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Automatic Cataract and Cholesterol Detection System Based on Recurrent Neural Network (RNN): A Comparison with Convolutional Neural Network (CNN) and Siamese Neural Network (SNN) 1

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19th International Conference of Intelligent Unmanned systems, ICIUS 2023

作者： Lie, Zener S. Widodo, Jatmiko A. F. Hanesi, Isabel K. P. Moningka, Matthew R. N. Mariana, Y. Astuti, Winda Akmeliawati, Rini Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta11480 Indonesia Product Design program Industrial Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta11480 Indonesia Robotics and Automation Research Group School of Electrical and Mechanical Engineering The University of Adelaide North Terrace Campus Adelaide Australia

ISBN: (数字)9789819765911

ISBN: (纸本)9789819765904

In Indonesia, the majority of blindness cases, approximately 81%, are attributed to cataract. Additionally, 35% of the population has higher cholesterol levels than the standard normal range. The lack of specialized medical equipment, such as the slit-lamp (Ruparelia in Dalhousie Med J 49(1), [1]) bio-microscope, and trained professionals, especially in rural areas, make it difficult to detect these conditions through eye examinations. To address this issue, a supervised machine learning method, the Recurrent Neural Network (RNN), has been utilized to create an iris scanner system that can identify cataract and high cholesterol in a patient. This system will classify eyes into three categories, i.e., cataract, high cholesterol, and normal iris. The computer simulations indicate that this technique is more accurate and requires less training time compared to the existing Convolutional Neural Network (ANN) and Siamese Neural Network (SNN)-based method, while achieving 100% accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Convex Equilibrium-Free Stability and Performance Analysis of Discrete-Time Nonlinear systems

arXiv

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arXiv 2024年

作者： Koelewijn, Patrick J.W. Weiland, Siep Tóth, Roland Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Netherlands Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Hungary

This paper considers the equilibrium-free stability and performance analysis of discrete-time nonlinear systems. We consider two types of equilibrium-free notions. Namely, the universal shifted concept, which considers stability and performance w.r.t. all equilibrium points of the system, and the incremental concept, which considers stability and performance between trajectories of the system. In this paper, we show how universal shifted stability and performance of discrete-time systems can be analyzed by making use of the time-difference dynamics. Moreover, we extend the existing results for incremental dissipativity for discrete-time systems based on dissipativity analysis of the differential dynamics to more general state-dependent storage functions for less conservative results. Finally, we show how both these equilibrium-free notions can be cast as a convex analysis problem by making use of the linear parameter-varying framework, which is also demonstrated by means of an example. Copyright © 2024, The Authors. All rights reserved.

关键词： Nonlinear systems

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MLFusion: Multilevel Data Fusion using CNNS for atrial fibrillation detection

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computers in Biology and Medicine 2025年 189卷 109901-109901页

作者： John, Arlene Parhi, Keshab K. Cardiff, Barry John, Deepu Biomedical Signals and Systems Group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede7522 NB Netherlands Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States School of Electrical and Electronic Engineering University College Dublin DublinD04 FX62 Ireland

Data fusion, involving the simultaneous integration of signals from multiple sensors, is an emerging field that facilitates more accurate inferences in instrumentation applications. This paper presents a novel fusion methodology for multi-sensor multimodal data using convolutional neural networks (CNNs), which introduces a data-driven approach to automatically select the optimal level of information abstraction for fusion. Unlike traditional methods, the proposed model allows for a self-learning fusion process, eliminating the need for manual selection of the fusion level by the designer. The model uniquely integrates feature extraction and fusion into a unified framework, enhancing efficiency and performance. Additionally, the fusion network incorporates signal quality indicators (SQIs) as input streams, enabling the model to account for the quality of each input signal during the fusion process. The methodology is evaluated on the task of atrial fibrillation detection through the fusion of two multimodal signal inputs taken from a portion of the MIMIC III database. The fusion network, using electrocardiogram (ECG) and photoplethsymogram (PPG) signals as inputs, and trained with an average loss function, achieved an accuracy of 99.33% and a sensitivity of 99.74%. The model's robustness in the presence of noisy inputs is also analyzed, demonstrating the effectiveness of the SQI-based multi-level fusion approach. This novel methodology represents a significant advancement in data fusion by offering a fully automated, quality-aware approach to multi-sensor multimodal signal fusion. © 2025

关键词： Convolutional neural networks

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Critical Thermal Characterization of Next-Generation Solid-State Batteries for Automotive Battery Management systems

Critical Thermal Characterization of Next-Generation Solid-S...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Chandan Chetri Sheldon Williamson Department of Electrical Computer and Software Engineering Smart Transportation Electrification and Energy Research (STEER) Group Ontario Tech University Oshawa Canada

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

Solid-state batteries (SSBs) are at the forefront of next-generation energy storage technologies, promising enhanced safety, energy density, and longevity compared to traditional lithium-ion batteries. However, managing the thermal behavior of SSBs during charging is critical to ensuring their safe and reliable operation. This paper presents an in-depth analysis of the thermal characteristics of SSBs under various charging conditions, based on laboratory experiments conducted in controlled environments. The study specifically evaluates the thermal performance of SSBs in comparison to traditional lithium nickel cobalt aluminum oxide (NCA) batteries, with an emphasis on their potential application in e-mobility. The insights gained from this research are expected to inform the development of advanced thermal management strategies, contributing to the design of safer and more efficient solid-state battery technologies for e-mobility applications.

关键词： Thermal management of electronics Battery management systems Amplitude modulation Thermal management Lithium Batteries Solid state batteries Thermal analysis Next generation networking Automotive engineering

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Robotic fish:Opening a new era of underwater detection

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The Innovation 2025年第3期6卷 13-14页

作者： Dongfang Li Suet To Zhicheng Xu Guanghong Liu Rob Law Edmond Q.Wu Limin Zhu State Key Laboratory of Ultraprecision Machining Technology Department of Industrial and Systems EngineeringThe Hong Kong Polytechnic UniversityHong Kong 999077China School of Electrical Engineering and Automation Fuzhou UniversityFujian 350108China China Electronics Technology Group Corporation Information Science Research InstituteBeijing 100086China School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China Asia-Pacific Academy of Economics and Management Faculty of Business AdministrationUniversity of MacaoMacao 999078China Department of Computer Science and Engineering Shanghai JiaoTong UniversityShanghai 200240China

Escalating competition within aquatic environments,particularly in oceans,underscores the imperative for innovative solutions in underwater *** solutions necessitate the development of intelligent underwater equipment,with robotic fish emerging as a promising contender.1 Leveraging advancements in robotics and artificial intelligence,robotic fish offer a suite of advantages that position them as transformative assets in underwater exploration and *** fish are autonomous robots designed based on biomimetics principles that mimic the appearance of fish and can autonomously swim and perform specific tasks in water.

关键词： robotic fish intelligent underwater equipmentwith autonomous robots underwater detection oceans artificial intelligencerobotic fish aquatic environments innovative solutions

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Information-Energy Capacity Region for SLIPT systems Over Lognormal-Fading Channels

Information-Energy Capacity Region for SLIPT Systems Over Lo...

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IEEE International Symposium on Information Theory

作者： Kapila W. S. Palitharathna Nizar Khalfet Constantinos Psomas George K. Karagiannidis Ioannis Krikidis Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Artificial Intelligence & Cyber Systems Research Center Lebanese American University (LAU) Lebanon

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

In this paper, we study the fundamental limits of simultaneous lightwave information and power transfer (SLIPT) systems over channels with path loss and lognormal fading conditions. We consider a system with a single transmitter transferring information to a photodiode-based receiver as well as transferring energy to a photovoltaic cell receiver. In particular, we study the information-energy capacity region and the optimal input distribution under (a) peak-power and average-power constraints at the transmitter, and (b) the minimum harvest energy at the energy harvesting receiver. To this end, an expression for the transition probability distribution function of the lognormal channel is derived. By extending Smith's framework and using Hermite polynomial bases, we prove that the optimal input distribution is discrete with a finite number of mass points. Information-energy capacity region for SLIPT over lognormal channel conditions is illustrated and compared with the case of additive white Gaussian noise channel.

关键词： Fading channels AWGN channels Transmitters Channel capacity Photovoltaic cells Receivers Probability distribution

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Optimal Deep Dense Convolutional Neural Network Based Classification Model for COVID-19 Disease

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computers, Materials & Continua 2022年第1期70卷 2031-2047页

作者： A.Sheryl Oliver P.Suresh A.Mohanarathinam Seifedine Kadry Orawit Thinnukool Department of Computer Science and Engineering St.Joseph’s College of Engineering600119ChennaiIndia Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Faculty of Engineering Karpagam Academy of Higher EducationCoimbatore641021India Faculty of Applied Computing and Technology Noroff University CollegeKristiansand4608Norway Research Group of Embedded Systems and Mobile Application in Health Science College of ArtsMedia and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Early diagnosis and detection are important tasks in controlling the spread of COVID-19.A number of Deep Learning techniques has been established by researchers to detect the presence of COVID-19 using CT scan images and ***,these methods suffer from biased results and inaccurate detection of the ***,the current research article developed Oppositional-based Chimp Optimization Algorithm and Deep Dense Convolutional Neural Network(OCOA-DDCNN)for COVID-19 prediction using CT images in IoT *** proposed methodology works on the basis of two stages such as pre-processing and ***,CT scan images generated from prospective COVID-19 are collected from open-source system using IoT *** collected images are then preprocessed using Gaussian *** filter can be utilized in the removal of unwanted noise from the collected CT scan ***,the preprocessed images are sent to prediction *** this phase,Deep Dense Convolutional Neural Network(DDCNN)is applied upon the pre-processed *** proposed classifier is optimally designed with the consideration of Oppositional-basedChimp Optimization Algorithm(OCOA).This algorithm is utilized in the selection of optimal parameters for the proposed ***,the proposed technique is used in the prediction of COVID-19 and classify the results as either COVID-19 or *** projected method was implemented in MATLAB and the performances were evaluated through statistical *** proposed method was contrasted with conventional techniques such as Convolutional Neural Network-Firefly Algorithm(CNN-FA),Emperor Penguin Optimization(CNN-EPO)*** results established the supremacy of the proposed model.

关键词： Deep learning deep dense convolutional neural network covid-19 CT images chimp optimization algorithm

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Precise Identification and Segmentation of Brain Tumour in MR Brain Images Using Salp Swarm Optimized K-Means Clustering Technique 2

Precise Identification and Segmentation of Brain Tumour in M...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Mahendran, N. Muthuvel, P. Arunprasath, T. Pallikonda Rajasekaran, M. Bridget Nirmala, J. Kottaimalai, R. Kalasalingam Academy of Research and Education Department of Electrical and Electronics Engineering Tamilnadu Krishnankoil India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Krishnankoil India Kalasalingam Academy of Research and Education Department of Biomedical Engineering Tamilnadu Krishnankoil India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamilnadu Krishnankoil India St. Michael Group of Institutions Tamilnadu kalaiyarkoil India

ISBN: (纸本)9798350347579

Brain tumour delineation is a challenging task from raw magnetic resonance images. To accurately delineate the different parts of tumours is the main aim of dissection process. Among the most common types of cerebral tumour, glioma that arises from glial cells. According to the World Health Organisation (WHO), tumour behaviours and microscopic images can be used to classify gliomas into four different levels. The popular imaging techniques used prior to and following surgical treatment is magnetic resonance imaging (MRI), which aims to provide vital details for the therapeutic plan. For effective tumour delineation from brain MRI, a novel combination of K-means and Salp Swarm Optimization (SSO) Algorithm is proposed. K-means clustering method groups the most similar pixels in to a single cluster. Salp Swarm Optimization Algorithm is one of the nature-inspired metaheuristic optimization algorithms based on the social and foraging behaviour of salps. In biomedical signal processing and control systems, SSO is used to tackle large-scale optimization problems. The proposed methodology's efficiency is validated through testing on various BraTS challenge datasets. The attained average computational time, MSE, PSNR, TC and DS are 16.9 Sec, 0.3787, 52.47 dB, 74.86 % and 83.44 %, respectively. © 2023 IEEE.

关键词： Tumors

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Construction and optimization of a computational holographic lithography system 8

Construction and optimization of a computational holographic...

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8th International Conference on Speckle Metrology, Speckle 2023

作者： Chen, Rui Lin, Angyi You, Jianfeng Lin, Huixiang Zhao, Wenqing Zhang, Fucai School of Control and Computer Engineering North China Electric Power University Baoding071003 China The Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China Engineering Research Center of Intelligent Computing for Complex Energy Systems Ministry of Education North China Electric Power University China

ISBN: (数字)9781510674592

ISBN: (纸本)9781510674585

Traditional lithography techniques are currently facing challenges, including high costs and the susceptibility of mask plates to damage. This research aims to elucidate the feasibility and technical constraints of a novel diffraction lithography approach. This method can reduce the size and cost of lithography machines, break through existing technological barriers, and offer new possibilities for the future development of lithography machines. We have built a basic computational holographic lithography system utilizing a 405nm laser and validated the system using holograms obtained with both classical GS algorithm and a newly proposed method. With the GS holograms, patterns with 700nm features have been successfully exposed on the photoresist. With our new algorithms, multiple holograms for the same target pattern can be generated with different settings in the algorithm. Sequential exposure of those holograms resulted in continuous lines 500 nm wide with reduced speckle artifacts, which was about a 1.4-time improvement over the result from the GS algorithm. Future research will focus on optimizing and enhancing the experimental system and the hologram calculation methods to reduce the line widths further and improve the exposure pattern quality. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Holograms

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Effect of the antenna slot numbers and position on the performance of microwave ablation

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Medicine in Novel Technology and Devices 2023年第4期20卷 105-113页

作者： Sabiha Binte Aziz Md Rejvi Kaysir Md Jahirul Islam Torikul Islam Mahmudur Rahman Department of Biomedical Engineering(BME) Khulna University of Engineering&TechnologyKhulna-9230Bangladesh Photonics Research Group Department of EEEKhulna University of Engineering&TechnologyKhulna-9230Bangladesh Department of Electrical and Computer Engineering Waterloo Institute for NanotechnologyUniversity of Waterloo200 University AveWaterlooONCanada Department of Biomedical Engineering(BME) New Jersey Institute of TechnologyNewarkNJUSA Department of Electrical and Electronic Engineering Dhaka University of Engineering&TechnologyGazipur 1707Bangladesh

Microwave ablation(MWA)is a type of thermal ablation used for cancer treatment in interventional *** induce localized tissue heating MWA employs electromagnetic waves within the microwave energy spectrum,which is done by the precisely designed *** study substantially emphasizes the design and performance ameliorating of slot(both single and double)antennae and compares the results with conventional monopole antennae in terms of temperature distribution,specific absorption ratio(SAR),and thermal tissue damage *** simulation has been done in COMSOL by solving the Bioheat equation along with Maxwell electromagnetic equations using the finite element *** simulation results reveal that the double-slot antenna has the most accurate and directional heat dissipation for liver tumors as well as the highest tissue damage rate and *** highest SAR was found to be 3500 W/kg and 3350 W/kg at the implant depth of 61 mm and 63 mm for double and single-slot antennae,*** addition,the fastest tissue damage occurred near the upper slot of the double-slot *** study helps to understand the basic design parameters for enhancing single and doubleslot antennae performance.

关键词： Microwave ablation(MWA) Finite element method(FEM) Liver tumor Microwave antenna Specific absorption rate(SAR)

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