Feature tracking is a key, underlying component in
many approaches to 3D reconstruction, detection, localization
and recognition of underwater objects. In this paper, we
proposed to adapt SIFT technique for feature tracking in
underwater video sequences. Over the past few years the
underwater vision is attracting researchers to investigate
suitable feature tracking techniques for underwater
applications. The researchers have developed many feature
tracking techniques such as KLT, SIFT, SURF etc., to track
the features in video sequence for general applications. The
literature survey reveals that there is no standard feature
tracker suitable for underwater environment. We proposed to
adapt SIFT technique for tracking features of objects in
underwater video sequence. The SIFT extracts features, which
are invariant to scale, rotation and affine transformations.
We have compared and evaluated SIFT with popular techniques
such as KLT and SURF on captured video sequence of
underwater objects. The experimental results shows that
adapted SIFT works well for underwater video sequence
HUMAN ACTION RECOGNITION IN VIDEOS USING STABLE FEATURES sipij
Human action recognition is still a challenging problem and researchers are focusing to investigate this
problem using different techniques. We propose a robust approach for human action recognition. This is
achieved by extracting stable spatio-temporal features in terms of pairwise local binary pattern (P-LBP)
and scale invariant feature transform (SIFT). These features are used to train an MLP neural network
during the training stage, and the action classes are inferred from the test videos during the testing stage.
The proposed features well match the motion of individuals and their consistency, and accuracy is higher
using a challenging dataset. The experimental evaluation is conducted on a benchmark dataset commonly
used for human action recognition. In addition, we show that our approach outperforms individual features
i.e. considering only spatial and only temporal feature.
This document discusses data analysis techniques for refraction tomography including data conversion, signal killing, picking approaches, and model geometry. It provides instructions on installing picking software, naming converted data files sequentially, fixing header sizes, deleting unwanted traces based on component, and approaches for manual and automated first break picking. Examples of clear seismic records that make first arrival picking easy are also shown.
Dealing with multiple source spatio-temporal data in urban dynamics analysis ...Beniamino Murgante
The document summarizes research on representing spatio-temporal data from multiple sources in urban mobility analysis. It proposes a framework with concepts like observations, places, stays, and trajectories to integrate GPS, Wi-Fi, and GSM data. The concepts are validated by mapping real-world smartphone data to observations and places. Future work includes handling larger datasets, improving the place learning algorithm, and extending the analysis to groups.
1) The document presents a new method for automatically registering synthetic aperture radar (SAR) imagery to light detection and ranging (LIDAR) data and optical images using digital elevation models (DEMs).
2) The method works by generating a predicted SAR image from the DEM and sensor model, registering this predicted image to the actual SAR image, and refining the sensor model.
3) The results show accurate registration of SAR imagery to LIDAR DEMs and multispectral imagery, with registration errors of 1-2 meters.
A New Watermarking Algorithm Based on Image Scrambling and SVD in the Wavelet...IDES Editor
A new watermarking algorithm which is based on
image scrambling and SVD in the wavelet domain is discussed
in this paper. In the proposed algorithm, chaotic signals are
generated using logistic mapping and are used for scrambling
the original watermark. The initial values of logistic mapping
are taken as private keys. The covert image is decomposed
into four bands using integer wavelet transform; we apply
SVD to each band and embed the
SOAR: SENSOR ORIENTED MOBILE AUGMENTED REALITY FOR URBAN LANDSCAPE ASSESSMENTTomohiro Fukuda
This slide is presented in CAADRIA2012 (The 17th International Conference on Computer Aided Architectural Design Research in Asia).
Abstract. This research presents the development of a sensor oriented mobile AR system which realizes geometric consistency using GPS, a gyroscope and a video camera which are mounted in a smartphone for urban landscape assessment. A low cost AR system with high flexibility is realized. Consistency of the viewing angle of a video camera and a CG virtual camera, and geometric consistency between a video image and 3DCG are verified. In conclusion, the proposed system was evaluated as feasible and effective.
The document presents a 3-D environment modeling and adaptive foreground detection system for multi-camera surveillance. The system constructs a 3-D model of the environment using planar patches approximated from camera views. Videos are integrated and displayed on the 3-D model using texture mapping. A novel method is proposed to detect moving shadows in two phases: an offline training phase determines pixel-wise thresholds, and an online phase updates the thresholds over time to adapt to different scenes. Foreground objects are extracted accurately after removing shadows and displayed using axis-aligned billboarding for 3-D visualization.
HUMAN ACTION RECOGNITION IN VIDEOS USING STABLE FEATURES sipij
Human action recognition is still a challenging problem and researchers are focusing to investigate this
problem using different techniques. We propose a robust approach for human action recognition. This is
achieved by extracting stable spatio-temporal features in terms of pairwise local binary pattern (P-LBP)
and scale invariant feature transform (SIFT). These features are used to train an MLP neural network
during the training stage, and the action classes are inferred from the test videos during the testing stage.
The proposed features well match the motion of individuals and their consistency, and accuracy is higher
using a challenging dataset. The experimental evaluation is conducted on a benchmark dataset commonly
used for human action recognition. In addition, we show that our approach outperforms individual features
i.e. considering only spatial and only temporal feature.
This document discusses data analysis techniques for refraction tomography including data conversion, signal killing, picking approaches, and model geometry. It provides instructions on installing picking software, naming converted data files sequentially, fixing header sizes, deleting unwanted traces based on component, and approaches for manual and automated first break picking. Examples of clear seismic records that make first arrival picking easy are also shown.
Dealing with multiple source spatio-temporal data in urban dynamics analysis ...Beniamino Murgante
The document summarizes research on representing spatio-temporal data from multiple sources in urban mobility analysis. It proposes a framework with concepts like observations, places, stays, and trajectories to integrate GPS, Wi-Fi, and GSM data. The concepts are validated by mapping real-world smartphone data to observations and places. Future work includes handling larger datasets, improving the place learning algorithm, and extending the analysis to groups.
1) The document presents a new method for automatically registering synthetic aperture radar (SAR) imagery to light detection and ranging (LIDAR) data and optical images using digital elevation models (DEMs).
2) The method works by generating a predicted SAR image from the DEM and sensor model, registering this predicted image to the actual SAR image, and refining the sensor model.
3) The results show accurate registration of SAR imagery to LIDAR DEMs and multispectral imagery, with registration errors of 1-2 meters.
A New Watermarking Algorithm Based on Image Scrambling and SVD in the Wavelet...IDES Editor
A new watermarking algorithm which is based on
image scrambling and SVD in the wavelet domain is discussed
in this paper. In the proposed algorithm, chaotic signals are
generated using logistic mapping and are used for scrambling
the original watermark. The initial values of logistic mapping
are taken as private keys. The covert image is decomposed
into four bands using integer wavelet transform; we apply
SVD to each band and embed the
SOAR: SENSOR ORIENTED MOBILE AUGMENTED REALITY FOR URBAN LANDSCAPE ASSESSMENTTomohiro Fukuda
This slide is presented in CAADRIA2012 (The 17th International Conference on Computer Aided Architectural Design Research in Asia).
Abstract. This research presents the development of a sensor oriented mobile AR system which realizes geometric consistency using GPS, a gyroscope and a video camera which are mounted in a smartphone for urban landscape assessment. A low cost AR system with high flexibility is realized. Consistency of the viewing angle of a video camera and a CG virtual camera, and geometric consistency between a video image and 3DCG are verified. In conclusion, the proposed system was evaluated as feasible and effective.
The document presents a 3-D environment modeling and adaptive foreground detection system for multi-camera surveillance. The system constructs a 3-D model of the environment using planar patches approximated from camera views. Videos are integrated and displayed on the 3-D model using texture mapping. A novel method is proposed to detect moving shadows in two phases: an offline training phase determines pixel-wise thresholds, and an online phase updates the thresholds over time to adapt to different scenes. Foreground objects are extracted accurately after removing shadows and displayed using axis-aligned billboarding for 3-D visualization.
Real-time 3D Object Pose Estimation and Tracking for Natural Landmark Based V...c.choi
1) The document describes a real-time method for estimating and tracking the 3D pose of a rigid object using either a mono or stereo camera.
2) The method combines scale invariant feature matching (SIFT) for initial pose estimation with optical flow-based tracking (KLT) for efficient local pose estimation.
3) Outliers in the tracking are removed using RANSAC to improve accuracy, and tracking restarts from initial pose estimation if the number of inliers falls below a threshold.
The document describes the Lightspeed Automatic Interactive Lighting Preview System. It aims to provide fast feedback for lighting design by precomputing a deep framebuffer cache of scene properties like normals and textures, and reevaluating shading on the GPU based on new lighting parameters. Key components include automatic program analysis to separate static and dynamic shader code, deep framebuffer generation from the preprocessed scene, and a GPU-based relighting engine to interactively preview lighting changes at high quality.
GOAR: GIS Oriented Mobile Augmented Reality for Urban Landscape AssessmentTomohiro Fukuda
This slide is presented in CMC2012 (2012 4th International Conference on
Communications, Mobility, and Computing).
Abstract. This research presents the development of a mobile AR system which realizes geometric consistency
using GIS, a gyroscope and a video camera which are mounted in a smartphone for urban landscape assessment. A low cost AR system with high flexibility is developed.
Geometric consistency between a video image and 3DCG are verified. In conclusion, the proposed system was evaluated as feasible and effective.
Availability of Mobile Augmented Reality System for Urban Landscape SimulationTomohiro Fukuda
This slide is presented in CDVE2012 (The 9th International Conference on Cooperative Design, Visualization, and Engineering).
Abstract. This research presents the availability of a landscape simulation method for a mobile AR (Augmented Reality), comparing it with photo montage and VR (Virtual Reality) which are the main existing methods. After a pilot experiment with 28 subjects in Kobe city, a questionnaire about three landscape simulation methods was implemented. In the results of the questionnaire, the mobile AR method was well evaluated for reproducibility of a landscape, operability, and cost. An evaluation rated as better than equivalent was obtained in comparison with the existing methods. The suitability of mobile augmented reality for landscape simulation was found to be high.
A STUDY OF VARIATION OF NORMAL OF POLY-GONS CREATED BY POINT CLOUD DATA FOR A...Tomohiro Fukuda
This slide is presented in CAADRIA2011 (The 16th International Conference on Computer Aided Architectural Design Research in Asia).
Abstracts: Acquiring current 3D space data of cities, buildings, and rooms rapidly and in detail has become indispensable. When the point cloud data of an object or space scanned by a 3D laser scanner is converted into polygons, it is an accumulation of small polygons. When object or space is a closed flat plane, it is necessary to merge small polygons to reduce the volume of data, and to convert them into one polygon. When an object or space is a closed flat plane, each normal vector of small polygons theoretically has the same angle. However, in practise, these angles are not the same. Therefore, the purpose of this study is to clarify the variation of the angle of a small polygon group that should become one polygon based on actual data. As a result of experimentation, no small polygons are converted by the point cloud data scanned with the 3D laser scanner even if the group of small polygons is a closed flat plane lying in the same plane. When the standard deviation of the extracted number of polygons is assumed to be less than 100, the variation of the angle of the normal vector is roughly 7 degrees.
A New Approach for video denoising and enhancement using optical flow EstimationIRJET Journal
This document proposes a new approach for video denoising and enhancement using optical flow estimation. It discusses using motion compensation via optical flow estimation along with principal component analysis (PCA) to provide fine video details. However, PCA has limitations in fully eliminating noise. The proposed method aims to replace PCA with wavelet transformation, which provides multi-resolution analysis and sparsity advantages for better denoising results in terms of PSNR and RMSE compared to PCA. It involves estimating optical flow between frames for motion compensation before applying wavelet transformation for noise removal and video reconstruction.
This document summarizes recent developments in action recognition using deep learning techniques. It discusses early approaches using improved dense trajectories and two-stream convolutional neural networks. It then focuses on advances using 3D convolutional networks, enabled by large video datasets like Kinetics. State-of-the-art results are achieved using inflated 3D convolutional networks and temporal aggregation methods like temporal linear encoding. The document provides an overview of popular datasets and challenges and concludes with tips on training models at scale.
The document discusses automatic geotagging of videos. It describes challenges in estimating the geographic location of videos using textual and visual information. Methods discussed include using textual tags, visual features, and gazetteers to determine location. The author also describes fusing multiple approaches and using spatial segmentation to improve accuracy while reducing computational costs.
The document summarizes two approaches to implementing foveated imaging in CMOS image sensors: (1) A pyramidal architecture with multiple rings of pixels having different integration times, allowing for dynamic range enhancement. (2) A universal multiresolution sensor using a 3T pixel design that allows pixels to be grouped and averaged, enabling adaptive resolution. Both designs aim to mimic the human retina and improve efficiency over traditional sensors. The pyramidal and multiresolution sensors were fabricated in 0.18um CMOS technology and are being tested for applications like video conferencing and industrial inspection.
DISTRIBUTED AND SYNCHRONISED VR MEETING USING CLOUD COMPUTING: Availability a...Tomohiro Fukuda
This slide is presented in CAADRIA2012 (The 17th International Conference on Computer Aided Architectural Design Research in Asia).
Abstract. The mobility of people's activities, and cloud computing technologies are becoming advanced in the modern age of information and globalisation. This study describes the availability of discussing spatial design while sharing a 3-dimensional virtual space with stakeholders in a distributed and synchronised environment. First of all, a townscape design support system based on a cloud computing type VR system is constructed. Next, an experiment of a distributed and synchronised discussion of townscape design is executed with subjects who are specialists in the townscape design field. After the experiment, both qualitative mental evaluation and quantitative evaluation were carried out. The conclusions are as follows: 1. Users who use VR frequently and who use videoconferencing consider that the difference with face-to-face discussion is small. 2. A Moiré pattern may occur in a gradation picture. 3. The availability of distributed and synchronised discussions with cloud computing type VR is high.
Self Attested Images for Secured Transactions using Superior SOMIDES Editor
Separate digital signals are usually used as the
digital watermarks. But this paper proposes rebuffed
untrained minute values of vital image as a digital watermark,
since no host image is needed to hide the vital image for its
safety. The vital images can be transformed with the self
attestation. Superior Self Organized Maps is used to derive
self signature from the vital image. This analysis work
constructs framework with Superior Self Organizing Maps
(SSOM) against Counter Propagation Network for watermark
generation and detection. The required features like
robustness, imperceptibility and security was analyzed to prove
that which neural network is appropriate for mining watermark
from the host image. SSOM network is proved as an efficient
neural trainer for the proposed watermarking technique. The
paper presents one more contribution to the watermarking
area.
The International Journal of Engineering and Science (The IJES)theijes
This document summarizes and compares different techniques for moving object detection in video surveillance systems. It discusses background subtraction, background estimation, and adaptive contrast change detection methods. It finds that while traditional methods work for single objects, correlation between frames performs better for multiple objects or poor lighting conditions, as it detects changes between frames. The document evaluates several algorithms and concludes correlation significantly improves output and performance even with multiple moving objects, making it suitable for night-time surveillance applications.
Image Denoising Based On Wavelet for Satellite Imagery: A ReviewIJMER
In this paper studied the use of wavelet and their family to denoising images. Satellite images
are extensively used in the field of RS and GIS for land possession, mapping use for planning and
decision support. As of many Satellite image having common problem i.e. noise which hold unwanted
information in an images. Different types of noise are addressing different techniques to denoising
remotely sense images. Noise within the remote sensing images identifying and denoising them is big
challenge before the researcher. Therefore we review wavelet for denoising of the remote sensing
images. Thus implementing wavelet is essential to get much higher quality denoising image. However,
they are usually too computationally demanding. In order to reduce the
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.
This document describes the development of the first updatable holographic 3D display based on photorefractive polymers. The display has a size of 4x4 inches, making it the largest photorefractive 3D display to date. It is capable of recording and displaying new holographic images every few minutes that can then be viewed for several hours without refreshing. The holograms can also be completely erased and updated whenever desired. This dynamic display overcomes limitations of other holographic technologies that either allow only static images or require high refresh rates to avoid flicker during playback.
Pontillo Semanti Code Using Content Similarity And Database Driven Matching T...Kalle
Laboratory eyetrackers, constrained to a fixed display and static (or accurately tracked) observer, facilitate automated analysis of fixation data. Development of wearable eyetrackers has extended environments and tasks that can be studied at the expense of automated analysis. Wearable eyetrackers provide 2D point-of-regard (POR) in scene-camera coordinates, but the researcher is typically interested in some high-level semantic property (e.g., object identity, region, or material) surrounding individual fixation points. The synthesis of POR into fixations and semantic information remains a labor-intensive manual task, limiting the application of wearable eyetracking.
We describe a system that segments POR videos into fixations and allows users to train a database-driven, object-recognition system. A correctly trained library results in a very accurate and semi-automated translation of raw POR data into a sequence of objects, regions or materials.
The document presents a system for detecting complex events in unconstrained videos using pre-trained deep CNN models. Frame-level features extracted from various CNNs are fused to form video-level descriptors, which are then classified using SVMs. Evaluation on a large video corpus found that fusing different CNNs outperformed individual CNNs, and no single CNN worked best for all events as some are more object-driven while others are more scene-based. The best performance was achieved by learning event-dependent weights for different CNNs.
This document discusses analyzing 3D human motion from 2D images. It covers:
1) The difficulties in inferring 3D information from 2D images, including depth ambiguities, self-occlusions, and data association challenges.
2) Two main approaches to modeling 3D human motion - generative/alignment-based methods that predict state distributions from images, and discriminative/predictive methods that optimize alignment with image features.
3) Key techniques for temporal inference in tracking human motion over time, including generative methods like particle filtering and discriminative conditional models.
Sudha radhika to upload in slide share [compatibility mode]radhikasabareesh
1) The document proposes using wavelet analysis as an effective tool for detecting wind damage from satellite images by identifying changes to damaged building structures.
2) It describes past research on disaster detection from aerial/satellite images for earthquakes, wildfires, floods, and landslides. For wind damage, past research used statistical analysis of image pixel values.
3) The proposed methodology extracts building structures from pre- and post-disaster satellite images, analyzes pixel radiance data and edge features using conventional and wavelet-based methods, and classifies damage levels using an artificial neural network trained on these extracted features.
IRJET - Underwater Object Identification using Matlab and MachineIRJET Journal
This document discusses underwater object identification using MATLAB and machine learning. It begins with an abstract that outlines using image processing techniques like color correction and enhancement to improve underwater image quality and resolution for object detection. The methodology section then describes the process, which includes image acquisition, preprocessing like color conversion and noise removal, feature extraction to determine object type, and using a NodeMCU to send data to the cloud. It tests this approach by capturing images of fish underwater and classifying them by type. The results show enhanced, higher quality images compared to the originals. In conclusion, this method effectively removes color distortions and increases contrast to identify underwater objects using deep learning frameworks.
IRJET- Human Fall Detection using Co-Saliency-Enhanced Deep Recurrent Convolu...IRJET Journal
This document summarizes a research paper that proposes a new method for detecting human falls in videos using deep learning. The method uses a recurrent convolutional neural network (RCN) that applies convolutional neural networks (CNNs) to video segments and connects them with long short-term memory (LSTM) to model temporal relationships. It also enhances video frames using co-saliency detection to highlight important human activity regions before feeding them to the RCN. The researchers tested the method on a dataset of 768 video clips from 4 activity classes and achieved 98.12% accuracy at detecting falls, demonstrating the effectiveness of the co-saliency-enhanced RCN approach.
IRJET- A Review on Moving Object Detection in Video Forensics IRJET Journal
This document reviews research on moving object detection in video forensics. It discusses challenges in analyzing large amounts of surveillance video data and summarizes several papers that propose methods for tasks like video synopsis, abandoned object detection, person identification, copy-move forgery detection, and assessing evidence quality. The goal is to develop techniques for efficiently analyzing video evidence and detecting anomalies or tampering.
Real-time 3D Object Pose Estimation and Tracking for Natural Landmark Based V...c.choi
1) The document describes a real-time method for estimating and tracking the 3D pose of a rigid object using either a mono or stereo camera.
2) The method combines scale invariant feature matching (SIFT) for initial pose estimation with optical flow-based tracking (KLT) for efficient local pose estimation.
3) Outliers in the tracking are removed using RANSAC to improve accuracy, and tracking restarts from initial pose estimation if the number of inliers falls below a threshold.
The document describes the Lightspeed Automatic Interactive Lighting Preview System. It aims to provide fast feedback for lighting design by precomputing a deep framebuffer cache of scene properties like normals and textures, and reevaluating shading on the GPU based on new lighting parameters. Key components include automatic program analysis to separate static and dynamic shader code, deep framebuffer generation from the preprocessed scene, and a GPU-based relighting engine to interactively preview lighting changes at high quality.
GOAR: GIS Oriented Mobile Augmented Reality for Urban Landscape AssessmentTomohiro Fukuda
This slide is presented in CMC2012 (2012 4th International Conference on
Communications, Mobility, and Computing).
Abstract. This research presents the development of a mobile AR system which realizes geometric consistency
using GIS, a gyroscope and a video camera which are mounted in a smartphone for urban landscape assessment. A low cost AR system with high flexibility is developed.
Geometric consistency between a video image and 3DCG are verified. In conclusion, the proposed system was evaluated as feasible and effective.
Availability of Mobile Augmented Reality System for Urban Landscape SimulationTomohiro Fukuda
This slide is presented in CDVE2012 (The 9th International Conference on Cooperative Design, Visualization, and Engineering).
Abstract. This research presents the availability of a landscape simulation method for a mobile AR (Augmented Reality), comparing it with photo montage and VR (Virtual Reality) which are the main existing methods. After a pilot experiment with 28 subjects in Kobe city, a questionnaire about three landscape simulation methods was implemented. In the results of the questionnaire, the mobile AR method was well evaluated for reproducibility of a landscape, operability, and cost. An evaluation rated as better than equivalent was obtained in comparison with the existing methods. The suitability of mobile augmented reality for landscape simulation was found to be high.
A STUDY OF VARIATION OF NORMAL OF POLY-GONS CREATED BY POINT CLOUD DATA FOR A...Tomohiro Fukuda
This slide is presented in CAADRIA2011 (The 16th International Conference on Computer Aided Architectural Design Research in Asia).
Abstracts: Acquiring current 3D space data of cities, buildings, and rooms rapidly and in detail has become indispensable. When the point cloud data of an object or space scanned by a 3D laser scanner is converted into polygons, it is an accumulation of small polygons. When object or space is a closed flat plane, it is necessary to merge small polygons to reduce the volume of data, and to convert them into one polygon. When an object or space is a closed flat plane, each normal vector of small polygons theoretically has the same angle. However, in practise, these angles are not the same. Therefore, the purpose of this study is to clarify the variation of the angle of a small polygon group that should become one polygon based on actual data. As a result of experimentation, no small polygons are converted by the point cloud data scanned with the 3D laser scanner even if the group of small polygons is a closed flat plane lying in the same plane. When the standard deviation of the extracted number of polygons is assumed to be less than 100, the variation of the angle of the normal vector is roughly 7 degrees.
A New Approach for video denoising and enhancement using optical flow EstimationIRJET Journal
This document proposes a new approach for video denoising and enhancement using optical flow estimation. It discusses using motion compensation via optical flow estimation along with principal component analysis (PCA) to provide fine video details. However, PCA has limitations in fully eliminating noise. The proposed method aims to replace PCA with wavelet transformation, which provides multi-resolution analysis and sparsity advantages for better denoising results in terms of PSNR and RMSE compared to PCA. It involves estimating optical flow between frames for motion compensation before applying wavelet transformation for noise removal and video reconstruction.
This document summarizes recent developments in action recognition using deep learning techniques. It discusses early approaches using improved dense trajectories and two-stream convolutional neural networks. It then focuses on advances using 3D convolutional networks, enabled by large video datasets like Kinetics. State-of-the-art results are achieved using inflated 3D convolutional networks and temporal aggregation methods like temporal linear encoding. The document provides an overview of popular datasets and challenges and concludes with tips on training models at scale.
The document discusses automatic geotagging of videos. It describes challenges in estimating the geographic location of videos using textual and visual information. Methods discussed include using textual tags, visual features, and gazetteers to determine location. The author also describes fusing multiple approaches and using spatial segmentation to improve accuracy while reducing computational costs.
The document summarizes two approaches to implementing foveated imaging in CMOS image sensors: (1) A pyramidal architecture with multiple rings of pixels having different integration times, allowing for dynamic range enhancement. (2) A universal multiresolution sensor using a 3T pixel design that allows pixels to be grouped and averaged, enabling adaptive resolution. Both designs aim to mimic the human retina and improve efficiency over traditional sensors. The pyramidal and multiresolution sensors were fabricated in 0.18um CMOS technology and are being tested for applications like video conferencing and industrial inspection.
DISTRIBUTED AND SYNCHRONISED VR MEETING USING CLOUD COMPUTING: Availability a...Tomohiro Fukuda
This slide is presented in CAADRIA2012 (The 17th International Conference on Computer Aided Architectural Design Research in Asia).
Abstract. The mobility of people's activities, and cloud computing technologies are becoming advanced in the modern age of information and globalisation. This study describes the availability of discussing spatial design while sharing a 3-dimensional virtual space with stakeholders in a distributed and synchronised environment. First of all, a townscape design support system based on a cloud computing type VR system is constructed. Next, an experiment of a distributed and synchronised discussion of townscape design is executed with subjects who are specialists in the townscape design field. After the experiment, both qualitative mental evaluation and quantitative evaluation were carried out. The conclusions are as follows: 1. Users who use VR frequently and who use videoconferencing consider that the difference with face-to-face discussion is small. 2. A Moiré pattern may occur in a gradation picture. 3. The availability of distributed and synchronised discussions with cloud computing type VR is high.
Self Attested Images for Secured Transactions using Superior SOMIDES Editor
Separate digital signals are usually used as the
digital watermarks. But this paper proposes rebuffed
untrained minute values of vital image as a digital watermark,
since no host image is needed to hide the vital image for its
safety. The vital images can be transformed with the self
attestation. Superior Self Organized Maps is used to derive
self signature from the vital image. This analysis work
constructs framework with Superior Self Organizing Maps
(SSOM) against Counter Propagation Network for watermark
generation and detection. The required features like
robustness, imperceptibility and security was analyzed to prove
that which neural network is appropriate for mining watermark
from the host image. SSOM network is proved as an efficient
neural trainer for the proposed watermarking technique. The
paper presents one more contribution to the watermarking
area.
The International Journal of Engineering and Science (The IJES)theijes
This document summarizes and compares different techniques for moving object detection in video surveillance systems. It discusses background subtraction, background estimation, and adaptive contrast change detection methods. It finds that while traditional methods work for single objects, correlation between frames performs better for multiple objects or poor lighting conditions, as it detects changes between frames. The document evaluates several algorithms and concludes correlation significantly improves output and performance even with multiple moving objects, making it suitable for night-time surveillance applications.
Image Denoising Based On Wavelet for Satellite Imagery: A ReviewIJMER
In this paper studied the use of wavelet and their family to denoising images. Satellite images
are extensively used in the field of RS and GIS for land possession, mapping use for planning and
decision support. As of many Satellite image having common problem i.e. noise which hold unwanted
information in an images. Different types of noise are addressing different techniques to denoising
remotely sense images. Noise within the remote sensing images identifying and denoising them is big
challenge before the researcher. Therefore we review wavelet for denoising of the remote sensing
images. Thus implementing wavelet is essential to get much higher quality denoising image. However,
they are usually too computationally demanding. In order to reduce the
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.
This document describes the development of the first updatable holographic 3D display based on photorefractive polymers. The display has a size of 4x4 inches, making it the largest photorefractive 3D display to date. It is capable of recording and displaying new holographic images every few minutes that can then be viewed for several hours without refreshing. The holograms can also be completely erased and updated whenever desired. This dynamic display overcomes limitations of other holographic technologies that either allow only static images or require high refresh rates to avoid flicker during playback.
Pontillo Semanti Code Using Content Similarity And Database Driven Matching T...Kalle
Laboratory eyetrackers, constrained to a fixed display and static (or accurately tracked) observer, facilitate automated analysis of fixation data. Development of wearable eyetrackers has extended environments and tasks that can be studied at the expense of automated analysis. Wearable eyetrackers provide 2D point-of-regard (POR) in scene-camera coordinates, but the researcher is typically interested in some high-level semantic property (e.g., object identity, region, or material) surrounding individual fixation points. The synthesis of POR into fixations and semantic information remains a labor-intensive manual task, limiting the application of wearable eyetracking.
We describe a system that segments POR videos into fixations and allows users to train a database-driven, object-recognition system. A correctly trained library results in a very accurate and semi-automated translation of raw POR data into a sequence of objects, regions or materials.
The document presents a system for detecting complex events in unconstrained videos using pre-trained deep CNN models. Frame-level features extracted from various CNNs are fused to form video-level descriptors, which are then classified using SVMs. Evaluation on a large video corpus found that fusing different CNNs outperformed individual CNNs, and no single CNN worked best for all events as some are more object-driven while others are more scene-based. The best performance was achieved by learning event-dependent weights for different CNNs.
This document discusses analyzing 3D human motion from 2D images. It covers:
1) The difficulties in inferring 3D information from 2D images, including depth ambiguities, self-occlusions, and data association challenges.
2) Two main approaches to modeling 3D human motion - generative/alignment-based methods that predict state distributions from images, and discriminative/predictive methods that optimize alignment with image features.
3) Key techniques for temporal inference in tracking human motion over time, including generative methods like particle filtering and discriminative conditional models.
Sudha radhika to upload in slide share [compatibility mode]radhikasabareesh
1) The document proposes using wavelet analysis as an effective tool for detecting wind damage from satellite images by identifying changes to damaged building structures.
2) It describes past research on disaster detection from aerial/satellite images for earthquakes, wildfires, floods, and landslides. For wind damage, past research used statistical analysis of image pixel values.
3) The proposed methodology extracts building structures from pre- and post-disaster satellite images, analyzes pixel radiance data and edge features using conventional and wavelet-based methods, and classifies damage levels using an artificial neural network trained on these extracted features.
IRJET - Underwater Object Identification using Matlab and MachineIRJET Journal
This document discusses underwater object identification using MATLAB and machine learning. It begins with an abstract that outlines using image processing techniques like color correction and enhancement to improve underwater image quality and resolution for object detection. The methodology section then describes the process, which includes image acquisition, preprocessing like color conversion and noise removal, feature extraction to determine object type, and using a NodeMCU to send data to the cloud. It tests this approach by capturing images of fish underwater and classifying them by type. The results show enhanced, higher quality images compared to the originals. In conclusion, this method effectively removes color distortions and increases contrast to identify underwater objects using deep learning frameworks.
IRJET- Human Fall Detection using Co-Saliency-Enhanced Deep Recurrent Convolu...IRJET Journal
This document summarizes a research paper that proposes a new method for detecting human falls in videos using deep learning. The method uses a recurrent convolutional neural network (RCN) that applies convolutional neural networks (CNNs) to video segments and connects them with long short-term memory (LSTM) to model temporal relationships. It also enhances video frames using co-saliency detection to highlight important human activity regions before feeding them to the RCN. The researchers tested the method on a dataset of 768 video clips from 4 activity classes and achieved 98.12% accuracy at detecting falls, demonstrating the effectiveness of the co-saliency-enhanced RCN approach.
IRJET- A Review on Moving Object Detection in Video Forensics IRJET Journal
This document reviews research on moving object detection in video forensics. It discusses challenges in analyzing large amounts of surveillance video data and summarizes several papers that propose methods for tasks like video synopsis, abandoned object detection, person identification, copy-move forgery detection, and assessing evidence quality. The goal is to develop techniques for efficiently analyzing video evidence and detecting anomalies or tampering.
This document discusses video segmentation for moving object detection using an entropy-based adaptive window thresholding algorithm. It begins with an introduction to motion detection and object segmentation in video processing. It then discusses challenges in detecting moving objects and related work, including change detection approaches, region-based segmentation techniques, and level set methods. The document proposes an entropy-based real-time adaptive thresholding algorithm to improve change detection and moving object segmentation.
VIDEO SEGMENTATION FOR MOVING OBJECT DETECTION USING LOCAL CHANGE & ENTROPY B...cscpconf
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 dentification 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.
This document discusses approaches for video segmentation. It describes tracking particles across frames to identify motion patterns, then clustering the particles to obtain a pixel-wise segmentation over space and time. This addresses limitations of segmentation based on motion boundaries. Reality-based 3D models can help address complex spatial motions by representing objects and their relationships in 3D space. The document also reviews direct and feature-based motion estimation methods, variational and level-set segmentation frameworks, and challenges including fitting motion models to data and handling outliers.
MIT 6.870 - Template Matching and Histograms (Nicolas Pinto, MIT)npinto
MIT 6.870 Object Recognition and Scene Understanding (Fall 2008)
http://people.csail.mit.edu/torralba/courses/6.870/6.870.recognition.htm
This class will review and discuss current approaches to object recognition and scene understanding in computer vision. The course will cover bag of words models, part based models, classifier based models, multiclass object recognition and transfer learning, concurrent recognition and segmentation, context models for object recognition, grammars for scene understanding and large datasets for semi supervised and unsupervised discovery of object and scene categories. We will be reading a mixture of papers from computer vision and influential works from cognitive psychology on object and scene recognition.
The Effectiveness of 2D-3D Converters in Rendering Natural Water Phenomenaidescitation
Several commercially available conversion
applications have been developed to generate 3D content from
existing 2D images or videos. In this study, five 2D-3D
converters are evaluated for their effectiveness in producing
high quality 3D videos with scenery containing water
phenomena. Such scenes are challenging to convert due to
scene complexity including detail, scene dynamics,
illumination, and reflective distortion. Comparisons are given
using quantitative and subjective evaluations.
Under water object classification using sonar signalIRJET Journal
The document presents a proposed system for classifying underwater objects using sonar signals. The objectives are to classify identified underwater objects, implement a framework for mine detection, and use an AI-based approach. The proposed model would use a dataset of sonar signals from mines and common objects to train a machine learning model. Data preprocessing would be performed to handle missing/noisy data. The trained model would then classify new sonar signals as either mines or common objects. Background research identified gaps in previous work such as reliance on image processing for classification which provides low accuracy. The proposed system aims to classify objects based on sonar signals to safely detect mines and protect submarines/ships.
Human action recognition using local space time features and adaboost svmeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Application of feature point matching to video stabilizationNikhil Prathapani
One of the significant application of computer vision is Stabilizing a video that was captured from a jittery or moving platform. One way to stabilize a video is to track a prominent feature in the image and utilize it as an anchor point to cancel out all perturbations relative to it. This technique, however, must be bootstrapped with knowledge of where such a salient feature remains in the first video frame. The paper presents method of video stabilization that works without any such erstwhile knowledge. The method is built on the basis of Random Sampling and Consensus (RANSAC) and adding few additions to the existing methodologies. It instead automatically investigates for the "background plane" in a video sequence, and utilizes its observed distortion to precise for camera motion. All the simulations have been performed using MATLAB tool.
This document summarizes an approach for video segmentation based on motion coherence. It uses tracking to identify 2-D motion patterns with clustering. Pixels are clustered to obtain a segmentation in both space and time domains. The limitations of segmentation based solely on spatial motion can be addressed using 3-D models generated from range, image, or CAD data. These models can be semantically segmented and organized in different ways depending on their geometry and detail levels.
This document discusses human action recognition in videos using two deep learning models: Long-Term Recurrent Convolutional Networks (LRCN) and Convolutional Long Short-Term Memory (ConvLSTM). LRCN combines convolutional and LSTM layers to analyze video frame sequences, while ConvLSTM integrates convolutional operations within LSTM cells. The document aims to compare the performance of these two models on the UCF101 action recognition dataset. It provides background on related work, describes the proposed methodology including the LRCN and ConvLSTM models, and outlines the experimental procedure to evaluate and compare the two approaches on video classification tasks.
Underwater image enhancement is a challenging task and has gained priority in recent years, as the human eye cannot clearly perceive underwater images. We introduce an effective technique to develop the images captured underwater which are degraded due to the medium scattering and absorption. Our proposed method is a single image approach that does not require specialized hardware or knowledge about the structure of the hardware or underwater conditions. We introduce a new underwater image enhancement approach based on object detection and gamma correction in this paper. In our method, we first obtain the restored image on the base of underwater image model. Then we detect objects using object detection. Finally, the image is gamma corrected. adapted. Experimental results on real world as well as mock underwater images demonstrate that the proposed method is a simplified approach on different underwater scenes and outperforms the existing methods of enhancement. Marshall Mathews | Riya Chummar | Sandra | Ms. R. Sreetha E, S "Underwater Image Enhancement" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31467.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/31467/underwater-image-enhancement/marshall-mathews
Automatic identification of animal using visual and motion saliencyeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Shadow Detection and Removal using Tricolor Attenuation Model Based on Featur...ijtsrd
Presently present TAM FD, a novel expansion of tricolor constriction model custom fitted for the difficult issue of shadow identification in pictures. Past strategies for shadow discovery center on learning the neighborhood appearance of shadow areas, while utilizing restricted nearby setting thinking as pairwise possibilities in a Conditional Random Field. Interestingly, the proposed methodology can display more elevated amount connections and worldwide scene attributes. We train a shadow locator that relates to the generator of a restrictive TAM, and expand its shadow precision by consolidating the run of the mill TAM misfortune with an information misfortune term utilizing highlight descriptor. Shadows happen when articles impede direct light from a wellspring of enlightenment, which is generally the sun. As indicated by the rule of arrangement, shadows can be separated into cast shadow and self shadow. Cast shadow is planned by the projection of articles toward the light source self shadow alludes to the piece of the item that isnt enlightened. For a cast shadow, the piece of it where direct light is totally hindered by an article is named the umbra, while the part where direct light is mostly blocked is named the obscuration. On account of the presence of an obscuration, there wont be an unequivocal limit among shadowed and non shadowed regions the shadows cause incomplete or all out loss of radiometric data in the influenced zones, and therefore, they make errands like picture elucidation, object identification and acknowledgment, and change recognition progressively troublesome or even inconceivable. SDI record improves by 1.76 . Shading segment record for safeguard shading difference during evacuation of shadow procedure is improved by 9.75 . Standardize immersion esteem discovery file NSVDI is improve by 1.89 for distinguish shadow pixel. Rakesh Dangi | Anjana Nigam ""Shadow Detection and Removal using Tricolor Attenuation Model Based on Feature Descriptor"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25127.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/25127/shadow-detection-and-removal-using-tricolor-attenuation-model-based-on-feature-descriptor/rakesh-dangi
IRJET-A Review of Underwater Image Enhancement By Wavelet Decomposition using...IRJET Journal
This document reviews underwater image enhancement techniques using wavelet decomposition implemented on a field programmable gate array (FPGA). It begins with an introduction to the poor quality of underwater images due to light scattering and color distortion. It then discusses prior work on underwater image enhancement using techniques like wavelet fusion and contrast adjustment. The proposed approach involves color correction, contrast enhancement, and multi-scale fusion via wavelet decomposition of the color-corrected and contrast-enhanced images. Low frequency components are fused with weighted averaging while high frequency components use local variance. Experimental results demonstrate this wavelet fusion approach improves underwater image visibility.
Internet data almost double every year. The need of multimedia communication
is less storage space and fast transmission. So, the large volume of video data has become
the reason for video compression. The aim of this paper is to achieve temporal compression
for three-dimensional (3D) videos using motion estimation-compensation and wavelets.
Instead of performing a two-dimensional (2D) motion search, as is common in conventional
video codec’s, the use of a 3D motion search has been proposed, that is able to better exploit
the temporal correlations of 3D content. This leads to more accurate motion prediction and
a smaller residual. The discrete wavelet transform (DWT) compression scheme has been
added for better compression ratio. The DWT has a high-energy compaction property thus
greatly impacted the field of compression. The quality parameters peak signal to noise ratio
(PSNR) and mean square error (MSE) have been calculated. The simulation results shows
that the proposed work improves the PSNR from existing work.
PREVENTING COPYRIGHTS INFRINGEMENT OF IMAGES BY WATERMARKING IN TRANSFORM DOM...ijistjournal
Images are undoubtedly the most efficacious and easiest means of communicating an idea. They are surely an indispensable part of human life .The trend of sharing images of various kinds for example typical technical figures, modern exceptional masterpiece from an artist, photos from the recent picnic to hill station etc, on the internet is spreading like a viral. There is a mandatory requirement for checking the privacy and security of our personal digital images before making them public via the internet. There is always a threat of our original images being illegally reproduced or distributed elsewhere. To prevent the misuse and protect the copyrights, an efficient solution has been given that can withstand many attacks. This paper aims at encoding of the host image prior to watermark embedding for enhancing the security. The fast and effective full counter propagation neural network helps in the successful watermark embedding without deteriorating the image perception. Earlier techniques embedded the watermark in the image itself but is has been observed that synapses of neural network provide a better platform for reducing the distortion and increasing the message capacity.
PREVENTING COPYRIGHTS INFRINGEMENT OF IMAGES BY WATERMARKING IN TRANSFORM DOM...ijistjournal
1) The document discusses a method for preventing copyright infringement of images using watermarking in the transform domain and a full counter propagation neural network.
2) It aims to encode the host image before watermark embedding to enhance security. The fast and effective full counter propagation neural network then helps successfully embed the watermark without deteriorating the image quality.
3) Previous techniques embedded watermarks directly in images, but the authors find neural network synapses provide a better way to reduce distortion and increase message capacity when embedding watermarks.
Similar to Feature Tracking of Objects in Underwater Video Sequences (20)
Power System State Estimation - A ReviewIDES Editor
This document provides a review of power system state estimation techniques. It discusses both static and dynamic state estimation algorithms. For static state estimation, it covers weighted least squares, decoupled, and robust estimation methods. Weighted least squares is commonly used but can have numerical instability issues. Decoupled state estimation approximates the gain matrix for faster computation. Robust estimation uses M-estimators and other techniques to handle outliers and bad data. Dynamic state estimation applies Kalman filtering, leapfrog algorithms, and other methods to continuously monitor system states over time.
Artificial Intelligence Technique based Reactive Power Planning Incorporating...IDES Editor
This document summarizes a research paper that proposes using artificial intelligence techniques and FACTS controllers for reactive power planning in real-time power transmission systems. The paper formulates the reactive power planning problem and incorporates flexible AC transmission system (FACTS) devices like static VAR compensators (SVC), thyristor controlled series capacitors (TCSC), and unified power flow controllers (UPFC). Evolutionary algorithms like evolutionary programming (EP) and differential evolution (DE) are applied to find the optimal locations and settings of the FACTS controllers to minimize losses and costs. Simulation results on IEEE 30-bus and 72-bus Indian test systems show that UPFC performs best in reducing losses compared to SVC and TCSC.
Design and Performance Analysis of Genetic based PID-PSS with SVC in a Multi-...IDES Editor
Damping of power system oscillations with the help
of proposed optimal Proportional Integral Derivative Power
System Stabilizer (PID-PSS) and Static Var Compensator
(SVC)-based controllers are thoroughly investigated in this
paper. This study presents robust tuning of PID-PSS and
SVC-based controllers using Genetic Algorithms (GA) in
multi machine power systems by considering detailed model
of the generators (model 1.1). The effectiveness of FACTSbased
controllers in general and SVC-based controller in
particular depends upon their proper location. Modal
controllability and observability are used to locate SVC–based
controller. The performance of the proposed controllers is
compared with conventional lead-lag power system stabilizer
(CPSS) and demonstrated on 10 machines, 39 bus New England
test system. Simulation studies show that the proposed genetic
based PID-PSS with SVC based controller provides better
performance.
Optimal Placement of DG for Loss Reduction and Voltage Sag Mitigation in Radi...IDES Editor
This paper presents the need to operate the power
system economically and with optimum levels of voltages has
further led to an increase in interest in Distributed
Generation. In order to reduce the power losses and to improve
the voltage in the distribution system, distributed generators
(DGs) are connected to load bus. To reduce the total power
losses in the system, the most important process is to identify
the proper location for fixing and sizing of DGs. It presents a
new methodology using a new population based meta heuristic
approach namely Artificial Bee Colony algorithm(ABC) for
the placement of Distributed Generators(DG) in the radial
distribution systems to reduce the real power losses and to
improve the voltage profile, voltage sag mitigation. The power
loss reduction is important factor for utility companies because
it is directly proportional to the company benefits in a
competitive electricity market, while reaching the better power
quality standards is too important as it has vital effect on
customer orientation. In this paper an ABC algorithm is
developed to gain these goals all together. In order to evaluate
sag mitigation capability of the proposed algorithm, voltage
in voltage sensitive buses is investigated. An existing 20KV
network has been chosen as test network and results are
compared with the proposed method in the radial distribution
system.
Line Losses in the 14-Bus Power System Network using UPFCIDES Editor
Controlling power flow in modern power systems
can be made more flexible by the use of recent developments
in power electronic and computing control technology. The
Unified Power Flow Controller (UPFC) is a Flexible AC
transmission system (FACTS) device that can control all the
three system variables namely line reactance, magnitude and
phase angle difference of voltage across the line. The UPFC
provides a promising means to control power flow in modern
power systems. Essentially the performance depends on proper
control setting achievable through a power flow analysis
program. This paper presents a reliable method to meet the
requirements by developing a Newton-Raphson based load
flow calculation through which control settings of UPFC can
be determined for the pre-specified power flow between the
lines. The proposed method keeps Newton-Raphson Load Flow
(NRLF) algorithm intact and needs (little modification in the
Jacobian matrix). A MATLAB program has been developed to
calculate the control settings of UPFC and the power flow
between the lines after the load flow is converged. Case studies
have been performed on IEEE 5-bus system and 14-bus system
to show that the proposed method is effective. These studies
indicate that the method maintains the basic NRLF properties
such as fast computational speed, high degree of accuracy and
good convergence rate.
Study of Structural Behaviour of Gravity Dam with Various Features of Gallery...IDES Editor
The size and shape of opening in dam causes the
stress concentration, it also causes the stress variation in the
rest of the dam cross section. The gravity method of the analysis
does not consider the size of opening and the elastic property
of dam material. Thus the objective of study is comprises of
the Finite Element Method which considers the size of
opening, elastic property of material, and stress distribution
because of geometric discontinuity in cross section of dam.
Stress concentration inside the dam increases with the opening
in dam which results in the failure of dam. Hence it is
necessary to analyses large opening inside the dam. By making
the percentage area of opening constant and varying size and
shape of opening the analysis is carried out. For this purpose
a section of Koyna Dam is considered. Dam is defined as a
plane strain element in FEM, based on geometry and loading
condition. Thus this available information specified our path
of approach to carry out 2D plane strain analysis. The results
obtained are then compared mutually to get most efficient
way of providing large opening in the gravity dam.
Assessing Uncertainty of Pushover Analysis to Geometric ModelingIDES Editor
Pushover Analysis a popular tool for seismic
performance evaluation of existing and new structures and is
nonlinear Static procedure where in monotonically increasing
loads are applied to the structure till the structure is unable
to resist the further load .During the analysis, whatever the
strength of concrete and steel is adopted for analysis of
structure may not be the same when real structure is
constructed and the pushover analysis results are very sensitive
to material model adopted, geometric model adopted, location
of plastic hinges and in general to procedure followed by the
analyzer. In this paper attempt has been made to assess
uncertainty in pushover analysis results by considering user
defined hinges and frame modeled as bare frame and frame
with slab modeled as rigid diaphragm and results compared
with experimental observations. Uncertain parameters
considered includes the strength of concrete, strength of steel
and cover to the reinforcement which are randomly generated
and incorporated into the analysis. The results are then
compared with experimental observations.
Secure Multi-Party Negotiation: An Analysis for Electronic Payments in Mobile...IDES Editor
This document summarizes and analyzes secure multi-party negotiation protocols for electronic payments in mobile computing. It presents a framework for secure multi-party decision protocols using lightweight implementations. The main focus is on synchronizing security features to avoid agreement manipulation and reduce user traffic. The paper describes negotiation between an auctioneer and bidders, showing multiparty security is better than existing systems. It analyzes the performance of encryption algorithms like ECC, XTR, and RSA for use in the multiparty negotiation protocols.
Selfish Node Isolation & Incentivation using Progressive ThresholdsIDES Editor
The problems associated with selfish nodes in
MANET are addressed by a collaborative watchdog approach
which reduces the detection time for selfish nodes thereby
improves the performance and accuracy of watchdogs[1]. In
the related works they make use of credit based systems, reputation
based mechanisms, pathrater and watchdog mechanism
to detect such selfish nodes. In this paper we follow an approach
of collaborative watchdog which reduces the detection
time for selfish nodes and also involves the removal of such
selfish nodes based on some progressively assessed thresholds.
The threshold gives the nodes a chance to stop misbehaving
before it is permanently deleted from the network.
The node passes through several isolation processes before it
is permanently removed. Another version of AODV protocol
is used here which allows the simulation of selfish nodes in
NS2 by adding or modifying log files in the protocol.
Various OSI Layer Attacks and Countermeasure to Enhance the Performance of WS...IDES Editor
Wireless sensor networks are networks having non
wired infrastructure and dynamic topology. In OSI model each
layer is prone to various attacks, which halts the performance
of a network .In this paper several attacks on four layers of
OSI model are discussed and security mechanism is described
to prevent attack in network layer i.e wormhole attack. In
Wormhole attack two or more malicious nodes makes a covert
channel which attracts the traffic towards itself by depicting a
low latency link and then start dropping and replaying packets
in the multi-path route. This paper proposes promiscuous mode
method to detect and isolate the malicious node during
wormhole attack by using Ad-hoc on demand distance vector
routing protocol (AODV) with omnidirectional antenna. The
methodology implemented notifies that the nodes which are
not participating in multi-path routing generates an alarm
message during delay and then detects and isolate the
malicious node from network. We also notice that not only
the same kind of attacks but also the same kind of
countermeasures can appear in multiple layer. For example,
misbehavior detection techniques can be applied to almost all
the layers we discussed.
Responsive Parameter based an AntiWorm Approach to Prevent Wormhole Attack in...IDES Editor
The recent advancements in the wireless technology
and their wide-spread deployment have made remarkable
enhancements in efficiency in the corporate and industrial
and Military sectors The increasing popularity and usage of
wireless technology is creating a need for more secure wireless
Ad hoc networks. This paper aims researched and developed
a new protocol that prevents wormhole attacks on a ad hoc
network. A few existing protocols detect wormhole attacks but
they require highly specialized equipment not found on most
wireless devices. This paper aims to develop a defense against
wormhole attacks as an Anti-worm protocol which is based on
responsive parameters, that does not require as a significant
amount of specialized equipment, trick clock synchronization,
no GPS dependencies.
Cloud Security and Data Integrity with Client Accountability FrameworkIDES Editor
This document summarizes a proposed cloud security and data integrity framework that provides client accountability. The framework aims to address issues like lack of user control over cloud data, need for data transparency and tracking, and ensuring data integrity. It proposes using JAR (Java Archive) files for data sharing due to benefits like portability. The framework incorporates client-side verification using MD5 hashing, digital signature-based authentication of JAR files, and use of HMAC to ensure data integrity. It also uses password-based encryption of log files to keep them tamper-proof. The framework is intended to provide both accountability and security for data sharing in cloud environments.
Genetic Algorithm based Layered Detection and Defense of HTTP BotnetIDES Editor
A System state in HTTP botnet uses HTTP protocol
for the creation of chain of Botnets thereby compromising
other systems. By using HTTP protocol and port number 80,
attacks can not only be hidden but also pass through the
firewall without being detected. The DPR based detection
leads to better analysis of botnet attacks [3]. However, it
provides only probabilistic detection of the attacker and also
time consuming and error prone. This paper proposes a Genetic
algorithm based layered approach for detecting as well as
preventing botnet attacks. The paper reviews p2p firewall
implementation which forms the basis of filtering.
Performance evaluation is done based on precision, F-value
and probability. Layered approach reduces the computation
and overall time requirement [7]. Genetic algorithm promises
a low false positive rate.
Enhancing Data Storage Security in Cloud Computing Through SteganographyIDES Editor
This document summarizes a research paper that proposes a method for enhancing data security in cloud computing through steganography. The method hides user data in digital images stored on cloud servers. When data needs to be accessed, it is extracted from the images. The document outlines the cloud architecture and security issues addressed. It then describes the proposed system architecture, security model, and data storage and retrieval process. Data is partitioned and hidden in multiple images to improve security. The goal is to prevent unauthorized access to user data stored on cloud servers.
The main tasks of a Wireless Sensor Network
(WSN) are data collection from its nodes and communication
of this data to the base station (BS). The protocols used for
communication among the WSN nodes and between the WSN
and the BS, must consider the resource constraints of nodes,
battery energy, computational capabilities and memory. The
WSN applications involve unattended operation of the network
over an extended period of time. In order to extend the lifetime
of a WSN, efficient routing protocols need to be adopted. The
proposed low power routing protocol based on tree-based
network structure reliably forwards the measured data towards
the BS using TDMA. An energy consumption analysis of the
WSN making use of this protocol is also carried out. It is
found that the network is energy efficient with an average
duty cycle of 0:7% for the WSN nodes. The OmNET++
simulation platform along with MiXiM framework is made
use of.
Permutation of Pixels within the Shares of Visual Cryptography using KBRP for...IDES Editor
The security of authentication of internet based
co-banking services should not be susceptible to high risks.
The passwords are highly vulnerable to virus attacks due to
the lack of high end embedding of security methods. In order
for the passwords to be more secure, people are generally
compelled to select jumbled up character based passwords
which are not only less memorable but are also equally prone
to insecurity. Multiple use of distributed shares has been
studied to solve the problem of authentication by algorithms
based on thresholding of pixels in image processing and visual
cryptography concepts where the subset of shares is considered
for the recovery of the original image for authentication using
correlation function[1][2].The main disadvantage in the above
study is the plain storage of shares and also one of the shares
is being supplied to the customer, which will lead to the
possibility of misuse by a third party. This paper proposes a
technique for scrambling of pixels by key based random
permutation (KBRP) within the shares before the
authentication has been attempted. Total number of shares to
be created is dependent on the multiplicity of ownership of
the account. By this method the problem of uncertainty among
the customers with regard to security, storage, retrieval of
holding of half of the shares is minimized.
This paper presents a trifocal Rotman Lens Design
approach. The effects of focal ratio and element spacing on
the performance of Rotman Lens are described. A three beam
prototype feeding 4 element antenna array working in L-band
has been simulated using RLD v1.7 software. Simulated
results show that the simulated lens has a return loss of –
12.4dB at 1.8GHz. Beam to array port phase error variation
with change in the focal ratio and element spacing has also
been investigated.
Band Clustering for the Lossless Compression of AVIRIS Hyperspectral ImagesIDES Editor
Hyperspectral images can be efficiently compressed
through a linear predictive model, as for example the one
used in the SLSQ algorithm. In this paper we exploit this
predictive model on the AVIRIS images by individuating,
through an off-line approach, a common subset of bands, which
are not spectrally related with any other bands. These bands
are not useful as prediction reference for the SLSQ 3-D
predictive model and we need to encode them via other
prediction strategies which consider only spatial correlation.
We have obtained this subset by clustering the AVIRIS bands
via the clustering by compression approach. The main result
of this paper is the list of the bands, not related with the
others, for AVIRIS images. The clustering trees obtained for
AVIRIS and the relationship among bands they depict is also
an interesting starting point for future research.
Microelectronic Circuit Analogous to Hydrogen Bonding Network in Active Site ...IDES Editor
A microelectronic circuit of block-elements
functionally analogous to two hydrogen bonding networks is
investigated. The hydrogen bonding networks are extracted
from â-lactamase protein and are formed in its active site.
Each hydrogen bond of the network is described in equivalent
electrical circuit by three or four-terminal block-element.
Each block-element is coded in Matlab. Static and dynamic
analyses are performed. The resultant microelectronic circuit
analogous to the hydrogen bonding network operates as
current mirror, sine pulse source, triangular pulse source as
well as signal modulator.
Texture Unit based Monocular Real-world Scene Classification using SOM and KN...IDES Editor
In this paper a method is proposed to discriminate
real world scenes in to natural and manmade scenes of similar
depth. Global-roughness of a scene image varies as a function
of image-depth. Increase in image depth leads to increase in
roughness in manmade scenes; on the contrary natural scenes
exhibit smooth behavior at higher image depth. This particular
arrangement of pixels in scene structure can be well explained
by local texture information in a pixel and its neighborhood.
Our proposed method analyses local texture information of a
scene image using texture unit matrix. For final classification
we have used both supervised and unsupervised learning using
K-Nearest Neighbor classifier (KNN) and Self Organizing
Map (SOM) respectively. This technique is useful for online
classification due to very less computational complexity.
QR Secure: A Hybrid Approach Using Machine Learning and Security Validation F...AlexanderRichford
QR Secure: A Hybrid Approach Using Machine Learning and Security Validation Functions to Prevent Interaction with Malicious QR Codes.
Aim of the Study: The goal of this research was to develop a robust hybrid approach for identifying malicious and insecure URLs derived from QR codes, ensuring safe interactions.
This is achieved through:
Machine Learning Model: Predicts the likelihood of a URL being malicious.
Security Validation Functions: Ensures the derived URL has a valid certificate and proper URL format.
This innovative blend of technology aims to enhance cybersecurity measures and protect users from potential threats hidden within QR codes 🖥 🔒
This study was my first introduction to using ML which has shown me the immense potential of ML in creating more secure digital environments!
Session 1 - Intro to Robotic Process Automation.pdfUiPathCommunity
👉 Check out our full 'Africa Series - Automation Student Developers (EN)' page to register for the full program:
https://bit.ly/Automation_Student_Kickstart
In this session, we shall introduce you to the world of automation, the UiPath Platform, and guide you on how to install and setup UiPath Studio on your Windows PC.
📕 Detailed agenda:
What is RPA? Benefits of RPA?
RPA Applications
The UiPath End-to-End Automation Platform
UiPath Studio CE Installation and Setup
💻 Extra training through UiPath Academy:
Introduction to Automation
UiPath Business Automation Platform
Explore automation development with UiPath Studio
👉 Register here for our upcoming Session 2 on June 20: Introduction to UiPath Studio Fundamentals: https://community.uipath.com/events/details/uipath-lagos-presents-session-2-introduction-to-uipath-studio-fundamentals/
The Department of Veteran Affairs (VA) invited Taylor Paschal, Knowledge & Information Management Consultant at Enterprise Knowledge, to speak at a Knowledge Management Lunch and Learn hosted on June 12, 2024. All Office of Administration staff were invited to attend and received professional development credit for participating in the voluntary event.
The objectives of the Lunch and Learn presentation were to:
- Review what KM ‘is’ and ‘isn’t’
- Understand the value of KM and the benefits of engaging
- Define and reflect on your “what’s in it for me?”
- Share actionable ways you can participate in Knowledge - - Capture & Transfer
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Introducing BoxLang : A new JVM language for productivity and modularity!Ortus Solutions, Corp
Just like life, our code must adapt to the ever changing world we live in. From one day coding for the web, to the next for our tablets or APIs or for running serverless applications. Multi-runtime development is the future of coding, the future is to be dynamic. Let us introduce you to BoxLang.
Dynamic. Modular. Productive.
BoxLang redefines development with its dynamic nature, empowering developers to craft expressive and functional code effortlessly. Its modular architecture prioritizes flexibility, allowing for seamless integration into existing ecosystems.
Interoperability at its Core
With 100% interoperability with Java, BoxLang seamlessly bridges the gap between traditional and modern development paradigms, unlocking new possibilities for innovation and collaboration.
Multi-Runtime
From the tiny 2m operating system binary to running on our pure Java web server, CommandBox, Jakarta EE, AWS Lambda, Microsoft Functions, Web Assembly, Android and more. BoxLang has been designed to enhance and adapt according to it's runnable runtime.
The Fusion of Modernity and Tradition
Experience the fusion of modern features inspired by CFML, Node, Ruby, Kotlin, Java, and Clojure, combined with the familiarity of Java bytecode compilation, making BoxLang a language of choice for forward-thinking developers.
Empowering Transition with Transpiler Support
Transitioning from CFML to BoxLang is seamless with our JIT transpiler, facilitating smooth migration and preserving existing code investments.
Unlocking Creativity with IDE Tools
Unleash your creativity with powerful IDE tools tailored for BoxLang, providing an intuitive development experience and streamlining your workflow. Join us as we embark on a journey to redefine JVM development. Welcome to the era of BoxLang.
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsDianaGray10
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
"What does it really mean for your system to be available, or how to define w...Fwdays
We will talk about system monitoring from a few different angles. We will start by covering the basics, then discuss SLOs, how to define them, and why understanding the business well is crucial for success in this exercise.
What is an RPA CoE? Session 2 – CoE RolesDianaGray10
In this session, we will review the players involved in the CoE and how each role impacts opportunities.
Topics covered:
• What roles are essential?
• What place in the automation journey does each role play?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
QA or the Highway - Component Testing: Bridging the gap between frontend appl...zjhamm304
These are the slides for the presentation, "Component Testing: Bridging the gap between frontend applications" that was presented at QA or the Highway 2024 in Columbus, OH by Zachary Hamm.
AI in the Workplace Reskilling, Upskilling, and Future Work.pptxSunil Jagani
Discover how AI is transforming the workplace and learn strategies for reskilling and upskilling employees to stay ahead. This comprehensive guide covers the impact of AI on jobs, essential skills for the future, and successful case studies from industry leaders. Embrace AI-driven changes, foster continuous learning, and build a future-ready workforce.
Read More - https://bit.ly/3VKly70
Getting the Most Out of ScyllaDB Monitoring: ShareChat's TipsScyllaDB
ScyllaDB monitoring provides a lot of useful information. But sometimes it’s not easy to find the root of the problem if something is wrong or even estimate the remaining capacity by the load on the cluster. This talk shares our team's practical tips on: 1) How to find the root of the problem by metrics if ScyllaDB is slow 2) How to interpret the load and plan capacity for the future 3) Compaction strategies and how to choose the right one 4) Important metrics which aren’t available in the default monitoring setup.
Must Know Postgres Extension for DBA and Developer during MigrationMydbops
Mydbops Opensource Database Meetup 16
Topic: Must-Know PostgreSQL Extensions for Developers and DBAs During Migration
Speaker: Deepak Mahto, Founder of DataCloudGaze Consulting
Date & Time: 8th June | 10 AM - 1 PM IST
Venue: Bangalore International Centre, Bangalore
Abstract: Discover how PostgreSQL extensions can be your secret weapon! This talk explores how key extensions enhance database capabilities and streamline the migration process for users moving from other relational databases like Oracle.
Key Takeaways:
* Learn about crucial extensions like oracle_fdw, pgtt, and pg_audit that ease migration complexities.
* Gain valuable strategies for implementing these extensions in PostgreSQL to achieve license freedom.
* Discover how these key extensions can empower both developers and DBAs during the migration process.
* Don't miss this chance to gain practical knowledge from an industry expert and stay updated on the latest open-source database trends.
Mydbops Managed Services specializes in taking the pain out of database management while optimizing performance. Since 2015, we have been providing top-notch support and assistance for the top three open-source databases: MySQL, MongoDB, and PostgreSQL.
Our team offers a wide range of services, including assistance, support, consulting, 24/7 operations, and expertise in all relevant technologies. We help organizations improve their database's performance, scalability, efficiency, and availability.
Contact us: info@mydbops.com
Visit: https://www.mydbops.com/
Follow us on LinkedIn: https://in.linkedin.com/company/mydbops
For more details and updates, please follow up the below links.
Meetup Page : https://www.meetup.com/mydbops-databa...
Twitter: https://twitter.com/mydbopsofficial
Blogs: https://www.mydbops.com/blog/
Facebook(Meta): https://www.facebook.com/mydbops/
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Keywords: AI, Containeres, Kubernetes, Cloud Native
Event Link: https://meine.doag.org/events/cloudland/2024/agenda/#agendaId.4211
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!