Browsing by Author "Lee, Jaehwan (John)"

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    Injector Waveform Monitoring of a Diesel Engine in Real-Time on a Hardware in the Loop Bench
    (2011-12) Farooqi, Quazi Mohammed Rushaed; Anwar, Sohel; Wasfy, Tamer; Lee, Jaehwan (John)
    This thesis presents the development, experimentation and validation of a reliable and robust system to monitor the injector pulse generated by an Engine Control Module (ECM) and send the corresponding fueling quantity to the real-time computer in a closed loop Hardware In the Loop (HIL) bench. The system can be easily calibrated for different engine platforms as well. The fueling quantity that is being injected by the injectors is a crucial variable to run closed loop HIL simulation to carry out the performance testing of engine, aftertreatment and other components of the vehicle. This research utilized Field Programmable Gate Arrays (FPGA) and Direct Memory Access (DMA) transfer capability offered by National Instruments (NI) Compact Reconfigurable Input-Output (cRIO) to achieve high speed data acquisition and delivery. The research was conducted in three stages. The first stage was to develop the HIL bench for the research. The second stage was to determine the performance of the system with different threshold methods and different sampling speeds necessary to satisfy the required accuracy of the fueling quantity being monitored. The third stage was to study the error and its variability involved in the injected fueling quantity from pulse to pulse, from injector to injector, between real injector stators and cheaper inductor load cells emulating the injectors, over different operating conditions with full factorial design of experimentation and mixed model Analysis Of Variance (ANOVA). Different thresholds were experimented to find out the best thresholds, the Start of Injection (SOI) threshold and the End of Injection (EOI) threshold that captured the injector “ontime” with best reliability and accuracy. Experimentation has been carried out at various data acquisition rates to find out the optimum speed of data sampling rate, trading off the accuracy of fueling quantity. The experimentation found out the expected error with a system with cheaper solution as well, so that, if a test application is not sensitive to error in fueling quantity, a cheaper solution with lower sampling rate and inductors as load cells can be used. The statistical analysis was carried out at highest available sampling rate on both injectors and inductors with the best threshold method found in previous studies. The result clearly shows the factors that affect the error and the variability in the standard deviations in error; it also shows the relation with the fixed and random factors. The real-time application developed for the HIL bench is capable of monitoring the injector waveform, using any fueling ontime table corresponding to the platform being tested, and delivering the fueling quantity in real-time. The test bench made for this research is also capable of studying injectors of different types with the automated test sequence, without occupying the resource of fully capable closed loop test benches for testing the ECM unctionality.
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    Real-time adaptive-optics optical coherence tomography (AOOCT) image reconstruction on a GPU
    (2014) Shafer, Brandon Andrew; Eberhart, Russell C.; Salama, Paul; Christopher, Lauren; Lee, Jaehwan (John); King, Brian
    Adaptive-optics optical coherence tomography (AOOCT) is a technology that has been rapidly advancing in recent years and offers amazing capabilities in scanning the human eye in vivo. In order to bring the ultra-high resolution capabilities to clinical use, however, newer technology needs to be used in the image reconstruction process. General purpose computation on graphics processing units is one such way that this computationally intensive reconstruction can be performed in a desktop computer in real-time. This work shows the process of AOOCT image reconstruction, the basics of how to use NVIDIA's CUDA to write parallel code, and a new AOOCT image reconstruction technology implemented using NVIDIA's CUDA. The results of this work demonstrate that image reconstruction can be done in real-time with high accuracy using a GPU.
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    A Security Analysis of Smartphones
    (2011-08) Verma, Ishita; King, Brian; Rizkalla, Maher E.; Lee, Jaehwan (John)
    This work analyzes and discusses the current security environment of today's (and future) smartphones, and proposes a security model which will reduce smartphone vulnerabilities, preserving privacy, integrity and availability of smartphone native applications to authorized parties. For this purpose, we begin with an overlook of current smartphone security standards, and explore the threats, vulnerabilities and attacks on them, that have been uncovered so far with existing popular smartphones. We also look ahead at the future uses of the smartphones, and the security threats that these newer applications would introduce. We use this knowledge to construct a mathematical model, which gives way to policies that should be followed to secure the smartphone under the model. We finally discuss existing and proposed security mechanisms that can be incorporated in the smartphone architecture to meet the set policies, and thus the set security standards.
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    TOWARDS MANY-CORE PROCESSOR SIMULATION ON CLOUD COMPUTING PLATFORMS
    (2011-08-23) Schmidt, James Michael; Lee, Jaehwan (John); King, Brian; Tuceryan, Mihran
    Growth of interest and need for many-core systems have steadily increased over the recent years. Industry trends lead many-core systems to become increasingly larger and more complex. Because of these realities it is important to researchers, academia, and industry that the design of these many-core systems be straightforward and comprehensive. There is a need for a many-core simulator that can be simple to use and learn from for students, dynamic and capable of emulating large systems for researchers, and flexible with fast turnover for industry designers. At the same time, as many-core systems have been becoming popular and complex, and hence their design, the long standing field of Cloud Computing has become more prevalent and feasible to use. Such cloud computing platforms as Windows Azure allow for the easy access and use of resources that in the past were simply not available to ordinary users. Large tasks can be performed in SaaS Cloud Computing models and be accessible from a small, lightweight device using nothing more than a web browser. As a solution to the needs for designing future many-core systems, we present a Many-Core Simulator on Azure Cloud Computing Platform called M3C Simulator. This is targeted at teaching, research, and industry and as such needs to be easy to use, flexible, and powerful. The Could Computing service model meets all these needs. This thesis discusses overall design of the M3C Simulator and how it leverages Cloud Computing resources, the simple-to-use and understand Interface layout, and the software design including program flow and dynamic compilation.