PhD, University of Cincinnati, 2014, Education, Criminal Justice, and Human Services: School Psychology

This study compared the effects of the Detect, Practice, and Repair (DPR) intervention package versus traditional Cover, Copy, and Compare (CCC) procedures in increasing multiplication math fact accuracy and fluency using an alternating treatments design with a modified control condition. Interventions were administered one-on-one across 4 fourth grade students. Three mutually exclusive multiplication sets were used with one set being assigned to each condition. Effectiveness was assessed through traditional curriculum-based measurement (CBM) procedures and through flashcard card procedures to measure accuracy. In addition, the efficiency of each intervention (i.e., amount of learning per instructional minute) was calculated. Maintenance data were collected to determine if newly learned math facts would be better maintained when taught with the DPR intervention or with the traditional CCC intervention procedures. Social validity data were collected with teachers and students to determine whether one intervention was preferred over another. Although DPR has been examined in five published research studies, it has never been examined through a one-on-one implementation or in a study directly comparing its effectiveness, efficiency, maintenance, and social validity against another intervention. In addition, this study serves as a component analysis since CCC is one component of the DPR package.

Committee: Julie Morrison Ph.D. (Committee Chair); Anne Bauer Ed.D. (Committee Member); Renee Oliver Hawkins Ph.D. (Committee Member) Subjects: Education

Master of Sciences (Engineering), Case Western Reserve University, 2012, EECS - Computer Engineering

With the increasing application of multiple processor cores (multicores) within embedded system applications, as well as the pervasive utilization of the field-programmable gate array (FPGA), the embedded system development community has been exploring the advantages of the dynamically reconfigurable nature of FPGAs. Given size and power limitations, a primary motivation for this interest is to enable dynamic customization of hardware to optimize system performance for the various algorithms that a system encounters. This work presents a hardware based platform for studying dynamic reconfiguration of FPGAs in the context of multicore embedded systems. It also presents a methodology for developing the hardware and software for these systems. An important aspect of this work was to maximize the utilization of open source hardware and software intellectual property (IP). An example of the basic implementation flow is also provided, along with some benchmarking results.

Committee: Chritos Papachristou PhD (Advisor); Francis Merat PhD (Committee Member); Francis Wolff PhD (Committee Member) Subjects:

Master of Science (M.S.), University of Dayton, 2013, Electrical Engineering

We present a 3D reconstruction algorithm designed to support various automation and navigation applications. The algorithm presented focuses on the 3D reconstruction of a scene using only a single moving camera. Utilizing video frames captured at different points in time allows us to determine the depths of a scene. In this way, the system can be used to construct a point cloud model of its unknown surroundings. In this thesis, we present the step by step methodology of the development of a reconstruction technique. The original reconstruction process, resulting with a point cloud was computed based on feature matching and depth triangulation analysis. In an improved version of the algorithm, we utilized optical flow features to create an extremely dense representation model. Although dense, this model is hindered due to its low disparity resolution. As feature points were matched from frame to frame, the resolution of the input images and the discrete nature of disparities limited the depth computations within a scene. With the third algorithmic modification, we introduce the addition of the preprocessing step of nonlinear super resolution. With this addition, the accuracy of the point cloud which relies on precise disparity measurement has significantly increased. Using a pixel by pixel approach, the super resolution technique computes the phase congruency of each pixel's neighborhood and produces nonlinearly interpolated high resolution input frames. Thus, a feature point travels a more precise discrete disparity. Also, the quantity of points within the 3D point cloud model is significantly increased since the number of features is directly proportional to the resolution and high frequencies of the input image. Our final contribution of additional preprocessing steps is designed to filter noise points and mismatched features, giving birth to the complete Dense Point-cloud Representation (DPR) technique. We measure the success of DPR by evaluating the visual appea (open full item for complete abstract)

Committee: Asari Vijayan PhD (Committee Chair); Raul Ordonez PhD (Committee Member); Eric Balster PhD (Committee Member) Subjects: Electrical Engineering; Engineering