-Nan Huang

Adaptive audio watermarking via the optimization point of view on the wavelet-based entropy

This study aims to present an adaptive audio watermarking method using ideas of wavelet-based entropy (WBE). The method converts low-frequency coefficients of discrete wavelet transform (DWT) into the WBE domain, followed by the calculations of mean values of each audio as well as derivation of some essential properties of WBE. A characteristic curve relating the WBE and DWT coefficients is also presented. The foundation of the embedding process lies on the approximately invariant property demonstrated from the mean of each audio and the characteristic curve. Besides, the quality of the watermarked audio is optimized. In the detecting process, the watermark can be extracted using only values of the WBE. Finally, the performance of the proposed watermarking method is analyzed in terms of signal to noise ratio, mean opinion score and robustness. Experimental results confirm that the embedded data are robust to resist the common attacks like re-sampling, MP3 compression, low-pass filtering, and amplitude scaling.

Hiding Patients Confidential Datainthe ECG Signal viaa Transform-Domain Quantization Scheme

Watermarking is the most widely used technology in the field of copyright and biological information protection. In this paper, we use quantization based digital watermark encryption technology on the Electrocardiogram (ECG) to protect patient rights and information. Three transform domains, DWT, DCT, and DFT are adopted to implement the quantization based watermarking technique. Although the watermark embedding process is not invertible, the change of the PQRST complexes and amplitude of the ECG signal is very small and so the watermarked data can meet the requirements of physiological diagnostics. In addition, the hidden information can be extracted without knowledge of the original ECG data. In other words, the proposed watermarking scheme is blind. Experimental results verify the efficiency of the proposed scheme.

Wavelet-Based Watermarking and Compression for ECG Signals with Verification Evaluation

In the current open society and with the growth of human rights, people are more and more concerned about the privacy of their information and other important data. This study makes use of electrocardiography (ECG) data in order to protect individual information. An ECG signal can not only be used to analyze disease, but also to provide crucial biometric information for identification and authentication. In this study, we propose a new idea of integrating electrocardiogram watermarking and compression approach, which has never been researched before. ECG watermarking can ensure the confidentiality and reliability of a users data while reducing the amount of data. In the evaluation, we apply the embedding capacity, bit error rate (BER), signal-to-noise ratio (SNR), compression ratio (CR), and compressed-signal to noise ratio (CNR) methods to assess the proposed algorithm. After comprehensive evaluation the final results show that our algorithm is robust and feasible.

A New Statistical-based Algorithm for ECG Identification

In this paper, a new statistical-based ECG algorithm, which applies the idea of matching Reduced Binary Pattern, is proposed to seek a timely and accurate human identity recognition. A comparison with previous researches, the proposed design requires neither waveform complex information nor de-noising pre-processing in advance. Our algorithm is tested on the public MIT-BIH arrhythmia and normal sinus rhythm databases. The experimental result confirms that the proposed scheme is feasible for high accuracy, low complexity, and fast processing for ECG identification.

Wavelet-Based Quantization Watermarking for ECG Signals

In this article, we use a self-synchronized watermark technology [7], to achieve the purpose of protection of electrocardiogram (ECG) signal. A Harr wavelet transform with 7 levels decomposition is adopted to transform the ECG signal and the synchronization code, combined with watermark, are quantized embedded in the low-frequency sub-band of level 7. The signal to noise ratio (SNR) between the embedded ECG and original one is greater than 30 such that the difference between these two ECG signals is very small and negligible in general. To test the robustness under the network transfer of ECG data, a white noise attack with various strengths is simulated that the bit error rate is quite small unless the SNR of the noise is very large. This study confirms the use of wavelet-based quantization watermarking scheme on ECG signal for patient protection is adequate.

Optimization-based image watermarking with integrated quantization embedding in the wavelet-domain

This study presents an optimization-based image watermarking scheme with integrated quantization embedding. First, peak signal to noise ratio (PSNR) is rewritten as a performance index in matrix form. In order to guarantee the robustness, this study embeds a watermark into the low-frequency coefficients of discrete wavelet transform (DWT). Unlike traditional way of single-coefficient quantization, this study applies amplitude quantization to embed the watermark and then rewrite this amplitude quantization as a constraint with embedding state. Then, an optimization-based equation connecting the performance index and amplitude-quantization constraint is obtained. Second, Lagrange Principle is used to solve the equation and then the optimal results are applied to embed the watermark. In detection, the hidden watermark can be extracted without original image. Finally, the performance of the proposed scheme is evaluated by PSNR and BER.

Optimization-Based Embedding for Wavelet-Domain Audio Watermarking

This work proposes a new blind digital audio watermarking system by using optimization-based modification on low-frequency amplitude of discrete wavelet transform (DWT). To modify the low-frequency amplitude under the best signal-to-noise ratio (SNR), the proposed embedding system minimizes the difference between the original and the embedded coefficients. Accordingly, an optimization-based embedding formula connecting the SNR and the embedding system is derived. The formula is then applied to embed the synchronization codes and the watermarks. Based on this formula, the number of DWT coefficients for embedding a binary bit can be increased to enhance the robustness without decreasing the audio quality. Consequently, the embedded audio has good quality and good robustness under high capacity. In addition, the system can extract the hidden data without the knowledge of original audio signal. Finally, the performance of the proposed watermarking method is tested. Experimental results indicate that the performance of proposed system is better than other DWT amplitude modification method.

Biometric Electrocardiogram Card for Access Control System

With the rapid development of access control system based on biometric technologies, the conventional identification exposed more and more weakness while they are easy to be counterfeited and imitated. In this paper, we bring the access control systems with electrocardiogram identification into a practical application. Moreover, a new prompt based ECG algorithm are proposed to seek a more secure and accurate identification. In our evaluation, a hardware board were designed to verified its feasibility, then the analysis and comparison among various biometric identification and previous ECG identification researches were further disused as well. The result shown our proposed design could provide a more secure, low cost and convenient identification for access control system.

Optimization-based audio watermarking with integrated quantization embedding

In general, the performance of a multimedia watermarking scheme is measured in terms of signal-to-noise ratio (SNR) and robustness. However, there is a tradeoff between them which issues a challenge in the field of the watermarking. To overcome this problem, the embedding rules using amplitude quantization instead of single-coefficient quantization are first integrated as an embedding system that incorporates a state and the SNR is rewritten as a cost function which is a wavelet-based functional. Then, a nonlinear function connecting the cost function and the embedding system is derived. Finally, the Lagrange Principle is used to obtain the optimal solution which is an important formula for watermarking. Based on this formula, the synchronization code and watermark are embedded into the lowest frequency sub-band coefficients in the wavelet domain. In addition, the hidden information can be extracted without knowledge of the original audio. In the experiments, the performance of the proposed system is tested and the results verify the high SNR and strong robustness against multimedia signal processing or attacks.

ECG Human Identification with Statistical Support Vector Machines

Electrocardiogram (ECG) as a biological information, it has some special feature. Different people will have different ECG information, even one person has different ECG when he is under different body state. In this paper we use the Electrocardiogram (ECG) to identify disease or to detect different person. Firstly, we collect the ECG information form different body state of the different people. Secondly we will preprocess the ECG data by using a method of statistical. Thirdly we can use the support vector machine to train the data, and then classify different peoples data into different class. And finally when there are one new ECG data, we can also use SVM to identify the new data. Because even one people have several ECG signal, with our statistical method, the classifier may gets better robust.