Search results for " Filtering"

Time-Frequency Filtering for Seismic Waves Clustering

2014

This paper introduces a new technique for clustering seismic events based on processing, in time-frequency domain, the waveforms recorded by seismographs. The detection of clusters of waveforms is performed by a k-means like algorithm which analyzes, at each iteration, the time-frequency content of the signals in order to optimally remove the non discriminant components which should compromise the grouping of waveforms. This step is followed by the allocation and by the computation of the cluster centroids on the basis of the filtered signals. The effectiveness of the method is shown on a real dataset of seismic waveforms.

HERMIA - GENERAL DESCRIPTION

1992

Advanced Motion Control in Induction Motor Systems - Modelling, Analysis and Control

Using a unified notation, this thesis collects and discusses the most important steps and issues in the design of estimation and control algorithms for induction motors. It contains many estimation and control algorithms. Their stability is analyzed and their performance is illustrated by simulations and experiments on the same induction motor. An intense and challenging collective research effort is carefully documented and analyzed, with the aim of providing and clarifying the basic intuition and tools required in the analysis and design of nonlinear feedback control algorithms. This material should be of specific interest to engineers who are engaged in the design of control algorithms f…

A Semantic Similarity Measure for the SIMS Framework

2008

The amount of currently available digital information grows rapidly. Relevant information is often spread over different information sources. An efficient and flexible framework to allow users to satisfy ef- fectively their information needs is required. The work presented in this paper describes SIMS (Semantic Information Management System), a ref- erence architecture for a framework performing semantic annotation, search and retrieval of information from multiple sources. The work pre- sented in this paper focuses on a specific SIMS module, the SIMS Semantic Content Navigator, proposing an algorithm and the related implementa- tion to calculate a semantic similarity measure inside an OWL …

Is There Anything New to Say About SIFT Matching?

2020

SIFT is a classical hand-crafted, histogram-based descriptor that has deeply influenced research on image matching for more than a decade. In this paper, a critical review of the aspects that affect SIFT matching performance is carried out, and novel descriptor design strategies are introduced and individually evaluated. These encompass quantization, binarization and hierarchical cascade filtering as means to reduce data storage and increase matching efficiency, with no significant loss of accuracy. An original contextual matching strategy based on a symmetrical variant of the usual nearest-neighbor ratio is discussed as well, that can increase the discriminative power of any descriptor. Th…

Exponential Entropy Driven HUM on Knee MR Images

2007

A very important artifact corrupting Magnetic Resonance Images is the RF inhomogeneity. This kind of artifact generates variations of illumination which trouble both direct examination by the doctor and segmentation algorithms. Even if homomorphic filtering approaches have been presented in literature, none of them has developed a measure to determine the cut-off frequency. In this work we present a measure based on information theory with a large experimental setup aimed to demonstrate the validity of our approach.

Particle Group Metropolis Methods for Tracking the Leaf Area Index

2020

Monte Carlo (MC) algorithms are widely used for Bayesian inference in statistics, signal processing, and machine learning. In this work, we introduce an Markov Chain Monte Carlo (MCMC) technique driven by a particle filter. The resulting scheme is a generalization of the so-called Particle Metropolis-Hastings (PMH) method, where a suitable Markov chain of sets of weighted samples is generated. We also introduce a marginal version for the goal of jointly inferring dynamic and static variables. The proposed algorithms outperform the corresponding standard PMH schemes, as shown by numerical experiments.

Pattern dynamics in a nonlinear electrical lattice

2003

International audience; In this paper, we present experiments using a nonlinear electrical line, modeling the FitzHugh-Nagumo equation, without recovery term. Different patterns are studied according to the para meters of this medium and initial conditions. We then propose to apply these results to the domain of signal processing. We show that erosion and dilation of a binary signal, two kinds,of binarization-one depending on an amplitude threshold, the other on an energetical threshold-and nonlinear filtering allowing noise removal can be obtained in the same medium.

Path to Overcome Material and Fundamental Obstacles in Spin Valves Based on MoS2 and Other Transition-Metal Dichalcogenides

2019

The recent introduction of two-dimensional materials into magnetic tunnel junctions (2D MTJs) offers very promising properties for spintronics, such as atomically defined interfaces, spin filtering, perpendicular anisotropy, and modulation of spin-orbit torque. Nevertheless, the difficulty of integrating exfoliated 2D materials into spintronic devices has limited exploration. Here the authors find a fabrication process leading to superior performance in MTJs based on transition-metal dichalcogenides, and further suggest a path to alleviate basic issues of technology and physics for 2D MTJs.

TB-Structure: Collective Intelligence for Exploratory Keyword Search

2017

In this paper we address an exploratory search challenge by presenting a new (structure-driven) collaborative filtering technique. The aim is to increase search effectiveness by predicting implicit seeker’s intents at an early stage of the search process. This is achieved by uncovering behavioral patterns within large datasets of preserved collective search experience. We apply a specific tree-based data structure called a TB (There-and-Back) structure for compact storage of search history in the form of merged query trails – sequences of queries approaching iteratively a seeker’s goal. The organization of TB-structures allows inferring new implicit trails for the prediction of a seeker’s i…