Activity Representation from Video Using Statistical Models on Shape Manifolds

Activity recognition from video data is a key computer vision problem with applications in surveillance, elderly care, etc. This problem is associated with modeling a representative shape which contains significant information about the underlying activity. In this dissertation, we represent several approaches for view-invariant activity recognition via modeling shapes on various shape spaces and Riemannian manifolds. The first two parts of this dissertation deal with activity modeling and recognition using tracks of landmark feature points. The motion trajectories of points extracted from objects involved in the activity are used to build deformation shape models for each activity, and these models are used for classification and detection of unusual activities. In the first part of the dissertation, these models are represented by the recovered 3D deformation basis shapes corresponding to the activity using a non-rigid structure from motion formulation. We use a theory for estimating the amount of deformation for these models from the visual data. We study the special case of ground plane activities in detail because of its importance in video surveillance applications. In the second part of the dissertation, we propose to model the activity by learning an affine invariant deformation subspace representation that captures the space of possible body poses associated with the activity. These subspaces can be viewed as points on a Grassmann manifold. We propose several statistical classification models on Grassmann manifold that capture the statistical variations of the shape data while following the intrinsic Riemannian geometry of these manifolds. The last part of this dissertation addresses the problem of recognizing human gestures from silhouette images. We represent a human gesture as a temporal sequence of human poses, each characterized by a contour of the associated human silhouette. The shape of a contour is viewed as a point on the shape space of closed curves and, hence, each gesture is characterized and modeled as a trajectory on this shape space. We utilize the Riemannian geometry of this space to propose a template-based and a graphical-based approaches for modeling these trajectories. The two models are designed in such a way to account for the different invariance requirements in gesture recognition, and also capture the statistical variations associated with the contour data

Adapting to Sparsity and Heavy Tailed Data

The Lasso and the Horseshoe, gold-standards in the frequentist and Bayesian paradigms, critically depend on learning the error variance. This causes a lack of scale invariance and adaptability to heavy-tailed data. The √ Lasso [Belloni et al., 2011] attempt to correct this by using the `1 norm on both the likelihood and the penalty for the objective function. In contrast, there is essentially no methods for uncertainty quantification or automatic parameter tuning via a formal Bayesian treatment of an unknown error distribution. On the other hand, Bayesian shrinkage priors lacking a local shrinkage term fails to adapt to the large signals embedded in noise. In this thesis, I propose to build a fully Bayesian method called √ DL that achieves scale invariance and robustness to heavy tails while maintaining computational efficiency. The classical √ Lasso estimate is then recovered as the posterior mode with an appropriate modification of the local shrinkage prior. The Bayesian √ DL leads to uncertainty quantification by yielding standard error estimates and credible sets for the underlying parameters. Furthermore, the hierarchical model leads to an automatic tuning of the penalty parameter using a full Bayes or empirical Bayes approach, avoiding any ad-hoc choice over a grid. We provide an efficient Gibbs sampling scheme based on Normal scale mixture representation of Laplace densities. Performance on real and simulated data exhibit excellent small sample properties and we establish some theoretical guarantees

Novel Algorithms and Instrumentation for Vibrational Spectroscopic Methods of Analysis

Raman spectroscopy is a form of vibrational spectroscopy that has been increasingly applied to qualitative analysis of chemicals, explosives, pharmaceuticals, and fuels due to its non-invasive and non-destructive nature; its ease of sampling; and its high molecular specificity. These characteristics of Raman spectroscopy also facilitate its use for both in-line and at-line analysis. The principle limitation of Raman spectroscopy is optical interference arising from both analyte and non-analyte fluorescence. In this dissertation, a solution to this problem is presented in the form of a novel spectrometer design which operates in a sequentially shifted excitation mode to eliminate fluorescence backgrounds, fixed pattern noise, and room lights, while keeping the Raman data in true spectral space. The Raman data are extracted from the shifted excitation spectra using a novel algorithm which is three orders of magnitude faster than conventional iterative algorithms. Near infrared spectroscopy is another form of vibrational spectroscopy which has also been increasingly used for the analysis of fuels in both at-line and in-line applications. Despite its popularity, near infrared spectroscopy lacks the spectral resolution of Raman spectroscopy and because of this, the transfer of quantitative calibration models is considered extremely difficult and expensive. This is considered one of the principal limitations in the use of near infrared spectroscopy for fuel analysis. In this dissertation, a solution to this problem is presented in the form of novel calibration transfer algorithms which allow the use of virtual fuels which are digitally synthesized from pure chemical standards and to transfer quantitative calibration models between different near infrared instruments. This solution eliminates the expense, time, and difficulty of traditional calibration transfer methods

Exploring Reality of Human Development at Educational Institutions in Oman from the Principals' Perspective

الملخص: هدفت الدراسة إلى التعرف على واقع التنمية البشرية في المؤسسات التعليمية في سلطنة عمان من وجهة نظر المديرين. ولتحقيق أهداف الدراسة، تم استخدام المنهج الوصفي من خلال استبيان كأداة رئيسية تضمنت 25 فقرة تتعلق بالتنمية البشرية. تم التحقق من صحة الاستبيان من صدق المحكمين. وتم التحقق من استقرارها باستخدام معامل ثبات ألفا كرونباخ (0.88) وتم جمع البيانات من عينة عشوائية من 200 مدير في مدارس ظفار. تم تحليل البيانات إحصائياً باستخدام برنامج (SPSS) ؛ حسب المتوسط ​​والانحراف المعياري والوزن النسبي واختبار t واختبار تحليل التباين. وجدت الدراسة أن المؤسسات التعليمية في عمان تساهم بشكل معتدل في تحقيق متطلبات التنمية البشرية بشكل عام. كما أوصت بزيادة الميزانية المخصصة للبحث العلمي في المدارس وتفعيل الشراكات الإيجابية بين المدارس ومؤسسات الإنتاج والاقتصاد في المجتمع. الكلمات الدلالية: الواقع، التنمية البشرية، المؤسسات التعليمية، مديري المدارس.Abstract: The study aimed to identify the reality of human development at the educational institutions in the Sultanate of Oman from the Principals' perspective. To achieve the objectives of the study, the descriptive approach was used through a questionnaire as a main tool, which included 25 paragraphs related to the human development. The validity of the questionnaire was verified by the sincerity of the arbitrators; and its stability was verified by using the Alpha-Cronbach stability factor (88%). Data were collected from a random sample of 200 principals in Dhofar schools. The data were analyzed statistically using the (SPSS) program; depending on mean, standard deviation, relative weight, t test, and variance analysis test. The study found that educational institutions in Oman moderately contribute to achieve human development requirements in general. It also recommended increasing the budget allocated to scientific research in schools and activating positive partnerships between schools and production institutions and the economy in society. Keywords: Reality, Human Development, Educational Institutions, Sultanate of Oman, School Principals

High speed operation design considerations for fractional slot axial flux PMSM

This paper discusses intensively the design considerations for the fractional slot axial flux permanent magnet synchronous (AFPMSMs) in order to work efficiently in the constant power speed range, also known as the field weakening (FW) region. The dominant parameter in the constant power speed region is called the characteristic current which equals the ratio of the magnet flux linkage over the synchronous inductance (− ψm/Ls). Several machine parameters is affecting the characteristic current including the machine geometry and the winding configurations. In this paper, the effect of many of these parameters on the FW has been discussed; including the outer diameter, inner to outer diameter ratio, magnet size, slot opening width, slots per poles combinations,and the multi phase configurations for the Axial flux permanent magnet synchronous machine (PMSM). Two main governors are considered to evaluate the parameters’ impact on the machine overall performance; the rated machine efficiency and the torque to weight ratio at the highest values. Selection of these governors is application driven where these governors are the most influencing factors on the axial flux PMSM design. The results of the present analysis show that the fine tuning of the discussed machine parameters would derive the motor to work in the required Constant Power Speed Region (CPSR) keeping the required high efficiency and torque to weight ratio. A previously proved analytical model has been used in this study to overcome the highly time consumption in the finite element model (FEM)