Information and Communication Technologies for Educational Development: The Case of Cybercafés at Obafemi Awolowo University, Ile-Ife, Nigeria

This study examines the use of Cybercafés/Internet resources and the evaluation of their usefulness. About eight Cybercafés located in the university community were used in this study. Questionnaires, interviews with the Cybercafé owners, staff and users as well as personal observations made during inspection of these cafés were used in this study. The data were analysed according to the background of the Internet users. The richness and high speed, accuracy, and authority were used by users to judge the quality of the Internet. Information such as the establishment of the café's facilities, membership and the future of the Cybercafés were also looked into. Finally, one can clearly see that the dominating impact of digital technology has crossed the Rubicon of controversy. The result of the survey shows that forty percent of the users learnt to use the internet by self instruction, thirty five percent learnt from colleagues or friends. Those in the sciences use the internet the most, the channel mostly used in obtaining information is the search engines. A large number of students, faculties and researchers make use of the internet in obtaining information. Many of those of those users make use of the Cybercafés in the university community.

Applications of Linear Programming to Coding Theory

Maximum-likelihood decoding is often the optimal decoding rule one can use, but it is very costly to implement in a general setting. Much effort has therefore been dedicated to find efficient decoding algorithms that either achieve or approximate the error-correcting performance of the maximum-likelihood decoder. This dissertation examines two approaches to this problem. In 2003 Feldman and his collaborators defined the linear programming decoder, which operates by solving a linear programming relaxation of the maximum-likelihood decoding problem. As with many modern decoding algorithms, is possible for the linear programming decoder to output vectors that do not correspond to codewords; such vectors are known as pseudocodewords. In this work, we completely classify the set of linear programming pseudocodewords for the family of cycle codes. For the case of the binary symmetric channel, another approximation of maximum-likelihood decoding was introduced by Omura in 1972. This decoder employs an iterative algorithm whose behavior closely mimics that of the simplex algorithm. We generalize Omura's decoder to operate on any binary-input memoryless channel, thus obtaining a soft-decision decoding algorithm. Further, we prove that the probability of the generalized algorithm returning the maximum-likelihood codeword approaches 1 as the number of iterations goes to infinity.