311 research outputs found
Sparse and Redundant Representations for Inverse Problems and Recognition
Sparse and redundant representation of data enables the
description of signals as linear combinations of a few atoms from
a dictionary. In this dissertation, we study applications of
sparse and redundant representations in inverse problems and
object recognition. Furthermore, we propose two novel imaging
modalities based on the recently introduced theory of Compressed
Sensing (CS).
This dissertation consists of four major parts. In the first part
of the dissertation, we study a new type of deconvolution
algorithm that is based on estimating the image from a shearlet
decomposition. Shearlets provide a multi-directional and
multi-scale decomposition that has been mathematically shown to
represent distributed discontinuities such as edges better than
traditional wavelets. We develop a deconvolution algorithm that
allows for the approximation inversion operator to be controlled
on a multi-scale and multi-directional basis. Furthermore, we
develop a method for the automatic determination of the threshold
values for the noise shrinkage for each scale and direction
without explicit knowledge of the noise variance using a
generalized cross validation method.
In the second part of the dissertation, we study a reconstruction
method that recovers highly undersampled images assumed to have a
sparse representation in a gradient domain by using partial
measurement samples that are collected in the Fourier domain. Our
method makes use of a robust generalized Poisson solver that
greatly aids in achieving a significantly improved performance
over similar proposed methods. We will demonstrate by experiments
that this new technique is more flexible to work with either
random or restricted sampling scenarios better than its
competitors.
In the third part of the dissertation, we introduce a novel
Synthetic Aperture Radar (SAR) imaging modality which can provide
a high resolution map of the spatial distribution of targets and
terrain using a significantly reduced number of needed transmitted
and/or received electromagnetic waveforms. We demonstrate that
this new imaging scheme, requires no new hardware components and
allows the aperture to be compressed. Also, it
presents many new applications and advantages which include strong
resistance to countermesasures and interception, imaging much
wider swaths and reduced on-board storage requirements.
The last part of the dissertation deals with object recognition
based on learning dictionaries for simultaneous sparse signal
approximations and feature extraction. A dictionary is learned
for each object class based on given training examples which
minimize the representation error with a sparseness constraint. A
novel test image is then projected onto the span of the atoms in
each learned dictionary. The residual vectors along with the
coefficients are then used for recognition. Applications to
illumination robust face recognition and automatic target
recognition are presented
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Extraction of anthropological data with ultrasound
This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.Human body scanners used to extract anthropological data have a significant drawback, the
subject is required to undress or wear tight fitting clothing. This thesis demonstrates an
ultrasonic based alternative to the current optical systems, that can potentially operate on a fully
clothed subject. To validate the concept several experiments were performed to determine the
acoustic properties of multiple garments. The results indicated that such an approach was
possible.
Beamforming is introduced as a method by which the ultrasonic scanning area can be increased,
the concept is thoroughly studied and a clear theoretical analysis is performed. Additionally,
Matlab has been used to demonstrate graphically, the results of such analysis, providing an
invaluable tool during the simulation, experimental and results stages of the thesis.
To evaluate beamfoming as a composite part of ultrasonic body imaging, a hardware solution
was necessary. During the concept phase, both FPGA and digital signal processors were
evaluated to determine their suitability for the role. An FPGA approach was finally chosen,
which allows highly parallel operation, essential to the high acquisition speeds required by some
beamforming methodologies. In addition, analogue circuitry was also designed to provide an
interface with the ultrasonic transducers, which, included variable gain amplifiers, charge
amplifiers and signal conditioning. Finally, a digital acquisition card was used to transfer data
between the FPGA and a desktop computer, on which, the sampled data was processed and
displayed in a coherent graphical manner.
The beamforming results clearly demonstrate that imaging multiple layers in air, with
ultrasound, is a viable technique for anthroplogical data collection. Furthermore, a wavelet
based method of improving the axial resolution is also proposed and demonstrated
Progressively communicating rich telemetry from autonomous underwater vehicles via relays
Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 2012As analysis of imagery and environmental data plays a greater role in mission construction
and execution, there is an increasing need for autonomous marine vehicles
to transmit this data to the surface. Without access to the data acquired by a
vehicle, surface operators cannot fully understand the state of the mission. Communicating
imagery and high-resolution sensor readings to surface observers remains
a significant challenge – as a result, current telemetry from free-roaming
autonomous marine vehicles remains limited to ‘heartbeat’ status messages, with
minimal scientific data available until after recovery. Increasing the challenge, longdistance
communication may require relaying data across multiple acoustic hops
between vehicles, yet fixed infrastructure is not always appropriate or possible.
In this thesis I present an analysis of the unique considerations facing telemetry
systems for free-roaming Autonomous Underwater Vehicles (AUVs) used in exploration.
These considerations include high-cost vehicle nodes with persistent storage
and significant computation capabilities, combined with human surface operators
monitoring each node. I then propose mechanisms for interactive, progressive
communication of data across multiple acoustic hops. These mechanisms include
wavelet-based embedded coding methods, and a novel image compression scheme
based on texture classification and synthesis. The specific characteristics of underwater
communication channels, including high latency, intermittent communication,
the lack of instantaneous end-to-end connectivity, and a broadcast medium,
inform these proposals. Human feedback is incorporated by allowing operators to
identify segments of data thatwarrant higher quality refinement, ensuring efficient
use of limited throughput. I then analyze the performance of these mechanisms
relative to current practices.
Finally, I present CAPTURE, a telemetry architecture that builds on this analysis.
CAPTURE draws on advances in compression and delay tolerant networking to
enable progressive transmission of scientific data, including imagery, across multiple acoustic hops. In concert with a physical layer, CAPTURE provides an endto-
end networking solution for communicating science data from autonomous marine
vehicles. Automatically selected imagery, sonar, and time-series sensor data
are progressively transmitted across multiple hops to surface operators. Human
operators can request arbitrarily high-quality refinement of any resource, up to an
error-free reconstruction. The components of this system are then demonstrated
through three field trials in diverse environments on SeaBED, OceanServer and
Bluefin AUVs, each in different software architectures.Thanks to the National Science Foundation, and the
National Oceanic and Atmospheric Administration for
their funding of my education and this work
Measure What Should be Measured: Progress and Challenges in Compressive Sensing
Is compressive sensing overrated? Or can it live up to our expectations? What
will come after compressive sensing and sparsity? And what has Galileo Galilei
got to do with it? Compressive sensing has taken the signal processing
community by storm. A large corpus of research devoted to the theory and
numerics of compressive sensing has been published in the last few years.
Moreover, compressive sensing has inspired and initiated intriguing new
research directions, such as matrix completion. Potential new applications
emerge at a dazzling rate. Yet some important theoretical questions remain
open, and seemingly obvious applications keep escaping the grip of compressive
sensing. In this paper I discuss some of the recent progress in compressive
sensing and point out key challenges and opportunities as the area of
compressive sensing and sparse representations keeps evolving. I also attempt
to assess the long-term impact of compressive sensing
Signal processing with Fourier analysis, novel algorithms and applications
Fourier analysis is the study of the way general functions may be represented or approximated by sums of simpler trigonometric functions, also analogously known as sinusoidal modeling. The original idea of Fourier had a profound impact on mathematical analysis, physics and engineering because it diagonalizes time-invariant convolution operators. In the past signal processing was a topic that stayed almost exclusively in electrical engineering, where only the experts could cancel noise, compress and reconstruct signals. Nowadays it is almost ubiquitous, as everyone now deals with modern digital signals. Medical imaging, wireless communications and power systems of the future will experience more data processing conditions and wider range of applications requirements than the systems of today. Such systems will require more powerful, efficient and flexible signal processing algorithms that are well designed to handle such needs. No matter how advanced our hardware technology becomes we will still need intelligent and efficient algorithms to address the growing demands in signal processing. In this thesis, we investigate novel techniques to solve a suite of four fundamental problems in signal processing that have a wide range of applications. The relevant equations, literature of signal processing applications, analysis and final numerical algorithms/methods to solve them using Fourier analysis are discussed for different applications in the electrical engineering/computer science. The first four chapters cover the following topics of central importance in the field of signal processing: • Fast Phasor Estimation using Adaptive Signal Processing (Chapter 2) • Frequency Estimation from Nonuniform Samples (Chapter 3) • 2D Polar and 3D Spherical Polar Nonuniform Discrete Fourier Transform (Chapter 4) • Robust 3D registration using Spherical Polar Discrete Fourier Transform and Spherical Harmonics (Chapter 5) Even though each of these four methods discussed may seem completely disparate, the underlying motivation for more efficient processing by exploiting the Fourier domain signal structure remains the same. The main contribution of this thesis is the innovation in the analysis, synthesis, discretization of certain well known problems like phasor estimation, frequency estimation, computations of a particular non-uniform Fourier transform and signal registration on the transformed domain. We conduct propositions and evaluations of certain applications relevant algorithms such as, frequency estimation algorithm using non-uniform sampling, polar and spherical polar Fourier transform. The techniques proposed are also useful in the field of computer vision and medical imaging. From a practical perspective, the proposed algorithms are shown to improve the existing solutions in the respective fields where they are applied/evaluated. The formulation and final proposition is shown to have a variety of benefits. Future work with potentials in medical imaging, directional wavelets, volume rendering, video/3D object classifications, high dimensional registration are also discussed in the final chapter. Finally, in the spirit of reproducible research we release the implementation of these algorithms to the public using Github
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