Analysis of physiological systems

The purpose of this paper is both a review and an assessment of techniques presently available for automatic test generation for analog systems. After recalling the general problems of automatic testing (definitions, faults in analog systems, different types of tests, main operations, and diagnosis procedures), characterization and description modes of analog systems, and the main software ingredients of automatic test equipment, a categorization of known techniques along several criteria can be proposed. Then, several techniques, respectively, proceeding from approaches based on deterministic and probabilistic estimation, taxonomical and topological analyses can be detailed. Techniques specific to linear systems (several of them belonging to the above three categories) are dealt with in a separate section. The main features of the techniques that are described are summed up in five synoptic tables. As a conclusion, several research areas that need further investigation in view of a possible industrial implementation of automatic analog test generation techniques are identified. Two appendixes deal briefly with fault tolerance and fault simulation in analog systems. An extensive bibliography ( \sim 500 entries) is provided.

Automatic test generation techniques for analog circuits and systems: A review

1. Introduction 2. Fundamentals of Fourier Analysis 3. Fourier Analysis in Systems Theory 4. Fourier Transforms in Probability, Random Variables and Stochastic Processes 5. The Sampling Theory 6. Generalizations of the Sampling Theorem 7. Noise and Error Effects 8. Multidimensional Signal Analysis 9. Time-Frequency Representations 10. Signal Recovery 11. Signal and Image Synthesis: Alternating Projections Onto Convex Sets 12. Mathematical Morphology and Fourier Analysis on Time Sales 13. Applications 14. Appendices 15. Reference

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Handbook of Fourier Analysis & Its Applications

This paper represents a survey of the recent literature in the area of process identification and parameter estimation techniques applicable to lumped-parameter, deterministic, dynamical systems. Methods reviewed include statistical estimation techniques, direct and indirect methods based on optimal control theory, functional expansion, impulse response, frequency response and a number of other specific methods. Each method is presented in a consistent format which includes an outline of the general characteristics, calculational techniques, experimental techniques, reliability estimates and applications. The overall objective is to provide a basis for comparison of the methods and a guide to particular applications which will assist the reader in selecting the best method for his specific problem Comments and additions are solicited: see page 263.

A review of process identification and parameter estimation techniques

In recent years, modulating the working temperature of metal-oxide gas sensors has been one of the most widely used methods to enhance sensor selectivity. When the working temperature of a gas sensor is modulated, the kinetics of the gas-sensor interaction are altered, and this leads to characteristic response patterns. Many works have shown that it is possible to identify and determine the concentration of gases in simple mixtures, even using a single temperature-modulated metal-oxide gas sensor. However, the selection of the frequencies used to modulate temperature remains an empirical process. In this paper, we introduce a method, borrowed from the field-of-system identification, to systematically determine the optimal set of modulating frequencies to solve a given gas-analysis application. The method consists of using maximum-length pseudorandom binary sequences to modulate the working temperature of metal-oxide gas sensors. Since these signals have a flat power spectrum (i.e., like white noise) in a wide frequency range, an estimate of the impulse response of each gas-sensor pair can be computed by the cross correlation of the excitatory and response sequences. Studying the impulse response estimates, the set of modulating frequencies that are useful to discriminate between different gases and to estimate gas concentration, is obtained in a systematic way. The method is demonstrated with tungsten oxide micro-hotplate gas sensors applied to detect ammonia, nitrogen dioxide, and their binary mixtures at different concentrations. It is shown that it is possible to find temperature-modulating frequencies to obtain high gas identification and quantification rates (95.55% and 100%, respectively).

Optimized temperature modulation of micro-hotplate gas sensors through pseudorandom binary sequences

A simple technique, exploiting a property of pseudorandom binary signals, reduces to zero the effect of linear disturbances in the estimation of a system weighting function by cross-correlation. Errors due to quadratic disturbances are then constant and may be minimised by choice of particular pseudorandom binary signals which are tabulated. The principal remaining error is also constant, and an unbiased estimate of the system weighting function is easily obtained.

Choice of pseudorandom binary signals for system identification

It is shown that the sampled output of a nonlinear system with sampled-data input is given by the discrete equivalent of a Volterra series in which the kernels are the sampled Volterra kernels of the system. This theorem is then extended to the common case in which the sampled-data input is applied to the nonlinear system through a zero-order hold.

Synchronous sampling theorem for nonlinear systems

Use of the reference phase of a pseudorandom binary signal in a linearly weighted crosscorrelation is shown to result in the elimination of errors due to both linear and quadratic disturbances in the estimation of a system weighting function.

Elimination of quadratic drift errors in system identification by pseudorandom binary signals

The sensitivity of the s plane pole position of a closed-loop system transfer function to variation of an open-loop system parameter is shown to be the coefficient of a complex frequency function in the response of the closed-loop system to a certain input signal. In many cases of practical interest, this signal is a simple one.

Sensitivity relationships for linear feedback systems

The general conditions under which a transformation of a p-level m-sequence yields a sequence with a primitive auto-correlation function are derived. Specific results are given for 3<p<13.

H.A. Barker

Papers

Choice of pseudorandom binary signals for system identification

Synchronous sampling theorem for nonlinear systems

Elimination of quadratic drift errors in system identification by pseudorandom binary signals

Sensitivity relationships for linear feedback systems

p-level sequences with primitive autocorrelation functions