Prediction-Based Channel Selection Prediction in Mobile Cognitive Radio Network

The emerging 5G wireless communications enabled diverse multimedia applications and smart devices in the network. It promises very high mobile traffic data rates, quality of service as in very low latency and improvement in user’s perceived quality of experience compared to current 4G wireless network. This encourages the increasing demand of significant bandwidth which results a significant urge of efficient spectrum utilization. In this paper, modelling, performance analysis and optimization of future channel selection for cognitive radio network by jointly exploiting both CR mobility and primary user activity to provide efficient spectrum access is studied.  The modelling and prediction method is implemented by using Hidden Markov Model algorithm. The movement of CR in wireless network yields location-varying spectrum opportunities. The current approaches in most literatures which only depend on reactive selection spectrum opportunities result of inefficient channel usages. Moreover, conventional random selection method tends to observe a higher handoff and operation delays in network performance.  This inefficiency can cause continuous transmission interruptions leading to the degradation of advance wireless services. This work goal is to improve the performance of CR in terms number of handoffs and operation delays. We perform simulation on our prediction strategy with a commonly used random sensing method with and without location. Through simulations, it is shown that the proposed prediction and learning strategy can obtain significant improvements in number of handoffs and operation delays performance parameters. It is also shown that future CR location is beneficial in increasing mobile CR performance. This study also shows that the number of primary user in the network and the PU protection range affect the performance of mobile CR channel selection for all methods

Development of a Hybrid Solar and Waste Heat Thermal Energy Harvesting System

This research aims to develop a Hybrid Solar and Waste Heat Thermal Energy Harvesting System that integrates Thermoelectric Generator (TEG) with a solar PV system. The main focus is given to the development of the hybrid solar and waste heat released from the solar panel by using the TEG system. This hybrid system consists of photovoltaic (PV) cells to absorb the solar energy and the TEG attached to the back of the panel to absorb heat waste and convert it into usable electricity. The PV cell and the TEG are integrated with each other in order to obtain maximum energy and increased system efficiency. The experimental results show that the maximum output voltage produced from the solar PV is 20.37V and the maximum output current generated is 203.72mA. The maximum output voltage obtained from the TEG is 18.92V and the maximum current produced is 189.265mA. This experimental result shows that the maximum voltage and current produced from solar and waste thermal heat from PV panels can be used to charge and to power up portable electronic devices. More efficiency is accomplished by combining the TEG to absorb waste heat loss from the PV cell, thus improving the performance of the PV panel system

A Delphi Study to Validate a New Model and Instruments for Assessment of Data Utilization of Flood Management

Since there are no methods for determining the extent to which data is used, it is currently difficult for the Malaysian government to identify potential improvements required for successful flood management. In the light of this situation, the development of a new model and instruments to assess data utilization of flood management in Malaysia using a performance measurement approach has been proposed. Therefore, validation of the assessment model and instruments is required to determine acceptability for successful data utilization assessment implementation. The initial model and instruments went through two Delphi rounds with nine validation panelists. Consensus was reached among all panelists, indicating relatively high acceptance and it is quite evident that they have accepted the proposed data utilization assessment model and instrument for this study