The document presents a research on detecting video forgery using machine learning. It proposes a novel approach that uses optical flow and coarse-to-fine detection strategy to detect copy-move image forgery in videos. The approach first divides video frames into overlapping blocks, then extracts GLCM features from blocks. It identifies duplicate blocks using k-means clustering and Euclidean distance calculation. Finally, it detects forged regions in frames by highlighting the duplicate blocks. The approach was implemented and experiments showed it could successfully detect forged regions in videos.
IRJET- Video Forgery Detection using Machine LearningIRJET Journal
This document proposes a method to detect video forgery using machine learning. It discusses extracting optical flow and GLCM features from video frames and using them to train a support vector machine classifier. The method segments video frames, applies k-means clustering to group similar regions, and extracts GLCM features for comparison. This allows the system to detect any duplicated or manipulated frames through feature analysis and machine learning classification.
An Image Based PCB Fault Detection and Its Classificationrahulmonikasharma
The field of electronics is skyrocketing like never before. The habitat for the electronic components is a printed circuit board (PCB). With the advent of newer and finer technologies it has almost become impossible to detect the faults in a printed circuit board manually which consumes lot of manpower and time. This paper proposes a simple and cost effective method of fault diagnosis in a PCB using image processing techniques. In addition to fault detection and its classification this paper addresses various problems faced during the pre-processing phase. This paper overcomes the drawbacks of the previous works such as improper orientations of the image and size variations of the image. Basically image subtraction algorithm is used for fault detection. The most commonly occurring faults are concentrated in this work and the same are implemented using MATLAB tool.
Blur Detection Methods for Digital Images-A SurveyEditor IJCATR
This paper described various blur detection methods along with proposed method. Digital photos are massively produced
while digital cameras are becoming popular; however, not every photo has good quality. Blur is one of the conventional image quality
degradation which is caused by various factors like limited contrast; inappropriate exposure time and improper device handling indeed,
blurry images make up a significant percentage of anyone's picture collections. Consequently, an efficient tool to detect blurry images
and label or separate them for automatic deletion in order to preserve storage capacity and the quality of image collections is needed.
There are various methods to detect the blur from the blurry images some of which requires transforms like DCT or Wavelet and some
doesn‟t require transform.
IRJET-Real-Time Object Detection: A SurveyIRJET Journal
This document provides an overview of real-time object detection techniques. It discusses several challenges in detecting objects including illumination variation, moving object appearance changes, abrupt motion, occlusion, shadows, and problems related to cameras. The document then reviews several existing object detection methods and algorithms. These include techniques using color segmentation, edge tracking, shape context features, image segmentation, and support vector machines or k-nearest neighbor classifiers applied to features like GIST or SIFT. The goal of the literature review is to analyze different object recognition and segmentation approaches that could be applied for real-time object detection.
Artifacts Detection by Extracting Edge Features and Error Block Analysis from...Md. Mehedi Hasan
This document presents a method for edge-based feature extraction to detect artifacts and analyze error patterns in broadcast videos. The method uses edge magnitude and direction features that are less sensitive to noise. Detected error frames are analyzed to classify error blocks based on edge direction, texture content, and shape. Experimental results show the method achieves high accuracy in detecting distorted frames and analyzing error patterns compared to other approaches. Future work will apply the error analysis to video error concealment.
This document discusses a digital image processing (DIP) based system for identifying defects in industrial materials like steel rods. Images of reference and test samples are taken and compared using techniques like thresholding, histograms, and cell segmentation in MATLAB. Defects are identified by variations between the images. The system is implemented on an FPGA for hardware acceleration. Images of steel rods with and without defects are compared to demonstrate the system's ability to detect cracks. The DIP based approach can replace manual inspection and provides faster quality evaluation of industrial materials compared to software-only methods.
IRJET- A Review Analysis to Detect an Object in Video Surveillance SystemIRJET Journal
This document reviews techniques for detecting objects in video surveillance systems. It discusses common object detection methods like frame differencing, optical flow, and background subtraction. Frame differencing detects motion by calculating pixel differences between frames but cannot detect still objects. Optical flow estimates pixel motion between frames to detect objects. Background subtraction models the static background and detects objects by subtracting current frames from the background model. The document analyzes these techniques and their use in video surveillance applications like traffic monitoring and security. It concludes more research is needed to improve object classification accuracy and handle challenges like camera motion.
Automated Traffic sign board classification system is one of the key technologies of Intelligent
Transportation Systems (ITS). Traffic Surveillance System is being more and important with improving
urban scale and increasing number of vehicles. This Paper presents an intelligent sign board
classification method based on blob analysis in traffic surveillance. Processing is done by three main
steps: moving object segmentation, blob analysis, and classifying. A Sign board is modelled as a
rectangular patch and classified via blob analysis. By processing the blob of sign boards, the meaningful
features are extracted. Tracking moving targets is achieved by comparing the extracted features with
training data. After classifying the sign boards the system will intimate to user in the form of alarms,
sound waves. The experimental results show that the proposed system can provide real-time and useful
information for traffic surveillance.
IRJET- Video Forgery Detection using Machine LearningIRJET Journal
This document proposes a method to detect video forgery using machine learning. It discusses extracting optical flow and GLCM features from video frames and using them to train a support vector machine classifier. The method segments video frames, applies k-means clustering to group similar regions, and extracts GLCM features for comparison. This allows the system to detect any duplicated or manipulated frames through feature analysis and machine learning classification.
An Image Based PCB Fault Detection and Its Classificationrahulmonikasharma
The field of electronics is skyrocketing like never before. The habitat for the electronic components is a printed circuit board (PCB). With the advent of newer and finer technologies it has almost become impossible to detect the faults in a printed circuit board manually which consumes lot of manpower and time. This paper proposes a simple and cost effective method of fault diagnosis in a PCB using image processing techniques. In addition to fault detection and its classification this paper addresses various problems faced during the pre-processing phase. This paper overcomes the drawbacks of the previous works such as improper orientations of the image and size variations of the image. Basically image subtraction algorithm is used for fault detection. The most commonly occurring faults are concentrated in this work and the same are implemented using MATLAB tool.
Blur Detection Methods for Digital Images-A SurveyEditor IJCATR
This paper described various blur detection methods along with proposed method. Digital photos are massively produced
while digital cameras are becoming popular; however, not every photo has good quality. Blur is one of the conventional image quality
degradation which is caused by various factors like limited contrast; inappropriate exposure time and improper device handling indeed,
blurry images make up a significant percentage of anyone's picture collections. Consequently, an efficient tool to detect blurry images
and label or separate them for automatic deletion in order to preserve storage capacity and the quality of image collections is needed.
There are various methods to detect the blur from the blurry images some of which requires transforms like DCT or Wavelet and some
doesn‟t require transform.
IRJET-Real-Time Object Detection: A SurveyIRJET Journal
This document provides an overview of real-time object detection techniques. It discusses several challenges in detecting objects including illumination variation, moving object appearance changes, abrupt motion, occlusion, shadows, and problems related to cameras. The document then reviews several existing object detection methods and algorithms. These include techniques using color segmentation, edge tracking, shape context features, image segmentation, and support vector machines or k-nearest neighbor classifiers applied to features like GIST or SIFT. The goal of the literature review is to analyze different object recognition and segmentation approaches that could be applied for real-time object detection.
Artifacts Detection by Extracting Edge Features and Error Block Analysis from...Md. Mehedi Hasan
This document presents a method for edge-based feature extraction to detect artifacts and analyze error patterns in broadcast videos. The method uses edge magnitude and direction features that are less sensitive to noise. Detected error frames are analyzed to classify error blocks based on edge direction, texture content, and shape. Experimental results show the method achieves high accuracy in detecting distorted frames and analyzing error patterns compared to other approaches. Future work will apply the error analysis to video error concealment.
This document discusses a digital image processing (DIP) based system for identifying defects in industrial materials like steel rods. Images of reference and test samples are taken and compared using techniques like thresholding, histograms, and cell segmentation in MATLAB. Defects are identified by variations between the images. The system is implemented on an FPGA for hardware acceleration. Images of steel rods with and without defects are compared to demonstrate the system's ability to detect cracks. The DIP based approach can replace manual inspection and provides faster quality evaluation of industrial materials compared to software-only methods.
IRJET- A Review Analysis to Detect an Object in Video Surveillance SystemIRJET Journal
This document reviews techniques for detecting objects in video surveillance systems. It discusses common object detection methods like frame differencing, optical flow, and background subtraction. Frame differencing detects motion by calculating pixel differences between frames but cannot detect still objects. Optical flow estimates pixel motion between frames to detect objects. Background subtraction models the static background and detects objects by subtracting current frames from the background model. The document analyzes these techniques and their use in video surveillance applications like traffic monitoring and security. It concludes more research is needed to improve object classification accuracy and handle challenges like camera motion.
Automated Traffic sign board classification system is one of the key technologies of Intelligent
Transportation Systems (ITS). Traffic Surveillance System is being more and important with improving
urban scale and increasing number of vehicles. This Paper presents an intelligent sign board
classification method based on blob analysis in traffic surveillance. Processing is done by three main
steps: moving object segmentation, blob analysis, and classifying. A Sign board is modelled as a
rectangular patch and classified via blob analysis. By processing the blob of sign boards, the meaningful
features are extracted. Tracking moving targets is achieved by comparing the extracted features with
training data. After classifying the sign boards the system will intimate to user in the form of alarms,
sound waves. The experimental results show that the proposed system can provide real-time and useful
information for traffic surveillance.
IRJET- Efficient Face Detection from Video Sequences using KNN and PCAIRJET Journal
1. The document proposes a new algorithm for efficient face detection from video sequences using K-Nearest Neighbors (KNN) and Principal Component Analysis (PCA).
2. PCA is used for feature extraction to reduce the dimensionality of the face images. KNN is then used for classification, where the k closest training examples are found based on Euclidean distance measures.
3. The proposed method achieves 99.47% accuracy on sample face images based on classification using 1NN, demonstrating the effectiveness of combining PCA for feature extraction with KNN for real-time face detection from video sequences.
Conference research paper_target_trackingpatrobadri
The document proposes a 3-stage algorithm for real-time video object tracking on the DaVinci processor:
1. A novel object segmentation and background subtraction algorithm is designed to handle noise, illumination changes, and multiple moving objects.
2. Binary Large OBject (BLOB) detection is used to identify image clusters and solve problems of abrupt object shapes, sizes, and counts.
3. A centroid-based tracking method is used to improve robustness to occlusion and contour sliding.
Optimizations are applied at both the algorithm and code levels to reduce memory usage and access and improve execution speed, allowing the tracking of 30 frames per second in real-time. The algorithm provides at least a 2
Video Key-Frame Extraction using Unsupervised Clustering and Mutual ComparisonCSCJournals
The document presents a novel method for extracting key frames from videos using unsupervised clustering and mutual comparison. It assigns weights of 70% to color (HSV histogram) and 30% to texture (GLCM) when computing frame similarity for clustering. It then performs mutual comparison of extracted key frames to remove near duplicates, improving accuracy. The algorithm is computationally simple and able to detect unique key frames, improving concept detection performance as validated on open databases.
Block matching algorithm (BMA) for motion estimation is extremely normally utilized in current video coding standard like H.26x and MPEG-x as a result of its simplicity and performance and also it is a very important content in video compression the motion estimation is becoming a problem in many video applications as it to estimate the motion of the object. There are homography between 2 frames within the video sequences captured by pan-tilt (PT) cameras in their unnatural movements and therefore the geometric relationship is used to reduce the spatial redundancy in the video. In this paper, I present a homography based motion estimation algorithm and a comparative study of different algorithms. Also I introduce a unique homography-based motion for block motion estimation. This study is to provide an idea about the important tradeoff between computational complexity, result quality and various applications. This algorithm can be done on Matlab.
Robust image processing algorithms, involving tools from digital geometry and...Antoine Vacavant
This document summarizes a seminar presentation about robust image processing algorithms involving tools from digital geometry and mathematical morphology. The presentation introduces the speaker and their background and research interests. It then discusses the need for a formal definition of robustness for image processing algorithms. Such a definition is proposed, involving evaluating algorithms over multiple noise scales and ensuring quality measures respect Lipschitz continuity as noise increases. Examples are given of algorithms from mathematical morphology and digital geometry that have been evaluated for robustness based on this definition. The talk concludes by discussing applications of these techniques to biomedical image analysis tasks.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
The document evaluates the performance of various foreground extraction algorithms for object detection in visual surveillance. It analyzes three background modeling techniques (change detection mask, median, histogram) and two background subtraction algorithms (frame difference, approximate median). Experimental results on test videos show that background modeling using the median value technique and background subtraction using frame differencing provides the most robust and efficient combination. Processing times are reported for different combinations of algorithms. The study concludes that the median-based approach has good computational efficiency and robustness for background modeling.
VIDEO SEGMENTATION FOR MOVING OBJECT DETECTION USING LOCAL CHANGE & ENTROPY B...csandit
Motion detection and object segmentation are an important research area of image-video
processing and computer vision. The technique and mathematical modeling used to detect and
segment region of interest (ROI) objects comprise the algorithmic modules of various high-level
techniques in video analysis, object extraction, classification, and recognition. The detection of
moving object is significant in many tasks, such as video surveillance & moving object tracking.
The design of a video surveillance system is directed on involuntary identification of events of
interest, especially on tracking and on classification of moving objects. An entropy based realtime
adaptive non-parametric window thresholding algorithm for change detection is
anticipated in this research. Based on the approximation of the value of scatter of sections of
change in a difference image, a threshold of every image block is calculated discriminatively
using entropy structure, and then the global threshold is attained by averaging all thresholds for
image blocks of the frame. The block threshold is calculated contrarily for regions of change
and background. Investigational results show the proposed thresholding algorithm
accomplishes well for change detection with high efficiency.
A Novel Approach for Tracking with Implicit Video Shot DetectionIOSR Journals
1) The document presents a novel approach that combines video shot detection and object tracking using a particle filter to create an efficient tracking algorithm with implicit shot detection.
2) It uses a robust pixel difference method for shot detection that is resistant to sudden illumination changes. It then applies a particle filter for tracking that uses color histograms and Bhattacharyya distance to track objects across frames.
3) The key innovation is that the tracking algorithm is only initiated after a shot change is detected, reducing computational costs by discarding unneeded frames and triggering tracking only when needed. This provides a more efficient solution for tracking large video datasets with minimal preprocessing.
IMAGE RECOGNITION USING MATLAB SIMULINK BLOCKSETIJCSEA Journal
The world over, image recognition are essential players in promoting quality object recognition especially in emergency and search-rescue operation. In this paper precise image recognition system using Matlab Simulink Blockset to detect selected object from crowd is presented. The process involves extracting object
features and then recognizes it considering illumination, direction and pose. A Simulink model has been developed to eliminate the tiny elements from the image, then creating segments for precise object recognition. Furthermore, the simulation explores image recognition from the coloured and gray-scale images through image processing techniques in Simulink environment. The tool employed for computation
and simulation is the Matlab image processing blockset. The process comprises morphological operation method which is effective for captured images and video. The results of extensive simulations indicate that this method is suitable for application identifying a person from a crow. The model can be used in emergency and search-rescue operation as well as in medicine, information security, access control, law enforcement, surveillance system, microscopy etc.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
HUMAN MOTION DETECTION AND TRACKING FOR VIDEO SURVEILLANCEAswinraj Manickam
An approach to detect and track groups of people in video-surveillance applications, and to automatically recognize their behavior.
This method keeps track of individuals moving together by maintaining a spacial and temporal group coherence.
First, people are individually detected and tracked. Second, their trajectories are analyzed over a temporal window and clustered using the Mean-Shift algorithm.
A coherence value describes how well a set of people can be described as a group. Furthermore, we propose a formal event description language.
The group events recognition approach is successfully validated on 4 camera views from 3 data sets: an airport, a subway, a shopping center corridor and an entrance hall.
IRJET - Traffic Density Estimation by Counting Vehicles using Aggregate Chann...IRJET Journal
This document presents a method for estimating traffic density by counting vehicles in images using aggregate channel features. The proposed method uses adaptive boosting and aggregate channel features to train an object detector to detect vehicles in images obtained from videos. Bounding boxes are placed around detected vehicles and overlapping boxes are removed. Traffic density is then estimated by counting the number of bounding boxes and dividing by the maximum possible number of vehicles in the area. The estimated densities can be used to control traffic light timing, with higher densities corresponding to shorter green light durations. The method is tested on real-world traffic images and is found to accurately detect vehicles and estimate densities.
Trajectory Based Unusual Human Movement Identification for ATM SystemIRJET Journal
This document summarizes a research paper on developing a system to identify unusual human movements at ATMs using trajectory analysis. The proposed system uses computer vision techniques like background subtraction and template matching to detect and track human movements. If a person's trajectory does not match expected patterns or if their face is covered, an alarm is triggered. The system is intended to prevent crimes and unauthorized access at ATMs by continuously monitoring movements and alerting administrators of any suspicious activity in real-time.
IRJET- Full Body Motion Detection and Surveillance System ApplicationIRJET Journal
1) The document discusses a system for real-time full-body motion detection and surveillance using computer vision techniques.
2) It involves comparing video frames over time to detect motion by treating videos as stacks of frames and looking for differences between frames.
3) The goal is to track body motion in real time using OpenCV for applications like surveillance systems, pose estimation, and other filters.
This document presents a method for identifying images that have been compressed using JPEG. It analyzes the quantization noise present in images after JPEG compression and decompression. The key findings are:
1) Decompressed JPEG images have a lower variance of "forward quantization noise" compared to uncompressed images.
2) An algorithm is developed to detect decompressed JPEG images based on comparing the forward quantization noise variance to a threshold.
3) Extensive experiments show the method outperforms state-of-the-art techniques, especially for images compressed with high quality settings.
4) The method is robust to small image sizes and chroma subsampling during JPEG compression.
IRJET- Study of SVM and CNN in Semantic Concept DetectionIRJET Journal
1) The document discusses approaches for semantic concept detection in videos using techniques like support vector machines (SVM) and convolutional neural networks (CNN).
2) It proposes a concept detection system that uses SVM and CNN together, extracting features from key frames using Hue moments and classifying the features with SVM and CNN.
3) The outputs of SVM and CNN are fused to improve concept detection accuracy compared to using the classifiers individually. Fusing the two classifiers is intended to better identify the concepts in video frames.
The document discusses an improved error detection and data recovery architecture for motion estimation testing applications. It presents a residue-and-quotient (RQ) code-based design to embed into motion estimation for detecting and recovering from errors in processing elements. Experimental results show the design can detect errors and recover data with acceptable overhead in area and timing. It also performs satisfactorily in terms of throughput and reliability for motion estimation testing.
Real Time Object Identification for Intelligent Video Surveillance ApplicationsEditor IJCATR
Intelligent video surveillance system has emerged as a very important research topic in the computer vision field in the
recent years. It is well suited for a broad range of applications such as to monitor activities at traffic intersections for detecting
congestions and predict the traffic flow. Object classification in the field of video surveillance is a key component of smart
surveillance software. Two robust methodology and algorithms adopted for people and object classification for automated surveillance
systems is proposed in this paper. First method uses background subtraction model for detecting the object motion. The background
subtraction and image segmentation based on morphological transformation for tracking and object classification on highways is
proposed. This algorithm uses erosion followed by dilation on various frames. Proposed algorithm in first method, segments the image
by preserving important edges which improves the adaptive background mixture model and makes the system learn faster and more
accurately. The system used in second method adopts the object detection method without background subtraction because of the static
object detection. Segmentation is done by the bounding box registration technique. Then the classification is done with the multiclass
SVM using the edge histogram as features. The edge histograms are calculated for various bin values in different environment. The
result obtained demonstrates the effectiveness of the proposed approach.
A New Deep Learning Based Technique To Detect Copy Move Forgery In Digital Im...IRJET Journal
This document proposes a new deep learning technique to detect copy move forgery in digital images. It uses a VGG16 CNN model to extract feature vectors from image blocks. Euclidean distance is used to measure similarity between feature vectors and detect matching blocks, indicating potential forgery. The proposed method is evaluated on the CoMoFoD dataset and achieves higher F1-scores than ResNet50 and EfficientNet models, detecting forged regions more accurately.
IRJET- Image Forgery Detection using Support Vector MachineIRJET Journal
This document presents research on detecting image forgery using support vector machines. It begins with an abstract discussing how easily images can be digitally manipulated today without leaving traces. It then discusses the most common forgery techniques of splicing, where one image region is cut and pasted into another image, and copy-move, where an image region is copied and pasted within the same image.
The document then reviews previous work on forgery detection techniques. It proposes a new approach that uses preprocessing, feature extraction from image blocks, and support vector machines to classify images as authentic or forged. If forged, principal component analysis is used to identify the forged regions. The approach is tested on splicing and copy-move forgeries and
IRJET- Framework for Image Forgery DetectionIRJET Journal
The document proposes a framework for detecting image forgeries using optical flow and stable parameters. It begins with coarse detection to find suspected tampered points by analyzing optical flow sum consistency. Then it performs fine detection for precise location of forgeries, including duplicated frame pair matching based on optical flow correlation and validation checks to reduce false detections. The framework is designed to balance detection efficiency, robustness, and applicability.
IRJET - Applications of Image and Video Deduplication: A SurveyIRJET Journal
This document discusses applications of image and video deduplication techniques. It begins by providing background on the growth of multimedia data and need for deduplication to reduce redundant data. It then describes key aspects of image and video deduplication, including extracting fingerprints from images and frames to identify duplicates. The document reviews several studies on image and video deduplication applications, such as identifying near-duplicate images on social media, detecting spoofed face images, verifying image copy detection, and eliminating near-duplicates from visual sensor networks. Overall, the document surveys various real-world implementations of image and video deduplication.
IRJET- Efficient Face Detection from Video Sequences using KNN and PCAIRJET Journal
1. The document proposes a new algorithm for efficient face detection from video sequences using K-Nearest Neighbors (KNN) and Principal Component Analysis (PCA).
2. PCA is used for feature extraction to reduce the dimensionality of the face images. KNN is then used for classification, where the k closest training examples are found based on Euclidean distance measures.
3. The proposed method achieves 99.47% accuracy on sample face images based on classification using 1NN, demonstrating the effectiveness of combining PCA for feature extraction with KNN for real-time face detection from video sequences.
Conference research paper_target_trackingpatrobadri
The document proposes a 3-stage algorithm for real-time video object tracking on the DaVinci processor:
1. A novel object segmentation and background subtraction algorithm is designed to handle noise, illumination changes, and multiple moving objects.
2. Binary Large OBject (BLOB) detection is used to identify image clusters and solve problems of abrupt object shapes, sizes, and counts.
3. A centroid-based tracking method is used to improve robustness to occlusion and contour sliding.
Optimizations are applied at both the algorithm and code levels to reduce memory usage and access and improve execution speed, allowing the tracking of 30 frames per second in real-time. The algorithm provides at least a 2
Video Key-Frame Extraction using Unsupervised Clustering and Mutual ComparisonCSCJournals
The document presents a novel method for extracting key frames from videos using unsupervised clustering and mutual comparison. It assigns weights of 70% to color (HSV histogram) and 30% to texture (GLCM) when computing frame similarity for clustering. It then performs mutual comparison of extracted key frames to remove near duplicates, improving accuracy. The algorithm is computationally simple and able to detect unique key frames, improving concept detection performance as validated on open databases.
Block matching algorithm (BMA) for motion estimation is extremely normally utilized in current video coding standard like H.26x and MPEG-x as a result of its simplicity and performance and also it is a very important content in video compression the motion estimation is becoming a problem in many video applications as it to estimate the motion of the object. There are homography between 2 frames within the video sequences captured by pan-tilt (PT) cameras in their unnatural movements and therefore the geometric relationship is used to reduce the spatial redundancy in the video. In this paper, I present a homography based motion estimation algorithm and a comparative study of different algorithms. Also I introduce a unique homography-based motion for block motion estimation. This study is to provide an idea about the important tradeoff between computational complexity, result quality and various applications. This algorithm can be done on Matlab.
Robust image processing algorithms, involving tools from digital geometry and...Antoine Vacavant
This document summarizes a seminar presentation about robust image processing algorithms involving tools from digital geometry and mathematical morphology. The presentation introduces the speaker and their background and research interests. It then discusses the need for a formal definition of robustness for image processing algorithms. Such a definition is proposed, involving evaluating algorithms over multiple noise scales and ensuring quality measures respect Lipschitz continuity as noise increases. Examples are given of algorithms from mathematical morphology and digital geometry that have been evaluated for robustness based on this definition. The talk concludes by discussing applications of these techniques to biomedical image analysis tasks.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
The document evaluates the performance of various foreground extraction algorithms for object detection in visual surveillance. It analyzes three background modeling techniques (change detection mask, median, histogram) and two background subtraction algorithms (frame difference, approximate median). Experimental results on test videos show that background modeling using the median value technique and background subtraction using frame differencing provides the most robust and efficient combination. Processing times are reported for different combinations of algorithms. The study concludes that the median-based approach has good computational efficiency and robustness for background modeling.
VIDEO SEGMENTATION FOR MOVING OBJECT DETECTION USING LOCAL CHANGE & ENTROPY B...csandit
Motion detection and object segmentation are an important research area of image-video
processing and computer vision. The technique and mathematical modeling used to detect and
segment region of interest (ROI) objects comprise the algorithmic modules of various high-level
techniques in video analysis, object extraction, classification, and recognition. The detection of
moving object is significant in many tasks, such as video surveillance & moving object tracking.
The design of a video surveillance system is directed on involuntary identification of events of
interest, especially on tracking and on classification of moving objects. An entropy based realtime
adaptive non-parametric window thresholding algorithm for change detection is
anticipated in this research. Based on the approximation of the value of scatter of sections of
change in a difference image, a threshold of every image block is calculated discriminatively
using entropy structure, and then the global threshold is attained by averaging all thresholds for
image blocks of the frame. The block threshold is calculated contrarily for regions of change
and background. Investigational results show the proposed thresholding algorithm
accomplishes well for change detection with high efficiency.
A Novel Approach for Tracking with Implicit Video Shot DetectionIOSR Journals
1) The document presents a novel approach that combines video shot detection and object tracking using a particle filter to create an efficient tracking algorithm with implicit shot detection.
2) It uses a robust pixel difference method for shot detection that is resistant to sudden illumination changes. It then applies a particle filter for tracking that uses color histograms and Bhattacharyya distance to track objects across frames.
3) The key innovation is that the tracking algorithm is only initiated after a shot change is detected, reducing computational costs by discarding unneeded frames and triggering tracking only when needed. This provides a more efficient solution for tracking large video datasets with minimal preprocessing.
IMAGE RECOGNITION USING MATLAB SIMULINK BLOCKSETIJCSEA Journal
The world over, image recognition are essential players in promoting quality object recognition especially in emergency and search-rescue operation. In this paper precise image recognition system using Matlab Simulink Blockset to detect selected object from crowd is presented. The process involves extracting object
features and then recognizes it considering illumination, direction and pose. A Simulink model has been developed to eliminate the tiny elements from the image, then creating segments for precise object recognition. Furthermore, the simulation explores image recognition from the coloured and gray-scale images through image processing techniques in Simulink environment. The tool employed for computation
and simulation is the Matlab image processing blockset. The process comprises morphological operation method which is effective for captured images and video. The results of extensive simulations indicate that this method is suitable for application identifying a person from a crow. The model can be used in emergency and search-rescue operation as well as in medicine, information security, access control, law enforcement, surveillance system, microscopy etc.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
HUMAN MOTION DETECTION AND TRACKING FOR VIDEO SURVEILLANCEAswinraj Manickam
An approach to detect and track groups of people in video-surveillance applications, and to automatically recognize their behavior.
This method keeps track of individuals moving together by maintaining a spacial and temporal group coherence.
First, people are individually detected and tracked. Second, their trajectories are analyzed over a temporal window and clustered using the Mean-Shift algorithm.
A coherence value describes how well a set of people can be described as a group. Furthermore, we propose a formal event description language.
The group events recognition approach is successfully validated on 4 camera views from 3 data sets: an airport, a subway, a shopping center corridor and an entrance hall.
IRJET - Traffic Density Estimation by Counting Vehicles using Aggregate Chann...IRJET Journal
This document presents a method for estimating traffic density by counting vehicles in images using aggregate channel features. The proposed method uses adaptive boosting and aggregate channel features to train an object detector to detect vehicles in images obtained from videos. Bounding boxes are placed around detected vehicles and overlapping boxes are removed. Traffic density is then estimated by counting the number of bounding boxes and dividing by the maximum possible number of vehicles in the area. The estimated densities can be used to control traffic light timing, with higher densities corresponding to shorter green light durations. The method is tested on real-world traffic images and is found to accurately detect vehicles and estimate densities.
Trajectory Based Unusual Human Movement Identification for ATM SystemIRJET Journal
This document summarizes a research paper on developing a system to identify unusual human movements at ATMs using trajectory analysis. The proposed system uses computer vision techniques like background subtraction and template matching to detect and track human movements. If a person's trajectory does not match expected patterns or if their face is covered, an alarm is triggered. The system is intended to prevent crimes and unauthorized access at ATMs by continuously monitoring movements and alerting administrators of any suspicious activity in real-time.
IRJET- Full Body Motion Detection and Surveillance System ApplicationIRJET Journal
1) The document discusses a system for real-time full-body motion detection and surveillance using computer vision techniques.
2) It involves comparing video frames over time to detect motion by treating videos as stacks of frames and looking for differences between frames.
3) The goal is to track body motion in real time using OpenCV for applications like surveillance systems, pose estimation, and other filters.
This document presents a method for identifying images that have been compressed using JPEG. It analyzes the quantization noise present in images after JPEG compression and decompression. The key findings are:
1) Decompressed JPEG images have a lower variance of "forward quantization noise" compared to uncompressed images.
2) An algorithm is developed to detect decompressed JPEG images based on comparing the forward quantization noise variance to a threshold.
3) Extensive experiments show the method outperforms state-of-the-art techniques, especially for images compressed with high quality settings.
4) The method is robust to small image sizes and chroma subsampling during JPEG compression.
IRJET- Study of SVM and CNN in Semantic Concept DetectionIRJET Journal
1) The document discusses approaches for semantic concept detection in videos using techniques like support vector machines (SVM) and convolutional neural networks (CNN).
2) It proposes a concept detection system that uses SVM and CNN together, extracting features from key frames using Hue moments and classifying the features with SVM and CNN.
3) The outputs of SVM and CNN are fused to improve concept detection accuracy compared to using the classifiers individually. Fusing the two classifiers is intended to better identify the concepts in video frames.
The document discusses an improved error detection and data recovery architecture for motion estimation testing applications. It presents a residue-and-quotient (RQ) code-based design to embed into motion estimation for detecting and recovering from errors in processing elements. Experimental results show the design can detect errors and recover data with acceptable overhead in area and timing. It also performs satisfactorily in terms of throughput and reliability for motion estimation testing.
Real Time Object Identification for Intelligent Video Surveillance ApplicationsEditor IJCATR
Intelligent video surveillance system has emerged as a very important research topic in the computer vision field in the
recent years. It is well suited for a broad range of applications such as to monitor activities at traffic intersections for detecting
congestions and predict the traffic flow. Object classification in the field of video surveillance is a key component of smart
surveillance software. Two robust methodology and algorithms adopted for people and object classification for automated surveillance
systems is proposed in this paper. First method uses background subtraction model for detecting the object motion. The background
subtraction and image segmentation based on morphological transformation for tracking and object classification on highways is
proposed. This algorithm uses erosion followed by dilation on various frames. Proposed algorithm in first method, segments the image
by preserving important edges which improves the adaptive background mixture model and makes the system learn faster and more
accurately. The system used in second method adopts the object detection method without background subtraction because of the static
object detection. Segmentation is done by the bounding box registration technique. Then the classification is done with the multiclass
SVM using the edge histogram as features. The edge histograms are calculated for various bin values in different environment. The
result obtained demonstrates the effectiveness of the proposed approach.
A New Deep Learning Based Technique To Detect Copy Move Forgery In Digital Im...IRJET Journal
This document proposes a new deep learning technique to detect copy move forgery in digital images. It uses a VGG16 CNN model to extract feature vectors from image blocks. Euclidean distance is used to measure similarity between feature vectors and detect matching blocks, indicating potential forgery. The proposed method is evaluated on the CoMoFoD dataset and achieves higher F1-scores than ResNet50 and EfficientNet models, detecting forged regions more accurately.
IRJET- Image Forgery Detection using Support Vector MachineIRJET Journal
This document presents research on detecting image forgery using support vector machines. It begins with an abstract discussing how easily images can be digitally manipulated today without leaving traces. It then discusses the most common forgery techniques of splicing, where one image region is cut and pasted into another image, and copy-move, where an image region is copied and pasted within the same image.
The document then reviews previous work on forgery detection techniques. It proposes a new approach that uses preprocessing, feature extraction from image blocks, and support vector machines to classify images as authentic or forged. If forged, principal component analysis is used to identify the forged regions. The approach is tested on splicing and copy-move forgeries and
IRJET- Framework for Image Forgery DetectionIRJET Journal
The document proposes a framework for detecting image forgeries using optical flow and stable parameters. It begins with coarse detection to find suspected tampered points by analyzing optical flow sum consistency. Then it performs fine detection for precise location of forgeries, including duplicated frame pair matching based on optical flow correlation and validation checks to reduce false detections. The framework is designed to balance detection efficiency, robustness, and applicability.
IRJET - Applications of Image and Video Deduplication: A SurveyIRJET Journal
This document discusses applications of image and video deduplication techniques. It begins by providing background on the growth of multimedia data and need for deduplication to reduce redundant data. It then describes key aspects of image and video deduplication, including extracting fingerprints from images and frames to identify duplicates. The document reviews several studies on image and video deduplication applications, such as identifying near-duplicate images on social media, detecting spoofed face images, verifying image copy detection, and eliminating near-duplicates from visual sensor networks. Overall, the document surveys various real-world implementations of image and video deduplication.
IRJET- Mosaic Image Creation in Video for Secure TransmissionIRJET Journal
This document proposes a new method for securely transmitting images over a medium using mosaic image creation in video. The method has two main phases:
1) Mosaic video creation: A video is selected and its frames are used to create a mosaic image that resembles a target secret image. Color transformations are applied to fit tiles of the secret image into blocks of frames. Relevant information for recovery is embedded into the mosaic video.
2) Secret image recovery: At the receiving end, the frames are extracted from the video. The embedded information is extracted to recover tiles of the secret image from the mosaic frames through inverse transformations. The secret image is thus reconstructed without any loss.
An Enhanced Method to Detect Copy Move Forgey in Digital Image Processing usi...IRJET Journal
This document presents a study on detecting copy-move forgery in digital images. It discusses an enhanced method using 2D discrete wavelet transform (DWT) approach. The key steps of the proposed method include preprocessing, feature extraction using DWT, block matching to identify duplicated regions, and filtering to reduce false matches. The method aims to develop an efficient, robust technique for copy-move forgery detection. It reviews existing literature on various detection techniques in the intensity and frequency domains. The proposed method extracts DWT features and uses a block matching algorithm to detect duplicated regions more precisely compared to other methods.
Real time Traffic Signs Recognition using Deep LearningIRJET Journal
This document discusses a deep learning model for real-time traffic sign recognition using convolutional neural networks. Specifically:
- The model uses a CNN architecture based on LeNet to classify images of traffic signs in real-time with a webcam.
- The model was trained on a dataset containing over 22,000 images across 43 traffic sign classes. It achieved 95% accuracy on the test set.
- The model consists of convolutional layers to extract features from images, max pooling layers, dropout layers, and dense layers to perform classification.
- Once trained, the model can continuously classify traffic signs from a webcam feed in real-time, displaying the predicted class and probability. This system has applications for autonomous vehicle navigation
USING IMAGE CLASSIFICATION TO INCENTIVIZE RECYCLINGIRJET Journal
This document discusses using image classification to incentivize recycling. It proposes a web application where users can upload images of recyclable materials. Using image processing and classification algorithms, the material is identified and points are awarded. When enough points are accumulated, users can exchange them for rewards. The system architecture includes image upload and classification, data storage, and transaction processing. Popular classification models like ResNet and FastAI are evaluated. Analysis shows some materials like plastic and metal are confused, indicating room for improvement. The goal is to promote recycling through gamification and make recycling more accessible.
1) The document discusses copy-move forgery detection using the Discrete Wavelet Transform (DWT) method. Copy-move forgery involves copying and pasting a part of an image within the same image to conceal information.
2) Previous work has used the PCA algorithm to detect incompatible pixels, but this study proposes using the DWT and GLCM algorithms instead. The proposed algorithm is tested in MATLAB and evaluated based on PSNR and MSE values.
3) The study finds that the proposed DWT and GLCM algorithm performs better than the previous PCA-only approach, providing more accurate forgery detection while maintaining good performance metrics.
An Efficient Image Forensic Mechanism using Super Pixel by SIFT and LFP Algor...IRJET Journal
This document summarizes a research paper that proposes an efficient image forensic mechanism using super pixels, scale-invariant feature transform (SIFT), and local fingerprint (LFP) algorithm to detect copy-move forgery. The mechanism applies wavelet decomposition to compute super pixel sizes for segmentation, extracts features using SIFT, and performs region growing to detect forged regions. Experimental results showed increased performance in precision, sensitivity, specificity, and F1 score measures for forgery detection compared to existing techniques. The document also reviews several related works on image forgery detection techniques.
Deep Learning Based Vehicle Rules Violation Detection and Accident AssistanceIRJET Journal
This document presents a deep learning based system for detecting traffic violations and assisting with accidents. The system uses techniques like YOLO, CNNs, and Image AI to detect violations like signal jumping, triple riding, helmet detection, no parking from video feeds. It can also detect accidents and provide swift assistance. When a violation is detected, the system recognizes the vehicle license plate using OCR and sends an SMS alert to the owner. The system is meant to assist traffic police by automatically detecting violations and accidents from video monitoring systems in real-time. It aims to help regulate traffic and reduce inconvenience to the public.
This document summarizes a project report on image segmentation using an advanced fuzzy c-means algorithm. The report was submitted by two students to fulfill requirements for a Bachelor of Technology degree in Electrical Engineering at the Indian Institute of Technology Roorkee. It describes implementing various clustering algorithms for image segmentation, including k-means, fuzzy c-means, bias-corrected fuzzy c-means, and Gaussian kernel fuzzy c-means. It then proposes improvements to the algorithms by automatically selecting the number of clusters and initial cluster centers based on a moving average filter on the image histogram. This approach removes problems of non-convergence and increases speed, enabling real-time video segmentation.
This document proposes a method for video copy detection using segmentation, MPEG-7 descriptors, and graph-based sequence matching. It extracts key frames from videos, extracts features from the frames using descriptors like CEDD, FCTH, SCD, EHD and CLD, and stores them in a database. When a query video is input, its features are extracted and compared to the database to detect if it matches any videos already in the database. Graph-based sequence matching is also used to find the optimal matching between video sequences despite transformations like changed frame rates or ordering. The method is shown to perform better than previous techniques at detecting copied videos through transformations.
IRJET- A Survey on Image Forgery Detection and RemovalIRJET Journal
This document summarizes a survey of techniques for detecting image forgery and removal. It discusses hashing methods that can be used to authenticate images, including transforms in both the spatial and frequency domains. Key hashing algorithms mentioned are based on histogram, singular value decomposition, non-negative matrix factorization, discrete wavelet transform, discrete cosine transform, and Zernike moments. The paper compares advantages and disadvantages of different algorithms and concludes that robust and secure hashes are needed and further study is required to improve robustness to content-preserving manipulations and sensitivity to small tampered regions.
Application of Digital Image Correlation: A ReviewIRJET Journal
This document reviews the application of digital image correlation (DIC) technique. DIC is a non-contact optical method used to measure full-field surface deformations and strains. It works by tracking random speckle patterns on a material's surface between images taken before and after deformation. The document discusses how DIC can be used to detect crack initiation in concrete, measure strain maps, and determine material properties like elastic modulus without being destructive. It also reviews several past studies where DIC was used to analyze strain in materials like gypsum, composites, and concrete. The document concludes that DIC provides an accurate alternative to conventional techniques and its use could be expanded in civil engineering.
IRJET - A Survey Paper on Efficient Object Detection and Matching using F...IRJET Journal
This document summarizes an approach for efficient object detection and matching in images and videos. It proposes a classification scheme that classifies extracted features as either object or non-object features. This binary classification approach can be used for object detection and matching in a way that is more robust and faster compared to traditional methods. The classification stage also enables faster object registration. The approach is evaluated to show advantages for object matching and registration compared to other methods. It has potential applications for real-time object tracking and detection.
IRJET - Simulation of Colour Image Processing Techniques on VHDLIRJET Journal
This document summarizes research on simulating color image processing techniques using VHDL. It discusses using VHDL to implement thresholding, brightness, and inversion operations on images. The goal is to perform these operations faster than software by taking advantage of the reconfigurability and parallelism of hardware. The paper reviews related work on image processing using FPGAs and proposes simulating the image processing system using a link between MATLAB and VHDL for testing and verification.
Video Stabilization using Python and open CVIRJET Journal
The document describes a video stabilization method using point feature matching. It involves detecting point features in video frames using Shi-Tomasi corner detection, tracking the features between frames using optical flow, estimating camera motion via feature matching, smoothing the camera path, and stabilizing frames. The method is implemented using Python and OpenCV. It is shown to effectively reduce unwanted camera motion and produce smoother videos. The system architecture involves preprocessing, feature detection/tracking, motion estimation, trajectory smoothing, stabilization, and output. Results on sample videos demonstrate reduced shakiness before and after stabilization.
Detection of a user-defined object in an image using feature extraction- Trai...IRJET Journal
The document proposes a method for detecting user-defined objects in images using feature extraction and training. The method combines contour detection, edge detection, k-means clustering, color identification, and image segmentation. It uses an original "source" object image to train the system to recognize and identify the target object in other images based on a feature set. The key steps include pre-processing images, extracting features like contours and edges, using k-means clustering to identify colors, and analyzing color and shape features to detect matching objects. The results demonstrate the ability to accurately detect target objects against complex backgrounds.
IRJET - Computer Vision-based Image Processing System for Redundant Objec...IRJET Journal
This document describes a proposed computer vision-based image processing system for detecting redundant objects using a Raspberry Pi. The system would utilize a Raspberry Pi connected to a USB camera to capture video frames and detect motion using OpenCV image processing libraries. When motion is detected, the system would segment the moving object from the background using thresholding techniques and morphological operations. It would then highlight and track the detected object using contour functions. Detected objects would be sent to a monitoring interface along with an alert to allow remote monitoring and response. The system aims to provide low-cost real-time surveillance and intruder detection capabilities.
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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.
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A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
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Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
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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.
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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
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3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
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P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
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Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
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Review on studies and research on widening of existing concrete bridgesIRJET Journal
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React based fullstack edtech web applicationIRJET Journal
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Online train ticket booking system project.pdfKamal Acharya
Rail transport is one of the important modes of transport in India. Now a days we
see that there are railways that are present for the long as well as short distance
travelling which makes the life of the people easier. When compared to other
means of transport, a railway is the cheapest means of transport. The maintenance
of the railway database also plays a major role in the smooth running of this
system. The Online Train Ticket Management System will help in reserving the
tickets of the railways to travel from a particular source to the destination.
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Particle Swarm Optimization–Long Short-Term Memory based Channel Estimation w...IJCNCJournal
Paper Title
Particle Swarm Optimization–Long Short-Term Memory based Channel Estimation with Hybrid Beam Forming Power Transfer in WSN-IoT Applications
Authors
Reginald Jude Sixtus J and Tamilarasi Muthu, Puducherry Technological University, India
Abstract
Non-Orthogonal Multiple Access (NOMA) helps to overcome various difficulties in future technology wireless communications. NOMA, when utilized with millimeter wave multiple-input multiple-output (MIMO) systems, channel estimation becomes extremely difficult. For reaping the benefits of the NOMA and mm-Wave combination, effective channel estimation is required. In this paper, we propose an enhanced particle swarm optimization based long short-term memory estimator network (PSOLSTMEstNet), which is a neural network model that can be employed to forecast the bandwidth required in the mm-Wave MIMO network. The prime advantage of the LSTM is that it has the capability of dynamically adapting to the functioning pattern of fluctuating channel state. The LSTM stage with adaptive coding and modulation enhances the BER.PSO algorithm is employed to optimize input weights of LSTM network. The modified algorithm splits the power by channel condition of every single user. Participants will be first sorted into distinct groups depending upon respective channel conditions, using a hybrid beamforming approach. The network characteristics are fine-estimated using PSO-LSTMEstNet after a rough approximation of channels parameters derived from the received data.
Keywords
Signal to Noise Ratio (SNR), Bit Error Rate (BER), mm-Wave, MIMO, NOMA, deep learning, optimization.
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Cricket management system ptoject report.pdfKamal Acharya
The aim of this project is to provide the complete information of the National and
International statistics. The information is available country wise and player wise. By
entering the data of eachmatch, we can get all type of reports instantly, which will be
useful to call back history of each player. Also the team performance in each match can
be obtained. We can get a report on number of matches, wins and lost.
Data Communication and Computer Networks Management System Project Report.pdfKamal Acharya
Networking is a telecommunications network that allows computers to exchange data. In
computer networks, networked computing devices pass data to each other along data
connections. Data is transferred in the form of packets. The connections between nodes are
established using either cable media or wireless media.
Sachpazis_Consolidation Settlement Calculation Program-The Python Code and th...Dr.Costas Sachpazis
Consolidation Settlement Calculation Program-The Python Code
By Professor Dr. Costas Sachpazis, Civil Engineer & Geologist
This program calculates the consolidation settlement for a foundation based on soil layer properties and foundation data. It allows users to input multiple soil layers and foundation characteristics to determine the total settlement.
This is an overview of my current metallic design and engineering knowledge base built up over my professional career and two MSc degrees : - MSc in Advanced Manufacturing Technology University of Portsmouth graduated 1st May 1998, and MSc in Aircraft Engineering Cranfield University graduated 8th June 2007.