/library/oar/handle/123456789/41696 2025-12-26T05:21:25Z 2025-12-26T05:21:25Z /library/oar/handle/123456789/123326 2024-06-10T08:14:11Z 1999-01-01T00:00:00Z

Title: Compiling Verilog into hardware : Appendix D : the source code Abstract: Library on has Appendix D - The Source Code. This booklet contains all the source code that makes up the project. Clearly, source files that are machine-generated (i.e. the output files of Flex and Bison) are not included. The files are present in this order: • myScan.l: This is the Flex file used to generate the lexical analyser. • myParse.y: This is the Bison file used to generate the parser. • TObject.h: This is the header file for TObject.cpp. It contains the declarations of all the classes used by the project. • TObject.cpp: This file implements all the classes declared in TObject.h. Description: B.SC.(HONS)IT

1999-01-01T00:00:00Z /library/oar/handle/123456789/101787 2024-05-06T13:17:51Z 2009-01-01T00:00:00Z

Title: Resilient wireless transmission of H.264/AVC through error localisation and control mechanisms Abstract: Current trends in wireless communications provide for fast and location independent access to multimedia services. Due to its high compression efficiency, H.264/AVC is expected to become the dominant underlying technology in the delivery of future wireless video applications. However, H.264/AVC is susceptible to transmission errors common in wireless environments where even a single corrupted bit may cause visual artefacts that propagate in the spatio-temporal domain. The standard incorporates several error resilient mechanisms to minimize the effect of transmission errors on the perceptual quality of the reconstructed video sequence. However, these mechanisms assume a packet-loss scenario where all macroblocks (MBs) contained within a corrupted slice, including numerous uncorrupted MBs, are discarded and concealed. This implies that the error resilient mechanisms operate at a lower bound and thus further performance gains can be achieved by exploiting the residual redundancies available at the decoder side. During this dissertation, decoder-based techniques aimed to enhance the quality of damaged video sequences were investigated. The first method considered in this work exploits the residual source redundancy left by the standard encoder after compression to derive the most likelihood H.264/AVC feasible bitstream. This method manages to completely recover an average of 30% of the corrupted slices at no additional cost in bandwidth. The second approach considered in this dissertation exploits the redundancy available at pixel level to detect and localise visually distorted regions within the damaged slice that would otherwise be discarded. The experimental results show that machine learning algorithms can be taught to automatically detect the regions affected by transmission errors. This method limits the area to be concealed since only visually impaired regions are concealed. Both these methods provide a significant gain in video quality when compared to the standard when adopted individually. The two methods were combined together in a single solution to form the Hybrid Error Control Artefact Detection (HECAD) method which further boosts the performance of the individual components. This gain in performance is achieved at no additional cost in bandwidth and a moderate increase in complexity of the decoder. Furthermore, this method can be applied in conjunction with other error resilient strategies adopted by the standard decoder and still register considerable performance gains. Description: PH.D.

2009-01-01T00:00:00Z /library/oar/handle/123456789/95972 2022-05-19T08:10:31Z 2004-01-01T00:00:00Z

Title: SharpHDL : a hardware description language embedded in C# Abstract: Digital systems can be very complex and may consist of millions of components. For many years logic schematics were used to design such systems but given the size of today's circuits, this technique is largely useless because it does not show the functionality of the design. Nowadays, engineers use Hardware Description Languages (HDL), which describe both the behavior and the structure of a circuit. Description: B.SC.(HONS)IT

2004-01-01T00:00:00Z /library/oar/handle/123456789/95970 2022-05-19T08:06:32Z 2009-01-01T00:00:00Z

Title: Automatic annotation of tennis videos (AAOTV) Abstract: Vision is a crucial must have for both humans and computers. As a general idea, Vision deals with the process of object recognition, objects localization in a specific space, tracking of objects of interest and also the recognition of certain actions which these objects exhibit. Computer Vision varies in some aspects when compared to human vision. This is due to the fact that computer vision is active while human vision is passive. Human vision relies on external sources to be efficient such as external energy sources which include sunlight, light bulbs and also fires which provide light that reflects the objects to our eyes. On the other hand computer vision is active since they can carry their own energy sources such as Radars. The basic idea of this thesis is to process a tennis video, taken via a static camera and to perform detection and tracking of both the tennis players and the tennis ball and finally produce annotations of the tennis game. In general words our work should successfully act as a Commentatory of a normal tennis match. The main steps involved in this process include a tennis court line detection to determine the coordinates of the lines of the court. Another module is an adaptive background subtraction technique, which is used to separate the background from the foreground and therefore detect the objects of interest. An important factor that this subtraction technique should have, is to successfully adapt to the changes in the environment, this is because the tennis match is played outside and therefore changes in the lightening are an a priori assumption. After that objects are detected the next thing is to successfully track their motion within the scene. In the case of my thesis the most important object to track is the tennis ball. By tracking the tennis ball important annotations would be known such as when the player stokes the tennis ball, when the ball bounced on the court, if the ball bounced outside the court and others. The result produced from our work should be a video with the ball tracked together with a set of annotations at certain intervals of the video. Our program should finally collate the tracked video and annotations together for the viewer to be able to see the tracked tennis video and the annotations in one view. Description: B.Sc. IT (Hons)(Melit.)

2009-01-01T00:00:00Z