No description available.

Interactively simulating visual appearance of natural objects under natural illumination is a fundamental problem in computer graphics. 3D computer games, geometry modeling, training and simulation, electronic commerce, visualization, lighting design, digital libraries, geographical information systems, economic and medical image processing are typical candidate applications. Recent advances in graphics hardware have enabled real-time rasterization of complex scenes under artificial lighting environment. Meanwhile, pre-computation based soft shadow algorithms are proven effective under low-frequency lighting environment. Under the most practical yet popular all-frequency natural lighting environment, however, real-time rendering of dynamic scenes still remains a challenging problem. In this dissertation, we propose a systematic approach to render dynamic glossy objects under the general all-frequency lighting environment. In our framework, lighting integration is reduced to two rather basic mathematical operations, efficiently computing multi-function product and product integral. The main contribution of our work is a novel mathematical representation and analysis of multi-function product and product integral in the wavelet domain. We show that, multi-function product integral in the primal is equivalent to summation of the product of basis coefficients and integral coefficients. In the dissertation, we give a novel Generalized Haar Integral Coefficient Theorem. We also present a set of efficient algorithms to compute multi-function product and product integral. In the dissertation, we demonstrate practical applications of these algorithms in the interactive rendering of dynamic glossy objects under distant time-variant all-frequency environment lighting with arbitrary view conditions. At each vertex, the shading integral is formulated as the product integral of multiple operand functions. By approximating operand functions in the wavelet domain, we demonstrate rendering dynamic glossy scenes interactively, which is orders of magnitude faster than previous work. As an important enhancement to the popular Pre-computation Based Radiance Transfer (PRT) approach, we present a novel Just-in-time Radiance Transfer (JRT) technique, and demonstrate its application in real-time realistic rendering of dynamic all-frequency shadows under general lighting environment. Our work is a significant step towards real-time rendering of arbitrary scenes under general lighting environment. It is also of great importance to general numerical analysis and signal processing.

<P> Video coding standards have been designed to support many applications such as broadcasting systems, movie industries and media storage. All video coding standards try to reduce data in video sequences as much as possible by exploiting spatial and temporal redundancies. Although those video coding standards are suitable for a wide variety of applications, some applications require low encoder complexity specifically for real time video encoding. Most of the computational complexity of a video encoder can be attributed to the motion estimation function. </p> <p> Motion estimation using multiple reference frames is widely used as the basis for recent video coding standards (eg. H.264/ AVC) to achieve increased coding efficiency. However, this increases the complexity of the encoding process. In this thesis, new techniques for efficient motion estimation are proposed. A combination of multiple reference frame selection and image residue-based mode selection is used to improve motion estimation time. By dynamic selection of an initial reference frame in advance, the number of reference frames to be considered is reduced. In addition, from examination of the residue between the current block and reconstructed blocks in preceding frames, variable block size mode decisions are made. Modified initial motion vector estimation and early stop condition detection are also adopted to speed up the motion estimation procedure. Experimental results compare the performance of the proposed algorithm with state of the art motion estimation algorithms and demonstrate significantly reduced motion estimation time while maintaining PSNR performance. </p> <p> In addition a new side information generation algorithm using dynamic motion estimation and post processing is proposed for improved distributed video coding. Multiple reference frames are employed for motion estimation at the side information frame generation block of the decoder. After motion estimation and compensation, post processing is applied to improve the hole and overlapped areas on the reconstructed side information frame. Both median filtering and residual-based block selecting algorithms are used to deal with hole and overlapped areas, respectively. The proposed side information method contributes to improving the quality of reconstructed frames at the distributed video decoder. The average encoding time of the distributed video coding is shown to be around 15% of H.264 inter coding and 40% of H.264 intra coding. The proposed side generation algorithm is implemented in a frequency domain distributed system and tested throughout various test sequences. The proposed side information based distributed video coding demonstrates improved performance compared with that of H.264 intra coding. </p> <p> Experimental implementations of the proposed algorithms are demonstrated using a set of video test sequences that are widely used and freely available. </p> / Thesis / Doctor of Philosophy (PhD)

This work describes a design of an agent model for robotic soccer based on the DEVS formalism. There is also presented a design of own DEVS simulator (based on classic DEVS simulator) for parallel realtime simulations. Functionality of the simulator and the model is shown on an example of a soccer client for RoboCup Soccer Server. Based on this client, there is also presented a design of a library for easier creation of soccer clients for RoboCup.

International Telemetering Conference Proceedings / October 20-23, 2003 / Riviera Hotel and Convention Center, Las Vegas, Nevada / This paper overviews the Research Environment for Vehicle-Embedded Analysis on Linux (REVEAL), which is an open standards framework for the creation and deployment of realtime embedded and network distributed data systems. REVEAL is an ongoing project at NASA Dryden to evaluate the feasibility and benefits of using Linux in a modern generic web-enabled data system for measurement and telemetry network research, by actually building such a system. Novel features are described, such as XML based self-configuring, self-verifying and self-documenting software, and automatic XML metadata generation. The REVEAL architecture is described, including the core server and scheduler, and the management of system and user job processing. Performance, timing, determinism, and security issues are discussed, as well as the advantages and limitations of Linux.

The purpose of this thesis is to automate the online detection of weaving defects by a computerized system based on image processing software. Obviously, fabric inspection has an importance to prevent risk of delivering inferior quality product. Until recently, the visual defect detection is still under taken offline and manually by humans with many drawbacks such as tiredness, boredom, and, inattentiveness. Fortunately, the continuous development in computer technology introduces the online automated fabric inspection as an effective alternative. Because the defect-free fabric has a periodic regular structure, the occurrence of a defect in the fabric breaks the regular structure. Therefore, the fabric defects can be detected by monitoring fabric structure. In our work, Fast Fourier Transform and Cross-correlation techniques, i.e. linear operations, are first implemented to examine the structure regularity features of the fabric image in the frequency domain. To improve the efficiency of the technique and overcome the problem of detection errors, further thresholding operation is implemented using a level selection filter. Through this filter, the technique is able to detect only the actual or real defects and highlight their exact dimensions. A software package such as Matlab or Scilab is used for this procedure. It is implemented firstly on a simulated plain fabric to determine the most important parameters during the process of defect detection and then to optimize each of them even considering noise. To verify the success of the technique, it is implemented on real plain fabric samples with different colours containing various defects. Finally, a vision-based fabric inspection prototype that could be accomplished on-loom to inspect the fabric under construction with 100% coverage is proposed.

This thesis is about digital control theory and presents an account of methods for enabling and analysing intentional non-uniform sampling in discrete compensators. Most conventional control algorithms cause numerical problems where data is collected at sampling rates that are substantially higher than the dynamics of the equivalent continuous-time operation that is being implemented. This is of relevant interest in applications of digital control, in which high sample rates are routinely dictated by the system stability requirements rather than the signal processing needs. Considerable recent progress in reducing the sample frequency requirements has been made through the use of non-uniform sampling schemes, so called alias-free signal processing. The approach prompts the simplification of complex systems and consequently enhances the numerical conditioning of the implementation algorithms that otherwise, would require very high uniform sample rates. Such means of signal representation and analysis presents a variety of options and thus is being researched and practiced in a number of areas in communications. However, the control communities have not yet investigated the use of intentional non-uniform sampling, and hence the ethos of this research project is to investigate the effectiveness of such sampling regimes, in the context of exploiting the benefits. Digital control systems exhibit bandwidth limitations enforced by their closed-loop frequency requirements, the calculation delays in the control algorithm and the interfacing conversion times. These limitations pave the way for additional phase lags within the control loop that demand very high sample rates. Since non-uniform sampling is propitious in reducing the sample frequency requirements of digital processing, it proffers the prospects of being utilised in achieving a higher control bandwidth without opting for very high uniform sample rates. The concept, to the author s knowledge, has not formally been studied and very few definite answers exist in control literature regarding the associated analysis techniques. The key contributions adduced in this thesis include the development and analysis of the control algorithm designed to accommodate intentional non-uniform sample frequencies. In addition, the implementation aspects are presented on an 8-bit microcontroller and an FPGA board. This work begins by establishing a brief historical perspective on the use of non-uniform sampling and its role for digital processing. The study is then applied to the problem of digital control design, and applications are further discoursed. This is followed by consideration of its implementation aspects on standard hardware.

An emerging trend towards non-laboratory based biological and microbiological marker analysis is occurring in multiple sectors of science and industry. In the medical sector, these trends have demonstrated that conducting sample analyses away from centralised laboratories not only makes analyses quicker and more convenient (e.g. a home pregnancy test), but can offer services that are otherwise impractical (e.g. mobile laboratories to diagnose disease in the developing world). In the environmental sector, similar benefits, plus the ability to develop and test hypotheses, protocols and sampling strategies within a field campaign, are possible with in-field analyses. Icy environments in particular would benefit from in situ or in-field life detection as they are typically remote, and hence impart high logistical costs for repeated field campaigns and associated sample return with the implication that the efficiency of scientific return is poor. Unfortunately, most equipment and protocols developed for microbiological analyses in other sectors of science and industry are unsuitable for direct application to in-field use in icy environments because of poor compatibility with icy environment sample matrices and frequently inappropriate microbiological targets. Hence within this work, two hypotheses were tested: that (i) microbiological detection infield in icy environments is possible and through this (ii) unique and more efficient scientific studies can be conducted. Cont/d.

<div>Unmanned Aerial Vehicle (UAV) technology is being increasingly used in a wide variety of applications ranging from remote sensing, to delivery, to security. As the number of UAVs increases, there is a growing need for UAV to UAV detection and tracking systems for both collision avoidance and coordination. Among possible solutions, autonamous “see-and-avoid” systems based on low-cost high-resolution video cameras offer the important advantages of light-weight and low power sensors. However, in order to be effective, camera based “see-and-avoid” systems will require sensitive, robust, and computationally efficient algorithms for UAV to UAV detect and tracking (U2U-D&T) from a moving camera.</div><div><br></div><div>In this thesis, we propose a general architecture for a highly accurate and computationally efficient U2U-D&T algorithms for detecting UAVs from a camera mounted on a moving UAV platform. The thesis contains three studies of U2U-D&T algorithms.<br></div><div><br></div><div>In the first study, we present a new approach to detect and track other UAVs from a single camera in our own UAV. Given the sequence of video frames, we estimate the background motion via perspective transformation model and then identify distinctive points in the background subtracted image to detect moving objects. We find spatio-temporal characteristics of each moving object through optical flow matching and then classify our targets which have very different motion compared with background. We also perform tracking based on Kalman filter to enforce the temporal consistency on our detection. The algorithm is tested on real videos from UAVs and results show that it is effective to detect and track small UAVs with limited computing resources.<br></div><div><br></div><div>In the second study, we present a new approach to detect and track UAVs from a single camera mounted on a different UAV. Initially, we estimate background motions via a perspective transformation model and then identify moving object candidates in the background subtracted image through deep learning classifier trained on manually labeled datasets. For each moving object candidates, we find spatio-temporal traits through optical flow matching and then prune them based on their motion patterns compared with the background. Kalman filter is applied on pruned moving objects to improve temporal consistency among the candidate detections. The algorithm was validated on video datasets taken from a UAV. Results demonstrate that our algorithm can effectively detect and track small UAVs with limited computing resources. </div><div><br></div><div>The system in the third study is based on a computationally efficient pipeline consisting of moving object detection from a motion stabilized image, classification with a hybrid neural network, followed by Kalmann tracking. The algorithm is validated using video data collected from multiple fixed-wing UAVs that is manually ground-truthed and publicly available. Results indicate that the proposed algorithm can be implemented on practical hardware and robustly achieves highly accurate detection and tracking of even distant and faint UAVs.<br></div>

I dagsläget finns en del problem vid beslutsprocesser som trafikledningen inom tågtrafiken dagligen får handskas med. Dessa beslutsprocesser kan effektiviseras genom ett förbättrat stöd och underlag och tanken med projektet var att skapa detta stöd. Detta projekt har resulterat i ett enkelt system som samlar in och presenterar data över tåg i ett grafiskt gränssnitt i form av en karta. Systemet är utvecklat för att enkelt kunna få fler funktioner applicerade i framtiden och var från början tänkt att bestå av högre utbud funktioner än vad det har i dagsläget. Projektet har tagit form med hjälp av tågbolaget SJ med information om de hinder de stöter på, och riktlinjer satta från vår handledare under projektet. Vi kommer i denna rapporten att beskriva mer detaljerat hur projektet tagit form, vilka problem vi fått handskas med och hur systemet är utvecklat. / Currently there exists several problems with the decision-making processes that the trafficmanagement within train traffic daily must put up with. The decision processes can be mademore efficient by having a good support and structure, and the goal of this project was to createthis support. This project has resulted in a simple system that collects and presents train data on a graphicalinterface in the form of a map. The system is designed to be expandable with new features andfunctionalities with relative ease and was initially supposed to include some of these extrafeatures. The project has taken shape with help from the train company SJ with informationabout problems they've encountered, and guidelines set by our supervisor during the project. In this report we will describe how the project has taken shape, what problems we have dealtwith and how the system is developed.