A kinesthetic washout filter for force-feedback rendering.

Today haptic feedback can be designed and associated to audiovisual content (haptic-audiovisuals or HAV). Although there are multiple means to create individual haptic effects, the issue of how to properly adapt such effects on force-feedback devices has not been addressed and is mostly a manual endeavor. We propose a new approach for the haptic rendering of HAV, based on a washout filter for force-feedback devices. A body model and an inverse kinematics algorithm simulate the user's kinesthetic perception. Then, the haptic rendering is adapted in order to handle transitions between haptic effects and to optimize the amplitude of effects regarding the device capabilities. Results of a user study show that this new haptic rendering can successfully improve the HAV experience.

Turning Tangent Empirical Mode Decomposition: A Framework for Mono- and Multivariate Signals.

A novel Empirical Mode Decomposition (EMD) algorithm, called 2T-EMD, for both mono- and multivariate signals is proposed in this paper. It differs from the other approaches by its computational lightness and its algorithmic simplicity. The method is essentially based on a redefinition of the signal mean envelope, computed thanks to new characteristic points, which offers the possibility to decompose multivariate signals without any projection. The scope of application of the novel algorithm is specified, and a comparison of the 2T-EMD technique with classical methods is performed on various simulated mono- and multivariate signals. The monovariate behaviour of the proposed method on noisy signals is then validated by decomposing a fractional Gaussian noise and an application to real life EEG data is finally presented.

Raman spectroscopy: a novel experimental approach to evaluating renal tumours.

BACKGROUND: New optical techniques of spectroscopy have shown promising results in the evaluation of solid tumours. OBJECTIVE: To evaluate the potential of Raman spectroscopy (RS) to assess renal tumours at surgery. DESIGN, SETTING, AND PARTICIPANTS: Over a 5-mo period, Raman optical spectra were prospectively acquired on surgical renal specimens removed due to suspicion of cancer. MEASUREMENTS: Raman measures were normalised to ensure comparison between spectra. A lower resolution signal was computed using a wavelet decomposition procedure to diminish the size of the signal and exploit the complete spectrum. A support vector machine (SVM) with a linear kernel and a sequential minimal optimisation solver was applied. A leave-one-out cross-validation technique was used to train and test the SVM. RESULTS AND LIMITATIONS: There were 36 patients with 34 malignant tumours (27 clear-cell, 6 papillary, and 1 chromophobe) and 2 benign (1 oncocytoma and 1 metanephric cyst) tumours. A total of 241 analysable Raman spectra were obtained. The SVM was able to classify tumoural and normal tissue with an accuracy of 84% (sensitivity 82%, specificity 87%). High-grade and low-grade tumours were differentiated with a precision of 82% (sensitivity 84%, specificity 80%). Histologic subtype could be categorised with an accuracy of 93% (sensitivity 96%, specificity 87%). SVM could not be applied to classify benign and malignant tumours because of the restricted number of benign spectra. CONCLUSIONS: RS can accurately differentiate normal and tumoural renal tissue, low-grade and high-grade renal tumours, and histologic subtype of renal cell carcinoma. Larger prospective studies are needed to confirm these preliminary data.

Modelling temporal evolution of cardiac electrophysiological features using Hidden Semi-Markov Models.

This paper presents a new method to analyse cardiac electrophysiological dynamics. It aims to classify or to cluster (i.e. to find natural groups) patients according to the dynamics of features extracted from their ECG. In this work, the dynamics of the features are modelled with Continuous Density Hidden Semi-Markovian Models (CDHSMM) which are interesting for the characterization of continuous multivariate time series without a priori information. These models can be easily used for classification and clustering. In this last case, a specific method, based on a fuzzy Expectation Maximisation (EM) algorithm, is proposed. Both tasks are applied to the analysis of ischemic episodes with encouraging results and a classification accuracy of 71%.

3D multi-object segmentation of cardiac MSCT imaging by using a multi-agent approach.

We propose a new technique for general purpose, semi-interactive and multi-object segmentation in N-dimensional images, applied to the extraction of cardiac structures in MultiSlice Computed Tomography (MSCT) imaging. The proposed approach makes use of a multi-agent scheme combined with a supervised classification methodology allowing the introduction of a priori information and presenting fast computing times. The multi-agent system is organised around a communicating agent which manages a population of situated agents which segment the image through cooperative and competitive interactions. The proposed technique has been tested on several patient data sets. Some typical results are finally presented and discussed.