Implementing Digital Filters and DSP Micro-Controller for Estimating the Frequency of a Time-Domain Signal

2021 ◽  
Author(s):  
Ratik Mittal ◽  
Vijay K. Jain
Keyword(s):  
Time Domain ◽  
Digital Filters ◽  
Time Domain Signal

Time-domain design of finite-memory digital filters

1968 ◽  
Vol 2 (4) ◽  
pp. 465-483 ◽  
Author(s):  
T.A Brubaker ◽  
D.R Stevens
Keyword(s):  
Time Domain ◽  
Digital Filters ◽  
Finite Memory

scholarly journals Is the time domain signal-averaged electrocardiogram helpful in patients with ventricular tachycardia without apparent structural heart disease?

1995 ◽  
Vol 18 (10) ◽  
pp. 568-572 ◽  
Author(s):  
Yelena S. K. Orlov ◽  
Michael A. Brodsky ◽  
Michael V. Orlov ◽  
Byron J. Allen ◽  
Rex J. Winters
Keyword(s):  
Heart Disease ◽  
Ventricular Tachycardia ◽  
Time Domain ◽  
Structural Heart Disease ◽  
Time Domain Signal ◽  
The Time Domain

Time-domain signal modelling in multidimensional NMR experiments for estimation of relaxation parameters

2019 ◽  
Vol 73 (3-4) ◽  
pp. 93-104 ◽  
Author(s):  
Yevgen Matviychuk ◽  
Mark J. Bostock ◽  
Daniel Nietlispach ◽  
Daniel J. Holland
Keyword(s):  
Time Domain ◽  
Multidimensional Nmr ◽  
Time Domain Signal ◽  
Relaxation Parameters ◽  
Signal Modelling

Time domain signal enhancement based on an optimized singular vector denoising algorithm

2012 ◽  
Vol 22 (5) ◽  
pp. 786-794 ◽  
Author(s):  
Hamid Hassanpour ◽  
Amin Zehtabian ◽  
S.J. Sadati
Keyword(s):  
Time Domain ◽  
Singular Vector ◽  
Signal Enhancement ◽  
Time Domain Signal

Fault Diagnosis of Motor Rotor Based on Fuzzy C-Means Clustering Analysis

2013 ◽  
Vol 273 ◽  
pp. 409-413 ◽  
Author(s):  
Yu Xiang Cao ◽  
Xue Jun Li ◽  
Ling Li Jiang
Keyword(s):  
Time Domain ◽  
Clustering Analysis ◽  
Statistical Features ◽  
Fuzzy C Means ◽  
Time Domain Signal ◽  
Fuzzy C Means Clustering ◽  
Important Means ◽  
Fault Diagnostic ◽  
Characteristic Features ◽  
The Time Domain

For the fuzziness of the fault symptoms in motor rotor, this paper proposes a fault diagnostic method which based on the time-domain statistical features and the fuzzy c-means clustering analysis (FCM). This method is to extract the characteristic features of time-domain signal via time-domain statistics and to import the extracted characteristic vector to classifier. And then the fuzzy c-means realizes the classification by confirming the distance among samples, which is based on the degree of membership between the sample and the clustering center. The fault diagnostic cases of motor rotor show that the method which bases on the time-domain statistical features-FCM can detect the rotor fault effectively and distinguish the different types of fault correctly. Therefore, it can be used as an important means of rotor fault identification.

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