Time-frequency bandwidth product estimation of Sinusoidal Non-linear Chirp Keying scheme

2017 ◽  
Vol 14 (8) ◽  
pp. 184-194 ◽  
Author(s):  
Zhiguo Sun ◽  
Xiaoyan Ning ◽  
Danyang Tian
Keyword(s):  
Frequency Bandwidth ◽  
Time Frequency ◽  
Linear Chirp ◽  
Non Linear

An Azimuth Frequency Non-Linear Chirp Scaling (FNCS) Algorithm for TOPS SAR Imaging With High Squint Angle

2014 ◽  
Vol 7 (1) ◽  
pp. 213-221 ◽  
Author(s):  
Yufeng Wu ◽  
Guang-Cai Sun ◽  
Xiang-Gen Xia ◽  
Mengdao Xing ◽  
Jun Yang ◽  
...  
Keyword(s):  
Sar Imaging ◽  
Linear Chirp ◽  
Non Linear

COMPREHENSIVE ANALYSIS OF NORMAL AND DIABETIC HEART RATE SIGNALS: A REVIEW

2012 ◽  
Vol 12 (05) ◽  
pp. 1240033 ◽  
Author(s):  
OLIVER FAUST ◽  
V. RAMANAN PRASAD ◽  
G. SWAPNA ◽  
SUBHAGATA CHATTOPADHYAY ◽  
TEIK-CHENG LIM
Keyword(s):  
Heart Rate ◽  
Organ Damage ◽  
Extraction Methods ◽  
Time Frequency ◽  
Ecg Signals ◽  
Cardiac Health ◽  
Non Linear ◽  
Non Stationary Signal ◽  
Early Diagnosis Of Diabetes ◽  
Typical Range

A large section of the world's population is affected by diabetes mellitus (DM), commonly referred to as "diabetes." Every year, the number of cases of DM is increasing. Diabetes has a strong genetic basis, hence it is very difficult to cure, but can be controlled with medications to prevent subsequent organ damage. Therefore, early diagnosis of diabetes is very important. In this paper, we examine how diabetes affects cardiac health, which is reflected through heart rate variability (HRV), as observed in electrocardiography (ECG) signals. Such signals provide clues for both the presence and severity of diabetes as well as diabetes-induced cardiac impairments. Heart rate (HR) is a non-linear and non-stationary signal. Thus, extracting useful information from HRV signals is a difficult task. We review several sophisticated signal processing and information extraction methods in order to establish measurable relationships between the presence and the extent of diabetes as well as the changes in the HRV signals. Furthermore, we discuss a typical range of values for several statistical, geometric, time domain, frequency domain, time–frequency, and non-linear features for HR signals from 15 normal and 15 diabetic subjects. We found that non-linear analysis is the most suitable approach to capture and analyze the subtle changes in HRV signals caused by diabetes.

Feature Extraction Technology for Rolling Bearings Based on Local Tangent Space Alignment

2015 ◽  
Vol 764-765 ◽  
pp. 274-279
Author(s):  
Zhi Wen ◽  
Chen Lu ◽  
Hong Mei Liu
Keyword(s):  
Tangent Space ◽  
Feature Vector ◽  
Wavelet Packet ◽  
Linear Structure ◽  
Extraction Technology ◽  
Rolling Bearings ◽  
Time Frequency ◽  
Non Linear ◽  
Non Stationary Signal ◽  
Local Tangent

Health assessment and fault diagnosis for rolling bearings mostly adopt traditional methods, such as time-frequency, spectral, and wavelet packet analyses, to extract the feature vector. These methods are suitable for processing data with a linear structure. However, for the non-linear and non-stationary signal, the result of these methods is not ideal. Thus, this study proposes a suitable method to extract the feature vector in nonlinear signals. Local tangent space alignment of a manifold algorithm is employed to extract the feature vector from the rolling bearings. Results verify the advantage of the manifold algorithm for non-linear and non-stationary signals.

Sign in / Sign up

Export Citation Format

Share Document