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Revolutionizing Personalized Health: The Frontier of Wearable Biomolecule Sensors Through 3D Printing Innovation

Biomedical Materials and Devices 2024年 1-17页

作者： Rajendran, Jerome Esfandyarpour, Rahim Department of Electrical Engineering and Computer Science University of California Irvine 92697 CA United States Laboratory for Integrated Nano BioElectronics Innovation The Henry Samueli School of Engineering University of California Irvine 92697 CA United States

This review article delves into the innovative intersection of 3D-printed technologies and wearable chemical sensors, highlighting a forward-thinking approach to biomarker monitoring. It emphasizes the transformative role of additive manufacturing in the development of wearable devices tailored for the precise detection of chemical biomarkers, crucial for proactive disease management and health assessment. By offering a detailed exploration of how 3D printing of nanomaterials contributes to pioneering sensor designs, this review underscores the practicality of sensor wearability, ensuring comfort and efficacy for users. We address the challenges of material resilience, sensor durability, and efficient data communication, while also charting the significant trends and future directions that promise to redefine the landscape of flexible and wearable chemical sensors. Through a comprehensive analysis, this article aims to showcase the pivotal advancements and ongoing innovations in the field, emphasizing the critical impact of 3D printing on enhancing personalized healthcare and wearable diagnostics. Graphical Abstract: (Figure presented.) © The Author(s) 2024.

关键词： 3D printing Biomarker monitoring Chemical sensors Flexible Personalized healthcare Wearable

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Survey on Cryptoprocessors Advances and Technological Trends 3rd

Survey on Cryptoprocessors Advances and Technological Tren...

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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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SiamCircle: Trajectory Representation Learning in Free Settings 23rd

SiamCircle: Trajectory Representation Learning in Free Sett...

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23rd International Symposium on Intelligent Data Analysis, IDA 2025

作者： Nasri, Maedeh Baratchi, Mitra Koutamanis, Alexander Rieffe, Carolien Department of Developmental Psychology Leiden University Leiden Netherlands Leiden Institute of Advanced Computer Science Leiden University Leiden Netherlands Faculty of Architecture and the Built Environment Delft University of Technology Delft Netherlands Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands Department of Psychology and Human Development Institute of Education UCL London United Kingdom

ISBN: (纸本)9783031913976

Trajectory representation learning (TRL) is an intermediate step in handling trajectory data to realize various downstream machine-learning tasks. While most previous TRL research focuses on modeling structured movements in large-scale urban spaces (e.g., cars or pedestrians on streets), this paper focuses on a more challenging scenario of modeling free movement in small-scale social spaces (e.g., children playing in a schoolyard). We present a TRL model, SiamCircle, to process raw trajectories without additional feature extraction to prevent information loss. SiamCircle adopts a Siamese network with Circle Loss to learn trajectory embeddings. Furthermore, SiamCircle employs a data augmentation process to enable self-supervised learning and enrich the input data to address the limited access to high-quality data and ground truth. We evaluate the performance of SiamCircle in downstream tasks using trajectory ranking and clustering performance via seven evaluation metrics collectively. Using an ablation study, we explored the impact of different loss functions on the model’s performance. Accordingly, we selected a 2-D convolutional design with Circle Loss as the best-performing model. In a comparative study, we compared our model against three other baselines. We observed up to 19% improvements in trajectory ranking tasks and achieved the highest average rank in supervised clustering tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Sorting

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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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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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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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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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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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