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IEEE Journal on Flexible Electronics 2024年第10期3卷 445-453页

作者： Mehmood, Muhammad Qasim Adnan, Malik Zulfiqar, Muhammad Hamza Aljaloud, Khaled A. Sarwar, Rimsha Hussain, Rifaqat Alqahtani, Ali H. Alomainy, Akram Punjab Lahore54000 Pakistan Department of Biomedical Engineering University of Engineering and Technology Narowal Campus Lahore54890 Pakistan College of Engineering King Saud University Muzahimiyah Branch Riyadh11451 Saudi Arabia Antenna and Electromagnetics Research Group School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United States

Nowadays, textile-based sensors are of great interest because of the significance of intelligent and smart textiles in wearable applications because of textiles’ washability, flexibility, and durability. We developed conductive yarn-based textile sensors for wearable multimode human–machine interfaces (HMIs), breathing, and walking pattern detection. The low-cost sewing process is used to develop interdigitated capacitive (IDC) sensor patterns on shirts, masks, and shoe soles using ultrafine highly conductive thread. Four sensor-based touchpads (SBTPs) were developed on the shirt and showed multiple modes of operation based on the pressure of the finger touch. The multimode capacitive sensors-based HMI is connected to the laptop wirelessly to perform three different functions from each sensor. The sensors exhibit a sensitivity of 34.675, 29.440, and 25.789 pF/N at low, medium, and high touch pressure. The developed mask detects the breathing pattern of humans, whether it’s slow, normal, or fast. Shoe Insole developed sensors to see the walking pattern, either slow, normal, or running. The response and recovery time of the sensor system is 11 and 10 ms, respectively. Sensors tested for 20 000 detection cycles and responded stability. Also, the sensors responded accurately after washing with water and detergent water. Reported textile sensors are washable, flexible, stretchable, comfortable, and reusable, showing the practicality of proposed sensors for personalized healthcare, smart textiles, and electronic textiles (e-textiles). © 2022 IEEE.

关键词： Smart textiles

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Deployment Strategy for Multiple Controllers Based on the Aviation On-Board Software-Defined Data Link Network

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computers, Materials & Continua 2023年第12期77卷 3867-3894页

作者： Yuting Zhu Yanfang Fu Yang Ce Pan Deng Jianpeng Zhu Huankun Su China Electronics Technology Group Corporation 20th Research Institute Xi’an710068China School of Computer Science and Engineering Xi’an Technological UniversityXi’an710021China Department of Statistics and Actuarial Science University of WaterlooWaterlooON N2L 3G1Canada

In light of the escalating demand and intricacy of services in contemporary terrestrial,maritime,and aerial combat operations,there is a compelling need for enhanced service quality and efficiency in airborne cluster communication ***-Defined Networking(SDN)proffers a viable solution for the multifaceted task of cooperative communication transmission and management across different operational domains within complex combat contexts,due to its intrinsic ability to flexibly allocate and centrally administer network *** study pivots around the optimization of SDN controller deployment within airborne data link clusters.A collaborative multi-controller architecture predicated on airborne data link clusters is thus *** this architectural framework,the controller deployment issue is reframed as a two-fold problem:subdomain partition-ing and central interaction node *** advocate a subdomain segmentation approach grounded in node value ranking(NDVR)and a central interaction node selection methodology predicated on an enhanced Artificial Fish Swarm Algorithm(AFSA).The advanced NDVR-AFSA(Node value ranking-Improved artificial fish swarm algorithm)algorithm makes use of a chaos algorithm for population initialization,boosting population diversity and circumventing premature algorithm *** the integration of adaptive strategies and incorporation of the genetic algorithm’s crossover and mutation operations,the algorithm’s search range adaptability is enhanced,thereby increasing the possibility of obtaining globally optimal solutions,while concurrently augmenting cluster *** simulation results verify the advantages of the NDVR-IAFSA algorithm,achieve a better load balancing effect,improve the reliability of aviation data link cluster,and significantly reduce the average propagation delay and disconnection rate,respectively,by 12.8%and 11.7%.This shows that the optimization scheme has important significance in pra

关键词： Aviation cluster software defined network controller deployment Airborne network data link

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Distributed Information Bottleneck for a Primitive Gaussian Diamond MIMO Channel

Distributed Information Bottleneck for a Primitive Gaussian ...

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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Song, Yi Xu, Hao Wong, Kai-Kit Caire, Giuseppe Shamai Shitz, Shlomo Technical University of Berlin Faculty of Electrical Engineering and Computer Science Berlin10587 Germany University College London Department of Electronic and Electrical Engineering LondonWC1E7JE United Kingdom Technion-Israel Institute of Technology Department of Electrical and Computer Engineering Haifa3200003 Israel

ISBN: (纸本)9781665475549

This paper considers the distributed information bottleneck (D-IB) problem for a primitive Gaussian diamond channel with two relays and MIMO Rayleigh fading. The channel state is an independent and identically distributed (i.i.d.) process known at the relays but unknown to the destination. The relays are oblivious, i.e., they are unaware of the codebook and treat the transmitted signal as a random process with known statistics. The bottleneck constraints prevent the relays to communicate the channel state information (CSI) perfectly to the destination. To evaluate the bottleneck rate, we provide an upper bound by assuming that the destination node knows the CSI and the relays can cooperate with each other, and also two achievable schemes with simple symbol-by-symbol relay processing and compression. Numerical results show that the lower bounds obtained by the proposed achievable schemes can come close to the upper bound on a wide range of relevant system parameters. © 2023 IEEE.

关键词： Channel state information

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Brain Tumor Segmentation from 3D MRI Scans Using U-Net

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SN computer science 2023年第4期4卷 386页

作者： Montaha, Sidratul Azam, Sami Rakibul Haque Rafid, A.K.M. Hasan, Md. Zahid Karim, Asif Department of Computer Science and Engineering Daffodil International University 102/1 Sukrabad Mirpur Rd Dhaka 1207 Bangladesh Faculty of Science and Technology Charles Darwin University Ellengowan Drive Casuarina 0909 NT Australia

A fully automated system based on three-dimensional (3D) magnetic resonance imaging (MRI) scans for brain tumor segmentation could be a diagnostic aid to clinical specialists, as manual segmentation is challenging, arduous, tedious and error prone. Employing 3D convolutions requires large computational cost and memory capacity. This study proposes a fully automated approach using 2D U-net architecture on BraTS2020 dataset to extract tumor regions from healthy tissue. All the MRI sequences are experimented with the model to determine for which sequence optimal performance is achieved. After normalization and rescaling, using optimizer Adam with learning rate 0.001 on T1 MRI sequence, we get an accuracy of 99.41% and dice similarity coefficient (DSC) of 93%, demonstrating the effectiveness of our approach. The model is further trained with different hyper-parameters to assess the robustness and performance consistency. © 2023, The Author(s).

关键词： 3D MRI Brain tumor segmentation BraTS dataset U-Net

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Accurate Distinction of Weak and Strong AC Grids for Emerging Hierarchical-Infeed LCC-UHVDC Systems

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CSEE Journal of Power and Energy Systems 2024年第2期10卷 772-777页

作者： Hao Xiao Xianzhong Duan Yi Zhang Department of Electrical and Computer Engineering University of ManitobaWinnipeg R3T 2N2ManitobaCanada State Key Laboratory of Advanced Electromagnetic Engineering and Technology Hubei Electric Power Security and High Efficiency Key LaboratorySchool of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan 430074HubeiChina RTDS Technologies Inc. Winnipeg R3T 2E1ManitobaCanada

Distinction of weak and strong AC grids for emerging hierarchical-infeed LCC-UHVDC systems is important for planning and operation departments. However, accuracy of earlier distinction methods is limited as they were developed by empirical reasoning without rigorous theoretical analysis. Hence in this letter, hierarchical-infeed interactive effective short-circuit ratio (HIESCR) index is first used for strength evaluation of HIDC systems with complex inter-inverter interactions considered. Boundary HIESCR (BHIESCR) is also introduced in the proposed distinction method of weak and strong AC grids. That is, weak (or strong) AC grids are, respectively, identified when HIESCR is less (or greater) than BHIESCR. Second, it is shown BHIESCR remains almost unchanged as 3.0 versus various system parameters and rated operation variables based on rigorous theoretical analysis. This salient feature makes the proposed method more accurate than earlier methods. Finally, the proposed method is validated by simulations based on the PSCAD/EMTDC program.

关键词： Hierarchical-Infeedinteractive effective short-circuit ratio hierarchical-infeed LCC-UHVDC systems inter-inverter interactions strong AC grids weak AC grids

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A systematic literature review to implement large language model in higher education: issues and solutions

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Discover Education 2025年第1期4卷 1-25页

作者： Guizani, Sghaier Mazhar, Tehseen Shahzad, Tariq Ahmad, Wasim Bibi, Afsha Hamam, Habib College of Engineering Alfaisal University Riyadh 11533 Saudi Arabia School of Computer Science National College of Business Administration and Economics Lahore 54000 Pakistan Department of Computer Science and Information Technology School Education Department Government of Punjab Layyah 31200 Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal 57000 Pakistan Department of Computing School of Arts and Creative Technology University of Greater Manchester Manchester BL15AB United Kingdom Department of Computer and Software Technology University of Swat Swat 19200 Pakistan Faculty of Engineering Uni de Moncton Moncton E1A3E9 NB Canada School of Electrical Engineering University of Johannesburg Johannesburg 2006 South Africa International Institute of Technology and Management (IITG) Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence - Spectrum Tunis Tunisia

Artificial intelligence-driven Chatbots, especially large language models (LLMs) like GPT-4, represent significant progress in digital education. These models excel in mimicking human-like text and transforming learning and teaching methods. This study examines the development, application, and impact of LLMs in education. It highlights their role in automating instructional tasks and promoting personalized learning experiences. Despite integration concerns and ethical debates, LLMs showcase the potential of AI to improve educational practices. Our research concludes that LLMs offer transformative opportunities for education. However, their incorporation requires careful ethical considerations, data privacy measures, and a balance between human educators and AI technologies. The findings suggest strategies for integrating LLMs into educational frameworks to enhance learning outcomes while preserving educational integrity. © The Author(s) 2025.

关键词： Artificial intelligence Education Large language models Learning systems Natural language processing systems Neural networks

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Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition

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植物表型组学（英文） 2024年第4期6卷 828-840页

作者： Jianwu Lin Xin Zhang Yongbin Qin Shengxian Yang Xingtian Wen Tomislav Cernava Quirico Migheli Xiaoyulong Chen Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China School of Biological Sciences Faculty of Environmental and Life SciencesUniversity of SouthamptonSouthampton S017 1BJUK Dipartimento di Agraria and NRD-Nucleo di Ricerca sulla Desertificazione Università degli Studi di SassariSassariItaly Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Life Sciences Guizhou UniversityGuiyang 550025China

Accurate identification of plant diseases is important for ensuring the safety of agricultural *** neural networks(CNNs)and visual transformers(VTs)can extract effective representations of images and have been widely used for the intelligent recognition of plant disease ***,CNNs have excellent local perception with poor global perception,and VTs have excellent global perception with poor local *** makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition *** this paper,we propose a local and global feature-aware dual-branch network,named LGNet,for the identification of plant *** specifically,we first design a dual-branch structure based on CNNs and VTs to extract the local and global ***,an adaptive feature fusion(AFF)module is designed to fuse the local and global features,thus driving the model to dynamically perceive the weights of different ***,we design a hierarchical mixed-scale unit-guided feature fusion(HMUFF)module to mine the key information in the features at different levels and fuse the differentiated information among them,thereby enhancing the model's multiscale perception ***,extensive experiments were conducted on the Al Challenger 2018 dataset and the self-collected corn disease(SCD)*** experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the Al Challenger 2018 dataset and the SCD dataset,with accuracies of 88.74％and 99.08％,respectively.

关键词： global plant disease dual-branch feature-aware local networks recognition

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A Temporal-Spatial Graph Network With a Learnable Adjacency Matrix for Appliance-Level Electricity Consumption Prediction

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第4期6卷 989-1002页

作者： Li, Dandan Xia, Jiaxing Li, Jiangfeng Xiao, Changjiang Stankovic, Vladimir Stankovic, Lina Shi, Qingjiang Tongji University School of Computer Science and Technology Shanghai201804 China China University of Mining and Technology Artificial Intelligence Research Institute Jiangsu Xuzhou221008 China Tongji University College of Surveying and Geo-Information Shanghai200092 China University of Strathclyde Department of Electronic and Electrical Engineering GlasgowG1 1XJ United Kingdom

Predicting the electricity consumption of individual appliances, known as appliance-level energy consumption (ALEC) prediction, is essential for effective energy management and conservation. Despite its importance, research in this area is limited and faces several challenges: 1) the correlation between the usage of different appliances has rarely been considered for ALEC prediction;2) a learnable strategy for obtaining the optimal correlation between different appliance behaviors is lacking;and 3) it is difficult to accurately quantify the usage relationship among different appliances. To address these issues, we propose a graph-based temporal-spatial network that employs a learnable adjacency matrix for appliance-level load prediction in this work. The network comprises a temporal graph convolutional network (TGCN) and a learnable adjacency matrix that enables us to utilize correlations between appliances and quantify their relationships. To validate our approach, we compared our model with six others: a TGCN model with a fixed adjacency matrix where all elements are set to 0;a TGCN model with a fixed adjacency matrix where all elements are set to 0.5, except for the diagonal;a TGCN model with a randomly generated adjacency matrix, except for the diagonal;an Aug-LSTM model;a model with ResNetPlus architecture;and a feed-forward deep neural network. Five houses in four datasets: AMPDs, REFIT, UK-DALE, and SC-EDNRR are utilized. The metrics used in this study include root mean square error, explained variance score, mean absolute error, F-norm and coefficient of determination. Our experiments have validated the accuracy and practicality of our proposed approach across different datasets. © 2024 IEEE.

关键词： Prediction models

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Green Computing for Industrial Wireless Sensor Networks: Energy oriented Cross Layer Modelling

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Recent Patents on engineering 2022年第3期16卷 97-109页

作者： Ram, Mahendra Kumar, Sushil Kumar, Arvind Kharel, Rupak School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi110067 India Department of Computer Science and Engineering in School of Computing Science and Engineering Galgotias University Gautam Buddh Nagar Uttar Pradesh Greater Noida India Department of Computing and Mathematics Manchester Metropolitan University ManchesterM1 5GD United Kingdom

Background: Enabling industrial environment with automation is growing trend due to the recent developments as industry 4.0 centric production. The industrial wireless sensor network environments have a number of constraints, including densely deployed nodes, delay constraint for mechanical operation, and access constraints due to node position within instruments. The related literature have applied existing models of wireless sensor network in industrial environment without appropriate updating in the different layers of communication, which results in performance degradation in realistic industrial scenario. Method: This paper presents a framework for Energy Oriented Cross Layer Data Dissemination Path (E-CLD2P) towards enabling green computing in industrial wireless sensor network environments. It is a cross-layer design approach considering deployment of sensors at the physical layer up to data dissemination at the network layer and smart services at application layer. In particular, an energy centric virtual circular deployment visualization model is presented focusing on physical layer signal transmission characteristics in industrial WSNs scenario. A delay centric angular striping is designed for cluster based angular transmission to support deadline constrained industrial operation in the WSNs environments. Algorithms for energy centric delivery path formulation and node’s role transfer are developed to support green computing in restricted access industrial WSNs scenario. Results: The green computing framework is implemented to evaluate the performance in a realistic industrial WSNs environment. Conclusion: The performance evaluation attests the benefits in terms of number of metrics in realistic industrial constrained environments. © 2022 Bentham science Publishers.

关键词： Sensor nodes

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Robust Transmission Design in Multiobjective RIS-Aided SWIPT IoT Communications

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IEEE Internet of Things Journal 2024年第10期11卷 18605-18618页

作者： Sharma, Vaibhav Allu, Raviteja Singh, Sandeep Kumar Singh, Keshav Duong, Trung Q. Tsiftsis, Theodoros A. Institute of Communications Engineering National Sun Yat-sen University Kaohsiung804 Taiwan Motilal Nehru National Institute of Technology Allahabad Electronics and Communication Engineering Department Prayagraj211004 India Memorial University of Newfoundland Faculty of Engineering and Applied Science St. John'sNLA1B 3X5 Canada Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom University of Thessaly Department of Informatics and Telecommunications Lamia35100 Greece

This work investigates the performance of simultaneous wireless information and power transfer (SWIPT) in a reconfigurable intelligent surface (RIS)-aided Internet of Things (IoT) communications under imperfect channel state information (CSI). We formulate a multiobjective optimization problem (MOOP) to design a transmit precoding vector (TPV) at the base station (BS) and phase shift matrix (PSM) at the RIS that jointly maximizes energy efficiency (EE) and harvested power (HP) under the norm-bounded CSI error model. Due to the conflicting objective functions and nonconvex nature of the above optimization problem, the MOOP is simplified using the ϵ-constraint method and subsequently adopting advanced optimization tools, such as the Dinkelbach method, S-procedure, general sign-definiteness, semidefinite programming and convex-concave procedure. Thereafter, we propose an alternating optimization-based algorithm which determines optimal TPV and PSM iteratively that jointly maximizes the EE and HP of the considered system. Through numerical simulations, we validate the robustness, optimality, convergence, accuracy, and effectiveness of our proposed algorithm. Furthermore, we assess the impact of several key parameters, such as the number of RIS elements, available transmit power at BS, and the minimum HP on the performance of the considered system. © 2014 IEEE.

关键词： Energy efficiency

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