed, and UKF and FNN transformations are combined to ensure that the data meet the preconditions for algorithmic application. To address the stability issues of positioning algorithms based on implicit function solutions in TDOA scenarios, Chan's algorithm is employed to convert range difference measurements into range measurements, thereby enhancing computational robustness. Ultimately, the TDOA-UKF-FNN-Chan-RIC algorithm is proposed, and its effectiveness is validated through simulation and practical experiments. The experimental results demonstrate that this algorithm significantly improves positioning accuracy and robustness, achieving millimeter-level fixed-point accuracy and centimeter-level track accuracy in practical experiments.

关键词： Accuracy Ultra wideband technology Robustness Position measurement Distance measurement signal processing algorithms Kalman filters Three-dimensional displays Optimization Filtering Chan feedforward neural network (FNN) redundant information correction (RIC) time difference of arrival (TDOA) unscented Kalman filter (UKF) ultrawideband (UWB) positioning

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IoT-Based System Using IMU Sensor Fusion for Knee Telerehabilitation Monitoring

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IEEE SENSORS JOURNAL 2025年第7期25卷 11906-11914页

作者： El Fezazi, Mohamed Achmamad, Abdelouahad Jbari, Atman Jilbab, Abdelilah Mohammed V Univ Rabat Natl Grad Sch Arts & Crafts ENSAM Elect Syst Sensors & Nanobiotechnol Rabat 10100 Morocco

High costs and clinical limitations restrict access to rehabilitation services, especially in low- and middle-income countries. There is a growing need for affordable, home-based solutions that enable continuous remote monitoring of patient rehabilitation progress. This work proposes an Internet of Thing (IoT)-based system for knee movement monitoring during telerehabilitation. The system comprises wearable inertial measurement units (IMUs) integrated into an IoT architecture. This architecture leverages edge and cloud computing to facilitate remote monitoring and real-time feedback. A sensor fusion algorithm was implemented on the edge to estimate knee joint angle, and a cloud-based application was developed to extract kinematic parameters and assess rehabilitation outcomes. The system was implemented using system-on-chip (SoC) technology, allowing embedded signal processing and wireless communication in a compact and low-power design. Three experimental validation tests were conducted: One hardware test evaluating the performance of the proposed sensor fusion algorithm;goniometer-based static test assessing the impact of environmental interference on system accuracy;dynamic test involving rehabilitation exercises to validate system performance against a gold-standard video-based system in the home context. The results demonstrated that the proposed algorithm achieved an optimal trade-off between accuracy, computational efficiency, and resilience to magnetic distortions. The system showed acceptable accuracy, with an average root mean square error (RMSE) ranging from 3.08 degrees to 6.43 degrees across all exercises. These results are consistent with the current state of the art, highlighting the system's potential for objective and remote monitoring of knee movement in home-based rehabilitation.

关键词： Sensors Knee Logic gates Accuracy Sensor fusion Cloud computing signal processing algorithms Biomedical monitoring Wireless communication Sensor systems Embedded edge-processing inertial sensor fusion Internet of Things (IoT) motion capture (MoCap) system wearable wireless sensor

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Distributed Downlink Precoding for Cell-Free Massive MIMO: A Quasi-Neural Network Approach

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IEEE TRANSACTIONS ON COMMUNICATIONS 2025年第2期73卷 1157-1168页

作者： Dai, Weijie Wang, Rui Liu, Junkai Jiang, Yi Fudan Univ Sch Informat Sci & Technol Dept Commun Sci & Engn Key Lab Informat Sci Electromagnet Waves MoE Shanghai 200438 Peoples R China

This paper proposes a novel downlink precoding method for a cell-free massive multiple-input multiple-output (CF-mMIMO) network, requiring no channel state information sharing between the access points via fronthaul links. By drawing analogies between a CF-mMIMO network and an artificial neural network, the proposed algorithm borrows the idea of backpropagation to train the precoders and the combiners through over-the-air ping-pong signaling between the access points and user equipments. It utilizes manifolds optimization to meet the per-AP power constraint and is named as distributed quasi-neural network precoding on manifold (DQNPM). The DQNPM algorithm can accommodate a large category of objective functions for fully distributed implementation. Numerical simulations show that our method outperforms the state-of-the-art approaches, and is robust against pilot contamination.

关键词： Precoding Downlink Manifolds Artificial neural networks Transmitting antennas signal to noise ratio signal processing algorithms Cell-free massive MIMO distributed precoding quasi-neural network Riemannian manifold

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Optimizing Optical Engine Features for Subsea Cable Systems

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2025年第4期43卷 1662-1670页

作者： Varughese, Siddharth Bennett, Geoff Lavery, Domanic Sun, Han Mertz, Pierre Infinera Corp San Jose MD 20701 USA Infinera Canada Inc Ottawa ON K2K 2X3 Canada

We introduce a taxonomy for subsea optical fiber cables as used for intercontinental telecommunications systems. With reference to this new classification, we describe how the design of optical engines has both influenced, and been influenced by, the development of subsea cables. We outline the pivotal role coherent transceivers have played in the development of high-capacity subsea cables, specifically with reference to advances in digital signal processing and coded modulation, and we highlight the opportunities and challenges of upgrading legacy systems with state-of-the-art coherent transceivers. In this work, we also look to future cable designs and ask how the availability of fundamentally new fiber types (specifically multicore and hollow core fibers) will have an influence on subsea cables and, once again, change transceiver requirements.

关键词： Transceivers Optical fiber cables Underwater cables Optical fiber dispersion Dispersion Optical fibers Optical fiber amplifiers Optical fiber networks optical receivers optical transmitters signal processing algorithms

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