Target signature extraction based on the continuous wavelet transform in ultra-wideband radar

1997 ◽  
Vol 33 (1) ◽  
pp. 89 ◽  
Author(s):  
T. Le-Tien ◽  
H. Talhami ◽  
D.T. Nguyen
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Ultra Wideband ◽  
Continuous Wavelet ◽  
Signature Extraction ◽  
Wideband Radar

scholarly journals Performance Evaluation of Adaptive Continuous Wavelet Transform based Rake Receiver for UWB Systems

2018 ◽  
Vol 8 (5) ◽  
pp. 3444
Author(s):  
Ch. Navitha ◽  
K. Sivani ◽  
K. Ashoka Reddy
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
High Speed ◽  
Channel Model ◽  
Rake Receiver ◽  
Training Data ◽  
Ultra Wideband ◽  
Line Of Sight ◽  
Continuous Wavelet ◽  
Channel Models

This paper proposes an adaptive continuous wavelet transform (ACWT) based Rake receiver to mitigate interference for high speed ultra wideband (UWB) transmission. The major parts of the receiver are least mean square (LMS) adaptive equalizer and N-selective maximum ratio combiner (MRC). The main advantage of using continuous wavelet rake receiver is that it utilizes the maximum bandwidth (7.5GHz) of the UWB transmitted signal, as announced by the Federal Communication Commission (FCC). In the proposed ACWT Rake receiver, the weights and the finger positions are updated depending upon the convergence error over a period in which training data is transmitted. Line of sight (LOS) channel model (CM1 from 0 to 4 meters) and the Non line of sight (NLOS) channel models (CM, CM3 and CM4) are the indoor channel models selected for investigating in this research . The performance of the proposed adaptive system   is evaluated by comparing with conventional rake and continuous wavelet transform (CWT) based rake. It showed an improved performance in all the different UWB channels (CM1 to CM4) for rake fingers of 2, 4 and 8. Simulations showed that for 8 rake fingers, the proposed adaptive CWT rake receiver has shown an SNR improvement of 2dB, 3dB, 10dB and 2dB respectively over CWT rake receiver in different UWB channels CM1, CM2, CM3 and CM4.

scholarly journals Continuous Wavelet Transform of Schwartz Distributions in DL2′(Rn), n ≥ 1

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1106
Author(s):  
Jagdish N. Pandey
Keyword(s):  
Wavelet Transform ◽  
Function Space ◽  
Continuous Wavelet Transform ◽  
Inversion Formula ◽  
Continuous Wavelet ◽  
The Real ◽  
Schwartz Distributions ◽  
Distributional Sense ◽  
Wavelet Inversion

We define a testing function space DL2(Rn) consisting of a class of C∞ functions defined on Rn, n≥1 whose every derivtive is L2(Rn) integrable and equip it with a topology generated by a separating collection of seminorms {γk}|k|=0∞ on DL2(Rn), where |k|=0,1,2,… and γk(ϕ)=∥ϕ(k)∥2,ϕ∈DL2(Rn). We then extend the continuous wavelet transform to distributions in DL2′(Rn), n≥1 and derive the corresponding wavelet inversion formula interpreting convergence in the weak distributional sense. The kernel of our wavelet transform is defined by an element ψ(x) of DL2(Rn)∩DL1(Rn), n≥1 which, when integrated along each of the real axes X1,X2,…Xn vanishes, but none of its moments ∫Rnxmψ(x)dx is zero; here xm=x1m1x2m2⋯xnmn, dx=dx1dx2⋯dxn and m=(m1,m2,…mn) and each of m1,m2,…mn is ≥1. The set of such wavelets will be denoted by DM(Rn).

scholarly journals Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 119
Author(s):  
Tao Wang ◽  
Changhua Lu ◽  
Yining Sun ◽  
Mei Yang ◽  
Chun Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Convolutional Neural Network ◽  
Continuous Wavelet Transform ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Ecg Signals ◽  
Rr Interval ◽  
Frequency Components ◽  
Fully Connected

Early detection of arrhythmia and effective treatment can prevent deaths caused by cardiovascular disease (CVD). In clinical practice, the diagnosis is made by checking the electrocardiogram (ECG) beat-by-beat, but this is usually time-consuming and laborious. In the paper, we propose an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN). CWT is used to decompose ECG signals to obtain different time-frequency components, and CNN is used to extract features from the 2D-scalogram composed of the above time-frequency components. Considering the surrounding R peak interval (also called RR interval) is also useful for the diagnosis of arrhythmia, four RR interval features are extracted and combined with the CNN features to input into a fully connected layer for ECG classification. By testing in the MIT-BIH arrhythmia database, our method achieves an overall performance of 70.75%, 67.47%, 68.76%, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively. Compared with existing methods, the overall F1-score of our method is increased by 4.75~16.85%. Because our method is simple and highly accurate, it can potentially be used as a clinical auxiliary diagnostic tool.

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