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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops, ICASSPW 2025

作者： Hsu, Yu-Chao Li, Tai-Yu Chen, Kuan-Cheng NARlabs National Center for High-Performance Computing Hsinchu Taiwan National Cheng Kung University Cross College Elite Program Tainan Taiwan Imperial College London Department of Electrical and Electronic Engineering London United Kingdom London United Kingdom

ISBN: (纸本)9798331519315

The integration of quantum computing into classical machine learning architectures has emerged as a promising approach to enhance model efficiency and computational capacity. In this work, we introduce the Quantum Kernel-Based Long Short-Term Memory (QK-LSTM) network, which utilizes quantum kernel functions within the classical LSTM framework to capture complex, non-linear patterns in sequential data. By embedding input data into a high-dimensional quantum feature space, the QK-LSTM model reduces the reliance on large parameter sets, achieving effective compression while maintaining accuracy in sequence modeling tasks. This quantum-enhanced architecture demonstrates efficient convergence, robust loss minimization, and model compactness, making it suitable for deployment in edge computing environments and resource-limited quantum devices (especially in the NISQ era). Benchmark comparisons reveal that QK-LSTM achieves performance on par with classical LSTM models, yet with fewer parameters, underscoring its potential to advance quantum machine learning applications in natural language processing and other domains requiring efficient temporal data processing. © 2025 IEEE.

关键词： Qubits

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Quantum-Train-Based Distributed Multi-Agent Reinforcement Learning

Quantum-Train-Based Distributed Multi-Agent Reinforcement Le...

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Multidisciplinary Computational Intelligence Incubators (MCII Companion), IEEE Symposium for

作者： Kuan-Cheng Chen Samuel Yen-Chi Chen Chen-Yu Liu Kin K. Leung Department of Electrical and Electronic Engineering Imperial College London London UK Centre for Quantum Engineering Science and Technology (QuEST) Imperial College London London UK Wells Fargo New York USA Graduate Institute of Applied Physics National Taiwan University Taipei Taiwan Hon Hai Research Institute Taipei Taiwan

ISBN: (数字)9798331519667

ISBN: (纸本)9798331519674

In this paper, we introduce Quantum-Train-Based Distributed Multi-Agent Reinforcement Learning (Dist-QTRL), a novel approach to addressing the scalability challenges of traditional Reinforcement Learning (RL) by integrating quantum computing principles. Quantum-Train Reinforcement Learning (QTRL) leverages parameterized quantum circuits to efficiently generate neural network parameters, achieving a poly $(\log (N))$ reduction in the dimensionality of trainable parameters while harnessing quantum entanglement for superior data representation. The framework is designed for distributed multi-agent environments, where multiple agents, modeled as Quantum Processing Units (QPUs), operate in parallel, enabling faster convergence and enhanced scalability. Additionally, the Dist-QTRL framework can be extended to high-performance computing (HPC) environments by utilizing distributed quantum training for parameter reduction in classical neural networks, followed by inference using classical CPUs or GPUs. This hybrid quantumHPC approach allows for further optimization in real-world applications. In this paper, we provide a mathematical formulation of the Dist-QTRL framework and explore its convergence properties, supported by empirical results demonstrating performance improvements over centric QTRL models. The results highlight the potential of quantum-enhanced RL in tackling complex, highdimensional tasks, particularly in distributed computing settings, where our framework achieves significant speedups through parallelization without compromising model accuracy. This work paves the way for scalable, quantum-enhanced RL systems in practical applications, leveraging both quantum and classical computational resources.

关键词： Training Quantum entanglement Scalability Computational modeling Neural networks Reinforcement learning Mathematical models Quantum circuit Integrated circuit modeling Convergence

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An Autonomous Mobile Robot Path Planner using Spline Curves and Differential Evolution

An Autonomous Mobile Robot Path Planner using Spline Curves ...

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Latin America Congress on Computational Intelligence (LA-CCI)

作者： Elias José de Rezende Freitas Miri Weiss Cohen Frederico Gadelha Guimarães Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Av. Antônio Carlos 6627 Belo Horizonte MG Brazil Federal Institute of Minas Gerais IFMG Department of Software Engineering Braude College of Engineering Karmiel Israel Department of Electrical Engineering Machine Intelligence and Data Science Laboratory (MINDS) Universidade Federal de Minas Gerais UFMG Belo Horizonte MG Brazil

Robots that operate autonomously require a path planner that ensures the robot reaches the desired position in a safe manner. The robot will be able to achieve greater speeds along the path if the path is smooth, minimizing the effort of the controller. This work presents two new path planners, called DE-Spline and DE-NURBS. These planners provide a smooth path directly by the cost function optimization using the algorithm L-SHADE (Success-History based Adaptive of Differential Evolution using linear population size reduction algorithm) and two path’s parametrization: Ferguson cubic spline and NURBS. Additionally, it ensures that the robot reaches the target in the desired orientation. The planners are compared with a Vector Field-based planner, widely used in robot soccer. Based on the results, it has been found that both planners provide a smoother path along the shortest path, by accounting for the robot’s starting angle and its goal angle. Furthermore, DE-NURBS provides a greater degree of planning freedom than DESpline.

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Fusion of Mixture of Experts and Generative Artificial Intelligence in Mobile Edge Metaverse

arXiv

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arXiv 2024年

作者： Liu, Guangyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Mao, Shiwen Kim, Dong In The College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Auburn University Auburn United States The School of Electrical and Computer Engineering University of Sydney Australia The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

In the digital transformation era, Metaverse offers a fusion of virtual reality (VR), augmented reality (AR), and web technologies to create immersive digital experiences. However, the evolution of the Metaverse is slowed down by the challenges of content creation, scalability, and dynamic user interaction. Our study investigates an integration of Mixture of Experts (MoE) models with Generative Artificial Intelligence (GAI) for mobile edge computing to revolutionize content creation and interaction in the Metaverse. Specifically, we harness an MoE model's ability to efficiently manage complex data and complex tasks by dynamically selecting the most relevant experts running various sub-models to enhance the capabilities of GAI. We then present a novel framework that improves video content generation quality and consistency, and demonstrate its application through case studies. Our findings underscore the efficacy of MoE and GAI integration to redefine virtual experiences by offering a scalable, efficient pathway to harvest the Metaverse's full potential. © 2024, CC BY.

关键词： Augmented reality

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A Smart Contract Based Blockchain Approach Integrated with Elliptic Curve Cryptography for Secure Email Application

A Smart Contract Based Blockchain Approach Integrated with E...

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International Symposium on Computing and Networking Workshops (CANDARW)

作者： Md. Biplob Hossain Maya Rahayu Md. Arshad Ali Samsul Huda Yuta Kodera Yasuyuki Nogami Graduate School of Environmental Life Natural Science and Technology Okayama University Japan Electrical and Electronic Engineering Department Khwaja Yunus Ali University Bangladesh Electrical Engineering Department Politeknik Negeri Bandung Indonesia Faculty of CSE Hajee Mohammad Danesh Science and Technology University Bangladesh Green Innovation Center Okayama University Japan

In today’s interconnected digital era, email stands as a primary platform for communication across various fields, such as business, academia, and personal interactions. Since it operates on shared mediums, email is inherently susceptible to interception and misuse of sensitive information. To cover this issue, Pretty Good Privacy (PGP) ensures the confidentiality of email contents. However, while PGP effectively encrypts the body of messages, a significant vulnerability emerges during key sharing. Blockchain technology comes with its immutability aspect. Once data is embedded into the blockchain, modifying it becomes exceedingly challenging. This makes it a potential safeguard against the vulnerabilities in PGP’s key sharing mechanism. Additionally, smart contracts remove the requirement of a Man-in-the-Middle for sharing key, which enhances the sharing key’s security and builds trust among users. In this paper, we integrate the strengths of blockchain into the PGP encryption process, specifically targeting the key sharing vulnerability. Also, we adopt Elliptic Curve Cryptography (ECC) to ensure the confidentiality of the PGP’s key. First, generate the PGP’s key. Then, encrypt it using ECC. After that, deploy a smart contract and continue with the encrypted PGP’s key transactions in blockchain networks. Through this integration, we aim to enhance the security, ensuring both confidentiality and integrity of the PGP’s key. We demonstrated that the proposed system is workable by experimental observation.

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Exploring Shifted Domain Problem within MNIST Dataset

Exploring Shifted Domain Problem within MNIST Dataset

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Computer Science, Information Technology and engineering (ICCoSITE), International Conference on

作者： Hidayaturrahman Yaya Heryadi Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program – Computer Science Bina Nusatara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Domain data can be shifted in any direction so it will be shared in different distributions to its original domain. This could be a problem since the model was trained with different distributions. It is found that adversarial domain adaptation using domain adversarial neural networks (DANN) can help to solve this problem on some scale. DANN can minimize the discrepancy between source and target data so the model can work well in both domains. The experiment is done by utilizing MNIST dataset that shifted into some conditions. In a condition when the shifting of distribution is too far, DANN is struggling to maintain the knowledge extracted from source data which leads to underperformance in the source and target domain. In contrast, when the shifting is closer, DANN can easily fit the model so it can perform well in both domains. It proves DANN is one of the good approaches to performing domain adaptation in small discrepancies.

关键词： Computer science Adaptation models Neural networks Feature extraction Data models Data mining Information technology

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3D Stackable Vertical Ferroelectric Tunneling Junction (V-FTJ) with on/off Ratio 1500x, Applicable Cell Current, Self-Rectifying Ratio 1000x, Robust Endurance of 10⁹ Cycles, Multilevel and Demonstrated Macro Operation Toward High-Density BEOL NVMs

3D Stackable Vertical Ferroelectric Tunneling Junction (V-FT...

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Symposium on VLSI Technology

作者： J.-Y. Lee F.-S. Chang K.-Y. Hsiang P.-H. Chen Z.-F. Luo Z.-X. Li J.-H. Tsai C. W. Liu M. H. Lee Institute and Undergraduate Program of Electro-Optical Engineering National Taiwan Normal University Taiwan Graduate School of Advance Technology National Taiwan University Taiwan Department of Electronics Engineering Institute of Electronics National Yang Ming Chiao Tung University Taiwan Department of Electrical Engineering National Taiwan Normal University Taiwan

A 1500x I LRS /I HRS with a high cell current of ~100 nA/ cell (J = 83 A/cm 2 ) is achieved by antiferroelectric (AFE) vertical ferroelectric tunnel junctions (V-FTJs) that demonstrates multilevel, a self-rectifying rate > 1000x, and macro operation. The stackable 3 D architecture integrating multiple layers of AFE V-FTJs with contact optimization further increases memory density. Robust endurance > 10 9 cycles at |4V| and stable nonvolatile data retention > 10 4 sec with extrapolation to 10 years are achieved. The proposed cell contact V-FTJ by AFE is a promising pathway toward BEOL NVMs by a NAND/NOR-based framework.

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Effective Loss Function for Unbalanced Breast Thermal Image Segmentation

Effective Loss Function for Unbalanced Breast Thermal Image ...

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2021 International Conference on Computer System, Information Technology, and electrical engineering, COSITE 2021

作者： Roslidar, Roslidar Saddami, Khairun Irhamsyah, Muhammad Arnia, Fitri Syukri, Maimun Munadi, Khairul Universitas Syiah Kuala Doctoral Program Post Graduate Program School of Engineering Banda Aceh23111 Indonesia Universitas Syiah Kuala Department of Electrical and Computer Engineering Banda Aceh23111 Indonesia Universitas Syiah Kuala Medical Faculty Banda Aceh23111 Indonesia

ISBN: (纸本)9781665425094

The convolutional neural network's ability to learn images has reigned in computer vision tasks of object detection, classification, and segmentation. In segmentation, the CNN architectures of U-Net and SegNet have shown a good performance;thus, we implemented these networks to take the region of interest (ROI) of the breast thermal images from database for mastology research (DMR). We fine-tuned the networks to find the optimal hyperparameter that can result in the best learning performance. The networks were trained using the stochastic gradient descent optimization algorithm, and weights were updated using the error backpropagation of the algorithm. To minimize the error, the loss function is applied to evaluate a candidate solution of the weights. Thus, we conducted a preliminary study on finding the optimal loss function for breast thermal image segmentation. We applied loss functions of cross-entropy, dice, and focal and figured out the one that provides the highest segmentation accuracy. Then, to evaluate the segmentation result, we calculated the pixel accuracy rate and the dice coefficient to obtain the overlapping index measuring the overlay area between ground truth and predicted output. The result shows that the SegNet model with cross-entropy loss function can take the ROI of breast thermal images at the highest pixel accuracy and optimum dice coefficient of 0.8845 and 0.7928, respectively. © 2021 IEEE.

关键词： Image segmentation

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On the Evaluation of Access-point Handovers for UAVs in Long-distance Missions

On the Evaluation of Access-point Handovers for UAVs in Long...

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2021 International Conference on Unmanned Aircraft Systems, ICUAS 2021

作者： Fagundes, Leonardo A. Souza, Vitor Barbosa Brandao, Alexandre S. Núcleo de Especialização em Robótica Federal University of Viçosa Department of Electrical Engineering and Graduate Program on Computer Science Viçosa Brazil Federal University of Viçosa Graduate Program on Computer Science Department of Informatics Viçosa Brazil

ISBN: (纸本)9780738131153

Unmanned Aerial Vehicle (UAV) systems are being increasingly used in a broad range of applications requiring extensive communications either to interconnect the UAVs with each other or to connect them with Ground Control Stations (GCS). Due to the complexity and difficulty of performing long distance experiments with these robots, simulation becomes a viable and necessary alternative. However, focusing either on the modeling of UAV operations or communication dynamics, available simulation tools are not able to capture the complex interdependencies between these two aspects, especially in applications where extensive communications are required. The main contribution of this work is the modeling and implementation of strategies for enhancing connectivity of a UAV performing long-distance navigation tasks, taking into account applications' requirements. The proposed strategies aim at assessing the impact of frequent handovers between wireless access points in terms of packet loss and available bandwidth. The first strategy prioritizes maintaining a connection until the signal-to-noise ratio (SNR) observed by the robot reaches a predefined threshold. For this, it is assumed that the data to be transmitted is small enough so that the robot may change its modulation to obtain smaller bandwidths while improving connection time. The second one prioritizes bandwidth availability, thus, it always searches for a network that is able to offer data rates higher than a minimum threshold. A hybrid model is validated by considering the characteristics of each individual strategy. The capabilities of the simulation tool designed for this work are illustrated through some case-study scenarios. © 2021 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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An integrated secured vehicular ad-hoc network leveraging Kerberos authentication and Blockchain technology

An integrated secured vehicular ad-hoc network leveraging Ke...

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International Symposium on Computing and Networking Workshops (CANDARW)

作者： Maya Rahayu Md. Biplob Hossain Md. Arshad Ali Samsul Huda Yuta Kodera Yasuyuki Nogami Graduate School of Environmental Life Natural Science and Technology Okayama University Japan Electrical Engineering Department Politeknik Negeri Bandung Indonesia Electrical and Electronic Engineering Department Khwaja Yunus Ali University Bangladesh Faculty of CSE Hajee Mohammad Danesh Science and Technology University Bangladesh Green Innovation Center Okayama University Japan

Vehicular Ad-Hoc Networks (VANETs) are a prominent technology in the drive towards establishing smart transportation systems in the Digital Transformation (DX) era. These networks provide users with critical road information for optimal route selection and accident avoidance. However, the openness of VANETs environment makes them susceptible to various cyber threats. Therefore, authenticating the entities that join the network is necessary and crucial for ensuring the security and integrity of VANET communications. Kerberos is one of the authentication protocols that ensure security, as the password and key are never directly sent among the entities. However, ensuring secure and efficient authentication, especially in VANET handovers, remains a challenge. This paper introduces an innovative authentication system for VANETs that leverages the combined power of blockchain and Kerberos. The system stores Kerberos authenticator messages in a distributed ledger within the blockchain, accessible to Trusted Authorities (TAS) and all RSUs. This approach streamlines vehicle handovers and safeguards authenticator messages against adversarial tampering. It exhibits minimal signalling overhead and authentication delay, ensuring swift and secure authentication processes. We verify the proposal’s effectiveness by simulating the VANETs environment with 100 vehicles, 4 RSUs, and 1 TAS using Omnet++ with the Tsushima, Japan area map.

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