Real-time processing of radar return on a parallel computer

NASA is working with the FAA to demonstrate the feasibility of pulse Doppler radar as a candidate airborne sensor to detect low altitude windshears. The need to provide the pilot with timely information about possible hazards has motivated a demand for real-time processing of a radar return. Investigated here is parallel processing as a means of accommodating the high data rates required. A PC based parallel computer, called the transputer, is used to investigate issues in real time concurrent processing of radar signals. A transputer network is made up of an array of single instruction stream processors that can be networked in a variety of ways. They are easily reconfigured and software development is largely independent of the particular network topology. The performance of the transputer is evaluated in light of the computational requirements. A number of algorithms have been implemented on the transputers in OCCAM, a language specially designed for parallel processing. These include signal processing algorithms such as the Fast Fourier Transform (FFT), pulse-pair, and autoregressive modelling, as well as routing software to support concurrency. The most computationally intensive task is estimating the spectrum. Two approaches have been taken on this problem, the first and most conventional of which is to use the FFT. By using table look-ups for the basis function and other optimizing techniques, an algorithm has been developed that is sufficient for real time. The other approach is to model the signal as an autoregressive process and estimate the spectrum based on the model coefficients. This technique is attractive because it does not suffer from the spectral leakage problem inherent in the FFT. Benchmark tests indicate that autoregressive modeling is feasible in real time

Real-time sound synthesis on a multi-processor platform

Real-time sound synthesis means that the calculation and output of each sound sample for a channel of audio information must be completed within a sample period. At a broadcasting standard, a sampling rate of 32,000 Hz, the maximum period available is 31.25 μsec. Such requirements demand a large amount of data processing power. An effective solution for this problem is a multi-processor platform; a parallel and distributed processing system. The suitability of the MIDI [Music Instrument Digital Interface] standard, published in 1983, as a controller for real-time applications is examined. Many musicians have expressed doubts on the decade old standard's ability for real-time performance. These have been investigated by measuring timing in various musical gestures, and by comparing these with the subjective characteristics of human perception. An implementation and its optimisation of real-time additive synthesis programs on a multi-transputer network are described. A prototype 81-polyphonic-note- organ configuration was implemented. By devising and deploying monitoring processes, the network's performance was measured and enhanced, leading to an efficient usage; the 88-note configuration. Since 88 simultaneous notes are rarely necessary in most performances, a scheduling program for dynamic note allocation was then introduced to achieve further efficiency gains. Considering calculation redundancies still further, a multi-sampling rate approach was applied as a further step to achieve an optimal performance. The theories underlining sound granulation, as a means of constructing complex sounds from grains, and the real-time implementation of this technique are outlined. The idea of sound granulation is quite similar to the quantum-wave theory, "acoustic quanta". Despite the conceptual simplicity, the signal processing requirements set tough demands, providing a challenge for this audio synthesis engine. Three issues arising from the results of the implementations above are discussed; the efficiency of the applications implemented, provisions for new processors and an optimal network architecture for sound synthesis

On the synthesis and processing of high quality audio signals by parallel computers

This work concerns the application of new computer architectures to the creation and manipulation of high-quality audio bandwidth signals. The configuration of both the hardware and software in such systems falls under consideration in the three major sections which present increasing levels of algorithmic concurrency. In the first section, the programs which are described are distributed in identical copies across an array of processing elements; these programs run autonomously, generating data independently, but with control parameters peculiar to each copy: this type of concurrency is referred to as isonomic}The central section presents a structure which distributes tasks across an arbitrary network of processors; the flow of control in such a program is quasi- indeterminate, and controlled on a demand basis by the rate of completion of the slave tasks and their irregular interaction with the master. Whilst that interaction is, in principle, deterministic, it is also data-dependent; the dynamic nature of task allocation demands that no a priori knowledge of the rate of task completion be required. This type of concurrency is called dianomic? Finally, an architecture is described which will support a very high level of algorithmic concurrency. The programs which make efficient use of such a machine are designed not by considering flow of control, but by considering flow of data. Each atomic algorithmic unit is made as simple as possible, which results in the extensive distribution of a program over very many processing elements. Programs designed by considering only the optimum data exchange routes are said to exhibit systolic^ concurrency. Often neglected in the study of system design are those provisions necessary for practical implementations. It was intended to provide users with useful application programs in fulfilment of this study; the target group is electroacoustic composers, who use digital signal processing techniques in the context of musical composition. Some of the algorithms in use in this field are highly complex, often requiring a quantity of processing for each sample which exceeds that currently available even from very powerful computers. Consequently, applications tend to operate not in 'real-time' (where the output of a system responds to its input apparently instantaneously), but by the manipulation of sounds recorded digitally on a mass storage device. The first two sections adopt existing, public-domain software, and seek to increase its speed of execution significantly by parallel techniques, with the minimum compromise of functionality and ease of use. Those chosen are the general- purpose direct synthesis program CSOUND, from M.I.T., and a stand-alone phase vocoder system from the C.D.P..(^4) In each case, the desired aim is achieved: to increase speed of execution by two orders of magnitude over the systems currently in use by composers. This requires substantial restructuring of the programs, and careful consideration of the best computer architectures on which they are to run concurrently. The third section examines the rationale behind the use of computers in music, and begins with the implementation of a sophisticated electronic musical instrument capable of a degree of expression at least equal to its acoustic counterparts. It seems that the flexible control of such an instrument demands a greater computing resource than the sound synthesis part. A machine has been constructed with the intention of enabling the 'gestural capture' of performance information in real-time; the structure of this computer, which has one hundred and sixty high-performance microprocessors running in parallel, is expounded; and the systolic programming techniques required to take advantage of such an array are illustrated in the Occam programming language

Theory and realization of novel algorithms for random sampling in digital signal processing

Random sampling is a technique which overcomes the alias problem in regular sampling. The randomization, however, destroys the symmetry property of the transform kernel of the discrete Fourier transform. Hence, when transforming a randomly sampled sequence to its frequency spectrum, the Fast Fourier transform cannot be applied and the computational complexity is N(^2). The objectives of this research project are (1) To devise sampling methods for random sampling such that computation may be reduced while the anti-alias property of random sampling is maintained : Two methods of inserting limited regularities into the randomized sampling grids are proposed. They are parallel additive random sampling and hybrid additive random sampling, both of which can save at least 75% of the multiplications required. The algorithms also lend themselves to the implementation by a multiprocessor system, which will further enhance the speed of the evaluation. (2) To study the auto-correlation sequence of a randomly sampled sequence as an alternative means to confirm its anti-alias property : The anti-alias property of the two proposed methods can be confirmed by using convolution in the frequency domain. However, the same conclusion is also reached by analysing in the spatial domain the auto-correlation of such sample sequences. A technique to evaluate the auto-correlation sequence of a randomly sampled sequence with a regular step size is proposed. The technique may also serve as an algorithm to convert a randomly sampled sequence to a regularly spaced sequence having a desired Nyquist frequency. (3) To provide a rapid spectral estimation using a coarse kernel : The approximate method proposed by Mason in 1980, which trades the accuracy for the speed of the computation, is introduced for making random sampling more attractive. (4) To suggest possible applications for random and pseudo-random sampling : To fully exploit its advantages, random sampling has been adopted in measurement Random sampling is a technique which overcomes the alias problem in regular sampling. The randomization, however, destroys the symmetry property of the transform kernel of the discrete Fourier transform. Hence, when transforming a randomly sampled sequence to its frequency spectrum, the Fast Fourier transform cannot be applied and the computational complexity is N"^. The objectives of this research project are (1) To devise sampling methods for random sampling such that computation may be reduced while the anti-alias property of random sampling is maintained : Two methods of inserting limited regularities into the randomized sampling grids are proposed. They are parallel additive random sampling and hybrid additive random sampling, both of which can save at least 75% , of the multiplications required. The algorithms also lend themselves to the implementation by a multiprocessor system, which will further enhance the speed of the evaluation. (2) To study the auto-correlation sequence of a randomly sampled sequence as an alternative means to confirm its anti-alias property : The anti-alias property of the two proposed methods can be confirmed by using convolution in the frequency domain. However, the same conclusion is also reached by analysing in the spatial domain the auto-correlation of such sample sequences. A technique to evaluate the auto-correlation sequence of a randomly sampled sequence with a regular step size is proposed. The technique may also serve as an algorithm to convert a randomly sampled sequence to a regularly spaced sequence having a desired Nyquist frequency. (3) To provide a rapid spectral estimation using a coarse kernel : The approximate method proposed by Mason in 1980, which trades the accuracy for the speed of the computation, is introduced for making random sampling more attractive. (4) To suggest possible applications for random and pseudo-random sampling : To fully exploit its advantages, random sampling has been adopted in measurement instruments where computing a spectrum is either minimal or not required. Such applications in instrumentation are easily found in the literature. In this thesis, two applications in digital signal processing are introduced. (5) To suggest an inverse transformation for random sampling so as to complete a two-way process and to broaden its scope of application. Apart from the above, a case study of realizing in a transputer network the prime factor algorithm with regular sampling is given in Chapter 2 and a rough estimation of the signal-to-noise ratio for a spectrum obtained from random sampling is found in Chapter 3. Although random sampling is alias-free, problems in computational complexity and noise prevent it from being adopted widely in engineering applications. In the conclusions, the criteria for adopting random sampling are put forward and the directions for its development are discussed

Multi-transputer based isolated word speech recognition system.

by Francis Cho-yiu Chik.Thesis (M.Phil.)--Chinese University of Hong Kong, 1996.Includes bibliographical references (leaves 129-135).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Automatic speech recognition and its applications --- p.1Chapter 1.1.1 --- Artificial Neural Network (ANN) approach --- p.3Chapter 1.2 --- Motivation --- p.5Chapter 1.3 --- Background --- p.6Chapter 1.3.1 --- Speech recognition --- p.6Chapter 1.3.2 --- Parallel processing --- p.7Chapter 1.3.3 --- Parallel architectures --- p.10Chapter 1.3.4 --- Transputer --- p.12Chapter 1.4 --- Thesis outline --- p.13Chapter 2 --- Speech Signal Pre-processing --- p.14Chapter 2.1 --- Determine useful signal --- p.14Chapter 2.1.1 --- End point detection using energy --- p.15Chapter 2.1.2 --- End point detection enhancement using zero crossing rate --- p.18Chapter 2.2 --- Pre-emphasis filter --- p.19Chapter 2.3 --- Feature extraction --- p.20Chapter 2.3.1 --- Filter-bank spectrum analysis model --- p.22Chapter 2.3.2 --- Linear Predictive Coding (LPC) coefficients --- p.25Chapter 2.3.3 --- Cepstral coefficients --- p.27Chapter 2.3.4 --- Zero crossing rate and energy --- p.27Chapter 2.3.5 --- Pitch (fundamental frequency) detection --- p.28Chapter 2.4 --- Discussions --- p.30Chapter 3 --- Speech Recognition Methods --- p.32Chapter 3.1 --- Template matching using Dynamic Time Warping (DTW) --- p.32Chapter 3.2 --- Hidden Markov Model (HMM) --- p.37Chapter 3.2.1 --- Vector Quantization (VQ) --- p.38Chapter 3.2.2 --- Description of a discrete HMM --- p.41Chapter 3.2.3 --- Probability evaluation --- p.42Chapter 3.2.4 --- Estimation technique for model parameters --- p.46Chapter 3.2.5 --- State sequence for the observation sequence --- p.48Chapter 3.3 --- 2-dimensional Hidden Markov Model (2dHMM) --- p.49Chapter 3.3.1 --- Calculation for a 2dHMM --- p.50Chapter 3.4 --- Discussions --- p.56Chapter 4 --- Implementation --- p.59Chapter 4.1 --- Transputer based multiprocessor system --- p.59Chapter 4.1.1 --- Transputer Development System (TDS) --- p.60Chapter 4.1.2 --- System architecture --- p.61Chapter 4.1.3 --- Transtech TMB16 mother board --- p.62Chapter 4.1.4 --- Farming technique --- p.64Chapter 4.2 --- Farming technique on extracting spectral amplitude feature --- p.68Chapter 4.3 --- Feature extraction for LPC --- p.73Chapter 4.4 --- DTW based recognition --- p.77Chapter 4.4.1 --- Feature extraction --- p.77Chapter 4.4.2 --- Training and matching --- p.78Chapter 4.5 --- HMM based recognition --- p.80Chapter 4.5.1 --- Feature extraction --- p.80Chapter 4.5.2 --- Model training and matching --- p.81Chapter 4.6 --- 2dHMM based recognition --- p.83Chapter 4.6.1 --- Feature extraction --- p.83Chapter 4.6.2 --- Training --- p.83Chapter 4.6.3 --- Recognition --- p.87Chapter 4.7 --- Training convergence in HMM and 2dHMM --- p.88Chapter 4.8 --- Discussions --- p.91Chapter 5 --- Experimental Results --- p.92Chapter 5.1 --- "Comparison of DTW, HMM and 2dHMM" --- p.93Chapter 5.2 --- Comparison between HMM and 2dHMM --- p.98Chapter 5.2.1 --- Recognition test on 20 English words --- p.98Chapter 5.2.2 --- Recognition test on 10 Cantonese syllables --- p.102Chapter 5.3 --- Recognition test on 80 Cantonese syllables --- p.113Chapter 5.4 --- Speed matching --- p.118Chapter 5.5 --- Computational performance --- p.119Chapter 5.5.1 --- Training performance --- p.119Chapter 5.5.2 --- Recognition performance --- p.120Chapter 6 --- Discussions and Conclusions --- p.126Bibliography --- p.129Chapter A --- An ANN Model for Speech Recognition --- p.136Chapter B --- A Speech Signal Represented in Fequency Domain (Spectrogram) --- p.138Chapter C --- Dynamic Programming --- p.144Chapter D --- Markov Process --- p.145Chapter E --- Maximum Likelihood (ML) --- p.146Chapter F --- Multiple Training --- p.149Chapter F.1 --- HMM --- p.150Chapter F.2 --- 2dHMM --- p.150Chapter G --- IMS T800 Transputer --- p.152Chapter G.1 --- IMS T800 architecture --- p.152Chapter G.2 --- Instruction encoding --- p.153Chapter G.3 --- Floating point instructions --- p.155Chapter G.4 --- Optimizing use of the stack --- p.157Chapter G.5 --- Concurrent operation of FPU and CPU --- p.15

A graph theoretic approach to transputer network design for computer vision

The work in this thesis is concerned with parallel architectures based on the Inmos transputer-type processors and parallelisation of some computer vision tasks chosen from low to high level. The transputer is a microprocessor with a micro-programmed scheduler and four serial communication links. It directly supports parallel processing since several transputers can be connected through their links to co-operate on solving a problem. Also several processes can be run on the same transputer. A major issue in parallel processing is the communication overhead introduced by parallelising a given task. This overhead is not present in sequential processing and must be curbed if the implementation of a task on a parallel machine is to be successful. The interconnection network underlying the architecture of a parallel computer is therefore of the utmost importance. Computer Vision consists of a hierarchy of tasks ranging from low-level operations dealing with large amounts of relatively simple data to high level operations handling increasingly complex structures. In this work a novel edge detector based on adaptive filtering and an edge detector operating on colour images are presented and implemented on a number of transputers. These parallel implementations together with implementations of vector quantisation, Fourier descriptors for shape discrimination, the Hough transform and the Maximum clique algorithm, offer a notable performance increase when compared with sequential implementations. However, every algorithm required the design of a specific network of transputers to take advantage of the parallelism and data dependencies inherent in each. Consequently, attention is focused on the topology of interconnection networks. In particular, the communication requirements of computer vision algorithms as identified by the various computer vision tasks are analysed. These requirements together with graph theoretical considerations are then used to suggest a topology for large transputer networks. The latter is based on sub-graphs, with proven performance when used to implement interconnection networks, combined to form an architecture with improved performance. This architecture consists of a fixed structure supplemented with a dynamically reconfigured network. After describing this topology, a routing algorithm that conveys messages along shortest paths in the network is given and implemented. And finally, some practical issues in the use of transputers are considered and solutions proposed

The implementation of a large-scale numerical model of the atmosphere on a pc-based transputer network

This thesis is a report of the study of a large-scale Numerical Weather Prediction Model. The study investigated the feasibility of applying parallel algorithms to the HIRLAM Model so that it could be implemented on a Transputer Network. A set of partial differential equations which describe the behaviour of the atmosphere are presented and numerical methods are explored. The investigations focused on time critical regions of the sequential programs. From these a criterion for task distribution was devised. Expressions for the computation of speedup and scaled speedup were derived. A special set of test data, extracted from the Analysis of Hurricane Charlie (August 1986) was used as Input data for a 24 hour forecast. We report on the Case Study and how the model predicted the storm over Ireland. For comparison purposes the Model was also run on a VAX 4200 and on a Dell 386-SX PC, with the same data. The execution of the critical program modules was monitored throughout and a table of results is presented