Search results for "algorithm"
showing 10 items of 4887 documents
Quantification of synchronization during atrial fibrillation by Shannon entropy: Validation in patients and computer model of atrial arrhythmias
2005
Atrial fibrillation (AF), a cardiac arrhythmia classically described as completely desynchronized, is now known to show a certain amount of synchronized electrical activity. In the present work a new method for quantifying the level of synchronization of the electrical activity recorded in pairs of atrial sites during atrial fibrillation is presented. A synchronization index (Sy) was defined by quantifying the degree of complexity of the distribution of the time delays between sites by Shannon entropy estimation. The capability of Sy to discriminate different AF types in patients was assessed on a database of 60 pairs of endocardial recordings from a multipolar basket catheter. The analysis…
Postprocessing algorithm for automated analysis of pelvic intraoperative neuromonitoring signals
2016
Abstract Two dimensional pelvic intraoperative neuromonitoring (pIONM®) is based on electric stimulation of autonomic nerves under observation of electromyography of internal anal sphincter (IAS) and manometry of urinary bladder. The method provides nerve identification and verification of its’ functional integrity. Currently pIONM® is gaining increased attention in times where preservation of function is becoming more and more important. Ongoing technical and methodological developments in experimental and clinical settings require further analysis of the obtained signals. This work describes a postprocessing algorithm for pIONM® signals, developed for automated analysis of huge amount of …
Deep learning algorithms for gravitational waves core-collapse supernova detection
2021
The detection of gravitational waves from core-collapse supernova (CCSN) explosions is a challenging task, yet to be achieved, in which it is key the connection between multiple messengers, including neutrinos and electromagnetic signals. In this work, we present a method for detecting these kind of signals based on machine learning techniques. We tested its robustness by injecting signals in the real noise data taken by the Advanced LIGO-Virgo network during the second observation run, O2. We trained three newly developed convolutional neural networks using time-frequency images corresponding to injections of simulated phenomenological signals, which mimic the waveforms obtained in 3D nume…
Understanding disease mechanisms with models of signaling pathway activities
2014
Background Understanding the aspects of the cell functionality that account for disease or drug action mechanisms is one of the main challenges in the analysis of genomic data and is on the basis of the future implementation of precision medicine. Results Here we propose a simple probabilistic model in which signaling pathways are separated into elementary sub-pathways or signal transmission circuits (which ultimately trigger cell functions) and then transforms gene expression measurements into probabilities of activation of such signal transmission circuits. Using this model, differential activation of such circuits between biological conditions can be estimated. Thus, circuit activation s…
An enhanced random walk algorithm for delineation of head and neck cancers in PET studies
2017
An algorithm for delineating complex head and neck cancers in positron emission tomography (PET) images is presented in this article. An enhanced random walk (RW) algorithm with automatic seed detection is proposed and used to make the segmentation process feasible in the event of inhomogeneous lesions with bifurcations. In addition, an adaptive probability threshold and a k-means based clustering technique have been integrated in the proposed enhanced RW algorithm. The new threshold is capable of following the intensity changes between adjacent slices along the whole cancer volume, leading to an operator-independent algorithm. Validation experiments were first conducted on phantom studies:…
A mutual GrabCut method to solve co-segmentation
2013
Publised version of an article from the journal:Eurasip Journal on Image and Video Processing. Also available on SpringerLink:http://dx.doi.org/10.1186/1687-5281-2013-20. Open Access Co-segmentation aims at segmenting common objects from a group of images. Markov random field (MRF) has been widely used to solve co-segmentation, which introduces a global constraint to make the foreground similar to each other. However, it is difficult to minimize the new model. In this paper, we propose a new Markov random field-based co-segmentation model to solve co-segmentation problem without minimization problem. In our model, foreground similarity constraint is added into the unary term of MRF model ra…
PORE Algorithm for Object Recognition in Photo Layers based on Parametric Characteristics of the Object Edges
2016
PORE stands for Photo-Object Recognition based on the Edges. Coincidentally, PORE means to examine something carefully and with due attention, so "we pore over the object layers in search for information about their characteristics with the aim at improving image recognition process". Therefore, this study presents a novel approach to object recognition based on the pattern by using photo layers and by defining the objects' specific characteristics. We select and introduce the parameters which determine a higher efficiency of image retrieval of the image objects. In this paper, we describe how the same photos are recognized in a process of classical retrieval compared to our model by analyz…
An Online Metric Learning Approach through Margin Maximization
2011
This work introduces a method based on learning similarity measures between pairs of objects in any representation space that allows to develop convenient recognition algorithms. The problem is formulated through margin maximization over distance values so that it can discriminate between similar (intra-class) and dissimilar (inter-class) elements without enforcing positive definiteness of the metric matrix as in most competing approaches. A passive-aggressive approach has been adopted to carry out the corresponding optimization procedure. The proposed approach has been empirically compared to state of the art metric learning on several publicly available databases showing its potential bot…
Contrasting Automatic and Manual Group Formation: A Case Study in a Software Engineering Postgraduate Course
2021
This paper proposes the comparison of a group formation approach based on an evolutionary algorithm with a manual approach performed by an instructor with ten years of experience on this task. The groups were created based on the professional, psychological, and experience profile of each student. The results obtained demonstrated the algorithm’s potential, reaching an average similarity of \(83.46\%\) with the groups formed manually by the instructor.
Optimisation des requêtes de similarité dans les espaces métriques répondant aux besoins des usagers
2012
The complexity of data stored in large databases has increased at very fast paces. Hence, operations more elaborated than traditional queries are essential in order to extract all required information from the database. Therefore, the interest of the database community in similarity search has increased significantly. Two of the well-known types of similarity search are the Range (Rq) and the k-Nearest Neighbor (kNNq) queries, which, as any of the traditional ones, can be sped up by indexing structures of the Database Management System (DBMS). Another way of speeding up queries is to perform query optimization. In this process, metrics about data are collected and employed to adjust the par…