Results 1 
5 of
5
Constrained Least Square Design Of Fir Filters Without Specified Transition Bands
 IEEE Trans. on Signal Processing
, 1995
"... We consider the design of digital filters and discuss the inclusion of explicitly specified transition bands in the frequency domain design of FIR filters. We put forth the notion that explicitly specified transition bands have been introduced in the filter design literature as an indirect and often ..."
Abstract

Cited by 19 (1 self)
 Add to MetaCart
(Show Context)
We consider the design of digital filters and discuss the inclusion of explicitly specified transition bands in the frequency domain design of FIR filters. We put forth the notion that explicitly specified transition bands have been introduced in the filter design literature as an indirect and often inadequate approach for dealing with discontinuities in the desired frequency response. We also present a rapidly converging, robust, simple algorithm for the design of optimal peak constrained least square lowpass FIR filters that does not require the use of transition bands. This versatile algorithm will design linear and minimum phase FIR filters and gives the best L2 filter and a continuum of Chebyshev filters as special cases. 1. INTRODUCTION We consider the definition of optimality for digital filter design and conclude that a constrained least squared error criterion with no transition band is often the best approximation measure for many physical filtering problems. This comes fro...
Exchange algorithm for the design of linear phase FIR filters and differentiators having flat monotonic passbands and equiripple stopbands
 IEEE Trans. Circuits Syst
, 1996
"... ..."
(Show Context)
Some Exchange Algorithms Complementing the ParksMcClellan Program for Filter Design
 In International Conference on Digital Signal Processing
, 1995
"... In this paper, several modifications of the ParksMcClellan (PM) program are described that treat the band edges differently than does the PM program. The first exchange algorithm we describe allows (1) the explicit specification of ffi p and ffi s and (2) the specification of the halfmagnitude fre ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
(Show Context)
In this paper, several modifications of the ParksMcClellan (PM) program are described that treat the band edges differently than does the PM program. The first exchange algorithm we describe allows (1) the explicit specification of ffi p and ffi s and (2) the specification of the halfmagnitude frequency, !o . The set of lowpass filters obtained with this algorithm is the same as the set of lowpass filters produced by the PM algorithm. We also find that if passband monotonicity is desired in the design of filters having very flat passbands it is also desirable to modify the usual way of treating the band edges. The second multiple exchange algorithm we describe produces filters having a specified ffi p and ffi s but also includes a measure of the integral square error. 1 Introduction In this paper, several modifications of the ParksMcClellan (PM) program [11, 13, 17] are described. Recall that in their approach to the design of digital filters, the band edges are specified and the ...
Optimal Filter Design to Compute the Mean of Cardiovascular Pressure Signals
"... Abstract—The mean pressure is a term used to describe the baseline trend of physiological pressure signals that excludes fluctuations due to the cardiac cycle and, in some cases, the respiratory cycle. In many clinical applications and bedside monitoring devices, the mean pressure is estimated with ..."
Abstract
 Add to MetaCart
Abstract—The mean pressure is a term used to describe the baseline trend of physiological pressure signals that excludes fluctuations due to the cardiac cycle and, in some cases, the respiratory cycle. In many clinical applications and bedside monitoring devices, the mean pressure is estimated with a 3–8 s moving average. We suggest that the mean pressure is best defined in terms of its frequency domain properties. This definition makes it possible to determine solutions that are both optimal and practical. We demonstrate that established methods of optimal finite impulse response (FIR) filter design produce estimates of the mean pressure that are significantly more accurate than the moving average. These filters have no more computational cost, are less sensitive to artifact, have shorter delays, and greater sensitivity to acute events. Index Terms—Clinical monitoring, filter design, finite impulse response, mean pressure, patient monitors, trend. I.