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Interpolated finite impulse response filters
IEEE Transactions on Acoustics, Speech, and Signal Processing, 1984A new approach to implement computationally efficient finite impulse response (FIR) digital filters is presented. The filter structure is a cascade of two sections. The first section generates a sparse set of impulse response samples and the other section generates the remaining samples by using interpolation.
Y. Neuvo, null Dong Cheng-Yu, S. Mitra
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Finite impulse response estimator (FIRE)
IEEE Transactions on Signal Processing, 1995FIRE is an optimal filter structure combining a deterministic digital filter and a stochastic filter. A model of the system is described by a stale equation and the output of the system is modeled by an FIR filter. This integrated structure has demonstrated the capability of processing signals contaminated by deterministic band-limited harmonic noises ...
Kwang Kim, Bahram Shafai
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A digital PLL with finite impulse responses
Proceedings of ISCAS'95 - International Symposium on Circuits and Systems, 2002Conventional phase-locked loops (PLLs) lack speed, because ordinary phase comparators cannot provide time-continuous operation thus they act as a time delay to cause instability. In contrast, a completely time-discrete scheme, with a special voltage-controlled oscillator and a digital controller instead of usual low-pass filter, can respond in the ...
Fuminori Kobayashi, Masayuki Haratsu
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Finite impulse response filters
2021An electrical filter modifies the spectrum of the signal in a desired way. In this chapter, Finite Impulse Response Filters, one type of filter called the FIR filter is designed and analyzed. The FIR filters can be designed easily with linear-phase response, which is desired in a variety of applications such as image processing.
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Finite impulse response estimator
SPIE Proceedings, 1990An attempt is made to combine a deterministic digital filter and a stochastic filter. The state equation is the standard form used for a Kalman filter derivation and the output equation is of the finite impulse response filter. An optimal estimator is derived for this combined structure, called a finite impulse response estimator (FIRE) which permits ...
Kwang H. Kim, Bahram Shafai
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Distance properties of finite-impulse response channels
IEEE Transactions on Communications, 2000High-speed data communication over channels with limited bandwidth gives rise to intersymbol interference at the receiver usually resulting in a poor receiver error performance. An important parameter that determines the receiver error performance is the minimum Euclidean distance between two received sequences.
Nandana Rajatheva, Ed Shwedyk
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Multiscale finite impulse response modeling
Engineering Applications of Artificial Intelligence, 2006Multiscale representation of data has shown great shrinkage abilities when used in data filtering. This paper shows that multiscale representation has similar advantages when used in empirical process modeling. One advantage is that it helps separate noise from important features, which helps improve the accuracy of estimated models.
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Optical finite impulse response neural networks
Applied Optics, 2002The finite impulse response neural network is described in detail. Different algorithms capable of temporal back-propagation are considered, including a novel modification to the conventional algorithm, called the delayed-feedback back-propagation algorithm.
Paulo E X, Silveira +2 more
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Growing filters for finite impulse response networks
Proceedings of ICNN'95 - International Conference on Neural Networks, 2002Time-delay neural networks are well-suited for prediction purposes. A particular implementation is the finite impulse response (FIR) neural net. A major design problem exists in establishing the optimal order of such filters while minimizing the number of weights. Here, a constructive solution inspired by cascade learning is outlined and illustrated by
Diepenhorst, M +3 more
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Finite impulse response utilizing the principle of superposition
IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 1997A critical parameter in any finite impulse response (FIR) design is the impulse response length, which must be optimized for the given design specifications in order to reduce the size of the filter. To this end, many design algorithms have been introduced, such as Remez exchange, linear programming, and least mean squares.
Carter, Scott E., Malocha, Donald C.
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