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6th International Conference on Medical and Health Informatics, ICMHI 2022

作者： Alizargar, Azadeh Tan, Tan-Hsu Chang, Yang-Lang Alkhaleefah, Mohammad College of Electrical Engineering and Computer Science Department of Electrical Engineering National Taipei University of Technology Taiwan

ISBN: (纸本)9781450396301

Hypertension is a common disease which may lead to incurable situation if it is not well-treated. By extracting knowledge from large datasets, data mining can be used for the hypertension prediction and diagnosis. In this research, three data mining techniques, the support vector machine (SVM), k-nearest neighbors (k-NN), and decision tree, were developed to predict the hypertension with two datasets. These techniques were implemented on Google Colab using Python codes. Experimental results illustrate that the accuracy and area under the receiver operating characteristic (ROC) curve performance of all these three techniques on first dataset is 81% and 80.64% for the SVM, 74% and 73.97% for the k-NN, and 74% and 73.86% for the decision tree, respectively. The accuracy and ROC curve performance of these three approaches on the second dataset were 73% and 72.48% for the SVM, 76% and 75.75% for the k-NN model, and 75% and 74.49% for the decision tree, respectively. The results indicate that the SVM, k-NN, and decision tree, are effective in predicting the hypertension based on the obtained important features of the patients, and the k-NN is among the best choice. © 2022 ACM.

关键词： Decision trees

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Dual-band and high-isolation shared-aperture antenna for broadband airborne applications

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Journal of Electronic science and Technology 2024年第4期22卷 9-18页

作者： Yuan Zhang Dong-Jun Wang Chuan Wu Qiang-Ming Cai Run-Ren Zhang Rong-Qiang Li Peng Gao Yan-Wen Zhao School of Electronic Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China AVIC Chengdu Aircraft Industrial(Group)Co. Ltd.Chengdu610091China Electronic Engineering College Chengdu University of Information TechnologyChengdu610225China School of Information Engineering Southwest University of Science and TechnologyMianyang621010China Department of Electrical and Computer Engineering Duke UniversityDurhamNC 27708USA

A dual-band and high-isolation shared-aperture antenna for unmanned aerial vehicle(UAV)platforms has been *** shared-aperture antenna consists of a rectangular monopole antenna and a 4-element multiple input multiple output(MIMO)*** order to increase the isolation,several double split ring metamaterial(MTM)structures are introduced between antenna *** antenna radiator and the MTM structure are located on the front and back of the same dielectric substrate,respectively,and are perpendicular to a circular metal *** overall size of the antenna substrate is 124 mm×38 mm×1.016 ***,the antenna prototype is constructed and measured,and the simulated and measured results are in good *** measured results show that the-10 dB bandwidth of the monopole antenna is 1.92 GHz to 2.75 GHz,and the common-6.0 dB bandwidth of the MIMO antenna element is 4.75 GHz to 4.91 GHz,covering 2.2 GHz to 2.4 GHz in the S-band and 4.8 GHz to 4.9 GHz in the 5G band,*** the 5G band,the isolation between any element of the MIMO antenna and the S-band monopole antenna is not less than 21 dB,and the isolation between the MIMO antenna elements is better than 23 dB,indicating t-hat the isolation between the antenna elements is *** proposed antenna is suitable for the application on UAV airborne platforms.

关键词： Dual-band antenna High-isolation Monopole antenna Multiple input multiple output(MIMO) Shared-aperture Unmanned aerial vehicle(UAV)

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Design of Medical Privacy Protection Circuit Based on Cross Obfuscation 9

Design of Medical Privacy Protection Circuit Based on Cross ...

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9th International Conference on Integrated Circuits and Microsystems, ICICM 2024

作者： Chen, Zhikang Zhang, Yuejun Ren, Mingze Xia, Donghao Wang, Pengjun Zhang, Huihong Wen, Liang Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China College of Electrical and Electronic Engineering Wenzhou University Wenzhou China China Coast Guard Academy Department of Electronic Technology Ningbo China

ISBN: (纸本)9798331509453

With the rise of the Internet of Things, smart healthcare with remote monitoring and diagnosis enables patients to reduce the number and duration of hospitalizations, thereby reducing the pressure on doctors. However, the transmission data of disease detection chips is susceptible to cache attacks, leading to user health data leakage. Hardware level encryption design has become a research hotspot. Firstly, by adding a Mask channel at the upper level of each convolution layer, the preprocessed data is transmitted simultaneously with the obfuscated data, and the channel exchange during transmission is utilized to protect user privacy;Secondly, there is a redundancy mechanism between the input matrix and weight matrix of the storage module, and invalid values are added to the redundant parts to prevent cache attacks from stealing user information;Finally, add multi temporal obfuscation to the first input data to achieve the different lengths between the first and subsequent data. The protection circuit designed in this article achieves user privacy protection on the basis of only increasing hardware overhead by 2.5%, power consumption overhead by 3.61%, and not affecting disease monitoring accuracy. © 2024 IEEE.

关键词： Information leakage

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Non-intrusive soil carbon content quantification methods using machine learning algorithms:A comparison of microwave and millimeter wave radar sensors

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Journal of Automation and Intelligence 2023年第3期2卷 152-166页

作者： Di An YangQuan Chen Electrical Engineering&Computer Science University of CaliforniaMerced95343CAUSA Department of Mechanical Engineering University of CaliforniaMerced95343CAUSA

Agricultural and forestry biomass can be converted to biochar through pyrolysis gasification,making it a significant carbon source for *** biochar to soil is a carbon-negative process that helps combat climate change,sustain soil biodiversity,and regulate water ***,quantifying soil carbon content conventionally is time-consuming,labor-intensive,imprecise,and expensive,making it difficult to accurately measure in-field soil carbon’s effect on storage water and *** address this challenge,this paper for the first time,reports on extensive lab tests demonstrating non-intrusive methods for sensing soil carbon and related smart biochar applications,such as differentiating between biochar types from various biomass feedstock species,monitoring soil moisture,and biochar water retention capacity using portable microwave and millimeter wave sensors,and machine *** methods can be scaled up by deploying the sensor in-field on a mobility platform,either ground or *** paper provides details on the materials,methods,machine learning workflow,and results of our *** significance of this work lays the foundation for assessing carbon-negative technology applications,such as soil carbon content *** validated our quantification method using supervised machine learning algorithms by collecting real soil mixed with known biochar contents in the *** results show that the millimeter wave sensor achieves high sensing accuracy(up to 100%)with proper classifiers selected and outperforms the microwave sensor by approximately 10%–15%accuracy in sensing soil carbon content.

关键词： Soil carbon content sensing Carbon sequestration Microwave radar Millimeter wave radar Proximal sensing Machine learning

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GPU Side-Channel Attack Classification for Targeted Secure Shader Mitigation

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SN computer science 2024年第8期5卷 1148页

作者： Lungu, Nelson Patra, Sudhansu Shekhar Mishra, Manoj Ranjan Dash, Bibhuti Bhusan Sasmal, Gopal Charan Pattnayak, Parthasarathi Singh, Satyendr Gourisaria, Mahendra Kumar Department of Electrical and Electronics Engineering University of Zambia Lusaka Zambia School of Computer Applications Kalinga Institute of Industrial Technology (KIIT) Deemed to be University Bhubaneswar India MCA Department Trident Academy of Creative Technology Bhubaneswar India Computer Science and Engineering Department BML Munjal University Gurugram India School of Computer Engineering Kalinga Institute of Industrial Technology (KIIT) Deemed to be University Bhubaneswar India

Graphics processing units (GPUs) provide massively parallel processing capabilities, enabling accelerated computation across diverse applications. However, their parallel architecture and shared resources also introduce significant security vulnerabilities, especially side-channel attacks that can compromise sensitive data. This article presents a systematic classification of GPU side-channel attack vectors, including power analysis, timing analysis, electromagnetic analysis, and combined attacks. This taxonomy proposes countermeasures for the NVIDIA GeForce RTX 3090 GPU to mitigate these threats. The countermeasures encompass hardware techniques such as isolated execution environments, secure memory partitioning, and hardware random number generators to reduce the attack surface. Software methods, including constant-time algorithms, data-independent execution paths, masking, and oblivious memory access, are also implemented to eliminate timing and cache side channels. Comprehensive experiments demonstrate the effectiveness of the countermeasures, achieving up to 95% reduction in power analysis leakage, 90% in timing analysis, and 92% in electromagnetic analysis with acceptable performance overheads. The secure shader programming framework provides robust mitigation against GPU side-channel attacks while maintaining efficiency and compatibility with the RTX 3090 architecture. This work contributes vital insights and solutions for analysing GPU security vulnerabilities, designing focused countermeasures, and enabling confidential computing leveraging modern GPU capabilities. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Graphics processing units security Hardware countermeasures NVIDIA GeForce RTX 3090 Secure shader Side-channel attacks Software mitigation

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FTA-DFI: a framework for generalizable deepfake detection based on distinctive features from various manipulations compared to genuine images

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Applied Intelligence 2025年第11期55卷 1-15页

作者： Zou, Zheng Peng, Dunlu Zhao, Yu Tian, Zekun Cai, Jun School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China

Deepfakes have attracted considerable attention due to their detrimental impact, notably the production of convincing fake images and their unregulated dissemination. However, existing detectors suffer from a common defect that obsessing with low-level local artifacts or predefined forgery patterns while neglecting the intrinsic differences between the forged and genuine images. This leads to significant performance decline when the model encounters with unseen domains and forgeries. To address this issue, this paper proposes a novel framework, named as FTA-DFI, which leverages disentanglement learning and adversarial learning to analyze forgery traces amplified from the perspective of key facial features and frequency domain. FTA-DFI consists of the Fake Trace Amplification Strategy (FTAS) and the Distinctive Feature Identification Strategy (DFIS). FTAS suppresses method-specific features and identity information using the Face Features Random Mask and amplifies manipulated artifacts through the Frequency Domain Converter. DFIS employs the Feature Separation Disentangler and Intrinsic-Adversarial Learning modules to discern distinctive features between fake and genuine images, thereby further mitigating the overfitting of forgery-irrelevant information and specific forgery approaches. Extensive experimental results demonstrate that FTA-DFI outperforms current state-of-the-art models in generalization evaluations across mainstream datasets, validating the superior performance of the proposed architecture in terms of generalization capability. The code is available at https://***/zouzhengcs/FTA-DFI .

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Identification and Containment of Intrusion Attacks in an IIoT Network based on Machine Learning and Blockchain Technology 2

Identification and Containment of Intrusion Attacks in an II...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Motamarri, Nikhita Satish, G. Uma, H.R. Barve, Amit Patil, Harshal Taqui, Syed Noeman Potti Sreeramulu Chalavadhi Mallikarjuna Rao College of Engineering and Technology Department of Computer Science and Engineering Vijayawada Andhra Pradesh 520001 India Sree Dattha Institute of Engineering and Science Department of Electrical and Electronics Engineering Sheriguda Ibrahimpatnam Telangana Hyderabad501510 India BGS Institute of Technology Adichunchanagiri University Department of Computer Science and Engineering Mandya Karnataka 571448 India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Gujarat 391760 India Department of Computer Science and Engineering Pune Maharashtra 412115 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of VLSI Microelectronics Tamilnadu Chennai602105 India

ISBN: (纸本)9798350327533

The Blockchain technology is leveraged to establish a secure and tamper-resistant decentralized ledger, providing a transparent and immutable record of all transactions and communications within the IoT network. By implementing smart contracts, we ensure the integrity and authenticity of device interactions, mitigating the risk of unauthorized access and data manipulation. Complementing the Blockchain, Machine Learning (ML) algorithms are employed to analyze and identify patterns indicative of malicious activities. The proposed system employs anomaly detection models, trained on historical data, to continuously monitor and adapt to emerging threats. This dynamic threat detection mechanism enhances the ability of the IoT network to recognize and respond to sophisticated intrusion attempts in real-Time. Furthermore, the proposed framework incorporates a decentralized identity management system using Blockchain, ensuring that only authenticated and authorized devices can participate in the IoT network. This not only safeguards against unauthorized access but also protects the privacy and integrity of the data generated and exchanged by IoT devices. To validate the effectiveness of our approach, we conducted experiments in a simulated IoT environment, considering various attack scenarios. The results demonstrate a significant improvement in the overall security posture of the IoT network, with reduced false positives and swift identification of intrusions. In conclusion, the integration of Blockchain and ML techniques provides a robust and adaptive defense mechanism for IoT device networks. This holistic approach addresses the challenges of security, privacy, and data integrity, paving the way for a more resilient and trustworthy IoT ecosystem in the face of evolving cyber threats. © 2024 IEEE.

关键词： Blockchain Integrity Internet of Things (IoT) Intrusion Intrusion Detection System Machine Learning Security

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FCOM: A Federated Collaborative Online Monitoring Framework via Representation Learning 39

FCOM: A Federated Collaborative Online Monitoring Framework ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Kosolwattana, Tanapol Wang, Huazheng Al Kontar, Raed Lin, Ying Department of Industrial Engineering University of Houston United States School of Electrical Engineering and Computer Science Oregon State University United States Department of Industrial & Operations Engineering University of Michigan United States

ISBN: (纸本)157735897X

Monitoring a large population of dynamic processes with limited resources presents a significant challenge across various industrial sectors. This is due to 1) the inherent disparity between the available monitoring resources and the extensive number of processes to be monitored and 2) the unpredictable and heterogeneous dynamics inherent in the progression of these processes. Online learning approaches, commonly referred to as bandit methods, have demonstrated notable potential in addressing this issue by dynamically allocating resources and effectively balancing the exploitation of high-reward processes and the exploration of uncertain ones. However, most online learning algorithms are designed for 1) a centralized setting that requires data sharing across processes for accurate predictions or 2) a homogeneity assumption that estimates a single global model from decentralized data. To overcome these limitations and enable online learning in a heterogeneous population under a decentralized setting, we propose a federated collaborative online monitoring method. Our approach utilizes representation learning to capture the latent representative models within the population and introduces a novel federated collaborative UCB algorithm to estimate these models from sequentially observed decentralized data. This strategy facilitates informed monitoring of resource allocation. The efficacy of our method is demonstrated through theoretical analysis, simulation studies, and its application to decentralized cognitive degradation monitoring in Alzheimer's disease. Copyright © 2025, Association for the Advancement of Artificial Intelligence.

关键词： Federated learning

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Sustainable rural electrification: Energy-economic feasibility analysis of autonomous hydrogen-based hybrid energy system

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International Journal of Hydrogen Energy 2025年 141卷 460-473页

作者： Khan, Shoaib Ahmed Tao, Zou Agyekum, Ephraim Bonah Fahad, Shah Tahir, Mustafa Salman, Muhammad School of Mechanical and Electrical Engineering Guangzhou University Guangzhou China Department of Nuclear and Renewable Energy Ural Federal University Named After the First President of Russia Boris Yeltsin Russia Department of Science and Innovations Western Caspian University 31 Istiglaliyyat Street BakuAZ1001 Azerbaijan College of Computer Science and Electronic Engineering Hunan University Changsha China College of Electrical Engineering Zhejiang University Hangzhou China

Pakistan has endured a severe power shortage for the past decade, with an installed capacity insufficient to meet the escalating demand. This dire situation results in catastrophic power breakdowns lasting 12 h in cities and 18 h in rural areas daily, and unfortunately, the outlook indicates it may worsen over time. The energy deficit is taking a toll on the country's overall economy, causing significant damage. This study focuses on designing a rural hybrid renewable energy system (HRES) to provide stable power to a remote village in Pakistan's Bannu district. The proposed HRES includes photovoltaic panels (PV), fuel cells (FC), and a biogas generator (BG). This paper addresses Pakistan's power shortage crisis and offers an economically viable solution through the rural HRES design, ensuring a sustainable and resilient energy future. Robustness, sensitivity, and breakeven analyses confirm the system's viability and ability to provide the community with uninterrupted electricity. The research demonstrates a cost of energy (COE) of $0.41/kWh and a total net present cost (TNPC) of $199,081 for the HRES. Additionally, the study compares the performance of the proposed system with state-of-the-art alternatives. © 2024

关键词： Biomass Cost of energy Hydrogen storage Net present cost Renewable energy system Rural electrification

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Social Force Embedded Mixed Graph Convolutional Network for Multi-Class Trajectory Prediction

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第9期9卷 5571-5580页

作者： Du, Quancheng Wang, Xiao Yin, Shouguo Li, Lingxi Ning, Huansheng University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Anhui University School of Artificial Intelligence Hefei230031 China Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Institute of Automation Beijing100190 China Indiana University-Purdue University Indianapolis School of Electrical and Computer Engineering IndianapolisIN46202 United States

Accurate prediction of agent motion trajectories is crucial for ensuring the safety and reliability of autonomous driving systems. Current research predominantly focuses on traditional deep learning methods, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which use relative distances to predict the trajectories of a single class of agents. However, in complex traffic scenarios, the motion behaviors of diverse traffic participants exhibit highly stochastic. Sole reliance on relative distances may not sufficiently capture nuanced interaction patterns between different classes of road users. To address this challenge, we propose a novel multi-class trajectory prediction method named the social force embedded mixed graph convolutional network (SFEM-GCN). SFEM-GCN comprises three graph topologies: the semantic graph (SG), position graph (PG), and velocity graph (VG). These graphs encode various social force relationships among different classes of agents in complex scenes. Specifically, SG utilizes one-hot encoding of agent-class information to guide the construction of graph adjacency matrices based on semantic information. PG and VG create adjacency matrices to capture motion interaction relationships between different classes of agents. These graph structures are then integrated into a mixed graph, where learning is conducted using a spatio-temporal graph convolutional neural network (ST-GCNN). To further enhance prediction performance, we adopt temporal convolutional networks (TCNs) to generate the predicted trajectory with fewer parameters. Extensive experiments on the widely recognized open-source dataset demonstrate that SFEM-GCN surpasses state-of-the-art methods in terms of accuracy and robustness. © 2016 IEEE.

关键词： Recurrent neural networks

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