The secure data transmission over internet is achieved using Steganography. It is the art and science of concealing information in unremarkable cover media so as not to arouse an observer’s suspicion. In this paper the color cover image is divided into equally four parts, for each part select one channel from each part( Red, or Green, or Blue), choosing one of these channel depending on the high color ratio in that part. The chosen part is decomposing into four parts {LL, HL, LH, HH} by using discrete wavelet transform. The hiding image is divided into four part n*n then apply DCT on each part. Finally the four DCT coefficient parts embedding in four high frequency sub-bands {HH} in cover image. Experiments show that this method gets stego image with perceptual invisibility, and better secrecy. The proposed method was implemented using MATLAB 7.8.
Steganography is an important class of security which is widely used in computer and network security nowadays. In this research, a new proposed algorithm was introduced with a new concept of dealing with steganography as an algorithmic secret key technique similar to stream cipher cryptographic system. The proposed algorithm is a secret key system suggested to be used in communications for messages transmission steganography
Time series analysis is the statistical approach used to analyze a series of data. Time series is the most popular statistical method for forecasting, which is widely used in several statistical and economic applications. The wavelet transform is a powerful mathematical technique that converts an analyzed signal into a time-frequency representation. The wavelet transform method provides signal information in both the time domain and frequency domain. The aims of this study are to propose a wavelet function by derivation of a quotient from two different Fibonacci coefficient polynomials, as well as a comparison between ARIMA and wavelet-ARIMA. The time series data for daily wind speed is used for this study. From the obtained results, the
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Some degree of noise is always present in any electronic device that
transmits or receives a signal . For televisions, this signal i has been to s the
broadcast data transmitted over cable-or received at the antenna; for digital
cameras, the signal is the light which hits the camera sensor. At any case, noise
is unavoidable. In this paper, an electronic noise has been generate on
TV-satellite images by using variable resistors connected to the transmitting cable
. The contrast of edges has been determined. This method has been applied by
capturing images from TV-satellite images (Al-arabiya channel) channel with
different resistors. The results show that when increasing resistance always
produced higher noise f
Currently, with the huge increase in modern communication and network applications, the speed of transformation and storing data in compact forms are pressing issues. Daily an enormous amount of images are stored and shared among people every moment, especially in the social media realm, but unfortunately, even with these marvelous applications, the limited size of sent data is still the main restriction's, where essentially all these applications utilized the well-known Joint Photographic Experts Group (JPEG) standard techniques, in the same way, the need for construction of universally accepted standard compression systems urgently required to play a key role in the immense revolution. This review is concerned with Different
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In this paper we study and design two feed forward neural networks. The first approach uses radial basis function network and second approach uses wavelet basis function network to approximate the mapping from the input to the output space. The trained networks are then used in an conjugate gradient algorithm to estimate the output. These neural networks are then applied to solve differential equation. Results of applying these algorithms to several examples are presented
Image compression is very important in reducing the costs of data storage transmission in relatively slow channels. Wavelet transform has received significant attention because their multiresolution decomposition that allows efficient image analysis. This paper attempts to give an understanding of the wavelet transform using two more popular examples for wavelet transform, Haar and Daubechies techniques, and make compression between their effects on the image compression.
This paper presents a study of wavelet self-organizing maps (WSOM) for face recognition. The WSOM is a feed forward network that estimates optimized wavelet based for the discrete wavelet transform (DWT) on the basis of the distribution of the input data, where wavelet basis transforms are used as activation function.
In this paper, a new analytical method is introduced to find the general solution of linear partial differential equations. In this method, each Laplace transform (LT) and Sumudu transform (ST) is used independently along with canonical coordinates. The strength of this method is that it is easy to implement and does not require initial conditions.
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