Extension of the phase and amplitude gradient estimation method for acoustic intensity to narrowband sources

2017 ◽  
Vol 141 (5) ◽  
pp. 3796-3796
Author(s):  
Kelli Succo ◽  
Scott D. Sommerfeldt ◽  
Kent L. Gee ◽  
Tracianne B. Neilsen
Keyword(s):  
Estimation Method ◽  
Gradient Estimation ◽  
Acoustic Intensity

Bias error analysis for phase and amplitude gradient estimation of acoustic intensity and specific acoustic impedance

2017 ◽  
Vol 142 (4) ◽  
pp. 2208-2218 ◽  
Author(s):  
Eric B. Whiting ◽  
Joseph S. Lawrence ◽  
Kent L. Gee ◽  
Tracianne B. Neilsen ◽  
Scott D. Sommerfeldt
Keyword(s):  
Error Analysis ◽  
Acoustic Impedance ◽  
Bias Error ◽  
Gradient Estimation ◽  
Acoustic Intensity

scholarly journals Blind Signal Processing Algorithmsbased based on Recursive Gradient Estimation

2015 ◽  
Vol 5 (3) ◽  
pp. 548
Author(s):  
Namyong Kim ◽  
Mingoo Kang
Keyword(s):  
Computational Complexity ◽  
Euclidean Distance ◽  
Estimation Method ◽  
Processing Method ◽  
Gradient Estimation ◽  
Blind Signal Processing ◽  
Dirac Delta ◽  
Blind Signal ◽  
Gradient Calculation ◽  
Delta Functions

Blind algorithms based on the Euclidean distance (ED) between the output distribution function and a set of Dirac delta functions have a heavy computational burden of  due to some double summation operations for the sample size and symbol points. In this paper, a recursive approach to the estimation of the ED and its gradient is proposed to reduce the computational complexity for efficient implementation of the algorithm. The ED of the algorithm is comprised of information potentials (IPs), and the IPs at the next iteration can be calculated recursively based on the currently obtained IPs. Utilizing the recursively estimated IPs, the next step gradient for the weight update of the algorithm can be estimated recursively with the present gradient. With this recursive approach, the computational complexity of gradient calculation has only . The simulation results show that the proposed gradient estimation method holds significantly reduced computational complexity keeping the same performance as the block processing method

Coaches estimation method for the athletes (CEM)

1995 ◽  
Author(s):  
Nagykaldi Csaba ◽  
Manohar Singh Badhan
Keyword(s):  
Estimation Method

Theoretical, methodological and practical issues of choice models

2018 ◽  
Vol 1 (1) ◽  
pp. 21-37
Author(s):  
Bharat P. Bhatta
Keyword(s):  
Discrete Choice ◽  
Model Building ◽  
Estimation Method ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Decision Makers ◽  
Model Formulation ◽  
Random Utility Maximization ◽  
The Relationship ◽  
Method Model

This paper analyzes and synthesizes the fundamentals of discrete choice models. This paper alsodiscusses the basic concept and theory underlying the econometrics of discrete choice, specific choicemodels, estimation method, model building and tests, and applications of discrete choice models. Thiswork highlights the relationship between economic theory and discrete choice models: how economictheory contributes to choice modeling and vice versa. Keywords: Discrete choice models; Random utility maximization; Decision makers; Utility function;Model formulation

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