Design and analysis of adaptive noise subspace estimation algorithms

Ph.DDOCTOR OF PHILOSOPH

Proceedings of the Fifth NASA/NSF/DOD Workshop on Aerospace Computational Control

The Fifth Annual Workshop on Aerospace Computational Control was one in a series of workshops sponsored by NASA, NSF, and the DOD. The purpose of these workshops is to address computational issues in the analysis, design, and testing of flexible multibody control systems for aerospace applications. The intention in holding these workshops is to bring together users, researchers, and developers of computational tools in aerospace systems (spacecraft, space robotics, aerospace transportation vehicles, etc.) for the purpose of exchanging ideas on the state of the art in computational tools and techniques

Advanced optimization algorithms for sensor arrays and multi-antenna communications

Optimization problems arise frequently in sensor array and multi-channel signal processing applications. Often, optimization needs to be performed subject to a matrix constraint. In particular, unitary matrices play a crucial role in communications and sensor array signal processing. They are involved in almost all modern multi-antenna transceiver techniques, as well as sensor array applications in biomedicine, machine learning and vision, astronomy and radars. In this thesis, algorithms for optimization under unitary matrix constraint stemming from Riemannian geometry are developed. Steepest descent (SD) and conjugate gradient (CG) algorithms operating on the Lie group of unitary matrices are derived. They have the ability to find the optimal solution in a numerically efficient manner and satisfy the constraint accurately. Novel line search methods specially tailored for this type of optimization are also introduced. The proposed approaches exploit the geometrical properties of the constraint space in order to reduce the computational complexity. Array and multi-channel signal processing techniques are key technologies in wireless communication systems. High capacity and link reliability may be achieved by using multiple transmit and receive antennas. Combining multi-antenna techniques with multicarrier transmission leads to high the spectral efficiency and helps to cope with severe multipath propagation. The problem of channel equalization in MIMO-OFDM systems is also addressed in this thesis. A blind algorithm that optimizes of a combined criterion in order to be cancel both inter-symbol and co-channel interference is proposed. The algorithm local converge properties are established as well

Subspace Representations for Robust Face and Facial Expression Recognition

Analyzing human faces and modeling their variations have always been of interest to the computer vision community. Face analysis based on 2D intensity images is a challenging problem, complicated by variations in pose, lighting, blur, and non-rigid facial deformations due to facial expressions. Among the different sources of variation, facial expressions are of interest as important channels of non-verbal communication. Facial expression analysis is also affected by changes in view-point and inter-subject variations in performing different expressions. This dissertation makes an attempt to address some of the challenges involved in developing robust algorithms for face and facial expression recognition by exploiting the idea of proper subspace representations for data. Variations in the visual appearance of an object mostly arise due to changes in illumination and pose. So we first present a video-based sequential algorithm for estimating the face albedo as an illumination-insensitive signature for face recognition. We show that by knowing/estimating the pose of the face at each frame of a sequence, the albedo can be efficiently estimated using a Kalman filter. Then we extend this to the case of unknown pose by simultaneously tracking the pose as well as updating the albedo through an efficient Bayesian inference method performed using a Rao-Blackwellized particle filter. Since understanding the effects of blur, especially motion blur, is an important problem in unconstrained visual analysis, we then propose a blur-robust recognition algorithm for faces with spatially varying blur. We model a blurred face as a weighted average of geometrically transformed instances of its clean face. We then build a matrix, for each gallery face, whose column space spans the space of all the motion blurred images obtained from the clean face. This matrix representation is then used to define a proper objective function and perform blur-robust face recognition. To develop robust and generalizable models for expression analysis one needs to break the dependence of the models on the choice of the coordinate frame of the camera. To this end, we build models for expressions on the affine shape-space (Grassmann manifold), as an approximation to the projective shape-space, by using a Riemannian interpretation of deformations that facial expressions cause on different parts of the face. This representation enables us to perform various expression analysis and recognition algorithms without the need for pose normalization as a preprocessing step. There is a large degree of inter-subject variations in performing various expressions. This poses an important challenge on developing robust facial expression recognition algorithms. To address this challenge, we propose a dictionary-based approach for facial expression analysis by decomposing expressions in terms of action units (AUs). First, we construct an AU-dictionary using domain experts' knowledge of AUs. To incorporate the high-level knowledge regarding expression decomposition and AUs, we then perform structure-preserving sparse coding by imposing two layers of grouping over AU-dictionary atoms as well as over the test image matrix columns. We use the computed sparse code matrix for each expressive face to perform expression decomposition and recognition. Most of the existing methods for the recognition of faces and expressions consider either the expression-invariant face recognition problem or the identity-independent facial expression recognition problem. We propose joint face and facial expression recognition using a dictionary-based component separation algorithm (DCS). In this approach, the given expressive face is viewed as a superposition of a neutral face component with a facial expression component, which is sparse with respect to the whole image. This assumption leads to a dictionary-based component separation algorithm, which benefits from the idea of sparsity and morphological diversity. The DCS algorithm uses the data-driven dictionaries to decompose an expressive test face into its constituent components. The sparse codes we obtain as a result of this decomposition are then used for joint face and expression recognition

Electricity Price Time Series Forecasting in Deregulated Markets Using Recurrent Neural Network Based Approaches

Ph.DDOCTOR OF PHILOSOPH

Dynamic modeling, property investigation, and adaptive controller design of serial robotic manipulators modeled with structural compliance

Research results on general serial robotic manipulators modeled with structural compliances are presented. Two compliant manipulator modeling approaches, distributed and lumped parameter models, are used in this study. System dynamic equations for both compliant models are derived by using the first and second order influence coefficients. Also, the properties of compliant manipulator system dynamics are investigated. One of the properties, which is defined as inaccessibility of vibratory modes, is shown to display a distinct character associated with compliant manipulators. This property indicates the impact of robot geometry on the control of structural oscillations. Example studies are provided to illustrate the physical interpretation of inaccessibility of vibratory modes. Two types of controllers are designed for compliant manipulators modeled by either lumped or distributed parameter techniques. In order to maintain the generality of the results, neither linearization is introduced. Example simulations are given to demonstrate the controller performance. The second type controller is also built for general serial robot arms and is adaptive in nature which can estimate uncertain payload parameters on-line and simultaneously maintain trajectory tracking properties. The relation between manipulator motion tracking capability and convergence of parameter estimation properties is discussed through example case studies. The effect of control input update delays on adaptive controller performance is also studied

Use of statistical modelling and analyses of malaria rapid diagnostic test outcome in Ethiopia.

Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2013.The transmission of malaria is among the leading public health problems in Ethiopia. From the total area of Ethiopia, more than 75% is malarious. Identifying the infectiousness of malaria by socio-economic, demographic and geographic risk factors based on the malaria rapid diagnosis test (RDT) survey results has several advantages for planning, monitoring and controlling, and eventual malaria eradication effort. Such a study requires thorough understanding of the diseases process and associated factors. However such studies are limited. Therefore, the aim of this study was to use different statistical tools suitable to identify socioeconomic, demographic and geographic risk factors of malaria based on the malaria rapid diagnosis test (RDT) survey results in Ethiopia. A total of 224 clusters of about 25 households were selected from the Amhara, Oromiya and Southern Nation Nationalities and People (SNNP) regions of Ethiopia. Accordingly, a number of binary response statistical analysis models were used. Multiple correspondence analysis was carried out to identify the association among socioeconomic, demographic and geographic factors. Moreover a number of binary response models such as survey logistic, GLMM, GLMM with spatial correlation, joint models and semi-parametric models were applied. To test and investigate how well the observed malaria RDT result, use of mosquito nets and use of indoor residual spray data fit the expectations of the model, Rasch model was used. The fitted models have their own strengths and weaknesses. Application of these models was carried out by analysing data on malaria RDT result. The data used in this study, which was conducted from December 2006 to January 2007 by The Carter Center, is from baseline malaria indicator survey in Amhara, Oromiya and Southern Nation Nationalities and People (SNNP) regions of Ethiopia. The correspondence analysis and survey logistic regression model was used to identify predictors which affect malaria RDT results. The effect of identified socioeconomic, demographic and geographic factors were subsequently explored by fitting a generalized linear mixed model (GLMM), i.e., to assess the covariance structures of the random components (to assess the association structure of the data). To examine whether the data displayed any spatial autocorrelation, i.e., whether surveys that are near in space have malaria prevalence or incidence that is similar to the surveys that are far apart, spatial statistics analysis was performed. This was done by introducing spatial autocorrelation structure in GLMM. Moreover, the customary two variables joint modelling approach was extended to three variables joint effect by exploring the joint effect of malaria RDT result, use of mosquito nets and indoor residual spray in the last twelve months. Assessing the association between these outcomes was also of interest. Furthermore, the relationships between the response and some confounding covariates may have unknown functional form. This led to proposing the use of semiparametric additive models which are less restrictive in their specification. Therefore, generalized additive mixed models were used to model the effect of age, family size, number of rooms per person, number of nets per person, altitude and number of months the room sprayed nonparametrically. The result from the study suggests that with the correct use of mosquito nets, indoor residual spraying and other preventative measures, coupled with factors such as the number of rooms in a house, are associated with a decrease in the incidence of malaria as determined by the RDT. However, the study also suggests that the poor are less likely to use these preventative measures to effectively counteract the spread of malaria. In order to determine whether or not the limited number of respondents had undue influence on the malaria RDT result, a Rasch model was used. The result shows that none of the responses had such influences. Therefore, application of the Rasch model has supported the viability of the total sixteen (socio-economic, demographic and geographic) items for measuring malaria RDT result, use of indoor residual spray and use of mosquito nets. From the analysis it can be seen that the scale shows high reliability. Hence, the result from Rasch model supports the analysis carried out in previous models

Machine Learning for Robot Grasping and Manipulation

Robotics as a technology has an incredible potential for improving our everyday lives. Robots could perform household chores, such as cleaning, cooking, and gardening, in order to give us more time for other pursuits. Robots could also be used to perform tasks in hazardous environments, such as turning off a valve in an emergency or safely sorting our more dangerous trash. However, all of these applications would require the robot to perform manipulation tasks with various objects. Today's robots are used primarily for performing specialized tasks in controlled scenarios, such as manufacturing. The robots that are used in today's applications are typically designed for a single purpose and they have been preprogrammed with all of the necessary task information. In contrast, a robot working in a more general environment will often be confronted with new objects and scenarios. Therefore, in order to reach their full potential as autonomous physical agents, robots must be capable of learning versatile manipulation skills for different objects and situations. Hence, we have worked on a variety of manipulation skills to improve those capabilities of robots, and the results have lead to several new approaches, which are presented in this thesis Learning manipulation skills is, however, an open problem with many challenges that still need to be overcome. The first challenge is to acquire and improve manipulation skills with little to no human supervision. Rather than being preprogrammed, the robot should be able to learn from human demonstrations and through physical interactions with objects. Learning to improve skills through trial and error learning is a particularly important ability for an autonomous robot, as it allows the robot to handle new situations. This ability also removes the burden from the human demonstrator to teach a skill perfectly, as a robot is allowed to make mistakes if it can learn from them. In order to address this challenge, we present a continuum-armed bandits approach for learning to grasp objects. The robot learns to predict the performances of different grasps, as well as how certain it is of this prediction, and selects grasps accordingly. As the robot tries more grasps, its predictions become more accurate, and its grasps improve accordingly. A robot can master a manipulation skill by learning from different objects in various scenarios. Another fundamental challenge is therefore to efficiently generalize manipulations between different scenarios. Rather than relearning from scratch, the robot should find similarities between the current situation and previous scenarios in order to reuse manipulation skills and task information. For example, the robot can learn to adapt manipulation skills to new objects by finding similarities between them and known objects. However, only some similarities between objects will be relevant for a given manipulation. The robot must therefore also learn which similarities are important for adapting the manipulation skill. We present two object representations for generalizing between different situations. Contacts between objects are important for many manipulations, but it is difficult to define general features for representing sets of contacts. Instead, we define a kernel function for comparing contact distributions, which allows the robot to use kernel methods for learning manipulations. The second approach is to use warped parameters to define more abstract features, such as areas and volumes. These features are defined as functions of known object models. The robot can compute these parameters for novel objects by warping the shape of the known object to match the unknown object. Learning about objects also requires the robot to reconcile information from multiple sensor modalities, including touch, hearing, and vision. While some object properties will only be observed by specific sensor modalities, other object properties can be determined from multiple sensor modalities. For example, while color can only be determined by vision, the shape of an object can be observed using vision or touch. The robot should use information from all of its senses in order to quickly learn about objects. We explain how the robot can learn low-dimensional representations of tactile data by incorporating cues from vision data. As touching an object usually occludes the surface, the proposed method was designed to work with weak pairings between the data in the two sensor modalities. The robot can also learn more efficiently if it reuses skills between different tasks. Rather than relearn a skill for each new task, the robot should learn manipulation skills that can be reused for multiple tasks. For an autonomous robot, this would require the robot to divide tasks into smaller steps. Dividing tasks into smaller parts makes it easier to learn the corresponding skills. If a step is a part of many tasks, then the robot will have more opportunities to practice the associated skill, and more tasks will benefit from the resulting performance improvement. In order to learn a set of useful subtasks, we propose a probabilistic model for dividing manipulations into phases. This model captures the conditions for transitioning between different phases, which represent subgoals and constraints of the overall tasks. The robot can use the model together with model-based reinforcement learning in order to learn skills for moving between phases. When confronted with a new task, the robot will have to select a suitable sequence of skills to execute. The robot must therefore also learn to select which manipulation to execute in the current scenario. Selecting sequences of motor primitives is difficult, as the robot must take into consideration the current task, state, and future actions when selecting the next motor skill to execute. We therefore present a value function method for selecting skills in an optimal manner. The robot learns the value function for the continuous state space using a flexible non-parametric model-based approach. Learning manipulation skills also poses certain challenges for learning methods. The robot will not have thousands of samples when learning a new manipulation skill, and must instead actively collect new samples or use data from similar scenarios. The learning methods presented in this thesis are, therefore, designed to work with relatively small amounts of data, and can generally be used during the learning process. Manipulation tasks also present a spectrum of different problem types. Hence, we present supervised, unsupervised, and reinforcement learning approaches in order to address the diverse challenges of learning manipulations skills