While a hearing-impaired individual depends on sign language and gestures, non-hearing-impaired person uses verbal language. Thus, there is need for means of arbitration to forestall situation when a non-hearing-impaired individual who does not understand the sign language wants to communicate with a hearing-impaired person. This paper is concerned with the development of a PC-based sign language translator to facilitate effective communication between hearing-impaired and non-hearing-impaired persons. Database of hand gestures in American sign language (ASL) is created using Python scripts. TensorFlow (TF) is used in the creation of a pipeline configuration model for machine learning of annotated images of gestures in the database with the real time gestures. The implementation is done in Python software environment and it runs on a PC equipped with a web camera to capture real time gestures for comparison and interpretations. The developed sign language translator is able to translate ASL/gestures to written texts along with corresponding audio renderings at an average duration of about one second. In addition, the translator is able to match real time gestures with the equivalent gesture images stored in the database even at 44% similarity.

Traffic congestion has been the major problem on most Nigeria roads. This is particularly due to the rapid increase in urban migration. Majority of the traffic control schemes adopted in the country to alleviate this problem are the fixed time controllers employed at all signalized intersections. This has resulted in increased traffic jam especially during the peak periods at most intersections on our highways. In this study, a fuzzy logic system to control traffic on signalized intersection has been proposed. The Fuzzy Logic Controller regulates the traffic signal timing, the green light extension and phase sequence to ensure smooth flow of traffic, thereby reducing traffic delays and thus increasing the intersection capacity. A fuzzy logic traffic control simulation model was developed and tested using MATLAB/ SIMULINK software. Comparative analysis was carried out between the fuzzy logic controller and a conventional fixed-time controller in order to determine the efficiency of the developed system. Evaluation results of the fuzzy logic traffic controller shows that vehicles spent less time at the intersection compared to the fixed time controller, that is, improved vehicular movement. Moreover, simulation results show that the fuzzy logic controller has better efficiency and that a huge improvement could be realized by adapting it in controlling traffic flow at intersections.

This paper is concerned with the analysis of consensus multi-agent networked system. Adopted in the analysis is the finite-level logarithmic quantizer, for the transmission of the networked-agents state. Two protocols are utilised in the analysed multi-agent networked system: the consensus protocol, which is determined from the outputs and states of a set of encoder-decoder pair that is employed in the system, and convergence rate protocol that is precisely characterized via the use of a dynamic scaling factor. With information exchange among neighbouring agents, the asymptotic consensus can be reached. The proof of protocols is based on proper selection of parameters of the logarithmic quantizer chosen for the connected network. As a demonstration of the validity of the protocols, a four-agent networked system is used. It is shown that an undirected network exchange of information via a communication channel that is equipped with a set of encoder and decoder can lead to attainment of estimates of neighbour state protocol for the networked system. Furthermore, desired asymptotic convergence can be reached through appropriate choice of parameters of the logarithmic quantizer.