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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Sabrina Karboua Fouzi Harrag Sarrah Karboua Amal Booutadjine Mohamed Deriche Department of Computer Science Setif 1 University-Ferhat ABBAS Abdelhamid MEHRI - Constantine 2 University Algeria College of Engineering and Information Technology AIRC Ajman University UAE

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Skin cancer is the most prevalent type of cancer worldwide, accounting for approximately one-third of all cancer cases, with its incidence steadily rising in recent years. This is why early and precise identification of skin cancer is crucial for effective treatment and enhancing survival rates. Leveraging advancements in deep learning and transfer learning, we developed a comprehensive system to address this challenge. In this study, we employed the HAM10000 dataset, a benchmark resource for skin cancer research, to train and evaluate deep learning models. Two architectures, DenseNet201 and ResNet50, were fine- tuned and modified to enhance their accuracy in classifying skin lesions as benign or malignant. Rigorous preprocessing, data augmentation, and model fine-tuning were utilized to address challenges such as dataset imbalance and improve classification accuracy. Our experiments revealed that the modified fine-tuned DenseNet201 model achieved superior performance compared to fined-tuned ResNet50 and other leading models reviewed in the literature with an accuracy of 95.04%. This model was then integrated into a mobile application, “SkinSafe,” to provide an accessible and user-friendly tool for real-time skin cancer classification.

关键词： Deep learning Accuracy Transfer learning Skin Real-time systems Data models Mobile applications Lesions Skin cancer Residual neural networks

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Blending Optimal Control and Biologically Plausible Learning for Noise-Robust Physical Neural Networks

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Physical Review Letters 2025年第1期134卷 017301-017301页

作者： Satoshi Sunada Tomoaki Niiyama Kazutaka Kanno Rin Nogami André Röhm Takato Awano Atsushi Uchida Faculty of Mechanical Engineering Institute of Science and Engineering Kanazawa University Kakuma-machi Kanazawa Ishikawa 920-1192 Japan Department of Information and Computer Sciences Saitama University 255 Shimo-Okubo Sakura-ku Saitama City Saitama 338-8570 Japan Department of Information Physics and Computing Graduate School of Information Science and Technology The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan

The rapidly increasing computational demands for artificial intelligence (AI) have spurred the exploration of computing principles beyond conventional digital computers. Physical neural networks (PNNs) offer efficient neuromorphic information processing by harnessing the innate computational power of physical processes; however, training their weight parameters is computationally expensive. We propose a training approach for substantially reducing this training cost. Our training approach merges an optimal control method for continuous-time dynamical systems with a biologically plausible training method—direct feedback alignment. In addition to the reduction of training time, this approach achieves robust processing even under measurement errors and noise without requiring detailed system information. The effectiveness was numerically and experimentally verified in an optoelectronic delay system. Our approach significantly extends the range of physical systems practically usable as PNNs.

关键词： Continuous time systems

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An End-to-End Deep Learning QoS Prediction Model Based on Temporal Context and Feature Fusion

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IEEE Transactions on Services Computing 2025年

作者： Zhang, Peiyun Fan, Jiajun Chen, Yutong Huang, Wenjun Zhu, Haibin Zhao, Qinglin Nanjing University of Information Science & Technology Engineering Research Center of Digital Forensics of Ministry of Education School of Computer Science Nanjing210044 China Nipissing University Department of Computer Science and Mathematics North BayONP1B 8L7 Canada Macau University of Science and Technology School of Computer Science and Engineering Taipa999078 China

Existing end-to-end quality of service (QoS) prediction methods based on deep learning often use one-hot encodings as features, which are input into neural networks. It is difficult for the networks to learn the information that is conducive to prediction. Aiming at the above problem, an end-to-end deep learning QoS prediction model based on a temporal context and feature fusion is proposed. In the proposed model, three blocks are designed for QoS prediction. Firstly, a user-service encoding conversion block is designed to convert the one-hot encodings of users and services into the latent features of users and services, which can make full use of the data in sparse matrices. Then a time feature extraction block is designed to extract time features based on the time-varying characteristics of QoS values. Finally, the time features are fused with the latent features of users and services to predict QoS values. The experimental results show that on existing datasets, the proposed model has better prediction accuracy than other advanced methods in response time and throughput. © 2025 IEEE.

关键词： Prediction models

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Digital Twin Empowered Wireless Positioning: Prospects, Architecture, and Challenges

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IEEE Network 2025年

作者： Shi, Xiufang Xuan, Xinlu Huang, Xinyu Xue, Jianzhe Shen, Xuemin Zhejiang University of Technology College of Information Engineering Hangzhou310023 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada Nanjing University School of Electronic Science and Engineering Nanjing210023 China

Achieving precise and reliable positioning in complex and dynamic environments is very challenging due to multipath propagation, environmental changes, and system limitations. This article explores the role of digital twin (DT) in wireless positioning and provides comprehensive study on the prospects of DT in enhancing positioning performance with respect to accuracy, coverage, latency, and reliability. Particularly, a general architecture for DT-empowered wireless positioning is proposed. The powerful simulation and prediction capabilities of DT are leveraged for fine-grained multi-path feature extraction, dynamic model update, seamless-transition, and system optimization. A case study based on fingerprinting positioning is presented to demonstrate the enhancement due to DT in feature extraction and model update. Some open research issues in DT-empowered wireless positioning are also discussed. © 1986-2012 IEEE.

关键词： Digital elevation model

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Study of New Generation Optical Networks with Regards to Type of Optical Amplifiers Connected in the Network 23

Study of New Generation Optical Networks with Regards to Typ...

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23rd Slovak-Czech-Polish Optical Conference on Wave and Quantum Aspects of Contemporary Optics

作者： Látal, Jan Šiška, Petr Hejduk, Stanislav Seyringer, Dana Dohnal, Fadi Chovan, Jozef Uherek, František Štípal, Jiří Trubák, Kamil Department of Telecommunications Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava 17. listopadu 2172/15 Ostrava-Poruba708 00 Czech Republic Research Centre for Microtechnology Vorarlberg University of Applied Sciences Hochschulstraße 1 Dornbirn6850 Austria Slovak Centre of Scientific and Technical Information International Laser Centre Ilkovičova 3 Bratislava841 04 Slovakia Institute of Electronics and Photonics Faculty of Electrical Engineering and Information Technology Slovak University of Technology Ilkovičova 3 Bratislava812 19 Slovakia

ISBN: (数字)9781510688094

ISBN: (纸本)9781510688087

Nowadays, there is strong demand for high-bitrate systems that can provide and distribute large bandwidth while assuring minimal error rate and high-quality communication with regards to the implementation of cloud centres, new 6G networks and smart applications. This all leads to continuous dynamic development in the field of xPON networks bringing new solutions for future optical networks. The presented work focuses on NG-PON2 networks, which are represented by Time and Wavelength Division Multiplexing Passive Optical Network (TWDM-PON) (G.989.1) or Super-PON (IEEE P802.3cs). The basis of the introduced topology is in combination of wave and time access through the TWDM-PON network. In our simulation, we were focusing on the utilization of eight wavelengths operating in the C and L band with amplifiers implementation, namely Erbium Doped Fiber Amplifier (EDFA) or Semiconductor Optical Amplifier (SOA). Regarding to used amplifier the ODN (Optical Distribution Network) reach of TWDM-PON were studied as well as the bitrate of 10 Gbps per wavelength in both upstream and downstream directions with the help of parameters such as bit error rate (BER), Q-factor. From the simulation results, it is shown that by implementing an EDFA amplifier, a longer range of up to 60 km and a lower BER than that of SOA can be achieved. The proposed TWDM-PON system provides a communication network for urban as well as rural areas with guaranteed high bit rates and range. © 2025 SPIE.

关键词： Erbium doped fiber amplifiers

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Simulation of Hybrid TWDM-PON with broadband AWG in topology 23

Simulation of Hybrid TWDM-PON with broadband AWG in topology

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23rd Slovak-Czech-Polish Optical Conference on Wave and Quantum Aspects of Contemporary Optics

作者： Šiška, Petr Látal, Jan Hejduk, Stanislav Seyringer, Dana Dohnal, Fadi Chovan, Jozef Uherek, František Štípal, Jiří Trubák, Kamil Department of Telecommunications Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava 17. listopadu 2172/15 Ostrava-Poruba708 00 Czech Republic Research Centre for Microtechnology Vorarlberg University of Applied Sciences Hochschulstraße 1 Dornbirn6850 Austria Slovak Centre of Scientific and Technical Information International Laser Centre Ilkovičova 3 Bratislava841 04 Slovakia Institute of Electronics and Photonics Faculty of Electrical Engineering and Information Technology Slovak University of Technology Ilkovičova 3 Bratislava812 19 Slovakia

ISBN: (数字)9781510688094

ISBN: (纸本)9781510688087

This work deals with the study of implementation possibilities of broadband optical multiplexer/demultiplexer in the form of AWG (Arrayed Waveguide Gratings) in TWDM-PON topology (Time and Wavelength Division Multiplexing Passive Optical Network) by norm G.989.1 as a key component of the hybrid network. The functionality possibilities of the TWDM-PON system are observed where the goal of AWG design is to support also wavelengths different from standard wavelengths used for TWDM-PON in C-band (Conventional) ranges from 1530 nm to 1565nm and L-band (Long-wavelength, 1565-1625nm) bands. This should allow for older communication systems (for example EPON (Ethernet Passive Optical Network) given by norm IEEE 802.3ah and GPON (Gigabit-capable Passive Optical Network) by norm ITU-T G.984) to be operating concurrently over ODN (optical distribution network) without mutual disturbance with newer xPON communication systems. The network topology operating in this manner makes efficient use of passive infrastructure, including reducing the cost of construction (CAPEx) or operation (OPEx) while creating hybrid types of optical networks. © 2025 SPIE.

关键词： Arrayed waveguide gratings

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pFedDHPO: A Differentiable Approach for Personalized Hyperparameter Optimization in Federated Learning

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Shen, Jinglong Cheng, Nan Xu, Wenchao Wang, Haozhao Quan, Wei Shen, Xuemin Xidian University School of Telecommunications Engineering Shaanxi Xi’an710126 China Hong Kong Polytechnic University Department of Computing China Huazhong University of Science and Technology School of Computer Science and Technology Wuhan430074 China Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Hyperparameter optimization (HPO) is crucial for federated learning (FL) performance. Given the inherent data heterogeneity across clients, recent research has focused on providing personalized hyperparameters for individual clients. However, such personalized approaches introduce exponential search complexity as the number of clients increases, significantly reducing the efficiency of existing HPO methods. To address this challenge, we propose pFedDHPO, a novel personalized HPO framework that efficiently optimizes hyperparameters in a differentiable manner. Specifically, pFedDHPO formulates personalized HPO as an optimization problem targeting joint distribution parameters within the clients’ search space and leverages gradient information from differentiable validation loss to substantially enhance the efficiency of the HPO process. Experimental results demonstrate that pFedDHPO achieves state-of-the-art performance compared to baseline methods, improving accuracy by up to 18.35% under extreme Non-IID data distributions. Additionally, the framework reduces communication overhead by 41.2% compared to conventional HPO methods, making it highly scalable for resource-constrained FL deployments. © 2015 IEEE.

关键词： Federated learning

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STARS Assisted Semi-Grant-Free NOMA Communications

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IEEE Transactions on Wireless Communications 2025年

作者： Xie, Jin Yue, Xinwei Zhao, Jingjing Zou, Yixuan Liu, Yuanwei Liu, Rongke Ding, Zhiguo Key Laboratory of Information and Communication Systems China Beijing Information Science and Technology University Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing100101 China Beihang University School of Electronic and Information Engineering Beijing100191 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of Khalifa University Department of Computer and Information Engineering Abu Dhabi United Arab Emirates

This paper investigates the performance of simultaneously transmitting and reflecting surface (STARS) assisted semi-grant-free non-orthogonal multiple access network with randomly distributed users. By deploying STARS, the transmit signals of grant-based user (GBU) and grant-free users (GFUs) can be exquisitely adjusted to reduce interference. We propose a maximum channel scheduling (MCS) protocol that allows a GFU to access GBU’s channel with the assistance of STARS. In particular, the impacts of perfect/imperfect successive interference cancellation (pSIC/ipSIC) on MCS protocol are taken into account. To characterize the performance of STARS aided MCS (STARS-MCS) network, we derive the expressions of outage probability for GBU and GFU with pSIC/ipSIC. By applying convolution theorem and Laplace transform, the asymptotic expressions of outage probability and diversity orders for GBU and GFU are attained. We further design a STARS-based power control (SPC) strategy to eliminate the outage probability error floor and improve the outage performance. Numerical results show that: 1) The performance of STARS-MCS outperforms the existing benchmarks in terms of outage probability and system throughput;2) The SPC strategy can effectively improve the performance of the STARS-MCS network and eliminate the outage probability error floor at high signal-to-noise ratios;and 3) By adjusting reflection and transmission coefficients of STARS, the outage performance of GBU and GFU can be greatly enhanced. © 2025 IEEE. All rights reserved.

关键词： Laplace transforms

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A Digital Twin-Based Intelligent Network Architecture for Underwater Acoustic Sensor Networks

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IEEE Transactions on Mobile Computing 2025年

作者： Song, Shanshan Huangfu, Bingwen Guo, Jiani Liu, Jun Cui, Junhong Shen, Xuemin Beihang University School of Electronic and Information Engineering Beijing100191 China Jilin University College of Computer Science and Technology Changchun130012 China UESTC Shenzhen Institute for Advanced Study Shenzhen China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Underwater acoustic sensor networks (UASNs) drive toward strong environmental adaptability, intelligence, and multifunctionality. However, due to unique UASN characteristics, such as long propagation delay, dynamic channel quality, and high attenuation, existing studies present untimeliness, inefficiency, and inflexibility in real practice. Digital twin (DT) technology is promising for UASNs to break the above bottlenecks by providing high-fidelity status prediction and exploring optimal schemes. In this article, we propose a Digital Twin-based Network Architecture (DTNA), enhancing UASNs' environmental adaptability, intelligence, and multifunctionality. By extracting real UASN information from local (node) and global (network) levels, we first design a layered architecture to improve the DT replica fidelity and UASN control flexibility. In local DT, we develop a resource allocation paradigm (RAPD), which rapidly perceives performance variations and iteratively optimizes allocation schemes to improve real-time environmental adaptability of resource allocation algorithms. In global DT, we aggregate decentralized local DT data and propose a collaborative Multi-agent reinforcement learning framework (CMFD) and a task-oriented network slicing (TNSD). CMFD patches scarce real data and provides extensive DT data to accelerate AI model training. TNSD unifies heterogeneous tasks' demand extraction and efficiently provides comprehensive network status, improving the flexibility of multi-task scheduling algorithms. Finally, practical and simulation experiments verify the high fidelity of DT. Compared with the original UASN architecture, experiment results demonstrate that DTNA can: (i) improve the timeliness and robustness of resource allocation;(ii) greatly reduce the training time of AI algorithms;(iii) more rapidly obtain network status for multi-task scheduling at a low cost. © 2002-2012 IEEE.

关键词： Resource allocation

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6G Communication New Paradigm: The Integration of Unmanned Aerial Vehicles and Intelligent Reflecting Surfaces

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IEEE Communications Surveys and Tutorials 2025年

作者： Ning, Zhaolong Li, Tengfeng Wu, Yu Wang, Xiaojie Wu, Qingqing Yu, Fei Richard Guo, Song Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Chongqing University of Posts and Telecommunications School of Cyber Security and Information Law Chongqing400065 China Department of Electronic Engineering Shanghai Jiao Tong University Shanghai Shanghai200240 China Carleton University School of Information Technology OttawaONK1S 5B6 Canada The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

With the continuous development of Intelligent Reflecting Surfaces (IRSs) and Unmanned Aerial Vehicles (UAVs), their combination has become foundational technologies to complement the terrestrial network by providing communication enhancement services for large-scale users. This article provides a comprehensive overview of IRS-assisted UAV communications for 6th-Generation (6G) networks. First, the applications supported by IRS-assisted UAV communications for 6G networks are introduced, and key issues originated from applications supported by IRSs and UAVs for 6G networks are summarized and analyzed. Then, prototypes and main technologies related to the integration of IRSs and UAVs are introduced. Driven by applications and technologies of IRS-assisted UAV communications, existing solutions in the realms of energy-constrained communications, secure communications, and enhanced communications are summarized, and corresponding empirical lessons are provided. Finally, we discuss some research challenges and open issues in IRS-assisted UAV communications, offering directions for the future development. © 1998-2012 IEEE.

关键词： Aircraft communication

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