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arXiv 2025年

作者： Reddy, Badri Ramanjaneya Dash, Soumya P. Alexandropoulos, George C. Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens16122 Greece Department of Electrical and Computer Engineering University of Illinois Chicago ChicagoIL60601 United States School of Electrical Sciences Indian Institute of Technology Bhubaneswar Argul Odisha Khordha752050 India

This paper analyzes the performance of a single-input multiple-output (SIMO) wireless communication system employing one- and two-sided amplitude shift keying (ASK) modulation schemes for data transmission and operating under correlated Rician fading channels. The receiver deploys an optimal noncoherent maximum likelihood detector, which exploits statistical knowledge of the channel state information for signal decoding. An optimal receiver structure is derived, from which series-form and closed-form expressions for the union bound on the symbol error probability (SEP) are obtained for general and massive SIMO systems, respectively. Furthermore, an optimization framework to derive the optimal one- and two-sided ASK modulation schemes is proposed, which focuses on minimizing SEP performance under an average transmit energy constraint. The conducted numerical investigations for various system parameters demonstrate that the proposed noncoherent SIMO system with the designed optimal ASK modulation schemes achieves superior error performance compared to traditional equispaced ASK modulation. It is also shown that, when the proposed system employs traditional two-sided ASK modulation, superior error performance from the case of using one-sided ASK is obtained. © 2025, CC BY.

关键词： Fading channels

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Introducing QuantConn: Overcoming Challenging Diffusion Acquisitions with Harmonization 15th

Introducing QuantConn: Overcoming Challenging Diffusion Acqu...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Newlin, Nancy Schilling, Kurt Koudoro, Serge Chandio, Bramsh Qamar Kanakaraj, Praitayini Moyer, Daniel Kelly, Claire E. Genc, Sila Yang, Joseph Yuan-Mou Wu, Ye Adluru, Nagesh Nath, Vishwesh Pathak, Sudhir Schneider, Walter Gade, Anurag Consagra, William Rathi, Yogesh Hendriks, Tom Vilanova, Anna Chamberland, Maxime Pieciak, Tomasz Ciupek, Dominika Vega, Antonio Tristán Aja-Fernández, Santiago Malawski, Maciej Ouedraogo, Gani Machnio, Julia Thompson, Paul M. Jahanshad, Neda Garyfallidis, Eleftherios Landman, Bennett Department of Computer Science Vanderbilt University NashvilleTN United States Department of Radiology and Radiological Sciences Vanderbilt University Medical Center Nashville United States Indiana University Bloomington BloomingtonIN United States Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of USC Los AngelesCA United States Developmental Imaging Murdoch Children’ Research Institute Melbourne Australia Murdoch Children’s Research Institute Melbourne Australia Turner Institute for Brain and Mental Health School of Psychological Sciences Monash University Melbourne Australia Department of Neurosurgery Royal Children’s Hospital Melbourne Australia Neuroscience Research Murdoch Children’s Research Institute Melbourne Australia Department of Paediatrics University of Melbourne Melbourne Australia School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China Waisman Center Department of Radiology University of Wisconsin Madison United States NVIDIA Santa Clara United States Learning Research and Development Center University of Pittsburgh Pittsburgh United States Brigham and Women’s Hospital Boston United States Brigham and Women’s Hospital Harvard Medical School Boston United States Department of Computer Science and Mathmatics Eindhoven University of Technology Eindhoven Netherlands LPI ETSI Telecomunicación Universidad de Valladolid Castilla y León Spain Sano Centre for Computational Medicine Kraków30-054 Poland Aix-Marseille Université Marseille France Vanderbilt University Institute of Imaging Science Vanderbilt University NashvilleTN United States Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States

ISBN: (纸本)9783031869198

White matter alterations are increasingly implicated in neurological diseases and their progression. Diffusion-weighted magnetic resonance imaging (DW-MRI) has been included in many international-scale studies to identify alterations in white matter microstructure and connectivity. Yet, quantitative investigation of DW-MRI data is hindered by a lack of consistency due to variations in acquisition protocols, sites, and scanners. Specifically, there is a need to harmonize the preprocessing of DW-MRI datasets to ensure that compatible and reproducible quantitative metrics are derived from each site, including (1) bundle-wise microstructure measures, (2) features of white matter fiber bundles, and (3) connectomics measures. In the MICCAI CDMRI 2023 QuantConn challenge, participants are provided raw data from the same individuals taken with two different acquisition protocols on a single 4 tesla scanner in the same scanning session and asked to preprocess the data in order to minimize acquisition differences while retaining biological variation. Here, we outline the testing framework, provide baseline pre-harmonized results, and discuss the learning implications of this challenge. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Photointerpretation

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Predicting land suitability for wheat and barley crops using machine learning techniques

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Scientific Reports 2025年第1期15卷 1-19页

作者： Ganati, Bikila Abebe Sitote, Tilahun Melak Department of Computer Science Faculty of Computing and Informatics Jimma Institute of Technology Jimma University P.O. Box 378 Jimma Ethiopia Department of Software Engineering College of Engineering Addis Ababa Science and Technology University P.O. Box 16417 Addis Ababa Ethiopia

Ensuring food security to meet the demands of a growing population remains a key challenge, especially for developing countries like Ethiopia. There are various policies and strategies designed by the government and stakeholders to confront the challenge. One of the strategies is using technology solutions to increase crop productivity. Precision agriculture using advanced technology has been utilized to increase crop yield. Identifying suitable land for a crop is one of the important factors that will affect the crop’s yield. The existing approach to land suitability identification for a crop is time-consuming, expensive, and inaccurate. In this study, land suitability has been predicted for the two widely grown cereal crops in Ethiopia—wheat and barley—using machine learning techniques. The dataset was obtained from the engineering Corporation of Oromia (ECO). To make it suitable for modelling, we have pre-processed it. Features have been selected with univariate feature selection (UFS), recursive feature elimination with cross validation (RFECV), and sequential forward selection (SFS). Then, random forest (RF), gradient boosting (GB), and K-nearest neighbour (KNN) were used to predict the land suitability of the two selected crops. To optimize the performance of the models, hyperparameters were tuned with cross-validated randomized searches. The performance of the models has been evaluated using stratified tenfold cross-validation with performance metrics such as accuracy, precision, recall, and F1-score. GB with the SFS has better performance than the other models, with accuracy of 99.41%, precision of 99.37%, recall of 99.34%, and an F1-score of 99.35%. We believe that predicting land suitability accurately using machine learning techniques for the two commonly cultivated cereal crops in Ethiopia will be helpful in increasing the crops’ productivity. The developed model is very accurate. It can be used to develop a decision support system to identify the land s

关键词： Barley Gradient boosting Land suitability Machine learning Sequential forward selection Wheat

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Exploiting Circular Shifts for Efficient Chaotic Image Encryption

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IEEE Access 2025年 13卷 92997-93016页

作者： Lazaros Moysis Marcin Lawnik Wassim Alexan Sotirios K. Goudos Murilo S. Baptista George F. Fragulis Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Department of Mechanical Engineering University of Western Macedonia Kozani Greece Department of Mathematical Methods in Technics and Informatics Faculty of Applied Mathematics Silesian University of Technology Gliwice Poland Communications Department Faculty of Information Engineering and Technology German University in Cairo Cairo Egypt ELEDIA@AUTH School of Physics Aristotle University of Thessaloniki Thessaloniki Greece Department of Physics Institute for Complex Systems and Mathematical Biology SUPA The University of Aberdeen Aberdeen U.K.

This work presents a chaotic image encryption algorithm that acts on the binary level of the image for effective permutation, and on the byte level for substitution. The permutation step combines actions of bit level rearranging, and bit shuffling. Circular shift operations are used for bit shuffling, to reduce the execution time. Circular shifts are also applied to shuffle the permutation and substitution rules, effectively increasing security at a very low execution cost. The encryption keys are plaintext dependent, and a key mixing step in included, so that each part of the key affects all the subsequent encryption operations. For the encryption, a new Soboleva hyperbolic tangent based map is first designed. The dynamical behavior of the map is studied and shows robust chaos, and an absence of equilibria, meaning that it can generate hidden attractors. This map is used as an entropy source, along with a chaotic pseudo random number generator (PRNG). The encryption process shows a good performance under a collection of tests, and has also a low execution time, 0.0134 sec for a $256\times 256$ image, 0.0530 sec for a $512\times 512$ image, and 0.2050 sec for a $1024\times 1024$ image. So the proposed algorithm is a viable option for fast, efficient, and secure data encryption.

关键词： Encryption Security Chaotic communication Generators Robustness Matrices Costs Standards Real-time systems Computational efficiency

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Multi-Agent Reinforcement Learning for Offloading Cellular Communications with Cooperating UAVs

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Mondal, Abhishek Mishra, Deepak Alexandropoulos, George C. Al-Nahari, Azzam Jantti, Riku National Institute of Technology Calicut Department of Electronics and Communication Engineering Kerala 673601 India University of New South Wales School of Electrical Engineering and Telecommunications Sydney2052 Australia National and Kapodistrian University of Athens Department of Informatics and Telecommunications Athens15784 Greece University of Illinois Department of Electrical and Computer Engineering ChicagoIL60601 United States Aalto University Department of Information and Communications Engineering Espoo02150 Finland Ibb University Department of Electrical Engineering Ibb70270 Yemen

Effective solutions for intelligent data collection in terrestrial cellular networks are crucial, especially in the context of Internet of Things (IoT) applications. The limited spectrum and coverage area of terrestrial base stations (BSs) pose challenges in meeting the escalating data rate demands of network users. Unmanned aerial vehicles (UAVs), known for their high agility, mobility, and flexibility, present an alternative means to offload data traffic from terrestrial BSs, serving as additional access points. In this paper, we aim to efficiently maximize the utilization of multiple UAVs for data traffic offloading from terrestrial BSs. Specifically, the focus is on maximizing user association with UAVs by jointly optimizing UAV trajectories and users' association indicators under quality-of-service (QoS) constraints. Since the formulated practical UAV control problem is non-convex and combinatorial, this study leverages the multi-agent reinforcement learning (MARL) framework. Employing MARL, each UAV acts as an independent agent, aiming to maintain inter-UAV cooperative behaviour. The proposed approach utilizes the finite-state Markov decision process (MDP) to account for UAVs' velocity constraints and the relationship between their trajectories and state space. A low-complexity distributed state-action-reward-state-action (SARSA) algorithm is presented to determine UAVs' optimal sequential decision-making policies over training episodes. The extensive simulation results validate the proposed analysis and offer valuable insights into the optimal UAV trajectories. The derived trajectories demonstrate superior average UAV association performance compared to benchmark techniques, such as Q-learning and particle swarm optimization. © 1965-2011 IEEE.

关键词： Markov processes

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Flicker Visibility With Different Spectra of White Light

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IEEE Transactions on Industry Applications 2025年

作者： Kukacka, Leos Hergesel, Jan Necasek, Jakub Bilek, Petr Vik, Michal Stiegler, Robert Pourarab, Morteza Meyer, Jan Drapela, Jiri Technical University of Liberec Faculty of Mechatronics Informatics and Interdisciplinary Studies Institute-of Mechatronics and Computer Engineering Liberec Czech Republic Technical University of Liberec Faculty of Textile Engineering Department of Material Engineering Czech Republic Technische Universität Dresden Institute of Electrical Power Systems and High Voltage Engineering Chair of Electrical Power Supply Dresden01069 Germany Brno University of Technology Faculty of Electrical Engineering and Communications Brno61600 Czech Republic

The standard flickermeter (voltage flickermeter) algorithm commonly used for the evaluation of flicker severity in current electromagnetic compatibility standards originates from the 1980 s. It was developed to mimic irritation of an average human observer by flickering incandescent lamp as a reference light source and optimized to voltage fluctuations caused by arc furnaces. However, the algorithm is questionable as the situation in the grid changed since the 1980 s;many modern devices are being connected to the grid, creating voltage distortion patterns which are not properly recognized by the voltage flickermeter. In addition, incandescent lamps have been largely replaced by discharge lamps and later by LEDs. Transitioning the algorithm from incandescent lamp light to LED light requires detailed revision of every part of the algorithm. This paper is concerned with verifying the frequency weighting function of the average human observer which is one of the bases of the voltage flickermeter, as well as of the derived light flickermeter. Flicker sensitivity of 34 human observers is measured with two different light sources (LED warm and neutral white, incandescent lamp) and compared with the frequency weighting function used in the current specification of the flickermeters in standards. Finally, an optimization of the weighting function is proposed based on the findings. © 1972-2012 IEEE.

关键词： Flickering

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Towards Bi-level Supply/Demand Balanced Charging Systems via Online Power Scheduling

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IEEE Transactions on Mobile Computing 2025年

作者： Lu, Xinyu Lou, Jiong Li, Jie Xu, Runhui Wu, Chentao Liu, Zhi Luo, Yuan Yang, Yang Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China Yancheng Blockchain Research Institute China Shanghai Jiao Tong University Shanghai200240 China Department of Computer Science and Engineering China Shanghai Jiao Tong University Innovation Center of Intelligent Connected Electric Vehicle in Global Institute of Future Technology Shanghai200240 China University of Electro-Communications Graduate School of Informatics and Engineering Tokyo182-8585 Japan Guangzhou511453 China Peng Cheng Laboratory Shenzhen518055 China Terminus Group Beijing100027 China

With the rise of transportation electrification, an increasing number of charging stations have been established, forming a city-scale charging system. These charging stations serve as intermediaries that connect supply and demand, drawing power from the grid and renewable energy sources to provide electricity to electric vehicles. Maintaining a delicate balance between supply and demand has emerged as a significant challenge for the charging system. On a macroscopic level, it impacts the power grid's peak load and reliability, while locally, it influences electric vehicle detour events. To comprehensively model the spatio-temporal characteristics in the charging system, we partition the charging system by adopting a supply-demand-aware approach and propose OPS, an online power scheduling algorithm based on the regularization technique. OPS aims to achieve a bi-level balance between supply and demand while constraining the power output of the charging system. We substantiate the efficacy of OPS through rigorous theoretical proofs, demonstrating its comparability to the optimal solution. Furthermore, we conduct extensive evaluation experiments with real-world data sets to establish the feasibility of the proposed methodology in alleviating the supply-demand imbalance. The results indicate that OPS attains an empirical competitive ratio of less than 1.2. © 2002-2012 IEEE.

关键词： Charging stations

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Understanding Older Adults' (Dis)Engagement with Design Materials 25

Understanding Older Adults' (Dis)Engagement with Design Mate...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

作者： Alisha Pradhan Ben Jelen Ramprabu Thangaraj Katie A. Siek Shannon Jette Amanda Lazar Department of Informatics Ying Wu College of Computing New Jersey Institute of Technology Newark New Jersey USA Computer Science and Software Engineering Rose-Hulman Institute of Technology Terre Haute Indiana USA Ying Wu College of Computing New Jersey Institute of Technology Newark New Jersey USA Luddy School of Informatics Computing and Engineering Indiana University Bloomington Indiana USA University of Maryland College Park Maryland USA College of Information Studies University of Maryland College Park Maryland USA

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Correction: Harnessing AI for sustainable higher education: ethical considerations, operational efficiency, and future directions

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Discover Sustainability 2025年第1期6卷 1-2页

作者： Khan, Sunawar Mazhar, Tehseen Shahzad, Tariq Khan, Muhammad Amir Rehman, Ateeq Ur Saeed, Mamoon M. Hamam, Habib Department of Software Engineering Islamia University of Bahawalpur Bahawalnagar Pakistan School of Computer Science National College of Business Administration and Economics Lahore Pakistan Department of Computer Science School Education Department Government of Punjab Layyah Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Malaysia School of Computing Gachon University Seongnam Republic of Korea Department of Communications and Electronics Engineering Faculty of Engineering University of Modern Sciences (UMS) Sana’a Yemen Faculty of Engineering Uni de Moncton Moncton Canada School of Electrical Engineering University of Johannesburg Johannesburg South Africa International Institute of Technology and Management (IITG) Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence-Spectrum Tunis Tunisia

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Non-Orthogonal Multiple Access Based Multi-Objective Optimization in Emergency Task Offloading

Non-Orthogonal Multiple Access Based Multi-Objective Optimiz...

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IEEE Conference on Wireless Communications and Networking

作者： Caijuan Chen Chaofeng Zhang Gaolei Li Yuchen Liu The Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan Advanced Institute of Industrial Technology Tokyo Japan School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science NC State University North Carolina US

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The escalating frequency of geological disasters, local conflicts, and public health crises necessitates robust emergency communication systems within Internet of Things (IoT) frameworks to ensure public safety. Employing Power-Domain NOMA (PD-NOMA) and Successive Interference Cancellation (SIC) enables effective multi-device communication that is crucial for emergency responses. However, the practical implementation of NOMA technologies in emergency scenarios is hindered by existing resource constraints and the requirement of prioritizing critical emergency information. To address these limitations, we propose a multi-objective optimization-based NOMA framework to maximize network throughput while ensuring quality of service (QoS) and energy efficiency. This framework incorporates a novel communication grouping algorithm based on queue penalty optimization and an Actor-Critic-based dynamic updating algorithm for multiple resource allocation. Our study proposes a practical approach to enhance emergency communication in disaster areas, ensuring efficient and reliable network performance under various constraints.

关键词： NOMA Interference cancellation Heuristic algorithms Disasters Quality of service Throughput Dynamic scheduling Internet of Things Resource management Optimization

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