1,100 research outputs found
Status and Future Perspectives for Lattice Gauge Theory Calculations to the Exascale and Beyond
In this and a set of companion whitepapers, the USQCD Collaboration lays out
a program of science and computing for lattice gauge theory. These whitepapers
describe how calculation using lattice QCD (and other gauge theories) can aid
the interpretation of ongoing and upcoming experiments in particle and nuclear
physics, as well as inspire new ones.Comment: 44 pages. 1 of USQCD whitepapers
Reliable and Efficient Parallel Processing Algorithms and Architectures for Modern Signal Processing
Least-squares (LS) estimations and spectral decomposition algorithms constitute the heart of modern signal processing and communication problems. Implementations of recursive LS and spectral decomposition algorithms onto parallel processing architectures such as systolic arrays with efficient fault-tolerant schemes are the major concerns of this dissertation. There are four major results in this dissertation. First, we propose the systolic block Householder transformation with application to the recursive least-squares minimization. It is successfully implemented on a systolic array with a two-level pipelined implementation at the vector level as well as at the word level. Second, a real-time algorithm-based concurrent error detection scheme based on the residual method is proposed for the QRD RLS systolic array. The fault diagnosis, order degraded reconfiguration, and performance analysis are also considered. Third, the dynamic range, stability, error detection capability under finite-precision implementation, order degraded performance, and residual estimation under faulty situations for the QRD RLS systolic array are studied in details. Finally, we propose the use of multi-phase systolic algorithms for spectral decomposition based on the QR algorithm. Two systolic architectures, one based on triangular array and another based on rectangular array, are presented for the multiphase operations with fault-tolerant considerations. Eigenvectors and singular vectors can be easily obtained by using the multi-pase operations. Performance issues are also considered
Two Rapid Power Iterative DOA Estimators for UAV Emitter Using Massive/Ultra-massive Receive Array
To provide rapid direction finding (DF) for unmanned aerial vehicle (UAV)
emitter in future wireless networks, a low-complexity direction of arrival
(DOA) estimation architecture for massive multiple input multiple output (MIMO)
receiver arrays is constructed. In this paper, we propose two strategies to
address the extremely high complexity caused by eigenvalue decomposition of the
received signal covariance matrix. Firstly, a rapid power-iterative rotational
invariance (RPI-RI) method is proposed, which adopts the signal subspace
generated by power iteration to gets the final direction estimation through
rotational invariance between subarrays. RPI-RI makes a significant complexity
reduction at the cost of a substantial performance loss. In order to further
reduce the complexity and provide a good directional measurement result, a
rapid power-iterative Polynomial rooting (RPI-PR) method is proposed, which
utilizes the noise subspace combined with polynomial solution method to get the
optimal direction estimation. In addition, the influence of initial vector
selection on convergence in the power iteration is analyzed, especially when
the initial vector is orthogonal to the incident wave. Simulation results show
that the two proposed methods outperform the conventional DOA estimation
methods in terms of computational complexity. In particular, the RPIPR method
achieves more than two orders of magnitude lower complexity than conventional
methods and achieves performance close to CRLB. Moreover, it is verified that
the initial vector and the relative error have a significant impact on the
performance of the computational complexity
Robust Face Recognition System Based on a Multi-Views Face Database
In this chapter, we describe a new robust face recognition system base on a multi-views face database that derives some 3-D information from a set of face images. We attempt to build an approximately 3-D system for improving the performance of face recognition. Our objective is to provide a basic 3-D system for improving the performance of face recognition. The main goal of this vision system is 1) to minimize the hardware resources, 2) to obtain high success rates of identity verification, and 3) to cope with real-time constraints. Using the multi-views database, we address the problem of face recognition by evaluating the two methods PCA and ICA and comparing their relative performance. We explore the issues of subspace selection, algorithm comparison, and multi-views face recognition performance. In order to make full use of the multi-views property, we also propose a strategy of majority voting among the five views, which can improve the recognition rate. Experimental results show that ICA is a promising method among the many possible face recognition methods, and that the ICA algorithm with majority-voting is currently the best choice for our purposes
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