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This paper proposes an approach to optimal planning under uncertainty with limited sensory information for an autonomous unmanned vehicle (UXV). We consider a surveillance application in which identification of environmental targets is impacted by controllable features commanded by the UXV as well as ambient features with dynamics that are uncontrollable. To manage computational complexity, mission...
This paper assesses the predictability of individual vehicle's mobility in Beijing expressway system using Electronic Toll Collection (ETC) data records. By examining the uncertainties of movements using entropy, considering both the frequencies and sequential correlations of vehicles' trajectories, we draw to the conclusion that the average limit of predictability of expressway vehicles mobility...
The problem of one-way file synchronization from the client to the data-center, namely, file updates, is studied. The problem is investigated in particular when an old version of a file which can be available at both client and data-center, is edited by the client to a new version. The edits are modeled as random insertions and deletions (InDels). Based on the updated and the previous version of the...
We are interested in inferring the set of waypoints (or intermediate destinations) of a mobility trajectory in the absence of timing information. We find that, by mining a dataset of real mobility traces, computing the entropy of conditional Markov trajectory enables us to uncover waypoints, even though no timing information nor absolute geographic location is provided. We build on this observation...
Measurements of various properties of the process models in the last few years become relatively widely explored area. These are properties such as uncertainty, complexity, readability or cohesion of process models. Quantification of these properties can provide better insight in term of, for instance, user-friendliness, predictability, clarity, etc. of the process model. The aim of this work is to...
We describe an information-theoretic approach to the analysis of music and other sequential data, which emphasises the predictive aspects of perception, and the dynamic process of forming and modifying expectations about an unfolding stream of data, characterising these using the tools of information theory: entropies, mutual informations, and related quantities. After reviewing the theoretical foundations,...
In communications, unknown variables are usually modelled as random variables, with concepts such as independence, entropy and information given in terms of the underlying probability distributions. In contrast, control theory often treats uncertainties and disturbances as bounded unknowns having no statistical structure. The area of networked control combines both fields and raises the question of...
Adaptive dynamical systems arise in a multitude of contexts, e.g., optimization, control, communications, signal processing, and machine learning. A precise characterization of their fundamental limitations is therefore of paramount importance. In this paper, we consider the general problem of adaptively controlling and/or identifying a stochastic dynamical system, where our a priori knowledge allows...
Passwords are the first line of defense for many computerized systems. The quality of these passwords decides the security strength of these systems. Many studies advocate using password entropy as an indicator for password quality where lower entropy suggests a weaker or less secure password. However, a closer examination of this literature shows that password entropy is very loosely defined. In...
We use the Bayesian Model Averaging (BMA) over Decision Trees (DTs) for assessing newborn brain maturity from clinical EEG. We found that within this methodology an appreciable part of EEG features is rarely used in the DT models, because these features make weak contribution to the assessment. It was identified that the portion of DT models using weak EEG features is large. The negative impact of...
Independent Component Analysis (ICA) can be used to decompose functional Magnetic Resonance Imaging (fMRI) data into a set of statistically independent images which are likely to be the sources of fMRI data. After applying ICA, a set of independent components are produced, and then, a “meaningful” subset from these components must be identified, because a large majority of components are non-interesting...
This paper proposes a novel relative entropy rate (RER) based approach for multiple HMM (MHMM) approximation of a class of discrete-time uncertain processes. Under different uncertainty assumptions, the model design problem is posed either as a min-max optimisation problem or stochastic minimization problem on the RER between joint laws describing the state and output processes (rather than the more...
Cameras are popular sensors for robot navigation tasks such as localization as they are inexpensive, lightweight, and provide rich data. However, fast movements of a mobile robot typically reduce the performance of vision-based localization systems due to motion blur. In this paper, we present a reinforcement learning approach to choose appropriate velocity profiles for vision-based navigation. The...
We consider a secure lossless source coding problem with a rate-limited helper. In particular, Alice observes an i.i.d. source Xn and wishes to transmit this source loss-lessly to Bob at a rate Rx. A helper, say Helen, observes a correlated source Yn and transmits at a rate Ry to Bob. A passive eavesdropper can observe the coded output of Alice. The equivocation ?? is measured by the conditional entropy...
In active perception systems for scene recognition the utility of an observation is determined by the information gain in the probability distribution over the state space. The goal is to find a sequence of actions which maximizes the system knowledge at low resource costs. Most current approaches focus either on optimizing the determination of the payoff neglecting the costs or develop sophisticated...
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