Musoke H. Sendaula

How to Update Dependable Secure Computing Systems from a Survivability Assessment Perspective

All practical applications contain some degree of non- determinism. When such applications are replicated to achieve Byzantine fault tolerance (BFT), their nondeterministic operations must be controlled to ensure replica consistency. To the best of our knowledge, only the most simplistic types of replica nondeterminism have been dealt with. Furthermore, there lacks a systematic approach to handling common types of nondeterminism. In this paper, we propose a classification of common types of replica nondeterminism with respect to the requirement of achieving Byzantine fault tolerance, and describe the design and implementation of the core mechanisms necessary to handle such nondeterminism within a Byzantine fault tolerance framework.In this paper, we investigate how to compute an updating time for a dependable secure computing (DSC) system from a survivability assessment perspective. Generally speaking, to prevent adversaries from learning secret information in a DSC system, the secret information has to be updated in the system. The proposed method is based on a novel concept of survivability of reconfigurable systems presented earlier in [1]. It is shown that secret updating time increases with k, which is the number of secret shares for reconstructing the secret. The proposed method presents a solution for an important problem in the DSC system which is how to determine an appropriate updating time.

Simultaneous solution of unit commitment and dispatch problems using artificial neural networks

Abstract In this paper a combination of Hopfield-Tank type and Chua-Lin type artificial neural networks is applied to solve simultaneously the unit commitment and the associated economic unit dispatch problems. The approach is based on imbedding the various economic and electrical constraints of the unit commitment and dispatch problems in a generalized energy function, and then defining the network dynamics in such a way that the generalized energy function is a Lyapunov function of the artificial neural network. The novel feature of the proposed approach is that the non-linear programming problem for unit dispatch and the combinatorial optimization problem for unit commitment are solved simultaneously by one network. The method is illustrated by an example.

Application of artificial neural networks to unit commitment

Artificial neural networks are currently being applied to a variety of complex combinatorial optimization and nonlinear programming problems. In this paper, a combination of Hopfield Tank type, and Chua-Lin type artificial neural networks is applied to solve simultaneously the unit commitment and the associated economic unit dispatch problems. The approach is based on imbedding the various constraints in a generalized energy function, and then defining the network dynamics in such a way that the generalized energy function is a Lyapunov function of the artificial neural network. The novel feature of the proposed approach is that the nonlinear programming and the combinatorial optimization problems are solved simultaneously by one network. An illustrative example is also presented.<<ETX>>

Electromagnetic crosstalk penalty in serial fiber optic modules

Electromagnetic crosstalk poses a serious problem within todays advanced serial communication modules. A major detrimental effect is the degradation of receiver sensitivity in the presence of crosstalk noise. The mitigation of crosstalk penalty becomes increasingly more challenging as data rates increase for higher throughput and as module sizes shrink for increased port density. This paper is a study of the primary sources of crosstalk penalty in a 2.5 GB/s serial fiber optic transceiver and a 10 Gb/s serial fiber optic transponder. A novel method for quantifying crosstalk penalty by observing a receivers bit-error-ratio (BER) versus transmitter to receiver signal phase is proposed. A simple coupled microstrip transmission line model is explored to demonstrate inductive crosstalk coupling.

Curriculum deceleration and on-line learning communities for working students

Online interactive learning communities and a decelerated five-year engineering program, are proposed as possible strategies for increasing the number of undergraduate students obtaining degrees in science, technology, engineering, and mathematics fields. These strategies are aimed at minimizing the differences in the learning environment and the support systems at residential campuses and those at urban commuter schools. The learning communities provide on-line mentoring and tutorial support to all the community members. Initial focus is to establish on-line interactive learning communities for all incoming students led by the student engineering professional societies. Five-year decelerated curricula for both electrical engineering and computer engineering programs have been developed, which are currently being used to advise students. Preliminary results from the on-line learning communities for incoming students are very encouraging.