115 research outputs found
Tungsten tip and atomic site tomography
Atomic electron tomography aims to precisely locate individual atoms of a nanoparticle in three-dimensional space. In this work, a tomography method based on tungsten tips is developed to allow images to be taken over a full angular range by placing a nanoparticle on the apex of an etched tungsten tip. There is no interference of signal from supporting materials with the suspended nanoparticle. A new reconstruction algorithm, atomic site tomography, is developed using the principle of regularisation in multiple linear regression. This algorithm is specifically designed for identifying the precise locations of individual atoms in three-dimensional space, and the algorithm is validated by an experimental dataset. A gold nanoparticle dataset is successfully obtained by tungsten tip tomography, and the dataset is processed to remove scanning artefacts. Selected region of the gold nanoparticle dataset is used to demonstrate the new reconstruction algorithm and the whole gold nanoparticle is then reconstructed. A tuning fork atomic force microscope is developed to provide a more flexible method to prepare samples for tungsten tip tomography and its progress is reported. This work contributes to the field of atomic electron tomography by improving the experimental techniques for acquiring high-quality tomography dataset and proposing a new reconstruction algorithm which aims at locating individual atoms of nanoparticles precisely
Image Restoration
This book represents a sample of recent contributions of researchers all around the world in the field of image restoration. The book consists of 15 chapters organized in three main sections (Theory, Applications, Interdisciplinarity). Topics cover some different aspects of the theory of image restoration, but this book is also an occasion to highlight some new topics of research related to the emergence of some original imaging devices. From this arise some real challenging problems related to image reconstruction/restoration that open the way to some new fundamental scientific questions closely related with the world we interact with
Advances in Image Processing, Analysis and Recognition Technology
For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches
Mobile Robots Navigation
Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described
Arrayed LiDAR signal analysis for automotive applications
Light detection and ranging (LiDAR) is one of the enabling technologies for advanced
driver assistance and autonomy. Advances in solid-state photon detector arrays offer
the potential of high-performance LiDAR systems but require novel signal processing
approaches to fully exploit the dramatic increase in data volume an arrayed detector
can provide.
This thesis presents two approaches applicable to arrayed solid-state LiDAR. First, a
novel block independent sparse depth reconstruction framework is developed, which
utilises a random and very sparse illumination scheme to reduce illumination density while improving sampling times, which further remain constant for any array
size. Compressive sensing (CS) principles are used to reconstruct depth information
from small measurement subsets. The smaller problem size of blocks reduces the
reconstruction complexity, improves compressive depth reconstruction performance
and enables fast concurrent processing. A feasibility study of a system proposal for
this approach demonstrates that the required logic could be practically implemented
within detector size constraints. Second, a novel deep learning architecture called
LiDARNet is presented to localise surface returns from LiDAR waveforms with high
throughput. This single data driven processing approach can unify a wide range
of scenarios, making use of a training-by-simulation methodology. This augments
real datasets with challenging simulated conditions such as multiple returns and
high noise variance, while enabling rapid prototyping of fast data driven processing
approaches for arrayed LiDAR systems.
Both approaches are fast and practical processing methodologies for arrayed LiDAR
systems. These retrieve depth information with excellent depth resolution for wide
operating ranges, and are demonstrated on real and simulated data. LiDARNet is
a rapid approach to determine surface locations from LiDAR waveforms for efficient point cloud generation, while block sparse depth reconstruction is an efficient method to facilitate high-resolution depth maps at high frame rates with reduced power and memory requirements.Engineering and Physical Sciences Research Council (EPSRC
The Fifth NASA/DOD Controls-Structures Interaction Technology Conference, part 2
This publication is a compilation of the papers presented at the Fifth NASA/DoD Controls-Structures Interaction (CSI) Technology Conference held in Lake Tahoe, Nevada, March 3-5, 1992. The conference, which was jointly sponsored by the NASA Office of Aeronautics and Space Technology and the Department of Defense, was organized by the NASA Langley Research Center. The purpose of this conference was to report to industry, academia, and government agencies on the current status of controls-structures interaction technology. The agenda covered ground testing, integrated design, analysis, flight experiments and concepts
Multi-modal Skill Memories for Online Learning of Interactive Robot Movement Generation
Queißer J. Multi-modal Skill Memories for Online Learning of Interactive Robot Movement Generation. Bielefeld: Universität Bielefeld; 2018.Modern robotic applications pose complex requirements with respect to the adaptation of
actions regarding the variability in a given task. Reinforcement learning can optimize for
changing conditions, but relearning from scratch is hardly feasible due to the high number of
required rollouts. This work proposes a parameterized skill that generalizes to new actions
for changing task parameters. The actions are encoded by a meta-learner that provides
parameters for task-specific dynamic motion primitives. Experimental evaluation shows that
the utilization of parameterized skills for initialization of the optimization process leads to a
more effective incremental task learning. A proposed hybrid optimization method combines
a fast coarse optimization on a manifold of policy parameters with a fine-grained parameter
search in the unrestricted space of actions. It is shown that the developed algorithm reduces
the number of required rollouts for adaptation to new task conditions. Further, this work
presents a transfer learning approach for adaptation of learned skills to new situations.
Application in illustrative toy scenarios, for a 10-DOF planar arm, a humanoid robot point
reaching task and parameterized drumming on a pneumatic robot validate the approach.
But parameterized skills that are applied on complex robotic systems pose further
challenges: the dynamics of the robot and the interaction with the environment introduce
model inaccuracies. In particular, high-level skill acquisition on highly compliant robotic
systems such as pneumatically driven or soft actuators is hardly feasible. Since learning of
the complete dynamics model is not feasible due to the high complexity, this thesis examines
two alternative approaches: First, an improvement of the low-level control based on an
equilibrium model of the robot. Utilization of an equilibrium model reduces the learning
complexity and this thesis evaluates its applicability for control of pneumatic and industrial
light-weight robots. Second, an extension of parameterized skills to generalize for forward
signals of action primitives that result in an enhanced control quality of complex robotic
systems. This thesis argues for a shift in the complexity of learning the full dynamics of the
robot to a lower dimensional task-related learning problem. Due to the generalization in
relation to the task variability, online learning for complex robots as well as complex scenarios
becomes feasible. An experimental evaluation investigates the generalization capabilities of
the proposed online learning system for robot motion generation. Evaluation is performed
through simulation of a compliant 2-DOF arm and scalability to a complex robotic system
is demonstrated for a pneumatically driven humanoid robot with 8-DOF
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