A new adaptive covariance symmetric algorithm and a fast initialization scheme for least squares FIR filters with symmetric impulse response

S. Theodoridis; K. Berberidis; N. Kalouptsidis

doi:10.1016/0165-1684(89)90047-9

A new adaptive covariance symmetric algorithm and a fast initialization scheme for least squares FIR filters with symmetric impulse response

Signal Processing ◽

10.1016/0165-1684(89)90047-9 ◽

1989 ◽

Vol 18 (2) ◽

pp. 153-167 ◽

Cited By ~ 4

Author(s):

S. Theodoridis ◽

K. Berberidis ◽

N. Kalouptsidis

Keyword(s):

Least Squares ◽

Impulse Response ◽

Fir Filters ◽

Initialization Scheme

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Least-Squares and Minimax Design of Polynomial Impulse Response FIR Filters for Reconstruction of Two-Periodic Nonuniformly Sampled Signals

IEEE Transactions on Circuits and Systems I Fundamental Theory and Applications ◽

10.1109/tcsi.2006.888765 ◽

2007 ◽

Vol 54 (4) ◽

pp. 877-888 ◽

Cited By ~ 29

Author(s):

Hkan Johansson ◽

Per Lwenborg ◽

Kameswaran Vengattaramane

Keyword(s):

Least Squares ◽

Impulse Response ◽

Fir Filters ◽

Minimax Design

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Weighted least squares design of two-dimensional zero-phase FIR filters in the continuous frequency domain

IEEE Transactions on Circuits and Systems II Analog and Digital Signal Processing ◽

10.1109/82.508431 ◽

1996 ◽

Vol 43 (7) ◽

pp. 534-537 ◽

Cited By ~ 6

Author(s):

M.T. Hanna

Keyword(s):

Least Squares ◽

Frequency Domain ◽

Weighted Least Squares ◽

Fir Filters ◽

Two Dimensional ◽

Continuous Frequency ◽

Zero Phase

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Comparison of Infinite Impulse Response (IIR) and Finite Impulse Response (FIR) Filters in Cardiac Optical Mapping Records

Communications in Computer and Information Science - Applied Technologies ◽

10.1007/978-3-030-71503-8_16 ◽

2021 ◽

pp. 207-224

Author(s):

David Rivas-Lalaleo ◽

Sergio Muñoz-Romero ◽

Monica Huerta ◽

Víctor Bautista-Naranjo ◽

Jorge García-Quintanilla ◽

...

Keyword(s):

Impulse Response ◽

Optical Mapping ◽

Finite Impulse Response ◽

Infinite Impulse Response ◽

Fir Filters

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Optimal Least-Squares Design of Sparse FIR Filters for Big-Data Signal Processing

2018 IEEE 23rd International Conference on Digital Signal Processing (DSP) ◽

10.1109/icdsp.2018.8631598 ◽

2018 ◽

Author(s):

Masayoshi Nakamoto ◽

Taro Itani ◽

Katsumi Konishi

Keyword(s):

Signal Processing ◽

Big Data ◽

Least Squares ◽

Fir Filters ◽

Data Signal

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Finite impulse response (FIR) filters

Information Technology: Transmission, Processing and Storage - Digital Signal Processing for Measurement Systems ◽

10.1007/978-0-387-28666-2_8 ◽

2007 ◽

pp. 179-220

Keyword(s):

Impulse Response ◽

Finite Impulse Response ◽

Fir Filters

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Reconstructing the rate of appearance of subcutaneous insulin by deconvolution

AJP Endocrinology and Metabolism ◽

10.1152/ajpendo.1987.253.5.e584 ◽

1987 ◽

Vol 253 (5) ◽

pp. E584-E590 ◽

Cited By ~ 1

Author(s):

C. Cobelli ◽

A. Mari ◽

S. Del Prato ◽

S. De Kreutzenberg ◽

R. Nosadini ◽

...

Keyword(s):

Least Squares ◽

Impulse Response ◽

Validation Study ◽

Least Squares Method ◽

Subcutaneous Insulin ◽

Independent Validation ◽

Intravenous Insulin ◽

Insulin Kinetics ◽

Deconvolution Problem ◽

Different Sources

In this paper a deconvolution scheme is presented to reconstruct the rate of appearance of subcutaneously injected insulin. Relevant aspects of experiment design are briefly described. Intravenous insulin kinetics are modeled to determine the impulse response of the system. The deconvolution problem is not ill conditioned and is solved using a least-squares method without imposing constraints on the input. An estimate of the error of the reconstructed input is provided. The reliability of the deconvolution scheme is tested by means of an independent validation study. Finally, the different sources of error that affect the method are discussed, and a figure of the global error is derived.

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