71,883 research outputs found
Extension of SBL Algorithms for the Recovery of Block Sparse Signals with Intra-Block Correlation
We examine the recovery of block sparse signals and extend the framework in
two important directions; one by exploiting signals' intra-block correlation
and the other by generalizing signals' block structure. We propose two families
of algorithms based on the framework of block sparse Bayesian learning (BSBL).
One family, directly derived from the BSBL framework, requires knowledge of the
block structure. Another family, derived from an expanded BSBL framework, is
based on a weaker assumption on the block structure, and can be used when the
block structure is completely unknown. Using these algorithms we show that
exploiting intra-block correlation is very helpful in improving recovery
performance. These algorithms also shed light on how to modify existing
algorithms or design new ones to exploit such correlation and improve
performance.Comment: Matlab codes can be downloaded at:
https://sites.google.com/site/researchbyzhang/bsbl, or
http://dsp.ucsd.edu/~zhilin/BSBL.htm
Deep Level Transient Spectroscopy (DLTS) System And Method
A computer-based deep level transient spectroscopy (DLTS) system (10) efficiently digitizes and analyzes capacitance and conductance transients acquired from a test material (13) by conventional DLTS methods as well as by several transient methods, including a covariance method of linear predictive modeling. A unique pseudo-logarithmic data storage scheme allows each transient to be tested at more than eleven different rates, permitting three to five decades of time constants Ï„ to be observed during each thermal scan, thereby allowing high resolution of closely spaced defect energy levels. The system (10) comprises a sensor (12) for detecting capacitance and/or conductance transients, a digitizing mechanism (14) for digitizing the capacitance and/or conductance transients, preamplifiers (16a, 16b) for filtering, amplifying, and for forwarding the transients to the digitizing mechanism (14), a pulse generator (18) for supplying a filling pulse to the test material (13) in a cryostat (24), a trigger conditioner for coordinating the timing between the digitizing mechanism (14) and the pulse generator (18), and a temperature controller (26) for changing the temperature of the cryostat (24).Georgia Tech Research Corporatio
Reliable Linear, Sesquilinear and Bijective Operations On Integer Data Streams Via Numerical Entanglement
A new technique is proposed for fault-tolerant linear, sesquilinear and
bijective (LSB) operations on integer data streams (), such as:
scaling, additions/subtractions, inner or outer vector products, permutations
and convolutions. In the proposed method, the input integer data streams
are linearly superimposed to form numerically-entangled integer data
streams that are stored in-place of the original inputs. A series of LSB
operations can then be performed directly using these entangled data streams.
The results are extracted from the entangled output streams by additions
and arithmetic shifts. Any soft errors affecting any single disentangled output
stream are guaranteed to be detectable via a specific post-computation
reliability check. In addition, when utilizing a separate processor core for
each of the streams, the proposed approach can recover all outputs after
any single fail-stop failure. Importantly, unlike algorithm-based fault
tolerance (ABFT) methods, the number of operations required for the
entanglement, extraction and validation of the results is linearly related to
the number of the inputs and does not depend on the complexity of the performed
LSB operations. We have validated our proposal in an Intel processor (Haswell
architecture with AVX2 support) via fast Fourier transforms, circular
convolutions, and matrix multiplication operations. Our analysis and
experiments reveal that the proposed approach incurs between to
reduction in processing throughput for a wide variety of LSB operations. This
overhead is 5 to 1000 times smaller than that of the equivalent ABFT method
that uses a checksum stream. Thus, our proposal can be used in fault-generating
processor hardware or safety-critical applications, where high reliability is
required without the cost of ABFT or modular redundancy.Comment: to appear in IEEE Trans. on Signal Processing, 201
Recovering Missing Coefficients in DCT-Transformed Images
A general method for recovering missing DCT coefficients in DCT-transformed
images is presented in this work. We model the DCT coefficients recovery
problem as an optimization problem and recover all missing DCT coefficients via
linear programming. The visual quality of the recovered image gradually
decreases as the number of missing DCT coefficients increases. For some images,
the quality is surprisingly good even when more than 10 most significant DCT
coefficients are missing. When only the DC coefficient is missing, the proposed
algorithm outperforms existing methods according to experimental results
conducted on 200 test images. The proposed recovery method can be used for
cryptanalysis of DCT based selective encryption schemes and other applications.Comment: 4 pages, 4 figure
Impact of Heterogeneity on Production in Tidal Sandstone Reservoirs: Application to the Linnorm Field, Offshore Norway
Imperial Users onl
Spatiotemporal Sparse Bayesian Learning with Applications to Compressed Sensing of Multichannel Physiological Signals
Energy consumption is an important issue in continuous wireless
telemonitoring of physiological signals. Compressed sensing (CS) is a promising
framework to address it, due to its energy-efficient data compression
procedure. However, most CS algorithms have difficulty in data recovery due to
non-sparsity characteristic of many physiological signals. Block sparse
Bayesian learning (BSBL) is an effective approach to recover such signals with
satisfactory recovery quality. However, it is time-consuming in recovering
multichannel signals, since its computational load almost linearly increases
with the number of channels.
This work proposes a spatiotemporal sparse Bayesian learning algorithm to
recover multichannel signals simultaneously. It not only exploits temporal
correlation within each channel signal, but also exploits inter-channel
correlation among different channel signals. Furthermore, its computational
load is not significantly affected by the number of channels. The proposed
algorithm was applied to brain computer interface (BCI) and EEG-based driver's
drowsiness estimation. Results showed that the algorithm had both better
recovery performance and much higher speed than BSBL. Particularly, the
proposed algorithm ensured that the BCI classification and the drowsiness
estimation had little degradation even when data were compressed by 80%, making
it very suitable for continuous wireless telemonitoring of multichannel
signals.Comment: Codes are available at:
https://sites.google.com/site/researchbyzhang/stsb
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