Browsing by Author "Stillwell, Mark Lee"

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    Automatic Rain Drop Detection for Improved Sensing in Automotive Computer Vision Applications
    (Cranfield University, 2014-04-04) Webster, Dereck D.; Breckon, Toby P.; Stillwell, Mark Lee
    The presence of raindrop induced distortion can have a significant negative impact on computer vision applications. Here we address the problem of visual raindrop distortion in standard colour video imagery for use in non-static, automotive computer vision applications where the scene can be observed to be changing over subsequent consecutive frames. We utilise current state of the art research conducted into the investigation of salience mapping as means of initial detection of potential raindrop candidates. We further expand on this prior state of the art work to construct a combined feature rich descriptor of shape information (Hu moments), isolation of raindrops pixel information from context, and texture (saliency derived) within an improved visual bag of words verification framework. Support Vector Machine and Random Forest classification were utilised for verification of potential candidates, and the effects of increasing discrete cluster centre counts on detection rates were studied. This novel approach of utilising extended shape information, isolation of context, and texture, along with increasing cluster counts, achieves a notable 13% increase in precision (92%) and 10% increase in recall (86%) against prior state of the art. False positive rates were also observed to decrease with a minimal false positive rate of 14% observed.
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    Internet Operation of Aero Gas Turbines
    (Cranfield University, 2014-10) Diakostefanis, Michail; Nikolaidis, Theoklis; Stillwell, Mark Lee; Barnes, S; Pilidis, Pericles
    Internet applications have been extended to various aspects of everyday life and offer services of high reliability and security. In the Academia, Internet applications offer useful tools for the remote creation of simulation models and real-time conduction of control experiments. The aim of this study was the design of a reliable, safe and secure software system for real time operation of a remote aero gas turbine, with the use of standard Internet technology at very low cost. The gas turbine used in this application was an AMT Netherlands Olympus micro gas turbine. The project presented three prototypes: operation from an adjacent computer station, operation within the Local Area Netwok (LAN) of Cranfield University and finally, remotely through the Internet. The gas turbine is a safety critical component, thus the project was driven by risk assessment at all the stages of the software process, which adhered to the Spiral Model. Elements of safety critical systems design were applied, with risk assessment present in every round of the software process. For the implementation, various software tools were used, with the majority to be open source API’s. LabVIEW with compatible hardware from National Instruments was used to interface the gas turbine with an adjacent computer work station. The main interaction has been established between the computer and the ECU of the engine, with additional instrumentation installed, wherever required. The Internet user interface web page implements AJAX technology in order to facilitate asynchronous update of the individual fields that present the indications of the operating gas turbine. The parameters of the gas turbine were acquired with high accuracy, with most attention given to the most critical indications, exhaust gas temperature (EGT) and rotational speed (RPM). These are provided to a designed real-time monitoring application, which automatically triggers actions when necessary. The acceptance validation was accomplished with a formal validation method – Model Checking. The final web application was inspired by the RESTful architecture and allows the user to operate the remote gas turbine through a standard browser, without requiring any additional downloading or local data processing. The web application was designed with provisions for generic applications. It can be configured to function with multiple different gas turbines and also integrated with external performance simulation or diagnostics Internet platforms. Also, an analytical proposal is presented, to integrate this application with the TURBOMATCH WebEngine web application, for gas turbine performance simulation, developed by Cranfield University.
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    Resource allocation algorithms for virtualized service hosting platforms
    (Elsevier Science B.V., Amsterdam, 2010-09-01T00:00:00Z) Stillwell, Mark Lee; Schanzenbach, David; Vivien, Frederic; Casanova, Henri
    Commodity clusters are used routinely for deploying service hosting platforms. Due to hardware and operation costs, clusters need to be shared among multiple services. Crucial for enabling such shared hosting platforms is virtual machine (VM) technology, which allows consolidation of hardware resources. A key challenge, however, is to make appropriate decisions when allocating hardware resources to service instances. In this work we propose a formulation of the resource allocation problem in shared hosting platforms for static workloads with servers that provide multiple types of resources. Our formulation supports a mix of best-effort and QoS scenarios, and, via a precisely defined objective function, promotes performance, fairness, and cluster utilization. Further, this formulation makes it possible to compute a bound on the optimal resource allocation. We propose several classes of resource allocation algorithms, which we evaluate in simulation. We are able to identify an algorithm that achieves average performance close to the optimal across many experimental scenarios. Furthermore, this algorithm runs in only a few seconds for large platforms and thus is usable in practice.
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    Virtual machine resource allocation for service hosting on heterogeneous distributed platforms
    (IEEE, 2012-08-16) Stillwell, Mark Lee; Vivien, Frédéric; Casanova, Henri
    We propose algorithms for allocating multiple resources to competing services running in virtual machines on heterogeneous distributed platforms. We develop a theoretical problem formulation and compare these algorithms via simulation experiments based in part on workload data supplied by Google. Our main finding is that vector packing approaches proposed in the homogeneous case can be extended to provide high-quality solutions in the heterogeneous case, and combined to provide a single efficient algorithm. We also consider the case when there may be bounded errors in estimates of performance-related resource needs. We provide a heuristic for compensating for such errors that performs well in simulation, as well as a proof of the worst-case competitive ratio for the single-resource, single-node case when there is no bound on the error.