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Transparent random access memory testing for pattern sensitive faults

JOURNAL OF ELECTRONIC TESTING-THEORY AND APPLICATIONS 1996年第3期9卷 251-266页

作者： Karpovsky, MG Yarmolik, VN Research Laboratory of Design and Testing of Computer Hardware Department of Electrical Computer and Systems Engineering Boston University Boston USA

This paper presents a new methodology for RAM testing based on the PS(n, k) fault model (the k out of n pattern sensitive fault model). According to this model the contents of any memory cell which belongs to an n-bit memory block, or the ability to change the contents, is influenced by the contents of any k-1 cells from this block. The proposed methodology is a transparent BIST technique, which can be efficiently combined with on-line error detection. This approach preserves the initial contents of the memory after the rest and provides for a high fault coverage for traditional fault and error models, as well as for pattern sensitive faults. This paper includes the investigation of testing approaches based on transparent pseudoexhaustive testing and its approximations by deterministic and pseudorandom circular tests. The proposed methodology can be used for periodic and manufacturing testing and require lower hardware and time overheads than the standard approaches.

关键词： random access memory memory testing exhaustive memory testing transparent memory testing built-in self-test pseudo-exhaustive memory testing pattern sensitive faults signature analysis

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Deficient Excitation in Parameter Learning

arXiv

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arXiv 2025年

作者： Cao, Ganghui Wang, Shimin Guay, Martin Wang, Jinzhi Duan, Zhisheng Polycarpou, Marios M. State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China Massachusetts Institute of Technology CambridgeMA02139 United States Queen’s University KingstonONK7L 3N6 Canada KIOS Research and Innovation Center of Excellence Department of Electrical and Computer Engineering University of Cyprus Nicosia1678 Cyprus

This paper investigates parameter learning problems under deficient excitation (DE). The DE condition is a rank-deficient, and therefore, a more general evolution of the well-known persistent excitation condition. Under the DE condition, a proposed online algorithm is able to calculate the identifiable and non-identifiable subspaces, and finally give an optimal parameter estimate in the sense of least squares. In particular, the learning error within the identifiable subspace exponentially converges to zero in the noise-free case, even without persistent excitation. The DE condition also provides a new perspective for solving distributed parameter learning problems, where the challenge is posed by local regressors that are often insufficiently excited. To improve knowledge of the unknown parameters, a cooperative learning protocol is proposed for a group of estimators that collect measured information under complementary DE conditions. This protocol allows each local estimator to operate locally in its identifiable subspace, and reach a consensus with neighbours in its non-identifiable subspace. As a result, the task of estimating unknown parameters can be achieved in a distributed way using cooperative local estimators. Application examples in system identification are given to demonstrate the effectiveness of the theoretical results developed in this paper. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Screening cognitive impairment in patients with atrial fibrillation: A deep learning model based on retinal fundus photographs

Heart Rhythm O2

引用

Heart Rhythm O2 2025年第5期6卷 678.0-686.0页

作者： Wang, Zhen Li, Mingxiao Xia, Peng Jiang, Chao Shen, Ting Ma, Jiaming Bai, Yu Zhang, Suhui Lai, Yiwei Li, Sitong Xu, Hui Xu, Yang Ma, Tong Ju, Lie He, Liu Dong, Li Sang, Caihua Long, Deyong Chen, Yuzhong Du, Xin Ge, Zongyuan Dong, Jianzeng Wei, Wen-Bin Ma, Changsheng Department of Cardiology Beijing Anzhen Hospital Capital Medical University Beijing China Engineering Research Center of Medical Devices for Cardiovascular Diseases Ministry of Education Capital Medical University Beijing China National Clinical Research Center for Cardiovascular Diseases Beijing China Beijing Airdoc Technology Co. Ltd. Beijing China Department of Electrical and Computer Systems Engineering Faculty of Engineering Monash University Clayton Australia Yale School of Medicine New Haven Connecticut United States School of Clinical Medicine Peking Union Medical College and Chinese Academy of Medical Science Beijing China Beijing Tongren Eye Center Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment Beijing Ophthalmology & Visual Sciences Key Lab Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology Beijing Tongren Hospital Capital Medical University Beijing China Monash e-Research Center Monash University Clayton Australia

Background: Patients with atrial fibrillation (AF) have a higher risk of cognitive impairment (CI). However, complexity of CI diagnosis and lack of simple screening approaches limited early screening and intervention of CI in AF patients. Objective: Our study aimed to develop deep learning models based on fundus photographs for easy screening of CI in AF patients. Methods: From May 2021 to April 2023, patients who completed fundus examination and cognitive function evaluation in the Chinese Atrial Fibrillation Registry Study were included. The training and validation sets were randomly split at an 8:2 ratio. Participants from the Beijing Eye Study served as the external validation set. Different deep learning models were trained, and their CI detection ability was validated. Results: A total of 899 patients in the Chinese Atrial Fibrillation Registry Study were included. In the validation set, the vision-ensemble model based on fundus images alone had an area under the receiver-operating characteristic curve (AUROC) of 0.855 (95% confidence interval 0.816–0.894) for CI screening. The multimodal model (AUROC 0.861, 95% confidence interval 0.823–0.898), based on fundus photographs and 4 clinical variables, performed comparably to the vision-ensemble model. The AUROC of the vision-ensemble model for CI screening achieved 0.773 (95% confidence interval 0.709–0.837) in the external test set. In the saliency map, the vision-ensemble model focused on areas around retinal vessels and the optic disc. Conclusion: A vision-ensemble model based on fundus images might be practical for preliminary screening of CI in AF patients. © 2025 Heart Rhythm Society

关键词： Artificial intelligence Atrial fibrillation Cognitive impairment Deep learning Fundus photography

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ADAPTIVE LATTICE FILTER IMPLEMENTATIONS ON PIPELINED MULTIPROCESSOR ARCHITECTURES

引用

IEEE TRANSACTIONS ON COMMUNICATIONS 1990年第1期38卷 122-124页

作者： MEYER, MD AGRAWAL, DP Computer Systems Laboratory Electrical and Computer Engineering Department North Carolina State University Raleigh NC USA

Three pipelined multiprocessor implementations of adaptive lattice filters are examined. A performance analysis is done for each multiprocessor system, and expressions for approximate computation time and speedup are derived. Each system is shown to be flexible with respect to the different algorithms and the different filter sizes it can implement.

关键词： Speeding Multiprocessor Approximate computation computational time lattice filters FLEXIBLE

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6D Movable Antenna Enhanced Interference Mitigation for Cellular-Connected UAV Communications

引用

IEEE Wireless Communications Letters 2025年

作者： Ren, Tianshi Zhang, Xianchao Zhu, Lipeng Ma, Wenyan Gao, Xiaozheng Zhang, Rui The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore The Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems China The Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing314001 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of HongKong Guangdong Shenzhen518172 China

Cellular-connected unmanned aerial vehicle (UAV) communications is an enabling technology to transmit control signaling or payload data for UAVs through cellular networks. Due to the line-of-sight dominant air-to-ground channels, efficient interference mitigation is crucial to UAV communications, while the conventional fixed-position antenna (FPA) arrays have limited degrees of freedom to suppress the interference between the UAV and its non-associated co-channel base stations (BSs). To address this challenge, we propose in this letter a new approach by utilizing the six-dimensional movable antenna (6DMA) arrays to enhance the interference mitigation for the UAV. Specifically, we propose an efficient block coordinate descent algorithm to iteratively optimize the antenna position vector, array rotation vector, receive beamforming vector, and associated BS of the UAV to maximize its signal-to-interference-plus-noise ratio. Numerical results show that the proposed 6DMA enhanced cellular-connected UAV communication can significantly outperform that with the traditional FPA arrays and other benchmark schemes in terms of interference mitigation. © 2025 IEEE. All rights reserved.

关键词： Aircraft communication

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Continual Reinforcement Learning for Digital Twin Synchronization Optimization

arXiv

引用

arXiv 2025年

作者： Tong, Haonan Chen, Mingzhe Zhao, Jun Hu, Ye Yang, Zhaohui Liu, Yuchen Yin, Changchuan The Beijing Key Laboratory of Network System Architecture and Convergence The Beijing Advanced Information Network Laboratory Beijing University of Posts and Telecommunications Beijing China Singapore The Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Department of Industrial and Systems Engineering University of Miami Coral GablesFL33146 United States The College of Information Science and Electronic Engineering Zhejiang University Zhejiang Hangzhou310027 China The Department of Computer Science North Carolina State University RaleighNC27695 United States The Beijing Key Laboratory of Network System Architecture and Convergence China The Beijing Advanced Information Network Laboratory Beijing University of Posts and Telecommunications Beijing100876 China

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods. © 2025, CC BY-NC-SA.

关键词： Markov processes

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Joint Design of Radar Receive Filter and Unimodular ISAC Waveform with Sidelobe Level Control

arXiv

引用

arXiv 2025年

作者： Zhang, Kecheng Liu, Ya-Feng Wang, Zhongbin Yuan, Weijie Keskin, Musa Furkan Wymeersch, Henk Xia, Shuqiang School of System Design and Intelligent Manufacturing The Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Electrical Engineering Chalmers University of Technology Gothenburg41296 Sweden ZTE Corporation The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China

Integrated sensing and communication (ISAC) has been considered a key feature of next-generation wireless networks. This paper investigates the joint design of the radar receive filter and dual-functional transmit waveform for the multiple-input multiple-output (MIMO) ISAC system. While optimizing the mean square error (MSE) of the radar receive spatial response and maximizing the achievable rate at the communication receiver, besides the constraints of full-power radar receiving filter and unimodular transmit sequence, we control the maximum range sidelobe level, which is often overlooked in existing ISAC waveform design literature, for better radar imaging performance. To solve the formulated optimization problem with convex and nonconvex constraints, we propose an inexact augmented Lagrangian method (ALM) algorithm. For each subproblem in the proposed inexact ALM algorithm, we custom-design a block successive upper-bound minimization (BSUM) scheme with closed-form solutions for all blocks of variable to enhance the computational efficiency. Convergence analysis shows that the proposed algorithm is guaranteed to provide a stationary and feasible solution. Extensive simulations are performed to investigate the impact of different system parameters on communication and radar imaging performance. Comparison with the existing works shows the superiority of the proposed algorithm. © 2025, CC BY-NC-SA.

关键词： Mean square error

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Discrete-Time Uncertain Event-Triggered H∞ Control for Slow Sampling Markov Jump systems With Strictly Dissipativity

引用

Mathematical Methods in the Applied Sciences 2025年

作者： Kchaou, Mourad Syed Ali, M. Vigneshwar, B. Sanober, Sumaya Ibrahim, Tarek F. D Alanazi, Faizah Electrical Department College of Engineering University of Hail Kingdom Saudi Arabia Complex Systems and Networked Science Research Laboratory Department of Mathematics Thiruvalluvar University Tamil Nadu Vellore India Department of Computer Science Old Dominion University NorfolkVA United States Department of Mathematics Sciences College King Khalid University Abha Saudi Arabia Department of Mathematics Faculty of Sciences Mansoura University Mansoura Egypt Department of Mathematics College of Science Northern Border University Arar Saudi Arabia

This paper investigates the (Formula presented.) control problem for discrete-time uncertain slow sampling Markov jump systems under the event-triggered scheme. Discrete-time Markov jumps are controlled using an event-triggered scheme. An event-based strategy is addressed to reduce the data communication and improve the control efficiency. By applying a switched Lyapunov functional and linear matrix inequalities (LMIs), we derive sufficient conditions for (Formula presented.) -dissipativity of the resulting system. An event-triggered scheme is introduced into LMIs approaches in order to solve the dissipative control problem. The control strategy is implemented by combining the disturbance output with state feedback control law in order to achieve asymptotically stable closed-loop systems. Convex combination approach is utilized to handle slow sampling input, while parameter uncertainty and external disturbances are both included, and the robustness (Formula presented.) performance is analyzed. The developed methods are illustrated with two examples that illustrate their flexibility and applicability. © 2025 John Wiley & Sons Ltd.

关键词： Markov processes

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Multimodal Emotion Recognition Based on Multi-Scale Facial Features and Cross-Modal Attention 26

Multimodal Emotion Recognition Based on Multi-Scale Facial F...

引用

26th International Conference on Industrial Technology, ICIT 2025

作者： Bao, Chengao Chen, Luefeng Li, Min Wu, Min Pedrycz, Witold Hirota, Kaoru School of Automation China University of Geosciences Wuhan430074 China School of Automation Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2G7 Canada Tokyo Institute of Technology Yokohama226-8502 Japan Tokyo Institute of Technology Tokyo226-8502 Japan

ISBN: (纸本)9798331521950

A multi-modal emotion recognition method based on facial multi-scale features and cross-modal attention (MS-FCA) network is proposed. The MSFCA model improves the traditional single-branch ViT network into a two-branch ViT architecture by using classification tokens in each branch to interact with picture embeddings in the other branch, which facilitates effective interactions between different scales of information. Subsequently, audio features are extracted using ResNet18 network. The cross-modal attention mechanism is used to obtain the weight matrices between different modal features, making full use of inter-modal correlation and effectively fusing visual and audio features for more accurate emotion recognition. Two datasets are used for the experiments: eNTERFACE'05 and REDVESS dataset. The experimental results show that the accuracy of the proposed method on the eNTERFACE'05 and REDVESS datasets is 85.42% and 83.84% respectively, which proves the effectiveness of the proposed method. © 2025 IEEE.

关键词： Modal analysis

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A Wideband WiFi/NB-IoT On-Glass Antenna for Smart Building Applications

A Wideband WiFi/NB-IoT On-Glass Antenna for Smart Building A...

引用

European Conference on Antennas and Propagation, EuCAP

作者： Muhammad Uzair Juin Acharjee Akkarat Boonpoonga Dirk Heberling Suramate Chalermwisutkul High Freqeuncy Systems Laboratory King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Electronics and Communication Engineering St. Thomas' College of Engineering and Technology Kolkata India Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Institute of High Frequency Technology RWTH Aachen University Aachen Germany Fraunhofer Institute for High Frequency Physics and Radar Techniques FHR Wachberg Germany

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

In this work, an on-glass antenna is proposed for IoT and smart building applications. The dielectric properties of five common glass types used in architecture were investigated. Clear glass, 3 mm thick, was selected as the substrate. The antenna is a rectangular monopole with a coplanar waveguide feed and an asymmetric ground. It was designed and simulated using Ansys HFSS, then fabricated using clear glass and copper tape. Measurements of the reflection coefficient and radiation pattern showed good agreement with simulations. The antenna exhibits a bidirectional radiation pattern, with 2.8 dBi gain and 94.82% radiation efficiency at 2.1 GHz and 3.9 dBi gain and 95.72% radiation efficiency at 2.4 GHz, respectively. A −10 dB bandwidth of 650 MHz (29.3%) centered at 2.215 GHz was achieved. The proposed antenna shows promise for integration with architectural glass elements in smart building applications, such as smart glass doors and windows.

关键词： Antenna measurements Smart buildings Indium tin oxide Glass Reflection coefficient Internet of Things Substrates Wideband Gain Antenna radiation patterns

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