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Advances in Wireless,Batteryless,Implantable Electronics for Real‑Time,Continuous Physiological Monitoring

Nano-Micro Letters 2024年第3期16卷 254-302页

作者： Hyeonseok Kim Bruno Rigo Gabriella Wong Yoon Jae Lee Woon‑Hong Yeo IEN Center for Wearable Intelligent Systems and Healthcare Georgia Institute of TechnologyAtlantaGA 30332USA George W.Woodruff School of Mechanical Engineering Georgia Institute of TechnologyAtlantaGA 30332USA School of Electrical and Computer Engineering Georgia Institute of TechnologyAtlantaGA 30332USA Wallace H.Coulter Department of Biomedical Engineering Georgia Tech and Emory University School of MedicineAtlantaGA 30332USA Parker H.Petit Institute for Bioengineering and Biosciences Institute for MaterialsInstitute for Robotics and Intelligent MachinesGeorgia Institute of TechnologyAtlantaGA 30332USA

This review summarizes recent progress in developing wireless,batteryless,fully implantable biomedical devices for real-time continuous physiological signal monitoring,focusing on advancing human health *** considerations,such as biological constraints,energy sourcing,and wireless communication,are discussed in achieving the desired performance of the devices and enhanced interface with human *** addition,we review the recent achievements in materials used for developing implantable systems,emphasizing their importance in achieving multi-functionalities,biocompatibility,and *** wireless,batteryless devices offer minimally invasive device insertion to the body,enabling portable health monitoring and advanced disease ***,we summarize the most recent practical applications of advanced implantable devices for human health care,highlighting their potential for immediate commercialization and clinical uses.

关键词： Implantable electronics Biomedical systems Batteryless devices Wireless electronics Physiological signal monitoring

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FAIR-FATE: Fair Federated Learning with Momentum 23rd

FAIR-FATE: Fair Federated Learning with Momentum

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23rd International Conference on Computational Science, ICCS 2023

作者： Salazar, Teresa Fernandes, Miguel Araújo, Helder Abreu, Pedro Henriques Centre for Informatics and Systems Department of Informatics Engineering of the University of Coimbra University of Coimbra Coimbra Portugal Institute of Systems and Robotics Department of Electrical and Computer Engineering of the University of Coimbra University of Coimbra Coimbra Portugal

ISBN: (纸本)9783031359941

While fairness-aware machine learning algorithms have been receiving increasing attention, the focus has been on centralized machine learning, leaving decentralized methods underexplored. Federated Learning is a decentralized form of machine learning where clients train local models with a server aggregating them to obtain a shared global model. Data heterogeneity amongst clients is a common characteristic of Federated Learning, which may induce or exacerbate discrimination of unprivileged groups defined by sensitive attributes such as race or gender. In this work we propose FAIR-FATE: a novel FAIR FederATEd Learning algorithm that aims to achieve group fairness while maintaining high utility via a fairness-aware aggregation method that computes the global model by taking into account the fairness of the clients. To achieve that, the global model update is computed by estimating a fair model update using a Momentum term that helps to overcome the oscillations of non-fair gradients. To the best of our knowledge, this is the first approach in machine learning that aims to achieve fairness using a fair Momentum estimate. Experimental results on real-world datasets demonstrate that FAIR-FATE outperforms state-of-the-art fair Federated Learning algorithms under different levels of data heterogeneity. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Momentum

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Dynamic Blockchain-Empowered Trustworthy End-Edge Collaborative Computing via Rotating Multi-Agent DRL

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4864-4878页

作者： Xu, Chi Zhang, Peifeng Yu, Haibin Li, Yonghui Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China University of Chinese Academy of Sciences Beijing China University of Sydney School of Electrical and Information Engineering Sydney Australia

Blockchain-empowered end-edge collaborative computing is a promising technology for enhancing the timeliness and trustworthiness of Industrial Internet of Things (IIoT). However, integrating task offloading with blockchain consensus inevitably escalates resource consumption across communication, computation, and energy domains. Thus, the joint optimization of task offloading, resource allocation and blockchain consensus is very important for IIoT. This paper studies a general end-edge collaborative computing scenario with multiple end devices and multiple edge servers. We first propose a novel dynamic blockchain (DBC) scheme by developing a dynamic leader election mechanism and designing a dynamic consensus waiting time window. Then, by fully considering the constraints of multi-task size and deadline, communication bandwidth, computing frequency, battery capacity, Byzantine fault tolerant and trustworthiness, we formulate the trustworthy processing efficiency (TPE) maximization problem with respect to end-edge task division, communication and computation resource allocation, leader election and consensus waiting window. To address this problem, we transform it into a Markov decision process and design a compound reward by fully considering the penalty for computing timeout and consensus failure. After that, we propose a rotating multi-agent deep reinforcement learning (R-MADRL) algorithm tailored to the proposed DBC scheme, where an entropy-based dual-critic DRL algorithm is proposed for rotating multi-agent training and decentralized execution. Extensive experiments validate the effectiveness and superiority of the proposed DBC with R-MADRL, where three benchmark DRL algorithms and three blockchain consensus schemes are compared. The results demonstrate that R-MADRL achieves stable convergence with more than 60.32% TPE reward than other algorithms while the task timeout ratio of DBC is reduced by more than 66.49% compared with other schemes. © 2025 institute of El

关键词： Markov processes

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A novel method for the detection and classification of multiple diseases using transfer learning-based deep learning techniques with improved performance

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Neural Computing and Applications 2024年第30期36卷 18979-18997页

作者： Natarajan, Krishnamoorthy Muthusamy, Suresh Sha, Mizaj Shabil Sadasivuni, Kishor Kumar Sekaran, Sreejith Charles Gnanakkan, Christober Asir Rajan A.Elngar, Ahmed School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Tamil Nadu Perundurai Erode India Centre for Advanced Materials Qatar University Doha Qatar Department of Mechanical and Industrial Engineering Qatar University PO Box 2713 Doha Qatar Department of Electrical Engineering National Institute of Technology Assam Silchar India Department of Electrical and Electronics Engineering Puducherry Technological University Puducherry India Faculty of Computers and Artificial Intelligence Beni-Suef University Beni-Suef City62511 Egypt

A disease is a distinct abnormal state that significantly affects the functioning of all or part of an individual and is not caused by external harm. Diseases are frequently understood as medical conditions connected with distinct indications and symptoms. According to a fairly wide categorization, diseases can also be categorized as mental disorders, deficient diseases, genetic diseases, degenerative diseases, self-inflicted diseases, infectious diseases, non-infectious diseases, social diseases, and physical diseases. Prevention of the diseases is of multiple instances. Primary prevention seeks to prevent illness or harm before it ever happens. Secondary prevention tries to lessen the effect of an illness or damage that has already happened. This is done through diagnosing and treating illness or injury as soon as feasible to stop or delay its course, supporting personal ways to avoid recurrence or reinjury, and implementing programs to restore individuals to their previous health and function to prevent long-term difficulties. Tertiary prevention tries to lessen the impact of a continuing sickness or injury that has enduring repercussions. Diagnosis of the disease at an earlier stage is important for the treatment of the disease. Hence, in this study, deep learning algorithms, such as VGG16, EfficientNetB4, and ResNet, are utilized to diagnose various diseases, such as Alzheimer's, brain tumors, skin diseases, and lung diseases. Chest X-rays, MRI scans, CT scans, and skin lesions are used to diagnose the mentioned diseases. Transfer learning algorithms, such as VGG16, VGG19, ResNet, InceptionV3, and EfficientNetB4, are utilized to categorize various diseases. EfficientNetB4 with the learning rate annealing, having obtained an accuracy of 94.04% on the test dataset, is observed. As a consequence, we observed that every network has unique particular skills on the multi-disease dataset, which includes chest X-rays, MRI scans, etc., © The Author(s) 2024.

关键词： Diseases

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第43届中国控制会议

作者： Zhen-Peng Chen Hong-Yue Li Tian-Shu Song Hui-Rang Hou Qing-Hao Meng Institute of Robotics and Autonomous Systems School of Electrical and Information EngineeringTianjin University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Detecting ignitable liquids(ILs) at the fire scene is essential for fire *** nose(e-nose) plays a crucial role in the detection of ILs due to its low cost and rapid ***,when we used different e-noses with the same sensor array(i.e.,identical model and number of gas sensors) and running the same algorithm to identify the same ILs,we found that the identification results were different because of the poor consistency of the gas sensors used in the *** address the issue of ILs identification across e-noses,we built a platform for sensor similarity detection,which is used to reject sensors with large *** on this platform,we constructed a similarity dataset using eight types of gas sensors(each containing eight sensors) to collect samples of four ILs(ethanol,gasoline,diesel and thinner).Moreover,a multi-branch residual convolutional neural network for evaluating sensor similarity was *** experiments on the dataset show that compared with the two-dimensional convolutional neural network(2D-CNN) and multilayer perceptron(MLP),the average accuracy of similarity evaluation of our method is improved by 13.1% and 3.23%,respectively.

关键词： E-nose Ignitable Liquids Deep Learning Similarity Evaluation Affinity Sequence

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Multimodal Human Detection using RGB, Thermal and LiDAR modalities for Robotic Perception

Multimodal Human Detection using RGB, Thermal and LiDAR moda...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Kennedy O. S. Mota Luís Garrote Cristiano Premebida Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Portugal

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

People detection is a relevant research topic in artificial perception with wide range of applications from security, surveillance, robotics to autonomous driving. Overcoming challenges in this field involves advanced algorithms, combination of machine learning approaches, as well as the use of sensory data e.g., from cameras and LiDARs. This work addresses the problem of people detection using YOLO, a state-of-the-art object detection method, trained on three distinct data sources LiDAR, RGB (color) and ‘thermal’ (long-wave infra-red) images. The rationale for combining multiple-sensory representation relies on the assumption that each sensor has its own advantages and disadvantages, but together they normally complement each other - specially in real-world conditions. LiDAR contributes to a physically-interpretable mapping of the environment, providing precise information regarding size/dimension and location of the objects, while RGB and thermal provide relevant textural features. The sensors have been calibrated w.r.t. each other thus, allowing the LiDAR’s point-clouds to be projected into the image plane, followed by an up-sampling step, to create dense-depth maps (DM) that enable direct use of the YOLO framework. To support the experiments, a new multi-sensory dataset has been collected using a mobile robot. Besides single-modality models, this paper also explores early and late-fusion strategies. Finally, the new dataset has been made available in a Github repository 1 .

关键词： YOLO Laser radar Machine learning algorithms Surveillance Soft sensors Thermal engineering Robot vision systems Security Mobile robots Software development management

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A Compliant Robot-Assisted Mold Polishing Application Using the Franka Emika Robot 49

A Compliant Robot-Assisted Mold Polishing Application Using ...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Xenya, Michael Christopher Cortesao, Rui Institute of Systems and Robotics University of Coimbra Electrical and Computer Engineering Department Coimbra3030-290 Portugal FOE GCTU PMB100 Accra Ghana

ISBN: (纸本)9798350331820

Metal mold casting for many application areas remains imperfect since this involves the control of numerous factors that affect workpiece quality. Surface roughness which remains one of the many issues related to metal mold cast is often mitigated by means of grinding and polishing. As many attempts are being made to address this challenge through robot-assisted techniques, we focus on rapid mold polishing using the Franka Emika (FE) robot and its programming resources, including a built-in impedance control architecture. To accomplish the objective, mold surface segmentation is necessary. For each segment, the polishing approach is based on surface normals, relative motion, and torque sensor data. Results were obtained with open platform communication-unified architecture (opc-ua) client and server that record torque and force data. Results show that tuning internal impedance control parameters for polishing tasks is feasible. © 2023 IEEE.

关键词： Polishing

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Saliency-SLAM: Saliency Prediction for Visual SLAM

Saliency-SLAM: Saliency Prediction for Visual SLAM

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2023 Tianjin University-IET electrical and Information engineering Doctoral Forum, TJU IET 2023

作者： Jin, Sheng Meng, Qinghao Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin University Tianjin China

ISBN: (纸本)9781839539824

This paper proposes a novel approach for visual simultaneous localization and mapping (vSLAM), called Saliency-SLAM. This method effectively emulates the visual attention mechanism by integrating a saliency prediction model. In contrast to conventional vSLAM methods, which treat all feature points as equally significant, salient feature points are given greater importance in Saliency-SLAM. Therefore, we construct a novel saliency dataset specifically for vSLAM task to train a saliency prediction model by integrating multi-level visual information. Then, we propose to use the salient value of predicted result as the weight of significance for the feature points during the optimization process. Exhaustive experiments have been conducted on the KITTI dataset, demonstrating that our proposed method outperforms existing approaches in terms of localization accuracy. © The Institution of engineering & Technology 2023.

关键词： Forecasting

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A Probabilistic Framework Applied to Multispectral Image Segmentation

A Probabilistic Framework Applied to Multispectral Image Seg...

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Intelligent Reality (ICIR), IEEE International Conference on

作者： Wilgo Cardoso Tiago Barros Gil Gonçalves Cristiano Premebida University of Coimbra Institute of Systems and Robotics Portugal Dept. of Mathematics University of Coimbra Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra) Portugal

ISBN: (数字)9798331534424

ISBN: (纸本)9798331534431

This paper describes a study on exploring probability densities applied to the logit values of deep models to obtain predictions with probabilistic interpretation. Kernel density estimation (KDE) is used to providing non-parametric modeling of the underlying logit-values distribution. While preliminary results show promising potential, this work lays the foundation for future research, where the combination of KDE with other advanced techniques could further enhance segmentation performance of deep models. This study reports segmentation results on a dataset collected by a UAV equipped with a multispectral camera flying over vineyards.

关键词： Measurement Image segmentation Estimation Predictive models Probabilistic logic Cameras Autonomous aerial vehicles Calibration Reliability Kernel

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Analog, Programmable Switched Capacitor FIR Filter Based on Rotator Architecture Implemented in CMOS Technology 26

Analog, Programmable Switched Capacitor FIR Filter Based on ...

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26th IEEE Signal Processing: Algorithms, Architectures, Arrangements, and Applications, SPA 2023

作者： Pawlowski, Pawel Dlugosz, Rafal Radkowski, Marek Watly, Mateusz Dabrowski, Adam Poznań University of Technology Division of Signal Processing and Electronic Systems Faculty of Control Robotics and Electrical Engineering Institute of Automatic Control and Robotics Poznań60-965 Poland Bydgoszcz University of Science and Technology Faculty of Telecommunication Computer Science and Electrical Engineering Bydgoszcz85-796 Poland

ISBN: (纸本)9798350304985

The paper presents project and its verification of a prototype integrated circuit containing an analog, programmable finite impulse response (FIR) filter, implemented in CMOS 350 nm technology. The structure of the filter is based on the switched capacitor technique. In circuits of this type, one of main challenges is an efficient implementation of filter coefficients, which result from several factors described in this work. When implementing such filters as programmable circuits, the values of their coefficients have to be limited to a selected range, i.e. a given resolution in bits. In the implemented prototype filter, the filter coefficients are represented by 6 bits in sign-magnitude notation, so they can take 63 different values only. In such filters, it is not possible to directly implement any frequency response of the filter. Each time, it is necessary to properly round the theoretical values of the coefficients so that they fit into the available range of discrete values resulting from the implementation. The authors of the work designed an algorithm that allows such matching. The paper also presents results of measurements of the prototype chip. © 2023 Division of Signal Processing and Electronic systems, Poznan University of Technology (DSPES PUT).

关键词： FIR filters

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