25,988 research outputs found
Accelerated Modeling of Near and Far-Field Diffraction for Coronagraphic Optical Systems
Accurately predicting the performance of coronagraphs and tolerancing optical
surfaces for high-contrast imaging requires a detailed accounting of
diffraction effects. Unlike simple Fraunhofer diffraction modeling, near and
far-field diffraction effects, such as the Talbot effect, are captured by
plane-to-plane propagation using Fresnel and angular spectrum propagation. This
approach requires a sequence of computationally intensive Fourier transforms
and quadratic phase functions, which limit the design and aberration
sensitivity parameter space which can be explored at high-fidelity in the
course of coronagraph design. This study presents the results of optimizing the
multi-surface propagation module of the open source Physical Optics Propagation
in PYthon (POPPY) package. This optimization was performed by implementing and
benchmarking Fourier transforms and array operations on graphics processing
units, as well as optimizing multithreaded numerical calculations using the
NumExpr python library where appropriate, to speed the end-to-end simulation of
observatory and coronagraph optical systems. Using realistic systems, this
study demonstrates a greater than five-fold decrease in wall-clock runtime over
POPPY's previous implementation and describes opportunities for further
improvements in diffraction modeling performance.Comment: Presented at SPIE ASTI 2018, Austin Texas. 11 pages, 6 figure
A Flexible Implementation of a Matrix Laurent Series-Based 16-Point Fast Fourier and Hartley Transforms
This paper describes a flexible architecture for implementing a new fast
computation of the discrete Fourier and Hartley transforms, which is based on a
matrix Laurent series. The device calculates the transforms based on a single
bit selection operator. The hardware structure and synthesis are presented,
which handled a 16-point fast transform in 65 nsec, with a Xilinx SPARTAN 3E
device.Comment: 4 pages, 4 figures. IEEE VI Southern Programmable Logic Conference
201
New Algorithms for Computing a Single Component of the Discrete Fourier Transform
This paper introduces the theory and hardware implementation of two new
algorithms for computing a single component of the discrete Fourier transform.
In terms of multiplicative complexity, both algorithms are more efficient, in
general, than the well known Goertzel Algorithm.Comment: 4 pages, 3 figures, 1 table. In: 10th International Symposium on
Communication Theory and Applications, Ambleside, U
Optimization of new Chinese Remainder theorems using special moduli sets
The residue number system (RNS) is an integer number representation system, which is capable of supporting parallel, high-speed arithmetic. This system also offers some useful properties for error detection, error correction and fault tolerance. It has numerous applications in computation-intensive digital signal processing (DSP) operations, like digital filtering, convolution, correlation, Discrete Fourier Transform, Fast Fourier Transform, direct digital frequency synthesis, etc. The residue to binary conversion is based on Chinese Remainder Theorem (CRT) and Mixed Radix Conversion (MRC). However, the CRT requires a slow large modulo operation while the MRC requires finding the mixed radix digits which is a slow process. The new Chinese Remainder Theorems (CRT I, CRT II and CRT III) make the computations faster and efficient without any extra overheads. But, New CRTs are hardware intensive as they require many inverse modulus operators, modulus operators, multipliers and dividers. Dividers and inverse modulus operators in turn needs many half and full adders and subtractors. So, some kind of optimization is necessary to implement these theorems practically. In this research, for the optimization, new both co-prime and non co-prime multi modulus sets are proposed that simplify the new Chinese Remainder theorems by eliminating the huge summations, inverse modulo operators, and dividers. Furthermore, the proposed hardware optimization removes the multiplication terms in the theorems, which further simplifies the implementation
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