WAVELET BASED NONLINEAR SEPARATION OF IMAGES

This work addresses a real-life problem corresponding to the separation of the nonlinear mixture of images which arises when we scan a paper document and the image from the back page shows through. The proposed solution consists of a non-iterative procedure that is based on two simple observations: (1) the high frequency content of images is sparse, and (2) the image printed on each side of the paper appears more strongly in the mixture acquired from that side than in the mixture acquired from the opposite side. These ideas had already been used in the context of nonlinear denoising source separation (DSS). However, in that method the degree of separation achieved by applying these ideas was relatively weak, and the separation had to be improved by iterating within the DSS scheme. In this paper the application of these ideas is improved by changing the competition function and the wavelet transform that is used. These improvements allow us to achieve a good separation in one shot, without the need to integrate the process into an iterative DSS scheme. The resulting separation process is both nonlinear and non-local. We present experimental results that show that the method achieves a good separation quality

A robust feature extraction for automatic speech recognition in noisy environments

This paper presents a method for extraction of speech robust features when the external noise is additive and has white noise characteristics. The process consists of a short time power normalisation which goal is to preserve as much as possible, the speech features against noise. The proposed normalisation will be optimal if the corrupted process has, as the noise process white noise characteristics. With optimal normalisation we can mean that the corrupting noise does not change at all the means of the observed vectors of the corrupted process. As most of the speech energy is contained in a relatively small frequency band being most of the band composed by noise or noise-like power, this normalisation process can still capture most of the noise distortions. For Signal to Noise Ratio greater than 5 dB the results show that for stationary white noise, the normalisation process where the noise characteristics are ignored at the test phase, outperforms the conventional Markov models composition where the noise is known. If the noise is known, a reasonable approximation of the inverted system can be easily obtained performing noise compensation still increasing the recogniser performance

Neural networks in B-ISDN flow control: ATM traffic prediction or network modeling?

The authors discuss a technique that offers the combination of shared bandwidth and rejection rate parameters, together with the quality of service predicted by neural networks in a novel strategy for connection admission control and call routing

ATM call control by neural networks

The resource allocation in the Broadband Integrated Services Digital Network (B-ISDN) must guarantee the quality of service negotiated with new and existing calls, taking into account the Asynchronous Transfer Mode (ATM) statistical characteristics. A quality of operation function, characterizing the overall network performance, is proposed, and based on this function, it is introduced a new strategy for the admission control and routing of the ATM call connections. As it is shown by simulation results, feedforword Neural Networks trained with the backpropagation algorithm, can learn the traffic patterns in previous traffic situations, and can be used to predict the quality of operation changes caused by each new call

Spectral multi-normalisation for robust speech recognition

This paper presents an improved version of a spectral normalisation based method for extraction of speech robust features in additive noise. The baseline normalisation method was developed by taking into consideration that, while the speech regions with less energy need more robustness, since in these regions the noise is more dominant, the “peaked” spectral regions which are the most reliable due to the higher speech energy must also be preserved as much as possible by the feature extraction process. The additive noise effect tends to flatten the “peaked” spectral zones while the spectral zones of less energy are usually raised. The algorithm proposed in this paper showed to alleviate the noise effect by emphasising the voiced nature of the speech signal by raising the spectral “peaks”, which are “flatten” by the noise effect. The clean speech database is assumed as lightly contaminated, the additive noise is estimated in a frame by frame basis and then used to restore both the “peaked” and the flat spectral zones of the speech spectrum

Adaptive technique for ATM call admission and routing control using traffic prediction by neural networks

This paper discusses a technique for call admission and routing control, based on a global quality function, which is dependent on the allocated bandwidth, the free network capacity and the call rejection rate, and incorporates quality of service functions, predicted by neural networks. The superior capability of this technique to support admission and routing decisions, according to the characteristics of the traffic generated by admitted calls, is demonstrated by simulation results carried out using suitable traffic and network models, which are equally discussed. It is also shown that the proposed technique, being based on several observed traffic parameters, offers better results than methods based only on declared bandwidth parameters

B-ISDN connection admission control and routing strategy with traffic prediction by neural networks

The resource allocation in the Broadband Integrated Services Digital Network (B-ISDN) can be based in an overall network performance function described in this paper and named quality of operation. The quality of operation function is determined itself by bandwidth and quality of service functions. The traffic patterns of the quality of service for each call are predicted by neural networks. The applicability of the quality of operation function to connection admission control and call routing is proposed and supported by simulation results

Clinical diagnostic reference levels in computed tomography examinations

For the accomplishment of this study, CT dose descriptors such as mAs, kVp, Computed Tomography Dose Index (CTDI) and Dose Length Product (DLP) were registered for CT examinations of the brain, face, chest, spine, abdomen and pelvis, through the examination of the file dose protocol grouped into 12 different clinical indications and using a sample of 20 patients per each clinical indication. A final total of 240 examinations were considered.info:eu-repo/semantics/publishedVersio