Wavelet shrinkage schemes are applied for reducing noise in synthetic and experimental ultrasonic A-scans, using the Discrete Wavelet Transform (DWT) and a cycle-spinning (CS) implementation of Undecimated Wavelet Transform (UWT). A new wavelet-based denoising procedure, which we call Random Partial Cycle Spinning (RPCS) is presented and its performance is compared with that of DWT and a CS implementation of UWT. Three well known threshold selection rules (Universal, Minimax and Sure), with decomposition level dependent threshold selection, are used in all cases. Denoising using the UWT has previously shown a robust and usually better performance than denoising using DWT but with a much higher computational cost. In this work, it is shown that the alternative procedure RPCS provides a good robust performance, close to CS performance, but with a much lower computational cost.
Wavelets are a powerful tool for signal and image denoising. Most of the denoising applications in different fields were based on the thresholding of the discrete wavelet transform (DWT) coefficients. Nevertheless, DWT transform is not a time or shift invariant transform and results depend on the selected shift. Improvements on the denoising performance can be obtained using the stationary wavelet transform (SWT) (also called shift-invariant or undecimated wavelet transform). Denoising using SWT has previously shown a robust and usually better performance than denoising using DWT but with a higher computational cost. In this paper, wavelet shrinkage schemes are applied for reducing noise in synthetic and experimental non-destructive evaluation (NDE) ultrasonic A-scans, using DWT and a cycle-spinning implementation of SWT.A new denoising procedure, which we call Random Partial Cycle Spinning (RPCS), is presented. It is based on a cycle-spinning over a limited number of shifts that are selected in a random way. Wavelet denoising based on DWT, SWT and RPCS have been applied to the same sets of ultrasonic A-scans and their performances in terms of SNR are compared. In all cases three well known threshold selection rules (Universal, Minimax and Sure), with decomposition level dependent selection, have been used. It is shown that the new procedure provides a good robust denoising performance, without the DWT fluctuating performance, and close to SWT but with a much lower computational cost.
The concept of Smart-Hospital is generally associated with a comprehensive care model capable of responding to the needs of health institutions, companies and patients in an optimum way in terms of economic, operative and environmental aspects aiming the improvement of care quality and sustainable use of resources. In this context, a Smart-Hospital is a technological and hyper-connected hospital in terms of telecommunications. A huge range of systems and devices generate information of a heterogeneous nature. In many cases, for reasons of efficiency and availability, this information is stored and processed in architectures external to the hospital itself. Centralized services housed in Cloud architectures or telemedicine / tele-assistance services are proof of this. Guaranteeing an adequate level of quality of service is a complex task when approached from an analytical point of view due to the large number of sources and their heterogeneous nature. The use of simulation tools allows this task to be undertaken and using different hypotheses in less time and at a reasonable cost. This article presents the results obtained, in terms of quality of communications, for a Smart-Hospital with an arbitrary collection of heterogeneous services connected by Metro-Ethernet access. The results obtained: loss of information, delays and jitter will be used to outline the capacities to be contracted from the telecommunications supplier.
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