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25th IEEE International Conference on Industrial technology, ICIT 2024

作者： Wu, Peng Jin, Feihang Yan, Tao Institute of Big Data Science and Industry Shanxi University Taiyuan China School of Computer and Information Technology Shanxi University Taiyuan China Institute of Big Data Science and Industry School of Computer and Information Technology Taiyuan China

We propose a new scheduling policy for performance asymmetric multiprocessors that have identical instruction sets but different processing speeds. The difference between it and the original Pfair scheduling is that when selecting scheduling parameters such as the pseudo deadline of the subtask and selecting the processor when running the highest priority subtask, it can be aware of the performance asymmetry of the underlying processor. In addition, we also propose a sufficient schedulability test for Pfair scheduling on performance asymmetric multiprocessors. © 2024 IEEE.

关键词： Real time systems

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Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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GAN-IVDS: An Intrusion Detection System for Intelligent Connected Vehicles Based on Generative Adversarial Networks 8

GAN-IVDS: An Intrusion Detection System for Intelligent Conn...

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8th International Conference on Data science in Cyberspace, DSC 2023

作者： Chen, Xiansen Xiao, Kun Luo, Lei Li, Yun Chen, Lirong School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Guangdong Weichen Information Technology Co. Ltd Dongguan China

ISBN: (纸本)9798350331035

This paper proposes an anomaly detection algorithm based on Generative Adversarial Networks (GANs) for intrusion detection in Intelligent Connected Vehicles (ICVs). To address the imbalance between normal network traffic and attack traffic in vehicular networks, as well as the difficulty in collecting attack data, we train the neural network solely with normal traffic. We employ the Gini index to perform data dimensionality reduction, selecting 34 features with the highest purity of information and enhancing model training speed via normalization functions. Our algorithm combines the generative capabilities of GANs and the reconstructive power of autoencoders, detecting abnormal network data by learning the distribution of normal network data, thus eliminating the need to analyze packet payloads and safeguarding user privacy. We also develop a vehicular network traffic data collection scheme to collect network traffic data from ICVs. Experimental results indicate that our algorithm demonstrates high accuracy in detecting anomalous network traffic in ICVs, with an accuracy rate of 94.9%. © 2023 IEEE.

关键词： Anomaly Detection Algorithm Generative Adversarial Networks Intelligent Connected Vehicles Intrusion Detection Network Security

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Design of Patterned Transcendental Metamaterial Cover Structures for Crosstalk Reduction in FBAR Parallel Feed Lines

Design of Patterned Transcendental Metamaterial Cover Struct...

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Chu, Tongchuan Cao, Xin Zhou, Longjian Cai, Qiangming Zhu, Yuyu Southwest University of Science and Technology School of Information Engineering Mianyang621010 China South China University of Technology School of Electronic and Information Engineering Guangzhou510640 China

ISBN: (纸本)9798350375909

With the rapid development of modern high-speed circuit technology, electronic devices have the properties of high data transmission rates, steeper edge changing rates, and large-scale integration which can lead to increased crosstalk challenges in high-speed planar circuit designs. Severe crosstalk problems can result in disrupted data transmission and increase bit error rates (BER). Therefore, in order to reduce crosstalk problems and increase signal transmission quality, this paper studies the far-end crosstalk (FEXT) suppression of film bulk acoustic resonator (FBAR) parallel feed lines in high-speed data rate transmission. First, the theory of FBAR crosstalk is analyzed, and the influential factors such as line spacing, trace coupling length on crosstalk in FBAR feedlines are analyzed from the perspectives of frequency domain and time domain. Then, several structures based on geometric fractal substrate covers are proposed. The proposed structures utilize modern mathematical transcendental planar geometry such as modified Hilbert fractal, Minkowski fractal and von Koch fractal metamaterial covers. By adding these structures, the distributed capacitance of the line is increased to neutralize the magnetic coupling between the lines, thus reducing the FEXT both in frequency and time domains. The optimization is both performed through full-wave simulation and circuit simulation. As the results show that, in the frequency domain, the FEXT can be maximumly reduced about 26 dB from 0.1 GHz to 16 GHz. For signal transmitting verification, the lines are fed with DDR5 (the fifth-generation double data rate) signals, and the FEXT can be reduced over 21 mV, which corresponds to over 95% of the FEXT reduction in the time domain. The proposed structure can be applied in modern high speed planar circuits and wireless communication systems. © 2024 IEEE.

关键词： Frequency domain analysis

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Space/Air Covert Communications:Potentials,Scenarios,and Key Technologies

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China Communications 2024年第3期21卷 1-18页

作者： Mao Haobin Liu Yanming Zhu Lipeng Mao Tianqi Xiao Zhenyu Zhang Rui Han Zhu Xia Xianggen School of Electronic and Information Engineering Beihang UniversityBeijing 100191China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore School of Science and Engineering Shenzhen Research Institute of Big Datathe Chinese University of Hong KongShenzhenGuangdong 518172China Department of Electrical and Computer Engineering University of HoustonHoustonTX 77004USA Department of Computer Science and Engineering Kyung Hee UniversitySeoulSouth Korea446-701 Department of Electrical and Computer Engineering University of DelawareNewarkDE 19716USA

Space/air communications have been envisioned as an essential part of the next-generation mobile communication networks for providing highquality global connectivity. However, the inherent broadcasting nature of wireless propagation environment and the broad coverage pose severe threats to the protection of private data. Emerging covert communications provides a promising solution to achieve robust communication security. Aiming at facilitating the practical implementation of covert communications in space/air networks, we present a tutorial overview of its potentials, scenarios, and key technologies. Specifically, first, the commonly used covertness constraint model, covert performance metrics, and potential application scenarios are briefly introduced. Then, several efficient methods that introduce uncertainty into the covert system are thoroughly summarized, followed by several critical enabling technologies, including joint resource allocation and deployment/trajectory design, multi-antenna and beamforming techniques, reconfigurable intelligent surface(RIS), and artificial intelligence algorithms. Finally, we highlight some open issues for future investigation.

关键词： artificial intelligence(AI) sixth generation(6G) space-air-ground integrated networks(SAGINs) space/air covert communications

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A Tug-of-War between Static and Dynamic Memory in Intel SGX 38

A Tug-of-War between Static and Dynamic Memory in Intel SGX

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38th International Conference on VLSI Design, VLSID 2025

作者： Kumar, Sandeep Panda, Abhisek Nerlikar, Advait Sarangi, Smruti R. School of Information Technology IIT Delhi Delhi India Computer Science and Engineering IIT Delhi Delhi India Electrical and Electronics Engineering BITS Pilani Goa India

ISBN: (纸本)9798331522445

Security of applications and data in a cloud setting has become a first-class design criterion. Hardware vendors have proposed trusted execution environments or TEEs where the hardware guarantees an application's data and code security both at rest and in-use, even from privileged entities such as operating systems and hypervisors. Software Guard eXtension, or SGX, is a popular, trusted execution environment or TEE solution from Intel. To ensure security guarantees, SGX provides secure sand-box environments called enclaves, which have encrypted physical memory. In the latest version of SGX, we start an enclave with a the specified amount of 'static' memory specified by a developer. Subsequently, we add additional memory pages 'dynamically' to an enclave depending on an application's memory usage. In this paper, we analyze the impact of the allocation and freeing of static and dynamic pages on an application's performance. We observe that inappropriately setting the static memory size may lead to a performance slowdown of up to 20x. We present Harmony - a profile-guided optimizer that measures the impact of dynamic memory management on an application's performance, and suggests a near-optimal distribution for static and dynamic memory pages. We show that Harmony improves the execution latency of an application by up to 68% and 29% when compared with the purely dynamic and purely static allocation schemes, respectively. © 2025 IEEE.

关键词： Memory management

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Cardio Care: A Predictive Model for Heart Disease Detection 3

Cardio Care: A Predictive Model for Heart Disease Detection

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3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Rohith, R. Senthilnayaki, B. Joshua Dayalan, M. Anna University College Of Engineering Department of Information Science and Technology India Anna University College Of Engineering Department of Computer Science and Engineering India

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391565

Heart Disease is still an epidemic global health concern, so trustworthy prediction models are vital for risk assessment and early detection. In this research, we propose a comprehensive predictive framework leveraging ensemble learning techniques alongside traditional Machine Learning (ML) models to increase forecasting of cardio disease accuracy. Utilizing a dataset sourced from Cardiovascular-Disease-Dataset, demographic, clinical, and lifestyle factors were incorporated as features. The performance of Naive Bayes (NB), AdaBoost, Support Vector Classifier (SVC) and Logistic Regression (LR) both individually and within ensemble frameworks, was evaluated using accuracy of 98% and AUROC (Area-Under the Receiver Operating Characteristic Curve) of 99.845% as a primary metric. Our results demonstrate how effective ensemble learning strategies are in obtaining higher accuracy, precision-recall and along with AUROC curve for heart disease prediction compared to individual classifiers. This study underscores the importance of ensemble methods in improving predictive performance and contributing to enhanced clinical decision-making in cardiovascular health management. © 2024 IEEE.

关键词： Contrastive Learning

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LV-auth: Lip Motion Fusion for Voiceprint Authentication 18th

LV-auth: Lip Motion Fusion for Voiceprint Authentication

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Liu, Wei Zhu, Xiaojing Liu, Qin Li, Peng Zhou, Man School of Cyber Science and Engineering Wuhan University Wuhan430072 China School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783031714634

Voiceprint authentication is a growing field of biometric authentication. However, voiceprint authentication is vulnerable to spoofing attacks and suffers significant degradation in noisy environments. Employing lip motion detection is an innovative solution to enhance security. By strategically selecting a clean channel, such as the ultrasonic frequencies generated by smartphones’ inherent audio capability, lip motion detection can be performed without disturbance from ambient noise. In this paper, we propose LV-auth, a smartphone-based multimodal biometric authentication system that integrates lip motion and voiceprint. LV-auth captures both the speech signals and the ultrasonic echo signals modulated by lip motion, enabling liveness detection while enhancing robustness. We employ a contrastive learning-based feature extractor to extract reliable and effective fused features from both modalities for authentication. Extensive experiments show that LV-auth achieves an authentication accuracy of 99.37% and an equal error rate (EER) of 0.73% on the smartphone. In particular, LV-auth demonstrates excellent robustness in noisy environments and effectively withstands spoofing attacks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Smartphones

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Efficient Radon Levels Detection in Residential Areas using Cloud-Enabled Decision Trees for Real-Time Analysis 8

Efficient Radon Levels Detection in Residential Areas using ...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Lenin, J. Rohit, V.Robin Nisha, F. Vasudevan, B. Sivakumar, S. Murugan, S. Alliance College of Engineering and Design Alliance University Department of Computer Science & Engineering Bangalore India Presidency University Computer Science & Engineering School of Computer Science & Engineering and Information Science Bengaluru India Cmr University School of Engineering and Technology Computer Science & Engineering Bangalore India Srm Institute of Science and Technology Department of Electronics and Communication Engineering Kattankulathur SRM Nagar Tamil Nadu Chennai India Department of Computer Science Tamil Nadu Perambalur India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376425

This research introduces a new approach to radon detection in homes utilizing Decision Trees (DTs) enabled by the cloud in real-time. High radon levels, a natural radioactive gas, are dangerous to human health. Quick evaluation and mitigation efforts are hindered by the long processes required by traditional detection systems. Using DTs, a machine learning methodology, the proposed method sorts of radon levels according to variables, including building materials, ventilation systems, and geographic location. These trees may handle both numerical and categorical data and provide interpretability. Fast processing of massive data sets and real-time analysis across varied residential areas are made possible by integrating cloud computing, increasing efficiency and scalability. Stakeholders may remotely access crucial information due to the solution's accessibility and flexibility. Results from controlled experiments demonstrate that this method can accurately identify radon levels in the air in real time, allowing for preventative actions to lessen potential health hazards. People living in areas prone to radon may benefit from this practice in terms of their health. © 2024 IEEE.

关键词： Indoor air pollution

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Efficient Structure Slimming for Spiking Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第8期5卷 3823-3831页

作者： Li, Yaxin Fang, Xuanye Gao, Yuyuan Zhou, Dongdong Shen, Jiangrong Liu, Jian K. Pan, Gang Xu, Qi Dalian University of Technology School of Computer Science and Technology Dalian116024 China Zhejiang University College of Computer Science and Technology Zhejiang310027 China University of Birmingham College of Engineering and Physical Sciences BirminghamB152TT United Kingdom

Spiking neural networks (SNNs) are deeply inspired by biological neural information systems. Compared to convolutional neural networks (CNNs), SNNs are low power consumption because of their spike based information processing mechanism. However, most of the current structures of SNNs are fully connected or converted from deep CNNs which poses redundancy connections. While the structure and topology in human brain systems are sparse and efficient. This article aims at taking full advantage of sparse structure and low power consumption which lie in human brain and proposed efficient structure slimming methods. Inspired by the development of biological neural network structures, this article designed types of structure slimming methods including neuron pruning and channel pruning. In addition to pruning, this article also considers the growth and development of the nervous system. Through iterative application of the proposed neural pruning and rewiring algorithms, experimental evaluations on Canadian Institute for Advanced Research (CIFAR)-10, CIFAR-100, and dynamic vision sensor (DVS)-gesture datasets demonstrate the effectiveness of the structure slimming methods. When the parameter count is reduced to only about 10% of the original, the performance decreases by less than 1%. © 2020 IEEE.

关键词： Energy efficiency

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