coding

Information security is currently one of the most important issues that have been considered by many researchers. The purpose of Steganography is to hide hidden messages in a non-secret file. In general, information Steganography is a method of secure communication that aims to hide data so that no data appears to be hidden. The principle of Steganography is to use spaces from the information carrier that do not harm the identity of the carrier.  By Steganography information from unauthorized recipients, the information is hidden and hidden inside it without harming the signals. This information may be transmitted around us and wherever any file is sent. It may contain very dangerous content for the security of the space in which we live. Audio signals are very used for steganography because Digital audio signals have higher redundancy and higher data transfer speeds, making them suitable for use as a cover. The LPC10, CELP, and MELP audio standards are widely used in audio and speech processing and are powerful high-quality speech coding methods that provide highly accurate estimates of audio parameters and are widely used in communications. Therefore, since considering that these audio standards are used in commercial and military telecommunication systems, they can be considered a suitable platform for sending the following message of audio content. We try to carefully examine these standards and the audio Steganography done in these standards.

The human ear is aware of a wide range of sound signal amplitudes, so signal-based amplitude marking techniques have their own complexity. This article uses Watermarking to insert the message in the audio coverage and its main purpose is to keep this information hidden for others. There are many benchmarks for evaluating insertion and extraction algorithms that, by performing multiple attacks on an algorithm, increase the ability of the method. (Resilience, Transparency and Capacity for Use). Although the LSB method is superior to other encryption techniques, it is highly vulnerable to all kinds of attacks and attacks, including Additive White Gaussian Noise. This article introduces a new idea for Watermarking voice data encryption, based on the LSB method, which follows similar bits with bits instead of pasting information. The message will be in 16-bit samples, given the introduction of the distortion reduction algorithm for the changes we have made to the signal bits for the receiver's awareness, which could be a new way of causing F Of its resistance additive white Gaussian noise and LSB standard will also improve the transparency of the procedure and the method for reducing the capacity has been used.

Data transmission security has become an extremely important field of research. Steganography is an art of hiding information in image, audio and video files in a way that would meet the security requirements in the form of overt or covert. In this study, we propose a new hybrid steganography technique for color images that hide secret messages in the frequency domain of a cover image's blue channel. Hence this method provide robustness against attacks, eavesdropping and capacity. In addition, we use coding and compression algorithms to obtain high capacity along with security and maintains the quality of the cover image with considerable high value PSNR. In this paper a secret message can be either an image or text and our purpose is to improve three important parameters in steganography. Peak Signal to Noise Ratio (PSNR) values measures any steganography technique’s performance. Higher PSNR values indicate that the performance of the system is better.

The Error Correcting Output Codes (ECOC) represent any number of the binary classifiers to model the multiclass problems successfully. In this paper, we have used Curve Fitting as a binary classifier in ECOC algorithm to solve multiclass classification problems. Curve Fitting is a classifier based on a nonlinear decision boundary that separates two pattern classes by the curves of the best fit, and arriving at optimal boundary points between two classes. Since we need a coding and a decoding strategy to design an ECOC system, this paper gives five coding and eight decoding strategies of ECOC and compares the results of Curve Fitting with Adaboost classification and Nearest Mean Classifier (NMC). This evaluation has been performed on different data sets of UCI machine learning repository. The results indicate that One-versus-one, ECOC-ONE coding and LAP, BDEN decoding having the best results in contrast with another coding and decoding strategies and Curve Fitting is a good base classifier in ECOC, also it is comparable with the other ECOC approaches.

As Cognitive Radio (CR) used in video applications، user-comprehended video quality practiced by secondary users is an important metric to judge effectiveness of CR technologies. We propose a new adaptive modulation and coding (AMC) scheme for CR، which is OFDM based system that is compliant with the IEEE. 802. 16. The proposed CR alters its modulation and coding rate to provide high quality system. In this scheme، CR using its ability to consciousness of various parameters including knowledge of the white holes in the channel spectrum via channel sensing، SNR، carrier to interference and noise ratio (CINR)، and Modulation order Product code Rate (MPR) selects an optimum modulation and coding rate. In this scheme، we model the AMC function using Artificial Neural Network (ANN). Since AMC is naturally a non-liner function، ANN is selected to model this function. In order to achieve more accurate model، Genetic algorithm (GA) and Particle Swarm Optimization (PSO) are selected to optimize the function representing relationship between inputs and outputs of ANN، i. e.، AMC model. Inputs of ANN are CR knowledge parameters، and the outputs are modulation type and coding rate. Presenting a perfect AMC model is advantage of this scheme because of considering all impressive parameters including CINR، available bandwidth، SNR and MPR to select optimum modulation and coding rate. Also، we show that in this application، GA rather than PSO is better choice for optimization algorithm.