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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Shaukat, Neelofar Khan, Bilal Qureshi, Muhamamd Bilal Jawad, Muhammad Shabbir, Noman Daniel, Kamran Abbottabad Pakistan Lahore Pakistan FinEST Center for Smart Cities Department of Electrical and Computer Engineering Estonia Tallinn University of Technology Department of Electrical Power Engineering & Mechatronics Tallinn Estonia Nova University Department of Electrical & Computer Engineering Lisbon Portugal

ISBN: (纸本)9781665471640

In this research study, we proposed a Distributed Average Integral (DAI) control-based Energy Management Model (EMM) to achieve the economic load dispatch and consensus within a distributed energy system. The proposed model incorporates the Laplacian graph theory for establishing communication among energy districts (acting as agents/nodes) and optimizing the power distribution while satisfying the load demand. In order to evaluate the effectiveness of the proposed model, simulations are performed using MATLAB. The simulation results illustrate that the DAI control mechanism ensures the optimized power distribution leading to enhanced resource utilization while effectively achieving economic consensus among energy districts. However, in this study ideal case scenario is assumed. Therefore, this research study may highlight the potential for future investigations on utilizing the DAI control for EMM in practical scenarios with real-world challenges such as system complexities, losses, and communication delays. © 2023 IEEE.

关键词： Distributed Average Integral Control Energy Districts Graph Theory Power Consensus

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Efficient Object Detection Acceleration Methods for Autonomous-driving Embedded Platforms

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IEIE Transactions on Smart Processing and Computing 2022年第4期11卷 255-261页

作者： Choi, Jiwoong Chun, Dayoung Lee, Hyuk-Jae Kim, Hyun Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Research Center for Electrical and Information Technology Department of Electrical and Information Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of

Object detection in autonomous vehicles is typically operated in an embedded system to reduce power consumption. The use of an object detection algorithm with high accuracy and real-time detection speed in the embedded systems is essential for ensuring safe driving. This study proposes a parallel processing method for GPU and CPU operations to enhance the detection speed of the model. In addition, this study proposes data augmentation and image resize techniques that consider the camera input size of autonomous driving, which increases the accuracy significantly while improving the detection speed. The application of these proposed schemes to a baseline algorithm, tiny Gaussian YOLOv3, improves the mean average precision by 1.14 percent points (pp) for the Berkeley Deep Drive (BDD) dataset and 1.34 pp for the KITTI dataset compared to the baseline. Furthermore, in the NVIDIA Jetson AGX Xavier, which is an embedded platform for autonomous driving, the proposed algorithm improves the detection speed by 22.54 % for the BDD, and 24.67 % for the KITTI compared to the baseline, thereby enabling high-speed real-time detection on both datasets. © 2022 Institute of Electronics and Information Engineers. All rights reserved.

关键词： Object detection

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Gradual Domain Adaptation via Manifold-Constrained Distributionally Robust Optimization 38

Gradual Domain Adaptation via Manifold-Constrained Distribut...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Saberi, Amirhossein Najafi, Amir Behjati, Amin Emrani, Ala Zolfi, Yasaman Shadrooy, Mahdi Motahari, Abolfazl Khalaj, Babak H. Department of Electrical Engineering Sharif University of Technology Tehran Iran Department of Computer Engineering Sharif University of Technology Tehran Iran Sharif Center for Information Systems and Data Science Sharif University of Technology Tehran Iran

The aim of this paper is to address the challenge of gradual domain adaptation within a class of manifold-constrained data distributions. In particular, we consider a sequence of T ≥ 2 data distributions P1, ..., PT undergoing a gradual shift, where each pair of consecutive measures Pi, Pi+1 are close to each other in Wasserstein distance. We have a supervised dataset of size n sampled from P0, while for the subsequent distributions in the sequence, only unlabeled i.i.d. samples are available. Moreover, we assume that all distributions exhibit a known favorable attribute, such as (but not limited to) having intra-class soft/hard margins. In this context, we propose a methodology rooted in Distributionally Robust Optimization (DRO) with an adaptive Wasserstein radius. We theoretically show that this method guarantees the classification error across all Pis can be suitably bounded. Our bounds rely on a newly introduced compatibility measure, which fully characterizes the error propagation dynamics along the sequence. Specifically, for inadequately constrained distributions, the error can exponentially escalate as we progress through the gradual shifts. Conversely, for appropriately constrained distributions, the error can be demonstrated to be linear or even entirely eradicated. We have substantiated our theoretical findings through several experimental results. © 2024 Neural information processing systems foundation. All rights reserved.

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Multidimensional Voronoi Constellations vs. Short Blocklength Probabilistic Shaping: A Comparison for Multilevel Coding Approach

Multidimensional Voronoi Constellations vs. Short Blocklengt...

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2024 Asia Communications and Photonics Conference, ACP 2024 and International Conference on Information Photonics and optical Communications, IPOC 2024

作者： Sheng, Yajie Chen, Bin Lei, Yi Deng, Jingxin Xu, Jiwei Fu, Mengfan Zhuge, Qunbi Li, Shen Department of Computer Science and Information Engineering Hefei University of Technology Hefei China State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China Department of Electrical and Computer Engineering Université Laval Québec Canada

Performance of concatenated multilevel coding with probabilistic shaping (PS) and Voronoi constellations (VCs) is analysed over AWGN channel. Numerical results show that VCs provide up to 1.3 dB SNR gains over PS-QAM ... 详细信息

ISBN: (纸本)9798350379266

关键词： Probability distributions

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Learning Compositional, Time-Varying Neural Barrier Contracts 27

Learning Compositional, Time-Varying Neural Barrier Contract...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Low, Matthew Wang, Timothy E. Nuzzo, Pierluigi Department of Electrical and Computer Engineering University of Southern California Los AngelesCA United States RTX Technology Research Center BerkeleyCA United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA United States

ISBN: (纸本)9781643685489

Certificate functions can be used to efficiently capture and prove various properties of a system or controller. Control barrier functions (CBFs) are certificate functions that define a region of forward-invariance, which makes them a natural choice to enforce a notion of "safety" for a system. However, synthesizing CBFs, especially in the case of complex systems with learning-enabled components, remains a challenge. Recent work achieves promising results by leveraging neural networks as function approximators to learn CBFs. However, a single CBF may not exist or be difficult to obtain for complex hybrid systems. To overcome this difficulty, this paper presents a framework to simultaneously learn simpler, time-varying control barrier functions (TV-CBFs) that are composable. We embed these neural certificates in a compositional framework based on assume-guarantee contracts. The resulting neural barrier contracts can then be combined by leveraging the rigorous contract algebra. Learning multiple, composable CBFs empowers the verification process (1) by simplifying the verification of complex systems via decomposition and (2) by broadening the expressivity in capturing complex systems and controllers. We illustrate the effectiveness of our approach on the verification of an aircraft's automatic landing system and a quadrotor navigating an indoor environment. © 2024 The Authors.

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Crack Detection for Non-Metallic Material Using Split Ring Resonator-based Chipless RFID Sensor 20

Crack Detection for Non-Metallic Material Using Split Ring R...

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20th International Conference on electrical engineering/Electronics, computer, Telecommunications and Information technology, ECTI-CON 2023

作者： Nongpromma, Sirisak Janin, Suwan Kaemarungsi, Kamol Boonpoonga, Akkarat Torrungrueng, Danai Bannawat, Lakkhana King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand Rajamangala University of Technology Lanna Lampang Faculty of Engineering Department of Engineering and Technology Lampang52000 Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Pathumthani Thailand Department of Electrical and Computer Engineering Bangkok Thailand Department of Teacher Training in Electrical Engineering Bangkok Thailand Rajamangala University of Technology Rattanakosin Faculty of Engineering Department of Computer Engineering Nakhon Pathom Thailand

ISBN: (纸本)9798350310467

The study presented a novel chipless RFID sensor designed for the detection of cracks in non-metallic materials. The sensor utilized square coplanar split-ring resonators (SRR) and was mounted on the material under test (MUT), including materials such as Teflon (PTFE), Polycarbonate, and Kapton (Polyimide), for detecting cracks. To assess the effectiveness of the sensor in detecting cracks, simulation experiments were conducted using the CST Microwave Studio software, which analyzed shifts in the resonant frequency. The resulting RCS spectra demonstrated a clear correlation between the size of the crack and the resonance frequencies of the chipless RFID sensor, confirming the sensor's ability to detect cracks in nonmetallic materials. © 2023 IEEE.

关键词： Nonmetallic materials

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Enhanced memory window and efficient resistive switching in stabilized BaTiO3-based RRAM through incorporation of Al2O3 interlayer

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材料科学技术（英文版） 2025年第10期213卷 125-134页

作者： Akendra Singh Chabungbam Minjae Kim Atul Thakre Dong-eun Kim Hyung-Ho Park Department of Materials Science and Engineering Yonsei UniversitySeoul 03722Korea Department of Materials Science and Engineering Yonsei UniversitySeoul 03722Korea Department of Electrical and Computer Engineering University of Southern CaliforniaLos AngelesCAUSA Department of Materials Science and Engineering Yonsei UniversitySeoul 03722Korea Centre of Functional Materials Vellore Institute of TechnologyTamil Nadu 632014India Department of Materials Science and Engineering Yonsei UniversitySeoul 03722Korea Aerogel Materials Research Center Yonsei UniversitySeoul 03722Korea

As artificial intelligence and big data become increasingly prevalent,resistive random-access memory(RRAM)has become one of the most promising alternatives for storing massive amounts of *** this study,we employed high-quality crystalline TiN/Al2O3/BaTiO3/Pt RRAM with an optimized thin Al2O3 in-terlayer around 12 nm thick prepared using atomic layer deposition since the thickness of the inter-layer affects the memory window *** insertion of the Al2O3 interlayer,the novel RRAM exhibited outstanding uniform resistive switching voltage and the ON/OFF memory window drastically increased from 10 to 103 without any discernible decline in ***,the low-resistance state and high-resistance state operating current values decreased by almost one order and three orders of magni-tude,respectively,thereby decreasing the power consumption for the RESET and SET processes by more than three and almost one order of magnitude,*** device also exhibits multilevel resistive switching behavior when varying the applied ***,we also developed a 6 × 6 crossbar array which demonstrated consistent and reliable resistive switching behavior with minimal ***,our approach holds great promise for producing state-of-the-art non-volatile resistive switching devices.

关键词： Resistive random-access memory Resistive switching Atomic layer deposition Al2O3 interlayer

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DRL-Driven Localization With UAV in Near-Field Communications

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IEEE Internet of Things Journal 2025年第13期12卷 22587-22598页

作者： Khan, Muhammad Fawad Peng, Limei Ho, Pin-Han Chen, Yuguang Dong, FangJie Kyungpook National University School of Computer Science and Engineering Daegu41566 Korea Republic of University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Guangdong China University of Waterloo Department of Electrical and Computer Engineering ON Canada Shenzhen Unicom Innovation Business Capability Center China National Health Commission's Center for Statistics and Information China

In this paper, we propose a Deep Reinforcement Learning (DRL)-based Multi-Point Localization Scheme (MLS) to efficiently localize IoT devices using a single Unmanned Aerial Vehicle (UAV) equipped with a large-scale multi-antenna configuration in near-field communication (NFC). By utilizing the spherical wave-based near-field steering vector, the multi-antenna array on the UAV captures both the Angle of Arrival (AoA) and Received Signal Strength Indicator (RSSI) measurements from IoT devices to estimate their locations relative to the position of the UAV. This approach eliminates the need for multiple hovering points required by a single-antenna UAV (SA-UAV) or the deployment of multiple SA-UAVs. To enhance localization accuracy, key hovering points for the multi-antenna UAV (MA-UAV) are strategically selected, with weights assigned based on signal strength to prioritize stronger and more reliable signals. Furthermore, DRL dynamically adjusts the position of the MA-UAV to optimize the trade-off between localization accuracy and energy consumption. Extensive simulations conducted across rural, urban, and dense urban scenarios demonstrate that the proposed DRL-based MLS significantly improves localization accuracy while reducing the energy consumption of the UAV. © 2025 IEEE.

关键词： Deep reinforcement learning

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Fundamental Limits of Distributed Covariance Matrix Estimation Under Communication Constraints 41

Fundamental Limits of Distributed Covariance Matrix Estimati...

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41st International Conference on Machine Learning, ICML 2024

作者： Rahmani, Mohammad Reza Yassaee, Mohammad Hossein Maddah-Ali, Mohammad Ali Aref, Mohammad Reza Department of Electrical Engineering Sharif University of Technology Tehran Iran Sharif Center for Information Systems and Data Science Sharif Institute for Convergence Science & Technology Tehran Iran Department of Electrical and Computer Engineering University of Minnesota Minneapolis United States

Estimating high-dimensional covariance matrices is crucial in various domains. This work considers a scenario where two collaborating agents access disjoint dimensions of m samples from a high-dimensional random vector, and they can only communicate a limited number of bits to a central server, which wants to accurately approximate the covariance matrix. We analyze the fundamental trade-off between communication cost, number of samples, and estimation accuracy. We prove a lower bound on the error achievable by any estimator, highlighting the impact of dimensions, number of samples, and communication budget. Furthermore, we present an algorithm that achieves this lower bound up to a logarithmic factor, demonstrating its near-optimality in practical settings. Copyright 2024 by the author(s)

关键词： Budget control

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Reversible Data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

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IEEE Internet of Things Journal 2025年第13期12卷 23933-23945页

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： Cryptography Encryption Security Data mining Adaptive coding Correlation Cloud computing Polynomials Image coding Streaming media

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