152 research outputs found
On Variational Data Assimilation in Continuous Time
Variational data assimilation in continuous time is revisited. The central
techniques applied in this paper are in part adopted from the theory of optimal
nonlinear control. Alternatively, the investigated approach can be considered
as a continuous time generalisation of what is known as weakly constrained four
dimensional variational assimilation (WC--4DVAR) in the geosciences. The
technique allows to assimilate trajectories in the case of partial observations
and in the presence of model error. Several mathematical aspects of the
approach are studied. Computationally, it amounts to solving a two point
boundary value problem. For imperfect models, the trade off between small
dynamical error (i.e. the trajectory obeys the model dynamics) and small
observational error (i.e. the trajectory closely follows the observations) is
investigated. For (nearly) perfect models, this trade off turns out to be
(nearly) trivial in some sense, yet allowing for some dynamical error is shown
to have positive effects even in this situation. The presented formalism is
dynamical in character; no assumptions need to be made about the presence (or
absence) of dynamical or observational noise, let alone about their statistics.Comment: 28 Pages, 12 Figure
Sparse Dynamic Factor Models with Loading Selection by Variational Inference
In this paper we develop a novel approach for estimating large and sparse
dynamic factor models using variational inference, also allowing for missing
data. Inspired by Bayesian variable selection, we apply slab-and-spike priors
onto the factor loadings to deal with sparsity. An algorithm is developed to
find locally optimal mean field approximations of posterior distributions,
which can be obtained computationally fast, making it suitable for nowcasting
and frequently updated analyses in practice. We evaluate the method in two
simulation experiments, which show well identified sparsity patterns and
precise loading and factor estimation
Signal processing for microwave imaging systems with very sparse array
This dissertation investigates image reconstruction algorithms for near-field, two dimensional (2D) synthetic aperture radar (SAR) using compressed sensing (CS) based methods. In conventional SAR imaging systems, acquiring higher-quality images requires longer measuring time and/or more elements in an antenna array. Millimeter wave imaging systems using evenly-spaced antenna arrays also have spatial resolution constraints due to the large size of the antennas. This dissertation applies the CS principle to a bistatic antenna array that consists of separate transmitter and receiver subarrays very sparsely and non-uniformly distributed on a 2D plane. One pair of transmitter and receiver elements is turned on at a time, and different pairs are turned on in series to achieve synthetic aperture and controlled random measurements. This dissertation contributes to CS-hardware co-design by proposing several signal-processing methods, including monostatic approximation, re-gridding, adaptive interpolation, CS-based reconstruction, and image denoising. The proposed algorithms enable the successful implementation of CS-SAR hardware cameras, improve the resolution and image quality, and reduce hardware cost and experiment time. This dissertation also describes and analyzes the results for each independent method. The algorithms proposed in this dissertation break the limitations of hardware configuration. By using 16 x 16 transmit and receive elements with an average space of 16 mm, the sparse-array camera achieves the image resolution of 2 mm. This is equivalent to six percent of the λ/4 evenly-spaced array. The reconstructed images achieve similar quality as the fully-sampled array with the structure similarity (SSIM) larger than 0.8 and peak signal-to-noise ratio (PSNR) greater than 25 --Abstract, page iv
Hierarchical Models in the Brain
This paper describes a general model that subsumes many parametric models for
continuous data. The model comprises hidden layers of state-space or dynamic
causal models, arranged so that the output of one provides input to another. The
ensuing hierarchy furnishes a model for many types of data, of arbitrary
complexity. Special cases range from the general linear model for static data to
generalised convolution models, with system noise, for nonlinear time-series
analysis. Crucially, all of these models can be inverted using exactly the same
scheme, namely, dynamic expectation maximization. This means that a single model
and optimisation scheme can be used to invert a wide range of models. We present
the model and a brief review of its inversion to disclose the relationships
among, apparently, diverse generative models of empirical data. We then show
that this inversion can be formulated as a simple neural network and may provide
a useful metaphor for inference and learning in the brain
Mathematical theory of the Goddard trajectory determination system
Basic mathematical formulations depict coordinate and time systems, perturbation models, orbital estimation techniques, observation models, and numerical integration methods
Deep learning for fast and robust medical image reconstruction and analysis
Medical imaging is an indispensable component of modern medical research as well as clinical practice. Nevertheless, imaging techniques such as magnetic resonance imaging (MRI) and computational tomography (CT) are costly and are less accessible to the majority of the world. To make medical devices more accessible, affordable and efficient, it is crucial to re-calibrate our current imaging paradigm for smarter imaging. In particular, as medical imaging techniques have highly structured forms in the way they acquire data, they provide us with an opportunity to optimise the imaging techniques holistically by leveraging data. The central theme of this thesis is to explore different opportunities where we can exploit data and deep learning to improve the way we extract information for better, faster and smarter imaging.
This thesis explores three distinct problems. The first problem is the time-consuming nature of dynamic MR data acquisition and reconstruction. We propose deep learning methods for accelerated dynamic MR image reconstruction, resulting in up to 10-fold reduction in imaging time. The second problem is the redundancy in our current imaging pipeline. Traditionally, imaging pipeline treated acquisition, reconstruction and analysis as separate steps. However, we argue that one can approach them holistically and optimise the entire pipeline jointly for a specific target goal. To this end, we propose deep learning approaches for obtaining high fidelity cardiac MR segmentation directly from significantly undersampled data, greatly exceeding the undersampling limit for image reconstruction. The final part of this thesis tackles the problem of interpretability of the deep learning algorithms. We propose attention-models that can implicitly focus on salient regions in an image to improve accuracy for ultrasound scan plane detection and CT segmentation. More crucially, these models can provide explainability, which is a crucial stepping stone for the harmonisation of smart imaging and current clinical practice.Open Acces
Sense, Think, Grasp: A study on visual and tactile information processing for autonomous manipulation
Interacting with the environment using hands is one of the distinctive
abilities of humans with respect to other species. This aptitude reflects on
the crucial role played by objects\u2019 manipulation in the world that we have
shaped for us. With a view of bringing robots outside industries for supporting
people during everyday life, the ability of manipulating objects
autonomously and in unstructured environments is therefore one of the basic
skills they need. Autonomous manipulation is characterized by great
complexity especially regarding the processing of sensors information to
perceive the surrounding environment. Humans rely on vision for wideranging
tridimensional information, prioprioception for the awareness of
the relative position of their own body in the space and the sense of touch
for local information when physical interaction with objects happens. The
study of autonomous manipulation in robotics aims at transferring similar
perceptive skills to robots so that, combined with state of the art control
techniques, they could be able to achieve similar performance in manipulating
objects. The great complexity of this task makes autonomous
manipulation one of the open problems in robotics that has been drawing
increasingly the research attention in the latest years.
In this work of Thesis, we propose possible solutions to some key components
of autonomous manipulation, focusing in particular on the perception
problem and testing the developed approaches on the humanoid robotic platform iCub. When available, vision is the first source of information
to be processed for inferring how to interact with objects. The object
modeling and grasping pipeline based on superquadric functions we designed
meets this need, since it reconstructs the object 3D model from partial
point cloud and computes a suitable hand pose for grasping the object.
Retrieving objects information with touch sensors only is a relevant skill
that becomes crucial when vision is occluded, as happens for instance during
physical interaction with the object. We addressed this problem with
the design of a novel tactile localization algorithm, named Memory Unscented
Particle Filter, capable of localizing and recognizing objects relying solely
on 3D contact points collected on the object surface. Another key point of
autonomous manipulation we report on in this Thesis work is bi-manual
coordination. The execution of more advanced manipulation tasks in fact
might require the use and coordination of two arms. Tool usage for instance
often requires a proper in-hand object pose that can be obtained via
dual-arm re-grasping. In pick-and-place tasks sometimes the initial and
target position of the object do not belong to the same arm workspace, then
requiring to use one hand for lifting the object and the other for locating it
in the new position. At this regard, we implemented a pipeline for executing
the handover task, i.e. the sequences of actions for autonomously passing an
object from one robot hand on to the other.
The contributions described thus far address specific subproblems of
the more complex task of autonomous manipulation. This actually differs
from what humans do, in that humans develop their manipulation
skills by learning through experience and trial-and-error strategy. Aproper
mathematical formulation for encoding this learning approach is given by
Deep Reinforcement Learning, that has recently proved to be successful in
many robotics applications. For this reason, in this Thesis we report also
on the six month experience carried out at Berkeley Artificial Intelligence
Research laboratory with the goal of studying Deep Reinforcement Learning
and its application to autonomous manipulation
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