Android Phone has power to access or fetch data from remote location and provide various facilities to the user. Hence android applications have more and more demand because of its user friendly nature and its power of computation. Many tourist are having problem to search proper tourist places due to communication overhead or less facility of tourist guide. It is impractical to search each and every tourist place at every location. So in order to provide feasible as well as user friendly solution for this problem we develop an android application which will automatically recognize famous and nearby places and send notification to android phone. This application also provides weather recommendation feature which notifies the tourist about weather conditions of the destination before visiting it. All places are properly categorized and also with review or rating. The application also provides facility of vehicle mark to reach your vehicle after site visit. We are using Triangulation method with LBS as well as GPS to track the location of user. And as per his location, relevant list of tourist places will be send in the form of pop up notification.
Abstract— Itinerary planning is the important process for the travelling agencies which need to be done carefully to satisfy the requirements of the users. The satisfaction level of users can be increased by creating the itinerary planning in the customized manner by gathering the details from the users itself. There will be more burden of processing the large search space will occur where the number of POI present will be large. The time complexity and cost of processing those requests will be increased due to the large search space. And also it will be more difficult when there is a need of creating an similar type of itinerary plan to the multiple users. To overcome this problem in this work multi-user itinerary plan generation was proposed which intends to generate the multi-user customized itinerary plan by using the fuzzy-c-means clustering algorithm. In this approach, membership values are defined for clustering process. Based on the membership values of users we are clustering the users. From the experimentation result, the proposed system is used to reduce the search complexity as well as time complexity of the system.
Matching Sketches with Digital Face Images using MCWLD and Image Moment Invar...iosrjce
Face recognition is an important problem in many application domains. Matching sketches with
digital face image is important in solving crimes and capturing criminals. It is a computer application for
automatically identifying a person from a still image. Law enforcement agencies are progressively using
composite sketches and forensic sketches for catching the criminals. This paper presents two algorithms that
efficiently retrieve the matched results. First method uses multiscale circular Weber’s local descriptor to encode
more discriminative local micro patterns from local regions. Second method uses image moments, it extracts
discriminative shape, orientation, and texture features from local regions of a face. The discriminating
information from both sketch and digital image is compared using appropriate distance measure. The
contributions of this research paper are: i) Comparison of multiscale circular Weber’s local descriptor with
image moment for matching sketch to digital image, ii) Analysis of these algorithms on viewed face sketch,
forensic face sketch and composite face sketch databases
IRJET- Facial Expression Recognition using GPA AnalysisIRJET Journal
This document discusses a method for facial expression recognition using geometric feature analysis (GPA). The method involves preprocessing an input face image, extracting the skin pixels and facial features, and then using a support vector machine (SVM) classifier trained on geometric features to recognize the expression. Specifically, it performs skin mapping using a gray level co-occurrence matrix to isolate the face, extracts features like the eyes, nose and lips, and then inputs geometric relationships between these features into the SVM to classify the expression based on previous training data. The goal is to develop an automated system for facial expression recognition using digital image processing techniques.
Novel Approach to Use HU Moments with Image Processing Techniques for Real Ti...CSCJournals
Sign language is the fundamental communication method among people who suffer from speech and hearing defects. The rest of the world doesn’t have a clear idea of sign language. “Sign Language Communicator” (SLC) is designed to solve the language barrier between the sign language users and the rest of the world. The main objective of this research is to provide a low cost affordable method of sign language interpretation. This system will also be very useful to the sign language learners as they can practice the sign language. During the research available human computer interaction techniques in posture recognition was tested and evaluated. A series of image processing techniques with Hu-moment classification was identified as the best approach. To improve the accuracy of the system, a new approach; height to width ratio filtration was implemented along with Hu-moments. System is able to recognize selected Sign Language signs with the accuracy of 84% without a controlled background with small light adjustments.
This document describes a proposed content-based image retrieval system using backpropagation neural networks (BPNN) and k-means clustering. It begins by discussing CBIR techniques and features like color, texture, and shape. It then outlines the proposed system which includes training a BPNN on image features, validating images, and testing by querying and retrieving similar images. Performance is analyzed based on metrics like accuracy, efficiency, and classification rate. Results show the system achieves up to 98% classification accuracy within 5-6 seconds.
The document summarizes a novel approach for multisensor biometric fusion of face and palmprint images using wavelet decomposition and SIFT features for person authentication. Face and palmprint images are decomposed using wavelets and fused to create an enhanced fused image. SIFT features are extracted from the fused image and used for matching based on a monotonic-decreasing graph approach. Experimental results on a 150 person database show the proposed fusion method achieves 98.19% accuracy, outperforming individual face and palmprint recognition.
This document discusses object detection techniques for images. It begins with an introduction to image processing and object detection. Object detection aims to find instances of real-world objects in images and is used in applications like surveillance and automotive safety. The document then reviews several papers on related topics, including object removal from videos, visual surveillance systems for tracking targets, and augmented reality object compositing. It proposes using skull detection and region growing techniques to detect particular objects while removing shadows and other background objects for improved detection. Region growing would detect the object, while skull detection removes it from the image to analyze the object separately. In summary, the document reviews object detection methods and proposes a hybrid approach using skull detection and region growing.
A developed GPS trajectories data management system for predicting tourists' POITELKOMNIKA JOURNAL
One of the areas that have challenges in the use of internet of things (IoT) is the field of tourism and travel. The issue here is how to employ this technology to serve the tourism and managing the produced data. This work is focus on the use of tourists' trajectories that are collected from global positioning system (GPS) mobile sensors as a source of information. The aim of work is to predict preferred tourism places for tourists by tracking tourists' behavior to extract the tourism places that have been visited by such tourists. Density based clustering algorithm is mainly used to extract stay points and point of interest (POI). By projecting GPS location (for user and places) on the Google map, the type and name of places favored by the tourists are determined. K nearest neighbor (KNN) algorithm with haversine distance has been adopted to find the nearest places for tourists. The evaluation of the obtained results shows superior and satisfactory performance that can reach the objective behind this work.
Abstract— Itinerary planning is the important process for the travelling agencies which need to be done carefully to satisfy the requirements of the users. The satisfaction level of users can be increased by creating the itinerary planning in the customized manner by gathering the details from the users itself. There will be more burden of processing the large search space will occur where the number of POI present will be large. The time complexity and cost of processing those requests will be increased due to the large search space. And also it will be more difficult when there is a need of creating an similar type of itinerary plan to the multiple users. To overcome this problem in this work multi-user itinerary plan generation was proposed which intends to generate the multi-user customized itinerary plan by using the fuzzy-c-means clustering algorithm. In this approach, membership values are defined for clustering process. Based on the membership values of users we are clustering the users. From the experimentation result, the proposed system is used to reduce the search complexity as well as time complexity of the system.
Matching Sketches with Digital Face Images using MCWLD and Image Moment Invar...iosrjce
Face recognition is an important problem in many application domains. Matching sketches with
digital face image is important in solving crimes and capturing criminals. It is a computer application for
automatically identifying a person from a still image. Law enforcement agencies are progressively using
composite sketches and forensic sketches for catching the criminals. This paper presents two algorithms that
efficiently retrieve the matched results. First method uses multiscale circular Weber’s local descriptor to encode
more discriminative local micro patterns from local regions. Second method uses image moments, it extracts
discriminative shape, orientation, and texture features from local regions of a face. The discriminating
information from both sketch and digital image is compared using appropriate distance measure. The
contributions of this research paper are: i) Comparison of multiscale circular Weber’s local descriptor with
image moment for matching sketch to digital image, ii) Analysis of these algorithms on viewed face sketch,
forensic face sketch and composite face sketch databases
IRJET- Facial Expression Recognition using GPA AnalysisIRJET Journal
This document discusses a method for facial expression recognition using geometric feature analysis (GPA). The method involves preprocessing an input face image, extracting the skin pixels and facial features, and then using a support vector machine (SVM) classifier trained on geometric features to recognize the expression. Specifically, it performs skin mapping using a gray level co-occurrence matrix to isolate the face, extracts features like the eyes, nose and lips, and then inputs geometric relationships between these features into the SVM to classify the expression based on previous training data. The goal is to develop an automated system for facial expression recognition using digital image processing techniques.
Novel Approach to Use HU Moments with Image Processing Techniques for Real Ti...CSCJournals
Sign language is the fundamental communication method among people who suffer from speech and hearing defects. The rest of the world doesn’t have a clear idea of sign language. “Sign Language Communicator” (SLC) is designed to solve the language barrier between the sign language users and the rest of the world. The main objective of this research is to provide a low cost affordable method of sign language interpretation. This system will also be very useful to the sign language learners as they can practice the sign language. During the research available human computer interaction techniques in posture recognition was tested and evaluated. A series of image processing techniques with Hu-moment classification was identified as the best approach. To improve the accuracy of the system, a new approach; height to width ratio filtration was implemented along with Hu-moments. System is able to recognize selected Sign Language signs with the accuracy of 84% without a controlled background with small light adjustments.
This document describes a proposed content-based image retrieval system using backpropagation neural networks (BPNN) and k-means clustering. It begins by discussing CBIR techniques and features like color, texture, and shape. It then outlines the proposed system which includes training a BPNN on image features, validating images, and testing by querying and retrieving similar images. Performance is analyzed based on metrics like accuracy, efficiency, and classification rate. Results show the system achieves up to 98% classification accuracy within 5-6 seconds.
The document summarizes a novel approach for multisensor biometric fusion of face and palmprint images using wavelet decomposition and SIFT features for person authentication. Face and palmprint images are decomposed using wavelets and fused to create an enhanced fused image. SIFT features are extracted from the fused image and used for matching based on a monotonic-decreasing graph approach. Experimental results on a 150 person database show the proposed fusion method achieves 98.19% accuracy, outperforming individual face and palmprint recognition.
This document discusses object detection techniques for images. It begins with an introduction to image processing and object detection. Object detection aims to find instances of real-world objects in images and is used in applications like surveillance and automotive safety. The document then reviews several papers on related topics, including object removal from videos, visual surveillance systems for tracking targets, and augmented reality object compositing. It proposes using skull detection and region growing techniques to detect particular objects while removing shadows and other background objects for improved detection. Region growing would detect the object, while skull detection removes it from the image to analyze the object separately. In summary, the document reviews object detection methods and proposes a hybrid approach using skull detection and region growing.
A developed GPS trajectories data management system for predicting tourists' POITELKOMNIKA JOURNAL
One of the areas that have challenges in the use of internet of things (IoT) is the field of tourism and travel. The issue here is how to employ this technology to serve the tourism and managing the produced data. This work is focus on the use of tourists' trajectories that are collected from global positioning system (GPS) mobile sensors as a source of information. The aim of work is to predict preferred tourism places for tourists by tracking tourists' behavior to extract the tourism places that have been visited by such tourists. Density based clustering algorithm is mainly used to extract stay points and point of interest (POI). By projecting GPS location (for user and places) on the Google map, the type and name of places favored by the tourists are determined. K nearest neighbor (KNN) algorithm with haversine distance has been adopted to find the nearest places for tourists. The evaluation of the obtained results shows superior and satisfactory performance that can reach the objective behind this work.
This document summarizes an article that proposes a new approach to improving image web search techniques. It begins with an abstract that describes how rapidly increasing internet users and multimedia search are increasing network traffic, with most traffic being for searching multimedia content online. It then provides background on existing issues with image search and commonly used algorithms. The document proposes a hybrid image search engine that searches by image-to-image comparison, text/keywords, and uses a hybrid matches graph and time sensitivity filter to improve results. It describes the training and testing phases, featuring feature extraction and annotation matching to retrieve relevant images from queries.
Novel Hybrid Approach to Visual Concept Detection Using Image AnnotationCSCJournals
Millions of images are being uploaded on the internet without proper description (tags) about these images. Image retrieval based on image tagging approach is much faster than Content Based Image Retrieval (CBIR) approach but requires an entire image collection to be manually annotated with proper tags. This requires a lot of human efforts and time, and hence not feasible for huge image collections. An efficient method is necessary for automatically tagging such a vast collection of images. We propose a novel image tagging method, which automatically tags any image with its concept. Our unique approach to solve this problem involves manual tagging of small exemplar image set and low-level feature extraction of all the images, hence called a hybrid approach. This approach can be used to tag a large image dataset from manually tagged small image dataset. The experiments are performed on Wang's Corel Dataset. In the comparative study, it is found that, the proposed concept detection system based on this novel tagging approach has much less time complexity of classification step, and results in significant improvement in accuracy as compared to the other tagging approaches found in the literature. This approach may be used as faster alternative to the typical Content Based Image Retrieval (CBIR) approach for domain specific applications.
SIGNIFICANCE OF DIMENSIONALITY REDUCTION IN IMAGE PROCESSING sipij
The aim of this paper is to present a comparative study of two linear dimension reduction methods namely
PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The main idea of PCA is to
transform the high dimensional input space onto the feature space where the maximal variance is
displayed. The feature selection in traditional LDA is obtained by maximizing the difference between
classes and minimizing the distance within classes. PCA finds the axes with maximum variance for the
whole data set where LDA tries to find the axes for best class seperability. The neural network is trained
about the reduced feature set (using PCA or LDA) of images in the database for fast searching of images
from the database using back propagation algorithm. The proposed method is experimented over a general
image database using Matlab. The performance of these systems has been evaluated by Precision and
Recall measures. Experimental results show that PCA gives the better performance in terms of higher
precision and recall values with lesser computational complexity than LDA
The document discusses techniques for object recognition in images. It begins by outlining some of the challenges in object recognition, such as varying lighting, position, scale, and occlusion. It then describes several common object recognition techniques:
1. Template matching involves comparing images to stored templates but can be affected by changes in lighting, position, etc.
2. Color-based techniques use color histograms to match objects but require photometric invariance.
3. Local features represent objects with descriptors of local image patches but have limitations, while global features provide better recognition but are more complex to extract.
4. Shape-based methods match edge maps and contours between images and templates but require good segmentation.
The document
A NOVEL WEB IMAGE RE-RANKING APPROACH BASED ON QUERY SPECIFIC SEMANTIC SIGNAT...Journal For Research
Image re-ranking, is an effective way to improve the results of web-based image search. Given a query keyword, a pool of images are initailly retrieved primarily based on textual data, the remaining images are re-ranked based on their visual similarities with the query image corresponding to the user input. A major challenge is that the similarities of visual features don't well correlate with images’ semantic meanings that interpret users’ search intention. Recently people proposed to match pictures in a semantic space that used attributes or reference categories closely associated with the semantic meanings of images as basis. Even though, learning a universal visual semantic space to characterize extremely diverse images from the internet is troublesome and inefficient. In this thesis, we propose a completely distinctive image re-ranking framework that learns completely different semantic spaces for numerous query keywords automatically at the on-line stage. The visual features of images are projected into their corresponding semantic spaces to induce semantic signatures. At the online stage, images are re-ranked by scrutiny their semantic signatures obtained from the semantic spaces such that by the query keyword. The proposed query-specific semantic signatures considerably improve both the accuracy and efficiency of image re-ranking.
An Automatic Color Feature Vector Classification Based on Clustering MethodRSIS International
In computer vision application, visual features such as
shape, color and texture are extracted to characterize images.
Each of the features is represented using one or more feature
descriptors. One of the important requirements in image
retrieval, indexing, classification, clustering, etc. is extracting
efficient features from images. The color feature is one of the
most widely used visual features. Use of color histogram is the
most common way for representing color feature. One of
disadvantage of the color histogram is that it does not take the
color spatial distribution into consideration. In this paper an
automatic color feature vector classification based on clustering
approach is presented, which effectively describes the spatial
information of color features. The image retrieval results are
compare to improved color feature vector show the acceptable
efficiency of this approach. It propose an automatic color feature
vector classification of satellite images using clustering approach.
The intention is to study cluster a set of satellite images in several
categories on the color similarity basis. The images are processed
using LAB color space in the feature extraction stage. The
resulted color-based feature vectors are clustered using an
automatic unsupervised classification algorithm. Some
experiments based on the proposed recognition technique have
also been performed. More research, however, is needed to
identify and reduce uncertainties in the image processing chain
to improve classification accuracy. The mathematical training
and prediction analysis of a general familiarity with satellite
classifications meet typical map accuracy standards.
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.
The document describes a proposed system for real-time object tracking and learning using template matching. The system uses a live video stream and enables the tracking, learning, and detection of real-time objects. It selects an object of interest via cropping and then tracks it with a bounding box. Template matching is used to match the selected object with regions of interest in subsequent frames to mark its location. If a match is found, principal component analysis is used. The system also introduces a PN discrimination algorithm using background subtraction to increase frame processing speed and improve template matching accuracy. This allows the system to overcome limitations of existing methods and enable long-term, real-time object tracking.
Scene Description From Images To SentencesIRJET Journal
This document presents an approach for generating sentences to describe images using distributed intelligence. It involves detecting objects in images using YOLO detection, finding relative positions of objects, labeling background scenes, generating tuples of objects/scenes/relations, extracting candidate sentences from Wikipedia containing tuple elements, searching images for each sentence and selecting the sentence whose images most closely match the input image. The approach is compared to the Babytalk model using BLEU and ROUGE scores, showing comparable performance. Future work to improve object detection and use larger knowledge sources is discussed.
Research on object detection and recognition using machine learning algorithm...YousefElbayomi
This document discusses research on using machine learning and deep learning for object detection. It examines applications in autonomous vehicles, image detection for agriculture, and credit card fraud detection. For autonomous vehicles, deep learning is discussed for object identification and perception, though speed and real-world performance need improvement. Image detection for agriculture uses feature extraction and machine learning for automatic fruit identification. Credit card fraud detection uses ensemble methods like LightGBM, XGBoost and CatBoost on preprocessed transaction data to identify fraudulent transactions. The document evaluates different approaches and their challenges for these applications of object detection.
This document presents a new interactive image segmentation method called Advanced Maximal Similarity Based Region Merging (MSRM) using user interactions. The method first segments the image using multi-level thresholding. The user then marks regions of the desired object with markers. Regions are represented by color histograms and similarity is measured using Euclidean distance of mean color values. Regions are merged based on similarity, first merging marked and unmarked object regions, then merging remaining unmarked regions. Results show the proposed MSRM method achieves higher true positive rates and lower false positive rates than other interactive segmentation methods.
This document provides a survey of content-based image retrieval (CBIR) techniques using relevance feedback, interactive genetic algorithms, and neuro-fuzzy logic. It discusses how relevance feedback can help reduce the semantic gap between low-level image features and high-level concepts to improve retrieval accuracy. Interactive genetic algorithms make the retrieval process more interactive by evolving image content based on user feedback. Neuro-fuzzy systems combine fuzzy logic and neural networks to establish decoupled subsystems that perform classification and retrieval. The paper analyzes various CBIR systems that use these relevance feedback techniques and their performance based on precision, recall, and convergence ratio. It also covers applications of CBIR in areas like crime prevention, security, medical diagnosis, and design.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
IRJET- Image based Information RetrievalIRJET Journal
This document discusses content-based image retrieval (CBIR) for retrieving images based on visual similarity. It focuses on using CBIR to match images of monuments for tourism applications. The paper describes extracting shape features using edge histogram descriptors to divide images into sub-images and compare edge distributions. An experiment matches images of Humayun's Tomb and the Statue of Liberty by comparing their edge magnitude values across sub-images. Similar edge distributions between two images' sub-images indicates similarity in shape and matches the images. The paper concludes CBIR using shape features can effectively match similar images of monuments to provide relevant information to users.
This document describes a proposed method to improve image classification accuracy and speed using the bag-of-features model with spatial pooling. The proposed method has two phases: a training phase to create an image feature database, and an evaluation phase to classify new images. In the evaluation phase, spatial pooling is applied to input image features before classification with KNN. Variance-based feature selection is also used to reduce features before KNN classification. Experimental results show the proposed method improves classification accuracy up to 5% and reduces classification time by up to 50% compared to the standard bag-of-features model.
This document provides an overview of image analysis, including:
1) It defines image analysis and discusses its use in recognizing, differentiating, and quantifying images across various fields including food quality assessment.
2) It describes the process of creating a digital image through digitization and discusses key aspects of digital images like resolution, pixel bit depth, and color.
3) It outlines common image processing actions like compression, preprocessing, and analysis and provides examples of applying image analysis to evaluate food products.
Mri brain image retrieval using multi support vector machine classifiersrilaxmi524
This document discusses content-based image retrieval (CBIR) for medical images. It proposes using multiple query images instead of a single query image to improve retrieval accuracy. The system works by preprocessing queries, extracting features like texture from the queries, optimizing the features, using classifiers like SVM to categorize images, and then using KNN to retrieve similar images from the database based on feature matching. It claims this approach improves on existing CBIR systems that rely on annotations and have difficulties bridging the semantic gap between low-level features and high-level meanings.
A Review on Matching For Sketch TechniqueIOSR Journals
This document summarizes several techniques for sketch-based image retrieval. It discusses methods using SIFT features, HOG descriptors, color segmentation, and gradient orientation histograms. It also reviews applications of these techniques to domains like facial recognition, graffiti matching, and tattoo identification for law enforcement. The techniques aim to extract visual features from sketches that can be used to match and retrieve similar images from databases. While achieving good results, the methods have limitations regarding database size and specificity, and accuracy with complex textures and shapes. Overall, the review examines advances in using sketches as queries for image retrieval.
This document presents a content-based image retrieval semantic model for shaped and unshaped objects. It proposes classifying objects into two categories: shaped objects with a fixed shape like animals and objects, and unshaped objects without a fixed shape like landscapes. For unshaped objects, local regions are classified by frequency of occurrence and semantic concepts are evaluated using color, shape, and regional dissimilarity factors. For shaped objects, semantic concepts are measured using normalized color, edge detection, particle removal, and shape similarity. Several existing content-based image retrieval techniques are also briefly discussed.
Engine explained in this ppt ,takes a query image as an input do some process on it ,compare this image with images present in database and retrieve similar images. It uses the concept of content based image retrieval.
Brian Winston is a senior executive with over 30 years of experience in luxury retail, e-commerce, and global marketing. He has led large organizations with revenues over $250 million and teams of 45 people. Winston speaks English and has proficiency in French, Italian, and German. He holds a BBA in Economics/Commerce and an EMBA. The presentation discusses Sisley's competitors in the luxury skincare space such as Nordstrom, Saks Fifth Avenue, Sephora, and reviews strategies being used to target and market to millennials and baby boomers. It also provides an overview of Chinese consumers in Canada.
This document summarizes an article that proposes a new approach to improving image web search techniques. It begins with an abstract that describes how rapidly increasing internet users and multimedia search are increasing network traffic, with most traffic being for searching multimedia content online. It then provides background on existing issues with image search and commonly used algorithms. The document proposes a hybrid image search engine that searches by image-to-image comparison, text/keywords, and uses a hybrid matches graph and time sensitivity filter to improve results. It describes the training and testing phases, featuring feature extraction and annotation matching to retrieve relevant images from queries.
Novel Hybrid Approach to Visual Concept Detection Using Image AnnotationCSCJournals
Millions of images are being uploaded on the internet without proper description (tags) about these images. Image retrieval based on image tagging approach is much faster than Content Based Image Retrieval (CBIR) approach but requires an entire image collection to be manually annotated with proper tags. This requires a lot of human efforts and time, and hence not feasible for huge image collections. An efficient method is necessary for automatically tagging such a vast collection of images. We propose a novel image tagging method, which automatically tags any image with its concept. Our unique approach to solve this problem involves manual tagging of small exemplar image set and low-level feature extraction of all the images, hence called a hybrid approach. This approach can be used to tag a large image dataset from manually tagged small image dataset. The experiments are performed on Wang's Corel Dataset. In the comparative study, it is found that, the proposed concept detection system based on this novel tagging approach has much less time complexity of classification step, and results in significant improvement in accuracy as compared to the other tagging approaches found in the literature. This approach may be used as faster alternative to the typical Content Based Image Retrieval (CBIR) approach for domain specific applications.
SIGNIFICANCE OF DIMENSIONALITY REDUCTION IN IMAGE PROCESSING sipij
The aim of this paper is to present a comparative study of two linear dimension reduction methods namely
PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The main idea of PCA is to
transform the high dimensional input space onto the feature space where the maximal variance is
displayed. The feature selection in traditional LDA is obtained by maximizing the difference between
classes and minimizing the distance within classes. PCA finds the axes with maximum variance for the
whole data set where LDA tries to find the axes for best class seperability. The neural network is trained
about the reduced feature set (using PCA or LDA) of images in the database for fast searching of images
from the database using back propagation algorithm. The proposed method is experimented over a general
image database using Matlab. The performance of these systems has been evaluated by Precision and
Recall measures. Experimental results show that PCA gives the better performance in terms of higher
precision and recall values with lesser computational complexity than LDA
The document discusses techniques for object recognition in images. It begins by outlining some of the challenges in object recognition, such as varying lighting, position, scale, and occlusion. It then describes several common object recognition techniques:
1. Template matching involves comparing images to stored templates but can be affected by changes in lighting, position, etc.
2. Color-based techniques use color histograms to match objects but require photometric invariance.
3. Local features represent objects with descriptors of local image patches but have limitations, while global features provide better recognition but are more complex to extract.
4. Shape-based methods match edge maps and contours between images and templates but require good segmentation.
The document
A NOVEL WEB IMAGE RE-RANKING APPROACH BASED ON QUERY SPECIFIC SEMANTIC SIGNAT...Journal For Research
Image re-ranking, is an effective way to improve the results of web-based image search. Given a query keyword, a pool of images are initailly retrieved primarily based on textual data, the remaining images are re-ranked based on their visual similarities with the query image corresponding to the user input. A major challenge is that the similarities of visual features don't well correlate with images’ semantic meanings that interpret users’ search intention. Recently people proposed to match pictures in a semantic space that used attributes or reference categories closely associated with the semantic meanings of images as basis. Even though, learning a universal visual semantic space to characterize extremely diverse images from the internet is troublesome and inefficient. In this thesis, we propose a completely distinctive image re-ranking framework that learns completely different semantic spaces for numerous query keywords automatically at the on-line stage. The visual features of images are projected into their corresponding semantic spaces to induce semantic signatures. At the online stage, images are re-ranked by scrutiny their semantic signatures obtained from the semantic spaces such that by the query keyword. The proposed query-specific semantic signatures considerably improve both the accuracy and efficiency of image re-ranking.
An Automatic Color Feature Vector Classification Based on Clustering MethodRSIS International
In computer vision application, visual features such as
shape, color and texture are extracted to characterize images.
Each of the features is represented using one or more feature
descriptors. One of the important requirements in image
retrieval, indexing, classification, clustering, etc. is extracting
efficient features from images. The color feature is one of the
most widely used visual features. Use of color histogram is the
most common way for representing color feature. One of
disadvantage of the color histogram is that it does not take the
color spatial distribution into consideration. In this paper an
automatic color feature vector classification based on clustering
approach is presented, which effectively describes the spatial
information of color features. The image retrieval results are
compare to improved color feature vector show the acceptable
efficiency of this approach. It propose an automatic color feature
vector classification of satellite images using clustering approach.
The intention is to study cluster a set of satellite images in several
categories on the color similarity basis. The images are processed
using LAB color space in the feature extraction stage. The
resulted color-based feature vectors are clustered using an
automatic unsupervised classification algorithm. Some
experiments based on the proposed recognition technique have
also been performed. More research, however, is needed to
identify and reduce uncertainties in the image processing chain
to improve classification accuracy. The mathematical training
and prediction analysis of a general familiarity with satellite
classifications meet typical map accuracy standards.
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.
The document describes a proposed system for real-time object tracking and learning using template matching. The system uses a live video stream and enables the tracking, learning, and detection of real-time objects. It selects an object of interest via cropping and then tracks it with a bounding box. Template matching is used to match the selected object with regions of interest in subsequent frames to mark its location. If a match is found, principal component analysis is used. The system also introduces a PN discrimination algorithm using background subtraction to increase frame processing speed and improve template matching accuracy. This allows the system to overcome limitations of existing methods and enable long-term, real-time object tracking.
Scene Description From Images To SentencesIRJET Journal
This document presents an approach for generating sentences to describe images using distributed intelligence. It involves detecting objects in images using YOLO detection, finding relative positions of objects, labeling background scenes, generating tuples of objects/scenes/relations, extracting candidate sentences from Wikipedia containing tuple elements, searching images for each sentence and selecting the sentence whose images most closely match the input image. The approach is compared to the Babytalk model using BLEU and ROUGE scores, showing comparable performance. Future work to improve object detection and use larger knowledge sources is discussed.
Research on object detection and recognition using machine learning algorithm...YousefElbayomi
This document discusses research on using machine learning and deep learning for object detection. It examines applications in autonomous vehicles, image detection for agriculture, and credit card fraud detection. For autonomous vehicles, deep learning is discussed for object identification and perception, though speed and real-world performance need improvement. Image detection for agriculture uses feature extraction and machine learning for automatic fruit identification. Credit card fraud detection uses ensemble methods like LightGBM, XGBoost and CatBoost on preprocessed transaction data to identify fraudulent transactions. The document evaluates different approaches and their challenges for these applications of object detection.
This document presents a new interactive image segmentation method called Advanced Maximal Similarity Based Region Merging (MSRM) using user interactions. The method first segments the image using multi-level thresholding. The user then marks regions of the desired object with markers. Regions are represented by color histograms and similarity is measured using Euclidean distance of mean color values. Regions are merged based on similarity, first merging marked and unmarked object regions, then merging remaining unmarked regions. Results show the proposed MSRM method achieves higher true positive rates and lower false positive rates than other interactive segmentation methods.
This document provides a survey of content-based image retrieval (CBIR) techniques using relevance feedback, interactive genetic algorithms, and neuro-fuzzy logic. It discusses how relevance feedback can help reduce the semantic gap between low-level image features and high-level concepts to improve retrieval accuracy. Interactive genetic algorithms make the retrieval process more interactive by evolving image content based on user feedback. Neuro-fuzzy systems combine fuzzy logic and neural networks to establish decoupled subsystems that perform classification and retrieval. The paper analyzes various CBIR systems that use these relevance feedback techniques and their performance based on precision, recall, and convergence ratio. It also covers applications of CBIR in areas like crime prevention, security, medical diagnosis, and design.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
IRJET- Image based Information RetrievalIRJET Journal
This document discusses content-based image retrieval (CBIR) for retrieving images based on visual similarity. It focuses on using CBIR to match images of monuments for tourism applications. The paper describes extracting shape features using edge histogram descriptors to divide images into sub-images and compare edge distributions. An experiment matches images of Humayun's Tomb and the Statue of Liberty by comparing their edge magnitude values across sub-images. Similar edge distributions between two images' sub-images indicates similarity in shape and matches the images. The paper concludes CBIR using shape features can effectively match similar images of monuments to provide relevant information to users.
This document describes a proposed method to improve image classification accuracy and speed using the bag-of-features model with spatial pooling. The proposed method has two phases: a training phase to create an image feature database, and an evaluation phase to classify new images. In the evaluation phase, spatial pooling is applied to input image features before classification with KNN. Variance-based feature selection is also used to reduce features before KNN classification. Experimental results show the proposed method improves classification accuracy up to 5% and reduces classification time by up to 50% compared to the standard bag-of-features model.
This document provides an overview of image analysis, including:
1) It defines image analysis and discusses its use in recognizing, differentiating, and quantifying images across various fields including food quality assessment.
2) It describes the process of creating a digital image through digitization and discusses key aspects of digital images like resolution, pixel bit depth, and color.
3) It outlines common image processing actions like compression, preprocessing, and analysis and provides examples of applying image analysis to evaluate food products.
Mri brain image retrieval using multi support vector machine classifiersrilaxmi524
This document discusses content-based image retrieval (CBIR) for medical images. It proposes using multiple query images instead of a single query image to improve retrieval accuracy. The system works by preprocessing queries, extracting features like texture from the queries, optimizing the features, using classifiers like SVM to categorize images, and then using KNN to retrieve similar images from the database based on feature matching. It claims this approach improves on existing CBIR systems that rely on annotations and have difficulties bridging the semantic gap between low-level features and high-level meanings.
A Review on Matching For Sketch TechniqueIOSR Journals
This document summarizes several techniques for sketch-based image retrieval. It discusses methods using SIFT features, HOG descriptors, color segmentation, and gradient orientation histograms. It also reviews applications of these techniques to domains like facial recognition, graffiti matching, and tattoo identification for law enforcement. The techniques aim to extract visual features from sketches that can be used to match and retrieve similar images from databases. While achieving good results, the methods have limitations regarding database size and specificity, and accuracy with complex textures and shapes. Overall, the review examines advances in using sketches as queries for image retrieval.
This document presents a content-based image retrieval semantic model for shaped and unshaped objects. It proposes classifying objects into two categories: shaped objects with a fixed shape like animals and objects, and unshaped objects without a fixed shape like landscapes. For unshaped objects, local regions are classified by frequency of occurrence and semantic concepts are evaluated using color, shape, and regional dissimilarity factors. For shaped objects, semantic concepts are measured using normalized color, edge detection, particle removal, and shape similarity. Several existing content-based image retrieval techniques are also briefly discussed.
Engine explained in this ppt ,takes a query image as an input do some process on it ,compare this image with images present in database and retrieve similar images. It uses the concept of content based image retrieval.
Brian Winston is a senior executive with over 30 years of experience in luxury retail, e-commerce, and global marketing. He has led large organizations with revenues over $250 million and teams of 45 people. Winston speaks English and has proficiency in French, Italian, and German. He holds a BBA in Economics/Commerce and an EMBA. The presentation discusses Sisley's competitors in the luxury skincare space such as Nordstrom, Saks Fifth Avenue, Sephora, and reviews strategies being used to target and market to millennials and baby boomers. It also provides an overview of Chinese consumers in Canada.
Mohamed Gamal Ahmed Elsafty is seeking a challenging position utilizing his experience and skills. He has a Bachelor's degree in Accounting and diplomas in IT and accounting software. His professional experience includes logistics, purchasing, and administration roles supporting oil field operations. He has strong skills in English, Microsoft Office, ERP systems, and supply chain management.
Calum Hardy presented on photographer Perou. The presentation provided biographical details on Perou, noting he was born in 1970 in Newick, East Sussex, and received A Levels in Religious Studies and Design as well as a BTEC in Design Photography. The presentation also included exemplar work from Perou's photography.
The document discusses visual communication inspiration sources such as nature, man-made environments, and fantasy. It describes the dimensions for a project using square modules that are 100x100mm arranged on a 300x300mm base with 10mm gaps between modules.
Using Adaboost Algorithm to Enhance the Prediction Accuracy of Decision TreesMohammed Al Hamadi
Using R programming language's package fastAdaboost, we use the adaboost algorithm created by Yoav Freund and Robert Schapire on a public data set (white wine quality) to see if we can enhance the performance of a single decision tree.
"FrancigenR" is a native iOS mobile application which allows users to plan their trip on the Via Francigena by choosing the POIs suggested according to their profiles (updated through gamification) and to meet like-minded people they might travel with. We developed and presented this app at the HackathonBiz in Milan, organized by Uvet and Talent Garden on November, 15-16th, and we won the final prize consisting of 5K€. Demo-video: https://youtu.be/2znzGyEJIb8
Dr Ho Siew Hong delivered a public lecture on differentiating prostate cancer from non cancer enlargement of the prostate during the Prostate Awareness Month 2008
Helping the University of Leicester save money and make better use of technology. Eduserv provides identity and access management services, negotiates online resource licenses, and assists with digital engagement projects. It estimates the university saves at least £250,000 per year. For Bristol City Council, Eduserv helped transform services through a cloud migration to reduce costs. For the British Red Cross, Eduserv optimized its website for mobile use and added personalized location-based content. Eduserv's OpenAthens service provides over 4 million users in 33 countries with single sign-on access to hundreds of online healthcare, education and other resources.
User Category Based Estimation of Location Popularity using the Road GPS Traj...Waqas Tariq
The mining of the user GPS trajectories and identifying the interesting places have been well studied based on the visitor’s frequency. However, every user is given the same importance in the majority of the trajectory mining methods. In reality, the popularity of the place also depends on the category of the visitor i.e. international vs local visitors etc. We are proposing user category based location popularity estimation using the trajectories databases. It includes mainly three steps. First , pre-processing – the error correction and the graph connection establishment in the road network in order to be able to carry the graph based computations. Second , find the stay regions where the travelers spent some time off-the-road. The visitors can be easily categorized for each POI based on the travel distance from the home location. Finally , normalization and popularity estimation – measure the frequency and stay time of the visitors of each category in the places in question. The weighted sum of the frequency and stay time for each category of the visitors is calculated. The final popularity of the places is computed with values of the pre-configured range. We have implemented and evaluated the proposed method using a large real road GPS trajectory of 182 users that was collected in a period of over three years by Microsoft Asia Research group.
This document describes an optimized travel planner mobile application that helps travelers plan trips in an efficient manner. The application uses inputs from the user about their preferences, interests and time available to provide optimized route options on a map. It utilizes the Google Places API to retrieve location information and Firebase for backend services and database storage. The application is built using Flutter for the frontend and Dart as the programming language. It analyzes the user's inputs to sort and select places of interest and then displays multiple path options on a map from the starting point to ending point of a trip within the user's specified time period.
This document discusses techniques for predicting the next location of a user based on their location history data. It proposes using incremental learning methods like multivariate multiple regression, spherical-spherical regression, and randomized spherical K-NN regression on a damped window model to solve the location prediction problem in a streaming data setup. The techniques allow planning travel by providing routes and nearby facilities to predicted and current locations using APIs like Google Maps.
A Survey of Privacy-Preserving Algorithms for Finding meeting point in Mobile...IJERA Editor
Location privacy in Location Based Services (LBS) is the capability to protect the connection between user’s identity, uncertainty sources, servers and database, thereby restraining an impending attacker from conveniently linking users of LBS to convinced locations. Smart Phones have become most important gadget for maintaining the daily activities, highly interconnected urban population is also increasingly dependent on these gadgets to regulate and schedule their daily lives. These applications often depend on current location of user or a class of user. Use of Smart Mapping technology is also increasing in large area; this system provides an easy attainable online platform that can be used for accessing many services. This survey paper projects the privacy-preserving algorithm to find the most favorable meeting location for a class of users. GSM calculates the location of all users.
This document describes a place recommendation system developed as an Android application. The application uses a user's current GPS location and collaborative filtering to recommend nearby places of interest based on the user's preferences and previous check-ins. It analyzes similar users and places they have both visited to find places the active user may like. The application recommendations are generated using a self-maintained database of places that can be updated over time. The system aims to reduce confusion for users by tailored recommendations based on their behaviors and location.
A Study of Mobile User Movements Prediction Methods IJECEIAES
For a decade and more, the Number of smart phone users count increasing day by day. With the drastic improvements in Communication technologies, the prediction of future movements of mobile users needs also have important role. Various sectors can gain from this prediction. Communication management, City Development planning, and locationbased services are some of the fields that can be made more valuable with movement prediction. In this paper, we propose a study of several Location Prediction Techniques in the following areas.
Android application to locate and track mobile phones(aaltm) an implementati...eSAT Journals
Abstract
With the advancement of technology and modern science, people are expecting information about the location of any object for
tracking purposes. GPS is a system which has been implemented and is accessible without any restriction. Having the facility of
GPS, we need a GPS device to calculate the location from the information taken from GPS. This paper presents a study on
Location Based Services and is directed on locating and tracking of Android devices. In this implementation I am using Global
Positioning System (GPS) and Web Services, and android smart phone device since Android devices have a built-in feature of
GPS service. Some latest demanding tools and technology used for this implementation are Java, AVD, WAMP etc.
Keywords: Location Based Services; Global Positioning System (GPS); location; path
This paper aims to provide an overview of the
contents and design of the all newspapers. Majority of the
newspapers use Blog, RSS and Facebook to connect with
their readers. An online newspaper service providing project.
In this software system users may register as users to read
newspapers online. Once they register they may pay via
dummy credit cards and get access to reading newspapers
online for a month
IRJET- University Campus Event Navigation System IRJET Journal
This document describes a proposed University campus event navigation system developed as an Android application. The application would allow administrators to add, edit, and delete events, and provide event details and locations to users. Users could register for events and receive real-time updates. The application would utilize GPS to display routes to event locations on a map. It aims to better inform students and others about campus events and make it easier to navigate to event venues. The proposed system was designed to address limitations of existing single-college applications that cannot be easily updated with location or other changes.
Location Based Services, Change in Profile and Notification IRJET Journal
This document discusses a proposed location-based services application for Android devices. It would allow automatic profile changing, weather detection, and message sending based on the user's location. The application would use GPS and internet access to determine the user's location. It describes how location-based services work and the typical components involved, including mobile devices, applications, communication networks, positioning systems like GPS, and backend servers. The proposed application aims to provide these location-based functions for Android smartphones in a simple, automatic way.
PREDICTING VENUES IN LOCATION BASED SOCIAL NETWORKcsandit
1) The document analyzes check-in data from Foursquare to examine user behaviors and venue popularity across three cities.
2) Analysis of the dataset revealed different mobility habits, preferred locations, and location patterns between users.
3) This information about user behaviors and venue popularity can be used to build recommendation systems and predict future locations users may visit based on their check-in history and preferred categories.
Predicting Venues in Location Based Social Network cscpconf
The circulation of the social networks and the evolution of the mobile phone devices has led to a
big usage of location based social networks application such as Foursquare, Twitter, Swarm
and Zomato on mobile phone devices mean that huge dataset which is containing a blend of
information about users behaviour’s, social society network of each users and also information
about each of venues, all these information available in mobile location recommendation
system .These datasets are much more different from those which is used in online recommender
systems, these datasets have more information and details about the users and the venues which
is allowing to have more clear result with much more higher accuracy of the analysing in the
result.
In this paper we examine the users behaviour’s and the popularity of the venue through a large
check-ins dataset from a location based social services, Foursquare: by using large scale
dataset containing both user check-in and location information .Our analysis expose across 3
different cities.On analysis of these dataset reveal a different mobility habits, preferring places
and also location patterns in the user personality. This information about the users behaviour’s
and each of the location popularity can be used to know the recommendation systems and to
predict the next move of the users depending on the categories that the users attend to visit and
according to the history of each users check-ins.
Basically the project is having two parts namely the recommender system and the planner system. The tour
recommender system firstly asks the user for logging in then he asks for the various details about his tour in order
to recommend a perfect tour. The recommender system collects the information regarding the type of place, the
travelling method the user would like to prefer and more importantly the cost which the user can afford. The
collected data is sent to the recommender algorithm which processes the data and finds a perfect place with
required specification from the system database. Three best results are displayed. Then the user selects one tour as
per his choice. Next the planner system displays all the possible travelling and accommodation methods with their
costs and offers. Then the user selects the services according to his convenience and enjoys the trip.
This document presents a research project that aims to analyze tourist behavior based on geo-tagged location data from community websites. The researchers plan to analyze sequences of places visited by tourists each day to identify popular tourist locations and understand tourism demographics. They will use data from the Tourpedia website about tourism in Paris. The analysis will help tourism industry stakeholders better plan services and manage destinations. It will involve clustering location data, constructing time series to show tourist numbers over time, and structuring demographic data for locations.
Location Based Service in Social Media: An Overview of Application Yuyun Wabula
This paper presents a literature review on the use of geolocation data on social media. Geolocation is one of the feature on the social media which utilize the GPS devices embedded in the smartphones, tablets, or computers gadget that can show a user’s location map. This is related to a virtual user activity in the parts of the world, when and where they are. The main objective of this research is to investigate the extent to which spread of articles related to the application of location-based data on social media, such as problem issues, techniques applied, problem solved especially in urban environment context, published from 2010 to 2016. We analyzed 35 references which accordance with this field. The attribute prepared based on the application area, years, and author's parts to simplify the organizing of geolocation data applications. Then, the data format summarized in the tabular form for helping a readers. Authors find that three important issues that we have identified related to this field; distances, locations, and movements. Our research can contribute for the researchers for them future work regarding to the developments and limitations of each articles.
IRJET- Bon Voyage: A Travel Guide based on Web ApplicationIRJET Journal
The document describes a proposed web application called Bon Voyage that aims to assist travelers in planning trips. The key features of the application include generating travel itineraries, providing information on places of interest, ensuring safety of users by providing risk and precaution information, and allowing users to post experiences. The application will be cross-platform and integrate a chatbot for assistance. It is designed to address common travel planning hurdles like generating itineraries, finding location details, and knowing what items to bring. The application will use technologies like React for the user interface and Node.js for the backend API.
Service Rating Prediction by check-in and check-out behavior of user and POIIRJET Journal
This document proposes a system to predict service ratings by analyzing users' check-in and check-out behaviors and points of interest (POI). It aims to mine relationships between user ratings and geographical distances between users/items. The system would integrate user-item geographical connections, user-user geographical connections, and interest similarities into a location-based rating prediction model. It was found that users often give higher ratings to items farther away from their activity centers. Users and their geographically distant friends also often give similar ratings. The proposed model is evaluated on a Yelp dataset and shows improved performance over existing approaches.
This document proposes privacy-preserving algorithms for determining an optimal meeting location for a group of users. It discusses challenges with location privacy in mobile environments and existing location-based services that rely on sharing user locations. The paper then presents three secure multiparty computation protocols for privately calculating distances that could be used to find an optimal meeting point while preserving users' location privacy. It also surveys existing work on meeting location algorithms and proposes a more comprehensive solution that considers additional factors like user preferences, constraints, and transportation costs but does not address privacy or security issues.
This document reviews 13 papers related to using data analytics and machine learning techniques to analyze tourist behavior from large datasets. The papers explore using geotagged photos, reviews, and location data to cluster and classify tourist activities and interests, identify representative landmarks and destinations, predict future locations, and uncover patterns in tourist flows and behaviors. Machine learning methods discussed include density-based clustering, convolutional neural networks, random forest classification, and deep learning models. The goal of this research is to better understand tourist preferences and decision-making to help tourism organizations improve destination management and personalize services.
The document describes an Android application called "My Places" that allows users to find routes to destinations by using GPS to track the user's location and displaying it on a map alongside the route. It works by interacting with the Google Places API, which provides location data like coordinates and nearby places within a proximity radius. The application aims to make tasks easier for users by allowing them to find nearby locations and reminders as they reach different areas.
Similar to TOURIST PLACE RECOMMENDATION SYSTEM (20)
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.
Height and depth gauge linear metrology.pdfq30122000
Height gauges may also be used to measure the height of an object by using the underside of the scriber as the datum. The datum may be permanently fixed or the height gauge may have provision to adjust the scale, this is done by sliding the scale vertically along the body of the height gauge by turning a fine feed screw at the top of the gauge; then with the scriber set to the same level as the base, the scale can be matched to it. This adjustment allows different scribers or probes to be used, as well as adjusting for any errors in a damaged or resharpened probe.
Accident detection system project report.pdfKamal Acharya
The Rapid growth of technology and infrastructure has made our lives easier. The
advent of technology has also increased the traffic hazards and the road accidents take place
frequently which causes huge loss of life and property because of the poor emergency facilities.
Many lives could have been saved if emergency service could get accident information and
reach in time. Our project will provide an optimum solution to this draw back. A piezo electric
sensor can be used as a crash or rollover detector of the vehicle during and after a crash. With
signals from a piezo electric sensor, a severe accident can be recognized. According to this
project when a vehicle meets with an accident immediately piezo electric sensor will detect the
signal or if a car rolls over. Then with the help of GSM module and GPS module, the location
will be sent to the emergency contact. Then after conforming the location necessary action will
be taken. If the person meets with a small accident or if there is no serious threat to anyone’s
life, then the alert message can be terminated by the driver by a switch provided in order to
avoid wasting the valuable time of the medical rescue team.
Impartiality as per ISO /IEC 17025:2017 StandardMuhammadJazib15
This document provides basic guidelines for imparitallity requirement of ISO 17025. It defines in detial how it is met and wiudhwdih jdhsjdhwudjwkdbjwkdddddddddddkkkkkkkkkkkkkkkkkkkkkkkwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwioiiiiiiiiiiiii uwwwwwwwwwwwwwwwwhe wiqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq gbbbbbbbbbbbbb owdjjjjjjjjjjjjjjjjjjjj widhi owqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq uwdhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhwqiiiiiiiiiiiiiiiiiiiiiiiiiiiiw0pooooojjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjj whhhhhhhhhhh wheeeeeeee wihieiiiiii wihe
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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.
Levelised Cost of Hydrogen (LCOH) Calculator ManualMassimo Talia
The aim of this manual is to explain the
methodology behind the Levelized Cost of
Hydrogen (LCOH) calculator. Moreover, this
manual also demonstrates how the calculator
can be used for estimating the expenses associated with hydrogen production in Europe
using low-temperature electrolysis considering different sources of electricity
AI in customer support Use cases solutions development and implementation.pdfmahaffeycheryld
AI in customer support will integrate with emerging technologies such as augmented reality (AR) and virtual reality (VR) to enhance service delivery. AR-enabled smart glasses or VR environments will provide immersive support experiences, allowing customers to visualize solutions, receive step-by-step guidance, and interact with virtual support agents in real-time. These technologies will bridge the gap between physical and digital experiences, offering innovative ways to resolve issues, demonstrate products, and deliver personalized training and support.
https://www.leewayhertz.com/ai-in-customer-support/#How-does-AI-work-in-customer-support
Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...PriyankaKilaniya
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.