scholarly journals Watermarking of Parkinson Disease Speech in Cloud-Based Healthcare Framework

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Musaed Alhussein ◽  
Ghulam Muhammad
Keyword(s):  
Additive White Gaussian Noise ◽  
Smart Phone ◽  
Singular Value ◽  
Discrete Wavelet ◽  
Speech Watermarking ◽  
Mobile Healthcare ◽  
Speech Data ◽  
Value Decomposition ◽  
Embed Watermark ◽  
Secure Transmission

Mobile healthcare in a cloud-based system increases the easiness and the ubiquitous nature of patient-doctor relationship. One of the major issues of this healthcare is secure transmission and data authenticity. If the data is not transmitted securely or not authenticated, the clients may face embarrassment. In this paper, we propose a cloud-based healthcare framework that will authenticate speech data from a patient suspected to have Parkinson’s disease. The patient sends his or her speech signal recorded via a smart phone through Internet to the cloud. A discrete wavelet transform- (DWT-) singular value decomposition (SVD) based speech watermarking module is run in the cloud to embed watermark to the signal. In case of authentication, watermark is extracted from the questioned signal and matched with the stored watermark. Experimental results indicate that the proposed DWT-SVD based watermarking system achieves imperceptibility and is robust against attacks such as additive white Gaussian noise and filtering.

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.

scholarly journals Implementasi Teknik Watermarking Menggunakan Metode Discrete Wavelet Transform (DWT) dan Singular Value Decomposition (SVD) pada Citra Digital

2021 ◽  
Vol 14 (2) ◽  
pp. 125
Author(s):  
Verryna Adzillatul Fathiha
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Discrete Wavelet Transform ◽  
Singular Value ◽  
Discrete Wavelet ◽  
Value Decomposition

Watermarking merupakan teknik penyembunyian data/informasi kedalam suatu citra digital yang tidak kasat mata atau tidak dapat diketahui secara visual. Penyembunyian data/informasi kedalam citra tersebut bersifat rahasia. Karena tahan terhadap proses digitalisasi, teknik watermarking dapat digunakan untuk melindungi kepemilikan suatu citra digital. Ada tiga kriteria yang harus diperhatikan dalam watermarking pada citra digital, diantaranya adalah security, impreceptibily, dan robustness. Pada tugas Penulisan Karya Ilmiah (PI) ini dibuat suatu program watermarking menggunakan metode Discrete Wavelet Transform (DWT) dan Singular Value Decomposition (SVD) yang kemudian juga akan dipaparkan bagaimana cara menggunakan program watermarking yang telah dibuat. Keunggulan dari program ini citra yang digunakan dalam program watermarking ini dapat berupa citra berwarna maupun citra grayscale. Program watermarking ini dibuat menggunakan bahasa pemrograman C++ melalui aplikasi Matlab Kata Kunci            : Wateramarking , Singular Value Decomposition (SVD), Discrete Wavelet Transform (DWT), Matlab, Bahasa Pemrograman C++.

AUDIO WATERMARKING WITH ANGLE QUANTIZATION BASED ON DISCRETE WAVELET TRANSFORM AND SINGULAR VALUE DECOMPOSITION

2018 ◽  
Vol 6 (2) ◽  
pp. 41-52
Author(s):  
Divya Chadar ◽  
Shailja Shukla
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Discrete Wavelet Transform ◽  
Auditory System ◽  
Audio Signal ◽  
Singular Value ◽  
New Method ◽  
Discrete Wavelet ◽  
Audio Watermark ◽  
Value Decomposition

An audio watermark is a unique electronic identifier embedded in an audio signal, typically used to identify ownership of copyright. Proposed work is a new method of audio watermark hiding inside another bigger cover standard audio cover. The method includes ‘harr’ wavelet based Discrete Wavelet Transform decomposition of frequencies hence the audio samples of watermark gets hidden only those parts of cover audio where human ears are less sensible according to Human Auditory System. Proposed method also includes the Singular Value Decomposition, which is required for making our method robust against the various communication of processing attacks like compression, filtering, fading or noise addition. Proposed work is also using the concept of angular modulation which initially modifies the audio watermark in to provide extra security and also extra robustness in communication. The design is been develop on MATLAB 2013b version and verification of design o the same. 

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