Dora María Ballesteros

MULTI-RESOLUTION ANALYSIS AND LOSSLESS ENCODERS IN THE COMPRESSION OF ELECTROCARDIOGRAPHIC SIGNALS

Many algorithms have been developed in the compression on biomedical signals. One of the most commonly compressed signals correspond to the electrocardiographic signal because is frequently measurement in environment hospitals. In this paper, we present the comparison of two methods based on multi-resolution analysis and lossless encoders, specifically Discrete Wavelet Transform and Huffman-Run length. In the performance of the algorithms, we analyzed two parameters: the compression ratio (CR) and the percentage of roots mean square (PRD). Our algorithms presented a CR of 8:1 to a PRD of 0,5%, which are good results in clinical applications, because the main characteristics in time and frequency are preserved.

Text Hiding in Images Based on QIM and OVSF

This paper presents an algorithm of digital image watermarking that uses a text as mark. Our proposal generates an optimal level of robustness according to the parameters considered for watermarking. The proposed algorithm consists in the insertion over an image of a text string, previously modified by permutation using a random key and an OVSF (Orthogonal Variable Spreading Factor) generator. The insertion is made in the wavelet domain and uses Quantization Index Modulation (QIM). Also, the conditions to reverse the process and restore the original information are set. Finally, the robustness of the proposed algorithm is evaluated implementing several attacks on the marked image.

FPGA compression of ECG signals by using modified convolution scheme of the Discrete Wavelet Transform

This paper presents FPGA design of ECG compression by using the Discrete Wavelet Transform (DWT) and one lossless encoding method. Unlike the classical works based on off-line mode, the current work allows the real-time processing of the ECG signal to reduce the redundant information. A model is developed for a fixed-point convolution scheme which has a good performance in relation to the throughput, the latency, the maximum frequency of operation and the quality of the compressed signal. The quantization of the coefficients of the filters and the selected fixed-threshold give a low error in relation to clinical applications.

Discrete Wavelet Transform in Compression and Filtering of Biomedical Signals

Biomedical signals are a kind of signals that are measured from a specific part of the body, for example from the hearth (electrocardiography: ECG), muscles (electromyography: EMG) and brain (electroencephalography: EEG). This kind of signals have a no-stationary behavior, it means the behavior through the time is changing every time window. For this reason, the pre-processing, processing, and analysis should be different of the deterministic and stationary signals. One of the methods used in the last years to examine biomedical signals is the Discrete Wavelet Transform (DWT), it represents both time and frequency the signal’s characteristics in a multi-resolution mode. In this chapter, we are going to present two applications of the DWT in biomedical signals, it known as filtering and compression. When you have a device that measures the body’s signals, it is desired that the information stored or transmitted have high quality and low redundancy; this corresponds to apply a filter and compress the signal. These two blocks (filtering and compression) are added once the signal is acquired and processed by digital signal processing methods. The goal of using the DWT in an algorithm of filtering and compression biomedical signals is the possibility of choosing the signal’s coefficients with a significant energy and discards the others that have a very low percentage of all energy. This is possible because in every level of decomposition, the energy of different frequencies and time position is related to a specific coefficient. In the first part, we present one model of filtering of biomedical signals based on Discrete Wavelet Transform. We analyze the different parameters in the model and its relation to the quality of the new signal. Every parameter affects in low or high manner the quality of the filtered signal and we present the most common test to probe the signals distortion when the coefficients with low energy have been removed. Additionally, we present some results with one real EMG signal with different configuration of the parameters. In the second part, we extend the model of filtering to include the stage of compression; we explain the encoding block, which is added to the compression model. Two lossless encoding methods are explained and compared. The compression of some records of ECG is presented.

Comunicación encubierta de imágenes a escala de grises en imágenes a color

This manuscript shows a proposal of covert communication of grayscale images into color images. The main parameter taken into account in the design of the scheme is the transparency of the covert image; meaning that the covert image should be highly similar (perceptually and statistically) to the original color image. Several tests were conducted in order to measure both the transparency of the covert image and the quality of the recovered secret image (i.e. gray image)

Ocultamiento de voz en audio basado en el desplazamiento de espectro en el dominio wavelet

Este articulo describe un modelo de ocultamiento de voz (mensaje secreto) en audio (senal huesped) basado en la tecnica de espectro desplazado, Shift Spectrum Algorithm (SSA), y la Transformada Wavelet Discreta (DWT). Las senales de voz y audio se descomponen utilizando la DWT multinivel. Los coeficientes del mensaje secreto se ocultan en los coeficientes de detalle de la senal huesped, utilizando un reordenamiento de sub-bandas basado en un criterio de similitud. La clave secreta contiene la informacion del reordenamiento de las sub-bandas del mensaje secreto. La reconstruccion de los coeficientes wavelet superpuestos de las dos senales corresponde a la senal estego, la cual tiene la misma escala de tiempo y rango dinamico de la senal huesped. La calidad de la senal estego se califica con la prueba de promedio de opinion, Mean Opinion Score (MOS) del estandar ITU-T P.835.

Filtrado ciego de ruido blanco en señales de voz

Discrete Wavelet Transform (DWT) has been used in the recent yearsin signal processing applications, i.e. filtering and compression. In thecase of denoising because the energy of the noise is spread in the entire wavelet coefficients and it has low amplitude, it can be rejected by thres holding. In this paper, we propose a model to evaluate the influence of the denoising parameters in the quality of the speech signals, by ablind process. We examine the residual signal to establish an objective and blind criteria for selecting the following parameters: base, levels of de composition, rule, and threshold. This model can be applied in anytype of speech signal, no matter its behavior in time and frequency.

Separación ciega de fuentes no-determinada aplicada a mezclas de voz con base en la transformada wavelet discreta

Blind Source Separation, BSS, is a signal processing technique which estimates sources from linearly mixed signals and it uses methods such as ICA for sources that are statistically independent. Among the best known BSS algorithms is the JADE method, which requires that the number of independent signals match the number of observed signals (sensors). In the real world, the number of sensors is lower than the number of sources (undetermined BSS) and therefore the problem has no solution. This work proposes a solution for undetermined BSS by pre-processing and decomposition stages based on the Discrete Wavelet Transform (DWT). Our proposal, which it is known as DWT+BSS, creates a virtual observed signal from a real observed signal and it uses the wavelet coefficients of the observed signals as the inputs of the classical JADE algorithm. We validated our model with voice and audio signals obtaining indexes of similarity over 0.7 between the original and the estimated sources.

Compresion de señales ecg utilizando dwt y codificacion huffman

Data compress algorithms are frequently used by data communication systems and data storage systems, with the objective of reduce the information transmitted, increase the number of channels and reduce the requirements for storage. This paper presents software models for the wavelet decomposition and Huffman encoding that form the completely system of data compression. The designs have been implemented in Matlab, version R2009a. Performance metrics are obtained, by estimating PRD and CR and the results are compared with prior development.