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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Zhu, Jingxuan Liu, Ji Zhejiang Lab China Stony Brook University Department of Applied Mathematics and Statistics United States Stony Brook University Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350316339

This paper studies a homogeneous decentralized multi-armed bandit problem, in which a network of multiple agents faces the same set of arms, and each agent aims to minimize its own regret. A fully decentralized upper confidence bound (UCB) algorithm is proposed for a multi-agent network whose neighbor relations are described by a directed graph. It is shown that the decentralized algorithm guarantees each agent to achieve a lower logarithmic asymptotic regret compared to the classic UCB algorithm, provided the neighbor graph is strongly connected. The improved asymptotic regret upper bound is reciprocally related to the maximal size of a local neighborhood within the network. The roles of graph connectivity, maximum local degree, and network size are analytically elucidated in the expression of regret. © 2024 IEEE.

关键词： Decentralized systems

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Revolutionizing Marine Stability: A Review of Cutting-Edge Gyro Stabilizer Designs and Techniques for Vessels

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IEEE Access 2025年 13卷 89542-89555页

作者： Rahiman, Wan Ghazali, Mohamad Hazwan Mohd Mahmood, Iskandar Al-Thani Universiti Sains Malaysia Engineering Campus School of Electrical and Electronic Engineering Penang Nibong Tebal14300 Malaysia Daffodil International University Daffodil Robotics Lab Department of Computer Science and Engineering Dhaka1216 Bangladesh Universiti Kebangsaan Malaysia Faculty of Engineering and Built Environment Department of Mechanical and Manufacturing Engineering Selangor Bangi43600 Malaysia PETRONAS Research Sdn Bhd Selangor Kajang43000 Malaysia

Advancement in marine vessel stabilization technologies has greatly improved the safety and comfort for crews, cargo, and passengers, while also lowering the risk of capsizing. Marine vessels operating in open waters experience disturbances from unpredictable waves and strong winds, leading to instability and potential accidents. To mitigate these risks, stabilizers are integrated into ships to maintain stability, safety, and seaworthiness. As modern vessels evolve, stabilizers play an increasingly vital role, with gyro stabilizers emerging as a highly effective solution for mitigating roll motion without external appendages. This paper provides a comprehensive review of gyro stabilizer designs and techniques, covering their historical development, mathematical modeling, advantages, limitations, and case studies. A review of current research highlights a strong emphasis on simulating gyro stabilizer control systems, demonstrating their potential in enhancing vessel stability. However, key challenges persist, including accurate wave moment prediction during the design phase and gyroscopic drift, which can impact long-term performance and efficiency. Despite these challenges, recent advancements in control algorithms and optimization techniques have improved gyro stabilizer adaptability under varying sea conditions. By exploring the latest developments, this review serves as a valuable reference for researchers, engineers, and maritime professionals seeking to advance marine stabilization technologies. The findings underscore the potential of next-generation gyro stabilizers, including fiber optic gyros, in achieving higher precision, stability, and operational efficiency. © 2013 IEEE.

关键词： Warships

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Unguided Self-exploration in Narrow Spaces with Safety Region Enhanced Reinforcement Learning for Ackermann-steering Robots 2

Unguided Self-exploration in Narrow Spaces with Safety Regio...

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2nd IEEE International Conference on Mobility, Operations, Services and Technologies, MOST 2024

作者： Tian, Zhaofeng Liu, Zichuan Zhou, Xingyu Shi, Weisong University of Delaware The CAR Lab Newark United States Wayne State University Department of Computer Science Detroit United States Wayne State University Department of Electrical and Computer Engineering Detroit United States

ISBN: (纸本)9798350307733

In narrow spaces, motion planning based on the traditional hierarchical autonomous system could cause collisions due to mapping, localization, and control noises, especially for car-like Ackermann-steering robots which suffer from non-convex and non-holonomic kinematics. To tackle these problems, we leverage deep reinforcement learning which is verified to be effective in self-decision-making, to self-explore in narrow spaces without a given map and destination while avoiding collisions. Specifically, based on our Ackermann-steering rectangular-shaped ZebraT robot and its Gazebo simulator, we propose the rectangular safety region to represent states and detect collisions for rectangular-shaped robots, and a carefully crafted reward function for reinforcement learning that does not require the waypoint guidance. For validation, the robot was first trained in a simulated narrow track. Then, the well-trained model was transferred to other simulation tracks and could outperform other traditional methods including classical and learning methods. Finally, the trained model is demonstrated in the real world with our ZebraT robot. https://***/view/rl4exploration © 2024 IEEE.

关键词： Reinforcement learning

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SECURITY SYSTEMS IN GREEK HEALTH CARE INSTITUTIONS: A SCOPING REVIEW TOWARDS AN EFFECTIVE BENCHMARKING APPROACH 22

SECURITY SYSTEMS IN GREEK HEALTH CARE INSTITUTIONS: A SCOPIN...

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22nd International Conference on e-Society 2024, ES 2024 and 20th International Conference on Mobile Learning 2024, ML 2024

作者： Mariettou, Savina Koutsojannis, Constantinos Triantafyllou, Vassilis University of Peloponnese Electrical and Computer Engineering Department Patras Greece Health Physics & Computational Intelligence Laboratory Physiotherapy Department School of Health Rehabilitation Sciences University of Patras Patras Greece Network Technologies and Digital Transformation lab Electrical and Computer Engineering Dpt. University of Peloponnese Patras Greece

Healthcare has undoubtedly brought many advancements through information technology. Specifically, healthcare informatics involves the use of various technologies, data management and communication systems to collect, store, analyze and effectively disseminate information. Therefore, the above serves to improve care as well as patient outcomes, improve efficiency, promote innovation and data analysis, as digitization has become an integral part of our daily lives in today's world. However, with the increased use of interconnected systems and electronic health records, hospitals have become prime targets for cyber-attacks. In this article we will delve into the critical topic of cybersecurity in hospitals as well as nursing facilities. The deepening of this issue was carried out with extensive analysis of the security systems of Greek hospitals. We investigated all their web sites and the security systems already in place on those sites. By checking we found a number vulnerabilities and potential risks. The most important weakness is the lack of legal as well as technical information for all interested citizens and patients. More organizational as well as technical work concerning internal architecture of information systems is needed, towards the improvement of security. In conclusion, we present our proposals on strategies to strengthen healthcare facilities against cyber threats. © 2024 International Conferences e-Society 2024 and Mobile Learning 2024. All rights reserved.

关键词： Cybersecurity Digitization Healthcare Information Patients Systems

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Technology Pipeline for Large Scale Cross-Lingual Dubbing of Lecture Videos into Multiple Indian Languages 24

Technology Pipeline for Large Scale Cross-Lingual Dubbing of...

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24th International Speech Communication Association, Interspeech 2023

作者： Anusha, P. Arun, K. Ashish, S. Bhagyashree, M. Ishika, G. Jom, K. Jordan, F. Vikram, K.V. Kumar M, Mano R. Metilda, S.M. Wajahat, M. Mohana, N. Batra, Mudit Navina, K. Nihal, G. Nithya, R. Pruthwik, M. Sudhanshu, S. Vasista, L. Vandan, M. Vineeth, K. Vrundha, S. Mishra, Dipti Murthy, Hema A. Bhattacharya, Pushpak Umesh, Srinivasan Sangal, Rajeev SMT Lab Department of Computer Science and Engineering IIT Madras India Speech Lab Department of Electrical Engineering IIT Madras India IIIT Hyderabad India IIT Bombay India

Cross-lingual dubbing of lecture videos requires the transcription of the original audio, correction and removal of disfluencies, domain term discovery, text-to-text translation into the target language, text-to-speech synthesis followed by isochronous lipsyncing to the original video. This task becomes challenging when the source and target languages belong to different language families, resulting in differences in generated audio duration. This is further compounded by the original speaker's rhythm, especially for extempore speech. This paper describes the challenges in regenerating English lecture videos in Indian languages semi-automatically. A prototype is developed for dubbing lectures into Indian languages. A demo for dubbing with supervision is available online. © 2023 International Speech Communication Association. All rights reserved.

关键词： Speech synthesis

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X-ray Image-Based COVID-19 Patient Detection Using Machine Learning-Based Techniques

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computer Systems Science & engineering 2022年第11期43卷 671-682页

作者： Shabana Habib Saleh Alyahya Aizaz Ahmed Muhammad Islam Sheroz Khan Ishrat Khan Muhammad Kamil Department of Information Technology College of ComputerQassim UniversityBuraidah52571Saudi Arabia Department of Electrical Engineering College of Engineering&Information TechnologyOnaizah CollegesOnaizah51911Saudi Arabia Digital Image Processing Lab Islamia CollegePeshawar25120KPKPakistan Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electrical Engineering National University of Computer and Emerging SciencesPeshawarPakistan

In early December 2019,the city of Wuhan,China,reported an outbreak of coronavirus disease(COVID-19),caused by a novel severe acute respiratory syndrome coronavirus-2(SARS-CoV-2).On January 30,2020,the World Health Organization(WHO)declared the outbreak a global pandemic *** the face of the COVID-19 pandemic,the most important step has been the effective diagnosis and monitoring of infected *** COVID-19 using Machine Learning(ML)technologies can help the health care unit through assistive diagnostic suggestions,which can reduce the health unit's burden to a certain *** paper investigates the possibilities of ML techniques in identifying/detecting COVID-19 patients including both conventional and exploring from chest X-ray images the effect of viral *** approach includes preprocessing,feature extraction,and ***,the features are extracted using the Histogram of Oriented(HOG)and Local Binary Pattern(LBP)feature ***,for the extracted features classification,six ML models of Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)is *** results show that the diagnostic accuracy of random forest classifier(RFC)on extracted HOG plusLBP features is as high as 94%followed by SVM at 93%.The sensitivity of the K-nearest neighbour model has reached an accuracy of 88%.Overall,the predicted approach has shown higher classification accuracy and effective diagnostic *** is a highly useful tool for clinical practitioners and radiologists to help them in diagnosing and tracking the cases of COVID-19.

关键词： Image pre-processing detection classification x-ray images filter

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Sharp-NeRF: Grid-based Fast Deblurring Neural Radiance Fields using Sharpness Prior

Sharp-NeRF: Grid-based Fast Deblurring Neural Radiance Field...

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IEEE Workshop on Applications of computer vision (WACV)

作者： Byeonghyeon Lee Howoong Lee Usman Ali Eunbyung Park Department of Artificial Intelligence Sungkyunkwan University Department of Electrical and Computer Engineering Sungkyunkwan University Hanwha Vision

Neural Radiance Fields (NeRF) has shown its remarkable performance in neural rendering-based novel view synthesis. However, NeRF suffers from severe visual quality degradation when the input images have been captured under imperfect conditions, such as poor illumination, defocus blurring and lens aberrations. Especially, defocus blur is quite common in the images when they are normally captured using cameras. Although few recent studies have proposed to render sharp images of considerably high-quality, yet they still face many key challenges. In particular, those methods have employed a Multi-Layer Perceptron (MLP) based NeRF which requires tremendous computational time. To overcome these shortcomings, this paper proposes a novel technique Sharp-NeRF—a grid-based NeRF that renders clean and sharp images from the input blurry images within a half an hour training. To do so, we used several grid-based kernels to accurately model the sharpness/blurriness of the scene. The sharpness level of the pixels is computed to learn the spatially varying blur kernels. We have conducted experiments on the benchmarks consisting of blurry images and have evaluated full-reference and non-reference metrics. The qualitative and quantitative results have revealed that our approach renders the sharp novel views with vivid colors and fine details, and it has considerably faster training time than the previous works. Our code is available at https://***/benhenryL/SharpNeRF.

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ChromaSense-Empowering Health, Empowering You

ChromaSense-Empowering Health, Empowering You

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2023 IEEE Biomedical Circuits and Systems Conference, BioCAS 2023

作者： Durbha, Ravi Koomson, Valencia Tufts University Advanced Integrated Circuits & Systems Lab Department of Electrical & Computer Engineering MedfordMA United States

ISBN: (纸本)9798350300260

Wearable devices have transformed from novelties into indispensable companions for millions, finding applications in health and wellness, empowering individuals to proactively manage their well-being. Among these, pulse oximeters play a vital role in detecting health issues like hypoxemia. Yet, traditional oximeters struggle with accuracy in individuals with darker skin tones and lack multi-vital sign monitoring using Photoplethysmogram (PPG) signals. Addressing these challenges, we introduce ChromaSense, a Skintone Compensated Pulse Oximeter (SCPO) equipped with advanced sensors and intelligent algorithms. It aims to provide precise oxygen saturation readings for all skin tones, ensuring equitable access to quality healthcare. By addressing pulse oximetry disparities, ChromaSense seeks to enhance patient outcomes, promote inclusivity in healthcare, revolutionize medical diagnostics and treatment, and drive a patient-centered healthcare transformation. © 2023 IEEE.

关键词： Accessible healthcare Business Canvas model Chronic health patient-centric diagnosis Photoplethysmogram Pulse Oximeter Purpose Launchpad radar Tele-medicine Value Preposition

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Hyperspectral anomaly detection:a performance comparison of existing techniques

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International Journal of Digital Earth 2022年第1期15卷 2078-2125页

作者： Noman Raza Shah Abdur Rahman M.Maud Farrukh Aziz Bhatti Muhammad Khizer Ali Khurram Khurshid Moazam Maqsood Muhammad Amin iVISION Lab Electrical Engineering DepartmentInstitute of Space TechnologyIslamabadPakistan AI Enabling Technologies Lab Center of Excellence in Artificial IntelligenceBahria UniversityIslamabadPakistan Department of Electrical and Computer Engineering Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripurPakistan Avionics Engineering Department Institute of Space TechnologyIslamabadPakistan

Anomaly detection in Hyperspectral Imagery(HSI)has received considerable attention because of its potential application in several *** anomaly detection algorithms for HSI have been proposed in the literature;however,due to the use of different datasets in previous studies,an extensive performance comparison of these algorithms is *** this paper,an overview of the current state of research in hyperspectral anomaly detection is presented by broadly dividing all the previously proposed algorithms into eight different *** addition,this paper presents the most comprehensive comparative analysis to-date in hyperspectral anomaly detection by evaluating 22 algorithms on 17 different publicly available *** indicate that attribute and edge-preserving filtering-based detection(AED),local summation anomaly detection based on collaborative representation and inverse distance weight(LSAD-CR-IDW)and local summation unsupervised nearest regularized subspace with an outlier removal anomaly detector(LSUNRSORAD)perform better as indicated by the mean and median values of area under the receiver operating characteristic(ROC)***,this paper studies the effect of various dimensionality reduction techniques on anomaly *** indicate that reducing the number of components to around 20 improves the performance;however,any further decrease deteriorates the performance.

关键词： Anomaly detection algorithms hyperspectral imagery deep learning dimensionality reduction

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Ensemble and Personalized Transformer Models for Subject Identification and Relapse Detection in E-Prevention Challenge

Ensemble and Personalized Transformer Models for Subject Ide...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Salvatore Calcagno Raffaele Mineo Daniela Giordano Concetto Spampinato Department of Electrical Electronics and Computer Engineering Pattern Recognition and Computer Vision Laboratory (PeRCeiVe Lab) University of Catania Italy

In this short paper, we present the devised solutions for the subject identification and relapse detection tasks, which are part of the e-Prevention Challenge hosted at the ICASSP 2023 conference [1] [2] [3]. We specifically design an ensemble scheme of six models - five transformer-based ones and a CNN model - for the identification of subjects from wearable devices, while a personalized - one for each subject - scheme is used for relapse detection in psychotic disorder. Our final submitted solutions yield top performance on both tracks of the challenge: we ranked 2 nd on the subject identification task (with an accuracy of 93.85%) and 1 st on the relapse detection task (with a ROC-AUC and PR-AUC of about 0.65). Code and details are available at https://***/perceivelab/e-prevention-icassp-2023.

关键词： Performance evaluation Codes Wearable computers Signal processing Transformers Acoustics Object recognition

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