UNLV Transmutation Research Program Proposal Year III: Design and Evaluation of Processes for Fuel Fabrication

The objective of this project is the design and evaluation of manufacturing processes for transmuter fuel fabrication. The large-scale deployment of remote fabrication and refabrication processes will be required for all transmutation scenarios. Current program emphasis is on a five-year effort to determine the feasibility of transmutation as a technology to limit the need for repository storage of spent commercial fuel. The evaluation of the fabrication processes will create a decision support data base to document design, operations, and costs. Fabrication processes required for different fuel types differ in terms of equipment types, throughput, and cost. Differential cost Implications of various fuel choices will be assessed. The ongoing year 1 project has been focusing on collecting information on existing technologies, equipment costs, and material throughput. Another aspect during years 1 and 2 has been the assessment of robotic technology and robot supervision and control, and the simulation of material handling operations using 3-D simulation tools with view towards the development of a fully automated and reliable, autonomous manufacturing process. Such development has the potential to decrease the cost of remote fuel fabrication and to make transmutation a more economically viable process. An added benefit would be the potential for exposure dose reductions to workers. This project is being conducted in close cooperation with the fabrication development group at Argonne National Lab. Year 3 of the project will be devoted to developing further data and knowledge regarding the cost and feasibility of automated fuel manufacture in a hot cell. The detailed simulation of manufacturing processes (as robotic operations supervised by remote operators and as virtual mock-up facilities) will be continued. Both normal operations as well as failure scenarios will be investigated, analyzed, and simulated. The results of this study will be documented in detail. The results of the simulations will be used by Advanced Fuel Cycle Initiative (AFCI) program personnel to perform sensitivity studies on the impact of different fuel types on AFCI system operation. Conceptual designs for plant designs and the accompanying supervision and control systems will be developed. Impacts on transmutation system capital cost, economics of operation, estimates of process loss, and environmental and safety issues will be estimated in further detail, continuing the work from year 2

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

Overcoming Pose Limitations of a Skin-Cued Histograms of Oriented Gradients Dismount Detector through Contextual Use of Skin Islands and Multiple Support Vector Machines

This thesis provides a novel visualization method to analyze the impact that articulations in dismount pose and camera aspect angle have on histograms of oriented gradients (HOG) features and eventual detections. Insights from these relationships are used to identify limitations in a state of the art skin cued HOG dismount detector\u27s ability to detect poses not in a standard upright stances. Improvements to detector performance are made by further leveraging available skin information, reducing false detections by an additional order of magnitude. In addition, a method is outlined for training supplemental support vector machines (SVMs) from computer generated data, for detecting a wider range of poses and camera configurations. The multi-SVM structure yields a 7-fold increase detection probability when applied to challenging crouching poses. These dramatic improvements clearly demonstrate the viability of such an approach, which can be extended to include other pose configurations

Parallel Evidence-Based Indexing of Complex Three-Dimensional Models Using Prototypical Parts and Relations (Dissertation Proposal)

This proposal is concerned with three-dimensional object recognition from range data using superquadric primitives. Superquadrics are a family of parametric shape models which represent objects at the part level and can account for a wide variety of natural and man-made forms. An integrated framework for segmenting dense range data of complex 3-D objects into their constituent parts in terms of bi-quadric surface patches and superquadric shape primitives is described in [29]. We propose a vision architecture that scales well as the size of its model database grows. Following the recovery of superquadric primitives from the input depth map, we split the computation into two concurrent processing streams. One is concerned with the classification of individual parts using viewpoint-invariant shape information while the other classifies pairwise part relationships using their relative size, orientation and type of joint. The major contribution of this proposal lies in a principled solution to the very difficult problems of superquadric part classification and model indexing. The problem is how to retrieve the best matched models without exploring all possible object matches. Our approach is to cluster together similar model parts to create a reasonable number of prototypical part classes (protoparts). Each superquadric part recovered from the input is paired with the best matching protopart using precomputed class statistics. A parallel, theoretically-well grounded evidential recognition algorithm quickly selects models consistent with the classified parts. Classified part relations (protorelations) are used to further reduce the number of consistent models and remaining ambiguities are resolved using sequential top-down search

Fast and robust image feature matching methods for computer vision applications

Service robotic systems are designed to solve tasks such as recognizing and manipulating objects, understanding natural scenes, navigating in dynamic and populated environments. It's immediately evident that such tasks cannot be modeled in all necessary details as easy as it is with industrial robot tasks; therefore, service robotic system has to have the ability to sense and interact with the surrounding physical environment through a multitude of sensors and actuators. Environment sensing is one of the core problems that limit the deployment of mobile service robots since existing sensing systems are either too slow or too expensive. Visual sensing is the most promising way to provide a cost effective solution to the mobile robot sensing problem. It's usually achieved using one or several digital cameras placed on the robot or distributed in its environment. Digital cameras are information rich sensors and are relatively inexpensive and can be used to solve a number of key problems for robotics and other autonomous intelligent systems, such as visual servoing, robot navigation, object recognition, pose estimation, and much more. The key challenges to taking advantage of this powerful and inexpensive sensor is to come up with algorithms that can reliably and quickly extract and match the useful visual information necessary to automatically interpret the environment in real-time. Although considerable research has been conducted in recent years on the development of algorithms for computer and robot vision problems, there are still open research challenges in the context of the reliability, accuracy and processing time. Scale Invariant Feature Transform (SIFT) is one of the most widely used methods that has recently attracted much attention in the computer vision community due to the fact that SIFT features are highly distinctive, and invariant to scale, rotation and illumination changes. In addition, SIFT features are relatively easy to extract and to match against a large database of local features. Generally, there are two main drawbacks of SIFT algorithm, the first drawback is that the computational complexity of the algorithm increases rapidly with the number of key-points, especially at the matching step due to the high dimensionality of the SIFT feature descriptor. The other one is that the SIFT features are not robust to large viewpoint changes. These drawbacks limit the reasonable use of SIFT algorithm for robot vision applications since they require often real-time performance and dealing with large viewpoint changes. This dissertation proposes three new approaches to address the constraints faced when using SIFT features for robot vision applications, Speeded up SIFT feature matching, robust SIFT feature matching and the inclusion of the closed loop control structure into object recognition and pose estimation systems. The proposed methods are implemented and tested on the FRIEND II/III service robotic system. The achieved results are valuable to adapt SIFT algorithm to the robot vision applications

Classification of Radar Targets Using Invariant Features

Automatic target recognition ATR using radar commonly relies on modeling a target as a collection of point scattering centers, Features extracted from these scattering centers for input to a target classifier may be constructed that are invariant to translation and rotation, i.e., they are independent of the position and aspect angle of the target in the radar scene. Here an iterative approach for building effective scattering center models is developed, and the shape space of these models is investigated. Experimental results are obtained for three-dimensional scattering centers compressed to nineteen-dimensional feature sets, each consisting of the singular values of the matrix of scattering center locations augmented with the singular values of its second and third order monomial expansions. These feature sets are invariant to translation and rotation and permit the comparison of targets modeled by different numbers of scattering centers. A metric distance metric is used that effectively identifies targets under real world conditions that include noise and obscuration

Image-Based View Synthesis

We present a new method for rendering novel images of flexible 3D objects from a small number of example images in correspondence. The strength of the method is the ability to synthesize images whose viewing position is significantly far away from the viewing cone of the example images ("view extrapolation"), yet without ever modeling the 3D structure of the scene. The method relies on synthesizing a chain of "trilinear tensors" that governs the warping function from the example images to the novel image, together with a multi-dimensional interpolation function that synthesizes the non-rigid motions of the viewed object from the virtual camera position. We show that two closely spaced example images alone are sufficient in practice to synthesize a significant viewing cone, thus demonstrating the ability of representing an object by a relatively small number of model images --- for the purpose of cheap and fast viewers that can run on standard hardware

Segmentation of experience and episodic memory across species

How continuous ongoing perceptual experience is processed by the brain and mind to form unique episodes in memory is a key scientific question. Recent work in Psychology and Neuroscience has proposed that humans perceptually segment continuous ongoing experience into meaningful units, which allows the successful formation of episodic memories. Despite accumulating work demonstrating that non- human animals also display a capability of episodic-‘like’ memory, whether non-human animals segment continuous ongoing experience into ‘meaningful’ episodic units is a question that has not been fully explored. Hence, the main goal of the research in this thesis aims to address whether a comparable segmentation process (or processes) of continuous ongoing experience occurs for non-human animals in their formation of episodic-like memory, as it does for humans in their formation of episodic memory. Chapter 2 argues that, similarly to humans, rats can use top-down like prediction-error processing in segmenting for subsequent memory to guide behaviour in an episodic-like spontaneous object recognition task. Chapter 3 suggests that mice readily incorporate conspecific-contextual information using episodic-like memory processing, indicating that conspecifics can act as a segmentation cue for non-human animals. Chapter 4 highlights that humans and rodents may similarly segment continuous ongoing experience during turns made around spatial boundaries. Chapter 5 argues that individual place cells can represent content of episodic nature, with the theoretical implication of this being discussed in relation to episodic memory. Thus, the results presented in this thesis, as well as re-interpretation of previous literature, would argue in favour of non-humans segmenting their experience for episodic-like memory. Finally, the evidence is evaluated in the context of whether episodic-like memory in non-human animals is simply just episodic memory as experienced in humans