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The approach of transforming human expert knowledge into computer program only allow a system to solve foreseen and tested outcomes compared to a system having selflearning capabilities. This paper will summarize and discuss the research, design and implementation of a novel self-learning algorithm which combines: (a) Q-Learning -A reinforcement learning algorithm; and (b) AutoWiSARD -An unsupervised weightless neural network learning algorithm. The self-learning algorithm was implemented in an autonomous mobile robot navigation and obstacle avoidance system in a simulated environment.The AutoWiSARD algorithm identifies, differentiates and classifies the obstacles and the Q-learning algorithm learns and tries to maneuver through these obstacles. This novel hybrid technique allows the autonomous system to acquire knowledge, learn and record experience thus attaining self-learning state. The final result shows the simulated mobile robot was able to differentiate various shapes of obstacles such as corners and walls; and create complex control sequences of movements to maneuver through these obstacles.

Universal mobile networks require enhanced capability and appropriate quality of service (QoS) and experience (QoE). To achieve this, Long Term Evolution (LTE) system operators have intensively deployed femtocells (HeNBs) along with macrocells (eNBs) to offer user equipment (UE) with optimal capacity coverage and best quality of service. To achieve the requirement of QoS in the handover stage among macrocells and femtocells we need a seamless cell selection mechanism. Cell selection requirements are considered a difficult task in femtocell-based networks and effective cell selection procedures are essential to reduce the ping-pong phenomenon and to minimize needless handovers. In this study, we propose a seamless cell selection scheme for macrocell-femtocell LTE systems, based on the Q-learning environment. A novel cell selection mechanism is proposed for high-density femtocell network topologies to evaluate the target base station in the handover stage. We used the LTE-Sim simulator to implement and evaluate the cell selection procedures. The simulation results were encouraging: a decrease in the control signaling rate and packet loss ratio were observed and at the same time the system throughput was increased.

This paper describes the development of a mid-line detection system on curve road as guidance for drivers to stay center in road-lane they are currently on using simulation model. The system will identify the curve road and detecting the tangent for each segmented curve. The purpose of detecting the tangent is to find a point that is normal to tangent of the curve. Then, using pixel distance calculation, we can get a midpoint for the curve road in order to detect and draw a virtual mid-line. This mid-line will be the guidance for the drivers to stay center when driving on the curve road. As a safety measure, the system will notify the driver with a warning message if the vehicle goes off the lane. Yet, if the driver decided to change lane, the system will automatically update the new detection on left and right boundaries. The warning message will turn off once it gets back on track. In this paper, we used B-spline as a base to study the curve behavior from simple to complicated curves. As for the method to measure the curve, we combine several algorithms from B-spline together with Generalised Hough Transform to determine the transformation parameter and the position of the model in the image. The proposed method gives a unified framework for detecting, refining and tracking the road lane. Experimental result using images of real road scene are presented.

-Two virtual windows are used to determine the path of a single uniformly moving obstacle. If the path of the obstacle crosses the two virtual windows, then its path can be easily determined. A few simulations were implemented to ascertain the viability and effectiveness of this technique. A table with recommended maximum speed of the uniformly moving obstacle was also provided, given the specified pixel resolution and gap between the virtual windows.Index Terms -virtual window, uniformly moving obstacle, path determination.

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<p><span lang="EN-US"><span style="font-size: small;">Two virtual windows are used to determine the path of a single uniformly moving obstacle. If the path of the obstacle crosses the two virtual windows, then its path can be easily determined. A simulation is implemented to ascertain the viability and accuracy of this technique.</span></span><em><span lang="EN-US"> </span></em></p>