Interference Alignment in 2-user X Channel System with Orthogonal and quasi-orthogonal Space-time Block Codes

본 논문에서는 각 단말에 2개 이상의 안테나의 간섭 정렬을 이용한 X채널에서 직교 및 준직교 시공간 블록 부호를 통하여 더 높은 다이버시티와 전력 이득을 달성하고자 했다. 제안한 방법으로 다이버시티 차수는 직교 시공간 블록부호에서 최대에 도달한 반면, 준직교 시공간 블록 부호에서는 유효 채널 행렬의 비 직교성에 의해 약간의 성능 저하가 나타났다. 수신기의 유효 채널 행렬에서의 유리한 구조에 의해 단순 제로 포싱 수신기는 최대 다이버시티 차수를 달성하는 반면, 간섭 제거 수신기는 성능이 저하되었다. 기존의 방법과 비교했을 때, 시뮬레이션 결과는 제안된 방법이 같은 스펙트럼 효율을 얻으면서, 3-4개의 안테나의 각 단의 직교 시공간 블록 부호의 경우 각각 목표 비트 에러율 10-4 에서 14dB와 16.5bB의 이득을 얻는 것을 증명하였다. 또한 4개의 안테나의 각 단의 준직교 시공간 블록 부호의 경우 같은 목표 비트 에러율에서 10dB의 이득을 얻었다

Performance evaluation of decode and forward cooperative diversity systems over nakagami-m fading channels with non-identical interferers

The deficiencies of regular cooperative relaying schemes were the main reason behind the development of Incremental Relaying (IR). Fixed relaying is one of the regular cooperative relaying schemes and it relies on using the relay node to help in transmitting the signal of the source towards the destination despite the channel’s condition. However, adaptive relaying methods allocate the channel resources efficiently; thus, such methods have drawn the attention of researchers in recent years. In this study, we analyze a two-hop Decode-and-Forward (DF) IR system’s performance via Nakagami-m fading channels with the existence of the several L distinguishable interferers placed close to the destination which diminishes the overall performance of the system due to the co-channel interference. Tight formulas for the Bit Error Rate (BER) and the Outage Probability (OP) are drawn. The assumptions are consolidated by numerical calculations

Automatic region selection method to enhance image-based steganography

Image-based steganography is an essential procedure with several practical applications related to information security, user authentication, copyright protection, etc. However, most existing image-based steganographic techniques assume that the pixels that hide the data can be chosen freely, such as random pixel selection, without considering the contents of the input image. So, the “region of interest” such as human faces in the input image might have defected after data hiding even at a low inserting rate, and this will degrade the visual quality especially for the images containing several human faces. With this view, we proposed a novel approach that combines human skin-color detection along with the LSB approach which can choose the embedding regions. The idea behind that is based on the fact that the Human Vision System HVS tends to focus its attention on selectively certain structures of the visual scene instead of the whole image. Practically, human skin-color is good evidence of the existence of human targets in images. To the best of our knowledge, this is the first attempt that employs skin detection in application to steganography which consider the contents of input image and consequently can choose the embedding regions. Moreover, an enhanced RSA algorithm and Elliptic Curve Equation are used to provide a double level of security. In addition, the system embeds noise bits into the resulting stego-image to make the attacker’s task more confusing. Two datasets are used for testing and evaluation. The experimental results show that the proposed approach achieves a significant security improvement with high image quality

Antibacterial Activity of River Water Bacteriophage against Multidrug-resistant Gram-negative Bacteria, An In vitro Study

Microbes show a high antimicrobial resistance due to a high rate of mutations predisposed by many factors, especially the abuse of antibiotics. Therefore, there is a great need for an alternative therapeutic agent for infectious diseases caused by microbes resistant to antibiotics. Bacteriophages are viruses parasitizing microbes, that got a big scientist’s attention due to their ability as an alternative therapy for severe bacterial infections. This study is devoted to identifying bacteriophage from river water on tested pathogenic isolates isolated from clinical cases of UTI in vitro and finding out the effect of phage on these bacterial isolates as an initial step of further in vivo phage therapeutic study on the same tested isolates. The results showed a significant bactericidal effect of the isolated bacteriophages against the pathogenic bacterial isolates.</jats:p

Intelligent OS X malware threat detection with code inspection

With the increasing market share of Mac OS X operating system, there is a corresponding increase in the number of malicious programs (malware) designed to exploit vulnerabilities on Mac OS X platforms. However, existing manual and heuristic OS X malware detection techniques are not capable of coping with such a high rate of malware. While machine learning techniques offer promising results in automated detection of Windows and Android malware, there have been limited efforts in extending them to OS X malware detection. In this paper, we propose a supervised machine learning model. The model applies kernel base Support Vector Machine (SVM) and a novel weighting measure based on application library calls to detect OS X malware. For training and evaluating the model, a dataset with a combination of 152 malware and 450 benign were is created. Using common supervised Machine Learning algorithm on the dataset, we obtain over 91% detection accuracy with 3.9% false alarm rate. We also utilize Synthetic Minority Over-sampling Technique (SMOTE) to create three synthetic datasets with different distributions based on the refined version of collected dataset to investigate impact of different sample sizes on accuracy of malware detection. Using SMOTE datasets we could achieve over 96% detection accuracy and false alarm of less than 4%. All malware classification experiments are tested using cross validation technique. Our results reflect that increasing sample size in synthetic datasets has direct positive effect on detection accuracy while increases false alarm rate in compare to the original dataset

Graph-Based Comparison Of Iot And Android Malware

The growth in the number of android and Internet of Things (IoT) devices has witnessed a parallel increase in the number of malicious software (malware) that can run on both, affecting their ecosystems. Thus, it is essential to understand those malware towards their detection. In this work, we look into a comparative study of android and IoT malware through the lenses of graph measures: we construct abstract structures, using the control flow graph (CFG) to represent malware binaries. Using those structures, we conduct an in-depth analysis of malicious graphs extracted from the android and IoT malware. By reversing 2,874 and 201 malware binaries corresponding to the IoT and android platforms, respectively, extract their CFGs, and analyze them across both general characteristics, such as the number of nodes and edges, as well as graph algorithmic constructs, such as average shortest path, betweenness, closeness, density, etc. Using the CFG as an abstract structure, we emphasize various interesting findings, such as the prevalence of unreachable code in android malware, noted by the multiple components in their CFGs, the high density, strong closeness and betweenness, and larger number of nodes in the android malware, compared to the IoT malware, highlighting its higher order of complexity. We note that the number of edges in android malware is larger than that in IoT malware, highlighting a richer flow structure of those malware samples, despite their structural simplicity (number of nodes). We note that most of those graph-based properties can be used as discriminative features for classification

Proceedings of the Workshop on Simplifying Complex Networks for Practitioners (SIMPLEX 2013)

The complex networks science has recently attracted much attention from the scientific community, mainly due to the pervasive presence of complex phenomena in real-world systems such as peer-to-peer systems, social networks, and communication networks. Understanding complex networks and simplifying complex phenomena in them for practitioners is a very challenging task, where many well-established disciplines, like machine learning, data mining, and graph theory, have found great applications in the recent years to give insights into such complex networks. Building on the success in the past four years, the Fifth Annual Workshop on Simplifying Complex Networks for Practitioners – SIMPLEX 2013 continues to serve as a forum for researcher s as well as practitioners to disseminate and discuss recent advances and emerging issues in understanding and simplifying complex networks