Browsing by Author "Department of Electrical and Computer Engineering, Purdue School of Engineering and Technology"

Now showing 1 - 4 of 4
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    A Dynamic Threshold Decryption Scheme Using Bilinear Pairings
    (2015-11) King, Brian; Department of Electrical and Computer Engineering, Purdue School of Engineering and Technology
    A dynamic threshold sharing scheme is one that allows the set of participants to expand and contract. In this work we discuss dynamic threshold decryption schemes using bilinear pairing. We discuss and analyze existing schemes, demonstrate an attack and construct a signi cantly more efficient secure scheme.
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    Hidden Wind Farms Potential for Residential Households Having Roofmounted Wind Arrester
    (IEEE, 2015) Amini, Amin; Kamoona, Mustafa; Department of Electrical and Computer Engineering, Purdue School of Engineering and Technology
    Small-scale energy-generating systems are being increasingly integrated into built environment, and the use of renewable energies is now spreading to old towns in developing countries. Despite the promise of free energy, the high-tech appearance of the harnessing tools of renewables has provoked criticism because of the incompatibility with the cultural/environmental characteristics of older towns in Iran. This paper presents a new concept of novel hidden wind farms in the residential households of Iranian desert-edge towns with roof-mounted wind-arresters. The results of this study show that a hidden wind farm integrated into old towns with the potential of tourism can eliminate the concern over the visibility and bird collisions as well as the use of land. In the present study, the old city of Ardakan, Yazd, with an arid climate located at the edge of a desert in the center of Iran, is selected as target case study. Calculations show that the application of one small-scale wind turbine per wind-arrester across the town can generate approximately 2.90 GWh a year. Moreover, the proposed concept could also be applied in other countries such as Afghanistan, Egypt, Pakistan, Iraq, UAE and some African countries.
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    Pedestrian Detection based on Clustered Poselet Models and Hierarchical And-Or Grammar
    (IEEE, 2015-04) Li, Bo; Chen, Yaobin; Wang, Fei-Yue; Department of Electrical and Computer Engineering, Purdue School of Engineering and Technology
    In this paper, a novel part-based pedestrian detection algorithm is proposed for complex traffic surveillance environments. To capture posture and articulation variations of pedestrians, we define a hierarchical grammar model with the and-or graphical structure to represent the decomposition of pedestrians. Thus, pedestrian detection is converted to a parsing problem. Next, we propose clustered poselet models, which use the affinity propagation clustering algorithm to automatically select representative pedestrian part patterns in keypoint space. Trained clustered poselets are utilized as the terminal part models in the grammar model. Finally, after all clustered poselet activations in the input image are detected, one bottom-up inference is performed to effectively search maximum a posteriori (MAP) solutions in the grammar model. Thus, consistent poselet activations are combined into pedestrian hypotheses, and their bounding boxes are predicted. Both appearance scores and geometry constraints among pedestrian parts are considered in inference. A series of experiments is conducted on images, both from the public TUD-Pedestrian data set and collected in real traffic crossing scenarios. The experimental results demonstrate that our algorithm outperforms other successful approaches with high reliability and robustness in complex environments.
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    Using computational swarm intelligence for real-time asset allocation
    (IEEE, 2015-05) Reynolds, Joshua; Christopher, Lauren; Eberhart, Russ; Shaffer, Patrick; Department of Electrical and Computer Engineering, Purdue School of Engineering and Technology
    Particle Swarm Optimization (PSO) is especially useful for rapid optimization of problems involving multiple objectives and constraints in dynamic environments. It regularly and substantially outperforms other algorithms in benchmark tests. This paper describes research leading to the application of PSO to the autonomous asset management problem in electronic warfare. The PSO speed provides fast optimization of frequency allocations for receivers and jammers in highly complex and dynamic environments. The key contribution is the simultaneous optimization of the frequency allocations, signal priority, signal strength, and the spatial locations of the assets. The fitness function takes into account the assets' locations in 2 and 3 dimensions maximizing their spatial distribution while maintaining allocations based on signal priority and power. The fast speed of the optimization enables rapid responses to changing conditions in these complex signal environments, which can have real-time battlefield impact. Initial results optimizing receiver frequencies and locations in 2 dimensions have been successful. Current run-times are between 300 (3 receivers, 30 transmitters) and 1000 (7 receivers, 30 transmitters) milliseconds on a single-threaded x86 based PC. Statistical and qualitative tests indicate the swarm has viable solutions, and finds the global optimum 99% of the time on a test case. The results of the research on the PSO parameters and fitness function for this problem is demonstrated.