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Eye Blink to Voice Communication Aid for Paralyzed Patients 2nd

Eye Blink to Voice Communication Aid for Paralyzed Patients

引用

2nd International conference on Data Science, machine Learning and applications, ICDSMLA 2020

作者： Gowramma, G.S. Arumugam, S.S. Pranav, K.P. Soundharya Lakshmi, V. Mithun Gowda, V. Don Bosco Institute of Technology Bengaluru India

ISBN: (纸本)9789811636899

Paralyzed is also known as Locked-in syndrome happens when an individual is a quadriplegic and the patient cannot speak or do a facial movement (*** ). The affected patient cannot communicate but he will be aware of his surrounding;the patient can hear and see has a normal person. The patient has thinking capacity and reasoning ability he can only use eye movement to communicate with others. Hence, the key purpose of this paper is to propose a real-time interactive system for a paralyzed patient using eye blink an eye motion detection algorithm by assisting them to communicate to caretakers to their needs through blink which is converted to voice. For example, clinical help, S.O.S, essential utility. This is done using a video outline that is handled by Open Computer vision libraries for image processing which is an open-source programming method which is aimed at real-time computer vision. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computer vision

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Volumetric Feature Extraction from 2D images Using Cubixels

Volumetric Feature Extraction from 2D Images Using Cubixels

引用

International conference on machine Learning and applications (ICMLA)

作者： Sanad Aburass Maha Abu Rumman Department of Computer Science Luther College Decorah Iowa USA Department of Computer Science Maharishi International University Fairfield Iowa USA

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

This paper introduces innovative volumetric feature extraction methods for 2D images using Cubixels and the Volume of the Void (VoV) metric. Cubixels transform traditional pixels into three-dimensional entities, capturing more detailed spatial and color relationships by assigning RGB values to the dimensions of width, length, and height. The VoV metric quantifies spatial gaps between adjacent Cubixels, providing valuable insights into textural characteristics and edge acuity. We developed two methods: the first focuses on calculating VoV between Cubixels, while the second integrates gradient and curvature features to enhance geometric feature extraction. These methods were applied to the CIFAR-10 and MNIST datasets and evaluated using four convolutional neural network (CNN) models incorporating residual connections, spatial dropout, and attention mechanisms. Our experimental results demonstrate significant improvements in model performance, including higher validation accuracy and reduced overfitting, as indicated by the Overfitting Index. This study underscores the potential of volumetric feature extraction in advancing image processing and computer vision, paving the way for more sophisticated and context-aware AI applications.

关键词： Measurement Computer vision Attention mechanisms Accuracy Computational modeling Transforms Feature extraction Convolutional neural networks Indexes Overfitting

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A FAST AND COST-EFFECTIVE IMAGING SYSTEM FOR FINE-SCALE TOOL CONDITION MONITORING IN ULTRASONIC METAL WELDING 18

A FAST AND COST-EFFECTIVE IMAGING SYSTEM FOR FINE-SCALE TOOL...

引用

ASME 18th Int Mfg Sci and Engn Conf held jointly with the 51st N Amer Res Conf / Japan-Soc-of-Mech-Engineers Int Conf on Leading Edge Mfg/Mat and Proc

作者： Dong, Zhiqiao Chen, Qianmeng Lu, Kuan-Chieh Shao, Chenhui Univ Illinois Dept Mech Sci & Engn Urbana IL 61801 USA

ISBN: (纸本)9780791887240

In industrial-scale applications of ultrasonic metal welding (UMW), tool condition monitoring (TCM) is one of the most important maintenance tasks because tool health degrades quickly, and tool condition impacts the process physics and joint quality significantly. Moreover, tool replacement constitutes a notable portion of maintenance costs. In UMW, tool health degradation occurs mainly in the form of changes in tool surface geometry. Online TCM, which uses in-situ sensing data to indirectly classify tool conditions, has demonstrated to be effective;however, such indirect methods cannot provide a detailed characterization of tool surface profiles. On the other hand, direct measurements of tool surfaces require expensive and time-consuming high-resolution 3D metrology, which substantially increases the cost of quality and delays maintenance decision-making. To overcome these challenges, this paper develops a fast and cost-effective imaging system for fine-scale TCMinUMW. The imaging system mainly consists of a macro lens and a Raspberry Pi (RPI) high quality camera mounted over a linear rail driven by a stepper motor, and the full system is controlled through RPI GPIO (general-purpose input/output). The imaging system is used in conjunction with coaxial illumination, which enhances tool surface features, to capture a photo of a cast reproducing tool surface geometry. Then, image processing techniques are developed to characterize tool surface profiles and features. We demonstrate the effectiveness of the proposed TCMstrategy using tools in three distinct conditions. Results show that the TCM imaging system can effectively reconstruct critical fine-scale geometric features of tools, thus enabling more responsive, interpretable, and reliable TCM for UMW.

关键词： tool condition monitoring surface metrology ultrasonic metal welding machine vision computer vision maintenance quality control

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Design of LQFP chip pin defect detection system based on machine vision

Design of LQFP chip pin defect detection system based on mac...

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2021 International conference on machine Learning and Intelligent Systems Engineering, MLISE 2021

作者： Ou, XingFu Chen, Wenfeng Zhang, Miao Institute of Intelligent Manufacturing Foshan Polytechnic Guangdong Province Foshan China College of Automation Guangdong University of Technology Guangzhou University Town Guangdong Province Guangzhou China

ISBN: (纸本)9781665417365

In order to solve the problems in manual detection of LQFP chip defects, an automatic detection system of LQFP chip pin defects based on machine vision is designed. The system can use ARM-DSP dual-core architecture to rapidly realize image acquisition, processing, feature extraction and peripheral applications. And then a fast segmentation algorithm based on dynamic threshold is designed to obtain the chip feature image. The automatic detection of LQFP chip pin defects can be realized by using the methods such as grayscale transition detector to detect the pin midpoint, midpoint line fitting and pin spacing statistics. The experimental results show that the system is fast, accurate and feasible. © 2021 IEEE.

关键词： image segmentation

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A Literature Review of machine Learning Techniques for Dance Recognition and Robotic vision

A Literature Review of Machine Learning Techniques for Dance...

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Robotics and Technologies for Industrial Automation (ROBOTHIA), IEEE International conference on

作者： Lee Wei San Samuel-Soma M. Ajibade Muhammed Basheer Jasser Adefemi Ayodele Babatunde Adedotun Ajayi Mbiatke Anthony Bassey Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Darul Ehsan Malaysia Research Centre for Nanomaterials and Energy Technology (RCNMET) Faculty of Engineering and Technology Sunway University Selangor Malaysia Research Centre for Human-Machine Collaboration (HUMAC) Faculty of Engineering and Technology Sunway University Petaling Jaya Selangor Malaysia University of East London London UK Department of Cyber security Faculty of Computing & Informatics Ladoke Akintola University of Technology Ogbomoso Nigeria Department of Business Administration Universiti Tun Hussein Onn Malaysia Batu Pahat Malaysia

ISBN: (数字)9798350356755

ISBN: (纸本)9798350356762

image recognition, powered by machine learning (ML), has significantly advanced applications in both dance movement recognition and robotic vision. This review examines key ML techniques, including Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Self-Organizing Maps (SOMs), and Long Short-Term Memory (LSTM) networks, alongside pose estimation methods like OpenPose and Part Affinity Fields (PAFs). These techniques enhance dance classification, real-time feedback, and motion analysis, with OpenPose + LSTMs and PAFs + LSTMs demonstrating the highest accuracy. Notwithstanding progress, obstacles such as high computational costs, data dependency, and real-time implementation challenges persist. Beyond dance, these methods are critical in robotic vision, intelligent automation, and industrial image processing, enabling autonomous robotic navigation, defect detection in manufacturing, and AI-driven motion tracking. By leveraging human movement analysis for robotics, ML improves human-robot interaction, robotic-assisted rehabilitation, and industrial automation. Despite progress, challenges such as high computational demands, data dependency, and real-time constraints remain. This review explores future directions, including multimodal data fusion, hybrid AI models, and real-time optimization, bridging the gap between AI-driven motion systems and intelligent automation to enhance adaptability and efficiency across domains.

关键词： Intelligent automation Humanities Solid modeling image recognition Accuracy Service robots Robot sensing systems Real-time systems Robots Long short term memory

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4th International conference on Communications and Cyber-Physical Engineering, ICCCE 2021

4th International Conference on Communications and Cyber-Phy...

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4th International conference on Communications and Cyber-Physical Engineering, ICCCE 2021

ISBN: (纸本)9789811679841

The proceedings contain 131 papers. The special focus in this conference is on Communications and Cyber-Physical Engineering. The topics include: machine Learning Based Solutions for Human Resource Systems Management;moisture Sensor Using Microstrip Patch Antenna;blockchain Technology for Assisting the Analysis of viruses and Other Pathogens;exploratory Analysis of Human Mood Swings;effective Classification of Autism Spectrum Disorder Using Adaptive Support Vector machine;particle Swarm Optimization Based Multiobjective Rank Aggregation;intelligent Beyond 5G Systems: Upcoming Wireless Communication Systems;WA-SCV Analysis for Scope Creep Management in a Software Project Requirements;Blockchain Based Electronic Healthcare Record (EHR);end-to-End Supporting System for IoT applications: Survey;spontaneous Detection of Potholes and Humps;text processing for Marathi Text-To-Speech Synthesis;device to Device Communication for Next Generation Cellular Network: A Survey;Recent Trends and Techniques of CBIR to Enhance Retrieval Performance;automatic Emotion Detection Using Electroencephalogram;wearable Electrocardiogram Feature Extraction for Real Time Monitoring applications;EEG Based Computationally Optimized Solution for Non-epileptic Seizure Detection;performance Evaluation of Cellular Networks Base Station Using Water Filling Algorithm;Classification and Performance Evaluation of Phishing Email or URL Using Random Forest;Novel Chest X-Ray 4-CH-CNN COviD-19 Diagnosis;machine vision Based Fruit Classification and Grading—A Review;Comprehensive Literature Survey for mm-Wave Massive MIMO Using machine Learning for 6G;maize Leaf Healthy and Unhealthy Classification Using image processing Technique and machine Learning Classifiers;graphology Based Human Behavior and Personality Identification Using Artificial Neural Networks;a Survey on Soft Biometric Techniques;a Survey on Liver Cancer Detection: Based on Deep Learning Technology;preface.

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Recognizing the Emotions Evoked by Artworks Through visual Features and Knowledge Graph-Embeddings 21st

Recognizing the Emotions Evoked by Artworks Through Visual F...

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21st International conference on image Analysis and processing (ICIAP)

作者： Aslan, Sinem Castellano, Giovanna Digeno, vincenzo Migailo, Giuseppe Scaringi, Raffaele Vessio, Gennaro Ca Foscari Univ Venice Dept Environm Sci Informat & Stat Venice Italy Univ Bari Aldo Moro Dept Comp Sci Bari Italy

ISBN: (纸本)9783031133213;9783031133206

Recognizing the emotion an image evokes in the observer has long attracted the interest of the community for its many potential applications. However, it is a challenging task mainly due to the inherent complexity and subjectivity of human feelings. Such a difficulty is exacerbated in the domain of visual arts, mainly because of their abstract nature. In this work, we propose a new version of the artistic knowledge graph we were working on, namely Artgraph, obtained by integrating the emotion labels provided by the ArtEmis dataset. The proposed graph enables emotion-based information retrieval and knowledge discovery even without training a learning model. In addition, we propose an artwork emotion classification system that jointly exploits visual features and knowledge graph-embeddings. Experimental evaluation revealed that while improvements in emotion classification depend mainly on the use of visual features, the prediction of style, genre and emotion can benefit from the simultaneous exploitation of visual and contextual features and can assist each other in a synergistic way.

关键词： visual arts Digital humanities Computer vision Deep learning Knowledge graphs Emotion classification

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Classification of Alzheimer’s Disease Using Improved MeshCNN based on Residuals Connection

Classification of Alzheimer’s Disease Using Improved MeshCN...

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machine vision, image processing and Imaging Technology (MviPIT), International conference on

作者： Yuankun Liu Duanyang Feng Lumin Xing Wenjian Liu Faculty of Data Science City University of Macau Macau S.A.R China Shandong Provincial Qianfoshan Hospital The First Affiliated Hospital of Shandong First Medical University Jinan China

ISBN: (数字)9798350306545

ISBN: (纸本)9798350306552

It’s great to see the potential of deep learning being applied to medical imaging for the diagnosis of Alzheimer’s disease. The challenges of small data size and overfitting are common issues in many deep learning applications, and it’s encouraging to see that the ADNI dataset was utilized to address these challenges. The use of MeshCNN network with residual connections borrowed from ResNet is a clever approach to improve the classification accuracy. The Mesh data representation of brain surfaces as triangular meshes is an interesting and innovative technique to incorporate into the classification model. The improvements in accuracy of 0.01 for AD / MCI / NC and 0.02 for AD / NC may seem small, but in the context of Alzheimer’s disease diagnosis, even small improvements in accuracy can have significant implications for early intervention and treatment. Overall, this study demonstrates the potential of deep learning to advance research on Alzheimer’s disease diagnosis and underscores the importance of continued innovation in medical imaging techniques to improve the accuracy and effectiveness of diagnosis and treatment.

关键词： Deep learning Technological innovation Accuracy machine vision Magnetic resonance Brain modeling Data models

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A Flexible machine vision AI System for Edge-Oriented Deep Learning Accelerators

A Flexible Machine Vision AI System for Edge-Oriented Deep L...

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International Technical conference o Circuits/Systems, Computers and Communications (ITC-CSCC)

作者： Joon Boum Song Yumi Kim Minkyu Lee Sang-Seol Lee Kyungho Kim Korea Electronics Technology Institute

In recent 10 years, deep learning has successfully shown its effectiveness in various computer vision fields such as autonomous vehicles, robotics, and AI surveillance. Numerous machine vision AI systems have been accordingly developed to run those deep learning algorithms. However, existing PC-based machine vision AI systems have the disadvantage of having to modify the entire system although just a small change is required. They are also considerably high cost/power consuming systems for edge device purpose which is targeted in this study. In addition, a number of FPGA-based machine vision AI systems are not suitable for multiple applications as they are dedicatedly designed. In this work, in order to overcome those disadvantages, we have developed a flexible FPGA-based machine vision AI system where various type of accelerator can be implemented. An accelerator integrated with a post processing unit(PPU) that runs an object detection was successfully demonstrated on the system satisfying the required input image resolution/format. The proposed system can also provide multiple resolution/format of input images which is proved via Xilinx integrated logic analyzer(ILA) debugging. We therefore ensure that this system can further support more accelerators which can be deployed in diverse applications.

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Regional Transformer for image Super-Resolution

Regional Transformer for Image Super-Resolution

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International conference on machine vision and Information Technology (CMviT)

作者： Sen Yang Jiahong Yang Dahong Xu Xi Li Hunan Normal University Hunan Normal University Key Laboratory of Sports Intelligence Research

In the image super-resolution algorithm model, a large receptive field can provide more valuable features, so the Transformer with strong information interaction ability has achieved excellent results in image super-resolution processing applications. However, when the range of the receptive field reaches a certain critical value, the restoration performance of the super-resolution algorithm also reaches a certain critical value, which indicates that unconditionally increasing the receptive field will not continue to promote the improvement of the restoration performance. At the same time, the larger the receptive field range, the more data the model needs to process, which also seriously increases the computational complexity of the algorithm. In order to exchange information in a wider range more effectively, in this paper, a new type of super-resolution network based on Transformer, namely Regional Transformer, is designed. The key element in the newly designed network structure is the Region Block (RB) with the Boundary Restriction (BR) mechanism. In addition, the paper designs a Boundary Restriction based on coarse-to-fine pipes. This paper conducts a large number of experiments on multiple datasets, and the experiments show that the network structure designed in this paper has a significant improvement in performance.

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