By Ivars Bilinskis
As call for for purposes operating in prolonged frequency levels raises, classical electronic sign processing (DSP) ideas, no longer protected from aliasing, have gotten much less powerful. electronic alias-free sign processing (DASP) is a method for overcoming the issues of aliasing at prolonged frequency levels. according to non-uniform or randomised sampling strategies and the advance of novel algorithms, it creates the means to suppress power aliasing the most important for top frequency functions and to lessen the complexity of designs.
This e-book presents functional and entire insurance of the speculation and methods at the back of alias-free electronic sign processing.
- Analyses problems with sampling, randomised and pseudo-randomised quantisation and direct and in some way randomised sampling.
- Examines periodic and hybrid sampling, together with info on processing algorithms and strength obstacles imposed via sign dynamics.
- Sets out top tools and methods for complexity lowered designs, particularly designs of enormous aperture sensor arrays, gigantic info acquisition and compression from a few sign resources and complexity-reduced processing of non-uniform data.
- Presents examples of engineering purposes utilizing those innovations together with spectrum research, waveform reconstruction and the estimation of varied parameters, emphasising the significance of the process for constructing new technologies.
- Links DASP and standard applied sciences through mapping them into embedded platforms with usual inputs and outputs.
Digital Alias-free sign Processing is perfect for practicing engineers and researchers engaged on the advance of electronic sign processing purposes at prolonged frequencies. it's also a necessary reference for electric and laptop engineering graduates taking classes in sign processing or electronic sign processing.
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Extra info for Digital Alias-free Signal Processing
8(3), 328–35. J. (1970) Alias-free randomly timed sampling of stochastic processes. IEEE Trans. Inf. Theory, IT-16(2), 147–52. J. (1974) Recovery of randomly sampled signals by simple interpolators. Inf. Control, 26(4), 312–40. J. A. (1966) Random sampling of random processes: stationary point processes. Inf. Control, 9(4), 325–46. J. A. (1966) The theory of stationary point processes. , 116, 159–97. J. A. (1968) The spectral analysis of impulse processes. Inf. Control, 12(3), 236–58. Bilinskis, I.
It is possible to reconstruct the signal waveform from such a sparse sequence of sample values because the signal is ergodic and quasi-stationary. The parameters do not vary during the time period it is being observed. Under these conditions, a reduced number of independent sample values are needed to reconstruct it by estimating all three parameters (amplitude, frequency and phase angle) of all signal components. In this case, the time intervals between the sampling instants might be large and the mean sampling rate used in this particular case is 80 MS/s.
Use of nonorthogonal transforms might be mentioned as an example illustrating this. Their first application was reconstruction of nonuniformly sampled signal waveforms. Then it was discovered that they are also good for processing signals at extremely low frequencies. For example, they can be used to remove the negative effect caused by cutting off part of a signal period when, according to the classical definition, the processing should be carried out over a number of integer signal periods (or the periods of their separate components).