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检索条件"任意字段=Conference on Visual Communications and Image Processing 94"

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CNN-Based Circular Economy in E-Waste Management with Blockchain and IoT Integration

CNN-Based Circular Economy in E-Waste Management with Blockc...

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visual Analytics and Data visualization (ICVADV), International conference on

作者： P. Santhuja A Suresh Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Networking and Communications Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Increasing electronic waste (e-waste) is a serious environmental and financial problem that calls for creative ideas for effective resource recovery and recycling. This paper proposes an IoT- and Blockchain-integrated e-waste management system based on Convolutional Neural Networks (CNNs) to boost automated waste classification and, thus, circular economy practices. While Blockchain guarantees open and safe tracking of waste transportation, IoT sensors and RFID tags allow real-time monitoring of e-waste. Training on a collection of 50,000 e-waste images, a CNN-based image classification model attained an accuracy of 96.2% in classifying components into recyclables, reusables, and hazardous items. Using automated decision-making, the system showed a 28% reduction in processing time and a 32% gain in sorting efficiency over conventional approaches. Blockchain incorporation enhanced traceability through 100% secure transaction records, lowering fraud and illegal disposal. Experimental data show that this method improves 23% resource recovery rates by promoting sustainable e-waste management. The proposed architecture minimizes environmental effects by encouraging ethical e-waste disposal and a closed-loop recycling system. These results show how Blockchain-secured, AI-driven IoT devices might help to advance circular economic ideas for world e-waste management.

关键词： Waste management Training Accuracy Electronic waste Real-time systems Recycling Blockchains Internet of Things Convolutional neural networks Sorting

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CNN-Based Circular Economy in E-Waste Management with Blockchain and IoT Integration

CNN-Based Circular Economy in E-Waste Management with Blockc...

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2025 International conference on visual Analytics and Data visualization, ICVADV 2025

作者： Santhuja, P. Suresh, A. School of Computing Srm Institute of Science and Technology Department of Networking and Communications Tamil Nadu Chennai India School of Computing Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Tamil Nadu Chennai India

ISBN: (纸本)9798331521394

关键词： Electronic Waste

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Enhanced image Deblurring with the Fusion of Generative Adversarial Networks and Transformer Models 1st

Enhanced Image Deblurring with the Fusion of Generative Adve...

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1st International conference on Innovation and Emerging Trends in Computing and Information Technologies, IETCIT 2024

作者： Ranjan, Arti Ravinder, M. New Delhi India Department of Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida India

ISBN: (纸本)9783031808418

In the domain of image processing, deblurring remains a challenging task, particularly in restoring high-fidelity details from blurred images. This paper introduces a novel approach that synergizes the strengths of Generative Adversarial Networks (GANs) and Transformer models to address this challenge. Our methodology leverages the generative capability of GANs to produce realistic and sharp images, while incorporating the Transformer’s prowess in capturing long-range dependencies and intricate details within images. This adversarial training regime is designed to refine the model’s ability to generate deblurred images that are not only accurate in detail but also high in perceptual quality. Through extensive experiments on various datasets, our model demonstrates superior performance in deblurring, outperforming existing state-of-the-art methods in terms of both objective metrics and subjective visual quality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Soft Computing Based Agricultural Crops Monitoring and Disease Detection

Soft Computing Based Agricultural Crops Monitoring and Disea...

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2025 International conference on Intelligent Control, Computing and communications, IC3 2025

作者： Abhilash, Pideka Kundil Rex Vijay, V. Kavitha, V. Alzubaidi, Laith H. Muthubalaji, S. Kaliappan, S. Gokaraju Rangaraju Institute of Engineering and Technology Department of Information Technology Telangana Hyderabad India Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India Sethu Institute of Technology Department of Electrical Communication Engineering Pulloor Tamil Nadu Kariapatti626115 India College of Technical Engineering The Islamic University Department of computers Techniques engineering Najaf Iraq Cmr College of Engineering & Technology Department of Eee Telangana Hydrabad India Lovely Professional University Division of Research and Development Jalandhar - Delhi G.T.Road Punjab Phagwara144411 India

ISBN: (纸本)9798331527495

In light of the growing need for intelligent farming, it is crucial to ensure effective development & enhanced output of plants. Tracking a plant from its development to its picking is essential in order to improve output & profitability. The paper presents the development of a system that utilizes image processing techniques to identify and monitor diseases in fruits during the entire process through planting to picking. This objective is accomplished by utilizing the idea of artificial neural network. The investigation focuses on four illnesses that affect tomato crops. The approach under consideration utilizes two picture databases. The initial dataset is utilized to train contaminated photos, while the subsequent dataset is employed for implementing additional inquiry photographs. The training dataset undergoes weight modification through the process of back propagation. The results of the study demonstrate the categorization & matching of pictures to their corresponding groups. The photographs are classified into colour, texture, & morphological categories. The morphological characteristic yields a 94%accuracy rate, surpassing the performance of the remaining two characteristics. The system's design exhibits a high level of efficacy in identifying the propagation of diseases. The technique has been implemented practically using MATLAB. © 2025 IEEE.

关键词： image segmentation

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Edge-IoT Architecture-Based visualization Service Platform for RTO Real-Time Monitoring

Edge-IoT Architecture-Based Visualization Service Platform f...

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International conference on Advanced Communication Technology (ICACT)

作者： Hoon-Min Park Dal-Hwan Yoon Hyun-Min Jung Dae-Hee Lee Tae-Yeung Lim Hyun Kim Myung-Ki Jang Emsolution Co. Ltd Suwon Korea Department of Electronic Engineering Semyung University Korea Devicenet Co. Ltd Korea

ISBN: (数字)9791188428137

ISBN: (纸本)9798331507602

In this study, we present the Edge IoT Architecture (Architecture) for analyzing the real-time state information of the regenerative thermal oxidizer (RTO) and implement a visualization method. The Edge-IoT architecture system interfaces with the programmable logic control (PLC) that controls the monitoring of RTO operations and transmits real-time data to a remote server, analyzes the channel data received through the IoT to build a database, and visualizes the characteristics of database (DB) analysis. IoT architecture implementation for fault diagnosis and maintenance of RTO designs IoT circuits on PLC control panels that control monitoring. Compatibility and information security are considered to facilitate remote maintenance. The received data is segmented by each channel and analyzed by a visualization algorithm to detect anomalies in the segmented data. At this time, the visualized data is judged to be normal, insufficient, or warning, depending on the threshold.

关键词： image edge detection Data visualization Signal processing algorithms Signal processing Real-time systems Maintenance Thermal analysis visual databases Servers Monitoring

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Compression and Transmission of 8K Stereoscopic VR Using VAE-GAN Latents and Standard Encoders

Compression and Transmission of 8K Stereoscopic VR Using VAE...

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IEEE conference on Wireless communications and Networking

作者： Sampreet Vaidya Hatem Abou-Zeid Diwakar Krishnamurthy Department of Electrical and Software Engineering University of Calgary Calgary AB Canada

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Despite the heightened popularity of Virtual Re-ality (VR), streaming high-resolution stereoscopic VR remains a challenge. This is primarily due to significant bandwidth demands of high-definition VR content. While advanced deep neural networks (DNN s) have demonstrated the potential to outperform standard codecs, their integration into real-world transmission frameworks is complex and not directly compatible with current encoding standards. To bridge this gap, this paper proposes a novel technique for compressing and transmitting 8K stereoscopic scenes using Variational Autoencoder (VAE) GAN latents represented as 3-channel RGB scenes that can be transmitted via standard encoders. The proposed method reduces bandwidth requirements by 45.1 % across different 8K scenes while maintaining visual quality, highlighting the effectiveness of the approach. This study also investigates the impact of varying patch-sizes of input frames for model training and evaluate its influence on client-side reconstructions. We then explore various transmission configurations of latent frames. Our findings suggest that while residual transmission offers limited benefits for 3-channel latent frame compression, raw transmission consistently yields better results, particularly for texture-heavy scenes. To the best of our knowledge, this is the first such transmission study on 8K stereoscopic scenes for cloud-based VR, providing valuable insights for optimizing high-resolution VR streaming systems. Code: ***/sampreetucalgary07/8K-VR-compression.

关键词： Training Cloud computing visualization Solid modeling Stereo image processing Bandwidth Virtual reality Streaming media Generative adversarial networks Standards

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3rd International conference on Electronic Governance with Emerging Technologies, EGETC 2024

3rd International Conference on Electronic Governance with E...

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3rd International conference on Electronic Governance with Emerging Technologies, EGETC 2024

ISBN: (纸本)9783031770289

The proceedings contain 17 papers. The special focus in this conference is on Electronic Governance with Emerging Technologies. The topics include: Ecological Development of a Novel Machine Learning Technique for Electricity Consumption and Cost Prediction in Cloud Computing;blockchain Based Decentralized Approach for Securing Fog Computing Environment;Enhancing Face Spoofing Detection Via CNN Model Integration with Normalized Features;Study of ZigBee-Based Communication to Share Operative Weather Information Between UASs in the Frame of Smart Cities Concept;automating Exam Assessment Through Digital Transformation and image processing;an Analysis of Ethical Dimension of Machine Learning Models in Software Defect Prediction in Healthcare Sector;leaf Diseases Detection in Peach Using EfficientNet;medInsight: A Personalized Prescription Recommendation Using Machine Learning;intelligent Machine Learning for Cybersecurity: Anomaly Detection in Network Intrusion Systems and Beyond;Enhancing Cyber Resilience: Implementing DFMCAI for Threat Detection and Analysis;forecasting Future Credit Card Delinquencies: Leveraging Data Tables and visual Analysis for Enhanced Accuracy;navigating the Strategic Landscape: An In-Depth Review of the Strategic Planning Process;exploring Text Vectorization on the Polarity Detection of Spanish Comments;Evolutionary Optimization Techniques for the Design of Three-Dimensional UAV’s Antenna Arrays;cooperative Multi-agent Pursuit-Evasion Using Convolutional Neural Networks.

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Residential Real Estate image Classification for Property Valuation

Residential Real Estate Image Classification for Property Va...

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28th International conference on image processing, Computer Vision, and Pattern Recognition, IPCV 2024, and 23rd International conference on Information and Knowledge Engineering, IKE 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

作者： Nejad, Mehrdad Ziaee Naderpour, Mohsen Behbood, Vahid Ramezani, Fahimeh Lu, Jie 15 Broadway UltimoNSW2007 Australia

ISBN: (纸本)9783031859328

Residential real estate price is one of the key components of our economic developments and has also been a major concern of the public, bank industry, government, and investors. The accurate estimation of the sale price and its changes have an important role in the decision-making of related departments and organizations. In Australia, one of the biggest investments for people is in residential real estate. Therefore, many studies and research works have been carried out to build an automated valuation model to predict sale prices of residential properties accurately as much as possible. Automatic and accurate image classification of residential real estate plays an important role in property valuation and decision making of both sellers and buyers. It can be used in real estate online websites to organize the images for each property or used as a component in a visual decision support system for predicting the property sale prices based on property images. As convolutional image classification models show valuable performance in comparison with traditional models, a convolutional classification model is developed in this paper which creates a highly reliable classification component to be used in the corresponding research areas. The performance of the proposed model is investigated through a real dataset of New Sales Wales, Australia. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Accelerating Traditional Object Detection Methods on Sophisticated Embedded Systems 4th

Accelerating Traditional Object Detection Methods on Sophis...

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4th International conference on Intelligent Systems and Pattern Recognition, ISPR 2024

作者： Sehairi, Kamal Bouhafs, Abdelkader Boucherit, Ibtissam Mama Bouwmans, Thierry Chouireb, Fatima Department of Physics Laboratory of Applied Sciences and Didactics École Normale Supérieure de Laghouat Laghouat Algeria Department of Electronics Telecommunication Signals and Systems Laboratory University Amar Telidji of Laghouat Laghouat Algeria Department of Computer Science Lab. Mathématiques Image et Applications Université de La Rochelle La Rochelle France

ISBN: (纸本)9783031821523

Identifying and locating objects in images and videos, including elements like traffic signs, vehicles, buildings, and people, constitutes a fundamental and demanding task in computer vision, known as object detection. Due to the higher computing complexity of this technique and the large amount of data carried by the video signal, it is nearly impossible for ordinary general-purpose processors GPPs or CPUs to run these techniques in real-time, especially for embedded systems applications. Therefore, special hardware that can acquire, control, or execute in parallel is required. These specialized hardware systems include Digital Signal Processors DSPs, Field Programmable Gate Arrays FPGAs, visual processing Units VPUs, Tensor processing Units TPUs, Neural processing Units NPUs or Graphics processing Units GPUs. This work presents the benefits of accelerating traditional object detection methods on a high-end embedded system, the Jetson Nano Developer Kit. This single computer board is equipped with the Tegra K1 System on Chip SoC, which is composed of a quad-core ARM A15 and 192 cores of Kepler-embedded GPU. Computing acceleration was ensured via the use of the CUDA OpenCV library for both the Histogram of Oriented Gradients HOG and the Haar Cascade Classifier. For VGA resolution, results reveal that the GPU implementation on this embedded system is 1.4× faster than the CPU for the HOG method and 2× for the Haar Cascade Classifier method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： System-on-chip

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MKD-YOLO: Multi-Scale and Knowledge-Distilling YOLO for Efficient PPE Compliance Detection

MKD-YOLO: Multi-Scale and Knowledge-Distilling YOLO for Effi...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Juntao Zan Yang Fang Qilie Liu Uswah Khairuddin Yan Li Kaiwei Sun School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing China Malaysia-Japan International Institute of Technology University of Technology Malaysia Kuala Lumpur Malaysia Department of Electrical and Computer Engineering Inha University Incheon South Korea Key Laboratory of Data Engineering and Visual Computing Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

YOLO-based models are widely used for personal protective equipment (PPE) compliance detection due to their excellent detection performance and efficiency. However, most YOLO models are not competent for detection tasks in complex industrial scenarios such as remote surveillance and extremely small targets. In addition, there is a lack of effective model lightweighting and knowledge transfer approaches for industrial deployment. To this end, this paper proposes a Multi-scale and Knowledge-Distilling YOLO (MKD-YOLO) based on YOLOv8n for efficient PPE compliance detection. Specifically, in backbone stage, we design an Efficient Multi-Scale Enhanced Convolution (C2f-EMSEC) module and Large Spatial Pyramid Pooling-Fast (LSPPF) module for multi-scale and global-contextual feature learning as well as reducing model complexity. Then, in neck stage, a refined Bidirectional feature Pyramid Network (BPNet) is designated to capture fine-grained details for extremely small object detection. Moreover, we apply channel-wise knowledge distillation to facilitate model lightweighting and domain-specific knowledge transfer learning. Experiments on our proposed dataset and public datasets show that the proposed MKD-YOLO achieves a new state-of-the-art (SOTA) detection performance and efficiency for practical PPE compliance detection tasks. Codes and the dataset are available at https://***/z1Zjt/MKD-YOLO.

关键词： Personal protective equipment YOLO Representation learning Convolution Surveillance Speech enhancement Feature extraction Neck Kernel Knowledge transfer

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