Search results for " Marko"
showing 10 items of 201 documents
Exploiting Semantic Trajectories Using HMMs and BIM for Worker Safety in Dynamic Environments
2020
International audience; Understanding dynamic behaviors of moving objects using positioning technologies for construction safety monitoring is still an open research issue. One task; that is a small subset in the widespread field of objects dynamics is the enrichment of the location data of users with the semantic information for studying their mobility patterns in the context of the environment. However, incorporating the semantics related to the environment gets complex in case of the dynamic construction sites where the site spaces are kept evolving with time. For instance, new walls and infrastructure supports are added often on sites, while others are detached. Similar situations open …
Analysis on channel bonding/aggregation for multi-channel cognitive radio networks
2010
Channel bonding/aggregation techniques, which assemble several channels together as one channel, could be used in cognitive radio networks for the purpose of achieving better bandwidth utilization. In existing work on this topic, channel bonding/aggregation is focused on the cases when primary channels are time slotted or stationary as compared with secondary users' activities. In this paper, we analyze the performance of channel bonding/aggregation strategies when primary channels are not time slotted and the time scale of primary activities is at the same level as the secondary users', given that spectrum handover is not allowed. Continuous time Markov chain models are built in order to a…
An adaptive probabilistic approach to goal-level imitation learning
2010
Imitation learning has been recognized as a promising technique to teach robots advanced skills. It is based on the idea that robots could learn new behaviors by observing and imitating the behaviors of other skilled actors. We propose an adaptive probabilistic graphical model which copes with three core issues of any imitative behavior: observation, representation and reproduction of skills. Our model, Growing Hierarchical Dynamic Bayesian Network (GHDBN), is hierarchical (i.e. able to characterize structured behaviors at different levels of abstraction), and growing (i.e. skills are learned or updated incrementally - and at each level of abstraction - every time a new observation sequence…
An adaptive probabilistic graphical model for representing skills in PbD settings
2010
Learning from Errors: Detecting ZigBee Interference in WiFi Networks
2014
In this work we show how to detect ZigBee interference on commodity WiFi cards by monitoring the reception errors, such as synchronization errors, invalid header formats, too long frames, etc., caused by ZigBee transmissions. Indeed, in presence of non-WiFi modulated signals, the occurrence of these types of errors follows statistics that can be easily recognized. Moreover, the duration of the error bursts depends on the transmission interval of the interference source, while the error spacing depends on the receiver implementation. On the basis of these considerations, we propose the adoption of hidden Markov chains for characterizing the behavior of WiFi receivers in presence of controlle…
Hidden Markov Random Field model and BFGS algorithm for Brain Image Segmentation
2016
Brain MR images segmentation has attracted a particular focus in medical imaging. The automatic image analysis and interpretation became a necessity. Segmentation is one of the key operations to provide a crucial decision support to physicians. Its goal is to simplify the representation of an image into items meaningful and easier to analyze. Hidden Markov Random Fields (HMRF) provide an elegant way to model the segmentation problem. This model leads to the minimization problem of a function. BFGS (Broyden-Fletcher-Goldfarb-Shanno algorithm) is one of the most powerful methods to solve unconstrained optimization problem. This paper presents how we combine HMRF and BFGS to achieve a good seg…
Dinámica, modelización y servicios ecosistémicos del paisaje. Metodología para el análisis de la franja costera del Mediterráneo occidental
2019
El análisis y la dinámica de los paisajes a lo largo del tiempo y el espacio es esencial para la caracterización, ordenación y gestión de los paisajes actuales. Los paisajes, entendidos en su amplia escala espacial y temporal, son una expresión del trabajo conjunto de la naturaleza y el ser humano y por tanto un recurso territorial, un patrimonio y una señal de identidad, que necesita atención, protección y gestión. En el Convenio Europeo del Paisaje se institucionaliza el interés y el derecho al paisaje, y se insta a que los estados y regiones firmantes articulen políticas de paisaje en el marco de la ordenación territorial. Políticas que se operativizan mediante la identificación, análisi…
Analysing Complex Life Sequence Data with Hidden Markov Modelling
2016
When analysing complex sequence data with multiple channels (dimensions) and long observation sequences, describing and visualizing the data can be a challenge. Hidden Markov models (HMMs) and their mixtures (MHMMs) offer a probabilistic model-based framework where the information in such data can be compressed into hidden states (general life stages) and clusters (general patterns in life courses). We studied two different approaches to analysing clustered life sequence data with sequence analysis (SA) and hidden Markov modelling. In the first approach we used SA clusters as fixed and estimated HMMs separately for each group. In the second approach we treated SA clusters as suggestive and …
HOWERD: A Hidden Markov Model for Automatic OWL-ERD Alignment
2016
The HOWERD model for estimating the most likely alignment between an OWL ontology and an Entity Relation Diagram (ERD) is presented. Automatic alignment between relational schema and ontology represents a big challenge in Semantic Web research due to the different expressiveness of these representations. A relational schema is less expressive than the ontology; this is a non trivial problem when accessing data via an ontology and for ontology storing by means of a relational schema. Existent alignment methodologies fail in loosing some contents of the involved representations because the ontology captures more semantic information, and several elements are left unaligned. HOWERD relies on a…
KERNEL ESTIMATION OF THE TRANSITION DENSITY IN BIFURCATING MARKOV CHAINS
2023
We study the kernel estimator of the transition density of bifurcating Markov chains. Under some ergodic and regularity properties, we prove that this estimator is consistent and asymptotically normal. Next, in the numerical studies, we propose two data-driven methods to choose the bandwidth parameters. These methods are based on the so-called two bandwidths approach.