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9th International Conference on Science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Yuvarani, P. Suvetha, C. Shree Subha, M. Subashri, V. Leena, G. M Kumarasamy College of Engineering Department of Ece Karur India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365092

The most serious issue that densely populated cities confront is traffic congestion. This effort primarily intends to provide a solution to the difficulty encountered by ambulances while approaching a traffic signal amid heavy traffic. This is accomplished by employing an innovative approach for enhancing emergency response through the integration of automatic ambulance siren recognition with traffic signal control systems. Using advanced audio processing and deep learning techniques, the system identifies the unique acoustic profile of ambulance sirens in real-time. This project involves creating a real-time system using Tensor Flow, a machine learning framework, to distinguish ambulance sounds from other noises. Through machine learning techniques, the system will learn to classify audio samples, identifying the unique siren patterns of ambulances even in the presence of ambient noise. When a siren is detected, the traffic signal dynamically adjusts to prioritize the ambulance's passage, minimizing response times and improving overall emergency vehicle maneuverability in urban environments. The objective is to develop a dependable system that accurately recognizes ambulance sounds, potentially enhancing emergency response and bolstering public safety measures. The proposed solution offers a practical and efficient way to optimize traffic flow during emergencies, contributing to enhanced public safety and potentially saving crucial time during emergency situations. © 2024 IEEE.

关键词： deep learning

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Evaluating deep learning Models for Human Activity Recognition: CNN, RCNN, and VGG Approaches 8

Evaluating Deep Learning Models for Human Activity Recogniti...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Jani, Ritika Garg, Dweepna U & P U Patel Departmet of Computer Engineering Anand Gujarat Changa India Anand Gujarat Changa India Department of Computer Engineering Anand Gujarat Changa India

ISBN: (纸本)9798350376425

Human Activity Recognition (HAR) in video surveillance is an evolving field with profound implications for security, safety, and efficiency across various domains. This paper presents an overview of challenges, applications, and algorithms in the context of HAR within video surveillance. Challenges in HAR stem from the complexities of real-world scenarios. Variability in lighting conditions, occlusions, camera viewpoints, and the diversity of human activities pose significant obstacles. Furthermore, scalability, real-time processing, and the need for robustness in uncontrolled environments are persistent challenges. Machine learning techniques, including Support Vector Machines, Random Forests, and k-Nearest Neighbors, have been applied with success. However, the integration of deep learning has revolutionized HAR, with Convolutional Neural Networks (CNNs), You Look Only Once (YOLO) and Recurrent Neural Networks (RNNs). With the help of these neural networks, more precise and context-aware activity detection is possible by extracting hierarchical characteristics from unprocessed data. This paper address the current state of the art in highlighting the crucial role of deep learning approaches which have played in pushing the limits of HAR in video surveillance. © 2024 IEEE.

关键词： Convolutional neural networks

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image processing Technique for Tracking and Counting of Vehicles Using ROI

Image Processing Technique for Tracking and Counting of Vehi...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Kumar, Ankit Chandel, Ritika Singh, Monika Apex Institute of Technology Chandigarh University Mohali India

ISBN: (纸本)9798350357769

This research paper presents a novel approach for vehicle tracking and counting utilizing the advanced object detection model YOLOv8 in the field of image processing. The accurate monitoring of vehicular traffic is crucial for various applications, including traffic management, urban planning, and safety analysis. Existing methods often struggle with real-time tracking and counting of vehicles due to challenges such as occlusions, varying lighting conditions, and complex traffic scenarios. In our proposed methodology, we leverage the power of YOLOv8, a deep learning-based object detection model, uses real-time detection to identify and monitor vehicles, ensuring high accuracy and robustness in various traffic environments. The proposed approach provides a valuable tool for traffic management authorities and urban planners, offering precise data for traffic flow analysis and infrastructure development. © 2023 IEEE.

关键词： deep learning image processing Machine Vision Object Detection ROI (Region of Interest) Vehicle Detection YOLO (You Only Look Once)

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Integrating Blockchain deep learning and Ensemble Methods for Optimized MQTT Protocol in IoT Networks

Integrating Blockchain Deep Learning and Ensemble Methods fo...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Senthil Murugan, M. Abdul Rahman, M. Bharath Krishna, S. Siva, T. St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543617

MQTT is a lightweight protocol used commonly that offers low power consumption and overhead, but suffers significant security threats such as Man-in-the-Middle (MiTM) attacks, Packet Sniffing, and network anomalies. The presented work introduces an enhanced MQTT protocol that integrates blockchain technology, deep learning, and ensemble methods in optimized ways connected with security and performance for IoT environments that are either resource-constrained or challenged. Blockchain technology ensures integrity of data and tamper-proof communication between IoT devices with brokers;deep learning models, including LSTM and CNNs, detect anomalies in real-time and prevent MiTM attacks. The techniques in Ensemble learning which combine Random Forest and XGBoost together enhance the ability of detection of attacks, improving accuracies and reliability. The system is designed to be lightweight yet effective for securing MQTT communications, especially when it ensures authenticity of data while optimizing the network traffic load with minimal processing load. The experimental results find good improvements in network threat detection capabilities and the efficiency of data transmission. Such results confirm the possibility of bringing such technologies into an IoT framework. It develops more secure IoT networks, particularly for applications that require the handling of high security and optimized performance. © 2024 IEEE.

关键词： deep learning

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AI-Driven Neuro-Monitoring: Advancing Schizophrenia Detection and Management Through deep learning and EEG Analysis

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FUTURE INTERNET 2024年第11期16卷

作者： Paraschiv, Elena-Anca Bajenaru, Lidia Petrache, Cristian Bica, Ovidiu Nicolau, Dragos-Nicolae ICI Bucharest Natl Inst Res & Dev Informat Bucharest 011455 Romania Natl Univ Sci & Technol Politehn Bucharest Doctoral Sch Elect Telecommun & Informat Tech Bucharest 060042 Romania Natl Univ Sci & Technol Fac Automatic Control & Comp Dept Comp Sci POLITEHN Bucharest 060042 Romania

Schizophrenia is a complex neuropsychiatric disorder characterized by disruptions in brain connectivity and cognitive functioning. Continuous monitoring of neural activity is essential, as it allows for the detection of subtle changes in brain connectivity patterns, which could provide early warnings of cognitive decline or symptom exacerbation, ultimately facilitating timely therapeutic interventions. This paper proposes a novel approach for detecting schizophrenia-related abnormalities using deep learning (DL) techniques applied to electroencephalogram (EEG) data. Using an openly available EEG dataset on schizophrenia, the focus is on preprocessed event-related potentials (ERPs) from key electrode sites and applied transfer entropy (TE) analysis to quantify the directional flow of information between brain regions. TE matrices were generated to capture neural connectivity patterns, which were then used as input for a hybrid DL model, combining convolutional neural networks (CNNs) and Bidirectional Long Short-Term Memory (BiLSTM) networks. The model achieved a performant accuracy of 99.94% in classifying schizophrenia-related abnormalities, demonstrating its potential for real-time mental health monitoring. The generated TE matrices revealed significant differences in connectivity between the two groups, particularly in frontal and central brain regions, which are critical for cognitive processing. These findings were further validated by correlating the results with EEG data obtained from the Muse 2 headband, emphasizing the potential for portable, non-invasive monitoring of schizophrenia in real-world settings. The final model, integrated into the NeuroPredict platform, offers a scalable solution for continuous mental health monitoring. By incorporating EEG data, heart rate, sleep patterns, and environmental metrics, NeuroPredict facilitates early detection and personalized interventions for schizophrenia patients.

关键词： schizophrenia EEG deep learning CNN-BiLSTM transfer entropy mental health monitoring

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A GPU Parallel Stitching Algorithm for Low-Altitude Parallax Scenes 5

A GPU Parallel Stitching Algorithm for Low-Altitude Parallax...

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5th International Conference on Machine learning and Computer Application, ICMLCA 2024

作者： Liu, Sha Zhou, Shan Hu, Yan China Electronics Technology Group Corp 54th Research Institute Shijiazhuang050081 China China Electronics Technology Group Corp 20th Research Institute Xi'an710068 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

ISBN: (纸本)9798331530334

Panorama stitching of low-altitude captured images is a very interesting and challenging task. Since 3D scenes are not planar, there may be significant changes in the relative positions of scene structures in each perspective in the images, thus causing parallax. Existing image stitching algorithms often fail in parallax scenes and are not efficient enough to meet the real-time processing requirements of drones. To address this problem, we introduce gridded dense projection estimation and design a parallel computing unit suitable for GPUs for rapid modeling of projection fields. Experimental results show that our algorithm significantly outperforms the comparison algorithms in terms of computational performance, has high real-time performance, and achieves nearly the same level of stitching quality and naturalness. © 2024 IEEE.

关键词： GPU image registration parallel computing

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An Advanced deep learning-Based High-Resolution μCT images Construction Method for Cement Hydration Microstructure 20th

An Advanced Deep Learning-Based High-Resolution μCT Images ...

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20th International Conference on Intelligent Computing (ICIC)

作者： Han, Ruiqi Zhang, Liangliang Zhang, Pan Wang, Lin Yang, Bo Xie, Yuhang Univ Jinan Shandong Prov Key Lab Network Based Intelligent C Jinan 250022 Peoples R China Quan Cheng Lab Jinan 250100 Peoples R China

ISBN: (纸本)9789819755806;9789819755813

Analyzing the microstructure images of cement can help identify and quantify the internal material structure, understand the hydration mechanism, and design cement materials. Micro-CT (mu CT), as an advanced physical imaging device, can image internal structural information of cement hydration at the microscopic scale. However, due to limitations in the equipment price, maintenance costs, and single imaging time of the equipment itself, the acquisition of high-definition mu CT images for cement hydration microstructure faces high scanning economic costs. Additionally, limited by the imaging mechanism of the physical devices, the size of the cement samples affects image resolution. This paper proposes a high-resolution mu CT images construction method for cement hydration microstructure based on deep learning. real-ESRGAN network is introduced to improve the resolution of mu CT images obtained from physical devices after imaging, on a super-resolution reconstruction approach. In addition, a refined high-order degradation is designed and applied to real-ESRGAN to suit the characteristics of mu CT image data. Experiment results validated the good performance of the proposed method in constructing high-resolution mu CT images and were extended to historical images imaged by devices that are currently technologically backward.

关键词： Micro-CT images Super-resolution Cement hydration deep learning

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GASSF-Net: Geometric Algebra Based Spectral-Spatial Hierarchical Fusion Network for Hyperspectral and LiDAR image Classification

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REMOTE SENSING 2024年第20期16卷

作者： Wang, Rui Ye, Xiaoxi Huang, Yao Ju, Ming Xiang, Wei Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China La Trobe Univ Sch Comp Engn & Math Sci Melbourne Vic 3086 Australia James Cook Univ Coll Sci & Engn Cairns Qld 4878 Australia

The field of multi-source remote sensing observation is becoming increasingly dynamic through the integration of various remote sensing data sources. However, existing deep learning methods face challenges in differentiating between internal and external relationships and capturing fine spatial features. These models often struggle to effectively capture comprehensive information across remote sensing data bands, and they have inherent differences in the size, structure, and physical properties of different remote sensing datasets. To address these challenges, this paper proposes a novel geometric-algebra-based spectral-spatial hierarchical fusion network (GASSF-Net), which uses geometric algebra for the first time to process multi-source remote sensing images, enabling a more holistic approach to handling these images by simultaneously leveraging the real and imaginary components of geometric algebra to express structural information. This method captures the internal and external relationships between remote sensing image features and spatial information, effectively fusing the features of different remote sensing data to improve classification accuracy. GASSF-Net uses geometric algebra (GA) to represent pixels from different bands as multivectors, thus capturing the intrinsic relationships between spectral bands while preserving spatial information. The network begins by deeply mining the spectral-spatial features of a hyperspectral image (HSI) using pairwise covariance operators. These features are then extracted through two branches: a geometric-algebra-based branch and a real-valued network branch. Additionally, the geometric-algebra-based network extracts spatial information from light detection and ranging (LiDAR) to complement the elevation data lacking in the HSI. Finally, a genetic-algorithm-based cross-fusion module is introduced to fuse the HSI and LiDAR data for improved classification. Experiments conducted on three well-known datasets, Trento, MUUFL,

关键词： multi-source remote sensing data geometric algebra cross-channel fusion multi-branch hierarchical fusion

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deep learning for Pothole Road Damage Detection and Classification 1

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Anusha, Poranki Sahas, Buse Sudeep SravanKumar, Gudla Jyotsna, G. Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (数字)9789819789467

ISBN: (纸本)9789819789450

Road damages including cracks and potholes pose significant challenges to road safety. Automated systems are being developed to address these issues. We use deep learning algorithms to analyze images of road surfaces to identify cracks and classify pothole roads. The model’s capacity to adjust to various road conditions gives its capability for real-time use in the identification of road damage. This achievement creates opportunities for additional research into deep learning models, to improve their capacity to handle more significant issues with infrastructure upkeep and safety. deep learning models can be very helpful in detecting these potholes mainly on busy roads with hectic traffic or even small roads. Combining the advanced model, VGG16 improves the effectiveness of road defect-detecting systems. This would be very helpful for the reconstruction of roads. With all these the effectiveness of applying deep learning methods—more especially VGG16—for detecting road conditions. The groundwork establishes future developments with deep learning to road infrastructure monitoring and maintenance while also adding to the research in intelligent transportation systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image segmentation

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image Monitoring System Based on deep Neural Network 2

Image Monitoring System Based on Deep Neural Network

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2nd International Conference on image processing, Computer Vision and Machine learning, ICICML 2023

作者： Weng, Junhong Zeng, Lingfeng Song, Yingjie Shenzhen Power Supply Bureau CO. LTD China Southern Power Grid Guangdong Shenzhen China

ISBN: (纸本)9798350331417

For safety and security reasons, the indoor/outdoor working environments of various industries require the use of many cameras for automated surveillance. In such context, a major challenge for automated monitoring system is achieving high-precision real-time performance for image classification and object detection. In this paper, we present a novel image surveillance system based on a combined approach derived from YOLO V5. The system first detects moving targets using background subtraction. Then, we propose a modified YOLO V5 algorithm for accurately detecting and categorizing different objects in images captured in a video stream. The system runs in real time and could analyze multiple video streams simultaneously. The results of the experiments show that this system has good performance and could be widely applied on several areas, such as security, surveillance, and traffic management. © 2023 IEEE.

关键词： deep nueral network monitoring system YOLOv5

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