Azzam Sleit

The OTIS hyper hexa-cell optoelectronic architecture

Optical transpose interconnection system (OTIS) is an optoelectronic architecture that promises to be a great choice for future-generation parallel systems. OTIS combines the advantages of electronic and optical links, where electronic links are used for short distances which require low material cost, and optical links are used for long distances which provide high speed network with low power consumption. Taking into account the advantageous characteristics of OTIS and based on the attractive properties of hyper hexa-cell (HHC) interconnection topology from low diameter and good minimum node degree, this paper introduces a new optoelectronic architecture referred to as OTIS hyper hexa-cell (OHHC). This paper also provides an evaluation and a comparison of the new topology with OTIS-mesh in terms of the following topological properties: size, diameter, maximum and minimum node degree, bisection width, total cost and optical cost. The results of this study proved the excellence of the proposed OHHC over OTIS-mesh in terms of diameter, minimum node degree, bisection width, and optical cost.

A histogram based speaker identification technique

Feature extraction has the capability to improve the performance of speaker identification systems. This paper proposes two new techniques for speaker identification based on utilizing a reduced set of the features generated from the Mel Frequency Cepstral Coefficient method (MFCC). These techniques are based on histograms for the features using pre-defined interval lengths. The first technique builds a histogram for all data in the feature vectors for each speaker while the second technique builds a histogram for each feature column in the feature set of each speaker. Speaker identification is based on the Euclidian distance measure.

Approximating images using minimum bounding rectangles

In surveillance systems, video cameras record specific scenes for long times. However, by the end of the recording period, such tapes may hold many useless scenes which need to be eliminated. In order to reduce the time in reviewing these worthless scenes while seeking for a specific object, an approximation technique must be considered. In this article, we propose a new technique for finding the minimum bounding rectangle of objects which appear in a specific bitmap image. The minimum bounding rectangle of an image object is the Rectangle containing the object such that the sides of the rectangle touch the object boundaries. The dimensions and locations of the minimum bounding rectangle of an object can be utilized as features to identify the corresponding object.

An enhanced semi-blind DWT-SVD-based watermarking technique for digital images

Abstract This paper presents a semi-blind hybrid watermarking technique based on singular value decomposition (SVD) and discrete wavelet transformation (DWT). The proposed technique decomposes the host image using DWT and combines the singular values (SVs) of the watermark and the selected sub-bands. A binary watermark is used to be embedded in the grey-scale original image. This watermark image passes through multiple operations before embedding. The watermark is converted into a vector, which is permuted into scrambled data by using a key as the initial random seed of this process. Experimental results show that the proposed technique is able to resist a variety of attacks.

Corner-based splitting: An improved node splitting algorithm for R-tree

We introduce an improved method to split overflowed nodes of R-tree spatial index called the Corner Based Splitting (CBS) algorithm. Good splits produce an efficient R-tree which has minimal height, overlap and coverage in each node. The CBS algorithm selects the splitting axis that produces the most even split according to the number of objects, using the distance from each object centre to the nearest node’s MBR corner. Experiments performed using both synthetic and real data files showed obvious performance improvement. The improvement percentage over the Quad algorithm reached 23%, while the improvement percentage over the NR algorithm reached 37%.

Authentication Techniques for the Internet of Things: A Survey

Internet of Things (IoT) consists of a large number of connected objects that are communicating with each other. In order to support trusted communication between IoT objects, effective authentication procedures should be applied between the communicating entities. In this paper a survey of IoT authentication techniques, which are proposed in the literature, is presented. The survey aims to help other researchers in delving into the details of such techniques by going through their classification and comparison. The classification has been done based on the inherent features of these authentication technique such as being distributed vs. centralized, flat vs. hierarchical, and more others. A comparison between these techniques according to the used evaluation models and their security analysis is illustrated.

Resource Utilization in Cloud Computing as an Optimization Problem

In this paper, an algorithm for resource utilization problem in cloud computing based on greedy method is presented. A privately-owned cloud that provides services to a huge number of users is assumed. For a given resource, hundreds or thousands of requests accumulate over time to use that resource by different users worldwide via the Internet. A prior knowledge of the requests to use that resource is also assumed. The main concern is to find the best utilization schedule for a given resource in terms of profit obtained by utilizing that resource, and the number of time slices during which the resource will be utilized. The problem is proved to be an NP-Complete problem. A greedy algorithm is proposed and analyzed in terms of its runtime complexity. The proposed solution is based on a combination of the 0/1 Knapsack problem and the activity-selection problem. The algorithm is implemented using Java. Results show good performance with a runtime complexity O((F-S)nLogn)

Efficient MapReduce Matrix Multiplication with Optimized Mapper Set

The efficiency of matrix multiplication is a popular research topic given that matrices compromise large data in computer applications and other fields of study. The proposed schemes utilize data blocks to balance processing overhead results from a small mapper set and I/O overhead results from a large mapper set. Balancing between the two processing steps, however, consumes time and resources. The proposed technique uses a single MapReduce job and pre-processing step. The pre-processing step reads an element from the first array and a block from the second array prior to merging both elements into one file. The map task performs the multiplication operations, whereas the reduce task performs the sum operations. Comparing the proposed and existing schemes reveals that the proposed schemes more efficiently consume time and memory.

Computational Modeling of Proteins based on Cellular Automata

The literature of building computational and mathematical models of proteins is rich and diverse, since its practical applications are of a vital importance in the development of many fields. Modeling proteins is not a straightforward process and in some modeling strategies, it requires to combine concepts from different fields including physics, chemistry, thermodynamics, and computer science. The focus here will be on models that are based on the concept of cellular automata and equivalent systems. Cellular automata are discrete computational models that are capable of universal computation, in other words, they are capable of doing any computation that a normal computer can do. What is special about cellular automata is its ability to produce complex and chaotic global behavior from local interactions. The paper discusses the effort done so far by the researchers community in this direction and proposes a computational model of protein folding that is based on 3D cellular automata. Unlike common models, the proposed model maintains the basic properties of cellular automata and keeps a realistic view of proteins operations. As in any cellular automata model, the dimension, neighborhood, boundary, and rules were specified. In addition, a discussion is given to clarify why these parameters are in place and what possible alternatives can be used in the protein folding context.

Application of local rules and cellular automata in representing protein translation and enhancing protein folding approximation

It is self-evident that the coarse-grained view of transcription and protein translation is a result of certain computations. Although there is no single definition of the term “computation,” protein translation can be implemented over mathematical models of computers. Protein folding, however, is a combinatorial problem; it is still unknown whether a fast, accurate, and optimal folding algorithm exists. The discovery of near-optimal folds depends on approximation algorithms and heuristic searches. The hydrophobic–hydrophilic (HP) model is a simplified representation of some of the realities of protein structure. Despite the simplified representation, the folding problem in the HP model was proven to be NP-complete. We use simple and local rules to model translation and folding of proteins. Local rules imply that at a certain level of abstraction an entity can move from a state to another based on its state and information collected from its neighborhood. Also, the rules are simple in a sense that they do not require complicated computation. We use one-dimensional cellular automata to describe translation of mRNA into protein. Cellular automata are discrete models of computation that use local interactions to produce a global behavior of some sort. We will also discuss how local rules can improve approximation algorithms of protein folding and give an example of a CA that accept a certain family of strings to achieve half H–H contacts.