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The problem of identifying disturbance lines in printed circuit boards is addressed. Identification is achieved by estimating the disturbance frequency and the separation distance between the coupled lines. Frequency estimation uses a wavelet coefficients thresholding method to reconstruct the interference caused by the disturbance line. The separation distance is computed using two empirical laws based on physical ground and verified by simulation. The performance of the proposed method is illustrated by simulation results. [ABSTRACT FROM AUTHOR]

Physiologically based models are attractive for seizure detection, as their parameters can be explicitly related to neurological mechanisms. We propose an early seizure detection algorithm based on parameter identification of a neural mass model. The occurrence of a seizure is detected by analysing the time shift of key model parameters. The algorithm was evaluated against the manual scoring of a human expert on intracranial EEG samples from 16 patients suffering from different types of epilepsy. Results suggest that the algorithm is best suited for patients suffering from temporal lobe epilepsy (sensitivity was 95.0% ± 10.0% and false positive rate was 0.20 ± 0.22 per hour)., (© 2013 Elsevier Ltd. All rights reserved.)

This paper presents a technological solution based on sensors controlled remotely in order to monitor, track and evaluate the gait quality in people with or without associated pathology. Special hardware simulating a shoe was developed, which consists of three pressure sensors, two bending sensors, an Arduino mini and a Bluetooth module. The obtained signals are digitally processed, calculating the standard deviation and establishing thresholds obtained empirically. A group of users was chosen with the aim of executing two modalities: natural walking and dragging the left foot. The gait was parameterized with the following variables: as far as pressure sensors are concerned, one pressure sensor under the first metatarsal (right sensor), another one under the fifth metatarsal (left) and a third one under the heel were placed. With respect to bending sensors, one bending sensor was placed for the ankle movement and another one for the foot sole. The obtained results show a rate accuracy oscillating between 85% (right sensor) and 100% (heel and bending sensors). Therefore, the developed prototype is able to differentiate between healthy gait and pathological gait, and it will be used as the base of a more complex and integral technological solution, which is being developed currently.

A synthesizer is based on a nonlinear wave-shaping model of the glottal area, an algebraic model of the glottal aerodynamics as well as concatenated-tube models of the trachea and vocal tract. Voice disorders are simulated by way of models of vocal frequency jitter and tremor, vocal amplitude shimmer and tremor, as well as pulsatile additive noise. Six experiments have been carried out to assess the synthesizer perceptually. Three experiments involve the perceptual categorization of male synthetic and human stimuli and one the auditory discrimination between synthetic and human tokens. A fifth experiment reports the auditory discrimination between synthetic tokens with different levels of additive and modulation noise. A sixth experiment reports the scoring by expert listeners of male synthetic stimuli on equal-appearing interval scales grade-roughness-breathiness (GRB). A first objective is to demonstrate the ability of the synthesizer to simulate vowel sounds that are valid exemplars of speech sounds produced by humans with voice disorders. A second objective is to learn how human expert raters perceptually map vocal frequency, additive and modulation noise as well as vowel categories into scores on GRB scales.

A continuous wavelet transform-based method is presented to study the nonstationary strength and phase delay of the respiratory sinus arrhythmia (RSA). The RSA is the cyclic variation of instantaneous heart rate at the breathing frequency. In studies of cardio-respiratory interaction during sleep, paced breathing or postural changes, low respiratory frequencies, and fast changes can occur. Comparison on synthetic data presented here shows that the proposed method outperforms traditional short-time Fourier-transform analysis in these conditions. On the one hand, wavelet analysis presents a sufficient frequency-resolution to handle low respiratory frequencies, for which time frames should be long in Fourier-based analysis. On the other hand, it is able to track fast variations of the signals in both amplitude and phase for which time frames should be short in Fourier-based analysis.

An adaptive formulation of the long-term bidirectional linear predictive analysis is proposed in the context of the acoustic assessment of disordered speech. Vocal dysperiodicities are summarized by means of a signal-to-dysperiodicity ratio (SDR) marker. It is shown that performing an adaptive forward and backward long-term linear prediction of each speech sample and retaining the minimal prediction error energy as a cue of vocal dysperiodicity results in an SDR that correlates with the perceived degree of hoarseness. The coefficients of the time-varying long-term linear predictive model are estimated by means of the recursive least squares algorithm. The corpora comprise sustained vowels and French sentences produced by male and female normophonic and dysphonic speakers. A perceptual assessment of speech samples, which rests on comparative judgments, is used to evaluate the ability of the acoustic marker to predict subjective measures of voice quality. Experimental results show that the adaptive approach gives rise to high correlations for sustained vowels as well as for sentences.

The article presents an analysis of vocal dysperiodicities in connected speech produced by dysphonic speakers. The processing is based on a comparison of the present speech fragment with future and past fragments. The size of the dysperiodicity estimate is zero for periodic speech signals. A feeble increase of the vocal dysperiodicity is guaranteed to produce a feeble increase of the estimate. No spurious noise boosting occurs owing to cycle insertion and omission errors, or phonetic segment boundary artifacts. Additional objectives of the study have been investigating whether deviations from periodicity are larger or more commonplace in connected speech than in sustained vowels, and whether sentences that comprise frequent voice onsets and offsets are noisier than sentences that comprise few. The corpora contain sustained vowels as well as grammatically- and phonetically matched sentences. An acoustic marker that correlates with the perceived degree of hoarseness summarizes the size of the dysperiodicities. The marker values for sustained vowels have been highly correlated with those for connected speech, and the marker values for sentences that comprise few voiced/unvoiced transients have been highly correlated with the marker values for sentences that comprise many.