This document summarizes recent techniques for human pose estimation and classification from images and videos. It discusses the following:
1. The main approaches for human pose estimation are generative, discriminative, top-down, and bottom-up. Generative models use a representation of the human body, while discriminative methods directly map inputs to poses.
2. Popular human body models include skeleton-based using joint positions, contour-based using silhouettes, and volume-based using 3D meshes.
3. Keypoint detection methods discussed are OpenPose, DeepCut, RMPE, and Mask RCNN. They detect body parts and connect them to estimate poses.
4. Benchmark datasets for
This document discusses using artificial intelligence and deep learning techniques for yoga pose estimation and classification. Specifically, it proposes training a model using the PoseNet and OpenPose frameworks on a dataset of yoga pose videos to identify key points in the human body and classify the pose. The model would use convolutional neural networks and long short-term memory to process video frames in real-time and provide classification scores for the accuracy of pose identification. This type of system could help improve health and provide feedback to users on yoga pose form without an instructor. However, it is currently limited to a small number of poses and requires internet and webcam access.
Study of AI Fitness Model Using Deep LearningIRJET Journal
1. The document describes an AI fitness app called "AI Fitness Genie" that uses computer vision algorithms like YOLO and OpenPose to track a user's exercise poses, count repetitions, and provide feedback to improve form.
2. It detects body position using skeleton or contour modeling and compares the user's pose to 3D models to assess accuracy and provide recommendations for corrections.
3. An evaluation of the app found it could detect poses with 98.51% accuracy to guide users through proper exercise form.
Yoga Pose Detection and Correction using Posenet and KNNIRJET Journal
This document discusses a system for detecting and correcting yoga poses using computer vision techniques. The system uses PoseNet and a KNN classifier to identify key points in images of humans performing yoga poses. It then compares the actual pose to the target pose and provides feedback to help the user correct their form. The system was trained on a dataset of images depicting different yoga poses and can identify poses with 98.51% accuracy in real-time using a webcam. It aims to help people improve their yoga practice and form to avoid injuries from performing poses incorrectly.
AI Personal Trainer Using Open CV and Media PipeIRJET Journal
This document summarizes previous work on developing an AI personal trainer using computer vision techniques. It discusses early research using the Kinect camera for body posture detection. Later works applied machine learning and deep learning models to activity recognition in gyms and used OpenPose for pose detection in pre-recorded videos. To enable real-time detection, approaches categorized models as kinematic, planar and volumetric. Convolutional neural networks were also used to estimate 2D poses from single images and increase accuracy. More recent works introduced datasets with whole-body annotations and used a single network to address scale variance across body parts. The goal of this project is to build upon these techniques to create an AI trainer that analyzes exercise repetitions in real-time videos
YOGA POSE DETECTION USING MACHINE LEARNING LIBRARIESIRJET Journal
1) The document presents a study on detecting yoga poses using machine learning libraries. The researchers collected a dataset of 5 popular yoga poses and used OpenCV and MediaPipe for pose detection.
2) Several machine learning classifiers were tested on the dataset including logistic regression, random forest, gradient boosting, KNN and ridge regression. The gradient boosting classifier achieved the highest accuracy of 98.87%.
3) A web application with a graphical user interface was developed to allow users to capture their pose and receive feedback on accuracy in real-time without an instructor, helping people learn yoga poses properly. The system has potential for improvements like detecting additional poses and developing mobile applications.
AI Personal Trainer Using Open CV and Media PipeIRJET Journal
This document summarizes a research paper that proposes an AI personal trainer system using computer vision techniques. The system uses OpenCV and MediaPipe to detect a user's body pose and angles in real-time video to correct their form during exercises. It aims to help users safely and effectively work out at home without a physical trainer. The system would also connect users with similar fitness goals to encourage motivation. The researchers believe this AI trainer could make exercise more accessible and convenient for users.
IRJET- Recurrent Neural Network for Human Action Recognition using Star S...IRJET Journal
This document discusses a Recurrent Neural Network (RNN) methodology for human action recognition using star skeletonization. Star skeletonization is a technique that connects the geometric center of an object to its contour extremes, representing the human body as a five-dimensional vector connecting the head and limbs. A series of star skeleton vectors over time can represent a human action. The RNN model is suitable because the extracted features are time-dependent. Previous research on human action recognition is discussed, including approaches using depth motion maps, augmented constraints between joints, and bag-of-visual-words models. The challenges of human action recognition from video sequences are also summarized.
Comparison Analysis of Gait Classification for Human Motion Identification Us...IJECEIAES
In this paper, it will be discussed about comparison between two kinds of classification methods in order to improve security system based of human gait. Gait is one of biometric methods which can be used to identify person. K-Nearest Neighbour has parallelly implemented with Support Vector Machine for classifying human gait in same basic system. Generally, system has been built using Histogram and Principal Component Analysis for gait detection and its feature extraction. Then, the result of the simulation showed that K-Nearest Neighbour is slower in processing and less accurate than Support Vector Machine in gait classification.
This document discusses using artificial intelligence and deep learning techniques for yoga pose estimation and classification. Specifically, it proposes training a model using the PoseNet and OpenPose frameworks on a dataset of yoga pose videos to identify key points in the human body and classify the pose. The model would use convolutional neural networks and long short-term memory to process video frames in real-time and provide classification scores for the accuracy of pose identification. This type of system could help improve health and provide feedback to users on yoga pose form without an instructor. However, it is currently limited to a small number of poses and requires internet and webcam access.
Study of AI Fitness Model Using Deep LearningIRJET Journal
1. The document describes an AI fitness app called "AI Fitness Genie" that uses computer vision algorithms like YOLO and OpenPose to track a user's exercise poses, count repetitions, and provide feedback to improve form.
2. It detects body position using skeleton or contour modeling and compares the user's pose to 3D models to assess accuracy and provide recommendations for corrections.
3. An evaluation of the app found it could detect poses with 98.51% accuracy to guide users through proper exercise form.
Yoga Pose Detection and Correction using Posenet and KNNIRJET Journal
This document discusses a system for detecting and correcting yoga poses using computer vision techniques. The system uses PoseNet and a KNN classifier to identify key points in images of humans performing yoga poses. It then compares the actual pose to the target pose and provides feedback to help the user correct their form. The system was trained on a dataset of images depicting different yoga poses and can identify poses with 98.51% accuracy in real-time using a webcam. It aims to help people improve their yoga practice and form to avoid injuries from performing poses incorrectly.
AI Personal Trainer Using Open CV and Media PipeIRJET Journal
This document summarizes previous work on developing an AI personal trainer using computer vision techniques. It discusses early research using the Kinect camera for body posture detection. Later works applied machine learning and deep learning models to activity recognition in gyms and used OpenPose for pose detection in pre-recorded videos. To enable real-time detection, approaches categorized models as kinematic, planar and volumetric. Convolutional neural networks were also used to estimate 2D poses from single images and increase accuracy. More recent works introduced datasets with whole-body annotations and used a single network to address scale variance across body parts. The goal of this project is to build upon these techniques to create an AI trainer that analyzes exercise repetitions in real-time videos
YOGA POSE DETECTION USING MACHINE LEARNING LIBRARIESIRJET Journal
1) The document presents a study on detecting yoga poses using machine learning libraries. The researchers collected a dataset of 5 popular yoga poses and used OpenCV and MediaPipe for pose detection.
2) Several machine learning classifiers were tested on the dataset including logistic regression, random forest, gradient boosting, KNN and ridge regression. The gradient boosting classifier achieved the highest accuracy of 98.87%.
3) A web application with a graphical user interface was developed to allow users to capture their pose and receive feedback on accuracy in real-time without an instructor, helping people learn yoga poses properly. The system has potential for improvements like detecting additional poses and developing mobile applications.
AI Personal Trainer Using Open CV and Media PipeIRJET Journal
This document summarizes a research paper that proposes an AI personal trainer system using computer vision techniques. The system uses OpenCV and MediaPipe to detect a user's body pose and angles in real-time video to correct their form during exercises. It aims to help users safely and effectively work out at home without a physical trainer. The system would also connect users with similar fitness goals to encourage motivation. The researchers believe this AI trainer could make exercise more accessible and convenient for users.
IRJET- Recurrent Neural Network for Human Action Recognition using Star S...IRJET Journal
This document discusses a Recurrent Neural Network (RNN) methodology for human action recognition using star skeletonization. Star skeletonization is a technique that connects the geometric center of an object to its contour extremes, representing the human body as a five-dimensional vector connecting the head and limbs. A series of star skeleton vectors over time can represent a human action. The RNN model is suitable because the extracted features are time-dependent. Previous research on human action recognition is discussed, including approaches using depth motion maps, augmented constraints between joints, and bag-of-visual-words models. The challenges of human action recognition from video sequences are also summarized.
Comparison Analysis of Gait Classification for Human Motion Identification Us...IJECEIAES
In this paper, it will be discussed about comparison between two kinds of classification methods in order to improve security system based of human gait. Gait is one of biometric methods which can be used to identify person. K-Nearest Neighbour has parallelly implemented with Support Vector Machine for classifying human gait in same basic system. Generally, system has been built using Histogram and Principal Component Analysis for gait detection and its feature extraction. Then, the result of the simulation showed that K-Nearest Neighbour is slower in processing and less accurate than Support Vector Machine in gait classification.
The document summarizes a survey on recent developments in human pose estimation. It begins with an introduction to the field and discusses previous surveys. It then covers classifications of pose estimation like 2D vs 3D, single vs multiple person, top-down vs bottom-up approaches. Popular datasets for training models are described. Finally, it discusses performance evaluation metrics and challenges in the field. The main focus is on deep learning approaches optimized for deployment on edge devices to make pose estimation more accessible.
BIOMETRIC AUTHORIZATION SYSTEM USING GAIT BIOMETRYIJCSEA Journal
ABSTRACT
Human gait, which is a new biometric aimed to recognize individuals by the way they walk have come to play an increasingly important role in visual surveillance applications. In this paper a novel hybrid holistic approach is proposed to show how behavioural walking characteristics can be used to recognize unauthorized and suspicious persons when they enter a surveillance area. Initially background is modelled from the input video captured from cameras deployed for security and the foreground moving object in the individual frames are segmented using the background subtraction algorithm. Then gait representing spatial, temporal and wavelet components are extracted and fused for training and testing multi class support vector machine models (SVM). The proposed system is evaluated using side view videos of NLPR database. The experimental results demonstrate that the proposed system achieves a pleasing recognition rate and also the results indicate that the classification ability of SVM with Radial Basis Function (RBF) is better than with other kernel functions.
This document summarizes research on human motion tracking techniques using skeleton models. It discusses how model-based approaches use a predefined human skeleton model to represent joints and segments. Video-based approaches can infer physical attributes and daily actions without sensors. The document reviews several papers on reconstructing 3D human pose from video, using reduced joint sets and shape models to filter noise and track landmarks, and developing multi-view pose tracking using generative sampling and physical constraints. It also discusses challenges like high degrees of freedom and self-occlusion, and the need for efficient algorithms to enable real-time 3D full-body motion tracking from multiple cameras.
Paper Gloria Cea - Goal-Oriented Design Methodology Applied to User Interface...WTHS
This document describes a user interface designed for a mobile application called the Functional Assessment System (FAS). The FAS allows users to assess their aerobic fitness on their own without specialized equipment. The design of the mobile application interface was guided by the Goal-Oriented Design methodology. This methodology focuses on representing users as characters with specific goals and designing scenarios to help users achieve those goals. The document also discusses evaluating the usability of the interface using the AttrakDiff questionnaire to assess pragmatic and hedonic qualities. The results showed satisfactory user interaction with the FAS mobile application interface.
Face Recognition Smart Attendance System: (InClass System)IRJET Journal
- The document describes a face recognition system called "InClass" to automate student attendance tracking. It aims to address issues with traditional manual attendance systems like being inaccurate, time-consuming, and difficult to maintain.
- The InClass system uses a CNN face detector to detect and identify students' faces from images captured with a camera. It can handle variations in lighting, angles, and occlusions. Matching faces to a database allows for automated attendance marking.
- The system aims to simplify the attendance process, reduce time and errors compared to existing biometric systems, and make attendance records easily accessible and storable digitally rather than on paper.
Personality Traits and Visualization Survey by Christy CaseChristy C Langdon
This document summarizes key findings from 5 research articles on personality traits and visualization. The articles found:
1) Machine learning can classify users as fast or slow based on eye gaze and interaction data, and predict personality traits. This could enable real-time adaptive visualization systems.
2) Studies found locus of control, a personality measure, was important - those with internal locus performed better on procedural tasks in different interfaces.
3) Gaze-cursor alignment varies by time, task, and cursor behavior. Models using multiple cursor features can predict gaze better than cursor position alone.
4) Manipulating locus of control influenced visualization task performance, showing personality affects problem-solving.
5) Intro
Feature selection using modified particle swarm optimisation for face recogni...eSAT Journals
Abstract
One of the major influential factors which affects the accuracy of classification rate is the selection of right features. Not all features have vital role in classification. Many of the features in the dataset may be redundant and irrelevant, which increase the computational cost and may reduce classification rate. In this paper, we used DCT(Discrete cosine transform) coefficients as features for face recognition application. The coefficients are optimally selected based on a modified PSO algorithm. In this, the choice of coefficients is done by incorporating the average of the mean normalized standard deviations of various classes and giving more weightage to the lower indexed DCT coefficients. The algorithm is tested on ORL database. A recognition rate of 97% is obtained. Average number of features selected is about 40 percent for a 10 × 10 input. The modified PSO took about 50 iterations for convergence. These performance figures are found to be better than some of the work reported in literature.
Keywords: Particle swarm optimization, Discrete cosine transform, feature extraction, feature selection, face recognition, classification rate.
This document presents a large dataset for 3D human motion estimation containing over 3.6 million poses from 11 actors performing everyday activities. The dataset was collected using motion capture systems and synchronized with 2D video and depth data from 4 cameras. It aims to complement existing datasets by providing a more diverse range of human motions captured in a controlled laboratory setting, as well as complex mixed reality scenes with occlusions. Online tools are provided for model visualization, feature extraction, and baseline predictive methods to facilitate the use of this dataset.
HUMAN IDENTIFIER WITH MANNERISM USING DEEP LEARNINGIRJET Journal
The document discusses human posture and mannerism identification using deep learning. It describes how a deep learning model can be trained on media data from humans to recognize certain personalities based on their postures. This could then be used for applications like home security systems to prevent unwanted entrance. The document reviews several related works that use techniques like pose estimation, skeleton modeling, and deep neural networks to identify body joints and estimate full-body poses from images and video. Accurately estimating poses of multiple people remains a challenge, but recent methods using techniques like part affinity fields have achieved real-time performance with good accuracy.
1. The document discusses various approaches that have been used to develop machine learning models for yoga pose detection and correction using computer vision techniques. It reviews 14 different studies that have applied methods like convolutional neural networks, pose estimation libraries, and other deep learning algorithms.
2. Prediction accuracies for the models ranged from 82% to 99.58% for tasks like pose classification, detection of errors compared to ideal poses, and providing feedback to users. Keypoint detection from video frames was a common initial step before classification.
3. The review found that combinations of CNNs with other models like LSTMs, transfer learning approaches, and using larger datasets tended to improve accuracy for tasks like real-time yoga pose recognition and guidance
Projective exploration on individual stress levels using machine learningIRJET Journal
This document describes a proposed system to predict stress levels in college students using machine learning. It discusses how stress is an increasing issue among college students and the challenges with current manual methods of assessing stress. The proposed system would use an automated approach based on student profiles and behaviors to identify those experiencing stress. It reviews several previous studies that developed machine learning models to detect stress using physiological sensor data. The objective of the proposed project is to build a real-time classification model to predict whether students are experiencing stress or not based on their answers to questions. It would provide personalized solutions and assistance to help manage student stress levels and mental well-being.
Mano Vaidya: Gateway to Relaxation Via Machine LearningIRJET Journal
1. The document describes a mobile application called Mano Vaidya that aims to help reduce stress levels using machine learning techniques.
2. The app uses questionnaires from the SF-36 to classify users into stress level clusters (positive, tolerable, toxic) using k-means clustering. It then recommends stress-relieving activities based on the user's classification.
3. The application was developed using Android and evaluates users' mental health status through a series of questions. Machine learning algorithms like Naive Bayes, Decision Trees, and k-means are used to predict user's stress levels and suggest support activities.
Principal component analysis for human gait recognition systemjournalBEEI
This paper represents a method for Human Recognition system using Principal Component Analysis. Human Gait recognition works on the gait of walking subjects to identify people without them knowing or without their permission. The initial step in this kind of system is to generate silhouette frames of walking human. A number of features couldb be exytacted from these frames such as centriod ratio, heifht, width and orientation. The Principal Component Analysis (PCA) is used for the extracted features to condense the information and produces the main components that can represent the gait sequences for each waiking human. In the testing phase, the generated gait sequences are recognized by using a minimum distance classifier based on eluclidean distance matched with the one that already exist in the database used to identify walking subject
Gait Recognition using MDA, LDA, BPNN and SVMIJEEE
Recognition of any individual is a task to identify the human beings. Human identification using Gait is method to identify an individual by the way he walk or manner of moving on foot of humans. Gait recognition is a type of biometric recognition and related to the behavioral characteristics of biometric recognition. Gait offers ability of distance recognition or at low resolution. In this paper it will present the review of gait recognition system where different approaches and classification categories of Gait recognition like model free and model based approach, MDA, BPNN, LDA, and SVM.
Influence Analysis of Image Feature Selection TechniquesOver Deep Learning ModelIRJET Journal
This document discusses using different image feature selection techniques and their impact on deep learning models for image classification. It analyzes shape, color, texture, and combined features extracted from images using techniques like local binary patterns (LBP), grid color moments, and Sobel operators. A convolutional neural network (CNN) is used as the deep learning classifier. The performance is evaluated on a diabetic retinopathy detection dataset in terms of classification accuracy. The goal is to determine which feature selection techniques improve accuracy while minimizing computational resources when used with CNNs. A system is proposed that extracts individual features and combined features from images, then classifies them using CNNs to compare the impact of different feature selection approaches.
Pose Trainer: “An Exercise Guide and Assessment in Physiotherapy”IRJET Journal
This document describes a project called Pose Trainer that uses computer vision and machine learning to assess and provide feedback on physiotherapy exercises. The system uses OpenPose models to extract key points from user-uploaded exercise videos. It then compares the user's pose to sample poses to evaluate accuracy and provide real-time feedback on form. This guidance helps users perform exercises correctly at home without an in-person trainer. The project aims to make physical therapy more accessible and avoid potential injuries from improper exercise form.
This document discusses human activity recognition using deep learning techniques. It describes using a 3D convolutional neural network model to classify human activities in videos. The model is trained on the Kinetics dataset, which contains videos of human actions. The proposed system aims to identify common human activities like reading, writing, and playing instruments through video classification. It evaluates using deep learning for automated activity recognition as a challenging task due to issues like background clutter and pose variations between individuals.
Human Face Detection and Tracking for Age Rank, Weight and Gender Estimation ...IJERA Editor
This paper describes the technique for real time human face detection and tracking for age rank, weight and gender estimation. Face detection is involved with finding whether there are any faces in a given image and if there are any faces present, track the face and returns the face region with features of each face. Here it describes a simple and convenient hardware implementation of face detection method using Raspberry Pi Processor, which itself is a minicomputer of a credit card size. This paper presents a cost-sensitive ordinal hyperplanes ranking algorithm for human age evaluation based on face images. Two main components for building an efficient age estimator are facial feature extraction and estimator learning. Using feature extraction and comparing with our input database in which we have different age group face images with weight is specified according to that we also specify weight category i.e. under weight, normal weight and overweight . In this article we present gender estimation technique, which effectively integrates the head as well as mouth motion information with facial appearance by taking advantage of a unified probabilistic framework. Facial appearance as well as head and mouth motion possess a potentially relevant discriminatory power, and that the integration of different sources of biometric data from video sequences is the key approach to develop more precise and reliable realization systems.
ACTIVITY RECOGNITION USING HISTOGRAM OF ORIENTED GRADIENT PATTERN HISTORYijcseit
Human activity recognition is an important task in computer vision because it has many application areas
such as, healthcare, security, entertainment, and tactical scenarios. This paper presents a methodology to
automatically recognize human activity from input video stream using Histogram of Oriented Gradient
Pattern History (HOGPH) features and SVM classifier. For this purpose, the proposed system extracts
HOG features from a sequence of consecutive video frames and analyzes them to construct HOGPH feature
vector. The HOGPH feature vectors are used to train a multi-class SVM classifier for different human
activities. In test mode, we use the classifier with HOGPH feature vector to recognize human activity. We
have experimented with video data of human activity in real environments for three different tasks
(browsing, reading, and writing). The experimental result and its accuracy reveal that the proposed system
is applicable to recognize human activity in real-life.
ACTIVITY RECOGNITION USING HISTOGRAM OF ORIENTED GRADIENT PATTERN HISTORYijcseit
Human activity recognition is an important task in computer vision because it has many application areas
such as, healthcare, security, entertainment, and tactical scenarios. This paper presents a methodology to
automatically recognize human activity from input video stream using Histogram of Oriented Gradient
Pattern History (HOGPH) features and SVM classifier. For this purpose, the proposed system extracts
HOG features from a sequence of consecutive video frames and analyzes them to construct HOGPH feature
vector. The HOGPH feature vectors are used to train a multi-class SVM classifier for different human
activities. In test mode, we use the classifier with HOGPH feature vector to recognize human activity. We
have experimented with video data of human activity in real environments for three different tasks
(browsing, reading, and writing). The experimental result and its accuracy reveal that the proposed system
is applicable to recognize human activity in real-life.
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
More Related Content
Similar to A SURVEY ON HUMAN POSE ESTIMATION AND CLASSIFICATION
The document summarizes a survey on recent developments in human pose estimation. It begins with an introduction to the field and discusses previous surveys. It then covers classifications of pose estimation like 2D vs 3D, single vs multiple person, top-down vs bottom-up approaches. Popular datasets for training models are described. Finally, it discusses performance evaluation metrics and challenges in the field. The main focus is on deep learning approaches optimized for deployment on edge devices to make pose estimation more accessible.
BIOMETRIC AUTHORIZATION SYSTEM USING GAIT BIOMETRYIJCSEA Journal
ABSTRACT
Human gait, which is a new biometric aimed to recognize individuals by the way they walk have come to play an increasingly important role in visual surveillance applications. In this paper a novel hybrid holistic approach is proposed to show how behavioural walking characteristics can be used to recognize unauthorized and suspicious persons when they enter a surveillance area. Initially background is modelled from the input video captured from cameras deployed for security and the foreground moving object in the individual frames are segmented using the background subtraction algorithm. Then gait representing spatial, temporal and wavelet components are extracted and fused for training and testing multi class support vector machine models (SVM). The proposed system is evaluated using side view videos of NLPR database. The experimental results demonstrate that the proposed system achieves a pleasing recognition rate and also the results indicate that the classification ability of SVM with Radial Basis Function (RBF) is better than with other kernel functions.
This document summarizes research on human motion tracking techniques using skeleton models. It discusses how model-based approaches use a predefined human skeleton model to represent joints and segments. Video-based approaches can infer physical attributes and daily actions without sensors. The document reviews several papers on reconstructing 3D human pose from video, using reduced joint sets and shape models to filter noise and track landmarks, and developing multi-view pose tracking using generative sampling and physical constraints. It also discusses challenges like high degrees of freedom and self-occlusion, and the need for efficient algorithms to enable real-time 3D full-body motion tracking from multiple cameras.
Paper Gloria Cea - Goal-Oriented Design Methodology Applied to User Interface...WTHS
This document describes a user interface designed for a mobile application called the Functional Assessment System (FAS). The FAS allows users to assess their aerobic fitness on their own without specialized equipment. The design of the mobile application interface was guided by the Goal-Oriented Design methodology. This methodology focuses on representing users as characters with specific goals and designing scenarios to help users achieve those goals. The document also discusses evaluating the usability of the interface using the AttrakDiff questionnaire to assess pragmatic and hedonic qualities. The results showed satisfactory user interaction with the FAS mobile application interface.
Face Recognition Smart Attendance System: (InClass System)IRJET Journal
- The document describes a face recognition system called "InClass" to automate student attendance tracking. It aims to address issues with traditional manual attendance systems like being inaccurate, time-consuming, and difficult to maintain.
- The InClass system uses a CNN face detector to detect and identify students' faces from images captured with a camera. It can handle variations in lighting, angles, and occlusions. Matching faces to a database allows for automated attendance marking.
- The system aims to simplify the attendance process, reduce time and errors compared to existing biometric systems, and make attendance records easily accessible and storable digitally rather than on paper.
Personality Traits and Visualization Survey by Christy CaseChristy C Langdon
This document summarizes key findings from 5 research articles on personality traits and visualization. The articles found:
1) Machine learning can classify users as fast or slow based on eye gaze and interaction data, and predict personality traits. This could enable real-time adaptive visualization systems.
2) Studies found locus of control, a personality measure, was important - those with internal locus performed better on procedural tasks in different interfaces.
3) Gaze-cursor alignment varies by time, task, and cursor behavior. Models using multiple cursor features can predict gaze better than cursor position alone.
4) Manipulating locus of control influenced visualization task performance, showing personality affects problem-solving.
5) Intro
Feature selection using modified particle swarm optimisation for face recogni...eSAT Journals
Abstract
One of the major influential factors which affects the accuracy of classification rate is the selection of right features. Not all features have vital role in classification. Many of the features in the dataset may be redundant and irrelevant, which increase the computational cost and may reduce classification rate. In this paper, we used DCT(Discrete cosine transform) coefficients as features for face recognition application. The coefficients are optimally selected based on a modified PSO algorithm. In this, the choice of coefficients is done by incorporating the average of the mean normalized standard deviations of various classes and giving more weightage to the lower indexed DCT coefficients. The algorithm is tested on ORL database. A recognition rate of 97% is obtained. Average number of features selected is about 40 percent for a 10 × 10 input. The modified PSO took about 50 iterations for convergence. These performance figures are found to be better than some of the work reported in literature.
Keywords: Particle swarm optimization, Discrete cosine transform, feature extraction, feature selection, face recognition, classification rate.
This document presents a large dataset for 3D human motion estimation containing over 3.6 million poses from 11 actors performing everyday activities. The dataset was collected using motion capture systems and synchronized with 2D video and depth data from 4 cameras. It aims to complement existing datasets by providing a more diverse range of human motions captured in a controlled laboratory setting, as well as complex mixed reality scenes with occlusions. Online tools are provided for model visualization, feature extraction, and baseline predictive methods to facilitate the use of this dataset.
HUMAN IDENTIFIER WITH MANNERISM USING DEEP LEARNINGIRJET Journal
The document discusses human posture and mannerism identification using deep learning. It describes how a deep learning model can be trained on media data from humans to recognize certain personalities based on their postures. This could then be used for applications like home security systems to prevent unwanted entrance. The document reviews several related works that use techniques like pose estimation, skeleton modeling, and deep neural networks to identify body joints and estimate full-body poses from images and video. Accurately estimating poses of multiple people remains a challenge, but recent methods using techniques like part affinity fields have achieved real-time performance with good accuracy.
1. The document discusses various approaches that have been used to develop machine learning models for yoga pose detection and correction using computer vision techniques. It reviews 14 different studies that have applied methods like convolutional neural networks, pose estimation libraries, and other deep learning algorithms.
2. Prediction accuracies for the models ranged from 82% to 99.58% for tasks like pose classification, detection of errors compared to ideal poses, and providing feedback to users. Keypoint detection from video frames was a common initial step before classification.
3. The review found that combinations of CNNs with other models like LSTMs, transfer learning approaches, and using larger datasets tended to improve accuracy for tasks like real-time yoga pose recognition and guidance
Projective exploration on individual stress levels using machine learningIRJET Journal
This document describes a proposed system to predict stress levels in college students using machine learning. It discusses how stress is an increasing issue among college students and the challenges with current manual methods of assessing stress. The proposed system would use an automated approach based on student profiles and behaviors to identify those experiencing stress. It reviews several previous studies that developed machine learning models to detect stress using physiological sensor data. The objective of the proposed project is to build a real-time classification model to predict whether students are experiencing stress or not based on their answers to questions. It would provide personalized solutions and assistance to help manage student stress levels and mental well-being.
Mano Vaidya: Gateway to Relaxation Via Machine LearningIRJET Journal
1. The document describes a mobile application called Mano Vaidya that aims to help reduce stress levels using machine learning techniques.
2. The app uses questionnaires from the SF-36 to classify users into stress level clusters (positive, tolerable, toxic) using k-means clustering. It then recommends stress-relieving activities based on the user's classification.
3. The application was developed using Android and evaluates users' mental health status through a series of questions. Machine learning algorithms like Naive Bayes, Decision Trees, and k-means are used to predict user's stress levels and suggest support activities.
Principal component analysis for human gait recognition systemjournalBEEI
This paper represents a method for Human Recognition system using Principal Component Analysis. Human Gait recognition works on the gait of walking subjects to identify people without them knowing or without their permission. The initial step in this kind of system is to generate silhouette frames of walking human. A number of features couldb be exytacted from these frames such as centriod ratio, heifht, width and orientation. The Principal Component Analysis (PCA) is used for the extracted features to condense the information and produces the main components that can represent the gait sequences for each waiking human. In the testing phase, the generated gait sequences are recognized by using a minimum distance classifier based on eluclidean distance matched with the one that already exist in the database used to identify walking subject
Gait Recognition using MDA, LDA, BPNN and SVMIJEEE
Recognition of any individual is a task to identify the human beings. Human identification using Gait is method to identify an individual by the way he walk or manner of moving on foot of humans. Gait recognition is a type of biometric recognition and related to the behavioral characteristics of biometric recognition. Gait offers ability of distance recognition or at low resolution. In this paper it will present the review of gait recognition system where different approaches and classification categories of Gait recognition like model free and model based approach, MDA, BPNN, LDA, and SVM.
Influence Analysis of Image Feature Selection TechniquesOver Deep Learning ModelIRJET Journal
This document discusses using different image feature selection techniques and their impact on deep learning models for image classification. It analyzes shape, color, texture, and combined features extracted from images using techniques like local binary patterns (LBP), grid color moments, and Sobel operators. A convolutional neural network (CNN) is used as the deep learning classifier. The performance is evaluated on a diabetic retinopathy detection dataset in terms of classification accuracy. The goal is to determine which feature selection techniques improve accuracy while minimizing computational resources when used with CNNs. A system is proposed that extracts individual features and combined features from images, then classifies them using CNNs to compare the impact of different feature selection approaches.
Pose Trainer: “An Exercise Guide and Assessment in Physiotherapy”IRJET Journal
This document describes a project called Pose Trainer that uses computer vision and machine learning to assess and provide feedback on physiotherapy exercises. The system uses OpenPose models to extract key points from user-uploaded exercise videos. It then compares the user's pose to sample poses to evaluate accuracy and provide real-time feedback on form. This guidance helps users perform exercises correctly at home without an in-person trainer. The project aims to make physical therapy more accessible and avoid potential injuries from improper exercise form.
This document discusses human activity recognition using deep learning techniques. It describes using a 3D convolutional neural network model to classify human activities in videos. The model is trained on the Kinetics dataset, which contains videos of human actions. The proposed system aims to identify common human activities like reading, writing, and playing instruments through video classification. It evaluates using deep learning for automated activity recognition as a challenging task due to issues like background clutter and pose variations between individuals.
Human Face Detection and Tracking for Age Rank, Weight and Gender Estimation ...IJERA Editor
This paper describes the technique for real time human face detection and tracking for age rank, weight and gender estimation. Face detection is involved with finding whether there are any faces in a given image and if there are any faces present, track the face and returns the face region with features of each face. Here it describes a simple and convenient hardware implementation of face detection method using Raspberry Pi Processor, which itself is a minicomputer of a credit card size. This paper presents a cost-sensitive ordinal hyperplanes ranking algorithm for human age evaluation based on face images. Two main components for building an efficient age estimator are facial feature extraction and estimator learning. Using feature extraction and comparing with our input database in which we have different age group face images with weight is specified according to that we also specify weight category i.e. under weight, normal weight and overweight . In this article we present gender estimation technique, which effectively integrates the head as well as mouth motion information with facial appearance by taking advantage of a unified probabilistic framework. Facial appearance as well as head and mouth motion possess a potentially relevant discriminatory power, and that the integration of different sources of biometric data from video sequences is the key approach to develop more precise and reliable realization systems.
ACTIVITY RECOGNITION USING HISTOGRAM OF ORIENTED GRADIENT PATTERN HISTORYijcseit
Human activity recognition is an important task in computer vision because it has many application areas
such as, healthcare, security, entertainment, and tactical scenarios. This paper presents a methodology to
automatically recognize human activity from input video stream using Histogram of Oriented Gradient
Pattern History (HOGPH) features and SVM classifier. For this purpose, the proposed system extracts
HOG features from a sequence of consecutive video frames and analyzes them to construct HOGPH feature
vector. The HOGPH feature vectors are used to train a multi-class SVM classifier for different human
activities. In test mode, we use the classifier with HOGPH feature vector to recognize human activity. We
have experimented with video data of human activity in real environments for three different tasks
(browsing, reading, and writing). The experimental result and its accuracy reveal that the proposed system
is applicable to recognize human activity in real-life.
ACTIVITY RECOGNITION USING HISTOGRAM OF ORIENTED GRADIENT PATTERN HISTORYijcseit
Human activity recognition is an important task in computer vision because it has many application areas
such as, healthcare, security, entertainment, and tactical scenarios. This paper presents a methodology to
automatically recognize human activity from input video stream using Histogram of Oriented Gradient
Pattern History (HOGPH) features and SVM classifier. For this purpose, the proposed system extracts
HOG features from a sequence of consecutive video frames and analyzes them to construct HOGPH feature
vector. The HOGPH feature vectors are used to train a multi-class SVM classifier for different human
activities. In test mode, we use the classifier with HOGPH feature vector to recognize human activity. We
have experimented with video data of human activity in real environments for three different tasks
(browsing, reading, and writing). The experimental result and its accuracy reveal that the proposed system
is applicable to recognize human activity in real-life.
Similar to A SURVEY ON HUMAN POSE ESTIMATION AND CLASSIFICATION (20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
Open Channel Flow: fluid flow with a free surfaceIndrajeet sahu
Open Channel Flow: This topic focuses on fluid flow with a free surface, such as in rivers, canals, and drainage ditches. Key concepts include the classification of flow types (steady vs. unsteady, uniform vs. non-uniform), hydraulic radius, flow resistance, Manning's equation, critical flow conditions, and energy and momentum principles. It also covers flow measurement techniques, gradually varied flow analysis, and the design of open channels. Understanding these principles is vital for effective water resource management and engineering applications.
Determination of Equivalent Circuit parameters and performance characteristic...pvpriya2
Includes the testing of induction motor to draw the circle diagram of induction motor with step wise procedure and calculation for the same. Also explains the working and application of Induction generator
Blood finder application project report (1).pdfKamal Acharya
Blood Finder is an emergency time app where a user can search for the blood banks as
well as the registered blood donors around Mumbai. This application also provide an
opportunity for the user of this application to become a registered donor for this user have
to enroll for the donor request from the application itself. If the admin wish to make user
a registered donor, with some of the formalities with the organization it can be done.
Specialization of this application is that the user will not have to register on sign-in for
searching the blood banks and blood donors it can be just done by installing the
application to the mobile.
The purpose of making this application is to save the user’s time for searching blood of
needed blood group during the time of the emergency.
This is an android application developed in Java and XML with the connectivity of
SQLite database. This application will provide most of basic functionality required for an
emergency time application. All the details of Blood banks and Blood donors are stored
in the database i.e. SQLite.
This application allowed the user to get all the information regarding blood banks and
blood donors such as Name, Number, Address, Blood Group, rather than searching it on
the different websites and wasting the precious time. This application is effective and
user friendly.
Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...Transcat
Join us for this solutions-based webinar on the tools and techniques for commissioning and maintaining PV Systems. In this session, we'll review the process of building and maintaining a solar array, starting with installation and commissioning, then reviewing operations and maintenance of the system. This course will review insulation resistance testing, I-V curve testing, earth-bond continuity, ground resistance testing, performance tests, visual inspections, ground and arc fault testing procedures, and power quality analysis.
Fluke Solar Application Specialist Will White is presenting on this engaging topic:
Will has worked in the renewable energy industry since 2005, first as an installer for a small east coast solar integrator before adding sales, design, and project management to his skillset. In 2022, Will joined Fluke as a solar application specialist, where he supports their renewable energy testing equipment like IV-curve tracers, electrical meters, and thermal imaging cameras. Experienced in wind power, solar thermal, energy storage, and all scales of PV, Will has primarily focused on residential and small commercial systems. He is passionate about implementing high-quality, code-compliant installation techniques.
Digital Twins Computer Networking Paper Presentation.pptxaryanpankaj78
A Digital Twin in computer networking is a virtual representation of a physical network, used to simulate, analyze, and optimize network performance and reliability. It leverages real-time data to enhance network management, predict issues, and improve decision-making processes.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
A high-Speed Communication System is based on the Design of a Bi-NoC Router, ...DharmaBanothu
The Network on Chip (NoC) has emerged as an effective
solution for intercommunication infrastructure within System on
Chip (SoC) designs, overcoming the limitations of traditional
methods that face significant bottlenecks. However, the complexity
of NoC design presents numerous challenges related to
performance metrics such as scalability, latency, power
consumption, and signal integrity. This project addresses the
issues within the router's memory unit and proposes an enhanced
memory structure. To achieve efficient data transfer, FIFO buffers
are implemented in distributed RAM and virtual channels for
FPGA-based NoC. The project introduces advanced FIFO-based
memory units within the NoC router, assessing their performance
in a Bi-directional NoC (Bi-NoC) configuration. The primary
objective is to reduce the router's workload while enhancing the
FIFO internal structure. To further improve data transfer speed,
a Bi-NoC with a self-configurable intercommunication channel is
suggested. Simulation and synthesis results demonstrate
guaranteed throughput, predictable latency, and equitable
network access, showing significant improvement over previous
designs
Supermarket Management System Project Report.pdfKamal Acharya
Supermarket management is a stand-alone J2EE using Eclipse Juno program.
This project contains all the necessary required information about maintaining
the supermarket billing system.
The core idea of this project to minimize the paper work and centralize the
data. Here all the communication is taken in secure manner. That is, in this
application the information will be stored in client itself. For further security the
data base is stored in the back-end oracle and so no intruders can access it.