Beam-based adaptive processing is an economical way to achieve good interference rejection performance from an adaptive receiving array, at much less computational cost than full element-based methods. However, to exp...
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Beam-based adaptive processing is an economical way to achieve good interference rejection performance from an adaptive receiving array, at much less computational cost than full element-based methods. However, to exploit this potential for planar arrays it is necessary to identify, in real time, which beams must be retained for adaptive cancellation. This paper analyzes the beam-selection problem and presents a computationally efficient algorithm that performs real-time beam selection.
An iterative solution is given for solving deblurring problems having nonnegativity constraints through the use of methods motivated by tomographic imaging. After briefly reviewing three versions of tomographic-imagin...
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An iterative solution is given for solving deblurring problems having nonnegativity constraints through the use of methods motivated by tomographic imaging. After briefly reviewing three versions of tomographic-imaging problems, the paper indicates how methods that have proven to be powerful for the third version, weighted-integral tomography, can be applied to the more general deblurring problem when nonnegativity constraints are present.
Exact characterization of the array in terms of its geometry and the sensor gain and phase is important in beamforming and directions-of-arrival estimation problems. In practice the sensor gain and phase may not be kn...
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We develop a Jacobi-like scheme for computing the generalized Schur form of a regular pencil of matrices λB - A. The method starts with a preliminary triangularization of the matrix B and iteratively reduces A to tri...
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The recently introduced Phase Gradient Autofocus (PGA) algorithm is a non-parametric autofocus technique which has been shown to be quite effective for phase correction of Synthetic Aperture Radar (SAR) imagery. This ...
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The performances of high-resolution array processing methods are known to degrade in random inhomogeneous media because the amplitude and phase of each wavefront tend to fluctuate and to loose their coherence between ...
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The performances of high-resolution array processing methods are known to degrade in random inhomogeneous media because the amplitude and phase of each wavefront tend to fluctuate and to loose their coherence between array sensors. As a result, in the presence of such a multiplicative noise, the conventional coherent wavefront model becomes inapplicable. Such a type of degradation may be especially strong for large aperture arrays. Below, we develop new high-resolution covariance matching (CM) techniques with an improved robustness against multiplicative noise and related coherence losses. Using a few unrestrictive physics-based assumptions on the environment, we show that reliable algorithms can be developed which take into account possible coherence losses. Computer simulation results and real sonar data processing results are presented. These results demonstrate drastic improvements achieved by our approach as compared with conventional high-resolution array processing techniques.
System developers have found that exploiting parallel architectures for control systems is challenging and often the resulting implementations do not provide the expected performance advantages over traditional unipro...
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System developers have found that exploiting parallel architectures for control systems is challenging and often the resulting implementations do not provide the expected performance advantages over traditional uniprocessor solutions. This paper presents a generic method and a suite of design tools for the implementation of control algorithms on parallel architectures. These tools allow a developer to translate a control system algorithm into efficient executable code, via a highly automated route, for a class of advanced parallel architectures. The tools are demonstrated and discussed by developing several case-study algorithms to full implementations with an emphasis on the problematic areas leading to performance degradation common to parallel systems.
Demand is increasing for high throughput processing of irregular streaming applications； examples of such applications from scientific and engineering domains include biological sequence alignment, network packet fil...
Demand is increasing for high throughput processing of irregular streaming applications； examples of such applications from scientific and engineering domains include biological sequence alignment, network packet filtering, automated face detection, and big graph algorithms. With wide SIMD, lightweight threads, and low-cost thread-context switching, wide-SIMD architectures such as GPUs allow considerable flexibility in the way application work is assigned to threads. However, irregular applications are challenging to map efficiently onto wide SIMD because data-dependent filtering or replication of items creates an unpredictable data wavefront of items ready for further processing. Straightforward implementations of irregular applications on a wide-SIMD architecture are prone to load imbalance and reduced occupancy, while more sophisticated implementations require advanced use of parallel GPU operations to redistribute work efficiently among *** dissertation will present strategies for addressing the performance challenges of wavefront-irregular applications on wide-SIMD architectures. These strategies are embodied in a developer framework called Mercator that 1) allows developers to map irregular applications onto GPUs according to the streaming paradigm while abstracting from low-level data movement and 2) includes generalized techniques fortransparently overcoming the obstacles to high throughput presented by wavefront-irregular applications on a GPU. Mercator forms the centerpiece of this dissertation, and we present its motivation, performance model, implementation, and extensions in this work.
Demand is increasing for high throughput processing of irregular streaming applications; examples of such applications from scientific and engineering domains include biological sequence alignment, network packet filt...
Demand is increasing for high throughput processing of irregular streaming applications; examples of such applications from scientific and engineering domains include biological sequence alignment, network packet filtering, automated face detection, and big graph algorithms. With wide SIMD, lightweight threads, and low-cost thread-context switching, wide-SIMD architectures such as GPUs allow considerable flexibility in the way application work is assigned to threads. However, irregular applications are challenging to map efficiently onto wide SIMD because data-dependent filtering or replication of items creates an unpredictable data wavefront of items ready for further processing. Straightforward implementations of irregular applications on a wide-SIMD architecture are prone to load imbalance and reduced occupancy, while more sophisticated implementations require advanced use of parallel GPU operations to redistribute work efficiently among threads. This dissertation will present strategies for addressing the performance challenges of wavefront-irregular applications on wide-SIMD architectures. These strategies are embodied in a developer framework called Mercator that (1) allows developers to map irregular applications onto GPUs according to the streaming paradigm while abstracting from low-level data movement and (2) includes generalized techniques for transparently overcoming the obstacles to high throughput presented by wavefront-irregular applications on a GPU. Mercator forms the centerpiece of this dissertation, and we present its motivation, performance model, implementation, and extensions in this work.
This paper evaluates several signalprocessing techniques for optical dual-polarization quadrature phase-shift keying (DP-QPSK) coherent receivers, using offline post-processing of experimental data. In addition to el...
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