Nonparametric estimation problem for a time-periodic signal in a periodic noise

2013 ◽  
Vol 83 (2) ◽  
pp. 608-615 ◽  
Author(s):  
D. Dehay ◽  
K. El Waled
Keyword(s):  
Nonparametric Estimation ◽  
Estimation Problem ◽  
Periodic Signal ◽  
Periodic Noise ◽  
Time Periodic

scholarly journals Sequentially Adapted Parallel Feedforward Active Noise Control of Noisy Sinusoidal Signals

2009 ◽  
Vol 2009 ◽  
pp. 1-13 ◽  
Author(s):  
Govind Kannan ◽  
Issa M. S. Panahi ◽  
Richard W. Briggs
Keyword(s):  
Noise Control ◽  
Acoustic Noise ◽  
Random Noise ◽  
Active Noise Control ◽  
Superior Performance ◽  
Filter Method ◽  
Periodic Signal ◽  
Noise Component ◽  
Periodic Noise ◽  
Active Noise

A large class of acoustic noise sources has an underlying periodic process that generates a periodic noise component, and thus their acoustic noise can in general be modeled as the sum of a periodic signal and a randomly fluctuating signal (usually a broadband background noise). Active control of periodic noise (i.e., for a mixture of sinusoids) is more effective than that of random noise. For mixtures of sinusoids in a background broadband random noise, conventional FXLMS-based single filter method does not reach the maximum achievable Noise Attenuation Level (NALmax⁡). In this paper, an alternative approach is taken and the idea of a parallel active noise control (ANC) architecture for cancelling mixtures of periodic and random signals is presented. The proposed ANC system separates the noise into periodic and random components and generates corresponding antinoises via separate noise cancelling filters, and tends to reach NALmax⁡ consistently. The derivation of NALmax⁡ is presented. Both the separation and noise cancellation are based on adaptive filtering. Experimental results verify the analytical development by showing superior performance of the proposed method, over the single-filter approach, for several cases of sinusoids in white noise.

scholarly journals Bootstrap Statistical Inference for the Variance Based on Fuzzy Data

2016 ◽  
Vol 38 (2) ◽  
Author(s):  
Mohammad Ghasem Akbari ◽  
Abdolhamid Rezaei
Keyword(s):  
Hypothesis Testing ◽  
Statistical Inference ◽  
Confidence Intervals ◽  
Nonparametric Estimation ◽  
Bootstrap Method ◽  
Estimation Problem ◽  
Fuzzy Data ◽  
Signed Distance ◽  
Straightforward Method ◽  
Statistical Hypotheses

The bootstrap is a simple and straightforward method for calculating approximated biases, standard deviations, confidence intervals, testing statistical hypotheses, and so forth, in almost any nonparametric estimation problem. In this paper we describe a bootstrap method for variance that is designed directly for hypothesis testing in case of fuzzy data based on Yao-Wu signed distance.

scholarly journals Computationally efficient filtering algorithms for engine torque estimation

2005 ◽  
Vol 219 (9) ◽  
pp. 1099-1107 ◽  
Author(s):  
A Stotsky
Keyword(s):  
Engine Speed ◽  
Spark Ignition ◽  
Estimation Problem ◽  
Quality Information ◽  
Periodic Signal ◽  
Computationally Efficient ◽  
Filtering Algorithms ◽  
Engine Torque ◽  
Filtering Algorithm ◽  
Torque Estimation

A new computationally efficient filtering algorithm for the reconstruction of the first harmonic of a periodic signal is presented. The algorithm allows the recovery of the combustion quality information from the engine speed measurements that are noise contaminated. The algorithm is verified by using a spark ignition V8 engine in the torque estimation problem.

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