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arXiv 2023年

作者： Cai, Han Miao, Ying Schwartz, Moshe Tang, Xiaohu The School of Information Science and Technology Southwest Jiaotong University Chengdu610031 China The Faculty of Engineering Information and Systems University of Tsukuba Tennodai 1-1-1 Tsukuba305-8573 Japan The Department of Electrical and Computer Engineering McMaster University HamiltonONL8S 4K1 Canada The School of Electrical and Computer Engineering Ben-Gurion University of the Negev Israel

n this paper, the repair problem for erasures beyond locality in locally repairable codes is explored under a practical system setting, where a rack-aware storage system consists of racks, each containing a few parity checks. This is referred to as a rack-aware system with locality. Two repair schemes are devised to reduce the repair bandwidth for Tamo-Barg codes under the rack-aware model by setting each repair set as a rack. Additionally, a cut-set bound for locally repairable codes under the rack-aware model with locality is introduced. Using this bound, the second repair scheme is proven to be optimal. Furthermore, the partial-repair problem is considered for locally repairable codes under the rack-aware model with locality, and both repair schemes and bounds are introduced for this scenario.n this paper, the repair problem for erasures beyond locality in locally repairable codes is explored under a practical system setting, where a rack-aware storage system consists of racks, each containing a few parity checks. This is referred to as a rack-aware system with locality. Two repair schemes are devised to reduce the repair bandwidth for Tamo-Barg codes under the rack-aware model by setting each repair set as a rack. Additionally, a cut-set bound for locally repairable codes under the rack-aware model with locality is introduced. Using this bound, the second repair scheme is proven to be optimal. Furthermore, the partial-repair problem is considered for locally repairable codes under the rack-aware model with locality, and both repair schemes and bounds are introduced for this scenario.I Copyright © 2023, The Authors. All rights reserved.

关键词： Bar codes

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An optimal power supervising strategy for a smart home assessed based on BPMN Framework

An optimal power supervising strategy for a smart home asses...

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International Conference on Sciences and Techniques of Automatic Control and computer engineering (STA)

作者： Leila Lanouar Mouna Rekik Olfa Bouchaala Lotfi Krichen Multimédia & Web Department University of Gabes Higher Institute of Computer Science and Multimedia of Gabes (ISIMG) Gabes Tunis Electrical Engineering Department Electrical systems and renewable energies Laboratory (LSEER) University of Sfax University of Gabes National Engineering School of Sfax (ENIS) Higher Institute of Computer Science and Multimedia of Gabes (ISIMG) Sfax Gabes Tunis Department of Computer Engineering and Applied Mathematics Research Laboratory on Development and Control and Distributed Applications (ReDCAD) University of Sfax National Engineering School of Sfax (ENIS) Sfax Tunis Electrical Engineering Department Electrical systems and renewable energies Laboratory (LSEER) University of Sfax National Engineering School of Sfax (ENIS) Sfax Tunis

ISBN: (纸本)9781665482622

For each smart home, the need of energy consumption supervision is necessary, which plays an important role to ensure the highest power quality and to enhance the stability of the whole grid. The current document implements a smart home supply strategy based on endless resources to reduce the electricity bill and confirm the energy balance. In this context, a proposed supervision algorithm operates in eight cases to reach optimal energy flow between renewable generators, home battery and grid in a smart home concept is presented. The system is evaluated using the framework “Business Process Model and Notation” (BPMN) Camunda basing on information stored in Firebase Cloud and results are presented in order to manifest the efficiency of this control strategy.

关键词： Renewable energy sources Energy consumption Power quality Process control Smart homes Power system stability Stability analysis

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MLFuse: Multi-Scenario Feature Joint Learning for Multi-Modality Image Fusion

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IEEE Transactions on Multimedia 2025年

作者： Lei, Jia Li, Jiawei Liu, Jinyuan Wang, Bin Zhou, Shihua Zhang, Qiang Wei, Xiaopeng Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1010 New Zealand Ulster University Intelligent Systems Research Center LondonderryBT52 1SA United Kingdom

Multi-modality image fusion (MMIF) entails synthesizing images with detailed textures and prominent objects. Existing methods tend to use general feature extraction to handle different fusion tasks. However, these methods have difficulty breaking fusion barriers across various modalities owing to the lack of targeted learning routes. In this work, we propose a multi-scenario feature joint learning architecture, MLFuse, that employs the commonalities of multi-modality images to deconstruct the fusion progress. Specifically, we construct a cross-modal knowledge reinforcing network that adopts a multipath calibration strategy to promote information communication between different images. In addition, two professional networks are developed to maintain the salient and textural information of fusion results. The spatial-spectral domain optimizing network can learn the vital relationship of the source image context with the help of spatial attention and spectral attention. The edge-guided learning network utilizes the convolution operations of various receptive fields to capture image texture information. The desired fusion results are obtained by aggregating the outputs from the three networks. Extensive experiments demonstrate the superiority of MLFuse for infrared-visible image fusion and medical image fusion. The excellent results of downstream tasks (i.e., object detection and semantic segmentation) further verify the high-quality fusion performance of our method. The code is publicly available at https://***/jialei-sc/MLFuse. © 1999-2012 IEEE.

关键词： Semantic Segmentation

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LRDB: LSTM Raw data DNA Base-caller based on long-short term models in an active learning environment

arXiv

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arXiv 2023年

作者： Rezaei, Ahmad Taheri, Mahdi Mahani, Ali Magierowski, Sebastian Department of Computer Science and Automation Technische Universität Ilmenau Ilmenau Germany Tallinn University of Technology Tallinn Estonia Reliable & Smart Systems Laboratory Shahid Bahonar University Kerman Iran Department of Electrical Engineering and Computer Science York University Toronto Canada

The first important step in extracting DNA characters is using the output data of MinION devices in the form of electrical current signals. Various cutting-edge base callers use this data to detect the DNA characters based on the input. In this paper, we discuss several shortcomings of prior base callers in the case of time-critical applications, privacy-aware design, and the problem of catastrophic forgetting. Next, we propose the LRDB model, a lightweight open-source model for private developments with a better read-identity (0.35% increase) for the target bacterial samples in the paper. We have limited the extent of training data and benefited from the transfer learning algorithm to make the active usage of the LRDB viable in critical applications. Henceforth, less training time for adapting to new DNA samples (in our case, Bacterial samples) is needed. Furthermore, LRDB can be modified concerning the user constraints as the results show a negligible accuracy loss in case of using fewer parameters. We have also assessed the noise-tolerance property, which offers about a 1.439% decline in accuracy for a 15dB noise injection, and the performance metrics show that the model executes in a medium speed range compared with current cutting-edge models. © 2023, CC BY-NC-ND.

关键词： DNA

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Photoelectric Feedback Mechanism for Acceleration of Runaway Electrons in Gas Discharges at High Overvoltages

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Physical Review Letters 2024年第23期133卷 235301-235301页

作者： Victor P. Pasko Sebastien Celestin Anne Bourdon School of Electrical Engineering and Computer Science Penn State University University Park Pennsylvania USA Laboratory of Physics and Chemistry of the Environment and Space (LPC2E) OSUC University of Orleans CNRS Orleans France Laboratory of Plasma Physics (LPP) CNRS Sorbonne Université École Polytechnique Institut Polytechnique de Paris Palaiseau France

Energy conservation dictates that an electron with elementary charge e traversing a vacuum gap formed by electrodes maintained at potential difference U volts acquires maximum energy of eU. In many experiments electrons with energies as high as 3eU have been observed. The experimental discovery of this effect was made over 50 years ago and is still a subject of significant controversy in applications related to x-ray generation from high voltage discharges. Here we explain these observations by a sequential increase of energy of runaway electrons released from the cathode due to the photoelectric absorption of bremsstrahlung radiation generated by the previous cycle of runaway electrons bombarding the anode.

关键词： Fast particle effects in plasmas High-frequency & RF discharges in plasmas Low-temperature plasma

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Efficient and Secure Spatial Range Query over Large-scale Encrypted Data

Efficient and Secure Spatial Range Query over Large-scale En...

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International Conference on Distributed Computing systems

作者： Yinbin Miao Chao Xu Yifeng Zheng Ximeng Liu Xiangdong Meng Robert H. Deng School of Cyber Engineering Xidian University Xi'an China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Mathematics and Computer Science Fuzhou University Fuzhou China Henan Key Laboratory of Network Cryptography Technology China School of Information Systems Singapore Management University Singapore

Spatial range query enjoys widespread application scenarios due to the ever-growing geo-positioning technology in recent years. Huge amounts of encrypted geo-location data are being outsourced to cloud servers to alleviate local storage and computational overheads without leaking sensitive information. However, most existing Privacy-preserving Spatial Range Query (PSRQ) cannot achieve high efficiency while satisfying strong security over large-scale encrypted spatial data. To strike a best possible balance between security and efficiency, we propose a novel efficient Privacy-preserving Spatial Range Query (eP-SRQ) scheme in dual-cloud architecture over large-scale dataset. Specifically, we propose an efficient PSRQ scheme by designing a novel index structure based on Geohash algorithm, Circular Shift and Coalesce Zero-Sum Garbled Bloom Filter (CSC-ZGBF) and Symmetric Homomorphic Encryption (SHE), which makes the computational complexity of query process independent of dataset size. Formal security analysis proves that our scheme can achieve Indistinguishability against Chosen-Plaintext Attack (IND-CPA), and extensive experiments prove that our scheme is feasible in real-world applications.

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Detecting Shared Data Manipulation in Distributed Optimization Algorithms

arXiv

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arXiv 2023年

作者： Alkhraijah, Mohannad Harris, Rachel Litchfield, Samuel Huggins, David Molzahn, Daniel K. The School of Electrical and Computer Engineering Georgia Institute of Technology United States The Cybersecurity Information Protection and Hardware Evaluation Research Laboratory Georgia Tech Research Institute United States

This paper investigates the vulnerability of the Alternating Direction Method of Multipliers (ADMM) algorithm to shared data manipulation, with a focus on solving optimal power flow (OPF) problems. Deliberate data manipulation may cause the ADMM algorithm to converge to suboptimal solutions. We derive two sufficient conditions for detecting data manipulation based on the theoretical convergence trajectory of the ADMM algorithm. We evaluate the detection conditions’ performance on three data manipulation strategies we previously proposed: simple, feedback, and bilevel optimization attacks. We then extend these three data manipulation strategies to avoid detection by considering both the detection conditions and a neural network (NN) detection model in the attacks. We also propose an adversarial NN training framework to detect shared data manipulation. We illustrate the performance of our data manipulation strategy and detection framework on OPF problems. The results show that the proposed detection conditions successfully detect most of the data manipulation attacks. However, a bilevel optimization attack strategy that incorporates the detection methods may avoid being detected. Countering this, our proposed adversarial training framework detects all the instances of the bilevel optimization attack. Copyright © 2023, The Authors. All rights reserved.

关键词： Electric load flow

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An Improved Variable Parameter Phase-Locked Loop for Single-Carrier Underwater Acoustic Communications

An Improved Variable Parameter Phase-Locked Loop for Single-...

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OCEANS

作者： Changxin Liu Jun Tao Weimin Huang School of Information Science and Engineering Southeast University Nanjing China Pengcheng Laboratory Shenzhen China Department of Electrical and Computer Engineering Memorial University of Newfoundland St. John's NL Canada

A direct adaptive equalizer (DAE) coupled with a phase-locked loop (PLL) has been proved as an efficient receiver scheme for single-carrier coherent underwater acoustic (UWA) communications. Ever since its birth, numerous improvements have been proposed for the DAE. In contrast, the PLL has been somehow overlooked. A PLL with predetermined coefficients suffers performance degradation when channel conditions change, then a variable-parameter (VP) PLL designed via gradient descent (GD) method was proposed. The GD-VP-PLL is sensitive to parameter initialization though. In this paper, we propose a new VP-PLL scheme in light of the recursive Gauss-Newton (RGN) method. The resulting RGN-VP-PLL has been tested by at-sea experimental data, and showed better performance than the GD-VP-PLL and a PLL with fixed coefficients. Moreover, it is insensitive to initialization of parameters.

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Bayesian Optimization Enhanced Deep Reinforcement Learning for Trajectory Planning and Network Formation in Multi-UAV Networks

arXiv

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arXiv 2022年

作者： Gong, Shimin Wang, Meng Gu, Bo Zhang, Wenjie Hoang, Dinh Thai Niyato, Dusit The School of Intelligent Systems Engineering Shenzhen Campus of Sun Yatsen University China The School of Computer Sciences Minnan Normal University China The School of Electrical and Data Engineering University of Technology Sydney Australia School of Computer Science and Engineering Nanyang Technological University Singapore

In this paper, we employ multiple UAVs coordinated by a base station (BS) to help the ground users (GUs) to offload their sensing data. Different UAVs can adapt their trajectories and network formation to expedite data transmissions via multi-hop relaying. The trajectory planning aims to collect all GUs’ data, while the UAVs’ network formation optimizes the multi-hop UAV network topology to minimize the energy consumption and transmission delay. The joint network formation and trajectory optimization is solved by a two-step iterative approach. Firstly, we devise the adaptive network formation scheme by using a heuristic algorithm to balance the UAVs’ energy consumption and data queue size. Then, with the fixed network formation, the UAVs’ trajectories are further optimized by using multiagent deep reinforcement learning without knowing the GUs’ traffic demands and spatial distribution. To improve the learning efficiency, we further employ Bayesian optimization to estimate the UAVs’ flying decisions based on historical trajectory points. This helps avoid inefficient action explorations and improves the convergence rate in the model training. The simulation results reveal close spatial-temporal couplings between the UAVs’ trajectory planning and network formation. Compared with several baselines, our solution can better exploit the UAVs’ cooperation in data offloading, thus improving energy efficiency and delay performance. © 2022, CC BY.

关键词： Reinforcement learning

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Task Offloading Optimization for Mobile Edge Computing System: A CNO Approach

Task Offloading Optimization for Mobile Edge Computing Syste...

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International Conference on Communication Technology (ICCT)

作者： Changqing Long Wenchao Meng Yongmin Zhang College of Control Science and Engineering Zhejiang University Hangzhou China The Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province School of Computer and Engineering Central South University Changsha China

Mobile edge computing (MEC) can extend computing power at the edge of wireless networks by migrating compute-intensive tasks to MEC servers, which has the potential to enable compute-intensive applications in 5G networks. However, whether to offload tasks and how much edge computing resources to allocate to it is an NP-hard problem. In this paper, a MEC system enabled multi-user wireless network is proposed, in which the access point (AP) is equipped with a MEC server that provides computing resources for mobile users through task offloading. Then, the issue of task offloading and resource allocation is formulated as a mixed integer nonlinear program to minimize the overhead of the entire MEC system, represented by a weighted sum of delay and energy consumption. Furthermore, a collaborative neurodynamic optimization (CNO) with levy mutation operator (CNO-LMO) algorithm is proposed for searching global optimal solutions to the formulated problem. Finally, the experimental results are given to verify the superiority of the proposed CNO-LMO in reducing the overhead of the MEC system compared to some other algorithms.

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