This paper presents a genetic based approach to the partitioning and mapping of multicore SoC cores over a NoC system that uses mesh topology. The proposed algorithm performs the partitioning and mapping by reducing communication cost and minimizing power consumption by placing those intercommunicated cores as close as possible together. A program developed in C++ in which the provided specification of the multicore MPSoC system captures all data dependencies before any start of the design process. Experimental results of several multimedia benchmarks demonstrates that the genetic-based approach able to find different satisfied implementations to the problem of partitioning and mapping of MPSoC cores over mesh-based NoC system that satisfies design goals
Azeddien M. Sllame, Walid Mokthar Salh(2-2021)

Deoxyribonucleic acid (DNA) micro-arrays present a powerful means of observing thousands of gene terms levels at the same time. They consist of high dimensional datasets, which challenge conventional clustering methods. The data’s high dimensionality calls for Self Organizing Maps (SOMs) to cluster DNA micro-array data. The DNA micro-array dataset are stored in huge biological databases for several purposes . The proposed methods are based on the idea of selecting a gene subset to distinguish all classes, it will be more effective to solve a multi-class problem, and we will propose a genetic programming (GP) based approach to analyze multi-class micro-array datasets. This biological dataset will be derived from multiple biological databases. The procedure responsible for extracting datasets called DNA-Aggregator. We will design a biological aggregator, which aggregates various datasets via DNA micro-array community-developed ontology based upon the concept of semantic Web for integrating and exchanging biological data. Our aggregator is composed of modules that retrieve the data from various biological databases. It will also enable queries by other applications to recognize the genes. The genes will be categorized in groups based on a classification method, which collects similar expression patterns. Using a clustering method such as k-mean is required either to discover the groups of similar objects from the biological database to characterize the underlying data distribution. arabic 10 English 60
Mariam Abojela Msaad, Zakaria Suliman Zubi(1-2010)

LTE’s study phase began in late 2004. The overall goal was to select technology that would keep 3GPP’s Universal Mobile Telecommunications System (UMTS) at the forefront of mobile wireless well into the next decade. Key project objectives were set in the following areas: peak data throughput; spectral efficiency; flexible channel bandwidths; latency; device complexity; and overall system cost. The main decision was whether to pursue the objectives by continuing to evolve the existing W-CDMA air interface (which incorporates HSPA (highspeed packet access) or adopt a new air interface based on OFDM. arabic 5 English 27
Mariam Abojela Msaad, Amer R. Zerek, Fareda A. Elmaryami(1-2014)