On joint parameterizations of linear and nonlinear functionals in neural networks.

The paper proposes a new class of nonlinear operators and a dual learning paradigm where optimization jointly concerns both linear convolutional weights and the parameters of these nonlinear operators. The nonlinear class proposed to perform a rich functional representation is composed by functions called rectified parametric sigmoid units. This class is constructed to benefit from the advantages of both sigmoid and rectified linear unit functions, while rejecting their respective drawbacks.

Sparse representation-based classification of mysticete calls.

This paper presents an automatic classification method dedicated to mysticete calls. This method relies on sparse representations which assume that mysticete calls lie in a linear subspace described by a dictionary-based representation. The classifier accounts for noise by refusing to assign the observed signal to a given class if it is not included into the linear subspace spanned by the dictionaries of mysticete calls.

Detection of artifacts on photoplethysmography signals using random distortion testing.

In this work, we describe a novel method based on waveform morphology for detecting artifacts in photoplethysmography (PPG) signals and, thus, improve reliability of PPG. By considering inter-individual and measure condition variability, specific parameters are estimated for each record. We introduce a novel metric for comparing pulses, which is the derivative of the correlation coefficient.

Mechanical ventilation system monitoring: automatic detection of dynamic hyperinflation and asynchrony.

Automatic monitoring of mechanical ventilation system becomes more and more important with respect to the number of patients per clinician. In this paper, the automatic detections of dynamic hyperinflation (PEEPi) and asynchrony in a monitoring framework are considered. The proposed detection methods are based on a robust non-parametric hypothesis testing, namely Random Distortion Testing (RDT), that requires no prior information on the signal distribution.

Automatic detection of AutoPEEP during controlled mechanical ventilation.

Background: Dynamic hyperinflation, hereafter called AutoPEEP (auto-positive end expiratory pressure) with some slight language abuse, is a frequent deleterious phenomenon in patients undergoing mechanical ventilation. Although not readily quantifiable, AutoPEEP can be recognized on the expiratory portion of the flow waveform. If expiratory flow does not return to zero before the next inspiration, AutoPEEP is present.

Protein interaction hotspot identification using sequence-based frequency-derived features.

Finding good descriptors, capable of discriminating hotspot residues from others, is still a challenge in many attempts to understand protein interaction. In this paper, descriptors issued from the analysis of amino acid sequences using digital signal processing (DSP) techniques are shown to be as good as those derived from protein tertiary structure and/or information on the complex. The simulation results show that our descriptors can be used separately to predict hotspots, via a random forest classifier, with an accuracy of 79% and a precision of 75%.

On normalized MSE analysis of speech fundamental frequency in the cochlear implant-like spectrally reduced speech.

In this paper, we present a quantitative study on the speech fundamental frequency (F0) of the cochlear implant-like spectrally reduced speech (SRS). The SRS was synthesized from the subband amplitude and frequency modulations (AM and FM) of original clean speech utterances selected from the TI-digits database. The SRS synthesis algorithm was derived from the frequency amplitude modulation encoding (FAME) strategy, proposed by Nie et al.