检索结果-内蒙古大学图书馆

Advancements in memory technologies for artificial synapses

JOURNAL OF MATERIALS CHEMISTRY C 2024年第15期12卷 5274-5298页

作者： Sehgal, Anubha Dhull, Seema Roy, Sourajeet Kaushik, Brajesh Kumar Indian Inst Technol Roorkee Roorkee 247667 Uttarakhand India

neural networks (NNs) have made significant progress in recent years and have been applied in a broad range of applications, including speech recognition, image classification, automatic driving, and natural language processing. The hardware implementation of NNs presents challenges, and research communities have explored various analog and digital neuronal and synaptic devices for resource-efficient implementation. However, these hardware NNs face several challenges, such as overheads imposed by peripheral circuitry, speed-area tradeoffs, non-idealities associated with memory devices, low on-off resistance ratio, sneak path issues, low weight precision, and power-inefficient converters. This article reviews different synaptic devices and discusses the challenges associated with implementing these devices in hardware, along with corresponding solutions, and prospecting future research directions. Several categories of emerging synaptic devices such as resistive random-access memory (RRAM), phase change memory (PCM), analog-to-digital hybrid volatile memory-based, ferroelectric field effect transistor (FeFET)-based, spintronic-based spin transfer, spin-orbit, magnetic domain wall (DW) and skyrmion synaptic devices have been explored, and a comparison between them is presented. This study provides insights for researchers engaged in the field of hardware neural networks. This article reviews different synaptic devices and discusses the challenges associated with implementing these devices in hardware, along with corresponding solutions, applications, and prospecting future research directions.

关键词： Speech recognition

来源：评论

学校读者我要写书评

暂无评论

End-to-End artificial Intelligence-Based System for Automatic Stereo Camera Self-Calibration

引用

IEEE ACCESS 2024年 12卷 160927-160945页

作者： Satouri, Boutaina El Abderrahmani, Abdellatif Satori, Khalid USMBA Fac Sci Fes Dept Math & Informat LISAC Fes 30000 Morocco

Stereo camera self-calibration is a complex challenge in computer vision applications such as robotics, object tracking, surveillance and 3D reconstruction. To address this, we propose an efficient, fully automated End-To-End AI-Based system for automatic stereo camera self-calibration with varying intrinsic parameters, using only two images of any 3D scene. Our system combines deep convolutional neural networks (CNNs) with transfer learning techniques and fine-tuning. First, our end-to-end convolutional neural network optimized model begins by extracting matching points between a pair of stereo images. These matching points are then used, along with their 3D scene correspondences, to formulate a non-linear cost function. Direct optimization is subsequently performed to estimate the intrinsic camera parameters by minimizing this non-linear cost function. Following this initial optimization, a fine-tuning layer refines the intrinsic parameters for increased accuracy. Our hybrid approach is characterized by a special optimized architecture that leverages the strengths of end-to-end CNNs for image feature extraction and processing, as well as the pillars of our nonlinear cost function formulation and fine-tuning, to offer a robust and accurate method for stereo camera self-calibration. Extensive experiments on synthetic and real data demonstrate the superior performance of the proposed technique compared to traditional camera self-calibration methods in terms of precision and faster convergence.

关键词： Cameras Cost function Convolutional neural networks Three-dimensional displays Robot vision systems Solid modeling Accuracy Calibration Nonlinear distortion Mathematical models Camera self-calibration varying intrinsic parameters nonlinear direct optimization convolutional neural network transfer learning fine tuning matching interest points

来源：评论

学校读者我要写书评

暂无评论

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

引用

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)

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Driving scene image Dehazing model based on multi-branch and multi-scale feature fusion

引用

neural networks 2025年 188卷 107495页

作者： Yin, Shi Liu, Hui Cent South Univ Sch Traff & Transportat Engn Inst Artificial Intelligence & Robot IAIR Key Lab Traff Safety TrackMinist Educ Changsha 410075 Hunan Peoples R China

image dehazing is critical for enhancing image quality in applications such as autonomous driving, surveillance, and remote sensing. This paper presents an innovative image dehazing model based on a multi-branch and multi scale feature fusion network that leverages spatial and frequency information. The model features a multi-branch architecture that combines local and global features through depthwise separable convolutions and state space models, effectively capturing both detailed and comprehensive information to improve dehazing performance. Additionally, a specialized module integrates spatial and frequency domain information by utilizing convolutional layers and Fourier transforms, enabling comprehensive haze removal through the fusion of these two domains. A feature fusion mechanism incorporates channel attention and residual connections, dynamically adjusting the importance of different channel features while preserving the global structural information of the input image. Furthermore, this is the first model to combine Mamba and convolution layers for driving scene image dehazing, achieving global feature extraction with linear complexity. Each image is processed in only 0.030 s, with a frame rate of 32.41 FPS and a processing efficiency of 67.96 MPx/s, ensuring high efficiency suitable for real-time applications. Extensive experiments on real-world foggy driving scene datasets demonstrate the superior performance of the proposed method, providing reliable visual perception capabilities and significantly improving adaptability and robustness in complex environments.

关键词： image dehazing State space model Frequency domain Autonomous driving

来源：评论

学校读者我要写书评

暂无评论

Transformer-based image and video inpainting: current challenges and future directions

引用

artificial INTELLIGENCE REVIEW 2025年第4期58卷 1-45页

作者： Elharrouss, Omar Damseh, Rafat Belkacem, Abdelkader Nasreddine Badidi, Elarbi Lakas, Abderrahmane United Arab Emirates Univ Coll Informat Technol Dept Comp Sci & Software Engn Al Ain U Arab Emirates United Arab Emirates Univ Coll Informat Technol Dept Comp & Network Engn Al Ain U Arab Emirates

image inpainting is currently a hot topic within the field of computer vision. It offers a viable solution for various applications, including photographic restoration, video editing, and medical imaging. Deep learning advancements, notably convolutional neural networks (CNNs) and generative adversarial networks (GANs), have significantly enhanced the inpainting task with an improved capability to fill missing or damaged regions in an image or a video through the incorporation of contextually appropriate details. These advancements have improved other aspects, including efficiency, information preservation, and achieving both realistic textures and structures. Recently, Vision Transformers (ViTs) have been exploited and offer some improvements to image or video inpainting. The advent of transformer-based architectures, which were initially designed for natural language processing, has also been integrated into computer vision tasks. These methods utilize self-attention mechanisms that excel in capturing long-range dependencies within data;therefore, they are particularly effective for tasks requiring a comprehensive understanding of the global context of an image or video. In this paper, we provide a comprehensive review of the current image/video inpainting approaches, with a specific focus on Vision Transformer (ViT) techniques, with the goal to highlight the significant improvements and provide a guideline for new researchers in the field of image/video inpainting using vision transformers. We categorized the transformer-based techniques by their architectural configurations, types of damage, and performance metrics. Furthermore, we present an organized synthesis of the current challenges, and suggest directions for future research in the field of image or video inpainting.

关键词： image inpainting Video inpainting Vision transformers

来源：评论

学校读者我要写书评

暂无评论

X-ray PCB defect automatic diagnosis algorithm based on deep learning and artificial intelligence

引用

neural COMPUTING & applications 2023年第36期35卷 25263-25273页

作者： Liu, Yaojun Wang, Ping Liu, Jingjing Liu, Chuanyang Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 210002 Jiangsu Peoples R China Wuhu State Owned Factory Machining Wuhu 241000 Anhui Peoples R China Chizhou Univ Coll Mech & Elect Engn Chizhou 247000 Anhui Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210002 Jiangsu Peoples R China

As a main electronic material, X-ray circuits are widely used in various electronic devices, and their quality has an important impact on the overall quality of electronic products. In the process of mass production of circuit boards, due to the large number of layers, tight lines and some harmful external factors, circuit board quality may be problematic. Detecting circuit board defects are important for improving the reliability of electronic products. This paper introduces deep learning and artificial intelligence technology to conduct research on the automatic detection of X-ray circuit board defects. The study used a defect detection system to study X-ray circuit boards as a detection object and obtained the structure, lighting system and composition of the detection system. The working principle of the detection system is explained, and the image is preprocessed. Testing the processing performance of the PCB defect detection system, when the number of pixels is 6526, 7028, 7530 and 8032, the time consumption ratios between the proposed detection system and image processing on a traditional PC are 35.17%, 35.4%, 35% and 35.28%, respectively. The experimental results make a certain contribution to the future artificial intelligence X-ray PCB defect automatic diagnosis algorithm.

关键词： artificial intelligence artificial neural networks Circuit board defects Diagnostic algorithms

来源：评论

学校读者我要写书评

暂无评论

An integrated approach to apple leaf disease detection: leveraging convolutional neural networks for accurate diagnosis

引用

Multimedia Tools and applications 2025年 1-36页

作者： Rohith, Dasari Saurabh, Praneet Bisen, Dhananjay Manipal University Jaipur Rajasthan Jaipur India Madhav Institute of Technology & Science Deemed University M.P Gwalior India

Apple, a globally revered fruit with diverse cultivars, often falls victim to various leaf diseases that compromise crop yield and quality. Precise and timely disease identification is crucial for effective orchard management and timely intervention. Recent years has witnessed the exponential growth of artificial Intelligence, Machine Learning and its subsequent applications to various diversified applications including image processing. This paper intends to utilise the potential of Deep Learning to transform apple leaf disease detection and classification. In this work, the best Convolutional neural Network pretrained model for identification of apple leaf disease is found using rich visual dataset of 3,200 apple leaf images, meticulously curated to encompass four distinct categories: healthy leaves, those afflicted by apple scab, black rot, and cedar rust. Experimental results revealed that Residual neural Network achieved highest validation accuracy of 98.9%, significantly exceeding the performance of benchmark models like Visual Geometry Group 19 (96%), InceptionV3 (97%), EfficientNet (97.5%), Visual Geometry Group 16 (98.1%), MobileNet (98.2%) and the traditional baseline Convolutional neural Network (95%). The extensive experimentation also revealed that an 80:20 training-to-validation split yields the highest classification accuracy. The exceptional accuracy achieved by Residual neural Network powered model, coupled with the robustness of the image dataset, opens doors for the development of advanced orchard management tools with real-time identification and combating harmful leaf diseases. This can potentially revolutionize agricultural practices, minimizing crop losses and ensuring sustainable apple production. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Fruits

来源：评论

学校读者我要写书评

暂无评论

Machine learning and image analysis in vascular surgery

引用

SEMINARS IN VASCULAR SURGERY 2023年第3期36卷 413-418页

作者： Tomihama, Roger T. Dass, Saharsh Chen, Sally Kiang, Sharon C. Linda Univ Sch Med Dept Radiol Sect Vasc & Intervent Radiol 11234 Anderson StSuite MC-2605E Loma Linda CA 92354 USA Linda Univ Sch Med Dept Surg Div Vasc Surg Loma Linda CA USA Vet Affairs Loma Linda Healthcare Syst Dept Surg Div Vasc Surg Loma Linda CA USA

Deep leaming, a subset of machine learning within artificial intelligence, has been successful in medical image analysis in vascular surgery. Unlike traditional computer-based segmentation methods that manually extract features from input images, deep leaming methods learn image features and classify data without making prior assumptions. Convolutional neural networks, the main type of deep learning for computer vision processing, are neural networks with multilevel architecture and weighted connections between nodes that can "auto-leam" through repeated exposure to training data without manual input or supervision. These networks have numerous applications in vascular surgery imaging analysis, particularly in disease classification, object identification, semantic segmentation, and instance segmentation. The purpose of this review article was to review the relevant concepts of machine leaming image analysis and its application to the field of vascular surgery. (c) 2023 Elsevier Inc. All rights reserved.

关键词： artificial intelligence Vascular surgery image segmentation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：