Malware detection is typically performed using either software scanners running inside the operating system or external devices designed to validate the integrity of the kernel. This thesis proposes a hypervisor-based malware that compromises the system by targeting the hard disk drive and leaving the kernel unmodified. The hypervisor is able to issue read and write commands to the disk while actively hiding these actions from the operating system and any detection software therein. Additionally, the hypervisor’s presence has minimal impact on the performance of the system. The ability to perform these commands compromises the confidentiality, integrity, and availability of the stored data. As a result, this thesis has widespread implications affecting personal, corporate, and government users alike.

Students who transfer from a community college to a university need to learn about their new environment in order to succeed academically at their new institution. The purpose of this study was to investigate the effect of attending an extended orientation on transfer students’ academic achievement. An extended orientation is a program hosted by the 4-year institution to teach new students about the culture, campus, resources, and opportunities offered. This study compared the academic achievement of transfer students who attended an extended orientation and a similar (based on GPA and number hours transferred) group of transfer students who did not attend an extended orientation. The academic achievement measures included the cumulative grade point average (GPA) at the transfer institution, the cumulative number of hours earned at the transfer institution, and the rate of retention to the following fall semester. This study used a quantitative research design using an independent t-test for GPA and number of hours completed, and a chi-squared test of independence for retention rates. The findings showed that there was no statistically significant difference between the transfer students who attended the extended orientation and those who did not. While the transfer students who attended had slightly higher GPA’s and number of hours completed, the difference was minimal and not significant. Similarly, although the retention rates for transfer students who attended the extended orientation were slightly higher than for those who did not attend, the difference was also minimal and not significant. The goal of the study was to give practitioners who develop and implement similar programs evidence on how to tailor specific programs to meet the needs of transfer students from community colleges to better assist them in their transition to their new institution.

This thesis work presents a non-parametric learning method, the Extended Nearest Neighbor (ENN) algorithm, as a tool for data disaggregation in smart grids. The ENN algorithm makes the prediction according to the maximum gain of intra-class coherence. This algorithm not only considers the K nearest neighbors of the test sample but also considers whether these K data points consider the test sample as their nearest neighbor or not. So far, ENN has shown noticeable improvement in the classification accuracy for various real-life applications. To further enhance its prediction capability, in this thesis we propose to incorporate a metric learning algorithm, namely the Large Margin Nearest Neighbor (LMNN) algorithm, as a training stage in ENN. Our experiments on real-life energy data sets have shown significant performance improvement compared to several other traditional classification algorithms, including the classic KNN method and Support Vector Machines.

Rapid advances in speech recognition theory, as well as computing hardware, have led to the development of machines that can take human speech as input, decode the information content of the speech, and respond accordingly. Real-time performance of such systems is often dominated by the evaluation of likelihoods in the statistical modeling component of the system. Statistical models are typically implemented using Gaussian mixture distributions. The primary objective of this thesis was to develop an extension of the Bucket Box Intersection algorithm in which the dimension with the optimal number of splits can be selected when multiple minima are present. The effects of normalization of mixture weights and Gaussian clipping have also been investigated. We show that the Extended BBI algorithm (EBBI) reduces run-time by 21% without introducing any approximation error. EBBI also produced a 12% lower word error rate than Gaussian clipping for the same computational complexity. These approaches were evaluated on a wide variety of tasks including conversational speech.

No description available.

No description available.

No description available.

No description available.

With the recent advances in sensor technology and resulting sensor resolution, conven- tional point-based target tracking algorithms are becoming insufficient, particularly in application domains such as autonomous vehicles, visual tracking and surveillance using high resolution sensors. This has renewed the interest in extended target (ET) tracking, which aims to track not only the centroid of a target, but also its shape and size over time. This thesis addresses three of the most challenging problems in the domain of ET tracking applications. The first investigated challenge is the need for an accu- rate shape and centre estimate for the ET object with an arbitrary unknown star- convex shape in presence of non-Gaussian noise. The proposed method is based on a Student’s-t process regression algorithm which is defined in a recursive framework to be applicable for on-line tracking problems. The second problem tries to relax any constraints, including the star-convex con- straint, that is imposed on the shape of the ET object during the course of estimation by defining a novel Random Polytopes shape descriptor. Also, the proposed solution introduces a method to mitigate the troubles caused as a result of self-occlusion in ET tracking applications which its ignorance may cause catastrophic divergence in the ET state estimate.Finally, a framework for tracking multiple ET objects in the presence of clutter and occlusion is studied and a solution is proposed. The proposed method can estimate the centre and shape of the ET objects in a realistically scenario with the self- and mutual-occlusion challenges being considered. The proposed approach defines a time varying state-dependent probability of detection for each ET that enables the track to prolong even under adverse conditions caused due to mutual-occlusion. Plus, the proposed algorithm uses set-membership uncertainty models to bound the association and target shape uncertainties of occluded ET, to obtain more accurate state and shape estimates of an ET object. The performance of the proposed methods are quantified on realistically simulated scenarios with self- and mutual-occlusions and their results are compared against existing state-of-the-art methods for ET tracking applications. / Thesis / Doctor of Philosophy (PhD)

This research focuses on the design of an Extended Reality simulator for training medical professionals in Advanced Trauma Life Support (ATLS) and pulse palpation. Existing pulse simulators have the disadvantages of being bulky, expensive, and unsuitable to be used as training tools. In addition, none of the simulators were designed to incorporate the auditory feedback of the pulse, a crucial component of continuous pulse monitoring. The developed simulator incorporates haptic, visual, and auditory feedback modes. In this work, we also conduct a comparative user study to determine the effect of multimodal feedback on different participants. Participants trained in the Audio-Haptic scenario outperformed those trained in the Haptic only scenario. These values could also be correlated with qualitative user feedback indicating that Audio-Haptic interactions were perceived as superior. With this simulator, we hope to provide medical professionals with an immersive and realistic training tool for learning the skill of palpating pulse. / Master of Science / The medical field demands accurate and precise procedures to be performed by doctors, with no room for error. Traditional training methods consist of the trainer demonstrating the technique and the student duplicating it, which increases the risk of medical errors. Advanced Trauma Life Support (ATLS) is a program designed by the American College of Surgeons to teach physicians a systematic approach to treating trauma patients. Palpating and classifying pulses is one of the steps involved in the ATLS procedure. The majority of existing ATLS and pulse simulators are not fully integrated with haptic and auditory feedback, and there has been very little research on this topic. This work describes the design and development of an Extended Reality ATLS simulator with a pulse simulator for medical student training. We conduct a user study to determine how the Audio-Haptic scenario affects the learnability of palpating pulses and ATLS procedures. Our ATLS simulator aims to provide a comprehensive training module for emergency trauma response practice for medical professionals.