scholarly journals Results on principal component filter banks: colored noise suppression and existence issues

2001 ◽  
Vol 47 (3) ◽  
pp. 1003-1020 ◽  
Author(s):  
S. Akkarakaran ◽  
P.P. Vaidyanathan
Keyword(s):  
Colored Noise ◽  
Noise Suppression ◽  
Filter Banks ◽  
Principal Component

AvDR-Based Wireless Secure Key Generation with Colored Noise for IoT

2019 ◽  
Vol 19 (01) ◽  
pp. 2050013
Author(s):  
Ankit Soni ◽  
Raksha Upadhyay ◽  
Abhay Kumar
Keyword(s):  
Dimensionality Reduction ◽  
System Performance ◽  
Colored Noise ◽  
Additive White Gaussian Noise ◽  
Principal Component ◽  
Research Area ◽  
Wireless Channel ◽  
Resource Constraint ◽  
Key Generation ◽  
Correlated Components

Physical layer key generation exploiting inherent channel randomness is an open research area in securing the networks with resource constraint nodes; therefore reduction of numerical computation is desirable to save battery power. However, the correlated components due to colored noise also affect the system performance. In this work, we consider the correlated colored noise components along with the additive white Gaussian noise (AWGN) in the wireless channel and analyze the effect of these correlated components on the system performance. We further propose a hybrid averaging and dimensionality reduction (AvDR), based received signal strength (RSS) preprocessing which is the combination of moving window averaging (Av) and principal component analysis (PCA) as dimensionality reduction technique (DR) to improve the system performance. Further, the system performance was evaluated by numerical simulations, and it is observed that the same improvement in system performance is achieved by generating keys from a fewer number of points selected after PCA as compared to processing all the points. Picking a few of the points in the data sequence instead of all reduces the total number of numerical calculations and saves system power, which is the primary requirement of resource constraint networks like the IoT.

FIR principal component filter banks

1998 ◽  
Vol 46 (4) ◽  
pp. 930-940 ◽  
Author(s):  
Bo Xuan ◽  
R.I. Bamberger
Keyword(s):  
Filter Banks ◽  
Principal Component

Research on the Speech Enhancement Method based on PCA/KLT Algorithms

2012 ◽  
Vol 239-240 ◽  
pp. 1274-1278
Author(s):  
Guang Yan Wang ◽  
Yan Xiang Geng ◽  
Xiao Qun Zhao
Keyword(s):  
Speech Enhancement ◽  
High Performance ◽  
Large Scale ◽  
Colored Noise ◽  
Minimum Description Length ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
White And Colored Noise ◽  
Karhunen Loeve Transform ◽  
Subspace Selection

In this paper, we propose a speech enhancement technique in terms of subspace methods to reduce the white or colored noise in strong background noise environment. This subspace approach based on Karhunen-Loève transform (KLT) and implemented via Principal Component Analysis (PCA). The subspace selection provided by the minimum description length (MDL) criterion. An offset factor generated from the white noise was used to modify the variance to adapt to the specified colored noise. The objective speech quality measures SegSNR have been introduced to evaluate the performance of the proposed method in time domain. A large amount of data and figures testify that our algorithm provides high performance for a large scale of input signal-to-noise ratio (-5~10dB). The performance of our algorithm is assessed in white and colored noise.

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