The Design Of A Benchmark For Geo-stream Management Systems

The Design Of A Benchmark For Geo-stream Management Systems

Date: December 2011
Creator: Shen, Chao
Description: The recent growth in sensor technology allows easier information gathering in real-time as sensors have grown smaller, more accurate, and less expensive. The resulting data is often in a geo-stream format continuously changing input with a spatial extent. Researchers developing geo-streaming management systems (GSMS) require a benchmark system for evaluation, which is currently lacking. This thesis presents GSMark, a benchmark for evaluating GSMSs. GSMark provides a data generator that creates a combination of synthetic and real geo-streaming data, a workload simulator to present the data to the GSMS as a data stream, and a set of benchmark queries that evaluate typical GSMS functionality and query performance. In particular, GSMark generates both moving points and evolving spatial regions, two fundamental data types for a broad range of geo-stream applications, and the geo-streaming queries on this data.
Contributing Partner: UNT Libraries
Kalman Filtering Approach to Optimize OFDM Data Rate

Kalman Filtering Approach to Optimize OFDM Data Rate

Date: August 2011
Creator: Wunnava, Sashi Prabha
Description: This study is based on applying a non-linear mapping method, here the unscented Kalman filter; to estimate and optimize data rate resulting from the arrival rate having a Poisson distribution in an orthogonal frequency division multiplexing (OFDM) transmission system. OFDM is an emerging multi-carrier modulation scheme. With the growing need for quality of service in wireless communications, it is highly necessary to optimize resources in such a way that the overall performance of the system models should rise while keeping in mind the objective to achieve high data rate and efficient spectral methods in the near future. In this study, the results from the OFDM-TDMA transmission system have been used to apply cross-layer optimization between layers so as to treat different resources between layers simultaneously. The main controller manages the transmission of data between layers using the multicarrier modulation techniques. The unscented Kalman filter is used here to perform nonlinear mapping by estimating and optimizing the data rate, which result from the arrival rate having a Poisson distribution.
Contributing Partner: UNT Libraries
1/F Baseband Noise Suppression in Ofdm Using Kalman Filter

1/F Baseband Noise Suppression in Ofdm Using Kalman Filter

Date: May 2012
Creator: Rodda, Lasya
Description: As the technology is advances the reduced size of hardware gives rise to an additive 1/f baseband noise. This additive 1/f noise is a system noise generated due to miniaturization of hardware and affects the lower frequencies. Though 1/f noise does not show much effect in wide band channels because of its nature to affect only certain frequencies, 1/f noise becomes a prominent in OFDM communication systems where narrow band channels are used. in this thesis, I study the effects of 1/f noise on the OFDM systems and implement algorithms for estimation and suppression of the noise using Kalman filter. Suppression of the noise is achieved by subtracting the estimated noise from the received noise. I show that the performance of the system is considerably improved by applying the 1/f noise suppression.
Contributing Partner: UNT Libraries
Study of the effects of background and motion camera on the efficacy of Kalman and particle filter algorithms.

Study of the effects of background and motion camera on the efficacy of Kalman and particle filter algorithms.

Date: August 2009
Creator: Morita, Yasuhiro
Description: This study compares independent use of two known algorithms (Kalmar filter with background subtraction and Particle Filter) that are commonly deployed in object tracking applications. Object tracking in general is very challenging; it presents numerous problems that need to be addressed by the application in order to facilitate its successful deployment. Such problems range from abrupt object motion, during tracking, to a change in appearance of the scene and the object, as well as object to scene occlusions, and camera motion among others. It is important to take into consideration some issues, such as, accounting for noise associated with the image in question, ability to predict to an acceptable statistical accuracy, the position of the object at a particular time given its current position. This study tackles some of the issues raised above prior to addressing how the use of either of the aforementioned algorithm, minimize or in some cases eliminate the negative effects
Contributing Partner: UNT Libraries
Occlusion Tolerant Object Recognition Methods for Video Surveillance and Tracking of Moving Civilian Vehicles

Occlusion Tolerant Object Recognition Methods for Video Surveillance and Tracking of Moving Civilian Vehicles

Date: December 2007
Creator: Pati, Nishikanta
Description: Recently, there is a great interest in moving object tracking in the fields of security and surveillance. Object recognition under partial occlusion is the core of any object tracking system. This thesis presents an automatic and real-time color object-recognition system which is not only robust but also occlusion tolerant. The intended use of the system is to recognize and track external vehicles entered inside a secured area like a school campus or any army base. Statistical morphological skeleton is used to represent the visible shape of the vehicle. Simple curve matching and different feature based matching techniques are used to recognize the segmented vehicle. Features of the vehicle are extracted upon entering the secured area. The vehicle is recognized from either a digital video frame or a static digital image when needed. The recognition engine will help the design of a high performance tracking system meant for remote video surveillance.
Contributing Partner: UNT Libraries