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A Novel Lightweight Joint Source-Channel Coding Design in Semantic Communications

IEEE Internet of Things Journal 2025年第11期12卷 18447-18450页

作者： Yu, Xianhua Li, Dong Zhang, Ning Shen, Xuemin Macau University of Science and Technology School of Computer Science and Engineering 999078 China University of Windsor Department of Electrical and Computer Engineering Canada University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Semantic communication has emerged as a promising solution to meet the growing demand for efficient data transmission in the information age. Unlike traditional communication methods that focus on transmitting raw data, semantic communication prioritizes preserving the meaning of transmitted information, which significantly reduces the data volume. However, implementing semantic communication systems in resource-constrained environments, such as Internet of Things (IoT) devices, remains challenging due to limited computational resources. In this letter, we propose a novel lightweight deep learning (DL) model, termed the lightweight image compression and reconstruction network (LICRnet). LICRnet leverages depthwise separable convolution (DSC) and a local and non-local mixture (LNLM) block to significantly reduce computational costs. Additionally, the LNLM incorporates a variable window size-based multi-scale attention mechanism (VW-MSA), enabling it to effectively learn from both local detailed features and global high-level meaningful features. Extensive simulations demonstrate that LICRnet significantly reduces computational complexity while maintaining satisfactory image compression and reconstruction performance, making it highly suitable for deployment in resource-constrained environments. © 2014 IEEE.

关键词： Channel coding

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On-chip dielectrophoretic single-cell manipulation

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Microsystems & Nanoengineering 2024年第4期10卷 87-114页

作者： Zuyuan Tian Xihua Wang Jie Chen Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6G 1H9Canada Academy for Engineering&Technology Fudan UniversityShanghai 200433China

Bioanalysis at a single-cell level has yielded unparalleled insight into the heterogeneity of complex biological *** with Lab-on-a-Chip concepts,various simultaneous and high-frequency techniques and microfluidic platforms have led to the development of high-throughput platforms for single-cell ***(DEP),an electrical approach based on the dielectric property of target cells,makes it possible to efficiently manipulate individual cells without *** review focusses on the engineering designs of recent advanced microfluidic designs that utilize DEP techniques for multiple single-cell ***-chip DEP is primarily effectuated by the induced dipole of dielectric particles,(i.e.,cells)in a non-uniform electric *** addition to simply capturing and releasing particles,DEP can also aid in more complex manipulations,such as rotation and moving along arbitrary predefined routes for numerous ***,DEP electrodes can be designed with different patterns to achieve different geometric boundaries of the electric *** many single-cell analyses require isolation and compartmentalization of individual cells,specific microstructures can also be incorporated into DEP *** article discusses common electrical and physical designs of single-cell DEP microfluidic devices as well as different categories of electrodes and *** addition,an up-to-date summary of achievements and challenges in current designs,together with prospects for future design direction,is provided.

关键词： manipulation utilize dielectric

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Integration of data science with the intelligent IoT(IIoT):Current challenges and future perspectives

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Digital Communications and Networks 2025年第2期11卷 280-298页

作者： Inam Ullah Deepak Adhikari Xin Su Francesco Palmieri Celimuge Wu Chang Choi Department of Computer Engineering Gachon UniversitySeongnamSujeong-gu 13120Republic of Korea University of Electronic Science and Technology of China Chengdu 611731China College of Information Science and Engineering Hohai University213000China Department of Computer Science University of SalernoItaly Graduate School of Informatics and Engineering The University of Electro-Communications 1-5-1ChofugaokaChofu-shiTokyo182-8585Japan

The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,such as Artificial Intelligence(AI)and machine learning,to make accurate *** science is the science of dealing with data and its relationships through intelligent *** state-of-the-art research focuses independently on either data science or IIoT,rather than exploring their ***,to address the gap,this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT)system by classifying the existing IoT-based data science techniques and presenting a summary of various *** paper analyzes the data science or big data security and privacy features,including network architecture,data protection,and continuous monitoring of data,which face challenges in various IoT-based *** insights into IoT data security,privacy,and challenges are visualized in the context of data science for *** addition,this study reveals the current opportunities to enhance data science and IoT market *** current gap and challenges faced in the integration of data science and IoT are comprehensively presented,followed by the future outlook and possible solutions.

关键词： Data science Internet of things(IoT) Big data Communication systems Networks Security Data science analytics

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Microstructural Analysis of Ceramic Materials Through Machine Learning Techniques: A Benchmarking Interdisciplinary Review for Ceramic Material Characterization

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Journal of The Institution of Engineers (India): Series D 2025年 1-19页

作者： Roy, Mahuya Kumar, Bijendra Chowdhury, Soumit Bhattacharya, Tapas Kumar Maity, Satyabrata Haldar, Partha Department of Computer Science and Engineering St. Thomas College of Engineering and Technology West Bengal Kolkata700023 India Gun and Shell Factory West Bengal Cossipore Kolkata700002 India Department of Computer Science and Engineering Jalpaiguri Government Engineering College West Bengal Jalpaiguri735102 India Department of Ceramic Technology Government College of Engineering and Ceramic Technology West Bengal Kolkata700010 India Department of Information Technology Techno International New Town 1/1 Service Rd. DG Block New Town Kolkata700156 India Department of Mechanical Engineering Government College of Engineering and Ceramic Technology West Bengal Kolkata700010 India

This study presents a revolutionary analytical study on ceramic microstructural images in the context of sophisticated image processing as well as machine learning techniques for automated and most accurate characterization of ceramic material properties. The main objective here is to scientifically correlate the derived microstructural image features with the concern material properties and this can be purely governed by specific material compositions that lead to better productivity. So, this particular unique study purely focuses on some efficient machine learning algorithms like K-means and fuzzy C-means in contrast to the traditional image processing concepts like Canny and Sobel for most effective classification as well as automated evaluation of ceramic microstructural image features. In addition, more advanced deep learning techniques like convolutional neural network also reflected here with in depth comparative analysis in view of its superiority for crack estimation, pore characterization, phase identification, grain boundary detection and grain area calculation that leads to accurate material characterizations. Further, continuous up gradations are still highlighted here with recent advanced deep learning models like Auto encoders, generative adversarial networks and recurrent neural networks for much more accurate characterization of ceramic microstructural image features boosting both material design and productivity in great manner. Apart from that, some latest image property related transformations are also investigated here for accurately tracing the critical image features while this study even adopts some exhaustive bibliometric analysis to precisely identify the effective microstructural evaluation methods from the existing literature. Hence this novel study not only provides a concrete pathway for identifying the most effective mechanism for ceramic microstructural image property evaluation and characterization in the present scenario but it als

关键词： Ceramic materials

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Class-conditional domain adaptation for semantic segmentation

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Computational Visual Media 2024年第5期10卷 1013-1030页

作者： Yue Wang Yuke Li James H.Elder Runmin Wu Huchuan Lu School of Information and Communication Engineering Dalian University of TechnologyDalian 116024China School of Computer Science Wuhan UniversityWuhan 430072China Department of Electrical Engineering and Computer Science York UniversityToronto M3J 1P3Canada Department of Computer Science the University of Hong KongHong Kong 999077China

Semantic segmentation is an important sub-task for many ***,pixel-level ground-truth labeling is costly,and there is a tendency to overfit to training data,thereby limiting the generalization *** domain adaptation can potentially address these problems by allowing systems trained on labelled datasets from the source domain(including less expensive synthetic domain)to be adapted to a novel target *** conventional approach involves automatic extraction and alignment of the representations of source and target domains *** limitation of this approach is that it tends to neglect the differences between classes:representations of certain classes can be more easily extracted and aligned between the source and target domains than others,limiting the adaptation over all ***,we address:this problem by introducing a Class-Conditional Domain Adaptation(CCDA)*** incorporates a class-conditional multi-scale discriminator and class-conditional losses for both segmentation and ***,they measure the segmentation,shift the domain in a classconditional manner,and equalize the loss over *** results demonstrate that the performance of our CCDA method matches,and in some cases,surpasses that of state-of-the-art methods.

关键词： domain adaptation generative adversarial networks semantic segmentation cityscapes

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Flexible Sensors for IoT-Based Health Monitoring

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2025年第2期4卷 63-88页

作者： Majumder, Sumit Roy, Arup Kumer Mondal, Tapas Deen, M. Jamal Chittagong University of Engineering and Technology Department of Biomedical Engineering Chattogram4349 Bangladesh Chittagong University of Engineering and Technology Department of Chemistry Chattogram4349 Bangladesh McMaster University Department of Pediatrics HamiltonONL8S 4K1 Canada McMaster University School of Biomedical Engineering Department of Electrical and Computer Engineering HamiltonONL8S 4K1 Canada

The global population is aging due to increased life expectancy and declining birth rates. As a result, there is a growing prevalence of chronic diseases such as heart disease, hypertension, and diabetes, among the older population. These conditions not only diminish the quality of life but also significantly drive up healthcare costs. Consequently, the demand for efficient and cost-effective healthcare solutions is rising. Traditional healthcare systems are often challenged by issues of accessibility and equity, particularly in regions with inadequate medical infrastructure and geographic barriers. In response to these challenges, this article explores the potential of advanced flexible sensor technologies, integrated with cutting-edge communication and computing tools such as the Internet of Things (IoT), artificial intelligence (AI), and big data analytics. These sensors enable continuous, unobtrusive monitoring of vital signs, and health parameters, facilitating personalized and preventive care in the comfort of an individual’s home. However, the widespread adoption of these technologies faces several obstacles, including challenges related to manufacturing scalability, cost, mechanical stability, and data security. This article reviews the current state of research and development in flexible sensors and their integration with modern technologies for IoT-based health monitoring. It also examines key challenges and concerns associated with their use and outlines the future potential for these sensors to revolutionize healthcare monitoring and management. © 2022 IEEE.

关键词： Costs

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Smart Textiles to Enable In-Home Health Care: State of the Art, Challenges, and Future Perspectives

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2024年第4期3卷 120-150页

作者： Majumder, Sumit Roy, Arup Kumer Jiang, Wei Mondal, Tapas Deen, M. Jamal Chittagong University of Engineering and Technology Department of Biomedical Engineering Chattogram4349 Bangladesh Chittagong University of Engineering and Technology Department of Chemistry Chattogram4349 Bangladesh McMaster University Department of Electrical and Computer Engineering HamiltonONL8S 4K1 Canada McMaster University Department of Pediatrics HamiltonONL8S 4K1 Canada McMaster University Department of Electrical and Computer Engineering The School of Biomedical Engineering HamiltonONL8S 4K1 Canada

The rapid advances in high-speed and low-power computing, communication, and sensor technologies have propelled the growth of Internet-of-Things (IoT) technology. IoT enables sensors, actuators, and devices to work together in applications such as a smart home to track the health, activity, comfort, and well-being of residents, thus potentially revolutionizing the healthcare system. Textile-based wearables offer a promising solution to monitor health and activity in a smart home setting while ensuring comfort and unobtrusiveness. In fact, there has been a growing trend among researchers to develop textile-based sensors for measuring and monitoring vital signs, such as heart rate, body temperature, respiratory rate, biochemicals, humidity, and activities of daily living (ADLs). Researchers are also actively pursuing the development of electronic components, which are based on textiles, thus creating a new generation of wearable technology. Therefore, it is no wonder that the smart-textile market is expected to grow to over 4 billion by 2030. However, factors such as cost-effectiveness, manufacturing scalability, device performance, environmental and safety concerns, and privacy issues may impede the market penetration of smart textiles. In this article, we present a comprehensive overview of the current state of research and development in the manufacturing of smart textiles and textile-based sensors with a focus on their application in monitoring and management of health in a smart home environment. © 2022 IEEE.

关键词： Smart textiles

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ClipArtGAN: An Application of Pix2Pix Generative Adversarial Network for Clip Art Generation

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Multimedia Tools and Applications 2025年 1-25页

作者： Elnabawy, Reham H. Abdennadher, Slim Hellwich, Olaf Eldawlatly, Seif Technische Universität Berlin Berlin Germany Computer Science and Engineering Department Faculty of Media Engineering and Technology German University in Cairo Cairo Egypt Computer Science Department Faculty of Informatics and Computer Science German International University New Administrative Capital Egypt Computer and Systems Engineering Department Faculty of Engineering Ain Shams University 1 El-sarayat st. Abbassia Cairo Egypt Computer Science and Engineering Department The American University in Cairo Cairo Egypt School of Information Technology New Giza University Giza Egypt

An image can convey a thousand words. This statement emphasizes the importance of illustrating ideas visually rather than writing them down. Although detailed image representation is typically instructive, there are several contexts where simplification of image representations may be more informative. One way to provide an abstract representation of an object is via clip art, which is a simple graphic illustration of an object that can be included in documents or presentations. Clip art representation can be utilized in graphic design for concepts expression and daily life objects illustration. Furthermore, clip art had shown remarkable advantages when compared to real images in terms of editing and manipulation since clip art geometry can be easily edited in addition to the feasibility of storing it in a resolution-independent manner. However, it can be difficult to automatically create clip arts that resemble any object. Here, we propose a novel deep learning model, named ClipArtGAN, that utilizes Generative Adversarial Networks (GANs) to convert a photo of an object to its corresponding clip art. To our knowledge, this is the first GAN model based on Pix2Pix architecture that is capable of automatically generating clip art for any arbitrary object. Three training datasets were collected to train and compare two GAN models based on Pix2Pix and CycleGAN architectures. Our results demonstrate that the mean accuracy obtained using ClipArtGAN utilizing Pix2Pix outperforms that of CycleGAN by 58% and that of Google Images-based search by 54%. These results indicate the efficacy of ClipArtGAN in generating representative clip art images. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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Modeling Long- and Short-Term Service Recommendations with a Deep Multi-Interest Network for Edge Computing

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Tsinghua Science and technology 2024年第1期29卷 86-98页

作者： Rui Yuan Shunmei Meng Ruihan Dou Xinna Wang Department of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210056China Faculty of Mathematics University of WaterlooWaterloo N2L 3G1Canada

Edge computing platforms enable application developers and content providers to provide context-aware services(such as service recommendations)using real-time wireless access network *** to recommend the most suitable candidate from these numerous available services is an urgent ***-through rate(CTR)prediction is a core task of traditional service ***,many existing service recommender systems do not exploit user mobility for prediction,particularly in an edge computing *** this paper,we propose a model named long and short-term user preferences modeling with a multi-interest network based on user *** uses a logarithmic network to capture multiple interests in different fields,enriching the representations of user short-term *** terms of long-term preferences,users'comprehensive preferences are extracted in different periods and are fused using a nonlocal *** experiments on three datasets demonstrate that our model relying on user mobility can substantially improve the accuracy of service recommendation in edge computing compared with the state-of-the-art models.

关键词： recommender system logarithmic network nonlocal network

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A location-based fog computing optimization of energy management in smart buildings:DEVS modeling and design of connected objects

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Frontiers of computer Science 2023年第2期17卷 179-195页

作者： Abdelfettah MAATOUG Ghalem BELALEM Saïd MAHMOUDI Computer Science Department Faculty of Exact and Applied SciencesUniversity of Oran 1 Ahmed Ben BellaOran 31000Algeria Science and Technology Department Faculty of Science and TechnologyUniversity of TIARETTiaret 14000Algeria Computer Science Department Faculty of EngineeringUniversity of MonsMons 7000Belgium

Nowadays,smart buildings rely on Internet of things(loT)technology derived from the cloud and fog computing paradigms to coordinate and collaborate between connected *** is characterized by low latency with a wider spread and geographically distributed nodes to support mobility,real-time interaction,and location-based *** provide optimum quality of user life in moderm buildings,we rely on a holistic Framework,designed in a way that decreases latency and improves energy saving and services efficiency with different *** EVent system Specification(DEVS)is a formalism used to describe simulation models in a modular *** this work,the sub-models of connected objects in the building are accurately and independently designed,and after installing them together,we easily get an integrated model which is subject to the fog computing *** results show that this new approach significantly,improves energy efficiency of buildings and reduces ***,with DEVS,we can easily add or remove sub-models to or from the overall model,allowing us to continually improve our designs.

关键词： smart building energy consumption IoT fog computing Framework DEVS simulation models

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