Search results for "recognition"

Supramolecular Organocatalysis in Water Mediated by Macrocyclic Compounds

2018

In the last decades many efforts have been devoted to design supramolecular organocatalysts able to work in water as the reaction medium. The use of water as solvent provides promising benefits with respect to environmental impact. In this context, macrocyclic compounds played a role of primary importance thanks to their ease of synthesis and their molecular recognition abilities toward the reactants. The aim of this review is to give an overview of the recent advances in the field of supramolecular organocatalysis in water, focusing the attention on calixarene and cyclodextrins derivatives. Calixarenes and cyclodextrins, thanks to their hydrophobic cavities, are able to host selectively th…

Variable fractional Fourier processor: a simple implementation: erratum

1997

Une nouvelle approche de classification de personnes à partir d'une plate-forme multi-kinect

2015

International audience; Dans ce papier nous présentons un descripteur 3D original pour la classification de personnes. Nous avons réalisé une plate-forme multi-kinect qui fait l'acquisition de nuages de points complets (c'est à dire selon une vue à 360°). Un post-traitement (suppression d'arrière-plan) isole les éléments qui sont candidats à représenter une personne. Notre descripteur exploite ces données et permet, au travers d'un classifieur SVM (Support Vector Machine), de classifier les personnes et de déterminer l'orientation du plan frontal de la personne. Il utilise la répartition spatiale et l'orientation des normales aux surfaces dans un procédé dérivé de celui des HOG (Histogram o…

Model selection using limiting distributions of second-order blind source separation algorithms

2015

Signals, recorded over time, are often observed as mixtures of multiple source signals. To extract relevant information from such measurements one needs to determine the mixing coefficients. In case of weakly stationary time series with uncorrelated source signals, this separation can be achieved by jointly diagonalizing sample autocovariances at different lags, and several algorithms address this task. Often the mixing estimates contain close-to-zero entries and one wants to decide whether the corresponding source signals have a relevant impact on the observations or not. To address this question of model selection we consider the recently published second-order blind identification proced…

Acoustic detection and classification of river boats

2011

We present a robust algorithm to detect the arrival of a boat of a certain type when other background noises are present. It is done via the analysis of its acoustic signature against an existing database of recorded and processed acoustic signals. We characterize the signals by the distribution of their energies among blocks of wavelet packet coefficients. To derive the acoustic signature of the boat of interest, we use the Best Discriminant Basis method. The decision is made by combining the answers from the Linear Discriminant Analysis (LDA) classifier and from the Classification and Regression Trees (CART) that is also accompanied with an additional unit, called Aisles, that reduces fal…

A novel heuristic memetic clustering algorithm

2013

In this paper we introduce a novel clustering algorithm based on the Memetic Algorithm meta-heuristic wherein clusters are iteratively evolved using a novel single operator employing a combination of heuristics. Several heuristics are described and employed for the three types of selections used in the operator. The algorithm was exhaustively tested on three benchmark problems and compared to a classical clustering algorithm (k-Medoids) using the same performance metrics. The results show that our clustering algorithm consistently provides better clustering solutions with less computational effort.

Gear classification and fault detection using a diffusion map framework

2015

This article proposes a system health monitoring approach that detects abnormal behavior of machines. Diffusion map is used to reduce the dimensionality of training data, which facilitates the classification of newly arriving measurements. The new measurements are handled with Nyström extension. The method is trained and tested with real gear monitoring data from several windmill parks. A machine health index is proposed, showing that data recordings can be classified as working or failing using dimensionality reduction and warning levels in the low dimensional space. The proposed approach can be used with any system that produces high-dimensional measurement data. peerReviewed

Exploiting ongoing EEG with multilinear partial least squares during free-listening to music

2016

During real-world experiences, determining the stimulus-relevant brain activity is excitingly attractive and is very challenging, particularly in electroencephalography. Here, spectrograms of ongoing electroencephalogram (EEG) of one participant constructed a third-order tensor with three factors of time, frequency and space; and the stimulus data consisting of acoustical features derived from the naturalistic and continuous music formulated a matrix with two factors of time and the number of features. Thus, the multilinear partial least squares (PLS) conforming to the canonical polyadic (CP) model was performed on the tensor and the matrix for decomposing the ongoing EEG. Consequently, we …

Combining PCA and multiset CCA for dimension reduction when group ICA is applied to decompose naturalistic fMRI data

2015

An extension of group independent component analysis (GICA) is introduced, where multi-set canonical correlation analysis (MCCA) is combined with principal component analysis (PCA) for three-stage dimension reduction. The method is applied on naturalistic functional MRI (fMRI) images acquired during task-free continuous music listening experiment, and the results are compared with the outcome of the conventional GICA. The extended GICA resulted slightly faster ICA convergence and, more interestingly, extracted more stimulus-related components than its conventional counterpart. Therefore, we think the extension is beneficial enhancement for GICA, especially when applied to challenging fMRI d…

Support vector machine integrated with game-theoretic approach and genetic algorithm for the detection and classification of malware

2013

Abstract. —In the modern world, a rapid growth of mali- cious software production has become one of the most signifi- cant threats to the network security. Unfortunately, wides pread signature-based anti-malware strategies can not help to de tect malware unseen previously nor deal with code obfuscation te ch- niques employed by malware designers. In our study, the prob lem of malware detection and classification is solved by applyin g a data-mining-based approach that relies on supervised mach ine- learning. Executable files are presented in the form of byte a nd opcode sequences and n-gram models are employed to extract essential features from these sequences. Feature vectors o btained are…