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NOMA-Based Ze-RIS Empowered Backscatter Communication with Energy-Efficient Resource Management

IEEE Transactions on Communications 2025年

作者： Asif, Muhammad Bao, Xu Ihsan, Asim Khan, Wali Ullah Li, Xingwang Chatzinotas, Symeon Dobre, Octavia A. The School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China The Department of Electrical Engineering University of Cambridge Cambridge United Kingdom University of Luxembourg Luxembourg City1855 Luxembourg The School of Physics and Electronic Information Engineering Henan Polytechnic University the Jiaozuo Key Laboratory of Crow-Sensing Network Jiaozuo454003 China The Department of Electrical and Computer Engineering Faculty of Engineering and Applied Science Memorial University St. John’sNLA1B 3X9 Canada

This manuscript introduces a novel energy-efficient optimization strategy for a zero-energy reconfigurable intelligent reflecting surface (Ze-RIS) supported backscatter communication system employing non-orthogonal multiple access (NOMA). The central objective is to maximize the energy efficiency of the system by optimizing the several key parameters, including the amplitude reflection coefficient of Ze-RIS, the reflection coefficients of the backscatter tags, transmit beamforming at the base station, and passive beamforming at the Ze-RIS node, while incorporating a practical non-linear energy harvesting model both for the Ze-RIS and backscatter nodes. The proposed algorithm addresses the complex non-convex problem through three stages. Firstly, the transmit beamforming vectors are determined by leveraging the semi-definite programming and successive-convex approximation, while handling the rank-1 constraint with the semi-definite relaxation. Secondly, we determine the amplitude reflection coefficient of Ze-RIS by leveraging the monotonicity property of the objective function. Simultaneously, we compute the reflection coefficients of backscatter tags using the Dinkelbach algorithm, Lagrange duality, and the sub-gradient method. Thirdly, we compute passive beamforming using successive-convex approximation and semi-definite programming techniques, achieving a rank-1 solution through the penalty-based method. Finally, the numerical simulations confirm the effectiveness of the proposed approach, demonstrating its superiority over the benchmark competitors with rapid convergence within a few iterations. © 1972-2012 IEEE. All rights reserved.

关键词： Beamforming

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Study on P-Type Doping of Mid-Wave and Long-Wave Infrared Mercury Cadmium Telluride

arXiv

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arXiv 2025年

作者： Dehzangi, Arash Armstrong, John Schaake, Chris Skokan, Mark Morrison, Tyler Wilks, Justin Ajmera, Sameer Kinch, Mike DRS Network & Imaging Systems LLC’ DallasTX United States Department of Electrical and Computer Engineering University of Texas DallasTX United States

We present in depth study of p-type doping concentration of mid-wave infrared (MWIR) and long-wave infrared (LWIR) mercury cadmium Telluride (HgCdTe) thin films. Annealing time was changed under specific conditions to achieve a stable copper (Cu) doping concentration for HgCdTe thin films. Both MWIR and LWIR HgCdTe material were grown by molecular beam epitaxy (MBE), where different trends were observed between LWIR and MWIR HgCdTe thin films by increasing anneal time. We also report the impact of different thickness (4µm, 6µm and 9µm) along with annealing time on doping level of LWIR HgCdTe thin films. © 2025, CC BY-NC-ND.

关键词： Thin films

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Service-Oriented Multipath Scheduling for Integrated Satellite-Terrestrial networks

Service-Oriented Multipath Scheduling for Integrated Satelli...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Ouyang, Man Zhang, Ran Wang, Bingqing Liu, Jiang Zhuang, Weihua Bupt State Key Laboratory of Networking and Switching Technology Beijing100876 China Purple Mountain Laboratories Future Network Research Center Nanjing211111 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

ISBN: (纸本)9798350351255

Low earth orbit (LEO) satellite networks can seamlessly supplement terrestrial networks by providing a high capacity, wide coverage, and cost-effective solution. Positioned to play a significant role in the upcoming 5G/6G era thanks to reduced launch expenses, LEO satellite networks offer benefits such as multi-path transmission, aggregated link bandwidth, redundant paths, and enhanced mobility support. These advantages necessitate further exploration in integrated satellite-terrestrial networks. In this work, we leverage network conditions, underlying link status, and real-time service characteristics to achieve effective synergy, aiming to fulfill application requirements. We formulate the service-oriented multi-path scheduling (SOMPS) problem as a bounded multi-knapsack problem and employ dynamic programming methods for its solution. Simulation results demonstrate that our proposed scheme provides high transmission rate, low latency, and customized information delivery for services, in comparison with baseline schemes. © 2024 IEEE.

关键词： Dynamic programming

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A Study on the Potency of Hybrid Models: Detecting Diseases in Cucumber Leaves with Pre-trained CNNs and SVM

A Study on the Potency of Hybrid Models: Detecting Diseases ...

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International Electrical engineering Congress (iEECON)

作者： Teerapon Yodrot Chalermkiat Sutacha Teerapong Orachon Nattapon Jangjongdee Sompol Boonyasuwanno Department of Electrical Engineering Technology Faculty of Industry and Technology Rajamangala University of Technology Rattanakosin Prachuap Khiri Khan Thailand Department of Computer Network Engineering Faculty of Industrial Technology Ubon Ratchathani Rajabhat University Ubon Ratchathani Thailand Department of Computer Engineering KOSEN of King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (数字)9798350383591

ISBN: (纸本)9798350383607

This study investigates a hybrid machine learning approach for the classification of cucumber leaf diseases, combining the strengths of pre-trained Convolutional Neural networks (CNNs) and Support Vector Machines (SVM). Focusing on a publicly available dataset, we evaluated four CNN architectures—VGG16, ResNet50, AlexNet, and EfficientNetV2—for feature extraction, followed by SVM for the final classification task. The performance of each model was assessed using F1 scores and confusion matrices, with ResNet50 yielding the highest F1 score, indicative of a strong balance between precision and recall. VGG16 and AlexNet showed potential for improvement, which could be realized through model tuning or integration into an ensemble framework. EfficientNetV2, despite a slightly lower F1 score, highlighted the importance of further training to enhance class distinction. The research underlines the importance of a hybrid approach, which harnesses deep learning's feature extraction capabilities with SVM's classification strength, to improve the accuracy of disease detection in cucumber plants.

关键词： Support vector machines Training Adaptation models Neural networks Feature extraction Convolutional neural networks Task analysis

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Text Analysis on Early Reactions to ChatGPT as a Tool for Academic Progress or Exploitation

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SN computer Science 2024年第4期5卷 366页

作者： Bukar, Umar Ali Sayeed, Md Shohel Razak, Siti Fatimah Abdul Yogarayan, Sumendra Amodu, Oluwatosin Ahmed Raja Mahmood, Raja Azlina Centre for Intelligent Cloud Computing (CICC) Faculty of Information Science & amp Technology Multimedia University Jalan Ayer Keroh Lama Bukit Beruang Melaka 75450 Malaysia Department of Electrical Electronics & amp Systems Engineering Universiti Kebangsaan Malaysia (UKM) Selangor Bangi 43600 Malaysia Information and Communication Engineering Department Elizade University Ondo State Ilara-Mokin Nigeria Department of Communication Technology and Network Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Darul Ehsan Serdang 43400 Malaysia

Large language models (LLMs) have garnered significant attention lately, and one particular implementation that has captivated users is ChatGPT, a first-of-its kind innovation that sparks intense debates among professionals regarding its ethical boundaries, especially after it was released for public use. Some of the debates are ongoing, with several people getting more used to this tool and some losing touch with several ethical concerns. Thus, it is crucial to explore the early professional’s perspectives surrounding ChatGPT to comprehend its potential impact on the educational sector and, in particular, society in general. This research aims to address this knowledge gap by employing data mining techniques on LinkedIn to analyze ChatGPT-related discussions and provide a comprehensive textual examination. The dataset for this investigation consists of 166 records written in English by LinkedIn users discussing ChatGPT in its early stage. The study utilized VOSviewer software to clean, analyze, mine, and visualize the data. Among the sample, it was observed that 49% of the users are PhD holders, predominantly with experience in scientific research and data science fields. Hence, the findings reveal that issues such as plagiarism, references, citations, manuscripts, papers, and literature reviews stand out as the major concerns. These results suggest that while ChatGPT can contribute to academic progress by fostering new knowledge, thoughts, and ideas, it can also facilitate academic misconduct, including plagiarism and inaccurate information dissemination. Therefore, this paper also provides discussions on the implications and ethical issues of ChatGPT. In particular, it is imperative to establish clear ethical boundaries and guidelines for the appropriate use of LLMs in academic settings. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Academic ChatGPT Ethics LinkedIn LLMs Text analysis Visualization VOSviewer

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A Deep Learning-Based Algorithm for Energy and Performance Optimization of Computational Offloading in Mobile Edge Computing

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Khan, Israr Raza, Salman Rehman, Waheed Ur Khan, Razaullah Nahida, Kiran Tao, Xiaofeng National Engineering Laboratory for Mobile Network Technologies Beijing University of Posts and Telecommunications Beijing100876 China Department of Computer Science National Textile University Faisalabad Pakistan Department of Computer Science University of Peshawar Peshawar Pakistan Department of Computer Science University of Engineering and Technology Mardan Pakistan Beijing Laboratory of Advanced Information Network Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications China

Mobile edge computing (MEC) has produced incredible outcomes in the context of computationally intensive mobile applications by offloading computation to a neighboring server to limit the energy usage of user equipment (UE). However, choosing a pool of application components to offload in addition to the volume of data transfer along with the latency in communication is an intricate issue. In this article, we introduce a novel energy-efficient offloading scheme based on deep neural networks. The proposed scheme trains an intelligent decision-making model that picks a robust pool of application components. The selection is based on factors such as the remaining UE battery power, network conditions, the volume of data transfer, required energy by the application components, postponements in communication, and computational load. We have designed the cost function taking all the mentioned factors, get the cost for all conceivable combinations of component offloading decisions, pick the robust decisions over an extensive dataset, and train a deep neural network as a substitute for the exhaustive computations associated. Model outcomes illustrate that our proposed scheme is proficient in the context of accuracy, root mean square error (RMSE), mean absolute error (MAE), and energy usage of UE. © 2023 Israr Khan et al.

关键词： Data transfer

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AI Test Modeling and Analysis for Intelligent Chatbot Mobile App - A Case Study on Wysa

AI Test Modeling and Analysis for Intelligent Chatbot Mobile...

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IEEE International Conference on Artificial Intelligence Testing (AITest)

作者： Jerry Gao Prerna Garsole Radhika Agarwal Shengqiang Liu Department of Computer Engineering College of Engineering San Jose State University ALPSTouchStone Inc. ALPSTouchStone Inc. San Jose California USA Computer Network Information Center Chinese Academy of Sciences Tiangong University Tianjin

ISBN: (数字)9798350365054

ISBN: (纸本)9798350365061

The functionalities of AI-powered mobile apps or systems heavily depend on the given training dataset. The challenge, in this case, is that a learning system will change its behavior due to a slight change in the dataset. Current alternative approaches for evaluating these apps either focus on individual performance measurements such as accuracy, etc. Inspired by principles of the decision tree test method in software engineering, this paper provides a tutorial discussion on intelligent AI test modeling chat systems including basic concepts, validation process, testing scopes, approaches, and needs. The report is about an intelligent AI test modeling chatbot system that is built and implemented based on an innovative 3D AI test model for AI-powered functions in intelligent mobile apps to support model-based AI function testing, test data generation, auto test scripting and execution, and adequate test coverage analysis.

关键词： Training Analytical models Solid modeling Three-dimensional displays Tutorials Chatbots Data models

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A QoE-Aware Adaptive Energy-Efficient Transmission Scheduling Method 30

A QoE-Aware Adaptive Energy-Efficient Transmission Schedulin...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Yuan, Yankun Wang, Lin Xiao, Chonghui Liu, Zijuan Dang, Fan Wang, Xu Zhao, Haitian Miao, Xin Yanshan University School of Information Science and Engineering China National Computer Network Emergency Response Technical Team/Coordination Center of China China Tsinghua University Department of Automation and BNRist China Tsinghua University Global Innovation Exchange China Tsinghua University School of Software and BNRist China

ISBN: (纸本)9798331515966

In this paper, we propose a dynamic data transmission strategy for smart home environments that aims to optimize the Quality of Experience (QoE) by adaptively adjusting the data upload frequency based on the predicted trends in sensor data. Using the home wireless sensors monitoring dataset, we implement a deep learning model for accurate time series forecasting. In addition, an anomaly detection mechanism is used to identify critical events, requiring more frequent data uploads when important changes are detected. The QoE is quantified through a weighted average of several influencing factors, including data timeliness, timely upload of critical events, and transmission frequency. Our optimization objective is to maximize QoE while minimizing the number of transmissions, with an emphasis on reducing energy consumption through intelligent scheduling. The results demonstrate that our approach effectively balances data timeliness, transmission efficiency, and energy savings, leading to improved user satisfaction in smart home applications. © 2024 IEEE.

关键词： Adaptive algorithm Anomaly detection Energy conservation IoT LSTM Models QoE

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DCGSD: Low-Light Image Enhancement with Dual-Conditional Guidance Sparse Diffusion Model

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Jin, Haiyan Wang, Jing Zuo, Fengyuan Su, Haonan Xiao, Zhaolin Wang, Bin Zhang, Yuanlin Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Department of Computer Science and Engineering NO. 5 South Jinhua Road Shaanxi Xi'an710048 China

When restoring low-light images, most methods largely overlook the ambiguity due to dark noise and lack discrimination for region and shape representations, resulting in invalid feature enhancement. In this work, we propose a physically explainable and prior guidance model for low-light image enhancement, termed Dual-Conditional Guidance Sparse Diffusion (DCGSD). Specifically, we introduce an elaborately designed Luminance Structure Guidance Head, which can be easily plugged into the existing diffusion model to emphasize the value of the luminance and structural representation. Furthermore, for reliable noise analysis, we provide a novel Sparse Attention Enhancement Module that is adaptively empowered to exploit the most useful region-to-region dependencies. This dynamic selection makes the diffusion process from dense to sparse, thus improving the efficiency of the reasoning noise distributions. To avoid noise amplification, we further present a Skip Calibration Module, which can be used to refine the local neighborhood that contains noisy and structural information. Extensive experiments have been performed to verify the superiority of the proposed method. DCGSD shows that leveraging dual-conditional guidance can support the diffusion model to produce sharper and more realistic results. © 1991-2012 IEEE.

关键词： Feature extraction

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EDGAR: An Autonomous Driving Research Platform – From Feature Development to Real-World Application

arXiv

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arXiv 2023年

作者： Karle, Phillip Betz, Tobias Bosk, Marcin Fent, Felix Gehrke, Nils Geisslinger, Maximilian Gressenbuch, Luis Hafemann, Philipp Huber, Sebastian Hübner, Maximilian Huch, Sebastian Kaljavesi, Gemb Kerbl, Tobias Kulmer, Dominik Maierhofer, Sebastian Mascetta, Tobias Pfab, Florian Rezabek, Filip Rivera, Esteban Sagmeister, Simon Seidlitz, Leander Sauerbeck, Florian Tahiraj, Ilir Trauth, Rainer Uhlemann, Nico Würsching, Gerald Zarrouki, Baha Althoff, Matthias Betz, Johannes Bengler, Klaus Carle, Georg Diermeyer, Frank Ott, Jörg Lienkamp, Markus Germany School of Computation Information and Technology Department of Computer Engineering Germany School of Computation Information and Technology Department of Computer Engineering Professorship Cyber-Physical Systems Cyprus School of Engineering and Design Department of Mechanical Engineering Germany School of Computation Information and Technology Department of Computer Engineering Chair of Network Architectures and Services Germany Germany Technical University of Munich Garching Germany

While current research and development of autonomous driving primarily focuses on developing new features and algorithms, the transfer from isolated software components into an entire software stack has been covered sparsely. Besides that, due to the complexity of autonomous software stacks and public road traffic, the optimal validation of entire stacks is an open research problem. Our paper focuses on these two aspects. We present our autonomous research vehicle EDGAR and its digital twin, a detailed virtual duplication of the vehicle. While the vehicle’s setup is closely related to the state of the art, its virtual duplication is a valuable contribution as it is crucial for a consistent validation process from simulation to real-world tests. In addition, different development teams can work with the same model, making integration and testing of software stacks much easier, significantly accelerating the development process. The real and virtual vehicles are embedded in a comprehensive development environment, which is also introduced. All parameters of the digital twin are provided open-source at https://***/TUMFTM/edgar_digital_twin. © 2023, CC BY.

关键词： Data mining

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