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Fast construction and exploration of performance-cost design space for belief propagation polar decoders

Science China(Information Sciences) 2025年第3期68卷 401-402页

作者： Chao JI You YOU Weikang QIAN Yongming HUANG Chuan ZHANG Laboratory of Efficient Architectures for Digital-communication and Signal-processing (LEADS) National Mobile Communications Research Laboratory Southeast University Purple Mountain Laboratories University of Michigan-Shanghai Jiao Tong University Joint Institute Ministry of Education Key Laboratory of Artificial Intelligence Shanghai Jiao Tong University

Considering polar codes in the channel coding field, belief propagation(BP) polar decoding stands out as a potential solution for high-throughput applications due to its inherent advantage on high parallelism, and also emerging other improved BP-based decoding such as BP flip(BPF) [1, 2] and BP list(BPL) [3,4] polar decoders.

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Bag-Level Multiple Instance Learning for Acute Stress Detection from Video Data

Bag-Level Multiple Instance Learning for Acute Stress Detect...

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18th International joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2025

作者： Brügge, Nele Sophie Korda, Alexandra Borgwardt, Stefan Andreou, Christina Giannakakis, Giorgos Handels, Heinz German Research Center for Artificial Intelligence AI in Medical Image and Signal Processing Lübeck Germany Translational Psychiatry Department of Psychiatry and Psychotherapy University of Luebeck Lübeck23562 Germany Heraklion Greece Department of Electronic Engineering Hellenic Mediterranean University Chania Greece Institute of Agri-food and Life Sciences University Research and Innovation Center Hellenic Mediterranean University Heraklion Greece Institute of Medical Informatics University of Luebeck Lübeck Germany

Stress detection is a complex challenge with implications for health and well-being. It often relies on sensors recording biomarkers and biosignals, which can be uncomfortable and alter behaviour. Video-based facial feature analysis offers a noninvasive alternative. This study explores video-level stress detection using top-k Multiple Instance Learning applied to medical videos. The approach is motivated by the assumption that subjects partly show normal behaviour while performing stressful experimental tasks. Our contributions include a tailored temporal feature network and optimised data utilisation by additionally incorporating bottom-k snippets. Leave-five-subjects-out stress detection results of 95.46% accuracy and 95.49% F1 score demonstrate the potential of our approach, outperforming the baseline methods. Additionally, through multiple instance learning, it is possible to show which temporal video segments the network pays particular attention to. © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Adversarial machine learning

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Thermal constraint control of reversible solid oxide cell for optimal system efficiency

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International Journal of Hydrogen Energy 2025年 134卷 198-211页

作者： Xu, Yuanwu Yin, Jie Wang, Jingjing Wu, Xiao-long Peng, Jingxuan Tang, Ruifeng Zou, Suhang Fu, Xiaowei Li, Xi School of Artificial Intelligence and Automation Wuhan University of Science and Technology Hubei Wuhan430081 China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Hubei Wuhan430074 China School of Information Engineering Nanchang University Jiangxi Nanchang330031 China School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore China-Poland Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Hubei Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518055 China

Reversible solid oxide cell (RSOC) demonstrates considerable promise in multimodal energy conversion architectures, particularly as a technology for hydrogen-based energy storage systems. Nevertheless, under dynamic operating regimes with time-varying current inputs, realizing time-efficient and reliable transitions to steady-state operation remains difficult. This difficulty arises from the intricate coupling of thermal, electrical, and gaseous processes within the RSOC system. This study aims to address these challenges by developing a 5 kW mathematical model of the RSOC system. The proposed model enables systematic investigation of thermal safety issues arising during current switching operations while introducing a thermal constraint control strategy to enhance both system efficiency and thermal safety performance. This efficiency-driven study employs the Grid Search algorithm to identify optimal steady-state operating points for the RSOC system, ensuring strict adherence to safety constraints across varying current inputs. Furthermore, by integrating the pre-identified optimal operating points as feedforward control with Model Predictive Control (MPC)-based feedback correction, this hybrid strategy effectively mitigates stack temperature gradient overshoot while synchronously ensuring optimal system efficiency throughout current transition processes. Under simulated variable current inputs, the proposed RSOC system model and control strategy demonstrate the capability to constrain stack temperature gradient overshoot within 2.3K while enhancing transient efficiency by 5 %–8 %, achieving a peak system efficiency of 85 %. It provides a novel methodology for safe and efficient working with intermittent renewable energy. © 2025 Hydrogen Energy Publications LLC

关键词： Thermal variables control

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Efficient Fault Detection in Nonlinear Industrial Process: A Reduced Kernel PCA-Based Spectral Clustering Approach

Efficient Fault Detection in Nonlinear Industrial Process: A...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Khadija Attouri Majdi Mansouri Mansour Hajji Kais Bouzrara Laboratory of Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Tunisia Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Muscat Oman Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir Tunisia

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

The challenges posed by nonlinearities in industrial systems necessitate innovative techniques that outperform the limitations of traditional methods such as principal component analysis (PCA). While Kernel Principal Component Analysis (KPCA) offers a robust solution to handle nonlinear data, its computational requirements are a significant issue, especially for large-sized datasets. In this work, we propose a novel technique, namely, reduced kernel principal component analysis-based spectral clustering (RKPCA SpC ), to monitor and detect faults in the benchmark Tennessee Eastman process. The suggested approach addresses the complexity associated with KPCA by reducing data size during the model training phase. This reduction involves retaining only the principal components, preserving informative features, and selecting pertinent samples without compromising the original data's content. The efficacy of the proposed method is evaluated through key performance metrics, including false alarm rate (FAR), missed detection rate (MDR), detection time delay (DTD), and computation time (CT). Additionally, gained execution time (GET), gained storage space (GSP), and loss function (LF) are considered, providing a comprehensive assessment of the developed paradigms' effectiveness. The results demonstrate the promising capabilities of our proposed scheme.

关键词： Training Measurement Technological innovation Fault detection Delay effects Computational modeling Complexity theory Kernel Monitoring Principal component analysis

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MOBILE ROBOT POSITION DETERMINATION USING VISUAL LANDMARKS

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 1994年第6期41卷 654-662页

作者： DORAZIO, T LOVERGINE, FP IANIGRO, M STELLA, E DISTANTE, A National Research Council-Institute of Signal and Image Processing Bari Italy

This paper is concerned with the problem of determining the position of a mobile vehicle during navigation. In order to achieve this objective a multisensor navigation system for self location of the robot has been developed. By tracking a few known landmarks with a vision module, the system is able to monitor continuously its position and to integrate these estimates with the measures provided by the vehicle odometers. This paper describes in detail the vision module used by the navigation system.

关键词： Mobile robots Vehicles Navigation Infrared sensors Robot sensing systems Cameras Monitoring Position measurement Layout Robot vision systems

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Short-Term Photovoltaic Power Forecasting-Based Kernel Ridge Regression

Short-Term Photovoltaic Power Forecasting-Based Kernel Ridge...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Manel Marweni Majdi Mansouri Mansour Hajji Mohamed Faouzi Mimouni Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir Tunisia Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Muscat Oman Laboratory of Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kasserine Tunisia

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

The modern energy landscape is undergoing a seismic change from traditional, finite energy sources and toward cleaner, renewable alternatives. The restrictions faced by traditional sources, which are not only finite but increasingly shrink in the face of burgeoning global energy demands driven by population increase and industrial expansion, are driving this change. Although promising, renewable energy poses complications, particularly the reliance on climatic conditions. An important aspect of addressing these difficulties is effective energy management within distribution systems, which includes forecasting and optimization phases. This research focuses on forecasting using an advanced machine learning (ML) approach. Accurately forecasting renewable energy generation over time is critical for improving energy management. This technique is evaluated using a variety of performance indicators, including Mean Error (ME), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R 2 ). Empirical studies support the method's usefulness, demonstrating noteworthy performance with low error rates.

关键词： Photovoltaic systems Renewable energy sources Technological innovation Accuracy Machine learning Predictive models Forecasting Kernel Energy management Optimization

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5G green cellular networks considering power allocation schemes

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Science China(Information Sciences) 2016年第2期59卷 112-125页

作者： Xiaohu GE Jiaqi CHEN Cheng-Xiang WANG John THOMPSON Jing ZHANG School of Electronics Information and Communications Huazhong University of Science and Technology Joint Research Institute for Signal and Image Processing School of Engineering & Physical SciencesHeriot-Watt University

It is important to assess the effect of transmit power allocation schemes on the energy consumption on random cellular networks. The energy efficiency of 5G green cellular networks with average and water-filling power allocation schemes is studied in this paper. Based on the proposed interference and achievable rate model,an energy efficiency model is proposed for MIMO random cellular networks. Furthermore, the energy efficiency with average and water-filling power allocation schemes are presented, respectively. Numerical results indicate that the maximum limits of energy efficiency are always there for MIMO random cellular networks with different intensity ratios of mobile stations(MSs) to base stations(BSs) and channel conditions. Compared with the average power allocation scheme, the water-filling scheme is shown to improve the energy efficiency of MIMO random cellular networks when channel state information(CSI) is attainable for both transmitters and receivers.

关键词： energy efficiency cellular networks MIMO achievable rate model power allocation scheme

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Parallel Markov chain Monte Carlo computation for varying-dimension signal analysis

Parallel Markov chain Monte Carlo computation for varying-di...

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17th European signal processing Conference, EUSIPCO 2009

作者： Ye, Jing Wallace, Andrew Thompson, John Edinburgh Joint Research Institute in Signal and Image Processing Heriot-Watt University United Kingdom Edinburgh Joint Research Institute in Signal and Image Processing University of Edinburgh United Kingdom

Parallel implementation of Markov Chain Monte Carlo (MCMC) algorithms for Bayesian inference has been effective but is usually restricted to the case where the dimension of the parameter vector is fixed. We propose an efficient parallel solution for the varying-dimension problem by constructing multiple within-model MCMC chains and then combining the separate results to analyze the posterior distribution of dimensionality. We aim for parallel speed-up by reducing the length of the burn-in period and the individual chains in comparison with a serial, reversible jump MCMC (RJMCMC) algorithm. The parallel methodology is illustrated with application to a benchmarking, change point problem. © EURASIP, 2009.

关键词： Chains

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Dynamics of Masked image Modeling in Hyperspectral image Classification

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Ma, Chen Li, Huayi Jiang, Junjun Aybar, Cesar Yao, Jiaqi Camps-Valls, Gustau Harbin Institute of Technology Research Center of Satellite Technology State Key Laboratory of Micro-Spacecraft Rapid Design and Intelligent Cluster School of Astronautics Harbin150001 China Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Image Signal Processing Laboratory University of València València46980 Spain Tianjin Normal University Academy of Eco-civilization Development for Jing-Jin-Ji Megalopolis Tianjin300387 China

Masked image modeling (MIM), a common selfsupervised learning (SSL) technique, has been extensively studied for remote sensing image processing. Nevertheless, its effectiveness for hyperspectral imagery (HSI) remains underexplored due to the distinct data structures and high dimensionality. This paper aims to provide a detailed understanding of MIM from different perspectives of representation learning and statistical analysis for HSI classification tasks. Our study reveals that the MIM paradigm injects inductive bias in the attention mechanism of the transformer model, which is advantageous for capturing the local discrepancies between the spectra. We also show that MIM can increase the diversity of the attention heads in every layer, which is beneficial for the model in extracting more discriminative features from different spectral bands. The similarity of representations from various layers further proves this. Furthermore, our investigation highlights how MIM introduces a dynamic perspective to spectral representations, enabling the model to learn more robust and discriminative features. The final numerical experiments indicate that a moderate mask ratio can enhance the performance of downstream tasks. This suggests that designing a more targeted masking strategy might be necessary to achieve higher and more stable gains in downstream task performance. Without bells and whistles, the vanilla MIM improves the overall classification accuracy by an average of 2.69% over its SL counterpart. We hope that our findings can advance the understanding of MIM in HSI and inspire the design of a more stable SSL paradigm for HSI processing. © 1980-2012 IEEE.

关键词： Self-supervised learning

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On capacity of cognitive radio networks with average interference power constraints

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IEEE Transactions on Wireless Communications 2009年第4期8卷 1620-1625页

作者： Wang, Cheng-Xiang Hong, Xuemin Chen, Hsiao-Hwa Thompson, John Joint Research Institute for Signal and Image Processing School of Engineering and Physical Sciences Heriot-Watt University United Kingdom Department of Electrical and Computer Engineering University of Waterloo ON Canada Department of Engineering Science National Cheng Kung University Taiwan Joint Research Institute for Signal and Image Processing Institute for Digital Communications University of Edinburgh United Kingdom

Cognitive radio (CR) has been considered as a promising technology to improve the spectrum utilization. In this paper we analyze the capacity of a CR network with average received interference power constraints. Under the assumptions of uniform node placements and a simple power control scheme, the maximum transmit power of a target CR transmitter is characterized by its cumulative distribution function (CDF). We study two CR scenarios for future applications. The first scenario is called the CR based central access network, which aims at providing broadband access to CR devices. In the second scenario, the so-called CR assisted virtual multiple-input multiple-output (MIMO) network, CR is used to improve the access capability of a cellular system. The uplink ergodic channel capacities of both scenarios are derived and analyzed with an emphasis on understanding the impact of numbers of primary users and CR users on the capacity. Numerical and simulation results suggest that the CR based central access network is more suitable for less-populated rural areas where a relatively low density of primary receivers is expected;while the CR assisted virtual MIMO network performs better in urban environments with a dense population of mobile CR users. © 2006 IEEE.

关键词： Cognitive radio

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