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SWIPTNet: a Unified Deep Learning Framework for SWIPT Based on GNN and Transfer Learning

IEEE Transactions on Mobile computing 2025年

作者： Han, Hong Lu, Yang Song, Zihan Zhang, Ruichen Chen, Wei Ai, Bo Niyato, Dusit Kim, Dong In Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Beijing Jiaotong University School of Electronics and Information Engineering Beijing100044 China Sungkyunkwan University Department of Electrical and Computer Engineering Suwon16419 Korea Republic of

This paper investigates the deep learning based approaches for simultaneous wireless information and power transfer (SWIPT). The quality-of-service (QoS) constrained sumrate maximization problems are, respectively, formulated for power-splitting (PS) receivers and time-switching (TS) receivers and solved by a unified graph neural network (GNN) based model termed SWIPT net (SWIPTNet). To improve the performance of SWIPTNet, we first propose a single-type output method to reduce the learning complexity and facilitate the satisfaction of QoS constraints, and then, utilize the Laplace transform to enhance input features with the structural information. Besides, we adopt the multi-head attention and layer connection to enhance feature extracting. Furthermore, we present the implementation of transfer learning to the SWIPTNet between PS and TS receivers. Ablation studies show the effectiveness of key components in the SWIPTNet. Numerical results also demonstrate the capability of SWIPTNet in achieving nearoptimal performance with millisecond-level inference speed which is much faster than the traditional optimization algorithms. We also show the effectiveness of transfer learning via fast convergence and expressive capability improvement. © 2002-2012 IEEE.

关键词： Laplace transforms

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Meta-Transfer Learning Based Cross-Domain Gesture Recognition Using WiFi Channel State Information

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IEEE Transactions on Consumer Electronics 2025年

作者： Dai, Penglin Zhou, Junfei Ma, Jialong Zhang, Hao Wu, Xiao Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu611756 China Ministry of Education Engineering Research Center of Sustainable Urban Intelligent Transportation China Tangshan Institute of Southwest Jiaotong University Tangshan063000 China Chongqing University of Posts and Telecommunications College of Computer Science and Technology Key Laboratory of Data Engineering and Visual Computing Chongqing400065 China

Gesture recognition plays a crucial role in a wide range of consumer electronics applications, including human-computer interaction and virtual reality, by enabling the identification and interpretation of human gestures. In recent times, WiFi-based gesture recognition has garnered significant attention due to its privacy protection and unobtrusive nature. However, this approach heavily depends on neural network-based models and is notably influenced by environmental conditions, such as specific location and orientation. To address these environmental impacts, we propose a network framework that leverages transfer learning and meta-learning. The primary focus is on utilizing transfer learning to train a convolutional neural network (ResNet) for feature extraction, enabling the extraction of domain-independent features specifically related to gestures, rather than being influenced by environmental factors. Additionally, we employ the meta-learning algorithm MAML to train the fully connected network for gesture classification. Following training, a small set of samples can be utilized for rapid adaptation to diverse domains, maintaining high accuracy across different domains to achieve cross-domain gesture recognition. Our performance evaluation is based on the Widar 3.0 dataset, encompassing both in-domain and cross-domain scenarios. The simulation results demonstrate that our proposed algorithm surpasses Widar3.0 and WiGRUNT by approximately 7.9% and 1.12% in gesture recognition accuracy across all scenarios. © 2025 IEEE.

关键词： Wi-Fi

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Analysis of Schema Formation in Genetic Algorithms: A Review 16th

Analysis of Schema Formation in Genetic Algorithms: A Review

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16th International Conference on Genetic and Evolutionary computing, ICGEC 2024

作者： Zhang, Tiancong Chu, Shu-Chuan Weng, Shaowei Dao, Thi-Kien Nguyen, Trong-The School of Computing and Information Science Fuzhou Institute of Technology Fuzhou350506 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qing-Dao266590 China University of Information Technology Ho Chi Minh City700000 Viet Nam

ISBN: (纸本)9789819615346

Genetic algorithms (GAs) are a powerful class of optimization techniques inspired by the principles of natural selection and genetics. One of the theoretical cornerstones of GAs is schema theory, which provides a framework for understanding how building blocks of solutions, known as schemas, are propagated and combined through generations. This review paper aims to provide a comprehensive theoretical analysis of schema formation in genetic algorithms. It delves into the foundational principles of schema theory, including the schema theorem and its implications for the efficiency and behavior of GAs. The paper also explores advanced topics such as schema disruption mechanisms, the impact of multimodal and epistatic fitness landscapes, and the integration of schema theory with hybrid approaches. Additionally, practical implications and real-world applications of schema theory are discussed through case studies and best practices. By elucidating the mechanisms of schema formation and their influence on genetic algorithm performance, this review seeks to offer valuable insights and directions for future research. Ultimately, this theoretical exploration aims to bridge the gap between abstract concepts and practical application, contributing to the advancement of genetic algorithms as robust optimization tools. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Genetic algorithms

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Invisible Watermark and Hypothesis Testing for Multi-Ownership Verification Framework of Deep Neural Network

Invisible Watermark and Hypothesis Testing for Multi-Ownersh...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Ruoxi Wang Yujia Zhu Daoxun Xia School of Big Data and Computer Science Guizhou Normal University Key Laboratory of Information and Computing Science Guizhou Province Guizhou Normal University Engineering Laboratory for Applied Technology of Big Data in Education Guizhou Normal University Guiyang China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

With the rapid development of information technology, artificial intelligence models have become the core asset of tech giants, highlighting the importance of copyright protection. Black-box watermarking has gained attention due to its ability to verify the legality of models remotely without relying on internal model information. The challenges lie in embedding and verifying watermarks without accessing internal model parameters, while ensuring the robustness and stealth of the watermark. This paper focuses on discussing effective ways to protect the copyright of black-box watermarking models and detect and prevent unauthorized user behavior that infringes on model ownership. Therefore, this paper proposes a multi-ownership verification framework based on invisible watermark and hypothesis testing, which mainly consists of two parts: watermark generation and copyright verification. The watermark generation uses a dynamic watermarking method to generate invisible backdoor watermark and verifies copyright through hypothesis testing. This framework has high assurance and effectiveness under various models of CIFAR-10 and MNIST datasets and shows strong robustness in VGG and Resnet model pruning attacks. This research provides new ideas and methods for the protection of model copyrights in deep neural networks.

关键词： Deep learning Education Closed box Watermarking Artificial neural networks Copyright protection Benchmark testing Robustness Protection Information technology

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Split Learning without Local Weight Sharing To Enhance Client-side data Privacy

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Pham, Ngoc Duy Phan, Tran Khoa Abuadbba, Alsharif Gao, Yansong Nguyen, Van-Doan Chilamkurti, Naveen La Trobe University School of Computing Engineering and Mathematical Sciences VIC Australia CSIRO's Data61 Cybersecurity CRC Australia The University of Western Australia Department of Computer Science and Software Computing Australia

Split learning (SL) aims to protect user data privacy by distributing deep models between the client-server and keeping private data locally. In SL training with multiple clients, the local model weights are shared among the clients for the local model updates. This paper first reveals data privacy leakage exacerbated by local weight sharing among the clients in SL through model inversion attacks. Then, to reduce the data privacy leakage issue, we propose and analyze privacy-enhanced SL (P-SL) (or SL without local weight sharing). We further propose a parallelized P-SL to expedite the training process by duplicating multiple server-side model instances without compromising accuracy. Finally, we explore P-SL with late participating clients and devise a server-side cache-based training method to address the forgetting phenomenon in SL when late clients join. The experiment results demonstrate that P-SL helps reduce up to 50\% of client-side data leakage, which essentially achieves a better privacy-accuracy trade-off than the current trend by using differential privacy mechanisms. Moreover, P-SL and its cache-based version achieve comparable accuracy to baseline SL under various data distributions, while incurring lower costs for computation and communication. Additionally, caching-based training in P-SL mitigates the negative effect of forgetting, stabilizes learning, and enables practical and low-complexity training in a dynamic environment with late-arriving clients. © 2004-2012 IEEE.

关键词： Sensitive data

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Evolving Effective Drug Therapies with Multi-objective Genetic Algorithm 16th

Evolving Effective Drug Therapies with Multi-objective Genet...

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16th International Conference on Genetic and Evolutionary computing, ICGEC 2024

作者： Zhang, Tiancong Weng, Shaowei Chu, Shu-Chuan Nguyen, Trong-The Dao, Thi-Kien School of Computing and Information Science Fuzhou Institute of Technology Fuzhou350506 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qing-dao266590 China University of Information Technology Ho Chi Minh City700000 Viet Nam

ISBN: (纸本)9789819615346

The development of effective drug therapies is a complex and multifaceted problem involving various biological, chemical, and computational challenges. Traditional drug development methods are often time-consuming and costly, necessitating innovative approaches. Multi-objective genetic algorithms (MOGAs) have emerged as a powerful tool for optimizing drug design by simultaneously considering multiple conflicting objectives, such as efficacy, safety, and cost. This paper reviews the application of MOGAs in drug discovery, highlighting their principles, advantages, and case studies, and discussing future directions in the field. Enhancing MOGAs in drug discovery are also outlined, emphasizing the integration with machine learning, advancements in computational infrastructure, and the potential for personalized medicine. The paper concludes by affirming the significant promise of MOGAs in accelerating the development of safe and effective drug therapies, thereby addressing critical needs in modern pharmacology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Drug discovery

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Development of a Sensor-Based Wearable Fetal and Maternal Health Monitoring System 1

Development of a Sensor-Based Wearable Fetal and Maternal He...

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1st International Conference on Electronics, Communication and Signal Processing, ICECSP 2024

作者： Jayalath, Yugashi Maduranga, M.W.P. Tilwari, Valmik Wijayarathna, Wmsrb Wanniarachchi, Waam Sharma, Debashree School Of Pharmacy & Biomolecular Sciences Liverpool John Moores University Liverpool United Kingdom Sri Lanka Technology Campus Faculty of Computing and IT Department of Data Science Padukka Sri Lanka Indian Institute of Information Technology Department of Electronics & Communication Engineering Guwahati India General Sir Jhon Kotelawala Defence University Department of Information Technology Colombo Sri Lanka

ISBN: (纸本)9798350364590

According to the World Health Organization, approximately 360,000 pregnant women are registered annually in Sri Lanka. Among these, about 328,000 babies are born healthy, while 5,800 are born with birth defects, some of which are severe and impact daily life. To address the Sri Lankan stillbirth rate of 6.01%, continuous monitoring of fetal and maternal health is essential. Although commercial devices exist to fulfill this requirement, their high costs and safety issues often preclude regular use. To overcome these challenges, there is a need for a passive, wearable, and portable monitoring system for fetal and maternal health. This system should track key parameters such as fetal heart rate, fetal movements, and maternal heart rate to mitigate health risks for both the fetus and the mother. In this study, we developed a device using the Arduino, appropriate sensors, and a microcontroller to monitor these vital parameters. The device is user-friendly and suitable for regular use. MATLAB was utilized for the signal processing requirements of the proposed device. Test results demonstrated that fetal movement detection was accurate, although potential errors may arise during real-time testing. For pulse sensor readings, an average percentage difference of 3.025% was observed compared to pulse oximeter readings, likely due to placement errors and sensor instability in the proposed device. © 2024 IEEE.

关键词： Electronic health record

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Towards Efficient Discrete Controller Synthesis: Semantics-Aware Stepwise Policy Design via LLM

Towards Efficient Discrete Controller Synthesis: Semantics-A...

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IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)

作者： Yusei Ishimizu Jialong Li Takuto Yamauchi Sinan Chen Jinyu Cai Takanori Hirano Kenji Tei School of Computing Tokyo Institute of Technology Tokyo Japan Department of Computer Science and Engineering Waseda University Tokyo Japan Center of Mathematical and Data Sciences Kobe University Kobe Japan

ISBN: (数字)9798331530839

ISBN: (纸本)9798331530846

Discrete Controller Synthesis (DCS) is an automated generation technique to generate controllers from environment models and requirements. The time and space complexities of DCS are critical because they directly govern the feasibility of using DCS for real-time generation of controllers to cope with environmental changes. Stepwise DCS is considered a promising approach to prevent the construction of ineffective state spaces in the subsequent analysis of the full controller by analyzing sub-controllers that meet part of the requirements at an early stage. However, constructing a stepwise policy is a challenge, as the current methods often require domain experts to manually build it by understanding and reasoning the semantics of each sub-environment model and sub-requirement. To reduce the effort of manual construction, this paper introduces a method that leverages large language models (LLMs) to automate the generation of stepwise policies by utilizing the LLM's ability to understand and reason about the semantics of state machines. The evaluation using GPT-4 on a drone control scenario demonstrates the effectiveness of our method, with an average reduction of 82.2% in states and 91.2% in transitions.

关键词： Large language models Semantics Manuals Aerospace electronics Real-time systems Cognition Complexity theory Drones

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Blockchain Integration for Robust Intellectual Property Protection in New Product Development (NPD)

Blockchain Integration for Robust Intellectual Property Prot...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： P. Chinnasamy Subashini. B Nijanthan Nijanthan Madasamy Raja. G Devulapally Swetha Sreenivasulu Reddy L Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Department of Data Science and Business Systems School of Computing SRMIST India Computer Science and Engineering Paavai Engineering College Namakkal Tamilnadu India Information Technology Paavai Engineering College Namakkal Tamilnadu India Dept. of Computer Science and Engineering Vaagdevi Engineering College Telangana India Department of Mathematics School of Advanced Sciences Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The rapid advancement of Blockchain technology has led to its widespread application in several domains of digital activities, such as e-government concerns and monetary security. This article presents a smart contract-based Blockchain solution for protecting intellectual property rights in new product development. The proposed solution effectively tackles the challenges associated with the confidentiality of personally identifiable information, efficiency in storing transaction-related information, as well as cooperation and monitoring in IPR. Initially, we create a mathematical model based on smart contract enabled Blockchain and intelligent contracts. Subsequently, we develop algorithms for Blockchain generation, and intelligent contracts. The system is suitable for protecting IPR in a blockchain network, by implementation of distributed identity authentication and real-time extensible storage. These features enhance the security and expandability of circuit copyright protection based on blockchain technology. The experimental results demonstrate that the suggested algorithm has decreased the cost for communication and enhanced the effectiveness of storing information and administration. Furthermore, it demonstrates resilience against many common attacks such as double-spending assaults, while maintaining low cost and overhead. Additionally, it exhibits a superior level of security in comparison to three other competing algorithms.

关键词： Patents Technological innovation Costs Smart contracts Intellectual property Product development Blockchains Security Protection Resilience

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ECG data Analysis with MSE and Machine Learning

ECG Data Analysis with MSE and Machine Learning

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2021 IEEE MIT Undergraduate Research Technology Conference, URTC 2021

作者： Shrestha, Abhigya Pote Yu, Chen-Hsiang School of Engineering Wentworth Institute of Technology BostonMA United States School of Computing and Data Science Wentworth Institute of Technology BostonMA United States

ISBN: (纸本)9781665405959

Time series data such as electrocardiogram (ECG) recordings display patterns that indicate regularity in heartbeat rhythms. Medical researchers normally analyze data statistically after collecting all data, but this kind of post-processing approach is difficult to be applied to real-Time issue, such as identifying abnormal heartbeats. To address this issue, we investigated applying supervised machine learning approaches to RR intervals of ECG data for classifying three different types of heartbeat patterns: Healthy Heartbeat, Heart Failure and Atrial Fibrillation. In our study, we also applied the same methods to Multiscale Entropy (MSE) of RR intervals to verify if we can have a better prediction result. The evaluation results show that simply using RR Intervals of ECG data can build machine learning models to predict unseen ECG data with an 80%+ accuracy. However, there is no significant improvement when using MSE of RR intervals. The findings of this study demonstrated that supervised machine learning approaches are relatively successful in predicting heartbeat types with RR interval data than using MSE technique of coarse graining data with higher time scales. Based on this finding, we believe there is a chance to build a mobile or Web-based system that can use a trained model to distinguish three heartbeat patterns for real-Time service. © 2021 IEEE.

关键词： Electrocardiograms

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