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We derive a local equation of motion for the electronic single-particle density matrix in the presence of one- as well as two-body scattering processes. This is done by applying the mean-field approximation to the many-electron dynamics obtained via a recently proposed Markov limit, able to furnish many-body Lindblad-type scattering superoperators. The resulting time evolution at finite/high carrier...
This paper addresses the probabilistic consensus problem in a network of Markovian agents. The dynamics of each agent ismodeled as a finite-state Markov chain, with transition rates that are affected by the communication with the neighbors, so inducing an emulation effect. Consensus is reached when all the agent probability vectors converge to a common steady-state probability vector. The main result...
A technique to approximate heat diffusion on Riemannian manifolds is presented. We provide a numerical way to approximate the solution to the heat equation by using the idea of random walks of particles, governed by a continuous-time Markov chain, where the transition rates of the Markov chain are characterized by the distances between nodes on a given grid with non-equally placed nodes. The emphasis...
The paper analyzes the optimal response of an individual consumer, with a deferrable demand for electricity, to exogenous stochastic prices. The main goal of the paper is (i) to introduce a model where many realistic features are taken into account (such as the presence of bounds on power consumption, the possibility of curtailment, and time correlation in the price process) (ii) determine an explicit...
This paper addresses a perimeter patrol problem involving control of unmanned air vehicles. Around the perimeter of a protected area are placed a number of unmanned ground stations. These stations send alert signals which are investigated by the unmanned air vehicles. We present an approach to optimization of vehicle tasking based on max-plus probabilistic models and computational schemes.
In this paper, we consider state estimation for Stochastic Hybrid Systems (SHS). These are systems that possess both continuous-valued and discrete-valued dynamics. For SHS with nonlinear hybrid dynamics and/or non-Gaussian disturbances, state estimation can be implemented as an Interacting Multiple Model (IMM) particle filter. However, a disadvantage of particle filtering is the computational load...
In this paper we present a framework for risk-averse model predictive control (MPC) of linear systems affected by multiplicative uncertainty. Our key innovation is to consider time-consistent, dynamic risk metrics as objective functions to be minimized. This framework is axiomatically justified in terms of time-consistency of risk preferences, is amenable to dynamic optimization, and is unifying in...
The present work is a sequel of our paper [1] where a Bayesian unnormalised smoother was proposed for the so-called class of partially observed reciprocal chains (RC). Within this Bayesian setting, an issue remained unsolved concerning practical implementation due to the unnormalised feature of the smoother. Here a normalised Bayesian smoother is developed for a class of signals even more general...
In a cognitive wireless network, the sudden arrival of a primary user (PU) can force one or more secondary users (SU) to terminate their ongoing communication. Buffers can be utilized to prevent them from dropping, but their effectiveness depends on the tolerance of the SUs to the buffer waiting time. In this paper, we propose to dynamically assign service rates to the SUs to complement the gain offered...
Cloud computing is becoming popular as the next infrastructure of computing platform. However, with data and business applications outsourced to a third party, how to protect cloud data centers from numerous attacks has become a critical concern. In this paper, we propose a clusterized framework of cloud firewall, which characters performance and cost evaluation. To provide quantitative performance...
In this study, land cover changes in continental Portugal are analyzed using samples of the Landyn research project. The modeling approach includes the test of the hypothesis that land cover changes are generated by a first-order Markov process for years 1980, 1995 and 2010. Results show that the changes in land cover are dependant of the previous moment in time, i.e., they follow a Markov process...
It is of great importance to control the elastic demand to follow the renewable energy supply in order to reduce its fluctuation on the grid. Electrical vehicle (EV) is a promising form of the elastic demand. Considering the random nature of the EV charging load, it would be ideal if the charging load of the EVs can be controlled to match the wind energy supply for improving wind power utilization...
On the basis of the sensitivity-based optimization, we develop a unified optimization approach for semi-Markov decision processes (SMDPs) with infinite horizon discounted and average reward criteria. We show that the sensitivity formula under average reward criteria is a limitation case of discounted reward criteria. On the basis of the performance sensitivity formulas, we provide a unified formulation...
Markov chains play an important role in the decision analysis. In the practical applications, decision-makers often need to decide in an uncertain condition which the traditional decision theory can't deal with. In this paper, we combine Markov chains with the fuzzy sets to build a fuzzy Markov chain model using a triangle fuzzy number to denote the transition probability. A method is given to compute...
OFDMA has been selected as the multiple access scheme for emerging broadband wireless communication systems. However, designing efficient resource allocation algorithms for OFDMA systems is a challenging task, especially in the uplink, due to the combinatorial nature of subcarrier assignment and the distributed power budget for different users. Inspired by Glauber dynamics, in this paper, we propose...
This paper deals with the problem of simultaneously detecting and tracking multiple maneuvering targets. The multitarget, multi-Bernoulli (MeMber) filter based track-before-detect (TBD) is an attractive approach to detect and track targets at low signal-to-noise (SNR). However, MeMber-TBD with a fixed motion model is not general enough to accommodate maneuvering targets. In this paper, a new MeMber...
Operators have recently resorted to WiFi offloading to deal with increasing data demand and induced congestion. Researchers have further suggested the use of “delayed offloading”: if no WiFi connection is available, (some) traffic can be delayed up to a given deadline, or until WiFi becomes available. Nevertheless, there is no clear consensus as to the benefits of delayed offloading, with a couple...
Modeling based approach is described for analyzing and evaluating Internet server system reliability and availability in this paper. In the given model the states are defined by the different kinds of failures of the server system. The Markov model evaluates the probability of jumping from one known state into the next logical state. The probabilities between transitions of the states are a function...
We consider the problem of binary code design for simultaneous energy and information transfer where the receiver completely relies on the received signal for fulfilling its real-time power requirements. The receiver, in this scenario, would need a certain amount of energy (derived from the received signal) within a sliding time window for its continuous operation. In order to meet this energy requirement...
We consider online learning in finite stochastic Markovian environments where in each time step a new reward function is chosen by an oblivious adversary. The goal of the learning agent is to compete with the best stationary policy in hindsight in terms of the total reward received. Specifically, in each time step the agent observes the current state and the reward associated with the last transition,...
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