Breast Imaging by Time Reverse Minimum Variance Distortionless Response

2020 ◽  
Author(s):  
Yoshihiko Kuwahara ◽  
Fujii Kimihito
Keyword(s):  
Breast Imaging ◽  
Minimum Variance ◽  
Minimum Variance Distortionless Response

PARALLEL ALGORITHMS FOR ADAPTIVE MATCHED-FIELD PROCESSING ON DISTRIBUTED ARRAY SYSTEMS

2004 ◽  
Vol 12 (02) ◽  
pp. 149-174 ◽  
Author(s):  
KILSEOK CHO ◽  
ALAN D. GEORGE ◽  
RAJ SUBRAMANIYAN ◽  
KEONWOOK KIM
Keyword(s):  
Parallel Algorithms ◽  
Real Time ◽  
Digital Signal Processor ◽  
High Performance ◽  
Digital Signal ◽  
Minimum Variance ◽  
Adaptive Beamforming ◽  
Matched Field Processing ◽  
Minimum Variance Distortionless Response ◽  
Distributed Array

Matched-field processing (MFP) localizes sources more accurately than plane-wave beamforming by employing full-wave acoustic propagation models for the cluttered ocean environment. The minimum variance distortionless response MFP (MVDR–MFP) algorithm incorporates the MVDR technique into the MFP algorithm to enhance beamforming performance. Such an adaptive MFP algorithm involves intensive computational and memory requirements due to its complex acoustic model and environmental adaptation. The real-time implementation of adaptive MFP algorithms for large surveillance areas presents a serious computational challenge where high-performance embedded computing and parallel processing may be required to meet real-time constraints. In this paper, three parallel algorithms based on domain decomposition techniques are presented for the MVDR–MFP algorithm on distributed array systems. The parallel performance factors in terms of execution times, communication times, parallel efficiencies, and memory capacities are examined on three potential distributed systems including two types of digital signal processor arrays and a cluster of personal computers. The performance results demonstrate that these parallel algorithms provide a feasible solution for real-time, scalable, and cost-effective adaptive beamforming on embedded, distributed array systems.

scholarly journals GSM-RF Channel Characterization Using a Wideband Subspace Sensing Mechanism for Cognitive Radio Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Soumaya El Barrak ◽  
Amina El Gonnouni ◽  
Salvatore Serrano ◽  
Antonio Puliafito ◽  
Abdelouahid Lyhyaoui
Keyword(s):  
Software Defined Radio ◽  
Spectrum Sharing ◽  
Spectral Estimation ◽  
Energy Detection ◽  
Minimum Variance ◽  
Detection Accuracy ◽  
Sensing Mechanism ◽  
Wideband Spectrum Sensing ◽  
Density Peaks ◽  
Minimum Variance Distortionless Response

In this paper, we examine a spectrum sharing opportunities over the existing Global System of Mobile Communication (GSM) networks, by identifying the unused channels at a specific time and location. For this purpose, we propose a wideband spectrum sensing mechanism to analyze the status of 51 channels at once, belonging to the 10  MHz bandwidth centered at the frequency 945  MHz, in four different areas. We propose a subspace based spectral estimation mechanism, adapted to deal with real measurements. The process begins with data collection using Secondary User (SU) device enabled with Software Defined Radio (SDR) technology, configured to operate in the GSM band. Obtained samples are used then to feed the sensing mechanism. Spectral analysis is delivered to estimate power density peaks and corresponding frequencies. Decision making phase brings together power thresholding technique and GSM control channel decoding to identify idle and busy channels. Experiments are evaluated using detection and false alarm probabilities emulated via Receiver Operating Characteristic (ROC) curves. Obtained performances show better detection accuracy and robustness against variant noise/fading effects, when using our mechanism compared to Energy Detection (ED) based ones as Welch method, and Beamforming based ones as Minimum Variance Distortionless Response (MVDR) method. Occupancy results exhibit considerable potential of secondary use in GSM based primary network.

scholarly journals Differences Amplitude Based Improvement of Minimum Variance Distortionless Response Filter

2020 ◽  
Vol 8 (5) ◽  
pp. 1635-1637
Keyword(s):  
A Priori ◽  
Minimum Variance ◽  
Coherent Noise ◽  
Noise Interference ◽  
Power Spectral ◽  
Minimum Variance Distortionless Response ◽  
Power Spectral Densities ◽  
Noisy Signals ◽  
A New Technique ◽  
Priori Information

In this work, the author introduces a new technique for improving the performance of minimum variance distortionless response filter in condition of coherent noise. The proposal algorithm exploits a priori information of differences amplitude to balance power spectral densities of observed noisy signals. The output signal of MVDR filter is then processed by an additional post-filtering, which based speech presence probability to suppress more noise interference and increase quality speech. In experiments using two noisy signal recordings in anechoeic room, the modified MVDR-filter results provides that the suggested algorithm increases speech quality compared to the conventional MVDR filter.

DISTRIBUTED PARALLEL PROCESSING TECHNIQUES FOR ADAPTIVE SONAR BEAMFORMING

2002 ◽  
Vol 10 (01) ◽  
pp. 1-23 ◽  
Author(s):  
ALAN D. GEORGE ◽  
JESUS GARCIA ◽  
KEONWOOK KIM ◽  
PRIYABRATA SINHA
Keyword(s):  
Parallel Processing ◽  
Minimum Variance ◽  
Adaptive Beamforming ◽  
Autonomous Operation ◽  
Minimum Variance Distortionless Response ◽  
Efficiency And Effectiveness ◽  
Execution Speed ◽  
Processing Techniques ◽  
Performance Results ◽  
Communication Demands

Quiet submarine threats and high clutter in the littoral environment increase computation and communication demands on beamforming arrays, particularly for applications that require in-array autonomous operation. By coupling each transducer node in a distributed array with a microprocessor, and networking them together, embedded parallel processing for adaptive beamformers can glean advantages in execution speed, fault tolerance, scalability, power, and cost. In this paper, a novel set of techniques for the parallelization of adaptive beamforming algorithms is introduced for in-array sonar signal processing. A narrowband, unconstrained, Minimum Variance Distortionless Response (MVDR) beamformer is used as a baseline to investigate the efficiency and effectiveness of this method in an experimental fashion. Performance results are also included, among them execution times, parallel efficiencies, and memory requirements, using a distributed system testbed comprised of a cluster of workstations connected by a conventional network.

scholarly journals A Comparison of Optimal SONAR Array Amplitude Shading Coefficients

Acoustics ◽  
2019 ◽  
Vol 1 (4) ◽  
pp. 808-815
Author(s):  
Benjamin Cray ◽  
Ivars Kirsteins
Keyword(s):  
Short Communication ◽  
Minimum Variance ◽  
Adaptive Processing ◽  
Physical Acoustics ◽  
First Order ◽  
Physical Knowledge ◽  
Directional Sensors ◽  
Minimum Variance Distortionless Response ◽  
Cross Spectral Density ◽  
Optimal Set

This paper compares two different approaches to deriving shading coefficients (weights) for optimal first order and second order directional sensors (that is; sonobuoys, vectors and dyadic sensors). The first approach is an analytical or a physics-based derivation, involving computations with gradients and linearized momentum; the second is an adaptive minimum variance distortionless response (MVDR) derivation, which finds weights that minimize the cross spectral density (CSD) matrix. The two approaches are shown to be equivalent. In other words, the adaptive MVDR processing procedure does indeed converge to a physics-based solution, without any pre-existing physical knowledge of the behavior of the acoustic field. This suggests that adaptive algorithms innately seek physics-based solutions when these solutions are optimum. The intent of this short communication is not to advocate for one type of adaptive processing method over another. The observation that is presented here is important though, it confirms that at least in an idealized noise field, adaptive processing converges on an optimal set of shading coefficients, similarly derived based on well-established physical acoustics.

scholarly journals MVDR Algorithm Based on Estimated Diagonal Loading for Beamforming

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Yuteng Xiao ◽  
Jihang Yin ◽  
Honggang Qi ◽  
Hongsheng Yin ◽  
Gang Hua
Keyword(s):  
Signal Processing ◽  
Covariance Matrix ◽  
Optimization Model ◽  
Matrix Theory ◽  
Minimum Variance ◽  
Signal Model ◽  
Diagonal Loading ◽  
Minimum Variance Distortionless Response ◽  
The Matrix ◽  
Beamforming Algorithm

Beamforming algorithm is widely used in many signal processing fields. At present, the typical beamforming algorithm is MVDR (Minimum Variance Distortionless Response). However, the performance of MVDR algorithm relies on the accurate covariance matrix. The MVDR algorithm declines dramatically with the inaccurate covariance matrix. To solve the problem, studying the beamforming array signal model and beamforming MVDR algorithm, we improve MVDR algorithm based on estimated diagonal loading for beamforming. MVDR optimization model based on diagonal loading compensation is established and the interval of the diagonal loading compensation value is deduced on the basis of the matrix theory. The optimal diagonal loading value in the interval is also determined through the experimental method. The experimental results show that the algorithm compared with existing algorithms is practical and effective.

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