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59th Annual Conference on Information sciences and Systems, CISS 2025

作者： Wei, Dongyu Zhang, Qianqian Liu, Yuchen Chen, Mingzhe Rowan University Department of Electrical and Computer Engineering GlassboroNJ08028 United States Nc State University Department of Computer Science RaleighNC27695 United States University of Miami Department of Electrical and Computer Engineering Coral GablesFL33146 United States University of Miami Frost Institute for Data Science and Computing Coral GablesFL33146 United States

ISBN: (纸本)9798331513269

In this paper, a deceptive signal-assisted private split learning is investigated. In our model, several edge devices jointly perform collaborative training, and some eavesdroppers aim to collect the model and data information from devices. To prevent the eavesdropper from collecting model and data information, a subset of devices can transmit deceptive signals. Therefore, it is necessary to determine the subset of devices used for deceptive signal transmission, the subset of model training devices, and the models assigned to each model training device. This problem is formulated as an optimization problem whose goal is to minimize the eavesdropped information while meeting the model training energy consumption and delay constraints. To solve this problem, we propose an actor-critic deep reinforcement learning (AC-DRL) framework that enables a centralized agent to dynamically determine the model training devices, the deceptive signal transmission devices, the transmit power, and sub-models assigned to each model training device while considering network topology and device properties (e.g., energy and privacy constraints). The proposed AC-DRL framework integrates an actor network to generate optimal actions and a critic network to stabilize the learning process by evaluating the cumulative rewards. Simulation results demonstrate that the proposed method improves the convergence rate by 3× and the accumulated reward by up to 40% compared with the proximal policy optimization (PPO) algorithm. © 2025 IEEE.

关键词： Deep reinforcement learning

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Chaotic Particle Swarm Optimization Based Shortest Path Routing in VANET 7

Chaotic Particle Swarm Optimization Based Shortest Path Rout...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Ramya, R. Yazhini, A. Geetha, T. Devi, R. Sahila Kavin, K.S. Dhanalakshmi, S. Saveetha School of Engineering Department of Nano Electronics Materials and Sensors ECE Chennai India Anna University College of Engineering Department of Computer Science & Engineering Chennai600025 India Agni College of Engineering and Technology Department of Artificial Intelligence and Data Science Chennai India Rohini College of Engineering and Technology Department of Computer Science and Engineering Palkulam629401 India Government College of Engineering Department of Electrical and Electronics Engineering Tamilnadu Tirunelveli India Bannari Amman Institute of Technology Department of Electronics and Communication Engineering Erode Sathyamangalam638401 India

ISBN: (纸本)9798350372816

The present study examines the Vehicle Ad hoc Networks (VANETs) exhibit unexpected behavior due to their high mobility and random network structure. Including ad hoc networks, a variety of problems, including repeated connected breakdowns, flexibility, multi-hop transmission of information, and information loss, impact Transmission Control Protocols (TCP). To overcome this problem, this paper suggests employing zone-based routing in VANETs while taking mobility into account. The routing technique is implemented with a hybrid optimization strategy. The most significant swarm intelligence (SI) algorithm for solving optimization issues is the chaotic particle swarm optimization (CPSO) algorithm. Due to its simplicity, which involves just a few parameters, CPSO has grown to be considered one of the most frequently used optimization techniques. When resolving challenging optimization problems, the routing algorithms experience high overhead or incompatibilities. A CPSO based shortest path routing method is presented for VANET that offers a high-quality path for connection among nodes, improving the distribution of packets percentage and preventing end-to-end delay, in order to enhanced routing effectiveness and throughput in prevailing approaches. These achievements show that CPSO performs adequately and is experiencing significant progress. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Multi-modal and Multi-scale Spatial Environment Understanding for Immersive Visual Text-to-Speech 39

Multi-modal and Multi-scale Spatial Environment Understandin...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liu, Rui He, Shuwei Hu, Yifan Li, Haizhou Inner Mongolia University China Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering National University of Singapore Singapore

ISBN: (纸本)157735897X

Visual Text-to-Speech (VTTS) aims to take the environmental image as the prompt to synthesize the reverberant speech for the spoken content. The challenge of this task lies in understanding the spatial environment from the image. Many attempts have been made to extract global spatial visual information from the RGB space of an spatial image. However, local and depth image information are crucial for understanding the spatial environment, which previous works have ignored. To address the issues, we propose a novel multi-modal and multi-scale spatial environment understanding scheme to achieve immersive VTTS, termed M2SE-VTTS. The multimodal aims to take both the RGB and Depth spaces of the spatial image to learn more comprehensive spatial information, and the multi-scale seeks to model the local and global spatial knowledge simultaneously. Specifically, we first split the RGB and Depth images into patches and adopt the Gemini-generated environment captions to guide the local spatial understanding. After that, the multi-modal and multi-scale features are integrated by the local-aware global spatial understanding. In this way, M2SE-VTTS effectively models the interactions between local and global spatial contexts in the multi-modal spatial environment. Objective and subjective evaluations suggest that our model outperforms the advanced baselines in environmental speech generation. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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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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SP-DSTS-MIMO Scheme-Aided H.266 for Reliable High data Rate Mobile Video Communication

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computers, Materials & Continua 2023年第1期74卷 995-1010页

作者： Khadem Ullah Nasru Minallah Durre Nayab Ishtiaque Ahmed Jaroslav Frnda Jan Nedoma Department of Computer Systems Engineering University of Engineering and Technology PeshawarPeshawar25000Pakistan National Centre in Big Data and Cloud Computing University of Engineering and Technology Peshawar(NCBC-UETP)Peshawar25000Pakistan Department of Quantitative Methods and Economic Informatics Faculty of Operation and Economics of Transport and CommunicationsUniversity of Zilina01026ZilinaSlovakia Department of Telecommunications Faculty of Electrical Engineering and Computer ScienceVSB-Technical University of OstravaOstrava-PorubaCzech Republic

With the ever growth of Internet users,video applications,and massive data traffic across the network,there is a higher need for reliable bandwidth-efficient multimedia *** Video Coding(VVC/H.266)is finalized in September 2020 providing significantly greater compression efficiency compared to Highest Efficient Video Coding(HEVC)while providing versatile effective use for Ultra-High Definition(HD)*** article analyzes the quality performance of convolutional codes,turbo codes and self-concatenated convolutional(SCC)codes based on performance metrics for reliable future video *** advent of turbo codes was a significant achievement ever in the era of wireless communication approaching nearly the Shannon *** codes are operated by the deployment of an interleaver between two Recursive Systematic Convolutional(RSC)encoders in a parallel *** RSC encoders may be operating on the same or different architectures and code *** proposed work utilizes the latest source compression standards H.266 and H.265 encoded standards and Sphere Packing modulation aided differential Space Time Spreading(SP-DSTS)for video transmission in order to provide bandwidth-efficient wireless video ***,simulation results show that turbo codes defeat convolutional codes with an averaged E_(b)/N_(0) gain of 1.5 dB while convolutional codes outperformcompared to SCC codes with an E_(b)/N_(0) gain of 3.5 dBatBit ErrorRate(BER)of 10−*** Peak Signal to Noise Ratio(PSNR)results of convolutional codes with the latest source coding standard of H.266 is plotted against convolutional codes with H.265 and it was concluded H.266 outperform with about 6 dB PSNR gain at E_(b)/N_(0) value of 4.5 dB.

关键词： H.265 RSC turbo codes SCC SP-DSTS BP-CNN BER PSNR

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Output Tracking Control of a Nonlinear System Based on Takagi-Sugeno Fuzzy Model: Generalized Partial Eigenstructure Assignment Approach

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IEEE Access 2024年 12卷 18520-18535页

作者： Soltanian, Farzad Shasadeghi, Mokhtar Mobayen, Saleh Skruch, Pawel University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2M9 Canada Shiraz University of Technology Electrical Engineering Faculty Shiraz71557-13876 Iran National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan AGH University of Science and Technology Department of Automatic Control and Robotics Kraków30-059 Poland

This paper introduces an innovative optimal control approach to achieve output tracking while incorporating H2-performance specifications in a specific class of nonlinear dynamics modeled by the Takagi-Sugeno fuzzy model (TSFM). The primary innovation lies in extending partial eigenstructure assignment to TSFM-based nonlinear systems within the framework of sliding mode control (SMC). We propose a two-step methodology for designing optimal sliding surface gains. Initially, optimal state feedback gains are computed for each rule consequence containing a linear subsystem, adhering to predetermined eigenvalues and satisfying H2-performance criteria. Subsequently, using a convex combination, the overall state feedback gain is calculated and utilized to design sliding matrix gains. The sliding matrix gains are then determined by strategically combining previously calculated state-feedback gains in a convex optimization problem. We reframe the output tracking strategy as a stabilization problem using a virtual control input and reformulate the optimization task concerning tracking state errors. This process yields state feedback gains, sliding gains, and the formulation of the virtual control input. The effectiveness of our approach is verified through comprehensive simulations, emphasizing its capability in addressing output tracking challenges within nonlinear systems. © 2013 IEEE.

关键词： Transient analysis

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An Efficient Shortest Path Optimized Algorithm for Vehicle Adhoc Networks System 7

An Efficient Shortest Path Optimized Algorithm for Vehicle A...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Shalini, K. Reddy, K. Harinath Punati, Kondalarao Kavin, K.S. Veena, P. Pushpalatha, K. Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Shamshabad India Annamacharya Institute of Technology and Sciences Department of Electrical and Electronics Engineering Rajampet India Hyderabad Institute of Technology and Management Faculty of Electronics and Communication Engineering Hyderabad500090 India Government College of Engineering Department of Electrical and Electronics Engineering Tamilnadu Tirunelveli India N. S. S. Polytechnic College Department of Computer Engineering Pandalam Pathanamthitta India Coimbatore Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore641109 India

ISBN: (纸本)9798350372816

VANET (Vehicular ad-hoc network) is a network of advanced automobiles designed to solve the problem of mobility and, as a result, reduce the number of accidents. Because VANET is a type of MANET (mobile ad hoc network) surrounded by human-driven nodes, the social concept can be used to explain VANET routing decisions. This motivates academics to look into the possibilities of using Social Network Analysis (SNA) to create routing strategies. The most critical problem in any system is parcel drop. Social characteristics-based architectures encounter comparable challenges, resulting in inefficient message delivery. In this research, the dragonfly method is used to the shortest path methodology for fixed line VANET with the purpose of reducing package drop and increasing network reliability. This is made achievable by sculpted system of values centralization analysis, which improves the decision via dragonfly optimization methodology. The behavior of the using nodes capability then be determined by the importance computation. In this study, the sustainability of the optimized shortest path system is measured in terms of End to End delay, Energy Consumption, Packet Loss, and Throughput. The dragonfly algorithm's throughput evaluation is 650 kbps as compared to the traditional way. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Thermal Simulation for Magnetic Coupler of Wireless Power Transfer Electric Vehicles by Using Heat Sink and Thermoelectric Cooler

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Progress In Electromagnetics Research M 2024年 126卷 89-98页

作者： Farooq, Umar Shafique, Shahryar Asif, Muhammad Arslan, Muhammad Wongjom, Poramed Ullah, Rizwan Zhilenkov, Anton Mobayen, Saleh Pijitrojana, Wanchai Electrical and Computer Engineering Department Thammasat School of Engineering Thammasat University Rangsit Campus Klong Luang Pathum Thani 12120 Thailand Ministry of IT & Telecommunications Islamabad Pakistan Department of Electrical Engineering University of Science and Technology Main Campus Bannu23200 Pakistan Department of Mechanical Engineering University of Engineering and Technology Lahore Pakistan Division of Physics Faculty of Science and Technology Thammasat University Rangsit Campus Klongluang Pathum Thani 12120 Thailand Department of Electrical Engineering Chulalongkorn University Bangkok10330 Thailand Department of Cyber-Physical Systems St. Petersburg State Marine Technical University Saint-Petersburg190121 Russia Graduate School of Intelligent Data Science National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou640301 Taiwan

In challenging operational environments such as underground buildings beneath roadways, reliability and performance of wireless power transfer (WPT) systems for electric vehicles (EVs) heavily hinge on the operating temperature of the magnetic couplers. Addressing this, this study introduces a novel approach employing heat sink and thermoelectric cooler technologies to mitigate temperature rise in magnetic couplers, which is particularly crucial for high-power applications. Utilizing ANSYS simulation, the study evaluates a WPT high-power application coil model with a total output power of 2 KW and an 18 cm air gap, with a 3.5 cm adjacent alignment to enhance thermal performance on both transmitter and receiver sides. Results demonstrate significant thermal enhancement, reducing the temperature of coils from 63◦ C to 54◦ C solely with the heat sink and further down to 48◦ C with the combined implementation of both heat sink and thermoelectric cooler. These measures effectively dissipate heat from the coils into the surrounding air, ensuring system efficiency and stability while facilitating functionality of system components. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Heat sinks

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Novel Audio Mosaic Using LPC-Coefficient and Excitation-Codeword Permutations

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IEEE Signal Processing Letters 2024年 31卷 1690-1694页

作者： Tsai, Morris C.-H. Wu, Hsiao-Chun Huang, Scott C.-H. Chang, Shih Yu National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center for Ai Applications Taoyuan City32003 Taiwan San Jose State University Department of Applied Data Science San JoseCA95192 United States

In this letter, a novel light-weight audio mosaic scheme using permutations of linear predictive coding (LPC) coefficients as LPC has been widely adopted in many audio codecs (coder-encoders) nowadays. We establish the theoretical secrecy analysis with respect to the degree of the original LPC polynomial and the minimum-phase population of permuted LPC polynomials. The mosaic (destructuring) performance in terms of DCT-KLD (Kullback-Leibler divergence of discrete cosine transform) is also evaluated through simulations. In comparison with the existing audio-mosaic method using waveform permutations, our proposed new audio-mosaic scheme using LPC-coefficient permutations can achieve a much better mosaic performance (a much higher DCT-KLD) subject to the same key size. © 1994-2012 IEEE.

关键词： Cryptography

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INFOCON: CONCEPT DISCOVERY WITH GENERATIVE AND DISCRIMINATIVE INFORMATIVENESS 12

INFOCON: CONCEPT DISCOVERY WITH GENERATIVE AND DISCRIMINATIV...

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12th International Conference on Learning Representations, ICLR 2024

作者： Liu, Ruizhe Luo, Qian Yang, Yanchao HKU Musketeers Foundation Institute of Data Science The University of Hong Kong Hong Kong School of Electronics Engineering and Computer Science Peking University China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong

We focus on the self-supervised discovery of manipulation concepts that can be adapted and reassembled to address various robotic tasks. We propose that the decision to conceptualize a physical procedure should not depend on how we name it (semantics) but rather on the significance of the informativeness in its representation regarding the low-level physical state and state changes. We model manipulation concepts - discrete symbols - as generative and discriminative goals and derive metrics that can autonomously link them to meaningful sub-trajectories from noisy, unlabeled demonstrations. Specifically, we employ a trainable codebook containing encodings (concepts) capable of synthesizing the end-state of a sub-trajectory given the current state - generative informativeness. Moreover, the encoding corresponding to a particular sub-trajectory should differentiate the state within and outside it and confidently predict the subsequent action based on the gradient of its discriminative score - discriminative informativeness. These metrics, which do not rely on human annotation, can be seamlessly integrated into a VQ-VAE framework, enabling the partitioning of demonstrations into semantically consistent sub-trajectories, fulfilling the purpose of discovering manipulation concepts and the corresponding sub-goal (key) states. We evaluate the effectiveness of the learned concepts by training policies that utilize them as guidance, demonstrating superior performance compared to other baselines. Additionally, our discovered manipulation concepts compare favorably to human-annotated ones while saving much manual effort. Our code is available at: https://***/InfoCon/. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Trajectories

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