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

Accelerated CTIS Using the Cell Processor

The Computed Tomography Imaging Spectrometer (CTIS) is a device capable of simultaneously acquiring imagery from multiple bands of the electromagnetic spectrum. Due to the method of data collection from this system, a processing intensive reconstruction phase is required to resolve the image output. This paper evaluates a parallelized implementation of the Vose-Horton CTIS reconstruction algorithm using the Cell processor. In addition to demonstrating the feasibility of a mixed precision implementation, it is shown that use of the parallel processing capabilities of the Cell may provide a significant reduction in reconstruction time

Adaptive Real Time Imaging Synthesis Telescopes

The digital revolution is transforming astronomy from a data-starved to a data-submerged science. Instruments such as the Atacama Large Millimeter Array (ALMA), the Large Synoptic Survey Telescope (LSST), and the Square Kilometer Array (SKA) will measure their accumulated data in petabytes. The capacity to produce enormous volumes of data must be matched with the computing power to process that data and produce meaningful results. In addition to handling huge data rates, we need adaptive calibration and beamforming to handle atmospheric fluctuations and radio frequency interference, and to provide a user environment which makes the full power of large telescope arrays accessible to both expert and non-expert users. Delayed calibration and analysis limit the science which can be done. To make the best use of both telescope and human resources we must reduce the burden of data reduction. Our instrumentation comprises of a flexible correlator, beam former and imager with digital signal processing closely coupled with a computing cluster. This instrumentation will be highly accessible to scientists, engineers, and students for research and development of real-time processing algorithms, and will tap into the pool of talented and innovative students and visiting scientists from engineering, computing, and astronomy backgrounds. Adaptive real-time imaging will transform radio astronomy by providing real-time feedback to observers. Calibration of the data is made in close to real time using a model of the sky brightness distribution. The derived calibration parameters are fed back into the imagers and beam formers. The regions imaged are used to update and improve the a-priori model, which becomes the final calibrated image by the time the observations are complete

Containing Analog Data Deluge at Edge through Frequency-Domain Compression in Collaborative Compute-in-Memory Networks

Edge computing is a promising solution for handling high-dimensional, multispectral analog data from sensors and IoT devices for applications such as autonomous drones. However, edge devices' limited storage and computing resources make it challenging to perform complex predictive modeling at the edge. Compute-in-memory (CiM) has emerged as a principal paradigm to minimize energy for deep learning-based inference at the edge. Nevertheless, integrating storage and processing complicates memory cells and/or memory peripherals, essentially trading off area efficiency for energy efficiency. This paper proposes a novel solution to improve area efficiency in deep learning inference tasks. The proposed method employs two key strategies. Firstly, a Frequency domain learning approach uses binarized Walsh-Hadamard Transforms, reducing the necessary parameters for DNN (by 87% in MobileNetV2) and enabling compute-in-SRAM, which better utilizes parallelism during inference. Secondly, a memory-immersed collaborative digitization method is described among CiM arrays to reduce the area overheads of conventional ADCs. This facilitates more CiM arrays in limited footprint designs, leading to better parallelism and reduced external memory accesses. Different networking configurations are explored, where Flash, SA, and their hybrid digitization steps can be implemented using the memory-immersed scheme. The results are demonstrated using a 65 nm CMOS test chip, exhibiting significant area and energy savings compared to a 40 nm-node 5-bit SAR ADC and 5-bit Flash ADC. By processing analog data more efficiently, it is possible to selectively retain valuable data from sensors and alleviate the challenges posed by the analog data deluge.Comment: arXiv admin note: text overlap with arXiv:2307.03863, arXiv:2309.0177

Applications of GPU Computing to Control and Simulate Systems

[Abstract] This work deals with the new programming paradigm that exploits the benefits of modern Graphics Processing Units (GPUs), specifically their capacity to carry heavy calculations out for simulating systems or solving complex control strategies in real time

Similarity of Inference Face Matching On Angle Oriented Face Recognition

Face recognition is one of the wide applications of image processing technique. In this paper complete image of face recognition algorithm is proposed. In the prepared algorithm the local information is extracted using angle oriented discrete cosine transforms and invokes certain normalization techniques. To increase the Reliability of the Face detection process, neighborhood pixel information is incorporated into the proposed method. Discrete Cosine Transform (DCT) are renowned methods are implementing in the field of access control and security are utilizing the feature extraction capabilities. But these algorithms have certain limitations like poor discriminatory power and disability to handle large computational load. The face matching classification for the proposed system is done using various distance measure methods like Euclidean Distance, Manhattan Distance and Cosine Distance methods and the recognition rate were compared for different distance measures. The proposed method has been successfully tested on image database which is acquired under variable illumination and facial expressions. It is observed from the results that use of face matching like various method gives a recognition rate are high while comparing other methods. Also this study analyzes and compares the obtained results from the proposed Angle oriented face recognition with threshold based face detector to show the level of robustness using texture features in the proposed face detector. It was verified that a face recognition based on textual features can lead to an efficient and more reliable face detection method compared with KLT (Karhunen Loeve Transform), a threshold face detector. Keywords: Angle Oriented, Cosine Similarity, Discrete Cosine Transform, Euclidean Distance, Face Matching, Feature Extraction, Face Recognition, Image texture features

Research of Order Processing Capabilities for Oscillatory Signals in Rotating Equipment

The Arnold Engineering Development Complex (AEDC) has identified a need to process data from oscillatory signals on a revolution basis, also known as order processing. Such oscillatory data is hereafter referred to as dynamic data. Order processing would serve to improve dynamic data accuracy as reported in the frequency and order domains, capture momentary integral responses, and facilitate organic comparisons between various types of oscillatory signals. Organizing data by revolutions would also be beneficial for time domain analysis.This paper explores the need for order processing, reviews similar methods employed in other data acquisition applications such as blade tip timing, and discusses options for making order processing possible for any oscillatory signal generated by rotating equipment. This paper primarily deals with turbine engine vibratory instrumentation and data acquisition. The content discussed herein may also be extended to other types of rotating equipment such as motors, compressors, and turbines. Order processing deals primarily with integral (synchronous) responses, which are forced responses as a function of rotational speed and natural frequencies. Non-synchronous responses, also known as non-integral (NIV) responses, are not considered.The focus of this paper lies in the research of conditioning time domain data sets of various sizes to be transformed to the frequency domain by means of FFTs with standard 2x sizes. This is to be accomplished while varying numbers of samples per revolution for a full range of rotational speeds. Succinctly stated, a comparison is made between standard FFT processing results and simplistic order processing methods. Since improved accuracy is one of the major drivers for developing this capability, the focal points are the acquisition, conditioning, and processing of virtual data sets that are of different sizes than specified FFT sizes. More specifically, the effects of decimation, zero padding, windowing functions, and other types of processing variables are evaluated. This research serves as a precursor for development of comprehensive order processing capabilities for turbine engines at AEDC

High-performance Parallel Solver for Integral Equations of Electromagnetics Based on Galerkin Method

A new parallel solver for the volumetric integral equations (IE) of electrodynamics is presented. The solver is based on the Galerkin method which ensures the convergent numerical solution. The main features include: (i) the memory usage is 8 times lower, compared to analogous IE based algorithms, without additional restriction on the background media; (ii) accurate and stable method to compute matrix coefficients corresponding to the IE; (iii) high degree of parallelism. The solver's computational efficiency is shown on a problem of magnetotelluric sounding of the high conductivity contrast media. A good agreement with the results obtained with the second order finite element method is demonstrated. Due to effective approach to parallelization and distributed data storage the program exhibits perfect scalability on different hardware platforms.Comment: The main results of this paper were presented at IAMG 2015 conference Frieberg, Germany. 28 pages, 11 figure