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Application of matrices in real life. how matrices dominate our real life? how to relate matrices in real life problem and solve those problems . matrices on engineering sector. some interesting examples are included. this is the presentation slide. department of Electrical and Electronic Engineering , University of Chittagong.

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Conference_paper.pdf

This document proposes using machine learning techniques to predict COVID-19 infections based on chest x-ray images. Specifically, it involves using discrete wavelet transform to extract space-frequency features from chest x-rays, reducing the dimensionality of features using Shannon entropy, and then training standard machine learning classifiers like logistic regression, support vector machine, decision tree, and convolutional neural network on the extracted features to classify images as COVID-19 positive or negative. The document provides background on the proposed techniques of discrete wavelet transform, entropy, and various machine learning models.

Number of sources estimation using a hybrid algorithm for smart antenna

The number of sources estimation is one of the vital key technologies in smart antenna. The current paper adopts a new system that employs a hybrid algorithm of artificial bee colony (ABC) and complex generalized Hebbian (CGHA) neural network to Bayesian information criterion (BIC) technique, aiming to enhance the accuracy of number of sources estimation. The advantage of the new system is that no need to compute the covariance matrix, since its principal eigenvalues are computed using the CGHA neural network for the received signals. Moreover, the proposed system can optimize the training condition of the CGHA neural network, therefore it can overcome the random selection of initial weights and learning rate, which evades network oscillation and trapping into local solution. Simulation results of the offered system show good responses through reducing the required time to train the CGHA neural network, fast converge speed, effectiveness, in addition to achieving the correct number of sources.

Performance analysis of transformation and bogdonov chaotic substitution base...

In this article, a combined Pseudo Hadamard transformation and modified Bogdonav chaotic generator based image encryption technique is proposed. Pixel position transformation is performed using Pseudo Hadamard transformation and pixel value variation is made using Bogdonav chaotic substitution. Bogdonav chaotic generator produces random sequences and it is observed that very less correlation between the adjacent elements in the sequence. The cipher image obtained from the transformation stage is subjected for substitution using Bogdonav chaotic sequence to break correlation between adjacent pixels. The cipher image is subjected for various security tests under noisy conditions and very high degree of similarity is observed after deciphering process between original and decrypted images.

mini prjt

This document discusses single object tracking and velocity determination. It begins with an introduction and objectives of the project which is to develop an algorithm for tracking a single object and determining its velocity in a sequence of video frames. It then provides details on preprocessing techniques like mean filtering, Gaussian smoothing and median filtering to reduce noise. It describes segmentation methods including histogram-based, single Gaussian background and frame difference approaches. Feature extraction methods like edges, bounding boxes and color are explained. Object detection using optical flow and block matching is covered. Finally, it discusses tracking and calculating velocity of the moving object. MATLAB is introduced as a technical computing language for solving these types of problems.

Linear programming assignment help

Statisticshelpdesk offers online linear programming assignment help and homework help. Their experts help students understand linear programming, a mathematical optimization technique, through effective learning strategies. They provide step-by-step solutions to help students solve problems themselves. Students can quickly upload linear programming homework to the website and get it completed before the due date. The document then provides an example linear programming problem involving minimizing transportation costs with decision variables, constraints, and the optimal solution found using Excel solver. Contact information is given at the end.

Performance Improvement of Vector Quantization with Bit-parallelism Hardware

Vector quantization is an elementary technique for image compression; however, searching for the nearest codeword in a codebook is time-consuming. In this work, we propose a hardware-based scheme by adopting bit-parallelism to prune unnecessary codewords. The new scheme uses a “Bit-mapped Look-up Table” to represent the positional information of the codewords. The lookup procedure can simply refer to the bitmaps to find the candidate codewords. Our simulation results further confirm the effectiveness of the proposed scheme.

Iaetsd traffic sign recognition for advanced driver

This document discusses a system for traffic sign recognition using principal component analysis to help drivers. A camera captures road sign images which are processed using techniques like segmentation, recognition and classification. Eigenvalues are calculated and compared to a database. The recognized sign is transmitted to an LPC2148 processor via RS232. It displays the sign type on an LCD and announces it using an audio amplifier. The system was tested on various sign images and accurately recognized and announced the signs.

Lect 03 - first portion

The document discusses various image enhancement techniques in the spatial domain. It covers basic gray level transformations like negatives, log transformations, and power law transformations. It also discusses histogram processing and enhancement using arithmetic operations. Furthermore, it explains smoothing and sharpening spatial filters, and how to combine different spatial enhancement methods. The document provides examples and background on these fundamental image enhancement concepts.

Conference_paper.pdf

This document proposes using machine learning techniques to predict COVID-19 infections based on chest x-ray images. Specifically, it involves using discrete wavelet transform to extract space-frequency features from chest x-rays, reducing the dimensionality of features using Shannon entropy, and then training standard machine learning classifiers like logistic regression, support vector machine, decision tree, and convolutional neural network on the extracted features to classify images as COVID-19 positive or negative. The document provides background on the proposed techniques of discrete wavelet transform, entropy, and various machine learning models.

Number of sources estimation using a hybrid algorithm for smart antenna

The number of sources estimation is one of the vital key technologies in smart antenna. The current paper adopts a new system that employs a hybrid algorithm of artificial bee colony (ABC) and complex generalized Hebbian (CGHA) neural network to Bayesian information criterion (BIC) technique, aiming to enhance the accuracy of number of sources estimation. The advantage of the new system is that no need to compute the covariance matrix, since its principal eigenvalues are computed using the CGHA neural network for the received signals. Moreover, the proposed system can optimize the training condition of the CGHA neural network, therefore it can overcome the random selection of initial weights and learning rate, which evades network oscillation and trapping into local solution. Simulation results of the offered system show good responses through reducing the required time to train the CGHA neural network, fast converge speed, effectiveness, in addition to achieving the correct number of sources.

Performance analysis of transformation and bogdonov chaotic substitution base...

In this article, a combined Pseudo Hadamard transformation and modified Bogdonav chaotic generator based image encryption technique is proposed. Pixel position transformation is performed using Pseudo Hadamard transformation and pixel value variation is made using Bogdonav chaotic substitution. Bogdonav chaotic generator produces random sequences and it is observed that very less correlation between the adjacent elements in the sequence. The cipher image obtained from the transformation stage is subjected for substitution using Bogdonav chaotic sequence to break correlation between adjacent pixels. The cipher image is subjected for various security tests under noisy conditions and very high degree of similarity is observed after deciphering process between original and decrypted images.

mini prjt

This document discusses single object tracking and velocity determination. It begins with an introduction and objectives of the project which is to develop an algorithm for tracking a single object and determining its velocity in a sequence of video frames. It then provides details on preprocessing techniques like mean filtering, Gaussian smoothing and median filtering to reduce noise. It describes segmentation methods including histogram-based, single Gaussian background and frame difference approaches. Feature extraction methods like edges, bounding boxes and color are explained. Object detection using optical flow and block matching is covered. Finally, it discusses tracking and calculating velocity of the moving object. MATLAB is introduced as a technical computing language for solving these types of problems.

Linear programming assignment help

Statisticshelpdesk offers online linear programming assignment help and homework help. Their experts help students understand linear programming, a mathematical optimization technique, through effective learning strategies. They provide step-by-step solutions to help students solve problems themselves. Students can quickly upload linear programming homework to the website and get it completed before the due date. The document then provides an example linear programming problem involving minimizing transportation costs with decision variables, constraints, and the optimal solution found using Excel solver. Contact information is given at the end.

Performance Improvement of Vector Quantization with Bit-parallelism Hardware

Vector quantization is an elementary technique for image compression; however, searching for the nearest codeword in a codebook is time-consuming. In this work, we propose a hardware-based scheme by adopting bit-parallelism to prune unnecessary codewords. The new scheme uses a “Bit-mapped Look-up Table” to represent the positional information of the codewords. The lookup procedure can simply refer to the bitmaps to find the candidate codewords. Our simulation results further confirm the effectiveness of the proposed scheme.

Iaetsd traffic sign recognition for advanced driver

This document discusses a system for traffic sign recognition using principal component analysis to help drivers. A camera captures road sign images which are processed using techniques like segmentation, recognition and classification. Eigenvalues are calculated and compared to a database. The recognized sign is transmitted to an LPC2148 processor via RS232. It displays the sign type on an LCD and announces it using an audio amplifier. The system was tested on various sign images and accurately recognized and announced the signs.

Lect 03 - first portion

The document discusses various image enhancement techniques in the spatial domain. It covers basic gray level transformations like negatives, log transformations, and power law transformations. It also discusses histogram processing and enhancement using arithmetic operations. Furthermore, it explains smoothing and sharpening spatial filters, and how to combine different spatial enhancement methods. The document provides examples and background on these fundamental image enhancement concepts.

Manifold Blurring Mean Shift algorithms for manifold denoising, presentation,...

(General) To retrieve a clean dataset by deleting outliers.
(Computer Vision) the recovery of a digital image that has been contaminated by additive white Gaussian noise.

A STUDY AND ANALYSIS OF DIFFERENT EDGE DETECTION TECHNIQUES

In the first study [1], a combination of K-means, watershed segmentation method, and Difference In Strength (DIS) map were used to perform image segmentation and edge detection
tasks. We obtained an initial segmentation based on K-means clustering technique. Starting from this, we used two techniques; the first is watershed technique with new merging
procedures based on mean intensity value to segment the image regions and to detect their boundaries. The second is edge strength technique to obtain accurate edge maps of our images without using watershed method. In this technique: We solved the problem of undesirable over segmentation results produced by the watershed algorithm, when used directly with raw data images. Also, the edge maps we obtained have no broken lines on entire image. In the 2nd study level set methods are used for the implementation of curve/interface evolution under various forces. In the third study the main idea is to detect regions (objects) boundaries, to isolate and extract individual components from a medical image. This is done using an active contours to detect regions in a given image, based on techniques of curve evolution, Mumford–Shah functional for segmentation and level sets. Once we classified our images into different intensity regions based on Markov Random Field. Then we detect regions whose boundaries are not necessarily defined by gradient by minimize an energy of Mumford–Shah functional forsegmentation, where in the level set formulation, the problem becomes a mean-curvature which will stop on the desired boundary. The stopping term does not depend on the gradient of the image as in the classical active contour. The initial curve of level set can be anywhere in the image, and interior contours are automatically detected. The final image segmentation is one
closed boundary per actual region in the image.

An Efficient Interpolation-Based Chase BCH Decoder

This document describes an efficient interpolation-based Chase decoder for Bose-Chaudhuri-Hocquenghem (BCH) codes. BCH codes are error-correcting codes commonly used in applications such as flash memory and digital video broadcasting. The proposed Chase decoder uses an interpolation-based approach inspired by Chase decoders for Reed-Solomon codes but modified to leverage the binary properties of BCH codes. This allows it to correct up to t + η errors, where t is the number of errors the underlying BCH code can correct and η is the number of bits flipped in the Chase algorithm. The decoder consists of syndrome calculation, key equation solving, error location via Chien search, and error correction

Development of stereo matching algorithm based on sum of absolute RGB color d...

This article presents local-based stereo matching algorithm which comprises a devel- opment of an algorithm using block matching and two edge preserving ﬁlters in the framework. Fundamentally, the matching process consists of several stages which will produce the disparity or depth map. The problem and most challenging work for matching process is to get an accurate corresponding point between two images. Hence, this article proposes an algorithm for stereo matching using improved Sum of Absolute RGB Differences (SAD), gradient matching and edge preserving ﬁlters. It is Bilateral Filter (BF) to surge up the accuracy. The SAD and gradient matching will be implemented at the ﬁrst stage to get the preliminary corresponding result, then the BF works as an edge-preserving ﬁlter to remove the noise from the ﬁrst stage. The second BF is used at the last stage to improve ﬁnal disparity map and increase the object boundaries. The experimental analysis and validation are using the Middlebury standard benchmarking evaluation system. Based on the results, the proposed work is capable to increase the accuracy and to preserve the object edges. To make the proposed work more reliable with current available methods, the quantitative measurement has been made to compare with other existing methods and it shows the proposed work in this article perform much better.

COLOR IMAGE ENCRYPTION BASED ON MULTIPLE CHAOTIC SYSTEMS

This document proposes a novel color image encryption scheme based on multiple chaotic systems. The scheme utilizes the ergodic properties of chaotic systems to perform pixel permutation and applies a substitution operation to achieve diffusion. In the permutation stage, two generalized Arnold maps are used to generate hybrid chaotic sequences to permute pixel positions. In the diffusion stage, four pseudo-random gray value sequences generated by another generalized Arnold map are used to diffuse the permuted image via bitwise XOR operations. Security analysis shows the scheme has a large key space and is highly secure against statistical attacks, differential attacks, and chosen/known plaintext attacks.

COLOR IMAGE ENCRYPTION BASED ON MULTIPLE CHAOTIC SYSTEMS

This paper proposed a novel color image encryption scheme based on multiple chaotic systems. The ergodicity property of chaotic system is utilized to perform the permutation process; a substitution
operation is applied to achieve the diffusion effect. In permutation stage, the 3D color plain-image matrix
is converted to a 2D image matrix, then two generalized Arnold maps are employed to generate hybrid chaotic sequences which are dependent on the plain-image’s content. The generated chaotic sequences are then applied to perform the permutation process. The encryption’s key streams not only depend on the
cipher keys but also depend on plain-image and therefore can resist chosen-plaintext attack as well as
known-plaintext attack. In the diffusion stage, four pseudo-random gray value sequences are generated by another generalized Arnold map. The gray value sequences are applied to perform the diffusion process by bitxoring operation with the permuted image row-by-row or column-by-column to improve the encryption rate. The security and performance analysis have been performed, including key space analysis, histogram analysis, correlation analysis, information entropy analysis, key sensitivity analysis, differential analysis etc. The experimental results show that the proposed image encryption scheme is highly secure thanks to its large key space and efficient permutation-substitution operation, and therefore it is suitable for practical image and video encryption.

COLOR IMAGE ENCRYPTION BASED ON MULTIPLE CHAOTIC SYSTEMS

This paper proposed a novel color image encryption scheme based on multiple chaotic systems. The ergodicity property of chaotic system is utilized to perform the permutation process; a substitution operation is applied to achieve the diffusion effect. In permutation stage, the 3D color plain-image matrix is converted to a 2D image matrix, then two generalized Arnold maps are employed to generate hybrid chaotic sequences which are dependent on the plain-image’s content. The generated chaotic sequences are then applied to perform the permutation process. The encryption’s key streams not only depend on the cipher keys but also depend on plain-image and therefore can resist chosen-plaintext attack as well as
known-plaintext attack. In the diffusion stage, four pseudo-random gray value sequences are generated by
another generalized Arnold map. The gray value sequences are applied to perform the diffusion process by bitxoring operation with the permuted image row-by-row or column-by-column to improve the encryption rate. The security and performance analysis have been performed, including key space analysis, histogram analysis, correlation analysis, information entropy analysis, key sensitivity analysis, differential analysis
etc. The experimental results show that the proposed image encryption scheme is highly secure thanks to its
large key space and efficient permutation-substitution operation, and therefore it is suitable for practical image and video encryption.

International Journal of Engineering Research and Development (IJERD)

1) The document discusses wavelet transforms as a recent algorithm for image compression. Wavelet transforms can capture variations at different scales in an image, making them well-suited for reducing spatial redundancy.
2) A typical lossy image compression system uses four main components - source encoding, thresholding, quantization, and entropy encoding - to achieve compression by removing different types of redundancy in images.
3) Experimental results on the Lena test image showed that soft thresholding followed by quantization achieved higher peak signal-to-noise ratios than hard thresholding and quantization, demonstrating the effectiveness of wavelet transforms for image compression.

Comparison of image segmentation

An evaluation of two popular segmentation algorithms, the mean shift-based segmentation algorithm and a graph-based segmentation scheme. We also consider a hybrid method which combines the other two methods.

Translation Invariance (TI) based Novel Approach for better De-noising of Dig...

1. The document discusses a novel Translation Invariance (TI) approach for improving the performance of various digital image processing filters for image denoising.
2. It describes applying filters like convolution, wiener, gaussian etc. both without TI (directly on noisy image) and with TI (by shifting the image and averaging results) to denoise images.
3. The results found that using the TI approach, where the filters are applied after shifting the image and averaging the outputs, produced better performance and noise removal compared to directly applying the filters without translation invariance. This was also verified using edge detection tests.

On image intensities, eigenfaces and LDA

The document reports on the results of three image processing projects. The first project implemented Lloyd-Max quantization to reduce image file sizes and Retinex theory to compensate for uneven illumination. The second project used principal component analysis to compute eigenfaces for face recognition. The third project performed linear discriminant analysis and tensor-based linear discriminant analysis for binary classification and visual object recognition. Illumination compensation subtracted an estimated illumination plane from image intensities to reduce shadows. Eigenfaces were the principal components of a training set of face images. Tensor-based linear discriminant analysis treated images as higher-order tensors to outperform conventional LDA.

SLIC Superpixel Based Self Organizing Maps Algorithm for Segmentation of Micr...

We can find the simultaneous monitoring of thousands of genes in parallel Microarray technology. As per these measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Gridding, Intensity extraction, Enhancement and Segmentation are important steps in microarray image analysis. This paper gives simple linear iterative clustering (SLIC) based self organizing maps (SOM) algorithm for segmentation of microarray image. The clusters of pixels which share similar features are called Superpixels, thus they can be used as mid-level units to decrease the computational cost in many vision applications. The proposed algorithm utilizes superpixels as clustering objects instead of pixels. The qualitative and quantitative analysis shows that the proposed method produces better segmentation quality than k-means, fuzzy cmeans and self organizing maps clustering methods.

Reed_Solomon_Implementation

This document discusses Reed-Solomon error correcting codes. It begins with an introduction to Reed-Solomon codes and their use in communication and data storage. It then provides details on Reed-Solomon encoding and decoding. The decoding process involves calculating syndromes, finding error locations using the Chien search algorithm, and determining error values using Forney's algorithm. Extensions of the inversionless Massey-Berlekamp algorithm are also described, which can compute the error locator and evaluator polynomials simultaneously without field inversions.

Support Vector Machine

This document provides an overview of support vector machines (SVMs). It discusses how SVMs can be used to perform classification tasks by finding optimal separating hyperplanes that maximize the margin between different classes. The document outlines how SVMs solve an optimization problem to find these optimal hyperplanes using techniques like Lagrange duality, kernels, and soft margins. It also covers model selection methods like cross-validation and discusses extensions of SVMs to multi-class classification problems.

A simple framework for contrastive learning of visual representations

Link: https://machine-learning-made-simple.medium.com/learnings-from-simclr-a-framework-contrastive-learning-for-visual-representations-6c145a5d8e99
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This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5% top-1 accuracy, which is a 7% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1% of the labels, we achieve 85.8% top-5 accuracy, outperforming AlexNet with 100X fewer labels.
Comments: ICML'2020. Code and pretrained models at this https URL
Subjects: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
Cite as: arXiv:2002.05709 [cs.LG]
(or arXiv:2002.05709v3 [cs.LG] for this version)
Submission history
From: Ting Chen [view email]
[v1] Thu, 13 Feb 2020 18:50:45 UTC (5,093 KB)
[v2] Mon, 30 Mar 2020 15:32:51 UTC (5,047 KB)
[v3] Wed, 1 Jul 2020 00:09:08 UTC (5,829 KB)

Performance Study of BCH Error Correcting Codes Using the Bit Error Rate Term...

The quality of a digital transmission is mainly dependent on the amount of errors introduced into the transmission channel. The codes BCH (Bose-Chaudhuri-Hocquenghem) are widely used in communication systems and storage systems. In this paper a Performance study of BCH error correcting codes is proposed. This paper presents a comparative study of performance between the Bose-Chaudhuri-Hocquenghem codes BCH (15, 7, 2) and BCH (255, 231, 3) using the bit error rate term (BER). The channel and the modulation type are respectively AWGN and PSK where the order of modulation is equal to 2. First, we generated and simulated the error correcting codes BCH (15, 7, 2) and BCH (255, 231, 3) using Math lab simulator. Second, we compare the two codes using the bit error rate term (BER), finally we conclude the coding gain for a BER = 10-4.

Segmentation and Classification of MRI Brain Tumor

This document presents a study comparing two techniques for detecting brain tumors in MRI images: level set segmentation and K-means segmentation. Features are extracted from the segmented tumors using discrete wavelet transform and gray level co-occurrence matrix. The features are then classified as benign or malignant using a support vector machine. The level set method and K-means method are evaluated based on accuracy, sensitivity, and specificity on a dataset of 41 MRI brain images. The level set method achieved slightly higher accuracy of 94.12% compared to the K-means method.

Introducing New Parameters to Compare the Accuracy and Reliability of Mean-Sh...

Mean shift algorithms are among the most functional tracking methods which are accurate and have almost simple computation. Different versions of this algorithm are developed which are differ in template updating and their window sizes. To measure the reliability and accuracy of these methods one should normally rely on visual results or number of iteration. In this paper we introduce two new parameters which can be used to compare the algorithms especially when their results are close to each other.

Detection of leaf diseases and classification using digital image processing

In this presentation you can learn how to find leaf disease using k mean algorithm and gray level co-occurrence matrix and support vector machine with complete results.
In this presentation , I mention all the data in very convenient way . I hope you can take it easy.
Thank you

V2 v posenet

본 논문은 single depth map으로부터의 정확한 3D hand pose estimation을 목표로 한다. 3D hand pose estimation은 HCI, AR등의 기술을 구현함에 있어서 매우 중요한 기술이다. 이를 위해 많은 연구자들이 정확도를 높이기 위해 여러 방법을 제시하였지만, 여전히 손가락들의 비슷한 생김새, 가려짐, 다양한 손가락의 움직임으로 인한 복잡성 때문에 정확도를 올리는데 한계가 있었다. 본 논문은 기존 방법들의 한계를 극복하기 위해 기존 방법들이 사용하는 입력 형태와 출력 형태를 바꾸었다. 2d depth image를 입력으로 받아 hand joint의 3D coordinate를 직접 regress하는 대부분의 기존 방법들과는 달리, 제안하는 모델은 3D voxelized depth map을 입력으로 받아 3D heatmap을 출력한다. 이를 위해 encoder-decoder 형식의 3D CNN을 사용하였고, 달라진 입력과 출력 형태로 인해 제안하는 모델은 널리 사용되는 3개의 3d hand pose estimation dataset, 1개의 3d human pose estimation dataset에서 가장 높은 성능을 내었다. 또한 ICCV 2017에서 주최된 HANDS 2017 challenge에서 우승 하였다.

Manifold Blurring Mean Shift algorithms for manifold denoising, presentation,...

(General) To retrieve a clean dataset by deleting outliers.
(Computer Vision) the recovery of a digital image that has been contaminated by additive white Gaussian noise.

A STUDY AND ANALYSIS OF DIFFERENT EDGE DETECTION TECHNIQUES

In the first study [1], a combination of K-means, watershed segmentation method, and Difference In Strength (DIS) map were used to perform image segmentation and edge detection
tasks. We obtained an initial segmentation based on K-means clustering technique. Starting from this, we used two techniques; the first is watershed technique with new merging
procedures based on mean intensity value to segment the image regions and to detect their boundaries. The second is edge strength technique to obtain accurate edge maps of our images without using watershed method. In this technique: We solved the problem of undesirable over segmentation results produced by the watershed algorithm, when used directly with raw data images. Also, the edge maps we obtained have no broken lines on entire image. In the 2nd study level set methods are used for the implementation of curve/interface evolution under various forces. In the third study the main idea is to detect regions (objects) boundaries, to isolate and extract individual components from a medical image. This is done using an active contours to detect regions in a given image, based on techniques of curve evolution, Mumford–Shah functional for segmentation and level sets. Once we classified our images into different intensity regions based on Markov Random Field. Then we detect regions whose boundaries are not necessarily defined by gradient by minimize an energy of Mumford–Shah functional forsegmentation, where in the level set formulation, the problem becomes a mean-curvature which will stop on the desired boundary. The stopping term does not depend on the gradient of the image as in the classical active contour. The initial curve of level set can be anywhere in the image, and interior contours are automatically detected. The final image segmentation is one
closed boundary per actual region in the image.

An Efficient Interpolation-Based Chase BCH Decoder

This document describes an efficient interpolation-based Chase decoder for Bose-Chaudhuri-Hocquenghem (BCH) codes. BCH codes are error-correcting codes commonly used in applications such as flash memory and digital video broadcasting. The proposed Chase decoder uses an interpolation-based approach inspired by Chase decoders for Reed-Solomon codes but modified to leverage the binary properties of BCH codes. This allows it to correct up to t + η errors, where t is the number of errors the underlying BCH code can correct and η is the number of bits flipped in the Chase algorithm. The decoder consists of syndrome calculation, key equation solving, error location via Chien search, and error correction

Development of stereo matching algorithm based on sum of absolute RGB color d...

This article presents local-based stereo matching algorithm which comprises a devel- opment of an algorithm using block matching and two edge preserving ﬁlters in the framework. Fundamentally, the matching process consists of several stages which will produce the disparity or depth map. The problem and most challenging work for matching process is to get an accurate corresponding point between two images. Hence, this article proposes an algorithm for stereo matching using improved Sum of Absolute RGB Differences (SAD), gradient matching and edge preserving ﬁlters. It is Bilateral Filter (BF) to surge up the accuracy. The SAD and gradient matching will be implemented at the ﬁrst stage to get the preliminary corresponding result, then the BF works as an edge-preserving ﬁlter to remove the noise from the ﬁrst stage. The second BF is used at the last stage to improve ﬁnal disparity map and increase the object boundaries. The experimental analysis and validation are using the Middlebury standard benchmarking evaluation system. Based on the results, the proposed work is capable to increase the accuracy and to preserve the object edges. To make the proposed work more reliable with current available methods, the quantitative measurement has been made to compare with other existing methods and it shows the proposed work in this article perform much better.

COLOR IMAGE ENCRYPTION BASED ON MULTIPLE CHAOTIC SYSTEMS

This document proposes a novel color image encryption scheme based on multiple chaotic systems. The scheme utilizes the ergodic properties of chaotic systems to perform pixel permutation and applies a substitution operation to achieve diffusion. In the permutation stage, two generalized Arnold maps are used to generate hybrid chaotic sequences to permute pixel positions. In the diffusion stage, four pseudo-random gray value sequences generated by another generalized Arnold map are used to diffuse the permuted image via bitwise XOR operations. Security analysis shows the scheme has a large key space and is highly secure against statistical attacks, differential attacks, and chosen/known plaintext attacks.

COLOR IMAGE ENCRYPTION BASED ON MULTIPLE CHAOTIC SYSTEMS

This paper proposed a novel color image encryption scheme based on multiple chaotic systems. The ergodicity property of chaotic system is utilized to perform the permutation process; a substitution
operation is applied to achieve the diffusion effect. In permutation stage, the 3D color plain-image matrix
is converted to a 2D image matrix, then two generalized Arnold maps are employed to generate hybrid chaotic sequences which are dependent on the plain-image’s content. The generated chaotic sequences are then applied to perform the permutation process. The encryption’s key streams not only depend on the
cipher keys but also depend on plain-image and therefore can resist chosen-plaintext attack as well as
known-plaintext attack. In the diffusion stage, four pseudo-random gray value sequences are generated by another generalized Arnold map. The gray value sequences are applied to perform the diffusion process by bitxoring operation with the permuted image row-by-row or column-by-column to improve the encryption rate. The security and performance analysis have been performed, including key space analysis, histogram analysis, correlation analysis, information entropy analysis, key sensitivity analysis, differential analysis etc. The experimental results show that the proposed image encryption scheme is highly secure thanks to its large key space and efficient permutation-substitution operation, and therefore it is suitable for practical image and video encryption.

International Journal of Engineering Research and Development (IJERD)

1) The document discusses wavelet transforms as a recent algorithm for image compression. Wavelet transforms can capture variations at different scales in an image, making them well-suited for reducing spatial redundancy.
2) A typical lossy image compression system uses four main components - source encoding, thresholding, quantization, and entropy encoding - to achieve compression by removing different types of redundancy in images.
3) Experimental results on the Lena test image showed that soft thresholding followed by quantization achieved higher peak signal-to-noise ratios than hard thresholding and quantization, demonstrating the effectiveness of wavelet transforms for image compression.

Comparison of image segmentation

An evaluation of two popular segmentation algorithms, the mean shift-based segmentation algorithm and a graph-based segmentation scheme. We also consider a hybrid method which combines the other two methods.

Translation Invariance (TI) based Novel Approach for better De-noising of Dig...

1. The document discusses a novel Translation Invariance (TI) approach for improving the performance of various digital image processing filters for image denoising.
2. It describes applying filters like convolution, wiener, gaussian etc. both without TI (directly on noisy image) and with TI (by shifting the image and averaging results) to denoise images.
3. The results found that using the TI approach, where the filters are applied after shifting the image and averaging the outputs, produced better performance and noise removal compared to directly applying the filters without translation invariance. This was also verified using edge detection tests.

On image intensities, eigenfaces and LDA

The document reports on the results of three image processing projects. The first project implemented Lloyd-Max quantization to reduce image file sizes and Retinex theory to compensate for uneven illumination. The second project used principal component analysis to compute eigenfaces for face recognition. The third project performed linear discriminant analysis and tensor-based linear discriminant analysis for binary classification and visual object recognition. Illumination compensation subtracted an estimated illumination plane from image intensities to reduce shadows. Eigenfaces were the principal components of a training set of face images. Tensor-based linear discriminant analysis treated images as higher-order tensors to outperform conventional LDA.

SLIC Superpixel Based Self Organizing Maps Algorithm for Segmentation of Micr...

We can find the simultaneous monitoring of thousands of genes in parallel Microarray technology. As per these measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Gridding, Intensity extraction, Enhancement and Segmentation are important steps in microarray image analysis. This paper gives simple linear iterative clustering (SLIC) based self organizing maps (SOM) algorithm for segmentation of microarray image. The clusters of pixels which share similar features are called Superpixels, thus they can be used as mid-level units to decrease the computational cost in many vision applications. The proposed algorithm utilizes superpixels as clustering objects instead of pixels. The qualitative and quantitative analysis shows that the proposed method produces better segmentation quality than k-means, fuzzy cmeans and self organizing maps clustering methods.

Reed_Solomon_Implementation

This document discusses Reed-Solomon error correcting codes. It begins with an introduction to Reed-Solomon codes and their use in communication and data storage. It then provides details on Reed-Solomon encoding and decoding. The decoding process involves calculating syndromes, finding error locations using the Chien search algorithm, and determining error values using Forney's algorithm. Extensions of the inversionless Massey-Berlekamp algorithm are also described, which can compute the error locator and evaluator polynomials simultaneously without field inversions.

Support Vector Machine

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A simple framework for contrastive learning of visual representations

Link: https://machine-learning-made-simple.medium.com/learnings-from-simclr-a-framework-contrastive-learning-for-visual-representations-6c145a5d8e99
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This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5% top-1 accuracy, which is a 7% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1% of the labels, we achieve 85.8% top-5 accuracy, outperforming AlexNet with 100X fewer labels.
Comments: ICML'2020. Code and pretrained models at this https URL
Subjects: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
Cite as: arXiv:2002.05709 [cs.LG]
(or arXiv:2002.05709v3 [cs.LG] for this version)
Submission history
From: Ting Chen [view email]
[v1] Thu, 13 Feb 2020 18:50:45 UTC (5,093 KB)
[v2] Mon, 30 Mar 2020 15:32:51 UTC (5,047 KB)
[v3] Wed, 1 Jul 2020 00:09:08 UTC (5,829 KB)

Performance Study of BCH Error Correcting Codes Using the Bit Error Rate Term...

The quality of a digital transmission is mainly dependent on the amount of errors introduced into the transmission channel. The codes BCH (Bose-Chaudhuri-Hocquenghem) are widely used in communication systems and storage systems. In this paper a Performance study of BCH error correcting codes is proposed. This paper presents a comparative study of performance between the Bose-Chaudhuri-Hocquenghem codes BCH (15, 7, 2) and BCH (255, 231, 3) using the bit error rate term (BER). The channel and the modulation type are respectively AWGN and PSK where the order of modulation is equal to 2. First, we generated and simulated the error correcting codes BCH (15, 7, 2) and BCH (255, 231, 3) using Math lab simulator. Second, we compare the two codes using the bit error rate term (BER), finally we conclude the coding gain for a BER = 10-4.

Segmentation and Classification of MRI Brain Tumor

This document presents a study comparing two techniques for detecting brain tumors in MRI images: level set segmentation and K-means segmentation. Features are extracted from the segmented tumors using discrete wavelet transform and gray level co-occurrence matrix. The features are then classified as benign or malignant using a support vector machine. The level set method and K-means method are evaluated based on accuracy, sensitivity, and specificity on a dataset of 41 MRI brain images. The level set method achieved slightly higher accuracy of 94.12% compared to the K-means method.

Introducing New Parameters to Compare the Accuracy and Reliability of Mean-Sh...

Mean shift algorithms are among the most functional tracking methods which are accurate and have almost simple computation. Different versions of this algorithm are developed which are differ in template updating and their window sizes. To measure the reliability and accuracy of these methods one should normally rely on visual results or number of iteration. In this paper we introduce two new parameters which can be used to compare the algorithms especially when their results are close to each other.

Detection of leaf diseases and classification using digital image processing

In this presentation you can learn how to find leaf disease using k mean algorithm and gray level co-occurrence matrix and support vector machine with complete results.
In this presentation , I mention all the data in very convenient way . I hope you can take it easy.
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V2 v posenet

본 논문은 single depth map으로부터의 정확한 3D hand pose estimation을 목표로 한다. 3D hand pose estimation은 HCI, AR등의 기술을 구현함에 있어서 매우 중요한 기술이다. 이를 위해 많은 연구자들이 정확도를 높이기 위해 여러 방법을 제시하였지만, 여전히 손가락들의 비슷한 생김새, 가려짐, 다양한 손가락의 움직임으로 인한 복잡성 때문에 정확도를 올리는데 한계가 있었다. 본 논문은 기존 방법들의 한계를 극복하기 위해 기존 방법들이 사용하는 입력 형태와 출력 형태를 바꾸었다. 2d depth image를 입력으로 받아 hand joint의 3D coordinate를 직접 regress하는 대부분의 기존 방법들과는 달리, 제안하는 모델은 3D voxelized depth map을 입력으로 받아 3D heatmap을 출력한다. 이를 위해 encoder-decoder 형식의 3D CNN을 사용하였고, 달라진 입력과 출력 형태로 인해 제안하는 모델은 널리 사용되는 3개의 3d hand pose estimation dataset, 1개의 3d human pose estimation dataset에서 가장 높은 성능을 내었다. 또한 ICCV 2017에서 주최된 HANDS 2017 challenge에서 우승 하였다.

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Segmentation and Classification of MRI Brain Tumor

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Detection of leaf diseases and classification using digital image processing

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A business may deal with both sales and purchases occasionally. They buy things from vendors and then sell them to their customers. Such dealings can be confusing at times. Because multiple clients may inquire about the same product at the same time, after purchasing those products, customers must be assigned to them. Odoo has a tool called Reception Report that can be used to complete this assignment. By enabling this, a reception report comes automatically after confirming a receipt, from which we can assign products to orders.

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- 1. Presentation On “Application Of Matrices” Presented By MD RASED KHAN ID: 22702031 SESSION:2021-22 Under The Guidance Of Mohammad Jahed Hasan Lecturer, dept. of EEE Department of Electrical and Electronics Engineering University Of Chittagong
- 2. Some basic methods of Matrix Addition Subtraction Multiplication Inverse Transpose
- 3. Eigen vectors: Vectors that are only scaled by matrices are called Eigen vectors of that corresponding matrix. Eigen value: How much the vector is scaled by is called the eigenvalue.
- 4. 2x + y + 2z = 0 2x – y + z = 10 X + 3y – z = 5 Linear Equation solving: 2 1 2 3 -1 1 1 3 -1 X Y z 0 10 5 𝐴𝑥 = 𝐵
- 6. Humans 150 Covid positive 150 20% 10% Virus spread prediction: Humans: .8(150) + .1(150) = 135 Covid positive : .2(150) + .9(150) = 165
- 7. Humans 135 Covid positive 165 20% 10% Virus spread prediction: Humans: .8(135) + .1(165) = 125 Covid positive : .2(135) + .9(165) = 175
- 8. Covid positive If we represent those percentages as matrices then we get a linear equation: h and z are initial values of humans and covid positive population And h and z are population after an hour. 𝑓 𝑓
- 9. Google page rank Algorithm: PageRank (PR) is an algorithm used by Google Search to rank web pages in their search engine results. It is named after both the term "web page” and co-founder Larry Page. The spectrum of eigenvalues of the Google matrix of University of Cambridge from Fig.1 at α = 1, blue points show eigenvalues of isolated subspaces, red points show eigenvalues of core component. Fig.1
- 10. Image processing: Blurred image Box Blur
- 11. If we take the standard arithmetic mean of the component matrices R, G and B from a color image A ,we will get a gray scale version of the image (non-integer values are rounded to the nearest integer)
- 12. Before After Box Blur Edge detection
- 13. Matrices in Cryptography: 1.Encode a message using matrix multiplication. 2.Decode a coded message using the matrix inverse and matrix multiplication
- 14. Some common applications of matrices include: Computer Graphics Statistics and Data Analysis Economics and Finance Machine Learning and Artificial Intelligence Network Analysis Optimization Neaural networks
- 15. Thank You