957 research outputs found
Nearly Optimal Deterministic Algorithm for Sparse Walsh-Hadamard Transform
For every fixed constant , we design an algorithm for computing
the -sparse Walsh-Hadamard transform of an -dimensional vector in time . Specifically, the
algorithm is given query access to and computes a -sparse satisfying , for an absolute constant , where is the
transform of and is its best -sparse approximation. Our
algorithm is fully deterministic and only uses non-adaptive queries to
(i.e., all queries are determined and performed in parallel when the algorithm
starts).
An important technical tool that we use is a construction of nearly optimal
and linear lossless condensers which is a careful instantiation of the GUV
condenser (Guruswami, Umans, Vadhan, JACM 2009). Moreover, we design a
deterministic and non-adaptive compressed sensing scheme based
on general lossless condensers that is equipped with a fast reconstruction
algorithm running in time (for the GUV-based
condenser) and is of independent interest. Our scheme significantly simplifies
and improves an earlier expander-based construction due to Berinde, Gilbert,
Indyk, Karloff, Strauss (Allerton 2008).
Our methods use linear lossless condensers in a black box fashion; therefore,
any future improvement on explicit constructions of such condensers would
immediately translate to improved parameters in our framework (potentially
leading to reconstruction time with a reduced exponent in
the poly-logarithmic factor, and eliminating the extra parameter ).
Finally, by allowing the algorithm to use randomness, while still using
non-adaptive queries, the running time of the algorithm can be improved to
Light field image processing: an overview
Light field imaging has emerged as a technology allowing to capture richer visual information from our world. As opposed to traditional photography, which captures a 2D projection of the light in the scene integrating the angular domain, light fields collect radiance from rays in all directions, demultiplexing the angular information lost in conventional photography. On the one hand, this higher dimensional representation of visual data offers powerful capabilities for scene understanding, and substantially improves the performance of traditional computer vision problems such as depth sensing, post-capture refocusing, segmentation, video stabilization, material classification, etc. On the other hand, the high-dimensionality of light fields also brings up new challenges in terms of data capture, data compression, content editing, and display. Taking these two elements together, research in light field image processing has become increasingly popular in the computer vision, computer graphics, and signal processing communities. In this paper, we present a comprehensive overview and discussion of research in this field over the past 20 years. We focus on all aspects of light field image processing, including basic light field representation and theory, acquisition, super-resolution, depth estimation, compression, editing, processing algorithms for light field display, and computer vision applications of light field data
Image compression and energy harvesting for energy constrained sensors
Title from PDF of title page, viewed on June 21, 2013Dissertation advisor: Walter D. Leon-SalasVitaIncludes bibliographic references (pages 176-[187])Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2013The advances in complementary metal-oxide-semiconductor (CMOS) technology
have led to the integration of all components of electronic system into a single integrated
circuit. Ultra-low power circuit techniques have reduced the power consumption of circuits.
Moreover, solar cells with improved efficiency can be integrated on chip to harvest
energy from sunlight. As a result of all the above, a new class of miniaturized electronic
systems known as self-powered system on a chip has emerged. There is an increasing research
interest in the area of self-powered devices which provide cost-effective solutions
especially when these devices are used in the areas that changing or replacing batteries is
too costly. Therefore, image compression and energy harvesting are studied in this dissertation.
The integration of energy harvesting, image compression, and an image sensor
on the same chip provides the energy source to charge a battery, reduces the data rate, and improves the performance of wireless image sensors. Integrated circuits of image compression,
solar energy harvesting, and image sensors are studied, designed, and analyzed
in this work. In this dissertation, a hybrid image sensor that can perform the tasks of sensing and
energy harvesting is presented. Photodiodes of hybrid image sensor can be programmed
as image sensors or energy harvesting cells. The hybrid image sensor can harvest energy
in between frames, in sleep mode, and even when it is taking images. When sensing
images and harvesting energy are both needed at the same time, some pixels have to
work as sensing pixels, and the others have to work as solar cells. Since some pixels are
devoted to harvest energy, the resolution of the image will be reduced. To preserve the
resolution or to keep the fair resolution when a lot of energy collection is needed, image
reconstruction algorithms and compressive sensing theory provide solutions to achieve
a good image quality. On the other hand, when the battery has enough charge, image
compression comes into the picture. Multiresolution decomposition image compression
provides a way to compress image data in order to reduce the energy need from data
transmission. The solution provided in this dissertation not only harvests energy but also
saves energy resulting long lasting wireless sensors. The problem was first studied at the system level to identify the best system-level
configuration which was then implemented on silicon. As a proof of concept, a 32 x 32 array of hybrid image sensor, a 32 x 32 array of image sensor with multiresolution decomposition compression, and a compressive sensing converter have been designed
and fabricated in a standard 0.5 [micrometer] CMOS process. Printed circuit broads also have been
designed to test and verify the proposed and fabricated chips. VHDL and Matlab codes
were written to generate the proper signals to control, and read out data from chips. Image
processing and recovery were carried out in Matlab. DC-DC converters were designed to
boost the inherently low voltage output of the photodiodes. The DC-DC converter has
also been improved to increase the efficiency of power transformation.Introduction -- Hybrid imager system and circuit design -- Hybrid imager energy harvesting and image acquisition results and discussion -- Detailed description and mathematical analysis for a circuit of energy harvesting using on-chip solar cells -- Multiresolution decomposition for lossless and near-lossless compression -- An incremental [sigma-delta] converter for compressive sensing -- Detailed description of a sigma-delta random demodulator converter architecture for compressive sensing applications -- Conclusion -- Appendix A. Chip pin-out -- Appendix B. Schematics -- Appendix C. Pictures of custom PC
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A multi-sensor sub-Nyquist power spectrum blind sampling approach for low-power wireless sensors in operational modal analysis applications
A novel multi-sensor power spectrum blind sampling (PSBS) approach is proposed supporting low-power wireless sensor networks (WSN) for Operational Modal Analysis (OMA) applications. The developed approach relies on arrays of wireless sensors, employing deterministic non-uniform in time multi-coset sampling to acquire structural response acceleration signals at sub-Nyquist sampling rates, treated as realizations of stationary random processes without making any assumption about the average signal frequency content and spectral support. The acquired compressed measurements are transmitted to a central server and collectively processed via a PSBS technique, herein extended to the multi-sensor case, to estimate the power spectral density matrix of an underlying spatially correlated stationary response acceleration random process directly from the compressed measurements. Structural modal properties are then extracted through standard frequency domain decomposition (FDD). The efficacy of the proposed approach to resolve closely-spaced modes is numerically tested for various data compression levels using noisy response acceleration signals of a white-noise excited finite element model of a space truss as well as field-recorded acceleration time-histories of an instrumented bridge under operational loading. It is shown that accurate mode shapes based on the modal assurance criterion can be obtained from as low as 89% less measurements compared to conventional non-compressive FDD at Nyquist sampling rate. Further, significant gains in energy consumption and battery lifetime prolongation of the order of years are estimated, assuming wireless sensors operating on multi-coset sampling at different data compression levels. It is, therefore, concluded that the proposed PSBS approach could provide long-term structural health monitoring systems with low-maintenance cost once wireless sensors with multi-coset sampling capabilities become commercially available
Sensor Signal and Information Processing II
In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing
Convolutional Deblurring for Natural Imaging
In this paper, we propose a novel design of image deblurring in the form of
one-shot convolution filtering that can directly convolve with naturally
blurred images for restoration. The problem of optical blurring is a common
disadvantage to many imaging applications that suffer from optical
imperfections. Despite numerous deconvolution methods that blindly estimate
blurring in either inclusive or exclusive forms, they are practically
challenging due to high computational cost and low image reconstruction
quality. Both conditions of high accuracy and high speed are prerequisites for
high-throughput imaging platforms in digital archiving. In such platforms,
deblurring is required after image acquisition before being stored, previewed,
or processed for high-level interpretation. Therefore, on-the-fly correction of
such images is important to avoid possible time delays, mitigate computational
expenses, and increase image perception quality. We bridge this gap by
synthesizing a deconvolution kernel as a linear combination of Finite Impulse
Response (FIR) even-derivative filters that can be directly convolved with
blurry input images to boost the frequency fall-off of the Point Spread
Function (PSF) associated with the optical blur. We employ a Gaussian low-pass
filter to decouple the image denoising problem for image edge deblurring.
Furthermore, we propose a blind approach to estimate the PSF statistics for two
Gaussian and Laplacian models that are common in many imaging pipelines.
Thorough experiments are designed to test and validate the efficiency of the
proposed method using 2054 naturally blurred images across six imaging
applications and seven state-of-the-art deconvolution methods.Comment: 15 pages, for publication in IEEE Transaction Image Processin
Combined Industry, Space and Earth Science Data Compression Workshop
The sixth annual Space and Earth Science Data Compression Workshop and the third annual Data Compression Industry Workshop were held as a single combined workshop. The workshop was held April 4, 1996 in Snowbird, Utah in conjunction with the 1996 IEEE Data Compression Conference, which was held at the same location March 31 - April 3, 1996. The Space and Earth Science Data Compression sessions seek to explore opportunities for data compression to enhance the collection, analysis, and retrieval of space and earth science data. Of particular interest is data compression research that is integrated into, or has the potential to be integrated into, a particular space or earth science data information system. Preference is given to data compression research that takes into account the scien- tist's data requirements, and the constraints imposed by the data collection, transmission, distribution and archival systems
Sparse representation based hyperspectral image compression and classification
Abstract
This thesis presents a research work on applying sparse representation to lossy hyperspectral image
compression and hyperspectral image classification. The proposed lossy hyperspectral image
compression framework introduces two types of dictionaries distinguished by the terms sparse
representation spectral dictionary (SRSD) and multi-scale spectral dictionary (MSSD), respectively.
The former is learnt in the spectral domain to exploit the spectral correlations, and the
latter in wavelet multi-scale spectral domain to exploit both spatial and spectral correlations in
hyperspectral images. To alleviate the computational demand of dictionary learning, either a
base dictionary trained offline or an update of the base dictionary is employed in the compression
framework. The proposed compression method is evaluated in terms of different objective
metrics, and compared to selected state-of-the-art hyperspectral image compression schemes, including
JPEG 2000. The numerical results demonstrate the effectiveness and competitiveness of
both SRSD and MSSD approaches.
For the proposed hyperspectral image classification method, we utilize the sparse coefficients
for training support vector machine (SVM) and k-nearest neighbour (kNN) classifiers. In particular,
the discriminative character of the sparse coefficients is enhanced by incorporating contextual
information using local mean filters. The classification performance is evaluated and compared
to a number of similar or representative methods. The results show that our approach could outperform
other approaches based on SVM or sparse representation.
This thesis makes the following contributions. It provides a relatively thorough investigation
of applying sparse representation to lossy hyperspectral image compression. Specifically,
it reveals the effectiveness of sparse representation for the exploitation of spectral correlations
in hyperspectral images. In addition, we have shown that the discriminative character of sparse
coefficients can lead to superior performance in hyperspectral image classification.EM201
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