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Low-Power Real-Time Seizure Monitoring Using AI-Assisted Sonification of Neonatal EEG

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2025年第1期13卷 80-89页

作者： Nguyen, Tien Daly, Aengus Gomez-Quintana, Sergi O'Sullivan, Feargal Temko, Andriy Popovici, Emanuel Univ Coll Cork Dept Elect & Elect Engn Coll Rd Cork T12 K8AF Ireland

Detecting seizures in neonates requires continuous electroencephalography (EEG) monitoring, a costly process that demands trained experts. Although recent advancements in machine learning offer promising solutions for automated seizure detection, the opaque nature of these algorithms poses significant challenges to their adoption in healthcare settings. A prior study demonstrated that integrating machine learning with sonification-an interpretation method that converts bio-signals into sound-can mitigate the black-box problem while enhancing seizure detection performance. This AI-assisted sonification algorithm can provide a valuable complementary tool in seizure monitoring besides the traditional visualization method. A low-power and affordable implementation of the algorithm is presented in this study using a microcontroller. To improve its practicality, we also introduce a real-time design that allows the sonification algorithm to function in parallel with data acquisition. The system consumes 12 mW in average, making it suitable for a battery-powered device.

关键词： Electroencephalography Sonification Pediatrics Real-time systems Instruction sets signal processing algorithms Vocoders Monitoring Interpolation Detection algorithms Artificial intelligence biomedical monitoring biomedical signal processing electroencephalography embedded software Edge AI internet of medical things machine learning microprocessors real-time systems sonification signal processing algorithms

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Distributed Multi-Kernel Maximum Correntropy State-Constrained Kalman Filter Under Deception Attacks

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2025年第1期12卷 533-546页

作者： Wang, Guoqing Zhu, Zhaolei Yang, Chunyu Ma, Lei Dai, Wei Chen, Xinkai China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Peoples R China Shibaura Inst Technol Dept Elect & Informat Syst Saitama 3378570 Japan

In this paper, we investigate the distributed robust state estimation of non-Gaussian systems under unknown deception attacks with the imprecise constraint information. Leveraging the advantage of multi-kernel maximum correntropy criterion (MK-MCC) in non-Gaussian signal processing, a novel maximum-a-posterior like utility function (MAP-LUF) is designed inspired by the traditional 2-norm form cost function, where the inaccurate constraint information is taken into consideration. The direct solution of MAP-LUF gives rise to the centralized MK-MCC based state-constrained Kalman filter (C-MKMCSCKF) through fixed point iteration. Subsequently, the corresponding distributed algorithm is obtained by incorporating the consensus average in the computation of sum terms existing in the C-MKMCSCKF algorithm, which enables local information sharing to approximate the centralized estimation accuracy. Furthermore, we also establish the connection between the proposed centralized algorithm and the Banach theorem through dimension extension, and provide the convergence condition. The effectiveness of our proposed algorithms is validated through comparisons with related works in typical target tracking scenarios over sensor network.

关键词： Noise State estimation Vectors signal processing algorithms Cost function Accuracy Target tracking Kernel Kalman filters Convergence Deception attacks distributed Kalman filter multi-kernel correntropy robust state estimation sensor networks state constraint

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A Dual Inexact Nonsmooth Newton Method for Distributed Optimization

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 188-203页

作者： Niu, Dunbiao Hong, Yiguang Song, Enbin Tongji Univ Coll Elect & Informat Engn Dept Control Sci & Engn Natl Key Lab Autonomous Intelligent Unmanned Syst Shanghai 201804 Peoples R China Sichuan Univ Coll Math Chengdu 610065 Sichuan Peoples R China

In this paper, we propose a novel dual inexact nonsmooth Newton (DINN) method for solving a distributed optimization problem, which aims to minimize a sum of cost functions located among agents by communicating only with their neighboring agents over a network. Our method is based on the Lagrange dual of an appropriately formulated primal problem created by introducing local variables for each agent and enforcing a consensus constraint among these variables. Due to the decomposed structure of the dual problem, the DINN method guarantees a superlinear (or even quadratic) convergence rate for both the primal and dual iteration sequences, achieving the same convergence rate as its centralized counterpart. Furthermore, by exploiting the special structure of the dual generalized Hessian, we design a distributed iterative method based on Nesterov's acceleration technique to approximate the dual Newton direction with suitable precision. Moreover, in contrast to existing second-order methods, the DINN method relaxes the requirement for the objective function to be twice continuously differentiable by using the linear Newton approximation of its gradient. This expands the potential applications of distributed Newton methods. Numerical experiments demonstrate that the DINN method outperforms the current state-of-the-art distributed optimization methods.

关键词： Convergence Jacobian matrices Vectors Newton method Linear programming Costs signal processing algorithms Approximation algorithms Distributed databases Cost function Inexact nonsmooth Newton method distributed optimization superlinear convergence rate communication cost

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A Wideband Lateral Multibeam Tiling Synthetic Aperture Method for High-Speed Side-Scan Sonar

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Mao, Weibo Zhao, Dongdong Chen, Peng Liu, Jian Liang, Shihui Shen, Yi Guo, Xinxin Liang, Ronghua Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Zhejiang Peoples R China Chinese Acad Sci Inst Deep Sea Sci & Engn Sanya 572000 Hainan Peoples R China

Side-scan sonar technology plays an important role in seabed topographic exploration and mapping. However, the mainstream matched filtering technique does not fully utilize the bandwidth and the distance matching is susceptible to the Doppler effect, which limits the imaging effect and towing speed. In addition, the synthetic aperture method of radar is difficult to be adapted to sonar due to the transmission characteristics of acoustic waves in water. To address these issues, we propose a lateral multi-beam tiling synthetic aperture algorithm framework for linear array side-scan sonar (LMBT-SAS). The core idea is to improve long-range information acquisition capabilities through beam focusing, and indirectly increase the azimuth pulse repetition rate to serve synthetic aperture processing to increase towing speed. To further improve image performance and towing speed, within this framework, we also proposed some specific technical means. Including short-time Fourier frequency domain beamforming with speed compensation, beam tiling with pseudo pulse repetition, distance-dependent beam weight allocation mechanism and two-stage migration correction. The simulation results and lake test data imaging results demonstrate the effectiveness of the proposed LMBT-SAS algorithm, with a performance of 250 meters of transverse scanning distance and 12 knots of towing speed at 400 KHz signal.

关键词： Synthetic aperture sonar Apertures signal processing algorithms Side-scan sonar Frequency-domain analysis Broadband communication Sonar applications Focusing Azimuth Array signal processing High-speed side-scan sonar multibeam synthetic aperture pseudo-pulse repetition beam tiling short-time Fourier beamforming

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Robust Airborne Radar Clutter Suppression Algorithm via Deep Neural Networks Under Small-Sample Conditions

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IEEE SENSORS JOURNAL 2025年第7期25卷 11587-11600页

作者： Huang, Weijun Wang, Tong Cui, Weichen Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China Beijing Aerosp Automat Control Inst Beijing 100854 Peoples R China Natl Key Lab Sci & Technol Aerosp Intelligence Con Beijing 100854 Peoples R China

Space-time adaptive processing (STAP) is a powerful technique for clutter suppression in airborne radar systems. However, in practical applications, STAP performance is often compromised by the limited availability of independent and identically distributed (i.i.d.) samples, which restricts its clutter suppression capabilities. Inspired by compressed sensing, researchers have extensively investigated sparse recovery-based STAP (SR-STAP) algorithms, which can achieve near-optimal performance under ideal conditions. Yet, these algorithms experience notable performance degradation in the presence of spatial and temporal errors. To address this issue, we propose a deep neural network framework for clutter suppression, cascading a gridless sparse recovery network (GLSRNet) with a generative adversarial network (GAN), ensuring accurate clutter covariance matrix estimation under small-sample conditions with errors. During GAN training, we observed that the traditional binary cross-entropy (BCE) loss function led to significant oscillations in the training loss, impeding effective convergence. To resolve this, we design a new loss function that achieves stable convergence. Extensive experiments validate the effectiveness of the proposed algorithm in clutter suppression.

关键词： Clutter Training Vectors Generative adversarial networks signal processing algorithms Generators Airborne radar Minimization Accuracy Radar clutter Clutter suppression deep unfolding generative adversarial network (GAN) space-time adaptive processing (STAP)

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A New Robot Navigation Algorithm Based on Bi-Directional Collaborative Ant Colony Optimization

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IEEE SENSORS JOURNAL 2025年第8期25卷 14295-14306页

作者： Chen, Xufei Hu, Yaowei Tang, Pingping Zhang, Hui Jin, Jiong Mao, Shiwen Anhui Normal Univ Sch Phys & Elect Informat Wuhu 241002 Peoples R China Nanjing Univ Posts & Telecommun Sch Commun & Informat Engn Nanjing 210003 Peoples R China Swinburne Univ Technol Sch Engn Melbourne Vic 3122 Australia Auburn Univ Dept Elect & Comp Engn Auburn AL 36849 USA

Swarm intelligence has been widely adopted and successfully applied in the field of autonomous robot navigation. Among various swarm intelligence algorithms, ant colony optimization (ACO) has shown significant potential in addressing complex navigation challenges. However, ACO faces challenges, for example, unclear initial search direction, slow convergence, limited ant flexibility, and the need to simplify robot motion control. To address these challenges, this article presents a novel bi-directional collaborative ACO (BC-ACO) algorithm with key innovations. First, the algorithm adopts a bi-directional ant colony with forward and reverse populations, achieving effective route planning through collaborative decision-making. Second, the algorithm employs an adaptive step-size strategy and a stage-based exploration ant quantity adjustment method. These innovations optimize the balance between exploration and exploitation, accelerate convergence, and address the inefficiencies of traditional ACO methods. Additionally, this article improves the heuristic function by integrating a node distance index within the bi-directional ant colony, guiding the transition of ants between nodes and further accelerating convergence. Simulation results show that BC-ACO reduces the computation time by 73.97% and improves the convergence stability by 63.64% compared to standard ACO. Additionally, BC-ACO successfully plans optimal paths, achieving a 20.85% reduction in path length. Further integration of a local quadratic segmented B-spline (LQ-S B-spline) curve results in paths with smooth transitions, reducing robot motion complexity. In summary, BC-ACO achieves fast global convergence, high stability, and short computation time.

关键词： Heuristic algorithms Bidirectional control Convergence Splines (mathematics) Robots Optimization Navigation Indexes signal processing algorithms Collaboration Bi-directional ant colony collaborative decision-making node distance index segmented B-spline stage-based exploration ant quantity adjustment

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Light-Weight Low-Latency Reconfigurable CORDIC Architecture With a New Non-Power-of-2 Angle Set of Microrotations

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2025年

作者： Verma, Anu Das, Bishnu Prasad Indian Inst Technol IIT Roorkee Dept Elect & Commun Engn Roorkee 247667 Uttarakhand India

Trigonometric functions are widely used in various scientific and engineering applications, including digital signal processing. This article proposes a low-complexity low-latency reconfigurable CORDIC (COordinate Rotation Digital Computer) algorithm that computes trigonometric functions, which is based on a novel angle set of microrotations. The CORDIC algorithm is an iterative algorithm that reduces the performance due to the high latency of computation. The proposed pipelined scale-free CORDIC algorithm computes the functions in only four iterations with an optimized angle set of microrotations with high accuracy. It can be configured in two modes with different trajectories 1) rotation-mode;and 2) vectoring-mode for circular and hyperbolic trajectories. On-board FPGA implementation of the proposed architecture consumes 34.95% less register, 48.12% less occupied slices, 19.06% less LUTs, 66.51% less slice-latency product (SLP), 59.29% less LUT-latency product (LLP), and 71.23% less register-latency product (RLP) compared to the best existing state-of-the-art CORDIC architectures. Moreover, the latency of the proposed architecture is 72.34% less than the best of existing designs. Besides, the parasitic-extracted layout simulation results of the proposed design across different word lengths are reported in TSMC 65-nm CMOS technology for the validation of the proposed architecture.

关键词： Computer architecture Accuracy Hardware Trajectory Convergence Taylor series Pipelines Complexity theory Table lookup signal processing algorithms Coordinate rotation digital computer (CORDIC) reconfigurable CORDIC FPGA ASIC

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A Reliable Offloaded processing GNSS Receiver for Low-Power and Low-Accuracy Scenarios

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IEEE SENSORS JOURNAL 2025年第7期25卷 11532-11544页

作者： Su, Yu Jin, Tian Ju, Yi Beihang Univ Sch Elect & Informat Engn Beijing 100083 Peoples R China

Under the energy consumption constraints of mobile IoT terminals, global navigation satellite system (GNSS)-based tracking devices encounter significant challenges. Offloaded processing can offer a viable solution for reducing the energy consumption of GNSS tracking devices. This approach reduces the complexity of signal processing and utilizes remote servers for positioning. The signal tracking stage in the terminal is computationally intensive, and forwarding large amounts of raw data to the processing server consumes much power. To overcome these challenges, an offloaded processing GNSS receiver based on coarse acquisition results is introduced. Additionally, to improve the robustness of the measurements and the reliability of positioning, a fault detection and exclusion (FDE) algorithm based on geometric consistency and factor subset combination is proposed. The vehicle experiments demonstrate that the proposed algorithm can effectively detect and exclude faulty reconstructed pseudorange measurements by using a small number of subsets and can achieve a horizontal positioning accuracy of 33.17 m with 10 ms snapshot data.

关键词： Global navigation satellite system Receivers signal processing algorithms Servers Sensors Satellites Accuracy Internet of Things Energy consumption Codes Factor subset fault detection and exclusion (FDE) geometric consistency global navigation satellite system (GNSS)-based tracking offloaded processing GNSS receiver

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CUSE-TD Algorithm for Precision Detection of Coherent and Uncorrelated signals

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IEEE ACCESS 2025年 13卷 11307-11319页

作者： Dakulagi, Rohini Raushan, Ravi Dutta, Soham Alosaimi, Mohammed Theeb Villagomez-Galindo, Miguel Natl Inst Technol Karnataka Dept Elect & Elect Engn Mangaluru 575025 Karnataka India Manipal Acad Higher Educ Dept Elect & Elect Engn Manipal Inst Technol Manipal 576104 Karnataka India King Saud bin Abdulaziz Univ Hlth Sci Coll Sci & Hlth Profess Riyadh 11426 Saudi Arabia Minist Natl Guard Hlth Affairs King Abdullah Int Med Res Ctr Riyadh 11481 Saudi Arabia Univ Michoacana Mech Engn Fac Morelia 58000 Michoacan Mexico

This paper presents a novel algorithm, Coherent and Uncorrelated Sources Estimation using Tensor Decomposition (CUSE-TD), designed to enhance the accuracy of Direction of Arrival (DOA) estimation in Multiple-Input Multiple-Output (MIMO) systems. The CUSE-TD algorithm addresses the complex challenge of estimating both coherent and uncorrelated sources by leveraging advanced sensor-driven tensor decomposition techniques, with an emphasis on Tucker decomposition. By meticulously analyzing factor matrices derived from sensor data, the algorithm effectively decodes intricate spatiotemporal patterns within the received tensor, offering precise source estimation even in scenarios where the number and nature of the sources are unknown. The paper provides an in-depth assessment of the algorithm's performance, highlighting its computational efficiency and ability to handle diverse sensor-driven scenarios. Extensive simulations in intricate sensing environments demonstrate the algorithm's robustness, affirming its potential to significantly improve DOA estimation accuracy in MIMO systems across a wide range of operational conditions. The usefulness of the CUSE-TD algorithm lies in its applicability CUSE -TD to real-world MIMO systems, particularly in complex environments such as radar, wireless communications, and surveillance systems, where accurate source detection is critical for operational effectiveness. Its adaptability to varying sensor configurations and source conditions makes it a powerful tool for enhancing overall system reliability and performance.

关键词： Direction-of-arrival estimation Tensors Estimation signal processing algorithms Accuracy MIMO radar Array signal processing Matrix decomposition signal resolution Radar CUSE-TD algorithm direction of arrival (DOA) estimation multiple-input multiple-output (MIMO) systems tensor decomposition Tucker decomposition coherent sources uncorrelated sources spatiotemporal sensor data computational complexity sensor arrays

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Low-Complexity Data-Driven Communication Neural Receivers

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IEEE ACCESS 2025年 13卷 9325-9334页

作者： Wu, Qingle Liang, Yuanhui Ng, Benjamin K. Lam, Chan-Tong Ma, Yan Sichuan Univ Sci & Engn Sch Automat & Informat Engn Zigong 643000 Peoples R China Macao Polytech Univ Fac Appl Sci Macau 999078 Peoples R China Artificial Intelligence Key Lab Sichuan Prov Yibin 644000 Peoples R China Beijing Univ Posts & Telecommun BUPT Network Informat Ctr Beijing 100876 Peoples R China

Communication receivers based on deep learning have become a research hotspot in recent years. However, the excessive computational complexity and storage complexity prevent it from being deployed on communication hardware with limited resources. In order to reduce the computational complexity and required storage resources of communication neural receivers based on deep learning, we propose to use candecomp parafac (CP), Tucker, tensor-train (TT) and tensor-ring (TR) decomposition respectively to compress the data-driven deep learning based communication neural receiver. Through compression, the storage resources required by the communication neural receiver are reduced by about half with bit error rate (BER) performance degradation of only 0.75dB to 1.3dB.

关键词： Receivers Tensors Decoding OFDM Hardware Deep learning Symbols signal processing algorithms Computer architecture Wireless communication Tensor-train decomposition tensor-ring decomposition computational complexity communication neural receiver storage complexity

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