Optimizing P300-speller sequences by RIP-ping groups apart

International audienceSo far P300-speller design has put very little emphasis on the design of optimized flash patterns, a surprising fact given the importance of the sequence of flashes on the selection outcome. Previous work in this domain has consisted in studying consecutive flashes, to prevent the same letter or its neighbors from flashing consecutively. To this effect, the flashing letters form more random groups than the original row-column sequences for the P300 paradigm, but the groups remain fixed across repetitions. This has several important consequences, among which a lack of discrepancy between the scores of the different letters. The new approach proposed in this paper accumulates evidence for individual elements, and optimizes the sequences by relaxing the constraint that letters should belong to fixed groups across repetitions. The method is inspired by the theory of Restricted Isometry Property matrices in Compressed Sensing, and it can be applied to any display grid size, and for any target flash frequency. This leads to P300 sequences which are shown here to perform significantly better than the state of the art, in simulations and online tests

Optimizing P300-speller sequences by RIP-ping groups apart

International audienceSo far P300-speller design has put very little emphasis on the design of optimized flash patterns, a surprising fact given the importance of the sequence of flashes on the selection outcome. Previous work in this domain has consisted in studying consecutive flashes, to prevent the same letter or its neighbors from flashing consecutively. To this effect, the flashing letters form more random groups than the original row-column sequences for the P300 paradigm, but the groups remain fixed across repetitions. This has several important consequences, among which a lack of discrepancy between the scores of the different letters. The new approach proposed in this paper accumulates evidence for individual elements, and optimizes the sequences by relaxing the constraint that letters should belong to fixed groups across repetitions. The method is inspired by the theory of Restricted Isometry Property matrices in Compressed Sensing, and it can be applied to any display grid size, and for any target flash frequency. This leads to P300 sequences which are shown here to perform significantly better than the state of the art, in simulations and online tests

Image processing application to a translate Braille black ink system Braille

We recall the enhancement by polynomial filtering principle of the numerical Braille relief image . We describe the recognition method based on two orthogonal axis projection of each Braille character . The five recognition steps are developed taking account ofform defaults of relief and using a maximum likehood method. The position axis dispersion of Braille characters permits to calculate estimated theoretical error . The error rate, verified in practice for manual made Braille reliefs is about 1 % .Après avoir rappelé le principe de rehaussement par filtrage polynomial de l'image du relief Braille numérisée, il est décrit la méthode de reconnaissance choisie pour ce type particulier de forme d'objets . Cette méthode est basée sur la projection de chaque graphème sur deux axes orthogonaux . Il est décrit les cinq étapes de reconnaissance de chaque rangée de relief Braille qui tiennent compte de ses irrégularités de forme, et qui exploitent une méthode de maximum de vraisemblance . Le relevé de la dispersion des positions des axes des graphèmes permet de donner une estimation théorique du taux de réussite de reconnaissance pour des reliefs fabriqués manuellement . Le taux vérifié dans la pratique est voisin de 99 %

Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network

Abstract Background Conventional methods of motor imagery brain computer interfaces (MI-BCIs) suffer from the limited number of samples and simplified features, so as to produce poor performances with spatial-frequency features and shallow classifiers. Methods Alternatively, this paper applies a deep recurrent neural network (RNN) with a sliding window cropping strategy (SWCS) to signal classification of MI-BCIs. The spatial-frequency features are first extracted by the filter bank common spatial pattern (FB-CSP) algorithm, and such features are cropped by the SWCS into time slices. By extracting spatial-frequency-sequential relationships, the cropped time slices are then fed into RNN for classification. In order to overcome the memory distractions, the commonly used gated recurrent unit (GRU) and long-short term memory (LSTM) unit are applied to the RNN architecture, and experimental results are used to determine which unit is more suitable for processing EEG signals. Results Experimental results on common BCI benchmark datasets show that the spatial-frequency-sequential relationships outperform all other competing spatial-frequency methods. In particular, the proposed GRU-RNN architecture achieves the lowest misclassification rates on all BCI benchmark datasets. Conclusion By introducing spatial-frequency-sequential relationships with cropping time slice samples, the proposed method gives a novel way to construct and model high accuracy and robustness MI-BCIs based on limited trials of EEG signals

Evolution spatiale de de la dépolarisation dans un milieu turbide par approche stochastique

International audienceEvolution spatiale de de la dépolarisation dans un milieu turbide par approche stochastique V. Devlaminck, et J.M. Charbois, Journée Traitement des signaux polarimétriques optiques et radar du GDR ISIS 18 Octobre 2018. (Invité) Nous étudions l'évolution spatiale de la dépolarisation dans un milieu turbide du type particules en suspension dans un fluide, au moyen du formalisme des matrices de Mueller différentielles. Les propriétés polarimétriques sont modélisées par des processus stochastiques de type Orstein-Uhlenbeck. Nous montrons comment un paramètre peut être intégré dans ces modèles afin de permettre une prise en compte de la configuration de mesure

Stochastic model for differential Mueller matrix of stationary and non-stationary turbid media

International audienceWe show the existence of different regimes in spatial evolution of depolarization in turbid media characterized by a diagonal Mueller matrix (pure depolarizer). Experimental results previously published already established the existence of a first regime, where the depolarization follows a parabolic law with the thickness of stationary medium traveled by light. New experiments first confirm the existence of a second regime, which we have previously demonstrated, where the depolarization follows a linear law on a large scale. They also confirm the existence of much more complex evolution laws even under small-scale approximation. A stochastic approach is proposed to model the phenomenon. It perfectly describes all these different experimental results and allows us to analyze the behavior of the polarization in the case of solid or liquid scattering media. The influence of themeasurement setup is also analyzed

Evolution spatiale de de la dépolarisation dans un milieu turbide par approche stochastique

International audienceEvolution spatiale de de la dépolarisation dans un milieu turbide par approche stochastique V. Devlaminck, et J.M. Charbois, Journée Traitement des signaux polarimétriques optiques et radar du GDR ISIS 18 Octobre 2018. (Invité) Nous étudions l'évolution spatiale de la dépolarisation dans un milieu turbide du type particules en suspension dans un fluide, au moyen du formalisme des matrices de Mueller différentielles. Les propriétés polarimétriques sont modélisées par des processus stochastiques de type Orstein-Uhlenbeck. Nous montrons comment un paramètre peut être intégré dans ces modèles afin de permettre une prise en compte de la configuration de mesure

A Prototype Neural-network To Perform Early Warning in Nuclear-power-plant

The paper presents some results of research work in the field of artificial neural networks (ANN) applied to nuclear safety. It shows how a priori knowledge in the form of qualitative physical reasoning can provide a powerful basis for designing a set of ANN-based detection subsystems. In particular, it explains how each ANN is in charge of modelling a physical relationship between a set of state variables (thermal balance, mass balance, etc.) by trying to predict one particular variable from other ones; then, the residual signal, defined by the difference between the predicted value and the real one is used to decide whether abnormalities are present. As far as the decision logic is concerned, the paper describes how robustness can be improved by adequate filters on the residuals. The proposed approach is then validated on data coming from a fullscope simulator of one of the Belgian nuclear power units: the neural-based detection system is trained on ''normal'' scenarios and is able, after learning, to detect reliably and rapidly most of the incidental situations chosen as tests