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Artificial visual perception neural system using a solution-processable MoS_(2)-based in-memory light sensor

Light(science & Applications) 2023年第8期12卷 1583-1594页

作者： Dayanand Kumar Lana Joharji Hanrui Li Ayman Rezk Ammar Nayfeh Nazek El-Atab Smart Advanced Memory Devices and Applications(SAMA)LaboratoryElectrical and Computer Engineering ProgramComputer Electrical Mathematical Science and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955Kingdom of Saudi Arabia Department of Electrical Engineering and Computer Science Khalifa UniversityAbu Dhabi 127788United Arab Emirates

Optoelectronic devices are advantageous in in-memory light sensing for visual information processing,recognition,and storage in an energy-efficient ***,in-memory light sensors have been proposed to improve the energy,area,and time efficiencies of neuromorphic computing *** study is primarily focused on the development of a single sensing-storage-processing node based on a two-terminal solution-processable MoS2 metal-oxide-semiconductor(MOS)charge-trapping memory structure—the basic structure for charge-coupled devices(CCD)—and showing its suitability for in-memory light sensing and artificial visual *** memory window of the device increased from 2.8 V to more than 6V when the device was irradiated with optical lights of different wavelengths during the program ***,the charge retention capability of the device at a high temperature(100 ℃)was enhanced from 36 to 64%when exposed to a light wavelength of 400 *** larger shift in the threshold voltage with an increasing operating voltage confirmed that more charges were trapped at the Al_(2)O_(3)/MoS_(2) interface and in the MoS_(2) layer.A small convolutional neural network was proposed to measure the optical sensing and electrical programming abilities of the *** array simulation received optical images transmitted using a blue light wavelength and performed inference computation to process and recognize the images with 91%*** study is a significant step toward the development of optoelectronic MOS memory devices for neuromorphic visual perception,adaptive parallel processing networks for in-memory light sensing,and smart CCD cameras with artificial visual perception capabilities.

关键词： visual neural process

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Transparent and Accountable Training Data Sharing in Decentralized Machine Learning Systems

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computers, Materials & Continua 2024年第6期79卷 3805-3826页

作者： Siwan Noh Kyung-Hyune Rhee Industrial Science Technology Research Center Pukyong National UniversityBusan48513South Korea Division of Computer Engineering Pukyong National UniversityBusan48513South Korea

In Decentralized Machine Learning(DML)systems,system participants contribute their resources to assist others in developing machine learning *** malicious contributions in DML systems is challenging,which has led to the exploration of blockchain *** leverages its transparency and immutability to record the provenance and reliability of training ***,storing massive datasets or implementing model evaluation processes on smart contracts incurs high computational ***,current research on preventing malicious contributions in DML systems primarily focuses on protecting models from being exploited by workers who contribute incorrect or misleading ***,less attention has been paid to the scenario where malicious requesters intentionally manipulate test data during evaluation to gain an unfair *** paper proposes a transparent and accountable training data sharing method that securely shares data among potentially malicious system ***,we introduce a blockchain-based DML system architecture that supports secure training data sharing through the IPFS ***,we design a blockchain smart contract to transparently split training datasets into training and test datasets,respectively,without involving system *** the system,transparent and accountable training data sharing can be achieved with attribute-based proxy *** demonstrate the security analysis for the system,and conduct experiments on the Ethereum and IPFS platforms to show the feasibility and practicality of the system.

关键词： Decentralized machine learning data accountability dataset sharing

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Distributed Resource Allocation in Dispersed Computing Environment Based on UAV Track Inspection in Urban Rail Transit

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computers, Materials & Continua 2024年第7期80卷 643-660页

作者： Tong Gan Shuo Dong Shiyou Wang Jiaxin Li Division of Consulting Beijing Metro Consultancy Corporation Ltd.Beijing100037China Department of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China

With the rapid development of urban rail transit,the existing track detection has some problems such as low efficiency and insufficient detection coverage,so an intelligent and automatic track detectionmethod based onUAV is urgently needed to avoid major safety *** the same time,the geographical distribution of IoT devices results in the inefficient use of the significant computing potential held by a large number of *** a result,the Dispersed Computing(DCOMP)architecture enables collaborative computing between devices in the Internet of Everything(IoE),promotes low-latency and efficient cross-wide applications,and meets users’growing needs for computing performance and service *** paper focuses on examining the resource allocation challenge within a dispersed computing environment that utilizes UAV inspection ***,the system takes into account both resource constraints and computational constraints and transforms the optimization problem into an energy minimization problem with computational *** Markov Decision Process(MDP)model is employed to capture the connection between the dispersed computing resource allocation strategy and the system ***,a method based on Double Deep Q-Network(DDQN)is introduced to derive the optimal ***,an experience replay mechanism is implemented to tackle the issue of increasing *** experimental simulations validate the efficacy of the method across various scenarios.

关键词： UAV track inspection dispersed computing resource allocation deep reinforcement learning Markov decision process

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A Comprehensive Food Identification and Waste Reduction Solution with Built-in Nutritional Tracking using Machine Learning 8

A Comprehensive Food Identification and Waste Reduction Solu...

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Jonathan, J. Benjamin, Ruth Moly Prasad, Gunji Prem Karunya Institute of Technology and Sciences Computer Science Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350386578

Food Solutions and Nutrition Checker is an innovative and comprehensive project aimed at providing users with a reliable tool to identify various foods and dishes, while also offering a custom-built database for detecting fresh and wasted food items. With this system, users can easily determine the nutritional value of different foods, making informed decisions about their dietary choices. This study incorporates advanced technology that enables the recognition of a wide range of food items. By simply capturing an image or specifying the dish, users can obtain accurate information about the food, including its ingredients and origin. This feature is particularly helpful for individuals with specific dietary needs or restrictions, allowing them to easily identify suitable menu options. Additionally, this study includes a custom-built data system that helps to identify fresh and wasted food. By analyzing factors such as expiration dates, storage conditions, and quality indicators, the proposed system can determine the freshness status of various food items. This capability promotes food waste reduction by enabling users to make informed decisions regarding the consumption or disposal of perishable goods. Moreover, this study incorporates a nutritional tracker that provides users with comprehensive information regarding the nutritional composition of different foods. This feature includes details such as calorie count, macronutrient breakdown, vitamins, and minerals, empowering users to make healthier food choices aligned with their dietary goals and requirements. Overall, the proposed model offers a holistic approach for food identification, waste reduction, and nutritional tracking. By incorporating advanced technology and a robust database, this study aims to facilitate informed decision-making and promote healthier dietary practices. © 2024 IEEE.

关键词： Food ingredients

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Buffer and Splitter Insertion for Adiabatic Quantum-Flux-Parametron Circuits

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第3期44卷 975-988页

作者： Fu, Rongliang Wang, Mengmeng Kan, Yirong Chen, Olivia Yoshikawa, Nobuyuki Yu, Bei Ho, Tsung-Yi The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Yokohama National University Department of Electrical and Computer Engineering Yokohama240-8501 Japan Nara Institute of Science and Technology Division of Information Science Nara630-0192 Japan Kyushu University Department of Advanced Information Technology Fukuoka819-0395 Japan

The extremely low-bit energy characteristic of the adiabatic quantum-flux-parametron (AQFP) circuit makes it a promising candidate for highly energy-efficient computing systems. However, in contrast with conventional circuit design, general logic synthesis tools can not make sure that the circuit functionality of generated AQFP circuits is correct. AQFP circuits require buffer and splitter insertion for dataflow synchronization at all clock phases of the circuit and multifan-out driving. Notably, buffers and splitters inserted take up much area and delay in AQFP circuits, also causing a significant increase in energy dissipation. To address this problem, this article analyses in detail why buffer and splitter insertion is necessary for AQFP circuits and proposes a global optimization framework for this purpose. This framework consists of three parts: 1) logic level assignment;2) splitter tree generation;and 3) buffer insertion. An integer linear programming algorithm is proposed for the logic level assignment to estimate the globally optimal number of inserted buffers and splitters. Subsequently, a dynamic programming-based multiway search tree generation algorithm is proposed to construct an optimal splitter tree for each net of the input circuit. Moreover, three optimization strategies are proposed to further enhance the effectiveness and efficiency of our framework. Experimental results on ISCAS'85 and EPFL benchmarks demonstrate the effectiveness and efficiency of our proposed framework compared with the state-of-the-art, particularly with significant advantages on large circuits. © 2024 IEEE.

关键词： Global optimization

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Wrist joint synovial hypertrophy and effusion detection in musculoskeletal ultrasound images using self-attention U-Net

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Multimedia Tools and Applications 2024年第41期83卷 89317-89334页

作者： Chang, Chuan-Wang Chang, Chuan-Yu Zhu, Yu-Xian Wang, Sz-Tsan Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Douliu Taiwan Division of Allergy Immunology and Rheumatology Dalin Tzu Chi Hospital Buddhist Tzu Chi Medical Foundation Dalin Chiayi Taiwan

Skeletal muscle ultrasound has emerged as a pivotal imaging modality in rheumatology clinics, offering unparalleled advantages such as radiation-free imaging, safety, and dynamic examination capabilities. However, its reliance on operator expertise often leads to inconsistent interpretations and diagnostic variability. In this study, we present a novel diagnostic system aimed at detecting rheumatoid arthritis (RA) in the wrist joint, with a focus on identifying synovial hypertrophy and effusion using musculoskeletal ultrasound images. Leveraging deep learning techniques, specifically semantic segmentation models, we introduce SEAT-UNet, which combines the U-Net architecture with a self-attention mechanism to enhance the accuracy of lesion classification and localization. SEAT-UNet addresses the challenge of discontinuous dispersion encountered in conventional segmentation models, particularly when delineating lesion areas. Our experimental results demonstrate exceptional performance, achieving a sensitivity and Dice coefficient of 100% and 84%, respectively, in synovial hypertrophy detection, and 86% sensitivity with an 84% Dice coefficient in effusion detection. These findings underscore the potential of SEAT-UNet as a valuable tool for early RA diagnosis, offering improved patient outcomes and facilitating more effective disease management strategies. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diagnosis

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Criminal Activity Forecasting using Machine Learning 10

Criminal Activity Forecasting using Machine Learning

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Augustin Sam, N. Bamini, A. M. Anusha Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350384369

Crime analysis is an important topic that uses cutting-edge machine learning. This study focuses on using machine learning algorithms to examine crime trends and give law enforcement organizations useful information. The ML models may find patterns and trends by using past crime data, which makes it possible to identify possible crime hotspots and allocate the necessary resources. To find hidden patterns in crime data, Random forest algorithm was applied. The algorithm has the ability to automatically group criminal episodes into clusters, categorize them according to various attributes, and spot anomalies-that is, deviations from the norm. Furthermore, by using previous data, machine learning algorithms can be trained to support proactive crime prevention tactics. The accuracy and efficacy of crime analysis models are further improved by the integration of many data sources, such as information. The proposed algorithm makes it easier to monitor crimes in real time and permits prompt decision-making in emergency scenarios. Law enforcement organizations can use their resources more wisely, plan out patrol routes, and create focused crime prevention programs. By giving law enforcement agencies useful insights, machine learning turns crime analysis into a potent weapon. © 2024 IEEE.

关键词： Adversarial machine learning

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Leveraging Sentiment Analysis and Emotion Detection for Mental Health Insights

Leveraging Sentiment Analysis and Emotion Detection for Ment...

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2024 IEEE International Conference on Future Machine Learning and Data science, FMLDS 2024

作者： Jose, Joysh Jayaron Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (数字)9798350391213

ISBN: (纸本)9798350391213

This paper aims to establish a comprehensive analysis of mental health patient reviews using various NLP models, to derive insights and propose improvements that will lead to the enhancement of various mental health facilities. These NLP techniques were further used to categorize key themes such as doctor behaviour, communication and cost. A thorough correlation analysis was also conducted, understanding the relationship between various themes and how their frequency affects the overall patient satisfaction. The findings indicate that attentive listening, respectful staff behavior, and affordable services are critical to improving patient satisfaction. While speaking of mental health is still a taboo in various places, it is important to understand the frequency of the rising mental health related cases such as depression, anxiety, stress and social stigma associated with it. It is indeed much more important to provide better resources and lend understanding hand to the unaddressed mental health problems that are prevalent in various countries and places. © 2024 IEEE.

关键词： Electronic health record

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Real Time Stock Inventory Management System 8

Real Time Stock Inventory Management System

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Roosevelt, Steve Caleb Veemaraj, Ebenezer Kirubakaran, Stewart Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350386578

For companies looking to stay competitive in the dynamic and fast-paced business world of today, effective stock inventory management has become critical. In order to provide organizations with an all-inclusive and flexible inventory control solution, this paper presents a Real-Time Stock Inventory Management System. The system provides real-time stock inventory tracking, monitoring, and management by utilizing cutting-edge technologies including cloud computing, data analytics, and the Internet of Things. Through the utilization of Internet of Things devices, the Real-Time Stock Inventory Management System's cloud-based infrastructure enhances scalability, accessibility, and flexibility while providing accurate real-time tracking of inventory quantities, locations, and statuses. © 2024 IEEE.

关键词： Cloud analytics

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Retrospective Analysis of an Interpretable Skin Cancer Classification using Deep Learning Models 1

Retrospective Analysis of an Interpretable Skin Cancer Class...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Abraham, Shaun Shaji Dinesh Peter, J. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350372748

Skin cancer, particularly malignant melanoma, presents major diagnostic challenges due to similarities with benign tumors. Automatic detection systems based on deep learning algorithms provide intriguing answers, but lack interpretability, limiting clinical application. This research presents an explainable artificial intelligence (XAI) approach to skin lesion classification, with the goal of improving diagnostic accuracy and clinician trust. The created algorithm accurately identifies types of skin lesions using the dataset. Using the local interpretable model-agnostic explanations (LIME) framework, the model creates visual explanations to assist dermatologists in rational diagnosis. Explainability is integrated into the model, increasing its usability in real-world clinical practice. Furthermore, the study emphasizes the importance of XAI in increasing the interpretability and trustworthiness of deep learning models for skin cancer diagnosis, focusing on crucial features such as asymmetry and pigment network classification. This study helps to address the critical need for transparent and accurate AI models in the medical realm, particularly in preventing the rising global prevalence of skin cancer. © 2024 IEEE.

关键词： Deep learning

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