Extraction of Partial Discharge Signal Feature Based on Dual-tree Complex Wavelet Transform and Singular-value Decomposition

2018 ◽  
Author(s):  
Junchen Zhu ◽  
Bangle He ◽  
Xiaodi Wang ◽  
Liang Cui ◽  
Yuhao LI
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Partial Discharge ◽  
Singular Value ◽  
Complex Wavelet Transform ◽  
Feature Based ◽  
Complex Wavelet ◽  
Value Decomposition

Shock filter based image super-resolution using dual-tree complex wavelet transform and singular value decomposition

2016 ◽  
Vol 35 (3) ◽  
Author(s):  
suryanarayana gunnam ◽  
Ravindra Dhuli
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Design Methodology ◽  
Super Resolution ◽  
Singular Value ◽  
Image Contrast ◽  
Complex Wavelet Transform ◽  
Shock Filter ◽  
Image Super Resolution ◽  
Value Decomposition

Purpose The purpose of this paper is to present an improved wavelet based approach in single image super resolution (SISR). The proposed method generates high resolution (HR) images by preserving the image contrast and edges simultaneously. Design/methodology/approach Covariance based interpolation algorithm is employed to obtain an initial estimate of the unknown HR image. The proposed method preserves the image contrast, by applying singular value decomposition (SVD) based correction on the dual-tree complex wavelet transform (DT-CWT) coefficients. In addition, the dual operating mode diffusion based shock filter (DBSF) ensures noise mitigation and edge preservation. Findings Experimental results on various test images reveal superiority of the proposed method over the existing SISR techniques in terms of peak signal to noise ratio (PSNR), structural similarity index measure (SSIM) and visual quality. Originality/value With SVD based correction, the proposed method preserves the image contrast and also the DBSF operation helps to achieve sharper edges.

LWT-DCT based Image Watermarking Scheme using Normalized SVD

2020 ◽  
Vol 13 ◽  
Author(s):  
Rahul Dixit ◽  
Amita Nandal ◽  
Arvind Dhaka ◽  
Vardan Agarwal ◽  
Yohan Varghese
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Discrete Cosine Transform ◽  
Image Watermarking ◽  
Singular Value ◽  
Cover Image ◽  
Lifting Wavelet Transform ◽  
Cosine Transform ◽  
Lifting Wavelet ◽  
Value Decomposition

Background: Nowadays information security is one of the biggest issues of social networks. The multimedia data can be tampered with, and the attackers can then claim its ownership. Image watermarking is a technique that is used for copyright protection and authentication of multimedia. Objective: We aim to create a new and more robust image watermarking technique to prevent illegal copying, editing and distribution of media. Method : The watermarking technique proposed in this paper is non-blind and employs Lifting Wavelet Transform on the cover image to decompose the image into four coefficient matrices. Then Discrete Cosine Transform is applied which separates a selected coefficient matrix into different frequencies and later Singular Value Decomposition is applied. Singular Value Decomposition is also applied to the watermarking image and it is added to the singular matrix of the cover image which is then normalized followed by the inverse Singular Value Decomposition, inverse Discrete Cosine Transform and inverse Lifting Wavelet Transform respectively to obtain an embedded image. Normalization is proposed as an alternative to the traditional scaling factor. Results: Our technique is tested against attacks like rotation, resizing, cropping, noise addition and filtering. The performance comparison is evaluated based on Peak Signal to Noise Ratio, Structural Similarity Index Measure, and Normalized Cross-Correlation. Conclusion: The experimental results prove that the proposed method performs better than other state-of-the-art techniques and can be used to protect multimedia ownership.

scholarly journals Biosignal Compression Toolbox for Digital Biomarker Discovery

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 516
Author(s):  
Brinnae Bent ◽  
Baiying Lu ◽  
Juseong Kim ◽  
Jessilyn P. Dunn
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Data Compression ◽  
Biomarker Discovery ◽  
Singular Value ◽  
Sensor Data ◽  
Discrete Wavelet ◽  
Wearable Sensor ◽  
Huffman Encoding ◽  
Value Decomposition

A critical challenge to using longitudinal wearable sensor biosignal data for healthcare applications and digital biomarker development is the exacerbation of the healthcare “data deluge,” leading to new data storage and organization challenges and costs. Data aggregation, sampling rate minimization, and effective data compression are all methods for consolidating wearable sensor data to reduce data volumes. There has been limited research on appropriate, effective, and efficient data compression methods for biosignal data. Here, we examine the application of different data compression pipelines built using combinations of algorithmic- and encoding-based methods to biosignal data from wearable sensors and explore how these implementations affect data recoverability and storage footprint. Algorithmic methods tested include singular value decomposition, the discrete cosine transform, and the biorthogonal discrete wavelet transform. Encoding methods tested include run-length encoding and Huffman encoding. We apply these methods to common wearable sensor data, including electrocardiogram (ECG), photoplethysmography (PPG), accelerometry, electrodermal activity (EDA), and skin temperature measurements. Of the methods examined in this study and in line with the characteristics of the different data types, we recommend direct data compression with Huffman encoding for ECG, and PPG, singular value decomposition with Huffman encoding for EDA and accelerometry, and the biorthogonal discrete wavelet transform with Huffman encoding for skin temperature to maximize data recoverability after compression. We also report the best methods for maximizing the compression ratio. Finally, we develop and document open-source code and data for each compression method tested here, which can be accessed through the Digital Biomarker Discovery Pipeline as the “Biosignal Data Compression Toolbox,” an open-source, accessible software platform for compressing biosignal data.

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