This paper explores novel techniques involving number theoretic concepts to perform real-time digital signalprocessing for high bandwidth data stream applications in digital signalprocessing. Often the arithmetic ma...
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ISBN:
(纸本)081940943X
This paper explores novel techniques involving number theoretic concepts to perform real-time digital signalprocessing for high bandwidth data stream applications in digital signalprocessing. Often the arithmetic manipulations are simple in form (cascades of additions and multiplications in a well defined structure) but the numbers of operations that have to be computed every second can be large. This paper discusses ways in which new number theoretic mapping techniques can be used to perform DSP operations by both reducing the amount of hardware involved in the circuitry and by allowing the construction of very benign architectures down to the individual cells. Such architectures can be used in aggressive VLSI/ULSI implementations. We restrict ourselves to the computation of linear filter and transform algorithms, with the inner product form, which probably account for the vast majority of digital signalprocessing functions implemented commercially.
We formulate in a simple fashion the concept of invariance for a linear system. We show that one must define what we call an "associated Hermitian operator"' which commutes with the system function. We s...
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ISBN:
(纸本)9780819468451
We formulate in a simple fashion the concept of invariance for a linear system. We show that one must define what we call an "associated Hermitian operator"' which commutes with the system function. We show that it is this Hermitian operator that defines the invariance and also determines the appropriate transform and other connections between input and output relations.
A model for an infrared (M) flame detection system using artificial neural networks (ANN) is presented. The joint time-frequency analysis (JTFA) in the form of a Short-Time Fourier Transform (STFT) is used for extract...
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ISBN:
(纸本)9780819468451
A model for an infrared (M) flame detection system using artificial neural networks (ANN) is presented. The joint time-frequency analysis (JTFA) in the form of a Short-Time Fourier Transform (STFT) is used for extracting relevant input features for a set of ANNs. Each ANN is trained using the backpropagation conjugate-gradient (CG) method to distinguish all hydrocarbon flames from a particular type of environmental nuisance and background noise. signal saturation caused by the increased intensity of IR sources at closer distances is resolved by an adjustable gain control. A classification scheme with trained ANN connection weights was implemented on a digital signal processor for use in an industrial hydrocarbon flame detector.
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