and Hartley transforms, (Bracewell, 1986). depending on the application, (spectral analysis for example), maybe model based signal processing is more appropriate than just calculating a window based FFT, but again, this will depend on the 2.12 References precise nature of the application (Jaynes, 1987). Banks, S. (1990) Signal Processing, Image Processing and Pattern Recognition, Prentice Hall. 2.11 Conclusions Bracewell, R.N. (1986) The Hartley Transform, Oxford University Press. Burrus, C.S., Parks, T.W. (1985) DFT/FFT and Convolution Algo- In this chapter, we have introduced the concept of orthogonal rithms, Wiley Interscience. basis functions and the methods of obtaining the generalised Fourier Harris, F.J. (1978) Proc. 66, Number 1 On the use of windows for coefficients which can be used in order to expand a particular signal harmonic analysis with the discrete Fourier transform, IEEE. of interest in terms of 'elementary' basis functions, for example Jaynes, E.T. (1987) Bayesian spectrum and Chirp analysis. In Maxi- sines and cosines. mum Entropy and Bayesian Spectral Analysis and Estimation Consideration was given to the representation of continuous sig- Problems, Kluwer. nals and functions, and the analysis then considered the case of Kamen, E.W. (1990) Introduction to Signals and Systems, Maxwell discrete data sequences. Macmillan. The Fourier transform was introduced and applied to aperiodic Walker, J.S. (1988) Fourier Analysis, Oxford University Press. signals and the relationship between the power in a signal and the Wax, N. (ed) (1954) Noise and Stochastic Processes, Dover. This page intentionally left blank Queuing theory Phil Whiting University of Strathclyde Contents Introduction 3/3 Models based on the exponential, Markov process and Markov chains 3/5 Transform methods 3/8 Queuing networks 3/12 Queuing models for multi-access channels 3/15 References 3/17 This page intentionally left blank Introduction 3/3 3.1 Introduction representative of system performance. A fast response is a strict requireme