The purpose of this project is to investigate the differences between psychoacoustic principles and genetic algorithms (GA0). These will be discussed separately. The review will also compare the compression ratio and the quality of the decompressed files decoded by these two methods.

Indiana University-Purdue University Indianapolis (IUPUI) / A massive amount of videos are uploaded on video websites, smooth video browsing, editing, retrieval, and summarization are demanded. Most of the videos employ several types of camera operations for expanding field of view, emphasizing events, and expressing cinematic effect. To digest heterogeneous videos in video websites and databases, video clips are profiled to 2D image scroll containing both spatial and temporal information for video preview. The video profile is visually continuous, compact, scalable, and indexing to each frame. This work analyzes the camera kinematics including zoom, translation, and rotation, and categorize camera actions as their combinations. An automatic video summarization framework is proposed and developed. After conventional video clip segmentation and video segmentation for smooth camera operations, the global flow field under all camera actions has been investigated for profiling various types of video. A new algorithm has been designed to extract the major flow direction and convergence factor using condensed images. Then this work proposes a uniform scheme to segment video clips and sections, sample video volume across the major flow, compute flow convergence factor, in order to obtain an intrinsic scene space less influenced by the camera ego-motion. The motion blur technique has also been used to render dynamic targets in the profile. The resulting profile of video can be displayed in a video track to guide the access to video frames, help video editing, and facilitate the applications such as surveillance, visual archiving of environment, video retrieval, and online video preview.

Indiana University-Purdue University Indianapolis (IUPUI) / In today's internet world with such a high traffic, it becomes inevitable to have multiple servers representing a single logical server to share enormous load. A very common network configuration consists of multiple servers behind a load balancer. The load balancer determines which server would service a clients request or incoming load from the client. Such a hardware is expensive, runs a fixed policy or algorithm and is a single point of failure. In this paper, we will implement and analyze an alternative load balancing architecture using OpenFlow. This architecture acquires flexibility in policy, costs less and has the potential to be more robust. This paper also discusses potential usage of OpenFlow based load balancing for media gateway selection in SIP-PSTN networks to improve VoIP performance.

Indiana University-Purdue University Indianapolis (IUPUI) / With the advances in embedded technology and the omnipresence of smartphones, tracking systems do not need to be confined to a specific tracking environment. By introducing mobile devices into a tracking system, we can leverage their mobility and the availability of multiple sensors such as camera, Wi-Fi, Bluetooth and Inertial sensors. This thesis proposes to improve the existing tracking systems, enhanced Distributed Tracking System (e-DTS 2.0) [19] and enhanced Distributed Object Tracking System (eDOTS)[26], in the form of e-DTS 3.0 and provides an empirical analysis of these improvements. The enhancements proposed are to introduce Android-based mobile devices into the tracking system, to use multiple sensors on the mobile devices such as the camera, the Wi-Fi and Bluetooth sensors and inertial sensors and to utilize possible resources that may be available in the environment to make the tracking opportunistic. This thesis empirically validates the proposed enhancements through the experiments carried out on a prototype of e-DTS 3.0.

Indiana University-Purdue University Indianapolis (IUPUI) / A Brain Computer Interface (BCI) is a hardware and software system that establishes direct communication between human brain and the environment. In a BCI system, brain messages pass through wires and external computers instead of the normal pathway of nerves and muscles. General work ow in all BCIs is to measure brain activities, process and then convert them into an output readable for a computer. The measurement of electrical activities in different parts of the brain is called electroencephalography (EEG). There are lots of sensor technologies with different number of electrodes to record brain activities along the scalp. Each of these electrodes captures a weighted sum of activities of all neurons in the area around that electrode. In order to establish a BCI system, it is needed to set a bunch of electrodes on scalp, and a tool to send the signals to a computer for training a system that can find the important information, extract them from the raw signal, and use them to recognize the user's intention. After all, a control signal should be generated based on the application. This thesis describes the step by step training and testing a BCI system that can be used for a person who has lost speaking skills through an accident or surgery, but still has healthy brain tissues. The goal is to establish an algorithm, which recognizes different vowels from EEG signals. It considers a bandpass filter to remove signals' noise and artifacts, periodogram for feature extraction, and Support Vector Machine (SVM) for classification.