Most of the current embedded systems operate on digital domain even though input and output is analog in nature. All these devices contain ADC (Analog to Digital converter) to convert the analog signal in to digital domain which is used for processing as per the application. Images, videos and other data can be exactly recovered from a set of uniformly spaced samples taken at the Nyquist rate. Due to the recent technology signal bandwidth is becoming wider and wider. To meet the higher demand, signal acquisition system need to be improved. Traditional Nyquist rate which is used in signal acquisition suggests taking more numbers of samples to increase the bandwidth but while reconstruction most of the samples are not used. If samples are as per Nyquist rate then, this increases the complexity of encoder, storage of samples and signal processing. To avoid this new concept Compressive Sensing is used as an alternative for traditional sampling theory. This paper presents a survey and simplified theoretical view on compressive sensing and reconstruction and proposed work is introduced. 

Fractal analysis is currently in full swing in particular in the medical field because of the fractal nature of natural phenomena (vascular system, nervous system, bones, breast tissue ...). For this, many algorithms for estimating the fractal dimension have emerged. Most of them are based on the principle of box counting. In this work we propose a new method for calculating fractal attributes based on contrast homogeneity and energy that have been extracted from gray level co-occurrence matrix. As application we are investigated in the characterization and classification of mammographic images with SuportVectorMachine classifier. We considered in particular images with tumor masses and architectural disorder to compare with normal ones. We calculate, for comparison the fractal dimension obtained by a reference method (triangular prism) and perform a classification similar to the previous. Results obtained with new algorithm are better than reference method (classification rate is 0.91 vs 0.65). Hence new fractal attributes are relevant. 

Texture is a powerful image property for object and scene characterization, consequently, a large number of techniques has been developed for describing, classifying and retrieving texture images. On the other hand, edge information is proven to be an important cue used by the human visual system. Several physiological experiments have shown that, when looking at an object, human eyes explore different locations of that object through saccadic eye movements but they spend more time fixating edge regions. Based on this result, we hypothesize that a better performance could be obtained when analyzing an image (texture images in this case) if the visual features extracted from edge regions are given higher weights than those extracted from uniform regions. To check the validity of this hypothesis, we have modified several existing texture retrieval techniques in a way that incorporates the proposed idea and compared their performance with that of the original techniques. The results of the experiments that have been conducted on three common datasets confirmed the effectiveness of the proposed approach, since a significant improvement in the retrieval rate is obtained for all tested techniques. The experiments have also shown an improvement in the robustness to noise. 

Images are an integral part of advertisements. Images make the web pages heavy. It increases the response time if the size of the image is large and or available bandwidth is low. The consequence of it is viewer may lose his interest in the particular advertisement if he has to wait for a longer time. Image compression is one of the solutions to this problem. In advertisement images, ROI is of prime importance. Though the context of ROI and background regions are not of prime importance, they cannot be totally discarded. This paper investigates the effect of ROI coding on JPEG2000 performance. It proposes Multiple ROI (MROI) coding for compression of natural and advertisement images at moderate compression ratio. The proposed MROI coding prioritizes ROI codeblocks according to the ROI importance, and contribution of ROI in the specific ROI codeblock. It improves fine-grain accuracy at codeblock level also efficiently utilize the given bit budget with a negligible increase in encoding time. 

The aim of this paper is to utilize Support Vector Machine (SVM) as feature selection and classification techniques for audio signals to identify human emotional states. One of the major bottlenecks of common speech emotion recognition techniques is to use a huge number of features per utterance which could significantly slow down the learning process, and it might cause the problem known as "the curse of dimensionality". Consequently, to ease this challenge this paper aims to achieve high accuracy system with a minimum set of features. The proposed model uses two methods, namely "SVM features selection" and the common "Correlation-based Feature Subset Selection (CFS)" for the feature dimensions reduction part. In addition, two different classifiers, one Support Vector Machine and the other Neural Network are separately adopted to identify the six emotional states of anger, disgust, fear, happiness, sadness and neutral. The method has been verified using Persian (Persian ESD) and German (EMO-DB) emotional speech databases, which yield high recognition rates in both databases. The results show that SVM feature selection method provides better emotional speech-recognition performance compared to CFS and baseline feature set. Moreover, the new system is able to achieve a recognition rate of (99.44%) on the Persian ESD and (87.21%) on Berlin Emotion Database for speaker-dependent classification. Besides, promising result (76.12%) is obtained for speaker-independent classification case; which is among the best-known accuracies reported on the mentioned database relative to its little number of features. 

Speech emotions have potential to provide valuable source of information which can lead us toward human perception and decision making process. This paper analyzes the variation and effect on prosodic features (Formant and Pitch) of female and male speakers in two different emotions (angry and neutral) and softwares (PRAAT and MATLAB) in Urdu language using two ways ANOVA testing. The objective of this paper is to determine the significant effect of emotions and softwares on prosodic features (Pitch and Formant) using recorded speech emotion of both male and female voices of same age group in Urdu language. Experimental results of two-way ANOVA testing considerably show that emotions have effect on pitch and formant both in male and female voice unlike software. 

Pixel Value Differencing (PVD) is a spatial steganography technique that is area sensitive and considers complete visual invisibility while data hiding. While Least Significant Bit Approach (LSB) still remains the most popular technique and is simplest in approach its simplicity makes it vulnerable against steganalysis. Our proposed technique is an enhancement over traditional Pixel Value Differencing. We have added a layer of security using chaotic encryption approaches. Also some PVD based hybrid techniques are compared and analyzed to draw conclusions on the basis of various statistical measures. 

Human recognition through faces has elusive challenges over a period of time. In this paper, an efficient method using three matrix decompositions for face recognition is proposed. The proposed model uses Discrete Wavelet Transform (DWT) with Extended Directional Binary codes (EDBC) in one branch. Three matrix decompositions combination with Singular Value Decomposition (SVD) is used in the other branch. Preprocessing uses Single Scale Retinex (SSR), Multi Scale Retinex (MSR) and Single scale Self Quotient (SSQ) methods. The Approximate (LL) band of DWT is used to extract one hundred EDBC features. In addition, Schur, Hessenberg and QR matrix decompositions are applied individually on pre-processed images and added. Singular Value Decomposition (SVD) is applied on the decomposition sum to yield another one hundred features. The combination EDBC and SVD features are final features. City-block or Euclidean Distance (ED) measures are used to generate the results. Performance on YALE, GTAV and ORL face datasets is better compared to other existing methods.