scholarly journals A New Variable Tap-Length LMS Algorithm to Model an Exponential Decay Impulse Response

2007 ◽  
Vol 14 (4) ◽  
pp. 263-266 ◽  
Author(s):  
Yonggang Zhang ◽  
Jonathon A. Chambers ◽  
Saeid Sanei ◽  
Paul Kendrick ◽  
Trevor J. Cox
Keyword(s):  
Impulse Response ◽  
Exponential Decay ◽  
Lms Algorithm

The quantitative advantages of using B-field sensors in time-domain EM measurement for mineral exploration and unexploded ordnance search

Geophysics ◽  
2012 ◽  
Vol 77 (4) ◽  
pp. WB137-WB148 ◽  
Author(s):  
Michael W. Asten ◽  
Andrew C. Duncan
Keyword(s):  
Impulse Response ◽  
Time Constant ◽  
Exponential Decay ◽  
Time Domain ◽  
Step Response ◽  
Late Time ◽  
Additional Parameter ◽  
Unexploded Ordnance ◽  
Response Measurement ◽  
Response Systems

The use of simple models for decay of conductive targets under conductive overburden and for the decay of magnetically permeable conductive steel objects allows quantitative consideration of the advantages of the use of magnetic-field detectors in time-domain electromagnetic (TEM) measurements, or more generally, the advantage of step response over impulse response TEM systems. We identified eight advantages of the step response versus impulse-response systems. The first two advantages relate to the inductive limit (early time) decay behavior, in which a target response amplitude is largely dependent on geometrical rather than conductivity parameters. Five further advantages occur when measuring response of a target in a conductive host or under conductive overburden; the maximum target-to-overburden response occurs 25%–30% earlier in time, the earliest target detection time occurs a factor 2–4 earlier, and the amplitude advantage of target-to-overburden response is a factor in the range of 1–10 for the step versus impulse-response systems, respectively. These advantages agree quantitatively with field observations on a chalcopyrite orebody under conductive cover. We used a model response for a conductive permeable sphere to derive mathematically consistent approximations for the power-law and exponential decay behaviors for step and impulse responses of metal objects, from which the onset of late-time exponential decay of EM responses of unexploded ordnance occurs about a factor of two earlier in time for the step response. This earlier-time transition together with the higher signal-to-noise ratio available from the step-response measurement makes measurement of the fundamental time-constant of unexploded ordnance (UXO) possible for medium and large UXO where the time constant is in the range of tens of milliseconds. This time-constant thus becomes accessible as an additional parameter for UXO characterization and discrimination.

A Deconvolution Technique for Determining the Intrinsic Fluorescence Decay Lifetimes of Crude Oils

1988 ◽  
Vol 42 (3) ◽  
pp. 406-410 ◽  
Author(s):  
M. F. Quinn ◽  
S. Joubian ◽  
F. Al-Bahrani ◽  
S. Al-Aruri ◽  
Oussama Alameddine
Keyword(s):  
Fourier Transform ◽  
Impulse Response ◽  
Exponential Decay ◽  
Fluorescence Decay ◽  
Average Lifetime ◽  
Nitrogen Laser ◽  
Alternative Approach ◽  
Deconvolution Technique ◽  
The Fourier Transform ◽  
Single Exponential

A simple deconvolution procedure using FT was developed for determining the average lifetime of samples excited by a nitrogen laser pumped dye laser operating at 428 nm. To overcome the noise limitations imposed by including higher frequency harmonics in the analysis, we used an alternative approach. This approach relied on taking the Fourier transform at 21 subharmonic frequencies and using an appropriate weighting procedure in the calculation of amplitude and lifetime of the sample impulse response. A single exponential decay was assumed.

An Improved ZA-LMS Algorithm for Sparse System Identification with Variable Sparsity

2014 ◽  
Vol 602-605 ◽  
pp. 2415-2419 ◽  
Author(s):  
Hui Luo ◽  
Yun Lin ◽  
Qing Xia
Keyword(s):  
System Identification ◽  
Impulse Response ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Sparse System ◽  
Sparse System Identification ◽  
Least Mean Square Algorithm ◽  
Improved Algorithm

The standard least mean square algorithm does not consider the sparsity of the impulse response,and the performs of the ZA-LMS algorithm deteriorates ,as the degree of system sparsity reduces or non-sparse . Concerning this issue ,the ZA-LMS algorithm is studied and modified in this paper to improve the performance of sparse system identification .The improved algorithm by modify the zero attraction term, which attracts the coefficients only in a certain range (the “inactive” taps), thus have a good performance when the sparsity decreases. The simulations demonstrate that the proposed algorithm significantly outperforms then the ZA-LMS with variable sparisity.

scholarly journals Adaptive Parameter Estimation of IIR System-Based WSN Using Multihop Diffusion in Distributed Approach

2020 ◽  
Vol 14 (4) ◽  
pp. 30-41
Author(s):  
Meera Dash ◽  
Trilochan Panigrahi ◽  
Renu Sharma ◽  
Mihir Narayan Mohanty
Keyword(s):  
Impulse Response ◽  
Sensor Node ◽  
Finite Impulse Response ◽  
Lms Algorithm ◽  
Sensor System ◽  
Superior Performance ◽  
Estimation Accuracy ◽  
Infinite Impulse Response ◽  
Communication Overhead ◽  
Least Mean Square

Distributed estimation of parameters in wireless sensor networks is taken into consideration to reduce the communication overhead of the network which makes the sensor system energy efficient. Most of the distributed approaches in literature, the sensor system is modeled with finite impulse response as it is inherently stable. Whereas in real time applications of WSN like target tracking, fast rerouting requires, infinite impulse response system (IIR) is used to model and that has been chosen in this work. It is assumed that every sensor node is equipped with IIR adaptive system. The diffusion least mean square (DLMS) algorithm is used to estimate the parameters of the IIR system where each node in the network cooperates themselves. In a sparse WSN, the performance of a DLMS algorithm reduces as the degree of the node decreases. In order to increase the estimation accuracy with a smaller number of iterations, the sensor node needs to share their information with more neighbors. This is feasible by communicating each node with multi-hop nodes instead of one-hop only. Therefore the parameters of an IIR system is estimated in distributed sparse sensor network using multihop diffusion LMS algorithm. The simulation results exhibit superior performance of the multihop diffusion LMS over non-cooperative and conventional diffusion algorithms.

A Bound on the Impulse Response of Stable Rational Plants

1991 ◽  
Vol 113 (2) ◽  
pp. 315-316 ◽  
Author(s):  
C. R. MacCluer
Keyword(s):  
Impulse Response ◽  
Exponential Decay ◽  
Implied Constant ◽  
Spectral Energy ◽  
The Real ◽  
Exact Location ◽  
Poles And Zeros ◽  
Zeros And Poles

The magnitude of the impulse response of a strictly proper stable rational plant at time t is estimated to be of order 0(1/t) where the implied constant is explicitly given in terms of the spectral energy and the real parts of the zeros and poles. When exact location of poles and zeros is uncertain, this estimate can usefully replace the actual but uncertain exponential decay.

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