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作者： Bai, Ting Johansson, Alexander Li, Shaoyuan Johansson, Karl Henrik Mårtensson, Jonas School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden KTH Royal Institute of Technology Stockholm Sweden Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Digital Futures Stockholm Sweden

This paper models a platooning system consisting of trucks and a third-party service provider (TPSP), which performs platoon coordination, distributes the platooning profit in platoons, and charges trucks in exchange for the services. Government subsidies used to incentivize platooning are also considered. We propose a pricing rule for the TPSP, which keeps part of the platooning profit including the subsidy each time a platoon is formed. In addition, a platoon coordination solution based on the distributed model predictive control (MPC) is proposed, in which the pricing rule under government subsidies is integrated. We perform a realistic simulation over the Swedish road network to evaluate the impact of the pricing rule and subsidies on the achieved profits and fuel savings. Our results show that subsidies are an effective mean to boost fuel savings from platooning. Moreover, the simulation study indicates that high pricing corresponds to a low platooning rate of the system, as trucks' incentives for platooning decrease. © 2023 The Authors. Asian Journal of Control published by John Wiley & Sons Australia, Ltd on behalf of Chinese Automatic Control Society.

关键词： Trucks

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Low Chirp Dual-Racetrack Modulator for PAM-4 Modulation with Low Driving Voltage

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Journal of Lightwave technology 2025年第10期43卷 4840-4847页

作者： Yu, Fuhao Zeng, Zhaobang Tang, Kaifei Yang, Shichong Ji, Xiang Xin, Yu Gu, Tingyi Jiang, Wei Nanjing University Key Laboratory of Intelligent Optical Sensing and Manipulation Engineering Research Center for Precision Photonics Integration & System Applications Ministry of Education Nanjing210023 China Nanjing University National Laboratory of Solid-State Microstructures College of Engineering and Applied Sciences Institute of Optical Communication Engineering Nanjing210023 China Nanjing University of Information Science & Technology School of Physics and Optoelectronic Engineering Nanjing210044 China University of Delaware Department of Electrical and Computer Engineering NewarkDE19716 United States

Micro-ring modulators with a small footprint and energy consumption have been explored for densely integrated high throughput optical interconnects. However, their strong dispersive response results in frequency chirp and pulse broadening, which limits their signal transmission distance. Here we explore the low chirp operation of a dual-racetrack modulator symmetrically coupled to a bus waveguide. With opposite index changes in the two resonators, the coherent couplings of two resonators' optical fields with the bus waveguide and their interference can cancel out the output phase variation due to a single resonator. This allows the push-pull operation to achieve a lower chirp compared to the single-drive operation. The advantage of lower chirp operation is experimentally verified through open eye diagram with 100 Gbps PAM-4 transmitted over 2 km single-mode fiber (SMF), with a peak-to-peak driving voltage of 1.6 V. © 1983-2012 IEEE.

关键词： Single mode fibers

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Adaptive resource allocation and routing for integrated sensing and communications for wireless technologies

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Eurasip Journal on Wireless Communications and Networking 2025年第1期2025卷 1-27页

作者： Subramaniyan, Manochandar Venkatasamy, Thiruppathy Kesavan Thotakuruchi Mathiyalagan, Nithya Jakir Hossen, Md. Faculty of Digital Business & Analytics Bharathidasan Institute of Management Tamil Nadu Tiruchirappalli 620014 India Faculty of Information Technology Dhanalakshmi Srinivasan Engineering College Tamil Nadu Perambalur 621212 India Center for Advanced Analytics (CAA) COE for Artificial Intelligence Faculty of Engineering & Technology (FET) Multimedia University Melaka 75450 Malaysia Faculty of Computer Science and Engineering K. Ramakrishnan College of Engineering Tamil Nadu Samayapuram 621112 India Post Doctoral Fellow Department of Engineering & Technology Multimedia University Melaka 75450 Malaysia

The integrated sensing and communications (ISC) application completely changes the game regarding collecting, analyzing, and dispensing facts in real time via combining sensing skills with communication structure. The present research delves into how ISC can enhance decision-making in healthcare, smart towns, and environmental monitoring. Issues with interoperability, electricity intake, ecological resilience, and the worrying latency requirements of task-critical packages are many obstacles to easy integration. Our proposed mechanism, adaptive resource allocation and routing for integrated sensing (ARAR-IS), which uses new wireless technologies, attempts to clear up these problems by allocating and routing assets for incorporated sensing. ARAR-IS dynamically allocates sources and routes information considering the real global and network dynamics. Optimizing resource utilization, mitigating interference, and enhancing dependability in ISC systems are achieved using ARAR-IS, which leverages cognitive routing algorithms and software program-defined networking ideas. With ARAR-IS, various computer-aided engineering regions can benefit from customized remedy plans, far-off patient tracking, and real-time attempts to lessen pollution. These domains include healthcare, proper infrastructure, industrial automation, and environmental monitoring. Thorough simulation-based assessments show that ARAR-IS is a pleasant alternative for ISC systems in many one-of-a-kind situations because of its high reliability, low-strength intake, excessive throughput, and low latency. © The Author(s) 2025.

关键词： Adaptive control Data communication Resource allocation Routing protocols Wireless channels

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A New Heterogeneous Hybrid Massive MIMO Receiver With an Intrinsic Ability of Removing Phase Ambiguity of DOA Estimation via Machine Learning

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2025年 3卷 17-29页

作者： Shu, Feng Shi, Baihua Chen, Yiwen Bai, Jiatong Li, Yifan Liu, Tingting Han, Zhu You, Xiaohu Hainan University School of Information and Communication Engineering Haikou570228 China Nanjing University of Science and Technology School of Electronic and Optical Engineering Nanjing210094 China Hainan University Collaborative Innovation Center of Information Technology Haikou570228 China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210003 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China

Massive multiple input multiple output (MIMO) antenna arrays eventuate a huge amount of circuit costs and computational complexity. To satisfy the needs of high precision and low cost in future green wireless communication, the conventional hybrid analog and digital MIMO receive structure emerges a natural choice. But it exists an issue of the phase ambiguity in direction of arrival (DOA) estimation and requires at least two time-slots to complete one-time DOA measurement with the first time-slot generating the set of candidate solutions and the second one to find a true direction by received beamforming over this set, which will lead to a low time-efficiency. To address this problem,a new heterogeneous sub-connected hybrid analog and digital ( H2 AD) MIMO structure is proposed with an intrinsic ability of removing phase ambiguity, and then a corresponding new framework is developed to implement a rapid high-precision DOA estimation using only single time-slot. The proposed framework consists of two steps: 1) form a set of candidate solutions using existing methods like MUSIC;2) find the class of the true solutions and compute the class mean. To infer the set of true solutions, we propose two new clustering methods: weight global minimum distance (WGMD) and weight local minimum distance (WLMD). Next, we also enhance two classic clustering methods: accelerating local weighted k-means (ALW-K-means) and improved density. Additionally, the corresponding closed-form expression of Cramer-Rao lower bound (CRLB) is derived. Simulation results show that the proposed frameworks using the above four clustering can approach the CRLB in almost all signal to noise ratio (SNR) regions except for extremely low SNR (SNR © 2024 The Authors.

关键词： Signal to noise ratio

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Explainable hybrid forecasting model for a 4-node smart grid stability

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Energy Reports 2025年 13卷 4948-4961页

作者： Ghazal, Taher M. Hasan, Mohammad Kamrul Hassan, Rosilah Abdullah, Mustafa Islam, Shayla Ahmad, Munir Department of Networks and Cybersecurity Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi43600 Malaysia Electric vehicles engineering department Hourani Center for Applied Scientific Research Al-Ahliyyaesearch Al-Ahliyya Amman University Amman Jordan Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur56000 Malaysia University College Korea University Seoul02841 Korea Republic of

The 4-Node architecture for smart grids (SGs) distinguishes the operational features to enhance its stability and interruption less power distribution. This architecture contains 1 generator node and 3 consumption nodes to retain the stability of SGs. However, the chances of grid component failure, deficient power distribution, or peak demands result in the 4-node architecture imbalance. To forecast such imbalance towards SG stability, this paper exploits the explainable artificial intelligence (AI) and deep learning models to verify the existence of bias in the 4-node architecture due to the above flaws. For this purpose, in this paper, the component functioning rate and demand-to-distribution ratio are computed and verified to compute the bias. The deep learning model trains the explainable AI model for the potential flaws from the distribution intervals. Using the training using the deep learning model, the 4-node architecture's performance is evaluated to forecast the grid stability for future distributions. The 4-node architecture stability is verified under different loads, peak demands, and device performances using the integrated hybrid model with EAI. The proposed model improved the forecast accuracy by 14.53 % and reduced the loss in flaw detection by 13.41 % for the varying dissemination hours. © 2025 The Authors

关键词： Power grids

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Accelerating Deep Neural Networks with a Low-Cost Lossless Compression

Accelerating Deep Neural Networks with a Low-Cost Lossless C...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Kim, Seongwook Byeon, Gwangeun Lee, Seungeui Bae, Youngseok Hong, Seokin Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of Advanced R&D Center Hanwha Systems Co. Ltd. Korea Republic of Agency for Defense Development Emerging Science and Technology Directorate Korea Republic of

ISBN: (纸本)9798331510756

Since deep learning inference involves a significant amount of computations, there have been a lot of efforts to accelerate the inference process by eliminating ineffectual compu-tations. As a solution to this problem, several sparsity-aware deep learning accelerators have been proposed. These accelerators perform the inference on compressed data (filters and feature maps) and dynamically remove the ineffectual multiplications with zero operands. For efficiently handling the compressed data on the accelerators, a simple and effective compression scheme, which encodes the whole data with its nonzero values and their positions, is necessary. However, prior approaches have several shortcomings, such as low compression efficiency and high complexity in compression and decompression hardware. This paper proposes a low-cost compression scheme that can be applied to sparsity-aware deep-learning accelerators. The compression scheme synergistically combines two low-cost compression schemes: Zero-Value Compression (ZVC) and Base-Delta-Immediate Compression (BDI). Our experimental evaluation shows that the proposed compression scheme achieves an average compression ratio of 2.26, resulting in 2.33x performance improvement, © 2025 IEEE.

关键词： Data handling

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Deep Learning Applications for Detecting Crop Diseases from Image Data

Deep Learning Applications for Detecting Crop Diseases from ...

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2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Praveen, R.V.S. Mittal, Mudit Parida, Prasanta Begum, Rowsonara Kumar, Yogendra Nijhawan, Ginni LTIMindtree Limited Digital Engineering and Assurance Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India School of Rural Management KIIT University Odisha Bhubaneswar India MLR Institute of Technology Department of Comp. Sc. & Engg. Telangana Hyderabad India GLA University Department of Electrical Engineering Mathura India Lovely Professional University Dept. of Comp. Sc. & Engineering Phagwara India

ISBN: (纸本)9798331512965

In order to optimize the productivity of crops and ensure food security the crop diseases should be identified as soon as possible. In this study, we study the image based crop disease identification using Deep Learning Methods. However, the traditional illnesses detection techniques are typically based on specialist knowledge and human inspection, and the latter also has the disadvantages of being very time consuming and error prone. Use of convolutional neural networks (CNNs), in particular, have made deep learning powerful for many imaging problems such as precise and automated identification of disease. The ultimate goal of this research is to build a CNN based model which, through the analysis of photos of a damaged leaf, can identify and categorize various crop diseases. With this large data set with excellent images of both healthy and sick crops, we take advantage of. The model is trained and tested based on F1 scores, accuracy, precision and recall. The findings indicate that the model proposed in this research outperforms generic machine learning techniques in diagnosing diseases such as rust, mildew, and blight at high accuracy. The real time implementation of the system is also scalable enough to monitor big areas in the smart farming set up. I show in this study that deep learning has the potential to significantly enhance agricultural disease management and to pave the way to new and more effective and sustainable ways to grow crops. © 2025 IEEE.

关键词： Smart agriculture

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A Vertical Federated Multiview Fuzzy Clustering Method for Incomplete Data

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IEEE Transactions on Fuzzy Systems 2025年第5期33卷 1510-1524页

作者： Li, Yan Hu, Xingchen Yu, Shengju Ding, Weiping Pedrycz, Witold Kiat, Yeo Chai Liu, Zhong National University of Defense Technology Laboratory for Big Data and Decision Changsha410073 China National University of Defense Technology School of Computer Changsha410073 China Nantong University School of Artificial Intelligence and Computer Science Nantong226019 China Gliwice44-100 Poland University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Nanyang Technological University College of Computing and Data Science 639798 Singapore

Multiview fuzzy clustering (MVFC) has gained widespread adoption owing to its inherent flexibility in handling ambiguous data. The proliferation of privatization devices has driven the emergence of new challenge in MVFC research works. Federated learning, a technique that can jointly train without directly using raw data, has gain significant attention in decentralized MVFC. However, their applicability depends on the assumptions of data integrity and independence between different views. In fact, while within distributed environments, data typically exhibits two challenging problems: 1) multiple views within a single client;2) incomplete data. Existing methods exhibit limitations in effectively addressing these challenges. Hence, in this study, we aim at achieving the effective clustering for incomplete data by a novel vertical federated MVFC framework. Specifically, a unified clustering framework is designed to capture both local client learning and global server training. For the local client learning, the data reconstruction strategy and prototype alignment strategy are introduced to ensure the preservation of data structure and refinement of clustering relationships, which mitigates the impact of incomplete data. Meanwhile, the global training process implements aggregation based on client-specific information. The whole process is realized based on the unified fuzzy clustering framework, promoting collaborative learning between client-specific and server information. Theoretical analyses and extensive experiments are carefully conducted to validate the effectiveness and efficiency of the proposed method from multiple perspectives. © 1993-2012 IEEE.

关键词： Data integrity

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Distributed Prescribed-Time Observer for Nonlinear Systems in Block-Triangular Form

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IEEE Control Systems Letters 2025年 9卷 222-227页

作者： Vincent de Heij M. Umar B. Niazi Karl H. Johansson Saeed Ahmed Jan C. Willems Center for Systems and Control and the Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen Groningen The Netherlands Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Electrical Engineering and Computer Science Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA

This letter proposes a design of a distributed prescribed-time observer for nonlinear systems representable in a block-triangular observable canonical form. Using a weighted average of neighbor estimates exchanged over a strongly connected digraph, each observer estimates the system state despite the limited observability of local sensor measurements. The proposed design guarantees that distributed state estimation errors converge to zero at a user-specified convergence time, irrespective of observers’ initial conditions. To achieve this prescribed-time convergence, distributed observers implement time-varying local output injection gains that monotonically increase and approach infinity at the prescribed time. The theoretical convergence is rigorously proven and validated through numerical simulations, where some implementation issues due to increasing gains have also been clarified.

关键词： Observers Convergence Nonlinear systems Symmetric matrices Observability Directed graphs Control systems Vectors Linear systems Electronic mail

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An Intelligent Traffic Management of Vehicles using Deep Learning Approach in Smart Cities

An Intelligent Traffic Management of Vehicles using Deep Lea...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Botir Elov Adilbek Dauletov Yadala Sucharitha Feruza Khalilova Mukhayyo Latipova Mukhtasarkhon Abdullayeva Computer Linguistics and Digital Technology Alisher Navo'i Tashkent State University of Uzbek Language and Literature Tashkent Uzbekistan Department of Digital Technologies Alfraganus University Tashkent Uzbekistan Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India Department of Electrical Power Engineering Fergana Polytechnic Institute Fergana Uzbekistan

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Smart traffic management of automobiles has been one of the utmost sought-after issues in the academic community due to the ever-increasing pace of urbanization and civilization over the last several centuries. Vehicle dissection, traffic density appraisal, and vehicle tracing are the three main components of smart traffic management. This problem becomes more difficult to resolve when there are occlusions, background clutter, and fluctuations in traffic density. In light of this need, we explore a deeper learning approach based on R-CNN s for faster vehicle segmentation in this study. There is adaptive backdrop modeling in the computational framework. It solves problems with lighting and shadows as well. The use of topological active net deformable models allows for segmentation that is more precise. The deformations mentioned can be accomplished with the aid of topological and stretched topological active nets. Minimizing energy allows for mesh deformation. Using a tweaked version of the stretched topological active net improves the segmentation accuracy to 98.3%. The investigational findings show that this framework is better than other methods.

关键词： Deep learning Integrated circuits Deformable models Fluctuations Smart cities Deformation Communication systems Computational modeling Lighting Traffic control

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