This project dives into the topic of facial recognition and facial detection in a digital communications system. Face recognition is a technology that is widely used today which brings various benefits to society. Facial recognition differs from facial detection in the aspect that facial detection only finds and detects the present face/s in an image whereas, in facial recognition, the computer finds the face/s present in a subject and is able to distinguish the face from a sample of different faces. This research focuses on the implementation of both a facial recognition system and a facial detection system in MATLAB. This research would use the different imaging toolboxes available in the program and would be judged on its ability to accurately detect and recognize a sample in a given database. Additionally, this system should be able to create and to read a database of different faces.

No abstract

Measurement of Power is circuits is important. It is used to determine the efficiency of the system. There are different types of loads. One of them is a three-phase load. Three phases of electric power are the usual method of alternating current transmission and distribution. This method is a type of polyphase system and is most commonly used in electrical grids to transfer power. An advantage of three-phase systems over single-phase systems is the power delivered is constant, it gives higher output, has a higher power factor, has uniform torque and it requires fewer conductors. However, these systems cannot be measured easily. These systems require two or more wattmeter when being measured. This research will develop a MATLAB Optimization model to measure power in three phase circuits. A database for power capacity will be developed and optimization algorithms will be applied to it.

It is apparent that the taxi industry has grown and developed over the years. In addition to that, it will presumably continue to grow as time goes on due to the increasing popularity of taxi-hailing applications. However, taxi origin and destination (O-D) locations are not clearly established since taxis are very flexible in terms of where they can pick up and drop off passengers. In this study, the taxi origin and destination hotspots are determined by first clustering the available O-D pairs from empirical mobility traces. The validity of these formed clusters is determined by utilizing the silhouette analysis. Finally, hotspots are located by measuring the cluster’s h-index. Simulation results reveal that more clusters tend to provide unreliable silhouette values due to the fact that origin/destination GPS points are very close to each other. For a given number of clusters, the h-index tend to locate clusters that can be considered as hotspots.

No abstract