Disparity compensation using geometric transforms

This dissertation describes the research and development of some techniques to enhance the disparity compensation in 3D video compression algorithms. Disparity compensation is usually performed using a block matching technique between views, disregarding the various levels of disparity present for objects at different depths in the scene. An alternative coding scheme is proposed, taking advantage of the cameras setup information and the object’s depth in the scene, to compensate more complex spatial distortions, being able to improve disparity compensation even with convergent cameras. In order to perform a more accurate disparity compensation, the reference picture list is enriched with additional geometrically transformed images, for the most relevant object’s levels of depth in the scene, resulting from projections of one view to another. This scheme can be implemented in any state-of-the-art video codec, as H.264/AVC or HEVC, in order to improve the disparity matching accuracy between views. Experimental results, using MV-HEVC extension, show the efficiency of the proposed method for coding stereo video, presenting bitrate savings up to 2.87%, for convergent camera sequences, and 1.52% for parallel camera sequences. Also a method to choose the geometrically transformed inter view reference pictures was developed, in order to reduce unnecessary overhead for unused reference pictures. By selecting and adding to the reference picture list, only the most useful pictures, all results improved, presenting bitrate savings up to 3.06% for convergent camera sequences, and 2% for parallel camera sequences

Radar systems for the water resources mission, volume 2

The application of synthetic aperture radar (SAR) in monitoring and managing earth resources was examined. The function of spaceborne radar is to provide maps and map imagery to be used for earth resource and oceanographic applications. Spaceborne radar has the capability of mapping the entire United States regardless of inclement weather; however, the imagery must have a high degree of resolution to be meaningful. Attaining this resolution is possible with the SAR system. Imagery of the required quality must first meet mission parameters in the following areas: antenna patterns, azimuth and range ambiguities, coverage, and angle of incidence

Spectroscopic interrogation of differential mobility spectrometry selected, isolated, gas-phase molecular clusters

One of the most elusive challenges facing mass spectrometry-based methods is the study of isomers. Oftentimes, isomers produce identical fragmentation spectra that make structural elucidation and assignment from MS data alone difficult. In this thesis, the problem of studying isomers is addressed by employing differential mobility spectrometry-mass spectrometry experiments coupled with ultraviolet photodissociation action spectroscopy (DMS-MS-UVPD). Using a combination of experimental and computational techniques, the validity of DMS-MS-UVPD studies for applications in isomer separation and distinction is verified. During the course of two subprojects, DMS-MS-UVPD is successfully applied to separate and distinguish between geometric isomers and tautomers. First, DMS-MS experiments are conducted, and the dynamic clustering behaviour of each species is determined. Once the clustering behaviour is well-characterized, the DMS-MS parameters are optimized to select for the species of interest and used as an ion filter for subsequent UVPD action spectroscopy experiments. The results from the action spectroscopy studies produce a vibronic spectrum that can be compared to theoretical models for correct isomer assignment. Due to the complexity of excited state phenomena, many computational models were used to accurately predict vibronic spectra, including Franck-Condon based approaches and non-adiabatic dynamics. The work presented in this thesis provides the framework for the use of DMS-MS-UVPD in other isomer systems

Incorporating Human Expertise in Robot Motion Learning and Synthesis

With the exponential growth of robotics and the fast development of their advanced cognitive and motor capabilities, one can start to envision humans and robots jointly working together in unstructured environments. Yet, for that to be possible, robots need to be programmed for such types of complex scenarios, which demands significant domain knowledge in robotics and control. One viable approach to enable robots to acquire skills in a more flexible and efficient way is by giving them the capabilities of autonomously learn from human demonstrations and expertise through interaction. Such framework helps to make the creation of skills in robots more social and less demanding on programing and robotics expertise. Yet, current imitation learning approaches suffer from significant limitations, mainly about the flexibility and efficiency for representing, learning and reasoning about motor tasks. This thesis addresses this problem by exploring cost-function-based approaches to learning robot motion control, perception and the interplay between them. To begin with, the thesis proposes an efficient probabilistic algorithm to learn an impedance controller to accommodate motion contacts. The learning algorithm is able to incorporate important domain constraints, e.g., about force representation and decomposition, which are nontrivial to handle by standard techniques. Compliant handwriting motions are developed on an articulated robot arm and a multi-fingered hand. This work provides a flexible approach to learn robot motion conforming to both task and domain constraints. Furthermore, the thesis also contributes with techniques to learn from and reason about demonstrations with partial observability. The proposed approach combines inverse optimal control and ensemble methods, yielding a tractable learning of cost functions with latent variables. Two task priors are further incorporated. The first human kinematics prior results in a model which synthesizes rich and believable dynamical handwriting. The latter prior enforces dynamics on the latent variable and facilitates a real-time human intention cognition and an on-line motion adaptation in collaborative robot tasks. Finally, the thesis establishes a link between control and perception modalities. This work offers an analysis that bridges inverse optimal control and deep generative model, as well as a novel algorithm that learns cost features and embeds the modal coupling prior. This work contributes an end-to-end system for synthesizing arm joint motion from letter image pixels. The results highlight its robustness against noisy and out-of-sample sensory inputs. Overall, the proposed approach endows robots the potential to reason about diverse unstructured data, which is nowadays pervasive but hard to process for current imitation learning

Multifrequency Aperture-Synthesizing Microwave Radiometer System (MFASMR). Volume 1

Background material and a systems analysis of a multifrequency aperture - synthesizing microwave radiometer system is presented. It was found that the system does not exhibit high performance because much of the available thermal power is not used in the construction of the image and because the image that can be formed has a resolution of only ten lines. An analysis of image reconstruction is given. The system is compared with conventional aperture synthesis systems

Signed path dependence in financial markets: Applications and implications

Despite decades of studies, there is still no consensus on what type of serial dependence, if any, might be present in risky asset returns. The serial dependence structure in asset returns is complex and challenging to study, it varies over time, it varies over observed time resolution, it varies by asset type, it varies with liquidity and exchange and it even varies in statistical structure. The focus of the work in this thesis is to capture a previously unexplored notion of serial dependence that is applicable to any asset class and can be both parameteric or non-parameteric depending on the modelling approach preferred. The aim of this research is to develop new approaches by providing a model-free definition of serial dependence based on how the sign of cumulative innovations for a given lookback horizon correlates with the future cumulative innovations for a given forecast horizon. This concept is then theoretically validated on well-known time series model classes and used to build a predictive econometric model for future market returns, which is applied to empirical forecasting by means of a profit seeking trading strategy. The empirical experiment revealed strong evidence of serial dependence in equity markets, being statistically and economically significant even in the presence of trading costs. Subsequently, this thesis provides an empirical study of the prices of Energy Commodities, Gold and Copper in the futures markets and demonstrates that, for these assets, the level of asymmetry of asset returns varies through time and can be forecast using past returns. A new time series model is proposed based on this phenomenon, also empirically validated. The thesis concludes by embedding into option pricing theory the findings of previous chapters pertaining to signed path dependence structure. This is achieved by devising a model-free empirical risk-neutral distribution based on Polynomial Chaos Expansion and Stochastic Bridge Interpolators that includes information from the entire set of observable European call option prices under all available strikes and maturities for a given underlying asset, whilst the real-world measure includes the effects of serial dependence based on the sign of previous returns. The risk premium behaviour is subsequently inferred from the two distributions using the Radon-Nikodym derivative of the empirical riskneutral distribution with respect to the modelled real-world distribution

A differential-based parallel force/velocity actuation concept : theory and experiments

textRobots are now moving from their conventional confined habitats such as factory floors to human environments where they assist and physically interact with people. The requirement for inherent mechanical safety is overarching in such human-robot interaction systems. We propose a dual actuator called Parallel Force/Velocity Actuator (PFVA) that combines a Force Actuator (FA) (low velocity input) and a Velocity Actuator (VA) (high velocity input) using a differential gear train. In this arrangement mechanical safety can be achieved by limiting the torque on the FA and thus making it a backdriveable input. In addition, the kinematic redundancy in the drive can be used to control output velocity while satisfying secondary operational objectives. Our research focus was on three areas: (i) scalable parametric design of the PFVA, (ii) analytical modeling of the PFVA and experimental testing on a single-joint prototype, and (iii) generalized model formulation for PFVA-driven serial robot manipulators. In our analysis, the ratio of velocity ratios between the FA and the VA, called the relative scale factor, emerged as a purely geometric and dominant design parameter. Based on a dimensionless parametric design of PFVAs using power-flow and load distributions between the inputs, a prototype was designed and built using commercial-off-the-shelf components. Using controlled experiments, two performance-limiting phenomena in our prototype, friction and dynamic coupling between the two inputs, were identified. Two other experiments were conducted to characterize the operational performance of the actuator in velocity-mode and in what we call ‘torque-limited’ mode (i.e. when the FA input can be backdriven). Our theoretical and experimental results showed that the PFVA can be mechanical safe to both slow collisions and impacts due to the backdriveability of the FA. Also, we show that its kinematic redundancy can be effectively utilized to mitigate low-velocity friction and backlash in geared mechanisms. The implication at the system level of our actuator level analytical and experimental work was studied using a generalized dynamic modeling framework based on kinematic influence coefficients. Based on this dynamic model, three design case studies for a PFVA-driven serial planar 3R manipulator were presented. The major contributions of this research include (i) mathematical models and physical understanding for over six fundamental design and operational parameters of the PFVA, based on which approximately ten design and five operational guidelines were laid out, (ii) analytical and experimental proof-of-concept for the mechanical safety feature of the PFVA and the effective utilization of its kinematic redundancy, (iii) an experimental methodology to characterize the dynamic coupling between the inputs in a differential-summing mechanism, and (iv) a generalized dynamic model formulation for PFVA-driven serial robot manipulators with emphasis on distribution of output loads between the FA and VA input-sets.Mechanical Engineerin