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3rd International Conference on Innovations in Computing Research, ICR 2024

作者： Kakoulli, Elena Zacharioudakis, Eleftherios Intelligent Systems Laboratory Department of Computer Science Neapolis University Pafos Paphos Cyprus Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine

ISBN: (纸本)9783031655210

Cryptoprocessors play a pivotal role in enhancing the security of modern computing systems by accelerating cryptographic operations and fortifying data protection. This survey delves into the world of cryptoprocessors, encompassing their historical development, architectural features, and various types based on their applications. The paper explores the crucial security considerations surrounding cryptoprocessors, covering both physical and logical aspects, as well as potential threats and countermeasures. Furthermore, the survey navigates through the latest technological trends and advancements in cryptoprocessor design. This includes discussions on post-quantum cryptography, secure enclaves, and mitigating side-channel attacks, all of which are shaping the future of cryptoprocessors. Real-world use cases and applications in domains such as secure communications, the Internet of Things (IoT), and data centers are presented, showcasing the practical significance of cryptoprocessors. However, the paper also highlights the persistent challenges faced by cryptoprocessor designers and provides insights into future research directions aimed at addressing emerging security and performance issues. As the digital landscape continues to evolve, the role of cryptoprocessors becomes increasingly vital in safeguarding sensitive information and fortifying the foundations of cybersecurity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Internet of things

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Internal Short Circuit Detection for Parallel-Connected Battery Cells Using Convolutional Neural Network

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Automotive Innovation 2022年第2期5卷 107-120页

作者： Niankai Yang Ziyou Song Mohammad Reza Amini Heath Hofmann Department of Naval Architecture and Marine Engineering University of MichiganAnn ArborMI 48109USA Department of Mechanical Engineering National University of SingaporeSingapore 117575Singapore Department of Electrical Engineering and Computer Science University of MichiganAnn ArborMI 48109USA

Reliable and timely detection of an internal short circuit(ISC)in lithium-ion batteries is important to ensure safe and efficient *** paper investigates ISC detection of parallel-connected battery cells by considering cell non-uniformity and sensor limitation(i.e.,no independent current sensors for individual cells in a parallel string).To characterize ISC-related signatures in battery string responses,an electro-thermal model of parallel-connected battery cells is first established that explicitly captures *** analyzing the data generated from the electro-thermal model,the distribution of surface tem-perature among individual cells within the battery string is identified as an indicator for ISC detection under the constraints of sensor limitations.A convolutional neural network(CNN)is then designed to estimate the ISC resistance by using the cell surface temperature and the total capacity of the string as *** on the estimated ISC resistance from CNN,the strings are classified as faulty or non-faulty to guide the examination or replacement of the *** algorithm is evaluated in the presence of signal noises in terms of accuracy,false alarm rate,and missed detection rate,verifying the effectiveness and robustness of the proposed approach.

关键词： Internal short circuit Parallel-connected battery cells Convolutional neural network Cell temperature distribution

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Cortical and Subcortical Articulatory Representations of Two Languages Revealed by Speech Decoding Based on sEEG 24

Cortical and Subcortical Articulatory Representations of Two...

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9th International Conference on Biomedical Signal and Image Processing, ICBIP 2024

作者： Wu, Yunying Li, Chenyang Zhang, Haiyan Chen, Feiyan Chen, Weidong Zhang, Shaomin Qiushi Academy for Advanced Studies Department of Biomedical Engineering Zhejiang University China Bio-X Laboratory School of Physics Zhejiang University China Qiushi Academy for Advanced Studies Department of Computer Science and Technology Zhejiang University China

ISBN: (纸本)9798400717970

Numerous studies have suggested that the sensorimotor area may play a role in speech decoding due to its involvement in controlling the movement of vocal organs and its close link to speech. However, due to limitations in surface neural recording methods such as EEG and ECoG, it remains uncertain whether deep brain regions are capable of speech decoding. In this study, we utilized two sets of stereo electroencephalography (sEEG) data to investigate how speech is represented in both cortical and subcortical areas when people speak two different languages. The first sEEG dataset involved 10 subjects speaking Dutch, while the second dataset involved 5 subjects speaking Chinese. Consistent with previous studies, specific brain gyrus locations were closely linked to speech. We also discovered that high decoding performance could be achieved in many deep regions when subjects speak two languages. Furthermore, despite the two languages sharing similar articulation representation, they exhibited differences in neural representation, particularly in terms of high and low-frequency differences of audio. The differences of Chinese speakers were greater than those of Dutch speakers. We anticipate that future studies analyzing more data will uncover more precise articulatory representations of cortical and subcortical structures. © 2024 Copyright held by the owner/author(s).

关键词： Electroencephalography

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Green Concerns in Federated Learning over 6G

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China Communications 2022年第3期19卷 50-69页

作者： Borui Zhao Qimei Cui Shengyuan Liang Jinli Zhai Yanzhao Hou Xueqing Huang Miao Pan Xiaofeng Tao National Engineering Laboratory for Mobile Network Technologies Beijing University of Posts and TelecommunicationsBeijing100876China Department of Computer Science New York Institute of TechnologyOld WestburyNY11568USA Department of Electrical and Computer Engineering University of HoustonHoustonTX77204USA

As Information,Communications,and Data Technology(ICDT)are deeply integrated,the research of 6G gradually ***,federated learning(FL)as a distributed artificial intelligence(AI)framework is generally believed to be the most promising solution to achieve“Native AI”in *** the adoption of energy as a metric in AI and wireless networks is emerging,most studies still focused on obtaining high levels of accuracy,with little consideration on new features of future networks and their possible impact on energy *** address this issue,this article focuses on green concerns in FL over *** first analyze and summarize major energy consumption challenges caused by technical characteristics of FL and the dynamical heterogeneity of 6G networks,and model the energy consumption in FL over 6G from aspects of computation and *** classify and summarize the basic ways to reduce energy,and present several feasible green designs for FL-based 6G network architecture from three *** to the simulation results,we provide a useful guideline to researchers that different schemes should be used to achieve the minimum energy consumption at a reasonable cost of learning accuracy for different network scenarios and service requirements in FL-based 6G network.

关键词： 6G native AI federated learning radio access network green communications

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Hardware Modeling of Diesel Engine Fuel System Failure Modes and Coupled Shipboard Dynamics

Hardware Modeling of Diesel Engine Fuel System Failure Modes...

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2023 IEEE Electric Ship Technologies Symposium, ESTS 2023

作者： Almquist, Ethan T. McCoy, Timothy J. Olson, Stephen A. Kerkmaz, Arianna Williamson, Micah Zayko, Henry University of Michigan Naval Architecture and Marine Engineering Department Ann ArborMI United States University of Michigan Electrical Engineering and Computer Science Department Ann ArborMI United States

ISBN: (纸本)9781665465151

Recent interest in autonomous vessels has pushed machinery reliability into the forefront. A significant amount of research has been accomplished in solving the safe navigation problem, however relatively little work has been done in solving the machinery reliability problems which are a key enabler for autonomous vessels. This paper considers one aspect of machinery reliability, namely engine fuel systems. Fuel system failures are one of the most common engine failure mechanisms for shipboard internal combustion engines. This paper looks at the most common failure modes, namely clogging of filters and fuel system leaks. Both of these failure modes have been modeled to understand their effect on an engine's ability to continue operating. The software simulations have been validated in hardware. © 2023 IEEE.

关键词： Diesel engines

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A Reliable Clinical Decision Support System for Posttraumatic Stress Disorder Using Functional Magnetic Resonance Imaging Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5605-5615页

作者： Bhattacharya, J. Gupta, A. Dretsch, M.N. Denney, T.S. Deshpande, G. TIET Department of Computer Science and Engineering Patiala147004 India Bennett University School of Computer Science Engineering and Technology Greater Noida201310 India Walter Reed Army Institute of Research U.S. Army Medical Research Directorate-West Joint Base Lewis McChordWA98433 United States Auburn University Auburn University Neuroimaging Center Department of Electrical and Computer Engineering AuburnAL36849 United States Alabama Advanced Imaging Consortium BirminghamAL36849 United States National Institute of Mental Health and Neurosciences Department of Psychiatry Bangalore560030 India Indian Institute of Technology Department of Heritage Science and Technology Hyderabad502284 India

In recent years, there has been an upsurge in artificial intelligence (AI) systems. These systems, along with efficient performance and predictability, also need to incorporate the power of explainability and interpretability. This can significantly aid clinical decision support by providing explainable predictions to assist clinicians. Explainability generally involves uncovering key input features important for classification. However, characterizing the uncertainty underlying the decisions of the AI system is an important aspect needed for interpreting the decisions. This is especially important in clinical decision support systems, given considerations of medical ethics such as nonmaleficence and beneficence. In this study, we develop methods for characterizing the decision certainty of machine learning (ML)-based clinical decision support systems. As an illustrative example, we introduce a framework for ML-based posttraumatic stress disorder (PTSD) diagnostic classification that classifies the subjects into pure and mixed classes. Accordingly, a clinician can have very high confidence (95% probability) about the diagnosis of a subject in a pure PTSD or combat control class. Remaining sample points for which the AI classification tool does not have very high confidence ( © 2024 IEEE.

关键词： Decision support systems

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Prediction of futile recanalisation after endovascular treatment in acute ischaemic stroke:development and validation of a hybrid machine learning model

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Stroke & Vascular Neurology 2024年第6期9卷 631-639页

作者： Ximing Nie Jinxu Yang Xinxin Li Tianming Zhan Dongdong Liu Hongyi Yan Yufei Wei Xiran Liu Jiaping Chen Guoyang Gong Zhenzhou Wu Zhonghua Yang Miao Wen Weibin Gu Yuesong Pan Yong Jiang Xia Meng Tao Liu Jian Cheng Zixiao Li Zhongrong Miao Liping Liu Department of Neurology Capital Medical UniversityBeijingChina China National Clinical Research Center for Neurological Diseases Beijing Tiantan HospitalBeijingChina School of Computer and Communication Engineering University of Science and TechnologyBeijingChina Department of Radiology Beijing Tiantan HospitalBeijingChina Key Laboratory of Biomechanics and Mechanobiology Ministry of EducationSchool of Biological Science and Medical Engineering International Research Institute for Multidisciplinary ScienceBeihang UniversityBeijingChina Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Computer Science and EngineeringBeihang UniversityBeijingChina Department of Interventional Neuroradiology Beijing Tiantan HospitalCapital Medical UniversityBeijingChina

Background Identification of futile recanalisation following endovascular therapy(EVT)in patients with acute ischaemic stroke is both crucial and ***,we present a novel risk stratification system based on hybrid machine learning method for predicting futile *** Hybrid machine learning models were developed to address six clinical scenarios within the EVT and perioperative management *** models were trained on a prospective database using hybrid feature selection technique to predict futile recanalisation following *** optimal model was validated and compared with existing models and scoring systems in a multicentre prospective cohort to develop a hybrid machine learning-based risk stratification system for futile recanalisation *** Using a hybrid feature selection approach,we trained and tested multiple classifiers on two independent patient cohorts(n=1122)to develop a hybrid machine learning-based prediction *** model demonstrated superior discriminative ability compared with other models and scoring systems(area under the curve=0.80,95%CI 0.73 to 0.87)and was transformed into a web application(RESCUE-FR Index)that provides a risk stratification system for individual prediction(accessible online atfr-index.biomind.cn/RESCUE-FR/).Conclusions The proposed hybrid machine learning approach could be used as an individualised risk prediction model to facilitate adherence to clinical practice guidelines and shared decision-making for optimal candidate selection and prognosis assessment in patients undergoing EVT.

关键词： database prediction optimal

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Privacy‐preserving remote sensing images recognition based on limited visual cryptography

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1166-1177页

作者： Denghui Zhang Muhammad Shafiq Liguo Wang Gautam Srivastava Shoulin Yin Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhouChina Department of New Networks Peng Cheng LaboratoryShenzhenChina College of Information and Communications Engineering Dalian Minzu UniversityDalianChina Department of Mathematics and Computer Science Brandon UniversityBrandonManitobaCanada Research Centre for Interneural Computing China Medical UniversityTaichungTaiwanChina Department of Computer Science and Math Lebanese American UniversityBeirutLebanon College of Information and Communications Engineering Harbin Engineering UniversityHarbinChina

With the arrival of new data acquisition platforms derived from the Internet of Things(IoT),this paper goes beyond the understanding of traditional remote sensing *** fusion of remote sensing and computer vision has hit the industrial world and makes it possible to apply Artificial intelligence to solve problems such as automatic extraction of information and image ***,due to the complex architecture of IoT and the lack of a unified security protection mechanism,devices in remote sensing are vulnerable to privacy leaks when sharing *** is necessary to design a security scheme suitable for computation‐limited devices in IoT,since traditional encryption methods are based on computational *** Cryptography(VC)is a threshold scheme for images that can be decoded directly by the human visual system when superimposing encrypted *** stacking‐to‐see feature and simple Boolean decryption operation make VC an ideal solution for privacy‐preserving recognition for large‐scale remote sensing images in *** this study,the secure and efficient transmission of high‐resolution remote sensing images by meaningful VC is *** diffusing the error between the encryption block and the original block to adjacent blocks,the degradation of quality in recovery images is *** fine‐tuning the pre‐trained model from large‐scale datasets,we improve the recognition performance of small encryption datasets for remote sensing *** experimental results show that the proposed lightweight privacy‐preserving recognition framework maintains high recognition performance while enhancing security.

关键词： activity recognition feature extraction image classification KNN privacy protection remote monitoring remote sensing

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FDD Massive MIMO: How to Optimally Combine UL Pilot and Limited DL CSI Feedback?

FDD Massive MIMO: How to Optimally Combine UL Pilot and Limi...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Kim, Jungyeon Choi, Jinseok Park, Jeonghun Alkhateeb, Ahmed Lee, Namyoon Pohang University of Science and Technology Department of Electrical Engineering Pohang Korea Republic of Korea Advanced Institute of Science and Technology School of Electrical Engineering Daejeon Korea Republic of Yonsei University School of Electrical and Electronic Engineering Seoul Korea Republic of Arizona State University School of Electrical Computer and Energy Engineering AZ United States Korea University School of Electrical Engineering Seoul Korea Republic of

ISBN: (纸本)9798350351255

In frequency-division duplexing (FDD) multipleinput multiple-output (MIMO) systems, obtaining accurate downlink channel state information (CSI) becomes challenging due to the tremendous feedback overhead that increases with the number of antennas. Using uplink pilots to estimate downlink CSI is a promising approach that can eliminate the need for CSI feedback, but its accuracy decreases significantly as the number of channel paths increases. In this paper, we propose a mean square error (MSE)-optimal downlink channel reconstruction method that jointly utilizes uplink CSI and limited downlink CSI. With the MSE-optimal estimator, we derive the MSE as a function of the number of feedback bits for channel phase quantization and show the optimal feedback bit allocation method to minimize the MSE. Harnessing robust downlink precoding, we demonstrate that the proposed downlink channel reconstruction is sufficient to achieve a sum-spectral efficiency comparable to that with perfect downlink CSI. © 2024 IEEE.

关键词： Mean square error

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Modelling and Optimal Allocation of CCHP-Integrated-Energy-System Considering Low-Carbon and Economic Benefits: Methods and Vision 6

Modelling and Optimal Allocation of CCHP-Integrated-Energy-S...

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6th International Conference on Energy Systems and electrical Power, ICESEP 2024

作者： Li, Maodong Lao, Kengweng Lu, Wentao University of Macau State Key Laboratory of Internet of Things for Smart City Department of Electrical and Computer Engineering China School of Information Science and Technology Fudan University Shanghai China

ISBN: (纸本)9798350353563

Along with the development of smart energy, we take typical integrated energy systems (IES) into research considering various coupling modes and constraints, building a model of the IES for combined cooling, heating and power system, and propose the using of gas micro-turbine as a power supply. On this basis, the three dimension particle swarm optimization algorithm is applied to conduct operational and management research in one typical day, which provides a reference for the economic and low-carbon planning and operation of the IES, moreover demonstrates the superiority and effectiveness of the integrated energy systems compared to the traditional discrete energy systems. Furthermore, the paper gives the effective steps of allocation and optimization methods and vision of integrated energy systems. © 2024 IEEE.

关键词： Energy development

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