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Conference on Lasers and Electro-Optics (CLEO)

作者： Zhichao Li Ciril S. Prasad Xielin Wang Ding Zhang Gururaj V. Naik Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston TX USA Department of Electrical and Computer Engineering Rice University Houston TX USA Department of Physics and Astronomy Rice University Houston TX USA

Exceptional point (EP)-based optical sensors exhibit exceptional sensitivity but poor detectivity. Slightly off EP operation boosts detectivity without much loss in sensitivity. We experimentally demonstrate a high-de... 详细信息

ISBN: (纸本)9798350369311

关键词： Proteins Optical losses Sensitivity Optical device fabrication Measurement by laser beam Lasers and electrooptics Optical sensors Laser applications Electro-optical waveguides

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A New Machine-Learning Methodology for Detection Of Epithelial Dysplasia in Oral Leukoplakia Lesions

SSRN

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SSRN 2025年

作者： Rocha, Karoline da Bermudez, José C.M. Rivero, Elena Riet Correa Costa, Márcio Holsbach Graduate Program in Electrical Engineering Federal University of Santa Catarina Florianópolis Brazil Department of Pathology Federal University of Santa Catarina Florianópolis Brazil

Background and Objectives: Oral squamous cell carcinoma (OSCC) is the most common oral cancer, with high mortality rates, making early detection crucial. Malignant transformation is preceded by oral potentially malignant disorders (OPMDs), with leukoplakia being the most frequent. Epithelial dysplasia (ED) is a key histological feature for predicting the risk of progression to OSCC. However, its diagnosis relies on subjective visual assessment by pathologists, which is influenced by experience and emotional state. This study aims to develop a Machine Learning (ML), based methodology to assist in ED detection in leukoplakia lesions. Methods: The proposed methodology integrates histopathological image analysis with complementary patient data. Epithelial regions were selected for cutout extraction based on pathologist knowledge and combined with risk factors from patient data. A multilayer perceptron artificial neural network (MLP-ANN) was trained and evaluated. Performance was compared with a pre-trained convolutional neural network (ResNet-50V2) using the McNemar test. Results: The MLP-ANN achieved an area under the curve (AUC) of 0.9484. No statistically significant difference was found between the proposed method and the pre-trained CNN. However, the proposed approach was approximately 9.7 times faster in computational time. Conclusions: The ML-based methodology demonstrated high classification performance and computational efficiency. Refining the confidence criterion for intermediate classification results may further enhance its diagnostic accuracy, supporting pathologists in ED detection. © 2025, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Intelligent Live Video Streaming for Object Detection 23

Intelligent Live Video Streaming for Object Detection

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Chen, Mingkang Sun, Jingtao Aida, Kento Figueiredo, Renato J. Ku, Yun-Jung Subratie, Kensworth The Graduate University for Advanced Studies Sokendai Department of Informatics Tokyo Japan National Institute of Informatics Tokyo Japan University of Florida Electrical and Computer Engineering Florida United States

ISBN: (纸本)9781665494571

These days, sensors and cameras are being deployed on an increasingly large scale. Furthermore, the rapid development of machine learning models for computer vision now presents novel opportunities for the use of artificial intelligence (AI) and Internet of Things (IoT) combinations in various application scenarios. However, challenges remain in supporting low-latency video streaming from distributed mobile IoT devices under dynamic network environments, and overcoming video data quality degradation that results from weather 'noise', which reduces the accuracy of AI-based data analyses such as object detection. In this paper, we propose a live video stream processing system for supporting intelligent services that integrates the following features. First, to cope with dynamic networks and achieve low latency, our approach employs a peer-to-peer (P2P)-based virtual network at the edge and a multi-tiered architecture composed of IoT cameras, edge, and cloud servers. Second, we construct a flexible messaging system for video analysis built upon SINETStream, which is a messaging system that adopts a topic-based pub/sub model. Third, we implement a framework that can remove weather-related (rain, snow, and fog) noise by applying weather classification and adaptive noise removal models that improve the accuracy of video analysis from data collected outdoors. The latency, throughput, and image quality benchmark experiments conducted to validate the feasibility of our proposed system showed that the process resulted in image quality improvements of approximately 30% (on average). © 2021 IEEE.

关键词： Image quality Adaptation models Image edge detection Object detection Streaming media Throughput Internet of Things

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Improved Metaheuristics with Machine Learning Enabled Medical Decision Support System

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computers, Materials & Continua 2022年第11期73卷 2423-2439页

作者： Sara A.Althubiti JoséEscorcia-Gutierrez Margarita Gamarra Roosvel Soto-Diaz Romany F.Mansour Fayadh Alenezi Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Research Center-CIENS Escuela Naval de Suboficiales A.R.C.“Barranquilla”BarranquillaColombia Electronics and Telecommunications Engineering Program Universidad Autónoma del CaribeBarranquilla080001Colombia Departament of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080001Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080001Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga 72511Egypt Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia

Smart healthcare has become a hot research topic due to the contemporary developments of Internet of Things(IoT),sensor technologies,cloud computing,and ***,the latest advances of Artificial Intelligence(AI)tools find helpful for decision-making in innovative healthcare to diagnose several *** Cancer(OC)is a kind of cancer that affects women’s ovaries,and it is tedious to identify OC at the primary stages with a high mortality *** OC data produced by the Internet of Medical Things(IoMT)devices can be utilized to differentiate *** this aspect,this paper introduces a new quantum black widow optimization with a machine learningenabled decision support system(QBWO-MLDSS)for smart *** primary intention of the QBWO-MLDSS technique is to detect and categorize the OC rapidly and ***,the QBWO-MLDSS model involves a Z-score normalization approach to pre-process the *** addition,the QBWO-MLDSS technique derives a QBWO algorithm as a feature selection to derive optimum feature ***,symbiotic organisms search(SOS)with extreme learning machine(ELM)model is applied as a classifier for the detection and classification of ELM model,thereby improving the overall classification *** design of QBWO and SOS for OC detection and classification in the smart healthcare environment shows the study’s *** experimental result analysis of the QBWO-MLDSS model is conducted using a benchmark dataset,and the comparative results reported the enhanced outcomes of the QBWO-MLDSS model over the recent approaches.

关键词： Ovarian cancer decision support system smart healthcare IoMT deep learning feature selection

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Voltage Control of Boost Converters: Feasibility Guarantees*

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IFAC-PapersOnLine 2023年第2期56卷 803-808页

作者： Morteza Nazari Monfared Yu Kawano Michele Cucuzzella Department of Electrical Computer and Biomedical Engineering University of Pavia 27100 Pavia Italy Graduate School of Advanced Science and Engineering Hiroshima University Higashi-hiroshima 739-8527 Japan

This paper deals with the design of a novel static nonlinear controller to regulate the voltage of boost converters while fulfilling physical constraints; at any time instant, the states need to be positive, and the duty cycle of the converter takes an admissible value between zero and one. The proposed controller is based on a shifted Lyapunov function and asymptotically stabilizes the desired equilibrium point of the closed-loop system as long as the voltage and current are positive at any time instant. To guarantee positivity, we derive a necessary and sufficient condition for identifying a feasible level set that is contained in the positive orthant and is positively invariant. Next, to ensure a feasible implementation and operation of the proposed controller, we derive a necessary and sufficient condition on the controller feedback gain to ensure that the converter duty cycle takes admissible values between zero and one. Finally, the most significant theoretical results are validated in simulation.

关键词： Stability of nonlinear systems Constrained control Power systems Asymptotic stabilization Domain of attraction estimation

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Circular eight-room maze for assessing spatial learning and memory functions in rats: An example using a traumatic brain injury model

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computers in Biology and Medicine 2024年 182卷 109086-109086页

作者： Chen, Chi-Chun Hung, Yu-Rui Department of Electronic Engineering National Chin-Yi University of Technology Taichung Taiwan Graduate Institute Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung Taiwan

Background and objectives: This study introduced an animal cognitive function assessment system using a novel circular eight-room maze (CERM). The CERM, designed for tracking path trajectories in animal models of cognitive impairment pathologies such as traumatic brain injury (TBI), comprised a 120-cm diameter disk with eight rooms (30 cm × 25 cm × 30 cm). Methods: These rooms have magnetic interfaces for modular assembly and disassembly. Notably, one room remained dark and contained food, while the remaining seven rooms automatically lit up when a rat entry, facilitating the assessment of the rat's learning and spatial memory. An infrared night vision camera captured the animal's search trajectory, and binary image processing techniques were employed to eliminate noise and extract the rat's position coordinates to record the rat's path trajectories. The system automatically calculated various cognitive assessment parameters, such as latency, distance traveled, time spent in each quadrant, inner and outer area exploration times, short-term and long-term memory errors, and the number of entries to all the rooms by chance/by memory. Results: The analysis of overall path trajectories revealed increasingly erratic movement and a growing reliance on chance to enter rooms in rats with TBI over time, likely due to declining memory and the consequent inability to locate the food room. Moreover, increased trajectories in the first quadrant and inner area characterized the behavior of rats with TBI, with statistically significant differences from the sham group observed on day 7. By day 28, all cognitive parameters except short-term memory error significantly differed between the two groups. Conclusion: Experimental data indicated a substantial increase in irregular search behavior in the TBI group over time, suggesting deterioration in cognitive function and an inability to accurately recall the food room. Conversely, the sham group exhibited consistent search trajectories,

关键词： Rats

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Long short-term memory (LSTM) model-based reinforcement learning for nonlinear mass spring damper system control

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Procedia computer Science 2023年 216卷 213-220页

作者： Santo Wijaya Yaya Heryadi Yulyani Arifin Wayan Suparta Lukas Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta 55281 Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta 12930 Indonesia

The Neural Networks (NN) model which is incorporated in the control system design has been studied, and the results show better performance than the mathematical model approach. However, some studies consider that only offline NN model learning and does not use the online NN model learning directly on the control system. As a result, the controller's performance decreases due to changes in the system environment from time to time. The Reinforcement Learning (RL) method has been investigated intensively, especially Model-based RL (Mb-RL) to predict system dynamics. It has been investigated and performs well in making the system more robust to environmental changes by enabling online learning. This paper proposes online learning of local dynamics using the Mb-RL method by utilizing Long Short-Term Memory (LSTM) model. We consider Model Predictive Control (MPC) scheme as an agent of the Mb-RL method to control the regulatory trajectory objectives with a random shooting policy to search for the minimum objective function. A nonlinear Mass Spring Damper (NMSD) system with parameter-varying linear inertia is used to demonstrate the effectiveness of the proposed method. The simulation results show that the system can effectively control high-oscillating nonlinear systems with good performance.

关键词： Model-based Reinforcement Learning Random-Shooting Policy LSTM MPC Nonlinear System

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Neural Network-based Genetic Algorithm for Autonomous Boat Pathfinding

Neural Network-based Genetic Algorithm for Autonomous Boat P...

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IEEE International Conference on Knowledge Innovation and Invention (ICKII)

作者： Nur Hamid Willy Dharmawan Hidetaka Nambo Department of Electrical Engineering and Computer Science Graduate School of Natural Science and Technology Kanazawa University Kanazawa Japan

Genetic algorithms become widely used in various optimization as a nature-inspired algorithm. This biological-based algorithm includes three genetic operators: selection, crossover or recombination, and mutation. In this study, the genetic operator processed neural network parameters, including the weight and bias. Three-ray sensors with different directions input data into the neural network. We applied the method to autonomous boats for pathfinding responding to dynamic environment challenges. Generally, autonomous boats face difficulty in path-finding in dynamic environments. To evaluate the method, we experimented with the scenario of the static and dynamic environment (with wave surface) in a three-dimensional simulation platform. The hyperparameter of layer number and mutation rate variation were used. The results showed that the method was useful in pathfinding in static and dynamic environments.

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LIRSRN: A Lightweight Infrared Image Super-Resolution Network

LIRSRN: A Lightweight Infrared Image Super-Resolution Networ...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Chun-An Lin Tsung-Jung Liu Kuan-Hsien Liu Department of Electrical Engineering and Graduate Institute of Communication Engineering National Chung Hsing University Taichung Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Infrared imaging is crucial for many applications, such as night vision and environmental monitoring. However, it often suffers from lower spatial resolution compared to visible light imaging, which hinders its effectiveness in scenes demanding fine detail discernment. This paper introduces a Lightweight Infrared Image Super-Resolution Network (LIRSRN), which is a novel architecture designed to enhance the resolution of IR images with minimal computation overhead. The core of LIRSRN is the Attention Enhancement Module, which synergizes various attention mechanisms to emphasize important features across both channel and spatial domains. It is further enhanced by spatial frequency processing, which helps to extract key image attributes. The training is conducted on the DIV2K dataset, and the test results from "results-A" and "results-C" demonstrate the superior performance of LIRSRN in terms of PSNR and SSIM. Ablation studies compare the trade-offs between performance and computational cost using different attention mechanisms. Overall, the proposed model strikes a balance between performance and complexity. The source code and trained model are available at https://***/yYDnA6.

关键词： Training Computational modeling Source coding Scalability Superresolution Refining computer architecture

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Balancing Dual DC Voltage Control in Quasi Single-Stage Converter for Low-Voltage Energy Storage System Grid-Connected

Balancing Dual DC Voltage Control in Quasi Single-Stage Conv...

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International Conference on electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON

作者： Warissara Manokum Yuttana Kumsuwan Department of Electrical Engineering Graduate Program in Electrical Engineering Faculty of Engineering Chiang Mai University Chiang Mai Thailand Provincial Electricity Authority Bangkok Thailand Department of Electrical Engineering Faculty of Engineering Chiang Mai University Chiang Mai Thailand

The aim of a bidirectional quasi single-stage converter (QSSC) for low-voltage energy storage systems that are grid-connected is to reduce the power conversion stage of the dc-dc converter, which results in improved overall efficiency. The QSSC configuration relies on a modified modulation strategy due to the unequal dual dc port (upper and lower dc ports) voltage. The control for ensuring that the dual dc port voltages are equal is presented in this paper. When the lower dc port is varied in response to battery voltage, another is controlled to track in response to the lower dc port, so that the voltage ratio between the upper and lower dc port is always equal to one. Furthermore, power sharing in a dual dc port is equal, which means that a dc-dc converter automatically operates at 50% of the battery power. Thus, the existing modulation can be used and the power stage conversion of the dc-dc converter can be decreased. The proposed control for the QSSC is verified with simulation results.

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