Search results for "Hidden Markov Model"
showing 10 items of 76 documents
isotracer: An R package for the analysis of tracer addition experiments
2021
AbstractTracer addition experiments, particularly using isotopic tracers, are becoming increasingly important in a variety of studies aiming at characterizing the flows of molecules or nutrients at different levels of biological organization, from the cellular and tissue levels, to the organismal and ecosystem levels.We present an approach based on Hidden Markov Models (HMM) to estimate nutrient flow parameters across a network, and its implementation in the R package isotracer.The isotracer package is capable of handling a variety of tracer study designs, including continuous tracer drips, pulse experiments, and pulse-chase experiments. It can also take into account tracer decay when radio…
Minimum Description Length Based Hidden Markov Model Clustering for Life Sequence Analysis
2010
In this article, a model-based method for clustering life sequences is suggested. In the social sciences, model-free clustering methods are often used in order to find typical life sequences. The suggested method, which is based on hidden Markov models, provides principled probabilistic ranking of candidate clusterings for choosing the best solution. After presenting the principle of the method and algorithm, the method is tested with real life data, where it finds eight descriptive clusters with clear probabilistic structures. nonPeerReviewed
An AMI System for User Daily Routine Recognition and Prediction
2014
Ambient Intelligence (AmI) defines a scenario involving people living in a smart environment enriched by pervasive sensory devices with the goal of assisting them in a proactive way to satisfy their needs. In a home scenario, an AmI system controls the environment according to a user’s lifestyle and daily routine. To achieve this goal, one fundamental task is to recognize the user’s activities in order to generate his daily activities profile. In this chapter, we present a simple AMI system for a home scenario to recognize and predict users’ activities. With this predictive capability, it is possible to anticipate their actions and improve their quality of life. Our approach uses a Hidden M…
Statistical identification with hidden Markov models of large order splitting strategies in an equity market
2010
Large trades in a financial market are usually split into smaller parts and traded incrementally over extended periods of time. We address these large trades as hidden orders. In order to identify and characterize hidden orders we fit hidden Markov models to the time series of the sign of the tick by tick inventory variation of market members of the Spanish Stock Exchange. Our methodology probabilistically detects trading sequences, which are characterized by a net majority of buy or sell transactions. We interpret these patches of sequential buying or selling transactions as proxies of the traded hidden orders. We find that the time, volume and number of transactions size distributions of …
A new Multi-Layers Method to Analyze Gene Expression
2007
In the paper a new Multi-Layers approach (called Multi-Layers Model MLM) for the analysis of stochastic signals and its application to the analysis of gene expression data is presented. It consists in the generation of sub-samples from the input signal by applying a threshold technique based on cut-set optimal conditions. The MLM has been applied on synthetic and real microarray data for the identification of particular regions across DNA called nucleosomes and linkers. Nucleosomes are the fundamental repeating subunits of all eukaryotic chromatin, and their positioning provides useful information regarding the regulation of gene expression in eukaryotic cells. Results have shown a good rec…
A probabilistic approach to learning a visually grounded language model through human-robot interaction
2010
A Language is among the most fascinating and complex cognitive activities that develops rapidly since the early months of infants' life. The aim of the present work is to provide a humanoid robot with cognitive, perceptual and motor skills fundamental for the acquisition of a rudimentary form of language. We present a novel probabilistic model, inspired by the findings in cognitive sciences, able to associate spoken words with their perceptually grounded meanings. The main focus is set on acquiring the meaning of various perceptual categories (e. g. red, blue, circle, above, etc.), rather than specific world entities (e. g. an apple, a toy, etc.). Our probabilistic model is based on a varia…
Hidden Markov Random Fields and Direct Search Methods for Medical Image Segmentation
2016
The goal of image segmentation is to simplify the representation of an image to items meaningful and easier to analyze. Medical image segmentation is one of the fundamental problems in image processing field. It aims to provide a crucial decision support to physicians. There is no one way to perform the segmentation. There are several methods based on HMRF. Hidden Markov Random Fields (HMRF) constitute an elegant way to model the problem of segmentation. This modelling leads to the minimization of an energy function. In this paper we investigate direct search methods that are Nelder-Mead and Torczon methods to solve this optimization problem. The quality of segmentation is evaluated on grou…
Mimicking biological mechanisms for sensory information fusion
2013
Current Artificial Intelligence systems are bound to become increasingly interconnected to their surrounding environment in the view of the newly rising Ambient Intelligence (AmI) perspective. In this paper, we present a comprehensive AmI framework for performing fusion of raw data, perceived by sensors of different nature, in order to extract higher-level information according to a model structured so as to resemble the perceptual signal processing occurring in the human nervous system. Following the guidelines of the greater BICA challenge, we selected the specific task of user presence detection in a locality of the system as a representative application clarifying the potentialities of …
Path Modeling and Retrieval in Distributed Video Surveillance Databases
2012
We propose a framework for querying a distributed database of video surveillance data in order to retrieve a set of likely paths of a person moving in the area under surveillance. In our framework, each camera of the surveillance system locally pro- cesses the data and stores video sequences in a storage unit and the metadata for each detected person in the distributed database. A pedestrian’s path is formulated as a dynamic Bayesian network (DBN) to model the dependencies between subsequent observa- tions of the person as he makes his way through the camera net- work. We propose a tool by which the analyst can pose queries about where a certain person appeared while moving in the site duri…
Human Activity Recognition Process Using 3-D Posture Data
2015
In this paper, we present a method for recognizing human activities using information sensed by an RGB-D camera, namely the Microsoft Kinect. Our approach is based on the estimation of some relevant joints of the human body by means of the Kinect; three different machine learning techniques, i.e., K-means clustering, support vector machines, and hidden Markov models, are combined to detect the postures involved while performing an activity, to classify them, and to model each activity as a spatiotemporal evolution of known postures. Experiments were performed on Kinect Activity Recognition Dataset, a new dataset, and on CAD-60, a public dataset. Experimental results show that our solution o…