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2023 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition, iWEM 2023

作者： Qi, Junwei Zhang, Chenwei Li, Yingsong Huang, Zhixiang Loh, Tian Hong Chung, Kwok L. Harbin Engineering University College of Information and Communications Engineering Harbin150001 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China National Physical Laboratory Electromagnetic and Eletrochemical Technologies Department Middlesex United Kingdom Huizhou University School of Computer Science and Engineering 516007 China

ISBN: (纸本)9798350336740

In this paper, Q-value theory of conventional antennas is used to analyze reasons of low radiation performance of acoustics promoted antennas, where we modify the radiating structure of existing acoustics promoting antenna using the simulations to get the required improvement on radiation performance. The simulation experiments are carried out in the finite element simulation software Comsol Multiphysics to analyze the radiating performance. © 2023 IEEE.

关键词： Antennas

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Personalized Multimedia Recommendation Systems Using Higher-Order Tensor Singular-Value-Decomposition

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IEEE Transactions on Broadcasting 2024年第1期70卷 148-160页

作者： Chang, Shih Yu Wu, Hsiao-Chun Yan, Kun Chen, Xinjia Huang, Scott Chih-Hao Wu, Yiyan San Jose State University Department of Applied Data Science San JoseCA95192 United States Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center for AI Applications Chungli32003 Taiwan Guilin University of Electronic Technology Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Department of Information and Telecommunication Guilin541004 China Northwestern State University Department of Engineering Technology NatchitochesLA71497 United States National Tsing Hua University Department of Electrical Engineering Hsinchu300 Taiwan Communications Research Centre Canada OttawaK2H 8S2 Canada

Because of rapid growth of multimedia data over the Internet, the infobesity has been emerging in recent years. Many recommender systems (RSs) have been proposed using a variety of techniques, including artificial intelligence, machine learning, statistical analysis, and data mining. Nowadays, E-commerce companies such as Amazon, eBay, Netflix, etc. have applied RSs conveniently to provide better customer services than before by enabling customized multimedia-content recommendations. It is well known that classical RSs are almost all based on the user-item attribute matrices. However, this two-fold user-item relationship should be extended to the emerging many-fold relationship among an arbitrary number of attribute groups so that the corresponding new RSs may lead to better performances. Therefore, in this paper, we introduce a novel personalized multimedia recommender system (PMRS) according to our proposed new parallel higher-order tensor singular-value-decomposition (PHOTSVD) algorithm. This PHOTSVD algorithm is capable of accommodating an arbitrary number of entities such as social-media channels, users, queries, webpages, etc. in a robust RS. Furthermore, we also investigate the cold start and data bias problems critical to RSs. Finally, we compare the performances of our proposed new PMRS and three other existing tensor-based RSs over realworld data with respect to the well-adopted metric, namely normalized root-mean-square error (NRMSE). Our proposed new PMRS greatly outperforms the other three existing tensor-based RSs in terms of NRMSE. © 1963-12012 IEEE.

关键词： Singular value decomposition

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Automatic Detection and Characterization of Human Veins Using Infra-Red Image processing

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Journal of computer and communications 2024年第9期12卷 141-159页

作者： Jean Ndoumbe Brice Ekobo Akoa Gaelle Patricia Talotsing Frederic Franck Kounga Samuel Kaissassou Bertin Chouanmo Njo Laboratory of Computer Engineering Data Science and Artificial Intelligence Department of Computer & Telecommunications Engineering National Higher Polytechnic School Douala Cameroon Laboratory of Electrical Engineering Mechatronics and Signal Processing Department of Electrical & Telecommunications Engineering National Advanced School of Engineering Yaound Cameroon Mobile Computing and Networking Research Laboratory Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada DigiPlus SARL Bonamoussadi Douala Cameroon

The detection and characterization of human veins using infrared (IR) image processing have gained significant attention due to its potential applications in biometric identification, medical diagnostics, and vein-based authentication systems. This paper presents a low-cost approach for automatic detection and characterization of human veins from IR images. The proposed method uses image processing techniques including segmentation, feature extraction, and, pattern recognition algorithms. Initially, the IR images are preprocessed to enhance vein structures and reduce noise. Subsequently, a CLAHE algorithm is employed to extract vein regions based on their unique IR absorption properties. Features such as vein thickness, orientation, and branching patterns are extracted using mathematical morphology and directional filters. Finally, a classification framework is implemented to categorize veins and distinguish them from surrounding tissues or artifacts. A setup based on Raspberry Pi was used. Experimental results of IR images demonstrate the effectiveness and robustness of the proposed approach in accurately detecting and characterizing human. The developed system shows promising for integration into applications requiring reliable and secure identification based on vein patterns. Our work provides an effective and low-cost solution for nursing staff in low and middle-income countries to perform a safe and accurate venipuncture.

关键词： Vein Detection Blood Radiation Infrared Image CLAHE Algorithm Raspberry Pi

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MCFTNet: Multimodal Cross-Layer Fusion Transformer Network for Hyperspectral and LiDAR Data Classification

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12803-12818页

作者： Huang, Wei Wu, Tianren Zhang, Xueyu Li, Liangliang Lv, Ming Jia, Zhenhong Zhao, Xiaobin Ma, Hongbing Vivone, Gemine Xinjiang University School of Computer Science and Technology Urumqi830017 China Xinjiang University Key Laboratory of Signal Detection and Processing Urumqi830017 China Guangxi University School of Computer and Electronic Information Nanning530004 China Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning530004 China Beijing Institute of Technology School of Information and Electronics Beijing100081 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Tsinghua University Department of Electronic Engineering Beijing100084 China National Research Council Tito85050 Italy National Biodiversity Future Center NBFC Palermo90133 Italy

Remote sensing image classification is a popular yet challenging field. Many researchers have combined convolutional neural networks (CNNs) and Transformers for hyperspectral imaging (HSI) classification tasks. However, in traditional Transformers, shallow-level information does not propagate well to deeper layers, which can lead to spatial variations and overfitting. Moreover, traditional Transformer models use an external classification token (CLS token) that is randomly initialized and often struggles to generalize effectively. In this article, we combine the strengths of HSI and light detection and ranging (LiDAR) data, using LiDAR as an external CLS token, which significantly enhances classification accuracy and reliability. We propose a new multimodal cross-layer fusion transformer network (MCFTNet), integrating CNNs with the latest Transformer networks. It includes a CNN for extracting spatial features and a hybrid cross-patch attention mechanism for land cover classification, leveraging LiDAR data to generate CLS and HSI patch tokens. More importantly, to reduce the loss of valuable information during the layer-by-layer propagation, we designed cross-layer skip connections. Through adaptive learning, these cross-layer fusions help address the gradient vanishing problem in deep networks while preserving early-layer features. This enables the model to better integrate information from different layers, enhancing both its stability and performance. We carried out in-depth experiments on commonly used benchmark datasets, specifically, the University of Houston dataset, the Trento dataset, and the University of Southern Mississippi Gulf Park dataset. We compared the results of the proposed MCFTNet model with those obtained from state-of-the-art Transformer models, classical CNNs, and traditional classifiers. As a result, the MCFTNet model outshones them all in terms of performance. © 2008-2012 IEEE.

关键词： Convolutional neural networks

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InfaSafe: A Comprehensive, Non-invasive Infant Monitoring System

InfaSafe: A Comprehensive, Non-invasive Infant Monitoring Sy...

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IEEE International Conference on Electro-Information Technology

作者： Lukas Klicker Alex Maliwat Joanna Findura Xinrui Yu Mikhail Gromov Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL USA

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

InfaSafe emerges as a novel approach to infant health monitoring, uniquely positioned at the convergence of advanced artificial intelligence and edge computing. This system is designed not as a definitive solution but as an advanced platform for comprehensive data archiving, offering valuable insights into the complex and elusive nature of Sudden Unexpected Infant Death (SUID). InfaSafe utilizes AI algorithms for real-time pose estimation, breathing surveillance, and cry analysis, all within an edge computing framework that facilitates prompt and efficient data handling. This paper explores the development and capabilities of InfaSafe, underscoring its role in providing crucial, real-time insights and alerts for caregivers and its potential to contribute significantly to our understanding of neonatal health and SUID. The focus is on leveraging technological advancements to gather comprehensive data, which can be instrumental in shaping future research and interventions in neonatal care.

关键词： Temperature sensors Temperature measurement Pediatrics Temperature distribution Prototypes Real-time systems Thermal analysis

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Peak Age of Information under Tandem of Queues

arXiv

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arXiv 2024年

作者： Sinha, Ashirwad Singhvi, Shubhransh Mankar, Praful D. Dhillon, Harpreet S. Signal Processing & Communications Research Center IIIT Hyderabad India Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24061 United States

This paper considers a communication system where a source sends time-sensitive information to its destination via queues in tandem. We assume that the arrival process as well as the service process (of each server) are memoryless, and each of the servers has no buffer. For this setup, we develop a recursive framework to characterize the mean peak age of information (PAoI) under preemptive and non-preemptive policies with N servers having different service rates. For the preemptive case, the proposed framework also allows to obtain mean age of information (AoI). Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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On the Service Rate Region of Reed-Muller Codes

arXiv

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arXiv 2025年

作者： Ly, Hoang Soljanin, Emina Lalitha, V. Department of Electrical and Computer Engineering Rutgers The State University of New Jersey PiscatawayNJ08854 United States Signal Processing and Communications Research Center IIIT Hyderabad India

We study the Service Rate Region (SRR) of Reed-Muller (RM) codes in the context of distributed storage systems. The SRR is a convex polytope comprising all achievable data access request rates under a given coding scheme. It represents a critical metric for evaluating system efficiency and scalability. Using the geometric properties of RM codes, we characterize recovery sets for data objects, including their existence, uniqueness, and enumeration. This analysis reveals a connection between recovery sets and minimum-weight codewords in the dual RM code, providing a framework for identifying small recovery sets. Using these results, we derive explicit and tight bounds for the maximal achievable demand for individual data objects, which define the maximal simplex within the service rate region. © 2025, CC BY.

关键词： Reed-Solomon codes

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Protect Before Generate: Error Correcting Codes within Discrete Deep Generative Models

arXiv

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arXiv 2024年

作者： Martínez-García, María Villacrés, Grace Mitchell, David Olmos, Pablo M. Department of Signal Theory and Communications Universidad Carlos III de Madrid Spain Department of Signal Processing and Communications Universidad Rey Juan Carlos Spain Klipsch School of Electrical and Computer Engineering New Mexico State University United States

Despite significant advancements in deep probabilistic models, learning low-dimensional discrete latent representations remains a challenging task. In this paper, we introduce a novel method that enhances variational inference in discrete latent variable models by leveraging Error-Correcting Codes (ECCs) to introduce redundancy in the latent representations. This redundancy is then exploited by the variational posterior to yield more accurate estimates, thereby narrowing the variational gap. Inspired by ECCs commonly used in digital communications and data storage, we demonstrate proof-of-concept using a Discrete Variational Autoencoder (DVAE) with binary latent variables and block repetition codes. We further extend this idea to a hierarchical structure based on polar codes, where certain latent bits are more robustly protected. Our method improves generation quality, data reconstruction, and uncertainty calibration compared to the uncoded DVAE, even when trained with tighter bounds such as the Importance Weighted Autoencoder (IWAE) objective. In particular, we demonstrate superior performance on MNIST, FMNIST, CIFAR10, and Tiny ImageNet datasets. The general approach of integrating ECCs into variational inference is compatible with existing techniques to boost variational inference, such as importance sampling or Hamiltonian Monte Carlo. We also outline the key properties ECCs must have to effectively enhance discrete variational inference. Copyright © 2024, The Authors. All rights reserved.

关键词： Importance sampling

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Adaptive IMM Algorithm Based on Variational Inference for Multiple Maneuvering Extended Targets Tracking

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Advances in Astronautics 2025年 1-15页

作者： Wang, Shenghua Li, Renxian Men, Chenkai Cao, Yunhe Yeo, Tat-Soon School of Communications and Information Engineering and School of Artificial Intelligence Xi’an University of Posts and Telecommunications Xi’an China National Lab of Radar Signal Processing Xidian University Xi’an China The Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore

In order to track multiple maneuvering extended targets accurately, an adaptive interacting multiple model algorithm based on variational inference (AIMM-VI) is proposed. An augmented state is constructed to cater for time-varying orientation angle and track realistic shape changes, resulting in better elliptical shape estimation and tracking accuracy. Multiple measurements from multiple extended targets are effectively assigned to corresponding targets through the marginal association probability distribution criterion, and the variational inference is used to accurately estimate the augmented state and shape information, which greatly improves the parameters estimation performance. The residual and likelihood functions are updated in real-time according to the results of variational inference, allowing for the updating of the model probability in real-time. The Markov probability transfer matrix is subsequently adaptively updated by the compression ratio, which makes the algorithm more adaptable to maneuvering target and significantly improves the adaptability and robustness of the algorithm. The final simulation and experiment results show that the proposed algorithm can effectively improve the tracking performance of multiple maneuvering extended targets.

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AI-Based Security Surveillance and Hazard Detection for Train Platform Safety

AI-Based Security Surveillance and Hazard Detection for Trai...

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IEEE International Conference on Electro-Information Technology

作者： Alvaro Aparicio Serna Xinrui Yu Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

Safety in train platforms is a common concern in railway systems around the world. There are grave risks associated with the tracks and the crowds that often end up in injuries or deaths. Traditional approaches for ensuring safety in train transport networks rely on passive closed-circuit television (CCTV) monitorization that needs constant human attention. This paper proposes an AI-based alternative for detecting anomalies in surveillance videos, that is more efficient and cost-effective than traditional methods. Through Video Instance Segmentation (VIS), this work detects common risks such as overcrowding on the platform, people or objects standing on the edge of the platform, individuals or objects falling onto the tracks, the presence of firearms, and the presence of unattended baggage. The proposed algorithm combines state-of-the-art models like YOLOv8, ByteTrack, and Segment Anything Model (SAM) to classify, track, and segment object detections respectively. Additionally, this paper presents a custom-trained YOLOv8 model for gun detection. The results show that the system can successfully analyze video, create surveillance annotations, detect hazardous situations to alert authorities, and help prevent accidents and incidents on train platforms.

关键词： Instance segmentation Annotations Surveillance Image edge detection Rail transportation Hazards Classification algorithms

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