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10th International conference on Fuzzy systems and Data Mining, FSDM 2024

作者： Su, Wensheng Wang, Xinren Xu, Jia Special Equipment Safety Supervision Inspection Institute of Jiangsu Province Nanjing210036 China

ISBN: (纸本)9781643685694

Digital twin technology has gradually gained widespread application in mechanical engineering fields such as crane operation and maintenance. We propose a digital twin-based crane cloud mobile virtual reality (VR) framework. This framework combines cloud rendering, digital twin technology, and panoramic stereo imaging technology to achieve high-quality interactive 3D crane simulation scenarios stereo display on smartphones. In the context of digital twins, enhancing the accuracy, real-time performance, and resource utilization efficiency of crane operation and maintenance scene simulations has become a crucial research issue. The study optimizes the digital twin model through a multi-scale feature extraction algorithm (PMFEA) and a pyramid convolution and grouped channel attention mechanism. Experimental results show that the improved PMFEA algorithm exhibits outstanding performance in multiple practical application scenarios. In static crane recognition scenarios, PMFEA achieves an accuracy of 0.93, significantly outperforming ResNet50's 0.90 and VGG16's 0.87. In dynamic crane management, PMFEA leads with an accuracy of 0.89, ahead of MobileNetV2's 0.84 and InceptionV3's 0.85. Additionally, PMFEA achieves an accuracy of 0.85 in crane driving simulation scenarios and 0.90 in smart grid management scenarios, both significantly outperforming other comparative algorithms. Notably, in equipment diagnosis, PMFEA stands out with the highest accuracy of 0.94, significantly ahead of other algorithms, demonstrating its immense potential in high-precision demanding fields. The results indicate that this framework can use digital twin technology to stereoscopically display high-precision and high-quality interactive crane VR 3D simulation scenarios on smartphone web browsers at low computational cost. Furthermore, it provides users with an excellent immersive experience through VR glasses. © 2024 The Authors.

关键词： Stereo image processing

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An Optimistic Review on Breast Cancer Disease Diagnosis

An Optimistic Review on Breast Cancer Disease Diagnosis

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Intelligent Cyber Physical systems and Internet of Things (ICoICI), International conference on

作者： Purnima Singh Bhati Vishal Shrivastava Akhil Pandey COMPUTER SCIENCE AND ENGINEERING Arya College of Engineering & I.T Jaipur India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

Breast cancer is now being discovered in more women than ever before. A complete recovery is possible if breast cancer is discovered and treated early. As a consequence, researchers have devised practical and precise methods for predicting the growth of cancer cells through the use of automated deep-learning technologies. However, there are just a few articles that summarize the current research on breast cancer diagnostics. However, these studies were unable to address novel breast cancer detection structures and approaches. Using DL (Deep Learning) in medical image analysis, clinicians may now spot illnesses sooner, treat them more effectively, or forecast how they will develop in the future. Architectures of deep learning have evolved for the detection of breast cancer. This research study will focus on existing deep learning architectures, analyze their strengths and shortcomings, examine the datasets they used, and the image pre-processing procedures.

关键词： Deep learning image analysis Reviews Prediction algorithms Breast cancer Classification algorithms Medical diagnosis Internet of Things Medical diagnostic imaging Tumors

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1st International conference on Advances in Signal processing and Communication Engineering, ICASPACE 2021

1st International Conference on Advances in Signal Processin...

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1st International conference on Advances in Signal processing and Communication Engineering, ICASPACE 2021

ISBN: (纸本)9789811955495

The proceedings contain 44 papers. The special focus in this conference is on Advances in Signal processing and Communication Engineering. The topics include: Single-Precision Floating-Point Multiplier Design Using Quantum-Dot Cellular Automata with Power Dissipation Analysis;compression Techniques for Low Power Hardware Accelerator Design: Case Studies;sequence Set Design for Radar System;design of an All Digital Phase-Locked Loop Using Cordic Algorithm;analysis of Deep Learning algorithms for image Denoising;an Extensive Survey on Assessment of Multicore Processors for Embedded systems;image Segmentation Techniques and Optimization algorithms for Lung Cancer Detection;handwritten to Text Document Converter;An Efficient Energy Aware for Reliable Route Discovery Using Energy with Movement Detection Technique in MANET;Lumped Circuit Modeling at Nanoscale (Part-II: Coupling Between Two Nanospheres);design and Implementation of Imprecise Adders for Low-Power Applications;speech Processed Public Addressing System;an Approach Towards Data Privacy Issues in Distributed Cyber Physical System;Classification of LPI Radar Signals Using Multilayer Perceptron (MLP) Neural Networks;a Systematic Review on Screening of Diabetic Retinopathy and Maculopathy Using Artificial Intelligence;Area Efficient and High-Throughput Radix-4 1024-Point FFT Processor for DSP Applications;air Quality Monitoring System Based on Artificial Intelligence;an Improved Technique for Identification of Forgery image Detection Using Clustering Method;EEG Signal Analysis During Stroop Task for Checking the Effect of Sleep Deprivation;UWB Localization Procedures with Range Control Methods—A Review;deep Learning Model for Multiclass Classification of Diabetic Retinal Fundus images Using Gradient Descent Optimization;multilevel Authentication to Wireless Sensor Networks Against Malicious Attacks Using Butterfly Method.

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An Automatic ROI Detection in Ultrasound images for Thyroid Cancer

An Automatic ROI Detection in Ultrasound Images for Thyroid ...

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Electronics and Sustainable Communication systems (ICESC), 2020 International conference on

作者： T. Veda Reddy Shadab Siddiqui Koneru Lakshmaiah Education Foundation Hyderabad Telangana India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Thyroid cancer cases seem to be increasing rapidly across the globe. The best way to get detailed information about thyroid is ultrasound image. By analyzing ultrasound image features an expert can advise whether the nodule may be cancer or benign. Recently, artificial intelligence algorithms employed not only for efficient early diagnosis of thyroid cancer but also for over diagnosis. The paper proposes an automatic region of interest detection in ultra-sonic images using intensity and edge based segmentation. Intensity based segmentation is an image processing approach which divides an image into distinct regions based on intensity values of the pixels. Next edge-based segmentation is employed which detects edges within the image that might define anatomical shapes. For this a canny edge detector is used to identify potential boundaries of the ROI. The extracted ROI is evaluated using support vector machine algorithm to classify whether the extracted nodule features are benign or malignant. The proposed method achieves an accuracy of 98.8% and 97.4% in training and test phases respectively. The experimental results are also compared with existing literature work.

关键词： Support vector machines Training image segmentation Ultrasonic imaging Thyroid cancer image edge detection Feature extraction Classification algorithms Thyroid Tumors

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Maximum power point tracking based on metaheuristic algorithms of Partially Shaded solar photovoltaic system

Maximum power point tracking based on metaheuristic algorith...

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International conference on image and Signal processing and their Applications (ISPA)

作者： Taha Lemdjed Belahcene Okba Kraa Hamza Afghoul Djallal Eddine Zabia Belgacem Med Nassim Bouzidi Feriel Abdelmalek Department of Electrical Engineering LI3CUB Laboratory University of Mohamed Khider Biskra Biskra Algeria Department of Electrical Engineering LMSE Laboratory University of Mohamed Khider Biskra Biskra Algeria Department of Electrotechnics LEPCI Laboratory University of Ferhat Abbas Setif-1 Setif Algeria Department of Electrotechnics LAADI Laboratory University of Djelfa Djelfa Algeria Department of Electronics LEPCI Laboratory University of Ferhat Abbas Setif-1 Setif Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

The effectiveness of photovoltaic energy systems is contingent upon climatic variables, particularly sun irradiance and temperature. These considerations have prompted significant advancements in the development of novel Maximum Power Point Tracking (MPPT) techniques. The presence of partial shadowing condition gives rise to significant challenges, including a reduction in the output power generation and the emergence of several peaks in the PV characteristic; Also, it effects on the power quality injected to the grid. Therefore, the optimization of the global maximum power point (GMPP) under partial shading conditions is a challenging issue. In order to address this issue. This work provides a comparison of MPPT procedures utilizing the Grey Wolf optimization (GWO) method and the Cuckoo Search (CS) method. Furthermore, it explores the impact of these MPPT algorithms on the total harmonic distortion (THD) when implemented under identical system characteristics and climatic conditions. Using MATLAB simulation, the results demonstrate that before injecting power from PV, the MPC achieves a good THD Valeu of 1.87%, and that after injecting power, GWO outperforms CS in response time and efficiency, and has a lower THD value of 4.68%.

关键词： Maximum power point trackers Photovoltaic systems Total harmonic distortion Temperature Signal processing algorithms Signal processing Time factors

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Automated Pomegranate Leaf Disease Identification: An image processing Perspectives

Automated Pomegranate Leaf Disease Identification: An Image ...

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International conference on Communication systems and Network Technologies (CSNT)

作者： B Meghana Janani J S Lalitha Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Agriculture is a predominant occupation in India, and to date, automated plant disease detection has been applied primarily to crops such as paddy, wheat, and various vegetables (e.g., tomato, cucumber, and potato). In this work, a novel approach is proposed for the automated detection and labeling of pomegranate leaf diseases by employing Support Vector Machine (SVM) for classification and K-Means Clustering for image segmentation. The study focuses on four specific plant diseases—Bacterial Blight, Alternaria Alternata, Cercospora Leaf Spot, and Anthracnose—as well as the detection of healthy leaves when no affected areas are present. To enhance the visibility of the leaf images, contrast enhancement is performed as a preliminary step. Subsequently, K-Means Clustering is applied to segment the input image in the Lab color space. A Multiclass SVM is then utilized to classify the segmented regions based on identified affected portions of the leaf. Finally, the Region of Interest (ROI) is determined to calculate and display the total affected area, facilitating precise disease-type detection. Experimental results indicate that the proposed method achieves an accuracy of 0.96, a precision of 0.95455, a recall of 0.9333, an F1 score of 0.978, and a kappa statistic of 0.947, thereby demonstrating the efficacy of the proposed system for automated pomegranate leaf disease detection.

关键词： Support vector machines image segmentation Plant diseases Machine learning algorithms image color analysis Clustering algorithms Crops Machine learning Labeling Diseases

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Automated Recognition of Yoga Mudras through image processing

Automated Recognition of Yoga Mudras through Image Processin...

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Distributed systems, Computer Networks and Cybersecurity (ICDSCNC), International conference on

作者： Deeksha Holla Shambbavi S Harish H Spoorthi K T Varshita Bai Maharani Lakshmi Ammanni College for Women Autonomous Bengaluru India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Yoga, an ancient practice, uses specific hand gestures called mudras, to reduce stress, enhance focus, and improve mental health. However, existing systems for Yoga Mudra Recognition have been limited in scope, recognizing only a small number of mudras and often lacking accuracy under diverse and challenging conditions. To address these shortcomings, this study introduces a novel approach that overcomes these limitations by employing advanced image processing techniques. By leveraging cutting-edge computer vision algorithms, the system is capable of accurately identifying 22 different yoga mudras, marking a significant expansion from previous models. The methodology involves capturing detailed hand landmarks, applying sophisticated preprocessing steps, extracting critical features such as finger positions, and utilizing SVM for precise classification. The refined dataset, combined with extensive experiments considering various hand shapes, lighting conditions, and backgrounds, resulted in an impressive accuracy of 99.87%. This gesture recognition technology enhances mindfulness and holistic wellness across various environments.

关键词： Hands Support vector machines Learning systems Accuracy image recognition Shape Lighting Mental health Feature extraction Skin

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Real-Time Object Detection in Microscopic image of Indian Herbal Plants using YOLOv5 on Jetson Nano 1

Real-Time Object Detection in Microscopic Image of Indian He...

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1st International conference on Connected systems and Intelligence, CSI 2022

作者： Jha, Yash Prajapati, Harsh Fataniya, Bhupendra Nirma University Ahmedabad Electronics and Communication Dept. Ahmedabad India

ISBN: (纸本)9781665458153

Object detection has been evolving greatly in recent years and the advancements in hardware and software technologies have made it possible to perform object detection with ease. Due to the enhanced capabilities of the modern processors and Graphics processing Unit (GPU) of doing an exponentially complex and extensive number of iterations in very less time. Real-time object detection has become highly popular and the center of attention in recent years because most of the hardware owned by common users is powerful enough to compute that which unlocks whole new possibilities for implementing real-time object detection in numerous applications in various domains. Real-time herbal plant detection is one such topic that has many applications in the field of ayurvedic medicines and many other pharmaceutical applications that can be used to spike the efficiency in identifying these herbal plants that can be used as a precaution and even as a cure for numerous health problems. There are many existing algorithms for real-time detection, but the evolution of new Artificial Neural Network (ANN) and Machine Learning (ML) techniques unlocks new ways to implement recent and advanced algorithms to apply for real-time detection of such powdered microscopic images to achieve better performance in various aspects compared to already existing methods. Our model is trained for detecting three types of microscopic herbal plants. © 2022 IEEE.

关键词： Graphics processing unit

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Research on image Stabilization Ray Tracing Algorithm in Smartphone Photography

Research on Image Stabilization Ray Tracing Algorithm in Sma...

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International conference on Information systems and Computer Aided Education (ICISCAE)

作者： Lei Zhao Wuhan Business University Wuhan Hubei China

ISBN: (数字)9798350350760

ISBN: (纸本)9798350350777

This paper studies the image stabilization ray tracing algorithm in smartphone photography, which combines ray tracing technology and image processing technology to reduce image blur caused by hand shaking or environmental factors. Firstly, the background of image stabilization and its importance in mobile phone photography are introduced. Then, the basic principle of ray tracing algorithm and its application in image stabilization are described in detail. A new image stabilization algorithm is proposed to recover a clear image by tracing the path of light in the imaging process and compensating the deviation of light path caused by camera movement. At the same time, the median filtering technology is introduced to remove the noise in the image, and the image repair algorithm is combined to further improve the image quality. The experimental results show that the algorithm can effectively improve the stability and clarity of images in different scenes, and provides an effective image stabilization scheme for smart phone photography.

关键词： Photography image quality Heuristic algorithms Noise Process control Ray tracing Stability analysis image restoration Reliability Smart phones

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Graph Signal Restoration Using Nested Deep Algorithm Unrolling

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IEEE TRANSACTIONS ON SIGNAL processing 2022年 70卷 3296-3311页

作者： Nagahama, Masatoshi Yamada, Koki Tanaka, Yuichi Chan, Stanley H. Eldar, Yonina C. Tokyo Univ Agr & Technol Dept Elect Engn & Comp Sci Koganei Tokyo 1848588 Japan Japan Sci & Technol Agcy PRESTO Kawaguchi Saitama 3320012 Japan Tokyo Univ Sci Dept Elect Engn Tokyo 1258585 Japan Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA Weizmann Inst Sci Fac Math & Comp Sci IL-7610001 Rehovot Israel

Graph signal processing is a ubiquitous task in many applications such as sensor, social, transportation and brain networks, point cloud processing, and graph neural networks. Often, graph signals are corrupted in the sensing process, thus requiring restoration. In this paper, we propose two graph signal restoration methods based on deep algorithm unrolling (DAU). First, we present a graph signal denoiser by unrolling iterations of the alternating direction method of multiplier (ADMM). We then suggest a general restoration method for linear degradation by unrolling iterations of Plug-and-Play ADMM (PnP-ADMM). In the second approach, the unrolled ADMM-based denoiser is incorporated as a submodule, leading to a nested DAU structure. The parameters in the proposed denoising/restoration methods are trainable in an end-to-end manner. Our approach is interpretable and keeps the number of parameters small since we only tune graph-independent regularization parameters. We overcome two main challenges in existing graph signal restoration methods: 1) limited performance of convex optimization algorithms due to fixed parameters which are often determined manually. 2) large number of parameters of graph neural networks that result in difficulty of training. Several experiments for graph signal denoising and interpolation are performed on synthetic and real-world data. The proposed methods show performance improvements over several existing techniques in terms of root mean squared error in both tasks.

关键词： Signal processing algorithms image restoration Signal restoration Noise reduction Task analysis Symmetric matrices Optimization Graph signal processing signal restoration deep algorithm unrolling Plug-and-Play ADMM

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