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Reachability Analysis of Nonlinear Discrete-Time Systems Using Polyhedral Relaxations and Constrained Zonotopes 63

Reachability Analysis of Nonlinear Discrete-Time Systems Usi...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Rego, Brenner S. Raffo, Guilherme V. Terra, Marco H. Scott, Joseph K. Georgia Institute of Technology Department of Chemical and Biomolecular Engineering 311 Ferst Dr. AtlantaGA30318 United States University of Säo Paulo Department of Electrical and Computer Engineering SP Säo Carlos13566-590 Brazil Federal University of Minas Gerais Department of Electronics Engineering and the Graduate Program in Electrical Engineering MG Belo Horizonte31270-901 Brazil

ISBN: (纸本)9798350316339

This paper presents a novel algorithm for reachability analysis of nonlinear discrete-time systems. The proposed method combines constrained zonotopes (CZs) with polyhedral relaxations of factorable representations of nonlinear functions to propagate CZs through nonlinear functions, which is normally done using conservative linearization techniques. The new propagation method provides better approximations than those resulting from linearization procedures, leading to significant improvements in the computation of reachable sets in comparison to other CZ methods from the literature. Numerical examples highlight the advantages of the proposed algorithm. © 2024 IEEE.

关键词： Numerical methods

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IoT based Smart U-Turn Vehicle Accident Prevention System 2

IoT based Smart U-Turn Vehicle Accident Prevention System

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2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023

作者： Pradeepkumar, G. Praveen Santhoshkumar, G. Rohith Bhat, C. Jeyalakshmi, M. Muthukumar, T. Kumar, Neelam Sanjeev Kpr Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India Nandha Engineering College Department of Electronics and Communication Engineering Tamilnadu Erode India Saveetha School of Engineering Simats Department of Computer Science and Engineering Chennai India Ssm Institute of Engineering and Technology Department of Electronics and Communication Engineering Dindigul India Kongunadu College of Engineering and Technology Department of Electrical and Electronics Engineering India Saveetha School of Engineering Simats Department of Biomedical Engineering Chennai India

ISBN: (纸本)9781665491990

Unintentional deaths occur at a very high rate in developing countries. Curved roads have significantly more fatalities than straight roads. This occurs mainly on U-turns, hairnin turns, and narrow mountain roads. Drivers in this position cannot see the vehicle approaching from the opposite direction. As a result, thousands of people are killed in car accidents every year. The best way to avoid further accidents is to alert the car driver approaching from the side. Place the ultrasonic range detection sensor on one side of the road before the bend and the light indicator system on the opposite side after the bend. When a vehicle approaches from afar, an ultrasonic sensor on one side of the road sends a signal to the other side of the road via a light system. In response to a warning, the driver may stop the car until the other vehicle has passed. A buzzer will also be used to warn the driver of the car that is approaching. © 2023 IEEE.

关键词： Ultrasonic applications

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SRL-EII: A Modular and Efficient Numerical Solver for electrical Impedance Imaging 22

SRL-EII: A Modular and Efficient Numerical Solver for Electr...

引用

22nd IEEE International Symposium on biomedical Imaging, ISBI 2025

作者： Ha, Gia-Bao Assylbek, Dinali Vasquez, Fernando Guevara Sanchez, Benjamin University of Utah Department of Electrical and Computer Engineering Salt Lake CityUT United States University of Utah Department of Mathematics Salt Lake CityUT United States University of Illinois Chicago Department of Electrical and Computer Engineering ChicagoIL United States University of Illinois Chicago Department of Biomedical Engineering ChicagoIL United States

ISBN: (纸本)9798331520526

electrical impedance imaging (EII) is a functional imaging modality with high temporal resolution. Image reconstruction in EII seeks to estimate the electrical conductivity distribution inside of an object from external voltage measurements. It is a nonlinear and ill-posed inverse problem and requires iterative techniques. Here, we describe the implementation and validation of the Sanchez Research Lab-EII (SRL-EII) solver, an efficient algorithm using finite element model to solve the linearized reconstruction problem. The SRL-EII solver is benchmarked against reference EIDORS algorithm using experimental data, achieving a reconstruction error of 1.38.10-10 and 6.76.10-10S/m for phantom tank and lung test experiments. The results demonstrate the numerical accuracy and scalability of the SRL-EII algorithm and its potential for standalone hardware implementations. © 2025 IEEE.

关键词： Electric impedance tomography

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Hybrid Blockchain and IoT Driven Framework for Supply Chain Optimization by Enhancing Transparency, Efficiency and Cost Reduction 3

Hybrid Blockchain and IoT Driven Framework for Supply Chain ...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Pradeep Kumar, S. Basha, C.H. Hussaian Prasath, C. Arun Amirthayogam, G. Lathif, Syed Ismail Abdul Leelavathi, R. Rajalakshmi Engineering College Computer Science Design Department Thandalam Chennai602105 India Sr University Department of Electrical and Electronics Engineering Warangal506371 India Mahendra Engineering College Department of Ece Namakkal637503 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai600062 India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai603203 India Velalar College of Engineering and Technology Department of Biomedical Engineering Erode638012 India

ISBN: (纸本)9798331509675

Blockchain technology represents a modern supply chain management revolution which fixes operational challenges while providing enhanced transparency at reduced costs. The research develops HB-SCOF (Hybrid Blockchain-Enabled Supply Chain Optimization Framework) which combines blockchain self-verification databases with automatic smart contracts to maintain continuous supply chain observation and evaluate data credibility. The framework integrates tamper-proof transaction tracking mechanisms with unified supply chain network connectivity for securing data transmission as a trust delivery method through unbreakable shared databases. The retail-industry case study demonstrated a 25% decrease in expenses stemming from agency elimination alongside enhanced flow management and better stock distribution. The system demonstrates its effectiveness through assessments of key performance indicators including transaction speed and data accuracy together with business cost efficiency. This part looks at implementation methods through training stakeholders in addition to transforming organizational policies which produce successful outcomes. Blockchain technology labs verify its capability to enhance supply chain management efficiency while showing realistic deployment methods. © 2025 IEEE.

关键词： Smart contract

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CCRL: Contrastive Cell Representation Learning 17th

CCRL: Contrastive Cell Representation Learning

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17th European Conference on computer Vision, ECCV 2022

作者： Nakhli, Ramin Darbandsari, Amirali Farahani, Hossein Bashashati, Ali School of Biomedical Engineering University of British Columbia Vancouver Canada Department of Electrical and Computer Engineering University of British Columbia Vancouver Canada Department of Pathology and Laboratory Medicine University of British Columbia Vancouver Canada

ISBN: (纸本)9783031250811

Cell identification within the H &E slides is an essential prerequisite that can pave the way towards further pathology analyses including tissue classification, cancer grading, and phenotype prediction. However, performing such a task using deep learning techniques requires a large cell-level annotated dataset. Although previous studies have investigated the performance of contrastive self-supervised methods in tissue classification, the utility of this class of algorithms in cell identification and clustering is still unknown. In this work, we investigated the utility of Self-Supervised Learning (SSL) in cell clustering by proposing the Contrastive Cell Representation Learning (CCRL) model. Through comprehensive comparisons, we show that this model can outperform all currently available cell clustering models by a large margin across two datasets from different tissue types. More interestingly, the results show that our proposed model worked well with a few number of cell categories while the utility of SSL models has been mainly shown in the context of natural image datasets with large numbers of classes (e.g., ImageNet). The unsupervised representation learning approach proposed in this research eliminates the time-consuming step of data annotation in cell classification tasks, which enables us to train our model on a much larger dataset compared to previous methods. Therefore, considering the promising outcome, this approach can open a new avenue to automatic cell representation learning. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cells

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Real-Time Object Detection Performance of YOLOv8 Models for Self-Driving Cars in a Mixed Traffic Environment 2

Real-Time Object Detection Performance of YOLOv8 Models for ...

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2nd International Conference on computer System, Information Technology, and electrical engineering, COSITE 2023

作者： Afdhal, Afdhal Saddami, Khairun Sugiarto, Sugiarto Fuadi, Zahrul Nasaruddin, Nasaruddin Doctoral Program School of Engineering Universitas Syiah Kuala Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Indonesia Department of Civil Engineering Universitas Syiah Kuala Indonesia Department of Mechanical dan Industrial Engineering Universitas Syiah Kuala Indonesia Indonesia

ISBN: (纸本)9798350343069

Self-driving cars have gained significant attention in recent years. Real-time object detection is a critical component of their perception system. One of the main challenges in developing safe and efficient self-driving cars lies in accurately and in real-time detecting objects in diverse and complex traffic environments. This paper presents the performance evaluation of real-time object detection of YOLOv8 models, a state-of-the-art deep learning framework for self-driving cars in mixed traffic environments. The objective is to assess the extent of YOLOv8's object detection capabilities can be used for self-driving car within complex realworld traffic scenarios. The experimental results show that the accuracy of YOLOv8 during normal daylight scenarios ranges from 0.60 ~ 0.80. In contrast, the accuracy values obtained in night scenarios fall between 0.15 ~ 0.25. Similarly, the FMeasure of YOLOv8 models under daylight conditions ranges from 0.75 ~ 0.87. Conversely, the F-Measure in night conditions falls between 0.27 ~ 0.46. Based on our thorough evaluation results, YOLOv8 has demonstrated its robustness in detecting objects. However, the algorithm requires improvement to effectively handle the challenges of self-driving cars' object detection systems in mixed traffic, such as diverse object classifications, small-scale objects, fast-moving objects, blur, glare, and low-light illumination, particularly in nighttime scenarios. © 2023 IEEE.

关键词： Object detection

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A Review of Machine Learning Methods for Screening Cardiac Abnormalities using Auscultation

A Review of Machine Learning Methods for Screening Cardiac A...

引用

International Conference on engineering and Emerging Technologies (ICEET)

作者： Sarah Alhasan Zahra Poorshamsi Loubna Nasser Alyaziah Alzaabi Mohammed Ghazal Jawad Yousaf Taimur Hassan Department of Electrical Computer and Biomedical Engineering Abu Dhabi University UAE

ISBN: (数字)9798331532895

ISBN: (纸本)9798331532901

This paper presents a thorough review of various methodologies employed in heart sound classification, combining both conventional machine learning (ML) algorithms and complex deep learning (DL) approaches. We systematically evaluate different data collection approaches, preprocessing techniques, methodologies, and classification mechanisms. Further-more, the review analyzes several feature extraction methods required for capturing relevant signal characteristics, spanning Mel-frequency cepstral coefficients (MFCC), continuous wavelet transform (CWT), and convolutional neural networks (CNNs). Additionally, we examine a variety of classification methods, from traditional methods like support vector machines (SVMs), Knearest neighbors (KNNs), and Decision Trees (DTs), to more sophisticated parametric approaches such as recurrent neural networks (RNNs), (CNNs), long-short term memory (LSTM), and even ensemble techniques, signifying the potential of combining classification approaches. Key findings reveal a trend toward employing hybrid models that merge the capabilities of multiple techniques to enhance cardiac diagnostics precision. This comprehensive overview aims to explore existing methodologies and move toward bridging the gap in today's technologies, establishing a robust diagnostic tool for medical applications.

关键词： Heart Deep learning Support vector machines Continuous wavelet transforms Reviews Stethoscope Signal processing Feature extraction Wavelet analysis Mel frequency cepstral coefficient

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Separation of fascicles for motor unit separability in reinnervated muscles for neuroprosthesis application 46

Separation of fascicles for motor unit separability in reinn...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Qin, Lang Wang, Siyu Quinn, Kiara N. Perkins, Pierce Tuffaha, Sami Thakor, Nitish V. Johns Hopkins School of Medicine Department of Plastic and Reconstructive Surgery BaltimoreMD21218 United States Johns Hopkins Whiting School of Engineering Department of Electrical and Computer Engineering BaltimoreMD21218 United States Johns Hopkins School of Medicine Department of Biomedical Engineering BaltimoreMD21218 United States

ISBN: (纸本)9798350371499

After amputation, muscle signals can be used as a signal source for advanced prosthetic control. Innovative surgical techniques like muscle reinnervation surgeries have proven to be an advanced option. This study introduces a novel variation of the traditional muscle reinnervation approaches to provide a more robust and flexible signal source in prosthetic limbs. By implanting two separated fascicles at distant locations within a single muscle, our approach facilitates more effective innervation of the muscle to increase the spatial separability of the signal. Electrophysiological and histological analyses are used to verify that muscle signal amplitude is increased and the separability of motor units within the signal is improved. This work holds potential for enhancing the signals used to control dexterous prostheses. © 2024 IEEE.

关键词： Artificial limbs

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Negative Voltage Sequence Control for an Electric Arc Furnace Power Supply based on a Multilevel AC-AC Converter

Negative Voltage Sequence Control for an Electric Arc Furnac...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Volpini, Andrea Granata, Samuele Postiglione, Gianluca Zanchetta, Pericle Nidec Industrial Solutions Research & Development Department Milan Italy University of Pavia Dept. of Electrical Computer and Biomedical Engineering Pavia Italy University of Nottingham Dept. Electrical and Electronic Engineering Nottingham United Kingdom

ISBN: (纸本)9798350316445

This paper proposes a novel control approach for a back to back multilevel AC-AC converter in Electric Arc Furnace (EAF) power supply applications. The study presents a feasibility analysis about the use of the chosen converter structure for EAF applications, along with considerations for control mechanisms. In particular, the effect of both positive and negative voltage sequences on the system is explored. Through comprehensive analysis, the paper introduces a novel control method utilizing negative voltage sequence, aiming to enhance the overall system performances. The results are presented using both a simplified model and the Cassie-Mayr (CM) model for the EAF. © 2023 IEEE.

关键词： AC-AC Converter Cascaded H-Bridge Electric Arc Furnace power supply

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Work in Progress: Remote Laboratory Delivery with an At-Home Biomechanical Kinematic Data Acquisition Method 129

Work in Progress: Remote Laboratory Delivery with an At-Home...

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129th ASEE Annual Conference and Exposition: Excellence Through Diversity, ASEE 2022

作者： Sayed, Ahmed M. Electrical Engineering and Computer Science Department MSOE University MilwaukeeWI United States Biomedical Engineering Department Faculty of Engineering Helwan University Helwan Egypt

Under the remote learning mode due to COVID-19, educational laboratory modules lacking the active data acquisition step tend to lose students' engagement and diminish their eagerness to explore further knowledge. Such shortcomings are more profound in practical fields of study, such as Biomechanics. The goal of this paper is to present a remote laboratory delivery and evaluation method where students can apply principles of kinematic and kinetic biomechanical analysis on their own body motions with a computer vision algorithm to interactively solve a motion analysis problem. In this preliminary study, students were given the freedom to choose a specific body motion to be captured and analyzed, such as elbow, knee, wrist, and neck joint movements. Motion specifications included determination of the motion type, and also the starting and ending angular or linear positions. Readily available labels were utilized as passive joint markers. Students were then instructed to video record their joint motions using their laptop cameras. A custom video tracking algorithm specifically designed to track spatial locations was then employed to capture relative positions of the recorded motions. Laboratory instructions asked the students to perform kinematic calculations on the algorithm's generated positional data to determine joint velocities and accelerations, and then perform kinetic analyses to estimate the associated muscle forces. Laboratory requirements were concluded with a reflection prompt to evaluate the activity's workload and effort perceived by the students. These activities were delivered twice in two different academic terms. Samples of the produced kinematic data using our methods were verified in comparison with a standard physical motion capture system, where similar joint motion descriptive results were observed. Results show that the completion rate of laboratory requirements was 97% in the first term of delivery, and 100% in the second term, as supported by t

关键词： Students

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