Thesis (Ph.D.)--University of Wyoming, 2008. / Title from PDF title page (viewed on August 3, 2009). Includes bibliographical references (p. 73-84).

We consider the prospects to use polarized dark-matter detectors to discriminate between various dark-matter models. If WIMPs are fermions and participate in parity-violating interactions with ordinary matter, then the recoil-direction and recoil-energy distributions of nuclei in detectors will depend on the orientation of the initial nuclear spin with respect to the velocity of the detector through the Galactic halo. If, however, WIMPS are scalars, the only possible polarization-dependent interactions are extremely velocity-suppressed and, therefore, unobservable. Since the amplitude of this polarization modulation is fixed by the detector speed through the halo, in units of the speed of light, exposures several times larger than those of current experiments will be required to be probe this effect. / text

The overall purpose of this study was to find a way to identify incorrect data in Sida’s statistics about their contributions. A contribution is the financial support given by Sida to a project. The goal was to build an algorithm that determines if a contribution has a risk to be inaccurate coded, based on supervised classification methods within the area of Machine Learning. A thorough data analysis process was done in order to train a model to find hidden patterns in the data. Descriptive features containing important information about the contributions were successfully selected and used for this task. These included keywords that were retrieved from descriptions of the contributions. Two Machine learning methods, Adaboost and Support Vector Machines, were tested for ten classification models. Each model got evaluated depending on their accuracy of predicting the target variable into its correct class. A misclassified component was more likely to be incorrectly coded and was also seen as an anomaly. The Adaboost method performed better and more steadily on the majority of the models. Six classification models built with the Adaboost method were combined to one final ensemble classifier. This classifier was verified with new unseen data and an anomaly score was calculated for each component. The higher the score, the higher the risk of being anomalous. The result was a ranked list, where the most anomalous components were prioritized for further investigation of staff at Sida.

No description available.

ERPs, specifically the P3, have been proposed as an alternative to traditional polygraphy, with one approach (i.e., Brain Fingerprinting) being promoted as infallible to justify its use on a commercial basis. Concerns have been voiced, however, that such techniques would have to undergo peer-reviewed studies to satisfy validity concerns. Rosenfeld et al. (2004) found, for example, that mental countermeasures were effective in reducing detection rates using an amplitude based, peak-to-peak measure. The present study attempted to replicate and extend Rosenfeld et al.'s study, and to test Brain Fingerprinting's vulnerability to participant manipulation by employing a highly realistic virtual reality crime scenario, multiple countermeasures, and Bayesian and bootstrapping analytic approaches to classify individuals as being guilty or innocent. Participants reported a high degree of realism supporting the external validity of this study and suggesting future uses of virtual environments. Hit rates across statistical methods were significantly lower for standard guilty and innocent participants as compared to previous studies; countermeasures reduced the overall hit rates even further. Brain Fingerprinting was as vulnerable to countermeasures as other statistical measures, and produced a significant number of indeterminate outcomes. Nevertheless, innocent participants remained protected from being falsely accused across statistical methods, consistent with findings of prior studies. Reaction times were determined unsuitable in determining guilt or innocence in this study. Results suggested that ERP based deception detection measures might lack the level of validity required for use in an applied setting.

A "low cost fault detection system" that identifies wheel flats and defective tracks is explored here. This is achieved with the conjunction of sensors, microcontrollers and Radio Frequency (RF) transceivers. The objective of the proposed research is to identify faults plaguing railcars and to be able to clearly distinguish the faults of a railcar from the inherent faults in the track. The focus of the research though, is mainly to identify wheel flats and defective tracks. The thesis has been written with the premise that the results from the simulation software GENSYS are close to the real time data that would have been obtained from an actual railcar. Based on the results of GENSYS, a suitable algorithm is written that helps segregate a fault in a railcar from a defect in a track. The above code is implemented using hardware including microcontrollers, accelerometers, RF transceivers and a real time monitor. An enclosure houses the system completely, so that it is ready for application in a real environment. This also involves selection of suitable hardware so that there is a uniform source of power supply that reduces the cost and assists in building a robust system.

In this thesis, we employ optical flow features for the detection of the rigid or non‐rigid single object on an input video. For optical flow estimation, we use the Point Line [PL] method [2] (as a local method) to estimate the motion of the image sequence which is generated from the input video stream. Although the Lukas and Kanade [LK] is a popular local method for estimation of the optical flow, it is weak in dealing with the linear symmetric images even by use of regularization [e.g. Tikhonov]. The PL method is more powerful than the LK method and can properly separate both line flow and point flow. For dealing with rapidly changing data in some part of an image (high motion problem), a gaussian pyramid with five levels (different image resolutions) is employed. In this way, the pyramid height (Level) must be chosen properly according to the maximum optical flow that we expect in each section of the image without iteration. After determining the best‐estimated optical flow vector for every pixel, the algorithm should detect an object on video with its direction to the right or left. By using techniques such as segmentation and averaging the magnitude of flow vectors the program can detect and distinguish rigid objects (e.g. a car) and non‐rigid objects (e.g. a human). Finally the algorithm makes a new video output that includes detected object with flow vectors, the pyramid levels map which has been used for optical flow estimation and a respective binary image.

Labels are used for managing warehouse environments by collecting information from existing items on shelves and racks. Labels enable description and identification of items accurately in a short time. Although lot of research have been done in the field of barcode detection, the present methods for detection are applicable at a short distance from the camera and with a clear background. Therefore, label detection from captured images is challenging especially with a large and complex background. Once a label is detected, it is ready for next process of recognition, to read out the stored information in texts and barcodes. In this thesis, we compared methods from previous works and implemented the most suitable one for detecting one-dimensional (1D) barcodes available on the captured images by standard lens. We created a dataset for label detection with an assumption on background color and we continued processing by K-means clustering and classification. After localizing label regions, a projection for determining a different candidate area is done. We have worked on two types of barcodes, one-dimensional (1D) and Data Matrix as a two-dimensional (2D) barcode. The results show a good performance of the system in terms of images, which are the most important issue in terms of industrial detection.

Mesh simplification is an important task in Computer Graphics due to the ever increasing complexity of polygonal geometric models. Specifically in real-time rendering, there is a necessity that these models, which can be acquired through 3D scanning or through artistic conception, have to be simplified or optimized to be rendered on today's hardware while losing as little detail as possible. This thesis proposes a mesh simplification algorithm that works by identifying and simplifying features. Then it simplifies the remaining mesh with the simplified features frozen. The algorithm is called Quadric Error with Feature Curves (QEFC). Quadric Error with Feature Curves works as a tool that allows the user to interactively select a percentage of the most important points of the feature curves to be preserved along with the points determined by the Quadric Error Metric algorithm.

Non-verbal cues can provide behavioural signals of deception to observers. Microexpressions are facial cues that indicate the presence of an emotion being concealed by a deceiver. During deception, deceivers often attempt to conceal an emotion by masking it with an expression of another emotion such as a smile. Despite this, micro-expressions may be leaked during masking to reveal the hidden emotion. Nonetheless, research has shown that the majority of people recognize the occurrence of deception no better than could be expected by chance. Micro-expression detection training has been suggested to improve micro-expression detection skill that is linked to improved deception detection. The present study examined the effectiveness of the Micro-expression Training Tool (METT) in improving students’ and police officers’ skills in detecting masking smiles. The visual attention of trainees and untrained controls was measured via eye tracking during a pre and post test masking smile detection task. Results revealed that training did not have an effect on task performance, but practice did alter task performance. Following practice, all groups showed better detection of true smiles but not for masking smile detection. Participants’ abilities to identify masked emotions and location of microexpressions on the face varied as a function of the emotion present, as did their attention to the relevant regions of the face that contained a micro-expression. These results suggest that traditional micro-expression training is not sufficient to train observers in masking smile detection. This result has significant implications for future training protocols and many professional groups, as masking smiles are often employed during attempts at deception.