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A Bayesian Model Based on Link Distribution Features for Multi-target Passive Localization in Visible Light Sensing

IEEE Sensors Journal 2025年第11期25卷 20037-20050页

作者： Zhang, Shuai Zhang, Liyi Liu, Kaihua Wang, Ya Tianjin University of Commerce School of Information engineering Tianjin300134 China Tianjin University School of Microelectronics Tianjin300072 China Tianjin Renai College Information Engineering Department Tianjin301636 China Texas A&M University Department of Electrical and Computer Engineering Department of Biomedical Engineering Department of Mechanical Engineering J. Mike Walker 66 College StationTX77843 United States

Passive localization using visible light sensing has been considered as a promising solution for indoor human detection. A major challenge is to avoid false target positioning in multi-target positioning scenarios. Besides, a reasonable probable target area model is also critical to the accuracy of positioning and counting targets. In this article, a novel passive localization scheme is proposed to locate multiple targets. This scheme introduces a rectangular probable target area model, which is used to calculate a rectangular area containing the potential location of the target to be located. Compared with existing models, it is more suitable for positioning scenarios under low-density deployment of sensing nodes. Furthermore, we present an improved successive cancellation (SC) algorithm to excluding false target localization. To determine the authenticity of targets by the SC algorithm, a Bayesian model is introduced to optimize the SC algorithm according to the multidimensional shad-owed link information of candidate targets. Numerous simulation results show that the proposed multi-target passive localization scheme can improve the problem of false target localization in multi-target localization scenarios. And it also can achieve outstanding performance in localization accuracy. © 2001-2012 IEEE.

关键词： Sensors Location awareness Transmitters Light emitting diodes Accuracy Bayes methods Vectors Receivers RF signals Time measurement

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Adaptive Control Design for Robust Quality Control of an Online Checkweighing and Rejection System 12

Adaptive Control Design for Robust Quality Control of an Onl...

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12th IEEE Conference on Systems, Process and Control, ICSPC 2024

作者： Aamir, Narmeen Masood, Syed Muhammad Sabih Abid, Rasheed Illinois Institute of Technology Department of Electrical & Computer Engineering ChicagoIL United States NED University of Engineering & Technology Department of Electrical Engineering Karachi Pakistan Illinois Institute of Technology Department of Biomedical Engineering ChicagoIL United States

ISBN: (纸本)9798350391398

In modern process industries, maintaining precise weight measurements is critical for ensuring product quality and operational efficiency. Accurate weight measurement systems not only aid in meeting regulatory standards but also play a crucial role in minimizing waste and optimizing production processes. This paper provides an overview of an online checkweighing system installed in a process industry and introduces an adaptive control algorithm that calculates the running average of box weights using the Simple Moving Average (SMA) algorithm. This average is applied recursively to account for the impact of process parameter variations, such as temperature or density, on the accuracy of the checkweighing and rejection system. The proposed algorithm was successfully implemented to verify product count and detect underweight and overweight conditions in packed products. The results show that the rejection accuracy of the proposed algorithm is unaffected by weight variations caused by process variables, unlike the existing control algorithm, which could only accommodate weight variations within the limits of a single soap bar weight. © 2024 IEEE.

关键词： Adaptive control systems

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IMU-Trans: imputing missing motion capture data with unsupervised transformers

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Neural Computing and Applications 2025年第7期37卷 5699-5717页

作者： Avdan, Goksu Onal, Sinan Lu, Chao Department of Engineering Technology Missouri Western State University St. JosephMO64507 United States Department of Industrial Engineering Southern Illinois University Edwardsville EdwardsvilleIL62026 United States Department of Electrical Computer and Biomedical Engineering Southern Illinois University Carbondale CarbondaleIL62901 United States

Motion capture (mocap) systems are extensively utilized in healthcare for monitoring rehabilitation programs, facilitating clinical gait assessments for early Alzheimer’s diagnosis, managing walking disorders, and developing exoskeleton suits. However, like many other healthcare technologies, mocap systems have some flaws, like missing markers and occlusions. Given mocap data’s sequential and temporal nature, understanding marker relationships and capturing global dependencies are crucial for effective human motion recovery applications. To address these challenges, we proposed an unsupervised transformers framework for human motion recovery, called IMU-Trans. We evaluated our framework’s generalizability across two clinical datasets and tested its robustness by adjusting the missing marker rates, comparing its performance against low-dimensional Kalman filtering, long short-term memory (LSTM), and gated recurrent unit (GRU) models. Our experimental results demonstrated that IMU-Trans outperforms state-of-the-art models by training in an unsupervised manner. The closest competitor, GRU, demonstrated an RMSE of 1.35 ± 0.82, 2.36 ± 1.26, 3.43 ± 1.73, and 4.39 ± 2.18 cm for 20%, 30%, 40%, and 50% missing rates, respectively. IMU-Trans outperformed GRU with an RMSE of 1.26 ± 0.60, 2.06 ± 0.88, 2.68 ± 1.04, and 3.05 ± 1.22 for the same rates. Notably, our framework performs well even with higher missing data rates, creating opportunities for advancements in data analytics and indicating a promising future for motion capture in healthcare. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Exoskeleton (Robotics)

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Ultrasound-based AI for COVID-19 detection: a comprehensive review of public and private lung ultrasound datasets and studies

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Multimedia Tools and Applications 2025年 1-55页

作者： Morshed, Abrar Al Shihab, Abdulla Jahin, Md Abrar Al Nahian, Md Jaber Hossain Sarker, Md Murad Ibne Wadud, Md Sharjis Uddin, Mohammad Istiaq Imtiaz Siraji, Muntequa Anjum, Nafisa Shristy, Sumiya Rajjab Rahman, Tanvin Khatun, Mahmuda Rubel Dewan, Md Hossain, Mosaddeq Sultana, Razia Chakma, Ripel Emon, Sonet Barua Islam, Towhidul Hussain, Mohammad Arafat Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka1208 Bangladesh Department of Electrical and Telecommunication Engineering Daffodil International University Dhaka1216 Bangladesh Khulna9203 Bangladesh Okinawa904-0412 Japan Department of Information and Communication Technology Bangladesh University of Professionals Dhaka1216 Bangladesh Department of Information and Communication Technology Comilla University Cumilla3506 Bangladesh Department of Biomedical Physics and Technology University of Dhaka Dhaka1000 Bangladesh Department of Computer Science and Engineering International Islamic University Chittagong Chittagong4318 Bangladesh Department of Electrical and Electronic Engineering Islamic University of Technology Gazipur1704 Bangladesh Department of Electronics and Telecommunication Engineering Chittagong University of Engineering and Technology Chittagong4349 Bangladesh Department of Computer Science and Engineering Notre Dame University Bangladesh Dhaka1000 Bangladesh Department of Electrical and Electronic Engineering Khulna University of Engineering & Technology Khulna9203 Bangladesh Department of Computer Science and Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Electronics and Communication Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Mathematics Jahangirnagar University Dhaka1342 Bangladesh Department of Electrical and Electronic Engineering Daffodil International University Dhaka1216 Bangladesh Department of Electrical and Electronic Engineering Dhaka University of Engineering and Technology Gazipur1707 Bangladesh Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Noakhali3814 Bangladesh Department of Computer Science and Engineering Northern University Bangladesh Dhaka1230 Bangladesh Boston Children’s Hospital Harvard Medical School BostonMA02

The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings for detecting COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as a low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on AI-driven studies utilizing lung ultrasound (LUS) for COVID-19 detection and analysis. We provide a detailed overview of both publicly available and private LUS datasets and categorize the AI studies according to the dataset they used. Additionally, we systematically analyzed and tabulated the studies across various dimensions, including data preprocessing methods, AI models, cross-validation techniques, and evaluation metrics. In total, we reviewed 60 articles, 41 of which utilized public datasets, while the remaining employed private data. Our findings suggest that ultrasound-based AI studies for COVID-19 detection have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： COVID-19

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Trade Ease with Machine Learning and AWS

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IEEE Access 2023年 11卷 25893-25905页

作者： Teja, Kamurthi Ravi Liu, Chuan-Ming National Taipei University of Technology International Program of Electrical Engineering and Computer Science Taipei10608 Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei10608 Taiwan

Global trading is undergoing significant changes, necessitating modifications to the trading strategies. This study presents a newly developed cloud-based trading strategy that uses Amazon Web Services (AWS), machine learning (ML), and data science to automate trading tasks. The study begins by creating a machine learning trading strategy using a customized deep neural network (DNN). The strategy was then tested using a trading station before deployment on a cloud machine. The execution of the strategy resulted in a total profit of 42.74526 USD, with a test accuracy score of 87.45% and a training accuracy score of 89.15% over 6047 epochs. The roadmap provides a step-by-step overview of the entire process from strategy development to execution. In addition, this study offers insights into related issues and solutions that can enhance the effectiveness of trading strategies. Overall, this study contributes significantly to the field of cloud-based trading strategies and opens avenues for future research. © 2013 IEEE.

关键词： Deep neural networks

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Navigating IoT Security:Insights into Architecture,Key Security Features,Attacks,Current Challenges and AI-Driven Solutions Shaping the Future of Connectivity

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computers, Materials & Continua 2024年第12期81卷 3499-3559页

作者： Ali Hassan N.Nizam-Uddin Asim Quddus Syed Rizwan Hassan Ateeq Ur Rehman Salil Bharany Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan Department of Biomedical Engineering HITEC UniversityTaxila47080Pakistan Department of Electronics Engineering University of ChakwalChakwal48800Pakistan Department of Computer Science NFC Institute of Engineering and Fertilizer ResearchFaisalabad38000Pakistan School of Computing Gachon UniversitySeongnam-si13120Republic of Korea Chitkara University Institute of Engineering and Technology Chitkara UniversityRajpura140401PunjabIndia

Enhancing the interconnection of devices and systems,the Internet of Things(IoT)is a paradigm-shifting *** security concerns are still a substantial concern despite its extraordinary *** paper offers an extensive review of IoT security,emphasizing the technology’s architecture,important security elements,and common *** highlights how important artificial intelligence(AI)is to bolstering IoT security,especially when it comes to addressing risks at different IoT architecture *** systematically examined current mitigation strategies and their effectiveness,highlighting contemporary challenges with practical solutions and case studies from a range of industries,such as healthcare,smart homes,and industrial *** results highlight the importance of AI methods that are lightweight and improve security without compromising the limited resources of devices and computational *** networks can ensure operational efficiency and resilience by proactively identifying and countering security risks by utilizing machine learning *** study provides a comprehensive guide for practitioners and researchers aiming to understand the intricate connection between IoT,security challenges,and AI-driven solutions.

关键词： Internet of Things(IoT) artificial intelligence(AI) IoT architecture security attacks in IoT

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Reinforcement Learning-based Receding Horizon Control using Adaptive Control Barrier Functions for Safety-Critical Systems 63

Reinforcement Learning-based Receding Horizon Control using ...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Sabouni, Ehsan Sabbir Ahmad, H.M. Giammarino, Vittorio Cassandras, Christos G. Paschalidis, Ioannis Ch. Li, Wenchao Boston University Division of Systems Engineering BostonMA02215 United States Boston University Department of Electrical and Computer Engineering The Division of Systems Engineering BostonMA02215 United States Boston University Department of Electrical and Computer Engineering Division of Systems Engineering Department of Biomedical Engineering BostonMA02215 United States

ISBN: (纸本)9798350316339

Optimal control methods provide solutions to safety-critical problems but easily become intractable. Control Barrier Functions (CBFs) have emerged as a popular technique that facilitates their solution by provably guaranteeing safety, through their forward invariance property, at the expense of some performance loss. This approach involves defining a performance objective alongside CBF-based safety constraints that must always be enforced with both performance and solution feasibility significantly impacted by two key factors: (i) the selection of the cost function and associated parameters, and (ii) the calibration of parameters within the CBF-based constraints, which capture the trade-off between performance and conservativeness. To address these challenges, we propose a Reinforcement Learning (RL)-based Receding Horizon Control (RHC) approach leveraging Model Predictive Control (MPC) with CBFs (MPC-CBF). In particular, we parameterize our controller and use bilevel optimization, where RL is used to learn the optimal parameters while MPC computes the optimal control input. We validate our method by applying it to the challenging automated merging control problem for Connected and Automated Vehicles (CAVs) at conflicting roadways. Results demonstrate improved performance and a significant reduction in the number of infeasible cases compared to traditional heuristic approaches used for tuning CBF-based controllers, showcasing the effectiveness of the proposed method. In order to guarantee reproducibility, our code is provided here: ***/EhsanSabouni/CDC2024_RL_adpative_MPC_CBF/tree/main © 2024 IEEE.

关键词： Adaptive control systems

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Implementation of a Multipath Fully Differential OTA in 0.18-μm CMOS Process

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IEEE Transactions on Very Large Scale Integration (VLSI) Systems 2023年第1期31卷 147-151页

作者： Akbari, Meysam Hussein, Safwan Mawlood Hashim, Yasir Khateb, Fabian Kulej, Tomasz Tang, Kea-Tiong University of Kurdistan Department of Electrical Engineering Sanandaj66177 Iran Tishk International University Faculty of Engineering Department of Computer Engineering Erbil44001 Iraq Brno University of Defence Department of Electrical Engineering Brno66210 Czech Republic Czech Technical University in Prague Faculty of Biomedical Engineering Kladno16636 Czech Republic Czestochowa University of Technology Department of Electrical Engineering Czestochowa42201 Poland National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan

This brief implements a highly efficient fully differential transconductance amplifier, based on several input-to-output paths. Some traditional techniques, such as positive feedback, nonlinear tail current sources, and current mirror-based paths, are combined to increase the transconductance, thus leading to larger dc gain and higher gain bandwidth (GBW) product. Two flipped voltage-follower (FVF) cells are employed as variable current sources to provide class-AB operation and adaptive biasing of all other drivers. The proposed structure includes several input-to-output paths that play the role of dynamic current boosters during the slewing phase, thus improving the slew rate (SR) performance. The circuit was fabricated in a TSMC 0.18-μm CMOS process with a silicon area of 54.5×30.1μm. Experimental results show a GBW of 173.3 MHz, a dc gain of 72.7 dB, and an SR of 139.4 V/μs for a capacitive load of 2×5 pF. The proposed circuit consumes 619 μW of power, under a supply voltage of 1.8 V. © 1993-2012 IEEE.

关键词： Operational amplifiers

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Real Time Chest X-Ray Interpretation Applying Vision Transformer for Tuberculosis Detection

Real Time Chest X-Ray Interpretation Applying Vision Transfo...

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2024 International Conference on computer engineering, Network and Intelligent Multimedia, CENIM 2024

作者： Irhamsyah, Muhammad Roslidar Yunidar Al Lail Qadrillah, M. Nasaruddin Arnia, Fitri Munadi, Khairul Universitas Syiah Kuala Post Graduate Program in Engineering Banda Aceh2311 Indonesia Faculty of Engineering Department of Electrical and Computer Engineering 2311 Indonesia

ISBN: (纸本)9798350368802

Tuberculosis (TB) is an infectious disease that can attack the lungs and other organs. In the diagnosis of TB, the interpretation of X-ray radiological images requires special skills from medical specialists. However, the lack of experience of medical personnel and focus on X-ray examinations can be a challenge for radiologists because it has the potential to cause diagnostic errors in image interpretation. Therefore, we proposed a web-based application of real-time chest X-ray interpreter by integrating the deep learning model of Vision Transformer (ViT) to interpret chest X-ray images. The inference model was trained and tested on 652 normal and 652 tuberculosis X-ray images. The data preprocessing of Contrast Adaptive Histogram Equalization (CLAHE) was implemented to increase chest X-ray image contrast as well as inference model performance. The proposed real time system can inteprete the tested chest x-ray images with an accuracy of 95% and with simulation duration time of 4.7 seconds. © 2024 IEEE.

关键词： Diagnosis

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Identification and ranking biomaterials for bone scaffolds using machine learning and PROMETHEE

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Research on biomedical engineering 2023年第1期39卷 129-138页

作者： Javaid, Sabah Gorji, Hamed Taheri Soulami, Khaoula Belhaj Kaabouch, Naima Department of Biomedical Engineering University of North Dakota Grand ForksND United States School of Electrical Engineering and Computer Science Grand ForksND United States Rabat Morocco

Purpose: Bones have a complex hierarchical structure that supports their diverse chemical, biological, and mechanical functions. High rates of bone susceptibility to fractures and injury have attracted extensive research interest to find alternate biomaterials for bone scaffolds. Natural bone healing is only successful if the defect is very small and when a defect exceeds 1 cm3 then bone grafting is required. Large bone defects or injuries are very serious problems in orthopedics as they bring great harm to health and normal function of daily life routine. A scaffold should have good strength to maintain its own structure after implantation in a load bearing environment and without being stiff that shields surrounding bone from the load. Therefore, mechanical properties of bone scaffolds should match those of the host tissue and should be part of the natural environment of the body without any harm or further damage. Methods: In this paper, we present two main contributions. First, we investigate the use of machine learning models in identifying biomaterials that are suitable for bone scaffolds. Second, we rank the best materials for biomedical scaffold applications using the multi-criteria decision analysis methods, the Preference Ranking Organization METhod for the Enrichment of Evaluations (PROMETHEE). Machine learning models investigated are AdaBoost, artificial neural network (ANN), Naïve Bayes (NB), Decision tree (DT), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN). Mechanical properties such as comprehensive strength, tensile strength, and Young’s modulus with the cortical bone are used as the standard reference for classification. Results: The results show that the ANN outperforms the other machine learning models in identifying the biomaterials suitable for bone tissue engineering, while the ranking results using PROMETHEE show that Brushite and Titanium alloy are the best appropriate biomaterials for the cancellous and cortical bones, respectiv

关键词： Decision trees

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