Shakir M. Hussain

An adaptive character wordlength algorithm for data compression

This paper presents a new and efficient data compression algorithm, namely, the adaptive character wordlength (ACW) algorithm, which can be used as complementary algorithm to statistical compression techniques. In such techniques, the characters in the source file are converted to a binary code, where the most common characters in the file have the shortest binary codes, and the least common have the longest; the binary codes are generated based on the estimated probability of the character within the file. Then, the binary coded file is compressed using 8 bits character wordlength. In this new algorithm, an optimum character wordlength, b, is calculated, where b>8, so that the compression ratio is increased by a factor of b/8. In order to validate this algorithm, it is used as a complement algorithm to Huffman code to compress a source file having 10 characters with different probabilities, and these characters are randomly distributed within the source file. The results obtained and the factors that affect the optimum value of b are discussed, and, finally, conclusions are presented.

A Framework for Building an Interactive Satellite TV Based M-Learning Environment

This paper presents a description of an interactive satellite TV based mobile learning (STV-ML) framework, in which a satellite TV station is used as an integral part of a comprehensive interactive mobile learning (M-Learning) environment. The proposed framework assists in building a reliable, efficient, and cost-effective environment to meet the growing demands of M-Learning all over the world, especially in developing countries. It utilizes recent advances in satellite reception, broadcasting technologies, and interactive TV to facilitate the delivery of gigantic learning materials. This paper also proposed a simple and flexible three-phase implementation methodology which includes construction of earth station, expansion of broadcasting channels, and developing true user interactivity. The proposed framework and implementation methodology ensure the construction of a true, reliable, and cost effective M-Learning system that can be used efficiently and effectively by a wide range of users and educational institutions to deliver ubiquitous learning.

Competition-Based Learning: A Model for the Integration of Competitions with Project-Based Learning using Open Source LMS

In an effort to enhance the learning process in higher education, a new model for Competition-Based Learning CBL is presented. The new model utilizes two well-known learning models, namely, the Project-Based Learning PBL and competitions. The new model is also applied in a networked environment with emphasis on collective learning as well as collective outcomes. The new model, which is referred to as CBL, provides educators with an alternative solution to overcome many of students deficiencies associated with traditional learning practices; such as lack of motivation, lack of self esteem, insufficient practical and real-life experience, and inadequate team work practices. The new CBL model makes a clear distinction between PBL and competitions and CBL. It avoids the disadvantages of competitions, while at the same time gaining from the many benefits of PBL. Identification features of CBL, components of CBL, as well as advantages are presented. An open source Learning Management System LMS, namely, Moodle is used for the implementation of a networked environment to support CBL.

A web search engine model based on index-query bit-level compression

In this paper, we propose a new web search engine model based on index-query bit-level compression. The model incorporates two bit-level compression layers both implemented at the backend processor (server) side, one layer resides after the indexer acting as a second compression layer to generate a double compressed index, and the second layer be located after the query parser for query compression to enable bit-level compressed index-query search. This contributes to reducing the size of the index file as well as reducing disk I/O overheads, and consequently yielding higher retrieval rate and performance. The data compression scheme used in this model is the adaptive character wordlength (ACW(n,s)) scheme, which is an asymmetric, lossless, bit-level scheme that permits compressed index-query search. Results investigating the performance of the ACW(n,s) scheme is presented and discussed.