Data steganography is a technique used to hide data, secret message, within another data, cover carrier. It is considered as a part of information security. Audio steganography is a type of data steganography, where the secret message is hidden in audio carrier. This paper proposes an efficient audio steganography method that uses LSB technique. The proposed method enhances steganography performance by exploiting all carrier samples and balancing between hiding capacity and distortion ratio. It suggests an adaptive number of hiding bits for each audio sample depending on the secret message size, the cover carrier size, and the signal to noise ratio (SNR). Comparison results show that the proposed method outperforms state of the art methods

One of the most difficult issues in the history of communication technology is the transmission of secure images. On the internet, photos are used and shared by millions of individuals for both private and business reasons. Utilizing encryption methods to change the original image into an unintelligible or scrambled version is one way to achieve safe image transfer over the network. Cryptographic approaches based on chaotic logistic theory provide several new and promising options for developing secure Image encryption methods. The main aim of this paper is to build a secure system for encrypting gray and color images. The proposed system consists of two stages, the first stage is the encryption process, in which the keys are genera

An intelligent software defined network (ISDN) based on an intelligent controller can manage and control the network in a remarkable way. In this article, a methodology is proposed to estimate the packet flow at the sensing plane in the software defined network-Internet of Things based on a partial recurrent spike neural network (PRSNN) congestion controller, to predict the next step ahead of packet flow and thus, reduce the congestion that may occur. That is, the proposed model (spike ISDN-IoT) is enhanced with a congestion controller. This controller works as a proactive controller in the proposed model. In addition, we propose another intelligent clustering controller based on an artificial neural network, which operates as a reactive co

The goal of fusing multi-focus images is to obtain one image that has all the significant features from each input. The fusion process is needed because of the limitations of the optical lens depth of field that is used to capture images, so images of various focused regions are produced. In this paper, a multi-focus image fusion algorithm is proposed. It is based on utilizing the biorthogonal wavelet transform to extract details and edges from the input images by making the approximation subband equal zero and applying an inverse transform to get images that have only edges, lines, and details. The average gradient metric, which is used to represent sharpness and clarity, is calculated as an activity measurement for each NxN block

Iron–phthalocyanine (FePc) organic photoconductive detector was fabricated using pulsed laser deposition (PLD) technique to work in ultraviolet (UV) and visible regions. The organic semiconductor material (iron phthalocyanine) was deposited on n-type silicon wafer (Si) substrates at different thicknesses (100, 200 and 300) nm. FePc organic photoconductive detector has been improved by two methods: the first is to manufacture the detector on PSi substrates, and the second is by coating the detector with polyamide–nylon polymer to enhance the photoconductivity of the FePc detector. The current–voltage (I–V) characteristics, responsivity, photocurrent gain, response time and the quantum efficiency of the fabricated photoconduc

Background: The treatment of schizophrenia typically involves the use of olanzapine (OLZ), a typical antipsychotic drug that has poor oral bioavailability due to its low solubility and first-pass effect.  Objective: To prepare and optimize OLZ as nanoparticles for transdermal delivery to avoid problems with oral administration. Methods: The nanoprecipitation technique was applied for the preparation of eight OLZ nanoparticles by using different polymers with various ratios. Nanoparticles were evaluated using different methods, including particle size, polydispersity index (PDI), entrapment efficiency (EE%), zeta potential and an in vitro release study. The morphology was evaluated by a field emission scanning electron microscope (F

This paper proposes a better solution for EEG-based brain language signals classification, it is using machine learning and optimization algorithms. This project aims to replace the brain signal classification for language processing tasks by achieving the higher accuracy and speed process. Features extraction is performed using a modified Discrete Wavelet Transform (DWT) in this study which increases the capability of capturing signal characteristics appropriately by decomposing EEG signals into significant frequency components. A Gray Wolf Optimization (GWO) algorithm method is applied to improve the results and select the optimal features which achieves more accurate results by selecting impactful features with maximum relevance