FPGA Implementation of Zero Frequency Filter

2018 ◽  
Author(s):  
Nagapuri Srinivas ◽  
Gayadhar Pradhan ◽  
Puli Kishore Kumar
Keyword(s):  
Fpga Implementation ◽  
Frequency Filter ◽  
Zero Frequency

Zero Frequency Filter Based Analysis of Voice Disorders

2017 ◽  
Author(s):  
Nagaraj Adiga ◽  
Vikram C.M. ◽  
Keerthi Pullela ◽  
S.R. Mahadeva Prasanna
Keyword(s):  
Voice Disorders ◽  
Frequency Filter ◽  
Zero Frequency

Early detection of bearing faults using minimum entropy deconvolution adjusted and zero frequency filter

2021 ◽  
pp. 107754632098636
Author(s):  
Keshav Kumar ◽  
Sumitra Shukla ◽  
Sachin K Singh
Keyword(s):  
Time Domain ◽  
Vibration Signal ◽  
Rolling Element Bearings ◽  
Frequency Filtering ◽  
Minimum Entropy ◽  
Frequency Filter ◽  
Rolling Element ◽  
Zero Frequency Filtering ◽  
Bearing Vibration ◽  
Zero Frequency

A method based on minimum entropy deconvolution with convolution adjustment and zero frequency filter is presented for the identification of weak faults in rolling element bearings. Localized fault present in rolling element bearings causes periodic impulses in the bearing vibration signal. The zero frequency filtering of the bearing vibration signal keeps only the localized disturbances at the impulse locations while attenuating the non-impulsive components of the signal. The effectiveness of zero frequency filtering depends on the strength of impulses present in the measured faulty bearing signal in time domain. In the present work, Minimum entropy deconvolution adjusted is used as a preprocessor to improve the strength of impulses in the measured time-domain bearing signal. The effectiveness of the proposed algorithm is tested with simulated signals for the faulty bearing vibration at different levels of added Gaussian noise. The algorithm is also validated using experimental bearing vibration dataset. Results from the proposed algorithm are compared with the results of the zero frequency filter and local mean subtraction-based technique for rolling element bearings’ fault identification. The proposed algorithm performs better detection in case of a weak fault signal.

A Simplified Realization of Zero Frequency Filter for Hardware Implementation

2020 ◽  
Vol 39 (9) ◽  
pp. 4717-4729
Author(s):  
Nagapuri Srinivas ◽  
Gayadhar Pradhan ◽  
D. Govind
Keyword(s):  
Hardware Implementation ◽  
Frequency Filter ◽  
Zero Frequency

Voice Activity Detection Algorithm Using Zero Frequency Filter Assisted Peaking Resonator and Empirical Mode Decomposition

2013 ◽  
Vol 22 (3) ◽  
pp. 269-282
Author(s):  
M.S. Rudramurthy ◽  
V. Kamakshi Prasad ◽  
R. Kumaraswamy
Keyword(s):  
White Noise ◽  
Empirical Mode Decomposition ◽  
Voice Activity Detection ◽  
Data Driven ◽  
Activity Detection ◽  
Frequency Filter ◽  
Source Information ◽  
Mode Decomposition ◽  
Voice Activity ◽  
Zero Frequency

AbstractIn this article, a new adaptive data-driven strategy for voice activity detection (VAD) using empirical mode decomposition (EMD) is proposed. Speech data are decomposed using an a posteriori, adaptive, data-driven EMD in the time domain to yield a set of physically meaningful intrinsic mode functions (IMFs). Each IMF preserves the nonlinear and nonstationary property of the speech utterance. Among a set of IMFs, the IMF that contains source information dominantly called characteristic IMF (CIMF) can be identified and extracted by designing a zero-frequency filter-assisted peaking resonator. The detected CIMF is used to compute energy using short-term processing. Choosing proper threshold, voiced regions in speech utterances are detected using frame energy. The proposed framework has been studied on both clean speech utterance and noisy speech utterance (0-dB white noise). The proposed method is used for voice activity detection (VAD) in the presence of white noise and shows encouraging result in the presence of white noise up to 0 dB.

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