Hiding information in image using circular distribuition

В статье рассматриваются метод скрытия информации при передаче сообщений через сеть Интернет. Основой метода является скрытие информации в носителе другого типа, или стеганография. Предлагается метод скрытия звуковых сообщений, в котором в качестве носителя выбран файл изображения в формате bmp. Алгоритм основан на применении кругового распределения.Today the art send & reserve the hidden information become largely used in information security system especially in public places. Because the Internet as a whole does not use secure links, thus information in transit may be vulnerable to interception as well. Therefore, different methods have been proposed so far for hiding information in different cover media. The data in one medium can be hidden in another medium. The carrier medium can be image, audio or video. Of the different carrier media, image is best chosen as the carrier due to its frequency on the internet. This project aim to hiding audio file in the pixels of the carrier image using the Steganography "circular distribution algorithm" in image type BMP. The hiding audio is manipulated in way to keep host image with same size and without producing any significant distortion, also this project aim to extracting hiding audio from image without affect any problem in image and audio

A dependency-based search strategy for feature selection

Feature selection has become an increasingly important field of research. It aims at finding optimal feature subsets that can achieve better generalization on unseen data. However, this can be a very challenging task, especially when dealing with large feature sets. Hence, a search strategy is needed to explore a relatively small portion of the search space in order to find "semi-optimal" subsets. Many search strategies have been proposed in the literature, however most of them do not take into consideration relationships between features. Due to the fact that features usually have different degrees of dependency among each other, we propose in this paper a new search strategy that utilizes dependency between feature pairs to guide the search in the feature space. When compared to other well-known search strategies, the proposed method prevailed. © 2009 Elsevier Ltd. All rights reserved

Effect of feature and channel selection on EEG classification

In this paper, we evaluate the significance of feature and channel selection on EEG classification. The selection process is performed by searching the feature/channel space using genetic algorithm, and evaluating the importance of subsets using a linear support vector machine classifier. Three approaches have been considered: (i) selecting a subset of features that will be used to represent a specified set of channels, (ii) selecting channels that are each represented by a specified set of features, and (iii) selecting individual features from different channels. When applied to a Brain-Computer Interface (BCI) problem, results indicate that improvement in classification accuracy can be achieved by considering the correct combination of channels and features. © 2006 IEEE

Fundamentalist Rules for the MenofAl-Asfiya School Using HashemJ ameel's Book Issues of Comparative Jurisprudence

All Praise is due Allah, Lord of the Worlds, and peace and blessings be upon our Messenger Muhammad and due all his companions and due all his household. This study reviews some of the fundamentalist issues raised by Dr. HashemJamil in his book Issues in Comparative Jurisprudence. It seeks to shed light on one of the most outstanding scholars across Fallujah and the larger Iraq, given that he is an Al-Asifiya alumna where Sheikh Abdulaziz Al-Samarrai was teaching (a school which produced hundreds of brilliant scholars). More specifically, I intend to review a number of key contributions made by Dr. Hashem Jamil during his scholarly career. I studied and analyzed some fundamentalist rules from Issues in Comparative Jurisprudence, given that it is one of the important textbooks taught in the faculties of Islamic Sciences which shows the way the inferential contradiction is removed between Sunni and other resources in addition to the contradictions among prophetic hadiths. It is noteworthy that Sheikh Hashem Jameel has relied on the concrete evidence in hadith and Sunna rather than being influenced by a certain doctrine. Keywords: Consensus, Measurement, Al- Asfiya School, Absolute, Restricte

A medical image steganography method based on integer wavelet transform and overlapping edge detection

© Springer International Publishing Switzerland 2015. Recently, there has been an increased interest in the transmission of digital medical images for e-health services. However, existing implementations of this service do not pay much attention to the confidentiality and protection of patients’ information. In this paper, we present a new medical image steganography technique for protecting patients’ confidential information through the embedding of this information in the image itself while maintaining high quality of the image as well as high embedding capacity. This technique divides the cover image into two areas, the Region of Interest (ROI) and the Region of Non- Interest (RONI), by performing Otsu’s method and then encloses ROI pixels in a rectangular shape according to the binary pixel intensities. In order to improve the security, the Electronic Patient Records (EPR) is embedded in the high frequency sub-bands of the wavelet transform domain of the RONI pixels. An edge detection method is proposed using overlapping blocks to identify and classify the edge regions. Then, it embeds two secret bits into three coefficient bits by performing an XOR operation to minimize the difference between the cover and stego images. The experimental results indicate that the proposed method provides a good compromise between security, embedding capacity and visual quality of the stego images

Fuzzy discriminant analysis based feature projection in myoelectric control.

The myoelectric signal (MES) from human muscles is usually utilized as an input to the controller of a multifunction prosthetic hand. In such a system, a pattern recognition approach is usually employed to discriminate between the MES from different classes. Since the MES is recorded using multi channels, the feature vector size can become very large. In order to reduce the computational cost and enhance the generalization capability of the classifier, a dimensionality reduction method is needed to identify an informative moderate size feature set. This paper proposes a novel feature projection technique based on a combination of Fisher's Linear Discriminant Analysis (LDA), and Fuzzy Logic. The new method, called FLDA, assigns different membership degrees to the data points thus reducing the effect of overlapping points in the discrimination process. Furthermore, the concept of Mutual Information (MI) is introduced in the fuzzy memberships in order to assign weights to the features (attributes) according to their contribution to the discrimination process. The FLDA method is tested on a seven classes MES dataset and compared with other feature projection techniques proving its superiority

Swarm Intelligence In Myoelectric Control: Particle Swarm Based Dimensionality Reduction

The myoelectric signals (MES) from human muscles have been utilized in many applications such as prosthesis control. The identification of various MES temporal structures is used to control the movement of prosthetic devices by utilizing a pattern recognition approach. Recent advances in this field have shown that there are a number of factors limiting the clinical availability of such systems. Several control strategies have been proposed but deficiencies still exist with most of those strategies especially with the Dimensionality Reduction (DR) part. This paper proposes using Particle Swarm Optimization (PSO) algorithm with the concept of Mutual Information (MI) to produce a novel hybrid feature selection algorithm. The new algorithm, called PSOMIFS, is utilized as a DR tool in myoelectric control problems. The PSOMIFS will be compared with other techniques to prove the effectiveness of the proposed method. Accurate results are acquired using only a small subset of the original feature set producing a classification accuracy of 99% across a problem of ten classes based on tests done on six subjects MES datasets