Search results for "Markov"
showing 10 items of 628 documents
A Novel Method to Characterize User Sessions of Educational Software
2013
Abstract Software applications destined for the educational environment have a long history and have evolved side by side with the progress of technology from simple computer assisted instruction programs to sophisticated eLearning platforms. A study that we have conducted on a sample of 395 children aged 6 through 12, coming from both the rural and the urban environments, shows that an increasing number of children use computer related technologies. Given their exposure to these technologies it is imperative that the educational applications be designed in a way that takes into account the children's abilities, interests and the demands for their development. We have proposed a 5-dimension…
Collision Avoidance in Wake-Up Radio Enabled WSNs: Protocol and Performance Evaluation
2018
In wake-up radio (WuR) enabled wireless sensor networks (WSNs), the envisaged application scenarios are primarily targeted at low traffic load conditions. When applying WuR to medium or heavy traffic load scenarios, however, collisions among wake-up calls (WuCs) may happen, resulting in a lower packet delivery ratio (PDR). In this paper, we propose a media access control protocol for WuR- enabled WSN that is capable of avoiding WuC collisions by activating a contention-based collision avoidance mechanism for WuC transmissions. The performance of the proposed protocol is evaluated by a Markov chain based mathematical model and is compared with a WuR protocol that performs only clear channel as…
Event-Triggered Sleeping for Synchronous DC MAC IN WSNs: Mechanism and DTMC Modeling
2016
Overhearing and idle listening are two primary sources for unnecessary energy consumption in wireless sensor networks. Although introducing duty cycling in medium access control (MAC) reduces idle listening, it cannot avoid overhearing in a network with multiple contending nodes. In this paper, we propose an event-triggered sleeping (ETS) mechanism for synchronous duty-cycled (DC) MAC protocols in order to avoid overhearing when a node is not active. This ETS mechanism applies to any synchronous DC MAC protocols and makes them more energy efficient. Furthermore, we develop a two dimensional discrete time Markov chain model to evaluate the performance of the proposed ETS mechanism by integra…
Probability Measures on Product Spaces
2020
In order to model a random time evolution, the canonical procedure is to construct probability measures on product spaces. Roughly speaking, the first step is to take a probability measure that models the initial distribution. In the second step, on a different probability space, the distribution after one time step is modeled. Then in each subsequent step, on a further probability space, the random state in the next time step given the full history is modeled. On a formal level, we consider products of probability spaces and Markov kernels between such spaces. Finally, the Ionescu-Tulcea theorem shows that the whole procedure can be realized on a single infinite product space. Furthermore,…
A new design of H ∞ filtering for continuous-time Markovian jump systems with time-varying delay and partially accessible mode information
2013
In this paper, the delay-dependent H"~ filtering problem for a class of continuous-time Markovian jump linear systems with time-varying delay and partially accessible mode information is investigated by an indirect approach. The generality lies in that the systems under consideration are subject to a Markov stochastic process with exactly known and partially unknown transition rates. By utilizing the model transformation idea, an input-output approach is employed to transform the time-delayed filtering error system into a feedback interconnection formulation. Invoking the results from the scaled small gain theorem, an improved version of bounded real lemma is obtained based on a Markovian L…
Energy Efficient Consecutive Packet Transmissions in Receiver-Initiated Wake-Up Radio Enabled WSNs
2018
In wake-up radio (WuR)-enabled wireless sensor networks, data communication among nodes is triggered in an on-demand manner, by either a sender or a receiver. For receiver-initiated WuR (RI-WuR), a receiving node wakes up sending nodes through a wake-up call. Correspondingly sending nodes transmit packets in a traditional way by competing with one another multiple times in a single operational cycle. In this paper, we propose a receiver-initiated consecutive packet transmission WuR (RI-CPT-WuR) medium access control (MAC) protocol, which eliminates multiple competitions to achieve higher energy efficiency. Furthermore, we develop two associated discrete time Markov chains (DTMCs) for evalua…
Robust Assembly Assistance Using Informed Tree Search with Markov Chains
2022
Manual work accounts for one of the largest workgroups in the European manufacturing sector, and improving the training capacity, quality, and speed brings significant competitive benefits to companies. In this context, this paper presents an informed tree search on top of a Markov chain that suggests possible next assembly steps as a key component of an innovative assembly training station for manual operations. The goal of the next step suggestions is to provide support to inexperienced workers or to assist experienced workers by providing choices for the next assembly step in an automated manner without the involvement of a human trainer on site. Data stemming from 179 experiment partici…
Markov chain Monte Carlo importance samplers for Bayesian models with intractable likelihoods
2019
Markov chain Monte Carlo (MCMC) is an approach to parameter inference in Bayesian models that is based on computing ergodic averages formed from a Markov chain targeting the Bayesian posterior probability. We consider the efficient use of an approximation within the Markov chain, with subsequent importance sampling (IS) correction of the Markov chain inexact output, leading to asymptotically exact inference. We detail convergence and central limit theorems for the resulting MCMC-IS estimators. We also consider the case where the approximate Markov chain is pseudo-marginal, requiring unbiased estimators for its approximate marginal target. Convergence results with asymptotic variance formula…
ℓ1-Penalized Methods in High-Dimensional Gaussian Markov Random Fields
2016
In the last 20 years, we have witnessed the dramatic development of new data acquisition technologies allowing to collect massive amount of data with relatively low cost. is new feature leads Donoho to define the twenty-first century as the century of data. A major characteristic of this modern data set is that the number of measured variables is larger than the sample size; the word high-dimensional data analysis is referred to the statistical methods developed to make inference with this new kind of data. This chapter is devoted to the study of some of the most recent ℓ1-penalized methods proposed in the literature to make sparse inference in a Gaussian Markov random field (GMRF) defined …
Regularity of Spike Trains and Harmony Perception in a Model of the Auditory System
2011
Spike train regularity of the noisy neural auditory system model under the influence of two sinusoidal signals with different frequencies is investigated. For the increasing ratio m/n of the input signal frequencies (m, n are natural numbers) the linear growth of the regularity is found at the fixed difference (m - n). It is shown that the spike train regularity in the model is high for harmonious chords of input tones and low for dissonant ones.