introduction: repetitive strain injuries are one of the most prevalent problems in occupational diseases. repetition, vibration and bad postures of the extremities are physical risk factors related to work that can cause chronic musculoskeletal disorders. repetitive work on a computer with low level contraction requires the posture to be maintained for a long time, which can cause muscle fatigu...

Bayesian wavelet shrinkage methods are defined through a prior distribution on the space of wavelet coefficients after a Discrete Wavelet Transformation has been applied to the data. Posterior summaries of the wavelet coefficients establish a Bayes shrinkage rule. After the Bayes shrinkage is performed, an Inverse Discrete Wavelet Transformation can be used to recover the signal that generated ...

Wavelets are powerful mechanisms for analyzing and processing digital signals. The wavelet transform translates the time-amplitude representation of a signal to a time-frequency representation that is encapsulated as a set of wavelet coefficients. These wavelet coefficients can be manipulated in a frequency-dependent manner to achieve various digital signal processing effects. The inverse wavel...

The Discrete Wavelet Transform (DWT) has been applied to data compression to decorrelate the data and concentrate the energy in a small portion of the coefficients. Compression can be achieved since most of the quantized wavelet coefficients are zeros. For the decoder, the traditional inverse discrete wavelet transform (IDWT) has a complexity proportional to the size of the data. In this paper,...

In the present work we analyze the performance of orthogonal and Biorthogonal wavelet for electrocardiogram (ECG) compression. Both types of wavelets applied on different types of records taken from MIT-BIH database. A thresholding algorithm based on energy packing efficiency of the wavelet coefficients is used to threshold the wavelet coefficients. We divide wavelet coefficients in two groups ...

Most simple nonlinear thresholding rules for wavelet-based denoising assume that the wavelet coefficients are independent. However, wavelet coefficients of natural images have significant dependencies. In this paper, we will only consider the dependencies between the coefficients and their parents in detail. For this purpose, new non-Gaussian bivariate distributions are proposed, and correspond...

Under the general regression setup, yi = gi + ǫi, we are interested in estimating a possibly multivariate regression function g. The wavelet thresholding is a simple operation in the wavelet domain that selects the subset of wavelet coefficients corresponding to an estimator of g when back-transformed. We propose the selection of this subset in a semi-supervised fashion, in which a neighbor str...

Current wavelet-based statistical signal and image processing techniques such as shrinkage and filtering treat the wavelet coefficients as though they were statistically independent. This assumption is unrealistic; considering the statistical dependencies between wavelet coefficients can yield substantial performance improvements. In this paper, we develop a new framework for wavelet-based sign...

In this paper, a robust digital image watermarking approach using adaptive quantization of wavelet packet coefficients was proposed. The original image is decomposed by discrete wavelet packet transform and the dominant wavelet coefficients are selected for watermark embedding from each sub-band except the lowest frequency one. Then, each watermark bit is adaptively embedded with different stre...