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All-In-One Neuromorphic Light-Emitting Memristor for Infrared image Visualization and Recognition

ADVANCED FUNCTIONAL MATERIALS 2025年

作者： Pan, Youjiang Wu, Chunyan Bu, Guohao Xiang, Chaoyu Wu, Rui Hu, Hailong Guo, Tailiang Li, Fushan Fuzhou Univ Inst Optoelect Technol Fuzhou 350116 Peoples R China Fujian Sci & Technol Innovat Lab Optoelect Informa Fuzhou 350108 Peoples R China Chinese Acad Sci Ningbo Inst Mat Technol & Engn Ningbo 315201 Peoples R China

Neuromorphic vision systems have been proven to have great potential in simulating the fundamental functions of the human visual system and can even be extended to the infrared (IR) region, which is invisible to the human eye. Here, an IR neuromorphic light-emitting memristor (INLEM) is presented, which achieves full-process functionality from IR image sensing and information processing to visible light output within an all-in-one structure. The INLEM dynamically adjusts its current in response to different IR stimuli, successfully simulating the transition from short-term potentiation (STP) to long-term potentiation (LTP) in biological synapses. More importantly, this synaptic behavior is visualized in real-time through visible light without the need for an additional display module. Leveraging this feature, the process by which INLEM enhances the contrast of target information by suppressing IR noise is successfully demonstrated. Furthermore, it is proved that the intelligent image processing based on INLEM improves the efficiency of artificial neural networks in image recognition and classification tasks (in both energy and speed). Therefore, this intelligent neuromorphic light-emitting hardware, which integrates IR sensing, memory processing, and display functions, contributes to the development of integrated optoelectronic systems and Internet of Things applications.

关键词： all-in-one artificial neuron network image recognition infrared light neuromorphic

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Unravelling Visual Narratives with Deep Learning for image Caption Generation 1

Unravelling Visual Narratives with Deep Learning for Image C...

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

作者： Yoga, M. Ramyasri, M.M. Bhavatharini, N. Harini, M. NivethaSri, A. Kongu Engineering College Department of Artificial Intelligence Tamil Nadu India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372748

image Caption Generation (ICG), situated at the confluence of computer vision and natural language processing, empowers machines to comprehend visual content and express it in human-like language. This research offers a comprehensive overview of key concepts, methodologies, and challenges in ICG. The process involves developing algorithms for the automatic generation of contextually relevant captions, utilizing deep neural networks for feature extraction, and employing natural language processing techniques for coherent composition. Recent advancements, particularly in convolutional neural networks for image processing and recurrent neural networks for language modelling, have significantly elevated the performance of image captioning systems. The study delves into the core components of an ICG system, including pre-processing techniques for image data, feature extraction mechanisms, and the integration of language models. Attention mechanisms, a key innovation in this field, enable the model to focus on relevant image regions while generating captions, closely mirroring human attention patterns. Despite notable progress, ICG faces several challenges, such as handling diverse and complex visual scenes, ensuring cross-modal coherence between images and captions, and addressing biases present in training data. Ethical considerations, particularly in applications like automated content generation, are also discussed. The study concludes by highlighting potential future directions in ICG research, including the incorporation of multimodal learning approaches, enhancing the interpretability of generated captions, and addressing societal concerns related to bias and fairness. As ICG continues to evolve, it holds promise for various applications, ranging from accessibility for the visually impaired to improving content indexing and retrieval in multimedia databases. The research also underscores the significance of the accuracy attainments, showcasing the success of the pr

关键词： Feature extraction

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An improved deep learning-based optimal object detection system from images

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MULTIMEDIA TOOLS AND applications 2024年第10期83卷 30045-30072页

作者： Yadav, Satya Prakash Jindal, Muskan Rani, Preeti de Albuquerque, Victor Hugo C. Nascimento, Caio dos Santos Kumar, Manoj GL Bajaj Inst Technol & Management GLBITM Dept Comp Sci & Engn Greater Noida 201306 India Fed Inst Educ Sci & Technol Ceara IFCE Grad Program Telecommun Engn PPGET Fortaleza CE Brazil Amity Univ Dept Comp Sci & Engn Noida 201313 India SRM Inst Sci & Technol Dept Elect & Commun Engn Delhi NCR CampusDelhi Meerut Rd Ghaziabad Uttar Pradesh India Univ Fed Ceara Dept Teleinformat Engn Fortaleza CE Brazil Univ Wollongong Dubai Sch Comp Sceince FEIS Dubai Knowledge Pk Dubai U Arab Emirates Middle East Univ MEU Res Unit Amman 11831 Jordan

Computer vision technology for detecting objects in a complex environment often includes other key technologies, including pattern recognition, artificial intelligence, and digital image processing. It has been shown that Fast Convolutional neural networks (CNNs) with You Only Look Once (YOLO) is optimal for differentiating similar objects, constant motion, and low image quality. The proposed study aims to resolve these issues by implementing three different object detection algorithms-You Only Look Once (YOLO), Single Stage Detector (SSD), and Faster Region-Based Convolutional neural networks (R-CNN). This paper compares three different deep-learning object detection methods to find the best possible combination of feature and accuracy. The R-CNN object detection techniques are performed better than single-stage detectors like Yolo (You Only Look Once) and Single Shot Detector (SSD) in term of accuracy, recall, precision and loss.

关键词： Object Detection Chess Piece Identification You Only Look Once (YOLO) Single Stage Detector (SSD) Faster Region-Based Convolutional neural networks (R-CNN)

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Advancing Human Action Recognition and Medical image Segmentation using GRU networks with V-Net Architecture

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND applications 2024年第2期15卷 743-756页

作者： Rao, Dustakar Surendra Rao, L. Koteswara Bhagyaraju, Vipparthi Rohini, P. Koneru Lakshmaiah Educ Fdn Dept ECE Hyderabad 500075 Telangana India Guru Nanak Inst Tech Campus Dept ECE Hyderabad Telangana India Siddhartha Inst Engn & Technol Dept ECE Hyderabad 501506 Telangana India ICFAI Fdn Higher Educ Dept Data Sci & Artificial Intelligence Hyderabad INDIA4 India

Human Action Recognition and Medical image Segmentation study presents a novel framework that leverages advanced neural network architectures to improve Medical image Segmentation and Human Action Recognition (HAR). Gated Recurrent Units (GRU) are used in the HAR domain to efficiently capture complex temporal correlations in video sequences, yielding better accuracy, precision, recall, and F1 Score than current models. In computer vision and medical imaging, the current research environment highlights the significance of advanced techniques, especially when addressing problems like computational complexity, resilience, and noise in real-world applications. Improved medical image segmentation and human action recognition (HAR) are of growing interest. While methods such as the V-Net architecture for medical picture segmentation and Spatial Temporal Graph Convolutional networks (ST-GCNs) for HAR have shown promise, they are constrained by things like processing requirement and noise sensitivity. The suggested methods highlight the necessity of sophisticated neural network topologies and optimisation techniques for medical picture segmentation and HAR, with further study focusing on transfer learning and attention processes. A Python tool has been implemented to perform min-max normalization, utilize GRU for human action recognition, employ V- net for medical image segmentation, and optimize with the Adam optimizer, with performance evaluation metrics integrated for comprehensive analysis. This study provides an optimised GRU network strategy for Human Action Recognition with 92% accuracy, and a V-Net-based method for Medical image Segmentation with 88% Intersection over Union and 92% Dice Coefficient.

关键词： Human action recognition medical image segmentation grated rectifier unit V-net architecture neural network

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image Reconstruction in Multimode Optical Fibers Using Nonlocal artificial neural networks

Image Reconstruction in Multimode Optical Fibers Using Nonlo...

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2024 International conference on Interdisciplinary Physics, ICIPs 2024

作者： Galio, Efrad Alatas, Husin Hardhienata, Hendradi Theoretical Physics Division Department of Physics IPB University Meranti Avenue Wing S Building Dramaga Campus of IPB West Java Bogor16680 Indonesia

image transmission on multimode optical fibers has not yet been successfully implemented due to the persistent challenge of modal dispersion effects. This paper introduces a novel approach leveraging Non-local artificial neural networks (ANN) and Convolutional neural networks (CNN) to address and mitigate these modal dispersion effects. By employing these advanced neural network models, we reconstruct input images based on the distorted output images from multimode optical fibers, showcasing a significant improvement in image transmission quality. Our simulation utilizes well-known datasets, including MNIST, Fashion-MNIST, and Muybridge Punch, to evaluate the performance of these models. The Convolutional neural Network model achieves average PSNR values of 1015, 1829, and 4350, and SSIM values of 0.689, 0.511, and 0.397 for each dataset, respectively. In comparison, the Non-local artificial neural Network model attains average PSNR values of 2305, 2015, and 4376, and SSIM values of 0.388, 0.399, and 0.255. Our results demonstrate the superior capability of the proposed neural network models in reconstructing high-quality images, paving the way for practical implementations of multimode optical fiber systems in various fields, from communications to medical applications. This innovative approach represents a significant advancement in overcoming the long-standing issue of modal dispersion in optical fibers. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Convolutional neural networks

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The Application of artificial Intelligence for Tooth Segmentation in CBCT images: A Systematic Review

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APPLIED SCIENCES-BASEL 2024年第14期14卷 6298页

作者： Tarce, Mihai Zhou, You Antonelli, Alessandro Becker, Kathrin Univ Hong Kong Fac Dent Periodontol & Implant Dent Div Pokfulam Rd Hong Kong Peoples R China Magna Graecia Univ Catanzaro Sch Dent Hlth Sci Dept I-88100 Catanzaro Italy Charite Univ Med Berlin Dept Orthodont & Dentofacial Orthopaed Assmannshauser Str 4-6 D-14197 Berlin Germany

Objective: To conduct a comprehensive and systematic review of the application of existing artificial intelligence for tooth segmentation in CBCT images. Materials and Methods: A literature search of the MEDLINE, Web of Science, and Scopus databases to find publications from inception through 21 August 2023, non-English publications excluded. The risk of bias and applicability of each article was assessed using QUADAS-2, and data on segmentation category, research model, sample size and groupings, and evaluation metrics were extracted from the articles. Results: A total of 34 articles were included. artificial intelligence methods mainly involve deep learning-based techniques, including Convolutional neural networks (CNNs), Fully Convolutional networks (FCNs), and CNN-based network structures, such as U-Net and V-Net. They utilize multi-stage strategies and combine other mechanisms and algorithms to further improve the semantic or instance segmentation performance of CBCT images, and most of the models have a Dice similarity coefficient greater than 90% and accuracy ranging from 83% to 99%. Conclusions: artificial intelligence methods have shown excellent performance in tooth segmentation of CBCT images, but still face problems, such as the small size of training data and non-uniformity of evaluation metrics, which still need to be further improved and explored for their application and evaluation in clinical applications.

关键词： artificial intelligence image processing, computer-assisted cone-beam computed tomography

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Implementation and applications of neural networks Based on FPGA

Implementation and Applications of Neural Networks Based on ...

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2024 International conference on Electrical Drives, Power Electronics and Engineering, EDPEE 2024

作者： Li, Jiajun Zhang, Chuhuan Ma, Xiangcheng Shenzhen College Of International Education Shenzhen China Nanjing University Of Aeronautics And Astronautics Nanjing China

ISBN: (纸本)9798350395631

Deep neural networks have been crucial in several recent developments in artificial intelligence and big data technology, including natural language processing, speech recognition, and computer vision. Given the numerous layers of deep neural network models, the computational complexity, and a large number of parameters, the performance of hardware such as computing power, memory bandwidth, and data storage is highly demanding. Due to their high parallelism, low power consumption, and reconfigurability, Field Programmable Gate Arrays (FPGAs), which are programmable logic devices, are frequently used as an alternative to CPUs and GPUs. Their combination with neural networks has become a current research hotspot in the field of artificial intelligence. This paper briefly describes firstly the current mainstream neural network models and the design ideas of FPGAs. Next, the applications of FPGA-based neural networks are introduced. Finally, this paper concludes with a summary and outlook on the development of FPGA-based applications for deep neural networks. © 2024 IEEE.

关键词： Deep neural networks

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Application of CNN in Visual image Design: image Feature Extraction and processing 24

Application of CNN in Visual Image Design: Image Feature Ext...

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International conference on Machine Intelligence and Digital applications (MIDA)

作者： Jin, Huijiao Wang, Fei Jiang, Lanying Tian, Meng Hainan Technol & Business Coll Sch Architectural Engn Haikou 570203 Hainan Peoples R China Ningbo Univ Univ Angers Joint Inst Dept Fash Design Ningbo 315211 Zhejiang Peoples R China Harbin Guangsha Coll Arts & Media Sch Harbin 150000 Heilongjiang Peoples R China

ISBN: (纸本)9798400718144

In response to the problems of insufficient personalization and unclear feature extraction in traditional visual image design, this paper studied an image feature extraction and processing method based on CNN (Convolutional neural networks). Firstly, the images were preprocessed and enhanced to enhance the generalization ability and feature extraction performance of the CNN model;a lightweight CNN architecture and MobileNet (Mobile Network) were designed to reduce computational complexity and parameters, and the model was optimized through technical means;generative adversarial networks (GANs) were utilized to integrate creative elements and enhance the innovation of advertising design. Through transfer learning, the CNN model was adjusted, and the Adam optimizer and early stop strategy were adopted for model training and optimization. In the experiment of feature extraction accuracy, the accuracy of the model in this paper reached 98.8%, 97.8%, and 98%, and it was also better than the comparison model in the comparative experiment. In the generalization ability test, the model did not overfit and demonstrated good generalization ability;in the processing time test, the architecture used in this paper had a shorter processing time than other architectures;in the survey on the integration effect of creative elements, the attractiveness rating reached 96% of the positive reviews.

关键词： Visual image Design Convolutional neural networks image Feature Extraction and processing Mobile Network Generative Adversarial networks

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Advancing Oncology Diagnostics: AI-Enabled Early Detection of Lung Cancer Through Hybrid Histological image Analysis

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IEEE ACCESS 2024年 12卷 64396-64415页

作者： Noaman, Naglaa F. Kanber, Bassam M. Al Smadi, Ahmad Jiao, Licheng Alsmadi, Mutasem K. Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Zarqa Univ Dept Data Sci & Artificial Intelligence Zarqa 13100 Jordan Imam Abdulrahman Bin Faisal Univ Coll Appl Studies & Community Serv Dept Management Informat Syst Dammam 34212 Saudi Arabia

Against the backdrop of the pervasive global challenge of cancer, with particular emphasis on lung cancer (LC), this study centers its investigation on the critical realm of early detection leveraging artificial intelligence (AI) within the domain of histological image analysis. Through the fusion of DenseNet201 with color histogram techniques, a novel hybrid feature set emerges, engineered to elevate classification accuracy. The comprehensive evaluation encompasses eight diverse machine learning (ML) algorithms, spanning from K-Nearest Neighbors (KNN) to Support Vector Machines (SVM), including notable contenders such as LightGBM (LGBM), CatBoost, XGBoost, decision trees (DT), random forests (RF), and multinomial naive Bayes (MultinomialNB). This rigorous examination illuminates a distinguished model, achieving a remarkable accuracy rate of 99.683% on the LC25000 dataset. The extension of this methodology to breast cancer detection, utilizing the BreakHis dataset, yields a commendable accuracy rate of 94.808%. These findings underscore the transformative potential of AI in the intricate landscape of histopathological analysis, positioning it as a pivotal force in advancing diagnostic capabilities. A meticulous comparative analysis not only underscores the merits but also elucidates the limitations of existing AI applications in medical imaging, thereby charting a roadmap for future refinements and clinical deployments. Consequently, continued research in AI within clinical settings is advocated, with the ultimate aim of fortifying early cancer diagnosis and subsequently enhancing patient outcomes through judicious therapeutic interventions.

关键词： Cancer Feature extraction image color analysis Histograms Convolutional neural networks Histopathology image processing Machine learning Lung cancer Medical diagnosis Oncology Densenet201 histopathological images image processing lung cancer machine learning

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Design of Adaptive Control System Driven by artificial Intelligence and Its Application in Industry 4.0 2

Design of Adaptive Control System Driven by Artificial Intel...

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2nd IEEE International conference on image processing and Computer applications, ICIPCA 2024

作者： Wen, Xiaoqian Business School Belarusian State University Minsk Belarus

ISBN: (纸本)9798350360240

In view of the difficulty of effective fusion of multi-source sensing data used in industrial production, a method based on multi-source sensing data fusion was proposed. A multi-sensor network model for real-time monitoring in industrial field is established. The key techniques of multi-dimensional data extraction, self-fuzzy inference, multi-dimensional group estimation and multi-dimensional grouping mechanism are studied by four-level data analysis method. This improves the control accuracy and effectiveness of the multidimensional system. This scheme is based on I2C bus structure and uses single chip microcomputer to control multiple sensor nodes, which improves the software reconfiguration ability and robustness, and reduces the maintenance and updating cost of the system. This project intends to establish an adaptive control model by combining FLMS and BP neural network. According to the system error, the weight coefficients of the two kinds of neural networks are determined. In the initial stage, the convergence rate of the neural networks is improved first, and then the nonlinear BP network is used as the main assistant to improve the control effect. © 2024 IEEE.

关键词： Microcomputers

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