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International Conference on Communications and Signal processing

作者： N Karthikeyan Soumya Ranjan Mahapatro R Akash P v Rakesh Teja School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Tamil Nadu India School of Electronics and Comm. Engineering Vellore Institute of Technology Chennai Campus Tamil Nadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

With the increasing prevalence of surveillance systems, the need for an automated system that detects the presence of intruders from the surveillance camera and send an alert message to the user immediately has become very crucial. The research paper aims to provide a deep learning (DL) model-based solution to accurately detect the presence of human figures in the frame. The approach includes using YOLOv8 (You Only Look Once – version 8) object detection model to detect human figures, paired with the implementation of the Non-Maximum Suppression technique to remove duplicate bounding boxes around humans. Additionally, Gaussian Noise Reduction technique has been used to remove Gaussian blur from the image for obtaining a clearer image of human figure present in the frame. The research paper demonstrates a procedure to quickly send alert message to the user when a human figure is detected in the camera using the Telegram application. The paper also demonstrates the implementation of frame skipping method based on periodic intervals for optimizing the computational efficiency. In addition to this, the paper also demonstrates a comparative study between the frequently used human detection algorithms namely YOLOv8, YOLOv4, SSD and HOG and provides statistical proof on how YOLOv8 outperforms the other standard models.

关键词： YOLO Measurement Computational modeling Surveillance Gaussian noise Intrusion detection Signal processing

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An Improved RT-DETR Model for Detection of Tobacco Impurities

An Improved RT-DETR Model for Detection of Tobacco Impuritie...

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International Conference on vision, image and Signal processing (ICvISP)

作者： Chengjin Tao Zhongxin Ma Lijun Yun Zaiqing Chen Bin Li Cheng Shen Yunnan Provincial Tobacco Company Kunming China Yunnan Normal University Engineering Research Center of Computer Vision and Intelligent Control Technology Kunming China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

Tobacco, as a crop of significant economic importance, plays a pivotal role in global agriculture and industry. However, during the processing of tobacco products, the presence of tobacco impurities (TIs) poses a major challenge, as it substantially diminishes product quality. Traditional methods for detecting TIs are often inefficient and limited in scope, underscoring the need for precise and reliable detection techniques. This study addresses these challenges by introducing an improved Real-Time Detection Transformer (RT-DETR) model. By integrating the ShuffleNet v2 architecture and a novel Multi-Branch-Edge-Information-Select (MBEIS) block, the model enhances feature extraction and fusion capabilities, enabling efficient detection even in complex scenarios. Furthermore, a standardized imaging environment, consisting of controlled lighting and a black box setup, was employed to ensure consistent data collection, and the dataset was augmented using advanced data enhancement techniques. Experimental evaluations demonstrate that the proposed model achieves a precision of 92.7%, a recall of 93.5 %, and a mean average precision (mAP) of 87.9%, outperforming mainstream detection algorithms. In addition, the reduction in model parameters and computational complexity ensures superior real-time performance, making it both lightweight and effective for industrial applications. The results highlight the model's strong potential as a robust solution for detection of TIs, providing a significant technological advancement for the tobacco industry. This research not only addresses critical quality assurance challenges but also offers a valuable reference for applying advanced detection technologies in other industrial domains.

关键词： Accuracy Impurities Computational modeling Computer architecture Signal processing Transformers Real-time systems Product design Quality assessment Sorting

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Analysis and visual exploration of prediction algorithms for public bicycle sharing systems 15

Analysis and visual exploration of prediction algorithms for...

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International Conference on Computer Graphics, visualization, Computer vision and image processing 2021, CGvCvIP 2021, Connected Smart Cities 2021, CSC 2021 and Big Data Analytics, Data Mining and Computational Intelligence 2021, BIGDACI 2021 - Held at the 15th Multi-Conference on Computer Science and Information systems, MCCSIS 2021

作者： Ordoñez, Alexandra Cortez vázquez, Pere-Pau ViRVIG Universitat Politècnica de Catalunya Carrer Llorens I Artigas 4-6 Barcelona08028 Spain

ISBN: (纸本)9789898704320

Public bicycle sharing systems have become an increasingly popular means of transportation in many cities around the world. However, the information shown in mobile apps or websites is commonly limited to the system's current status and is of little use for both citizens and responsible planning entities. The vast amount of data produced by these managing systems makes it feasible to elaborate and present predictive models that may help its users in the decision-making process. For example, if a user finds a station empty, the application could provide an estimation of when a new bicycle would be available. In this paper, we explore the suitability of several prediction algorithms applied to this case of bicycle availability, and we present a web-based tool to visually explore their prediction errors under different time frames. Even though a quick quantitative analysis may initially suggest that Random Forest yields a lower error, our visual exploration interface allows us to perform a more thorough analysis and detect subtle but relevant differences between algorithms depending on variables such as the station's behavior, hourly intervals, days, or types of days (weekdays and weekends). This case illustrates the potential of visual representation together with quantitative metrics to compare prediction algorithms with a higher level of detail, which can, in turn, assist application designers and decision-makers to dynamically adjust the best model for their specific scenarios. © 2021 International Conference on Computer Graphics, visualization, Computer vision and image processing 2021, CGvCvIP 2021, Connected Smart Cities 2021, CSC 2021 and Big Data Analytics, Data Mining and Computational Intelligence 2021, BIGDACI 2021 - Held at the 15th Multi-Conference on Computer Science and Information systems, MCCSIS 2021. All rights reserved.

关键词： Websites

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Automated Detection and Analysis of Road Cracks and Pothole Depths using Computer vision and Depth Imaging

Automated Detection and Analysis of Road Cracks and Pothole ...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Manohar Makkena Geyani Lingamallu veda Harshitha Digavalli vamshidhar Reddy Gudupalli v. M. Manikandan Shaiju Panchikkil SRM University-AP Andhra Pradesh India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Maintaining road infrastructure is essential to effective transportation systems and public safety. This research provides a new method for pothole depth estimation and automatic road crack detection using computer vision techniques. Our method utilizes convolutional neural networks (CNNs) for classifying road images into “With Crack” or “Without Crack” categories with high accuracy. Additionally, we employ image processing algorithms to detect and highlight cracks, providing insights into their lengths and percentages. Furthermore, we introduce a monocular depth estimation model to assess pothole depths, aiding in prioritizing road repair efforts. Experimental results demonstrate the effectiveness of our approach in accurately identifying road defects and estimating their severity. This research contributes to the advancement of intelligent infrastructure management systems, enabling proactive maintenance and ensuring safer roads for communities.

关键词： Computer vision Accuracy Thresholding (Imaging) Roads Scalability Transportation Maintenance Convolutional neural networks Surface cracks Surface treatment

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Eye Movement analysis And Prediction Using image processing

Eye Movement analysis And Prediction Using Image Processing

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International Conference on Communication, Computing and Internet of Things (IC3IoT)

作者： v.K.G Kalaiselvi D. Doss Information Technology Sri Sairam Engineering College Chennai

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

The predominant function of most facial analysis systems revolves around facial alignment and eye tracking, crucial for locating key facial landmarks in images or videos. While developers have access to various models and techniques within popular computer vision libraries, the real-time demands and challenges posed by extreme head positions, occlusions, and inadequate lighting often limit their *** this article, we propose a range of software optimization techniques and training strategies centered on data augmentation. These strategies aim to enhance a diverse array of models integral to real-time face alignment algorithms. We advocate for a broader set of evaluation criteria, enabling innovative assessments that mitigate common issues encountered in real-time monitoring *** experimental findings showcase that models developed using our suggested methodologies exhibit smoother tracking, enhanced speed, reduced size, heightened accuracy, and greater resilience in challenging conditions.

关键词： Training Tracking Computational modeling Lighting Gaze tracking Real-time systems Software

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The Discrete System of Fibonacci Functions for the Construction of a Global Automated System for Monitoring Objects Littering the Environment Using Remote Sensing 27

The Discrete System of Fibonacci Functions for the Construct...

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Conference on image and Signal processing for Remote Sensing XXvII

作者： Kazaryan, M. Semenishchev, E. voronin, v. North Ossetian State Med Acad Vladikavkaz Russia Moscow State Univ Technol STANKIN Ctr Cognit Technol & Machine Vis Moscow Russia

ISBN: (数字)9781510645691

ISBN: (纸本)9781510645691;9781510645684

The article explores the detection of unauthorized landfills based on remote sensing data. We have developed a mathematical approach that translates ordinary images to the 2-D manifold or 3-D manifolds in stereo images. At the same time, the posed task when developing algorithms is the transformation (convolution) of these manifolds into a one-dimensional sample. The developed method corresponds to the following two conditions: 1) preservation of the topological proximity of the elements of the original and expanded spaces, 2) preservation of correlations between the elements of the original and transformed spaces. The basic idea that underlies the mathematical support of the developed automated system is the concept of fractal sets. The concept of continuous orthogonal transformations, including the Fibonacci transform, is used as a mathematical basis for determining anomalous signal structures in the surrounding background. The problem of monitoring and decoding space images from the point of view of the synthesis of orthogonal systems with predetermined properties (speed of calculations, order of transformation, etc.) is presented. Examples of processing by the proposed algorithm are presented on the satellite images of the Moscow Region territories of the Russian Federation.

关键词： space image of waste disposal facility landfill chemical process waste degradation fractals discrete orthogonal transformations Fibonacci transformations

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Advanced Security systems for Home Surveillance 4

Advanced Security Systems for Home Surveillance

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4th International Conference on Cognitive Computing and Information processing, CCIP 2022

作者： Savitha, G. Aashish Ramana, S. Jain, Kuldeep R v Institute of Technology and Management Department of Compter Science and Engineering Bangalore560076 India

ISBN: (纸本)9781665456487

In modern times, Security and surveillance in households have become somewhat of an especially important necessity. Advancement in technology has made it possible for everyone to access or break into different houses easily. The main purpose of our project is to build an advanced surveillance system that can be used to detect the different faces or any movement that may occur while in the view of the surveillance camera. This system is also supported by an application that has unique features to make it more user friendly for the users. Not only is the user notified when an unauthorized entity is detected, the user is also allowed to add different faces or objects that will be ignored during the process of theft detection. This has been achieved using unsupervised machine learning where a given set of data is compared with the actual live feed from the surveillance camera to check for any anomalies in its surroundings. The Dataset or the data used in the proposed system are a few images in the format of. JPEG and. JPG which can be stored in the given location manually by the user or through the application itself. The proposed model recognizes the images in any of the available formats. The modules used in this system are powered by a strong python module named Open Cv. This module supports various face recognition algorithms such as Haar Cascade, Eigen Faces, Fischer Faces, Local Binary Pattern Histogram (LBPH), etc and this module is responsible for all the image recognition, classification, and identification. The images extracted from the dataset are real time image frames obtained from the user webcam, both are compared using the Face Recognition module in python which uses the Regions for-Convolutional Neural Network Algorithm (R-CNN) and Unsupervised learning approach to detect and differentiate between objects in Real Time. This system also includes a message transmitting feature which works with the help of the Simple Message Transfer Protocol (SMTP) module

关键词： Python

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Moving vehicle Registration Plate Detection Using Machine Learning

Moving Vehicle Registration Plate Detection Using Machine Le...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： B Sreedhar Gowtham Prasad T v S Sivudu Macherla K. S. Raghuram M. Sunil Kumar Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram A.P India Dept of Electronics and Mohan Babu University (erstwhile Sree Vidyanikethan Engineering College) Tirupathi AP India Department of IT PACE Institute of Technology and Sciences Ongole AP India Department of Mechanical Engineering Vignan's Institute of Information Technology (A) Visakhapatnam A. P. India Department of Computer Science and Engineering School of Computing Mohan Babu University (erstwhile Sree Vidyanikethan Engineering College) Tirupathi AP India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

This research investigates the application of machine learning techniques in the detection of moving vehicle registration plates. The study explores the effectiveness of various algorithms in accurately identifying and localizing license plates in dynamic and real-time scenarios. By utilizing image processing and pattern recognition methods, the research aims to develop a robust and efficient system capable of detecting license plates under challenging conditions, such as varying lighting conditions and different vehicle orientations. The proposed approach offers potential benefits for law enforcement, traffic monitoring, and parking management systems. Through empirical evaluations, the study aims to validate the effectiveness and reliability of the machine learning-based approach for moving vehicle registration plate detection, providing insights for future research and development in this field.

关键词： Quantum computing Heuristic algorithms Machine learning Real-time systems Pattern recognition Reliability vehicle dynamics

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A Multi-Channel Spike Sorting Processor With Accurate Clustering Algorithm Using Convolutional Autoencoder

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IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND systems 2021年第6期15卷 1441-1453页

作者： Seong, Changyu Lee, Wonjae Jeon, Dongsuk Seoul Natl Univ Res Inst Convergence Sci Grad Sch Convergence Sci & Technol Seoul 08826 South Korea Seoul Natl Univ Inter Univ Semicond Res Ctr Seoul 08826 South Korea

This paper presents a spike sorting processor based on an accurate spike clustering algorithm. The proposed spike sorting algorithm employs an L2-normalized convolutional autoencoder to extract features from the input, where the autoencoder is trained using the proposed spike sorting-aware loss. In addition, we propose a similarity-based K-means clustering algorithm that conditionally updates the means by observing the cosine similarity. The modified K-means algorithm exhibits better convergence and enables online clustering with higher classification accuracy. We implement a spike sorting processor based on the proposed algorithm using an efficient time-multiplexed hardware architecture in a 40-nm CMOS process. Experimental results show that the processor consumes 224.75 mu W/mm(2) when processing 16 input channels at 7.68 MHz and 0.55 v. Our design achieves 95.54% clustering accuracy, outperforming prior spike sorting processor designs.

关键词： Clustering algorithms Feature extraction Sorting Training image reconstruction Real-time systems Autoencoder neural recording real-time recording spike sorting

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A Comprehensive Review on Deep Learning algorithms and its Applications 2

A Comprehensive Review on Deep Learning Algorithms and its A...

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2nd International Conference on Electronics and Sustainable Communication systems, ICESC 2021

作者： Kumar, P. Santhosh Sakthivel, v.P Raju, Manda Sathya, P.D. Annamalai University Dept of ECE Tamil nadu Chidambaram India Government College of Engineering Department of EEE Tamilnadu India Kakatiya Institute of Technology and Science Dept of ECE Warangal India

ISBN: (纸本)9781665428675

A very active expansion of data and convenience in modern period accept to motivate extremely significant automation tasks with the advanced algorithmic models with the technologies such as Artificial Intelligence, Deep Learning (Portion belonging to Machine Learning) to overcome the challenges in speech synthesis and brain tumor analysis. This research study explores different deep learnign algorithms and its applications. This review paper investigates the incoporation of Deep Learning techniques in signal processing, communications, medical image processing, particularly the models, which manage through effective activation of Artificial Intelligence. At this moment, many researchers attempt to obtain novel solutions to several issues that occur in different domains. This investigation focuses on anticipating threats that arise in different domains, which will be clarified through Deep Learning and it unquestionably be an upcoming generation of certain issues that are valuable and may help the analysts. © 2021 IEEE.

关键词： Field programmable gate arrays (FPGA)

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