Radar signal compression using wavelet transforms

2001 ◽  
Author(s):  
Yan Zhou ◽  
Fei-peng Li ◽  
Yu Xu ◽  
Qian-qing Qin ◽  
Deren Li
Keyword(s):  
Wavelet Transforms ◽  
Radar Signal ◽  
Signal Compression

Compression Performance Analysis of Electrogastrogram (Egg) Using Different Wavelet Transforms For Telemedicine

2021 ◽  
Vol 23 (05) ◽  
pp. 286-293
Author(s):  
Gokul M ◽  
◽  
Jothiraj S ◽  
Pradeep Murugesan ◽  
Monisha R ◽  
...  
Keyword(s):  
Compression Ratio ◽  
Wavelet Transforms ◽  
Graphical Representation ◽  
Transmission Rate ◽  
Signal Compression ◽  
Signal Loss ◽  
Compression Performance ◽  
Non Invasive ◽  
Egg Signal ◽  
Mean Square Difference

Electrogastrogram (EGG) is the non-invasive graphical representation of stomach’s electrical activity for diagnosing stomach Disorders. EGG signal compression has an important role in Tele-diagnosis, Tele-prognosis and survival analysis of all stomach dysrhythmias, when the patient is geographically isolated. There are plenty of signal compression techniques available and proposed over years. Due to some drawbacks like high cost, signal loss and poor compression ratio leads the signal into inefficient at receiver’s end. The compression of digital EGG in telemedicine holds three major advantages like efficient & economic usage of storage data, reduction of the data transmission rate and good transmission bandwidth conversation. In this study EGG signals are tested with different wavelet transforms such as Biorthogonal, coiflet, Daubechies, Haar, reverse biorthogonal and symlet wavelet transforms using MATLAB software, in order to find best performance wavelet for telemedicine. The performance is mathematically analyzed using the values of Percent Root Mean Square Difference (PRD), Compression ratio (CR) and recovery ratio. The result of better compression performance in signal compression could definitely be a great asset in telemedicine field for transferring more quantities of Biological signals.

scholarly journals Denoising of MST RADAR Signal usingCWT and Overlapping Group Shrinkage

2021 ◽  
Vol 12 (5) ◽  
pp. 1950-1954
Author(s):  
P. Suresh Babu, Et. al.
Keyword(s):  
Wavelet Transforms ◽  
Large Amplitude ◽  
Radar Signal ◽  
Mathematical Tool ◽  
Group Sparsity ◽  
L1 Norm ◽  
Convex Cost ◽  
Mst Radar ◽  
Radar Signals ◽  
Powerful Mathematical Tool

Existing algorithmsare generally denouncing the existence of clusters with large amplitude coefficients. The L1 norm as well as other distinct models of sparsity does not attract a cluster tendency (group sparsity). In the light of a minimisation of convex cost work fusing the blended norm, this work introduces the technique "overlapping group shrinking." The groups are completely overlapping in order to abstain from blocking relics. A basic minimization calculation, in light of progressive replacement, is inferred. A straightforward strategy for setting the regularization boundary, in view of constricting the noise to a predefined level, is portrayed in detail by combining OGS with one of the most powerful mathematical tool wavelet transforms. In fact, the CWT coefficients are processed by OGS to produce a noise-free signal. The CWT coefficients are also processed.The proposed approach is represented on MST RADAR signals, the denoised signals delivered by CWT combined with OGS are liberated from noise.

scholarly journals Denoising of MST RADAR Signal usingCWT and Overlapping Group Shrinkage

2021 ◽  
Vol 12 (5) ◽  
pp. 714-718
Author(s):  
P. Suresh Babu, Dr. G. Sreenivasulu
Keyword(s):  
Wavelet Transforms ◽  
Large Amplitude ◽  
Radar Signal ◽  
Mathematical Tool ◽  
Group Sparsity ◽  
L1 Norm ◽  
Convex Cost ◽  
Mst Radar ◽  
Radar Signals ◽  
Powerful Mathematical Tool

Existing algorithmsare generally denouncing the existence of clusters with large amplitude coefficients. The L1 norm as well as other distinct models of sparsity does not attract a cluster tendency (group sparsity). In the light of a minimisation of convex cost work fusing the blended norm, this work introduces the technique "overlapping group shrinking." The groups are completely overlapping in order to abstain from blocking relics. A basic minimization calculation, in light of progressive replacement, is inferred. A straightforward strategy for setting the regularization boundary, in view of constricting the noise to a predefined level, is portrayed in detail by combining OGS with one of the most powerful mathematical tool wavelet transforms. In fact, the CWT coefficients are processed by OGS to produce a noise-free signal. The CWT coefficients are also processed.The proposed approach is represented on MST RADAR signals, the denoised signals delivered by CWT combined with OGS are liberated from noise.

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