Read e-book online Advanced Digital Signal Processing and Noise Reduction PDF

By Saeed V. Vaseghi

ISBN-10: 047009494X

ISBN-13: 9780470094945

Sign processing performs an more and more critical position within the improvement of recent telecommunication and knowledge processing platforms, with quite a lot of purposes in components reminiscent of multimedia expertise, audio-visual sign processing, mobile cellular communique, radar platforms and monetary information forecasting. the idea and alertness of sign processing bargains with the identity, modelling and utilisation of styles and constructions in a sign method. The statement indications are frequently distorted, incomplete and noisy and for that reason, noise relief and the elimination of channel distortion is a vital a part of a sign processing method. complicated electronic sign Processing and Noise aid, 3rd version, presents an absolutely up to date and established presentation of the speculation and functions of statistical sign processing and noise aid tools. Noise is the everlasting bane of communications engineers, who're continually striving to discover new how you can enhance the signal-to-noise ratio in communications structures and this source might help them with this activity.

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Extra resources for Advanced Digital Signal Processing and Noise Reduction

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To design a classifier, a set of models is trained for the classes of signals that are of interest in the application. The simplest form that the models can assume is a bank, or code book, of waveforms, each representing the prototype for one class of signals. A more complete model for each class of signals takes the form of a probability distribution function. In the classification phase, a signal is labelled with the nearest or the most likely class. For example, in communication of a binary bit stream over a band-pass channel, the binary phase-shift keying (BPSK) scheme signals the bit ‘1’ using the waveform Ac sin c t and the bit ‘0’ using −Ac sin c t.

The parametric model normally describes the predictable structures and the expected patterns in the signal process, and can be used to forecast the future values of a signal from its past trajectory. Model-based methods normally outperform nonparametric methods, since they utilise more information in the form of a model of the signal process. However, they can be sensitive to the deviations of a signal from the class of signals characterised by the model. The most widely used parametric model is the linear prediction model, described in Chapter 8.

The choice of the threshold level affects the sensitivity of the detector. The higher the threshold, the lower the likelihood that noise would be classified as signal is, so the false alarm rate falls, but the probability of misclassification of signal as noise increases. 11) The choice of the threshold reflects a trade-off between the misclassification rate PMiss and the false alarm rate PFalse Alarm . 8 DIRECTIONAL RECEPTION OF WAVES: BEAM-FORMING Beam-forming is the spatial processing of plane waves received by an array of sensors such that the waves’ incidents at a particular spatial angle are passed through, whereas those arriving from other directions are attenuated.

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Advanced Digital Signal Processing and Noise Reduction by Saeed V. Vaseghi

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