Search results for "markov chain"
showing 10 items of 288 documents
A study on forecasting electricity production and consumption in smart cities and factories
2019
Abstract The electrical power sector must undergo a thorough metamorphosis to achieve the ambitious targets in greenhouse gas reduction set forth in the Paris Agreement of 2015. Reducing uncertainty about demand and, in case of renewable electricity generation, supply is important for the determination of spot electricity prices. In this work we propose and evaluate a context-based technique to anticipate the electricity production and consumption in buildings. We focus on a household with photovoltaics and energy storage system. We analyze the efficiency of Markov chains, stride predictors and also their combination into a hybrid predictor in modelling the evolution of electricity producti…
Comparison of different uncertainty techniques in urban stormwater quantity and quality modelling
2011
Abstract Urban drainage models are important tools used by both practitioners and scientists in the field of stormwater management. These models are often conceptual and usually require calibration using local datasets. The quantification of the uncertainty associated with the models is a must, although it is rarely practiced. The International Working Group on Data and Models, which works under the IWA/IAHR Joint Committee on Urban Drainage, has been working on the development of a framework for defining and assessing uncertainties in the field of urban drainage modelling. A part of that work is the assessment and comparison of different techniques generally used in the uncertainty assessm…
A dynamic channel access strategy for underlay cognitive radio networks: Markov modelling and performance evaluation
2015
Unlike in overlay cognitive radio networks, secondary users in underlay cognitive radio networks can access licensed spectrum even at the presence of a primary user, given that the interference caused by the secondary transmission is lower than a pre-specified threshold. Based on this underlay access principle, we propose in this paper a dynamic channel access strategy for multi-channel cognitive radio networks. Different from existing underlay access techniques, channel assembling, spectrum adaptation and restricted channel occupancy are also considered in the proposed strategy in order to achieve better performance in the secondary network. Accordingly, a partial channel occupancy mode, w…
Capacity Upper Bound of Channel Assembling in Cognitive Radio Networks with Quasistationary Primary User Activities
2013
In cognitive radio networks (CRNs) with multiple channels, various channel-assembling (ChA) strategies may be applied to secondary users (SUs), resulting in different achieved capacity. However, there is no previous work on determining the capacity upper bound (UB) of ChA for SUs under given system configurations. In this paper, we derive the maximum capacity for CRNs with ChA through Markov chain modeling, considering that primary user (PU) activities are relatively static, compared with SU services. We first deduce a closed-form expression for the maximum capacity in a dynamic ChA strategy and then demonstrate that no other ChA strategy can provide higher capacity than that achieved by th…
Aggregated Packet Transmission in Duty-Cycled WSNs: Modeling and Performance Evaluation
2017
[EN] Duty cycling (DC) is a popular technique for energy conservation in wireless sensor networks (WSNs) that allows nodes to wake up and sleep periodically. Typically, a single-packet transmission (SPT) occurs per cycle, leading to possibly long delay. With aggregated packet transmission (APT), nodes transmit a batch of packets in a single cycle. The potential benefits brought by an APT scheme include shorter delay, higher throughput, and higher energy efficiency. In the literature, different analytical models have been proposed to evaluate the performance of SPT schemes. However, no analytical models for the APT mode on synchronous DC medium access control (MAC) mechanisms exist. In this …
A cubic defining algebra for the Links–Gould polynomial
2013
Abstract We define a finite-dimensional cubic quotient of the group algebra of the braid group, endowed with a (essentially unique) Markov trace which affords the Links–Gould invariant of knots and links. We investigate several of its properties, and state several conjectures about its structure.
Enabling Backoff for SCM Wake-Up Radio: Protocol and Modeling
2017
In sub-carrier modulation (SCM) wake-up radio (WuR) enabled wireless sensor networks, a node can initiate data transmission at any instant of time. In this letter, we propose to activate a backoff procedure before sending wake-up calls (WuCs) in order to avoid potential collisions among WuCs. Consequently, no backoff is needed for the main radio after a WuC is received. A discrete-time Markov chain model is developed to evaluate the performance. Numerical results on network throughput, energy efficiency, average delay, and collision probability reveal the benefits of enabling backoff for SCM-WuRs, especially under heavy traffic loads or saturated traffic conditions.
On the use of approximate Bayesian computation Markov chain Monte Carlo with inflated tolerance and post-correction
2020
Approximate Bayesian computation allows for inference of complicated probabilistic models with intractable likelihoods using model simulations. The Markov chain Monte Carlo implementation of approximate Bayesian computation is often sensitive to the tolerance parameter: low tolerance leads to poor mixing and large tolerance entails excess bias. We consider an approach using a relatively large tolerance for the Markov chain Monte Carlo sampler to ensure its sufficient mixing, and post-processing the output leading to estimators for a range of finer tolerances. We introduce an approximate confidence interval for the related post-corrected estimators, and propose an adaptive approximate Bayesi…
Uncommon Suffix Tries
2011
Common assumptions on the source producing the words inserted in a suffix trie with $n$ leaves lead to a $\log n$ height and saturation level. We provide an example of a suffix trie whose height increases faster than a power of $n$ and another one whose saturation level is negligible with respect to $\log n$. Both are built from VLMC (Variable Length Markov Chain) probabilistic sources; they are easily extended to families of sources having the same properties. The first example corresponds to a ''logarithmic infinite comb'' and enjoys a non uniform polynomial mixing. The second one corresponds to a ''factorial infinite comb'' for which mixing is uniform and exponential.
Group Importance Sampling for particle filtering and MCMC
2018
Bayesian methods and their implementations by means of sophisticated Monte Carlo techniques have become very popular in signal processing over the last years. Importance Sampling (IS) is a well-known Monte Carlo technique that approximates integrals involving a posterior distribution by means of weighted samples. In this work, we study the assignation of a single weighted sample which compresses the information contained in a population of weighted samples. Part of the theory that we present as Group Importance Sampling (GIS) has been employed implicitly in different works in the literature. The provided analysis yields several theoretical and practical consequences. For instance, we discus…