Image compression is a crucial task in image processing and in the process of sending and receiving files. There is a need for effective techniques for image compression as the raw images require large amounts of disk space to defect during transportation and storage operations. The most important objective of image compression is to decrease the redundancy of the image which helps in increasing the storage capacity and then efficient transmission. This study introduces a system for lossless image compression that is built to work on fingerprint image compression. It uses lossless compression to take care of the first image during processing. However, there is a serious problem which is the low ratio of compression. In order to make the ratio higher, there are five lossless compression techniques used in this study which are Elias Gamma Coding (EGC), Huffman Coding (HC), Arithmetic Coding (AC), Run-Length Encoding (RLE) and Lempel Ziv Welch (LZW). With these techniques, there are three types of transforms are used; they are Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and Discrete Shearlet Transform (DST). The results conclude that discrete shearlet transform with the Lempel-Ziv Welch coding technique outperforms the other lossless compression techniques and its Compression Ratio (CR) is 3.678023.

This study presents a new approach for lossy medical image compression using vector quantization.Recently, the digital image has been a reliable replacement for a hard copy of medical images, therefore, an effort has been made to ensure maintaining high-quality images to use for archiving, classification, or automated diagnostics support.Although the medical application contains all sorts of the images like microscopic, X-rays, tomography, and fiber optics imaging by angioplasty, all of this comes at the cost of using digital storage that needs to be regularly backed up and maintained and to help minimize the need for larger storage media, this study is focusing on applying Non-Decimated Wavelet Transform (NDWT) and combined lossy and lossless compression techniques that will allow the images to take much smaller storage space while maintaining the high level of quality for these images.This study is focusing on chest X-ray images compression using a combination of lossy compression techniques using two Vector Quantization (VQ) algorithms such as k-means clustering and Linde, Buzo, and Gray (LBG) algorithm, and three lossless compression techniques such as Arithmetic Coding (AC), Run Length Encoding (RLE) and Huffman Coding (HC) and choose the optimum combination of them.Then, the performance is measured using Compression Ratio (CR), processing time, or called run time, Peak Signal to Noise Ratio (PSNR), and Bit Rate.

In this paper, we introduce an approach to classifying the biometric signal using Convolutional Neural Network (CNN) technology, Image processing, Improved image, Extract features for images

Compression is the art of representing information in a compressed form and not in its original or uncompressed form. In other words, using data compression. Image compression is a critical task in image processing, the use of discrete shearlet and improved image compression and quality. With the event within the field of networking and within the process of sending and receiving files, we would have liked effective techniques for compression because the raw images required large amounts of disc space to effect during transportation and storage operations. In this paper, we have proposed a system for lossless picture compression using Discrete Shearlet Transform (DST), Discrete Wavelet Transform (DWT), and Discrete Cosine Transform (DCT). We use lossless compression to take care of the first image during that process, but there's a serious problem which is frequently the low compression ratios. The main objective of image compression is to reduce the frequency of image data, which helps to increase storage capacity and provide effective transport capacity. Image compression aids for decreasing the dimensions in bytes of a digital image without degrading the standard of that picture. There are various techniques available for compressing in this paper, we use the arithmetic coding, Huffman coding. This is to increase the compression percentage and indicate the effect on increasing the compression percentage. Also, three types of wavelet transformation were used, which are a separate shearlet transformation, separate wavelet transformation, and a separate cosine transformation. Multi-Knowledge Electronic Comprehensive Journal For Education And Science Publications (MECSJ) ISSUE (29), February (2020) ISSN: 2616-9185

Spread Spectrum techniques (SS) were first developed for military applications, but currently, they have commercial applications. SS provides secure communication and allows multiple accesses for same radio spectrum. So, most Wireless Local Area Network (WLAN) systems use it, as do Cognitive Radios (CR), space systems and Global Positioning Systems (GPS). Direct Sequence Spread Spectrum (DSSS) and frequency hopped spread spectrum (FHSS) are the two most-used techniques today. Nowadays radio spectrum has become very crowded and so now there is a need for spectrum efficiency. Automatic SS classification presents a rather difficult problem, especially if the parameters, such as the signal power, carrier frequency, etc., are unknown. This research takes a new direction; it deploys the Gray Level Co-occurrence Matrix (GLCM) to capture statistical features of SS signals. Using GLCM, 22 features are extracted for each vector of signal. Analyzing the signals is done in the time domain which measures the variation of amplitude of signals with time. Therefore, the main contribution is to apply and show how GLCM improves the identification accuracy of the two signals in presence of noise. The proposed model achieves considerably accurate results even with a low SNR. GLCM features help classifiers to achieve average accuracy 84% and reach 100% signal identification at a zero SNR. To prove the superiority of these features, a variety of clustering methods are applied, such as centroid, connectivity, model-based and message-passing models. Clustering performance results based on GLCM features are compared With Principal Components Analysis (PCA), Kernel-based Principal Components Analysis (KPCA) and fast Independent Components Analysis (Fast-ICA). Clustering results are evaluated with external and internal validity indices. The accuracy was tested over 26 levels of Signal-to-Noise Ratios (SNR).

In this study, the comparative techniques have been developed to perform features extraction for the regression of the ECG images. Two regression methods have been used that are the linear and nonlinear regression. The features extraction techniques developed in this study are the nonnegative matrix factorization used to extract the feature from the ECG images and compare the results with different techniques such as principal component analysis, kernel principal component analysis and independent principal component analysis. These features are used for image regression using two regression techniques and compare between these two regressions techniques. The performance evaluation through this comparison is the error rate that is the root mean square error between the actual data and the data predicted from the regression and the results conclude the principal component analysis technique outperforms the other techniques.

The aim of the study is to reduce the size required for storage along with decreasing the bitrate and the bandwidth for the process of sending and receiving the image. It also aims to decrease the time required for the process as much as possible. This study proposes a novel system for efficient lossy volumetric medical image compression using Stationary Wavelet Transform and Linde-Buzo-Gray for Vector Quantization. The system makes use of a combination of Linde-Buzo-Gray vector quantization technique for lossy compression along with Arithmetic coding and Huffman coding for lossless compression. The system proposed uses Stationary Wavelet Transform and then compares the results obtained to Discrete Wavelet Transform, Lifting Wavelet Transform and Discrete Cosine Transform at three decomposition levels. The system also compares the results obtained using transforms with only Arithmetic Coding and Huffman Coding for Lossless Compression.The results show that the system proposed outperforms the others.

Authentication and integrity are very essential security requirements for a secure transaction. To achieve these security goals, we use a combined technology of Rivest-Shamir-Adleman cryptosystem algorithm and digital watermarking. This work proposes the Rivest-Shamir-Adleman cryptosystem algorithm to work with quantum computing idea as simulation to encrypt the image and the goal of the quantum idea is to speed the process of the encryption. After that, the encrypted image is embedded in the cover image using its least significant bit. Digital watermarking is the process of embedding information into a digital signal. This paper uses the hybrid discrete wavelet transform and singular value decomposition algorithms for embedding and extracting process of digital watermarking. This scheme favorably preserves the quality for both the sender and receiver. The experimental results showed the efficiency of the proposed system in terms of time, integrity, and the authentication. The results showed the accelerate of the encryption process using RSA with quantum ideas compared with using RSA only, the results showed the histogram for both the sender or decrypted image and the receiver or the watermark image is the same. From the histogram diagrams, it is observed that they are quite similar and the difference is insignificant which the human eye cannot easily differentiate. Also the results showed the correlation coefficient between the original watermark and received watermark. The correlation coefficient has the value one if the two images are absolutely identical, has the value zero if the two images are completely uncorrelated. From the correlation coefficient results, it is observed that they are nearly one. This model maintains the image quality is good.

In this paper two new highly efficient hybrid lossless audio coding techniques based on the Burrows-Wheeler Transform (BWT) and the distance transform (DT) are presented. In both techniques, floating point samples of the audio signal are first applied to the BWT and the resulting coefficients are then applied to the DT to obtain more suitable coefficients for the next step of lossless compression. In the first proposed method, two entropy-based lossless compression methods are considered, namely Arithmetic coding and Huffman coding. On the other hand, in the second proposed method the entropy coding is first preceded by Run Length Encoding (RLE).

In this paper, efficient techniques for the classification of chaotic codes are presented. Four different clustering techniques, namely, k-mean clustering, hierarchical clustering, fuzzy c mean clustering, and subtractive clustering are used for classification. Higher order statistics features obtained from some different types of wavelet transform are utilized. The codes to be classified are assumed to be generated by two different methods. The first method is generating different chaotic codes using different chaotic maps with the same initial values. Two types of chaotic maps are considered, namely the logistic map and bended-up-down map. The second method of code generation is to use the same chaotic map with different initial values.

This paper presents a lossless audio coding using Burrows-Wheeler Transform (BWT) and a combination of a Move-To-Front coding (MTF) and Run Length Encoding (RLE). Audio signals used are assumed to be of floating point values. The BWT is applied to this floating point values to get the transformed coefficients; and then these resulting coefficients are converted using the Move-to-Front coding to coefficients can be better compressed and then these resulting coefficients are compressed using a combination of the Run Length Encoding, and entropy coding. Two entropy coding are used which are Arithmetic and Huffman coding. Simulation results show that the proposed lossless audio coding method outperforms other lossless audio coding methods; using only Burrows-Wheeler Transform method, using combined Burrows-Wheeler Transform and Move-to-Front coding method, and using combined Burrows-Wheeler Transform and Run Length Encoding method.

This paper investigates the classification a new type of Pseudo-Random(PN) codes which are generated using noise-like chaotic signals. The classification made using different discriminant analysis classifiers such as linear,diagonal linear,quadratic,diagonal quadratic, and mahalanobis discriminant analysis classifiers. These classification compared with other classifiers such as neural networks,support vector machines,k-nearest neighbor,and maximum likelihood classifiers. Higher order statistical (HOS)moments and cumulants of the eighth order are used as features. Simulation results illustrates the dependence of the proposed classification method on the type of the used classification, the type of chaotic map and the initial values used for generating the chaotic code. Two types of one dimensional chaotic maps are considered in this work,namely,the logistic map and the Bended up down map.The performance,considered as the probability of correct decision, is shown to direr according to the type of the used classifier and is dependent on the signal-to-noise ratio. The results show that the quadratic discriminant analysis classifiers out performs the other discriminant analysis classifiers. Also,the performance of two codes generated from one logistic map with two different initial values out performs the performance of two codes generated from one Bended up down map with two different initial values, two codes generated from logistic map with initial value and Bended up down map with the same initial value, and two codes generated from logistic map with one initial value and Bended up down map with an other initial value.

Signal classification has many important applications in both of the civilian and military domains. This paper presents a classification of multi-user chirp modulation signals using wavelet higher order statistics features and neural network classifier (NN). In this paper, even higher order moments and cumulants up to order eight from the discrete wavelet transform (DWT) coefficients are proposed as effective features. These features are used for classification of eight multi-user chirp modulation signals using neural network classifier. Simulation results show that the proposed technique is able to classify these eight chirp signals in additive white Gaussian noise (AWGN) channels with high accuracy and the performance using features extracted from wavelet transform outperforms that extracted from the signals themselves. Also the features extracted from only details coefficients outperforms the features extracted from the total wavelet coefficients and from the approximation coefficients only and different decomposition levels for wavelet are used.

Higher order statistical features have been recently proved to be very efficient in the classification of wideband communications and radar signals with great accuracy. On the other hand, the denoising properties of the wavelet transform make WT an efficient signal processing tool in noisy environments. A novel technique for the classification of multi-user chirp modulation signals is presented in this paper. A combination of the higher order moments and cumulants of the wavelet coefficients as well as the peaks of the bispectrum and its bi-frequencies are proposed as effective features. Different types of artificial intelligence based classifiers and clustering techniques are used to identify the chirp signals of the different users. In particular, neural networks (NN), maximum likelihood (ML), k-nearest neighbor (KNN) and support vector machine (SVMs) classifiers as well as fuzzy c-means (FCM) and fuzzy k-means (FKM) clustering techniques are tested. The Simulation results show that the proposed technique is able to efficiently classify the different chirp signals in additive white Gaussian noise (AWGN) channels with high accuracy. It is shown that the NN classifier outperforms other classifiers. Also, the simulations prove that the classification based on features extracted from wavelet transform results in more accurate results than that using features directly extracted from the chirp signals, especially at low values of signal-to-noise ratios.

Automatic Digital signal type classification (ADSTC) has many important applications in both of the civilian and military domains. Most of the proposed classifiers can only recognize a few types of digital signals. This paper presents a novel technique that deals with the classification of multi-user chirp modulation signals. In this paper, the peak of the bispectrum and its bi-frequencies are proposed as the effective features and different types of classifiers are used. Simulation results show that the proposed technique is able to classify the different types of chirp signals in additive white Gaussian noise (AWGN) channels with high accuracy and the neural network classifier (NN) outperforms other classifiers, namely, maximum likelihood classifier (ML), the knearest neighbor classifier (KNN) and the support vector machine classifiers (SVMs).