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A Power-and-Area-Efficient Channel-Interleaved Neural Signal Processor for Wireless Brain-Computer Interfaces With Unsupervised Spike Sorting

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IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2025年第1期19卷 108-119页

作者： Hu, Zichen Zhou, Zhining Lyu, Hongming ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Shanghai 200050 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China ShanghaiTech Univ State Key Lab Adv Med Mat & Devices Shanghai 201210 Peoples R China Shanghai Engn Res Ctr Energy Efficient & Custom AI Shanghai 201210 Peoples R China

Next generation of wireless brain-computer-interface (BCI) devices require dedicated neural signal processors (NSPs) to extract key neurological information while operating within given power consumption and transmission bandwidth limits. Spike detection and clustering are important signal processing steps in neuroscience research and clinical applications. Computational-friendly spike detection and feature extraction algorithms are first systematically evaluated in this work. The nonlinear energy operator (NEO) and the first-and-second-derivative (FSDE) together with the 'perturbed' K-mean clustering achieve the highest accuracy performance. An NSP ASIC is implemented in a channel-interleaved architecture and the folding ratio of 16 leads to the minimum power-and-area product. As the result, the NSP consumes 2-mu W power consumption and occupies 0.0057 mm(2) for each channel in a 65-nm CMOS technology. The proposed system achieves the unsupervised spike classification accuracy of 92% and a data-rate reduction of 98.3%, showing the promise for realizing high-channel-count wireless BCIs.

关键词： Feature extraction Signal processing algorithms Wireless communication clustering algorithms Bandwidth Noise level Hardware Neural signal processing spike detection feature extraction unsupervised clustering spike sorting ASIC channel-interleaving CMOS low-power hardware-efficiency

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Cluster Optimization for Exponential, Right-Triangular, and Uniformly Distributed Data

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IEEE ACCESS 2025年 13卷 36273-36284页

作者： Bulivou, Gabiriele Reddy, Karuna G. Khan, M. G. M. Univ South Pacific Sch Informat Technol Engn Math & Phys Suva Fiji

Cluster analysis aims to categorize data objects into cohesive groups based on their intrinsic characteristics, often modeled by probability distributions. This paper presents a novel Mathematical Programming-Dynamic Programming (MP-DP) clustering method developed by the authors, applied to datasets characterized by exponential, right-triangular, and uniform distributions. The MP-DP technique optimizes cluster partitions by leveraging the probability distributions inherent in the data. We conducted a comparative evaluation to assess the performance of MP-DP against four established clustering methodologies: K-Means, Fuzzy C-Means, expectation-maximization, and Genie++ hierarchical clustering. Results from extensive simulations and real-world datasets consistently demonstrate the superior efficacy of MP-DP in achieving optimal clustering outcomes. Specifically, MP-DP excels in handling diverse data distributions and effectively mitigating the effects of noise and uncertainty, thereby enhancing clustering accuracy and reliability. This study highlights the significant advancement offered by MP-DP in clustering research. It underscores the method's potential for applications across various domains, such as healthcare, environmental monitoring, and manufacturing, where robust and efficient data clustering is essential for insightful data analysis and decision-making.

关键词： clustering algorithms Probability distribution Dynamic programming Programming Noise clustering methods Partitioning algorithms Heuristic algorithms Distributed databases Uncertainty Cluster analysis dynamic programming technique mathematical programming problem optimum cluster partitions probability distribution function

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Channel Knowledge Map-Aided Channel Prediction With Measurements-Based Evaluation

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IEEE TRANSACTIONS ON COMMUNICATIONS 2025年第5期73卷 3622-3636页

作者： Wang, Xianling Shi, Yi Wang, Tianci Huang, Yingyujiao Hu, Zeyu Chen, Lin Jiang, Zhiyuan Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China Stevens Inst Technol Dept Elect & Comp Engn Hoboken NJ 07030 USA

Gaining accurate channel state information (CSI) through a low-cost scheme has always been difficult in wireless communication systems. One of the current research directions is to obtain the CSI from the channel knowledge map (CKM) based on the users' location. However, the direct utilization of CSI in CKM is hindered due to the sensitivity of instantaneous CSI to time-varying scattering environments and positioning errors. To address this issue, this paper proposes a channel prediction scheme that combines the CKM with historical user CSI to enhance the beamforming performance in multiple-input multiple-output (MIMO) systems. Specifically, the joint-orthogonal matching pursuit algorithm is used to accurately reconstruct the user channel with high precision using a limited number of pilots, and the multi-path components tracking algorithm is employed to extract the common and independent support sets of paths from the estimated channel and the CKM. Lastly, an adaptive and low-complexity predictor is utilized to obtain the future user CSI. The proposed scheme has been evaluated using multiple measured channel datasets, the results indicate a significant improvement in predicting channel cosine similarity compared to directly using the CSI from CKM and existing schemes.

关键词： Channel estimation Prediction algorithms clustering algorithms Wireless communication Vectors Matching pursuit algorithms Array signal processing Accuracy Time-frequency analysis Predictive models Channel knowledge map channel prediction channel sounding campaign joint-orthogonal matching pursuit MIMO

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Underwater Multiple AUV Cooperative Target Tracking Based on Minimal Reward Participation-Embedded MARL

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第5期24卷 4169-4182页

作者： Zhu, Shengchao Han, Guangjie Lin, Chuan Zhang, Fan Hohai Univ Coll Comp & Software Engn Nanjing 210013 Peoples R China Hohai Univ Key Lab Maritime Intelligent Network Informat Tech Minist Educ Nanjing 210013 Peoples R China Northeastern Univ Software Coll Shenyang 110819 Peoples R China Hohai Univ Coll Informat Sci & Engn Changzhou 213200 Peoples R China

Recently, the rapid advancement of Multi-Agent Reinforcement Learning (MARL) has introduced a new paradigm for intelligent underwater target tracking within Autonomous Underwater Vehicle (AUV) cluster networks, enabling these networks to intelligently collaborate in target tracking. However, the limited scalability of MARL poses significant challenges to the performance of AUV cluster networks in tracking tasks. Specifically, MARL models trained on a fixed agents lose their effectiveness when the agent count changes, underscoring the critical need to enhance MARL's scalability to accommodate an arbitrary number of agents. This paper addresses the pressing issue of MARL's scalability in the context of AUV cluster network-based target tracking. Specifically, we propose an Elastic Software-Defined Multi-Agent Reinforcement Learning (ESD-MARL) architecture to enhance the scalability of AUV cluster networks. Moreover, we propose an Incremental Multi-Agent Reinforcement Learning algorithm based on Minimal Reward Participation (IMARL-MRP) that allows for the expansion of the agents without retraining. By integrating the ESD-MARL with the IMARL-MRP, we propose an elastic underwater target tracking scheme, achieving high-performance target tracking with enhanced scalability. Evaluation results demonstrate that the proposed approach effectively enhances the scalability of MARL, enabling the arbitrary expansion of the AUV cluster network, thus supporting scalable and efficient underwater target tracking.

关键词： Scalability Target tracking Computer architecture clustering algorithms Reinforcement learning Training Mobile computing Network architecture Mathematical models Heuristic algorithms Multi-agent reinforcement learning autonomous underwater vehicle underwater target tracking software-defined networking

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Dual AAV Cluster-Assisted Maritime Physical-Layer Secure Communications via Collaborative Beamforming

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IEEE INTERNET OF THINGS JOURNAL 2025年第9期12卷 12589-12607页

作者： Huang, Jiawei Wang, Aimin Sun, Geng Li, Jiahui Wang, Jiacheng Du, Hongyang Niyato, Dusit Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Nanyang Technol Univ Coll Comp & Data Sci Singapore 639798 Singapore Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China

Autonomous aerial vehicles (AAVs) can be utilized as relay platforms to assist maritime wireless communications. However, complex channels and multipath effects at sea can adversely affect the quality of AAV transmitted signals. Collaborative beamforming (CB) can enhance the signal strength and range to assist the AAV relay for remote maritime communications. However, due to the open nature of AAV channels, security issue requires special consideration. This article proposes a dual AAV cluster-assisted system via CB to achieve physical-layer security in maritime wireless communications. Specifically, one AAV cluster forms a maritime AAV-enabled virtual antenna array (MUVAA) relay to forward data signals to the remote legitimate vessel, and the other AAV cluster forms an MUVAA jammer to send jamming signals to the remote eavesdropper. In this system, we formulate a secure and energy-efficient maritime communication multiobjective optimization problem (SEMCMOP) to maximize the signal-to-interference-plus-noise ratio (SINR) of the legitimate vessel, minimize the SINR of the eavesdropping vessel and minimize the total flight energy consumption of AAVs. Since the SEMCMOP is an NP-hard and large-scale optimization problem, we propose an improved swarm intelligence optimization algorithm with chaotic solution initialization and hybrid solution update strategies to solve the problem. Simulation results indicate that the proposed algorithm outperforms other comparison algorithms, and it can achieve more efficient signal transmission by using the CB-based method.

关键词： Autonomous aerial vehicles Jamming Optimization Wireless communication Relays Maritime communications Security Eavesdropping clustering algorithms Antenna arrays Autonomous aerial vehicles (AAVs)-assisted collaborative beamforming maritime communications multiobjective optimization physical-layer secure

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An Interval-Probability-Based Distribution System State Estimation Quantification Framework Considering Nonlinear Correlations of Uncertain Distributed Generators With Limited Information

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IEEE TRANSACTIONS ON POWER SYSTEMS 2025年第3期40卷 2349-2362页

作者： Liu, Bi Wang, Huaifeng Huang, Qi Xu, Lijia Sichuan Agr Univ Coll Mech & Elect Engn Yaan 625014 Peoples R China Southwest Univ Sci & Technol SWUST Mianyang 621010 Peoples R China Univ Elect Sci & Technol China UESTC Chengdu 611731 Peoples R China

The distribution system state estimation (DSSE) is critical for the operation and control of electric distribution systems, but faces significant challenges due to the integration of distributed generators (DGs). The existing uncertain DSSE frameworks struggle with managing correlations, particularly nonlinear correlations among DGs, and it is exacerbated by limited available observations of DGs in practical distribution systems. In light of these problems, initially, this paper utilizes the partition around medoids clustering algorithm and evidence theory to propose a joint Dempster-Shafer (DS) structure for modeling the multiple DGs with limited information, while accounting for their nonlinear correlations. The entire uncertainty hyperspace of DGs is partitioned into a specific number of sub-hyperspaces with corresponding basic probability assignments, according to the limited observations. Subsequently, the uncertainties of DGs are propagated to DSSE outputs by integrating affine arithmetic with evidence theory and multidimensional parallelepiped model, while facilitating further correlation characterization among DGs. Eventually, a probability box (P-box) about each DSSE output, comprising finite intervals with interval probabilities, can be achieved for demonstrating its uncertainty. The proposed interval-probability-based DSSE framework's effectiveness, accuracy, computational efficiency, and scalability are validated through comprehensive tests across various distribution systems.

关键词： Uncertainty Correlation Mathematical models clustering algorithms Computational modeling Probabilistic logic Generators Accuracy State estimation Computational efficiency Uncertain distribution system state estimation Distributed generators Limited information Nonlinear correlations Interval probability

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Learning-Based Cooperative Cache Strategy Integrating Content Recommendation in IoV

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第2期74卷 2080-2093页

作者： Liang, Yan Zhang, Haijun Ma, Hui Wang, Lina Wang, Xianmei Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China

As the proliferation of in-vehicle information services continues to grow, the size of cached content expands, consequently extending the caching duration for vehicle users. Internet of Vehicles (IoV) technology can provide a multitude number of communication services between vehicles and Roadside Units (RSUs), making vehicle cooperative caching a viable solution. Nevertheless, for new energy vehicles such as electric ones, how to reduce the energy consumption of the vehicle network system remains a formidable challenge. Additionally, the task delay of vehicle cooperative caching can also be further reduced. In this paper, a clustering caching model is proposed by considering constraints for tolerable delay of tasks and the residence time of vehicles, and the kinetic energy recovery scheme of vehicles and the Energy Harvesting (EH) mechanism of RSUs are designed to reduce the system energy consumption. Terahertz (THz) communication is employed in vehicle to vehicle (V2V) to accelerate the completion of tasks. Subsequently, the energy consumption optimization problem for the vehicle cooperative caching system is constructed. Furthermore, the continuity and high-dimensionality properties of the decision variables for the optimization problem are analyzed, and we propose the joint Download Size and Bandwidth Allocation (DSBA) algorithm to explore an approximate optimal solution for task decomposition and resource allocation in the high-dynamic environment. We also propose an algorithm named Vehicle Cache Content Recommendation (VCCR) to avoid the additional delay and the waste of energy caused by the vehicle user cache miss-hit problem. The simulation results demonstrate that our proposed algorithms have significant advantages in saving task delay and system energy consumption compared to other solutions.

关键词： Delays Terahertz communications Cooperative caching Optimization Energy consumption Resource management clustering algorithms Internet of Vehicle (IoV) resource allocation vehicle cooperative cache deep reinforcement learning recommendation system

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First Arrival Picking of Aircraft-Excited Seismic Waves Based on Energy Distribution

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Li, Shuang Jiang, Tao Pang, Zhongqin Jilin Univ Coll Instrumentat & Elect Engn Minist Educ China Changchun 130026 Peoples R China Jilin Univ Minist Educ China Key Lab Geophys Explorat Equipment Changchun 130026 Peoples R China

When the acoustic-to-seismic coupling effect is used to detect low-altitude aircrafts, picking up the exact first arrival of the seismic waves excited by aircrafts directly affects the accuracy of the detection. There are random noise and frequent impulse noise in seismic data collection due to wind blowing, grass hitting, and so on. The existing seismic wave picking algorithms are sensitive to the impulse noise, which leads to high false picking rate. In order to solve this problem, first arrival picking of aircraft-excited seismic waves based on energy distribution (ED) is proposed in this article. Compared with the commonly used pickup algorithms, ED can avoid the false pickup caused by single impulse noise and effectively reduce the false picking caused by impulse noise clusters consisted of multiple consecutive single impulse noise. ED ensures both high accuracy and high computational efficiency. More importantly, the actual collected data may contain multiple aircraft events, and ED can pick up all the aircraft events at the same time and does not need to intercept each aircraft signal one by one. By picking up the actual data, the effectiveness and practicability of ED are further verified. The advantages and limitations of the ED algorithm are also discussed to provide future research directions.

关键词： Couplings Accuracy Noise clustering algorithms Interference Data collection Aerospace electronics Computational efficiency Seismic waves Aircraft Acoustic-to-seismic coupling aircraft detection energy distribution (ED) first-arrival picking impulse noise cluster

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Efficiently Transfer User Profile Across Networks

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IEEE TRANSACTIONS ON BIG DATA 2025年第1期11卷 271-285页

作者： Diao, Mengting Zhang, Zhongbao Su, Sen Gao, Shuai Cao, Huafeng Ye, Junda Beijing Univ Posts ands Telecommun BUPT State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

User profiling has very important applications for many downstream tasks. Most existing methods only focus on modeling user profiles of one social network with plenty of data. However, user profiles are difficult to acquire, especially when the data is scarce. Fortunately, we observed that similar users have similar behavior patterns in different social networks. Motivated by such observations, in this paper, we for the first time propose to study the user profiling problem from the transfer learning perspective. We design two efficient frameworks for User Profile transferring acrOss Networks, i.e., UPON and E-UPON. In UPON, we first design a novel graph convolutional networks based characteristic-aware domain attention model to find user dependencies within and between domains (i.e., social networks). We then design a dual-domain weighted adversarial learning method to address the domain shift problem existing in the transferring procedure. In E-UPON, we optimize UPON in terms of computational complexity and memory. Specifically, we design a mini-cluster gradient descent based graph representation algorithm to shrink the searching space and ensure parallel computation. Then we use an adaptive cluster matching method to adjust the clusters of users. Experimental results on Twitter-Foursquare dataset demonstrate that UPON and E-UPON outperform the state-of-the-art models.

关键词： Social networking (online) Transfer learning Knowledge engineering Computational complexity clustering algorithms Big Data Adversarial machine learning User profiling transfer learning social network

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DCMM: Dynamic Cluster-Based Mobile Node Migration Scheme for Blockchain Collaborative Storage in Mobile IoT Networks

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2025年第2期12卷 584-598页

作者： Peng, Kai Liao, Tongxin Liao, Xi Xie, Jiangshan Xu, Bo Deng, Tianping Hu, Menglan Huazhong Univ Sci & Technol Sch Elect Informat & Commun Hubei Key Lab Smart Internet Technol Wuhan 430074 Peoples R China

As blockchain technology becomes widely adopted in Mobile Internet of Things (MIoT) networks, the growing volume of blockchain data significantly increases storage pressure on peer nodes. Collaborative storage, which distributes blockchain data across nodes in cluster, offers a promising solution. However, the frequent movement of mobile nodes disrupts cluster structures, and existing static solutions fail to address this dynamic nature, rendering them ineffective. To address this issue, we propose a Dynamic Cluster-based Mobile Node Migration Scheme (DCMM), comprising two key components: new cluster selection and block redistribution. The Dynamic Node Synchronization Algorithm (DNSA) optimizes cluster selection, and the Dynamic Block Allocation Algorithm (DBAA) manages efficient block redistribution. Comparative analysis with five baseline approaches shows that DCMM improves performance by over 16.69% in the weighted optimization objective, which considers access costs, migration costs, and dwell times. These results demonstrate that our approach significantly optimizes network costs compared to baseline algorithm.

关键词： Collaboration Heuristic algorithms Costs Blockchains Internet of Things Vehicle dynamics Dynamic scheduling Resource management clustering algorithms Mobile nodes Blockchain MIoT network collaborative storage data migration

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