Computer Engineering Commons^™

Hardware-Software Co-Design, Acceleration And Prototyping Of Control Algorithms On Reconfigurable Platforms, Desta Kumsa Edosa

UNLV Theses, Dissertations, Professional Papers, and Capstones

Differential equations play a significant role in many disciplines of science and engineering. Solving and implementing Ordinary Differential Equations (ODEs) and partial Differential Equations (PDEs) effectively are very essential as most complex dynamic systems are modeled based on these equations. High Performance Computing (HPC) methodologies are required to compute and implement complex and data intensive applications modeled by differential equations at higher speed. There are, however, some challenges and limitations in implementing dynamic system, modeled by non-linear ordinary differential equations, on digital hardware. Modeling an integrator involves data approximation which results in accuracy error if data values are not considered …

A Parameterized Stereo Vision Core For Fpgas, Mark Chang, Stephen Longfield

Mark L. Chang

We present a parameterized stereo vision core suitable for a wide range of FPGA targets and stereo vision applications. By enabling easy tuning of algorithm parameters, our system allows for rapid exploration of the design space and simpler implementation of high-performance stereo vision systems. This implementation utilizes the census transform algorithm to calculate depth information from a pair of images delivered from a simulated stereo camera pair. This work advances our previous work through implementation improvements, a stereo camera pair simulation framework, and a scalable stereo vision core.

Precis: A Usercentric Word-Length Optimization Tool, Mark Chang, Scott Hauck

Mark L. Chang

Translating an algorithm designed for a general-purpose processor into an algorithm optimized for custom logic requires extensive knowledge of the algorithm and the target hardware. Precis lets designers analyze the precision requirements of algorithms specified in Matlab. The design time tool combines simulation, user input, and program analysis to help designers focus their manual precision optimization efforts.

Low-Cost Stereo Vision On An Fpga, Chris A. Murphy, Daniel Lindquist, Ann Marie Rynning, Thomas Cecil, Sarah Leavitt, Mark L. Chang

Mark L. Chang

We present a low-cost stereo vision implementation suitable for use in autonomous vehicle applications and designed with agricultural applications in mind. This implementation utilizes the Census transform algorithm to calculate depth maps from a stereo pair of automotive-grade CMOS cameras. The final prototype utilizes commodity hardware, including a Xilinx Spartan-3 FPGA, to process 320times240 pixel images at greater than 150 frames per second and deliver them via a USB 2.0 interface.

Multi-Tier Exploration Concept Demonstration Mission, Jeremy Straub

Jeremy Straub

A multi-tier, multi-craft mission architecture has been proposed but, despite its apparent promise, limited use and testing of the architecture has been conducted. This paper proposes and details a mission concept and its implementation for testing this architecture in the terrestrial environment. It is expected that this testing will allow significant refinement of the proposed architecture as well as providing data on its suitability for use in both terrestrial and extra-terrestrial applications. Logistical and technical challenges with this testing are discussed.

Error Estimation Techniques To Refine Overlapping Aerial Image Mosaic Processes Via Detected Parameters, William Glenn Bond

Dissertations

In this paper, I propose to demonstrate a means of error estimation preprocessing in the assembly of overlapping aerial image mosaics. The mosaic program automatically assembles several hundred aerial images from a data set by aligning them, via image registration using a pattern search method, onto a GIS grid.

The method presented first locates the images from a data set that it predicts will not align well via the mosaic process, then it uses a correlation function, optimized by a modified Hooke and Jeeves algorithm, to provide a more optimal transformation function input to the mosaic program. Using this improved …

Adaptive Algorithms For Coverage Control And Space Partitioning In Mobile Robotic Networks, Jerome Le Ny, George J. Pappas

George J. Pappas

We consider deployment problems where a mobile robotic network must optimize its configuration in a distributed way in order to minimize a steady-state cost function that depends on the spatial distribution of certain probabilistic events of interest. Three classes of problems are discussed in detail: coverage control problems, spatial partitioning problems, and dynamic vehicle routing problems. Moreover, we assume that the event distribution is a priori unknown, and can only be progressively inferred from the observation of the location of the actual event occurrences. For each problem we present distributed stochastic gradient algorithms that optimize the performance objective. The stochastic …

Adaptive Algorithms For Coverage Control And Space Partitioning In Mobile Robotic Networks, Jerome Le Ny, George J. Pappas

George J. Pappas

We consider deployment problems where a mobile robotic network must optimize its configuration in a distributed way in order to minimize a steady-state cost function that depends on the spatial distribution of certain probabilistic events of interest. Three classes of problems are discussed in detail: coverage control problems, spatial partitioning problems, and dynamic vehicle routing problems. Moreover, we assume that the event distribution is a priori unknown, and can only be progressively inferred from the observation of the location of the actual event occurrences. For each problem we present distributed stochastic gradient algorithms that optimize the performance objective. The stochastic …

Adaptive Algorithms For Coverage Control And Space Partitioning In Mobile Robotic Networks, Jerome Le Ny, George J. Pappas

George J. Pappas

We consider deployment problems where a mobile robotic network must optimize its configuration in a distributed way in order to minimize a steady-state cost function that depends on the spatial distribution of certain probabilistic events of interest. Three classes of problems are discussed in detail: coverage control problems, spatial partitioning problems, and dynamic vehicle routing problems. Moreover, we assume that the event distribution is a priori unknown, and can only be progressively inferred from the observation of the location of the actual event occurrences. For each problem we present distributed stochastic gradient algorithms that optimize the performance objective. The stochastic …

Extreme Learning Machine Terrain-Based Navigation For Unmanned Aerial Vehicles, Ee May Kan, Meng Hiot Lim, Yew Soon Ong, Ah-Hwee Tan, Swee Ping Yeo

Research Collection School Of Computing and Information Systems

Unmanned aerial vehicles (UAVs) rely on global positioning system (GPS) information to ascertain its position for navigation during mission execution. In the absence of GPS information, the capability of a UAV to carry out its intended mission is hindered. In this paper, we learn alternative means for UAVs to derive real-time positional reference information so as to ensure the continuity of the mission. We present extreme learning machine as a mechanism for learning the stored digital elevation information so as to aid UAVs to navigate through terrain without the need for GPS. The proposed algorithm accommodates the need of the …