Sahibzada Muhammad Ali

— The new and improved Smart Grid (SG) is replacing conventional electrical grid due to: (a) Integration of renewable energy sources, (b) Demand response programs, (c) energy management system, and (d) supply versus demand management. The electrical demand is typically supplied by conventional fossil fuel generation that induces high cost and increased carbon dioxide (CO2) emissions. Therefore, to overcome abovementioned issues, there is a pressing need to supply the consumer demand using hybrid energy system with low cost and reduced CO2 emissions. The analytical assessment of optimal hybrid power generation system at University campus is performed during load shedding hours or unavailability of power supply from the conventional power system. Further, this paper analyzes four various configurations of the power system that includes: (a) Diesel based, (b) Photo Voltaic (PV) based, (c) Wind based, and (d) Wind and PV based power generation system. Cost of Energy (COE) and Net Present Cost (NPC) plays a vital role in opting for power generation system. Therefore, this research will analyze COE and NPC for the abovementioned configurations using Hybrid Optimization Model for Electric Renewables (HOMER) software. A configuration with reduced COE and NPC will be proposed for University campus.

This paper presents the work on Automatic Speech Recognition of Urdu language, using a comparative analysis for Discrete Wavelets Transform (DWT) based features and Mel Frequency Cepstral Coefficients (MFCC). These features have been extracted for one hundred isolated words of Urdu, each word uttered by ten different speakers. The words have been selected from the most frequently used words of Urdu. A variety of age and dialect has been covered by using a balanced corpus approach. After extraction of features, the classification has been achieved by using Linear Discriminant Analysis. After the classification  task, the confusion matrix obtained for the DWT features has been compared with the one obtained for Mel-Frequency Cepstral Coefficients based speech recognition.  The framework has been trained and tested for speech data recorded under controlled environments. The experimental results are useful in determination of the optimum features for speech recognition task.