Dealing with Massive Data with a Distributed Expectation Propagation Particle Filter for Object Tracking

Target tracking in distributed networks faces the challenge in coping with large volumes of distributed data which requires efficient methods for real time applications with minimal communication overhead. The complexity considered in this paper is when each sensor in a distributed network observes a large number of measurements which are all required to be processed at each time step. The particle filter has been widely used for localisation and tracking in distributed networks with a small number of measurements [1]. This paper goes beyond the current state-of-the-art and presents a novel particle filter approach, combined with the expectation propagation framework, that is capable of dealing with the challenges presented by a large volume of measurements in a distributed network. In the proposed algorithm, the measurements are processed in parallel at each sensor node in the network and the communication overhead is minimised substantially. We show results with large improvements in communication overhead, with a negligible lossin tracking performance, compared with the standard centralised particle filter

Methodological Choices For Research In Information Science: Contributions To Domain Analysis

The article focuses on the ways of organizing studies according to their methodological choices in the Base Referencial de Artigos de Periódicos em Ciência da Informação (Reference Database of Journal articles in Information Science). We highlight how the organization of scientific production by the methodological choices in Information Science contributes to the identification of its production features and domain analysis. We studied research categories and proposed five classification criteria: research purposes, approaches, focus, techniques and type of analysis. The proposal of a corpus in Information Science is empirically applied, represented by 689 articles, 10% of the production indexed in Base Referencial de Artigos de Periódicos em Ciência da Informação from 1972 to 2010. We adopt content analysis to interpret the methodological choices of authors identified in the corpus. The results point out that exploratory studies are more predominant when considering the research purpose; regarding the research approach, bibliographic and documentary studies are more predominant; systematic observation, questionnaire and interview were the most widely used techniques; document analysis and content analysis are the most widely used types of analysis; the research focus of theoretical, historical and bibliometric studies are more predominant. We found that some studies use two methodological choices and explicit epistemological approaches, such as the studies following the positivist approach in the 1970s, and those influenced by the phenomenological approach in the 1980s, which increased the use of methods in qualitative research.28151

An Interval Approach to Multiple Unmanned Aerial Vehicle Collision Avoidance

Small/micro Unmanned Aerial Systems (UAVs) require the ability to operate with constraints of a diverse, automated airspace where obstacle telemetry is denied. This paper proposes a novel Sense, Detect and Avoid (SDA) algorithm with inherit resilience to sensor uncertainty. This is achieved through the interval geometric formulation of the avoidance problem, which by the use of interval analysis, can be extended to consider multiple obstacles. The approach is shown to demonstrate the ability to both tolerate sensor uncertainty and enact generated 3D avoidance trajectories. Monte-Carlo simulations demonstrate successful avoidance rates of 88%, 96% and 91% in two example collision scenarios and one multi-agent conflict scenario respectively

Treacher Collins syndrome with choanal atresia: a case report and review of disease features

SummaryTreacher Collins Syndrome - or mandibulofacial dysostosis – is a rare condition that presents several craniofacial deformities of different levels. This is a congenital malformation involving the first and second branchial arches. Incidence is estimated to range between 1-40,000 to 1-70,000 of live births. The disorder is characterized by abnormalities of the auricular pinna, hypoplasia of facial bones, antimongoloid slanting palpebral fissures with coloboma of the lower eyelids and cleft palate. Treacher Collins Syndrome is rarely associated with choanal atresia. A multidisciplinary team, including craniofacial surgeon, ophthalmologist, speech therapist, dental surgeon and otorhinolaryngologist, is the most appropriate setting to manage these patients. This study reports a rare case of Treacher Collins Syndrome with choanal atresia, presenting literature review and multidisciplinary intervention

Spatio-temporal Gaussian process models for extended and group object tracking with irregular shapes

Extended object tracking has become an integral part of many autonomous systems during the last two decades. For the first time, this paper presents a generic spatio-temporal Gaussian process (STGP) for tracking an irregular and non-rigid extended object. The complex shape is represented by key points and their parameters are estimated both in space and time. This is achieved by a factorization of the power spectral density function of the STGP covariance function. A new form of the temporal covariance kernel is derived with the theoretical expression of the filter likelihood function. Solutions to both the filtering and the smoothing problems are presented. A thorough evaluation of the performance in a simulated environment shows that the proposed STGP approach outperforms the state-of-the-art approach, with up to 90% improvement in the accuracy in position, 95% in velocity and 7% in the shape, while tracking a simulated asymmetric non-rigid object. The tracking performance improvement for a non-rigid irregular real object is up to 43% in position, 68% in velocity, 10% in the recall and 115% in the precision measures

Bayesian Processing of Big Data using Log Homotopy Based Particle Flow Filters

Bayesian recursive estimation using large volumes of data is a challenging research topic. The problem becomes particularly complex for high dimensional non-linear state spaces. Markov chain Monte Carlo (MCMC) based methods have been successfully used to solve such problems. The main issue when employing MCMC is the evaluation of the likelihood function at every iteration, which can become prohibitively expensive to compute. Alternative methods are therefore sought after to overcome this difficulty. One such method is the adaptive sequential MCMC (ASMCMC), where the use of the confidence sampling is proposed as a method to reduce the computational cost. The main idea is to make use of the concentration inequalities to sub-sample the measurements for which the likelihood terms are evaluated. However, ASMCMC methods require appropriate proposal distributions. In this work, we propose a novel ASMCMC framework in which log-homotopy based particle flow filters form adaptive proposals. We show the performance can be significantly enhanced by our proposed algorithm, while still maintaining a comparatively low processing overhead

Autonomous crowds tracking with box particle filtering and convolution particle filtering

Autonomous systems such as Unmanned Aerial Vehicles (UAVs) need to be able to recognise and track crowds of people, e.g. for rescuing and surveillance purposes. Large groups generate multiple measurements with uncertain origin. Additionally, often the sensor noise characteristics are unknown but measurements are bounded within certain intervals. In this work we propose two solutions to the crowds tracking problem— with a box particle filtering approach and with a convolution particle filtering approach. The developed filters can cope with the measurement origin uncertainty in an elegant way, i.e. resolve the data association problem. For the box particle filter (PF) we derive a theoretical expression of the generalised likelihood function in the presence of clutter. An adaptive convolution particle filter (CPF) is also developed and the performance of the two filters is compared with the standard sequential importance resampling (SIR) PF. The pros and cons of the two filters are illustrated over a realistic scenario (representing a crowd motion in a stadium) for a large crowd of pedestrians. Accurate estimation results are achieved

Numerical convergence of the block-maxima approach to the Generalized Extreme Value distribution

In this paper we perform an analytical and numerical study of Extreme Value distributions in discrete dynamical systems. In this setting, recent works have shown how to get a statistics of extremes in agreement with the classical Extreme Value Theory. We pursue these investigations by giving analytical expressions of Extreme Value distribution parameters for maps that have an absolutely continuous invariant measure. We compare these analytical results with numerical experiments in which we study the convergence to limiting distributions using the so called block-maxima approach, pointing out in which cases we obtain robust estimation of parameters. In regular maps for which mixing properties do not hold, we show that the fitting procedure to the classical Extreme Value Distribution fails, as expected. However, we obtain an empirical distribution that can be explained starting from a different observable function for which Nicolis et al. [2006] have found analytical results.Comment: 34 pages, 7 figures; Journal of Statistical Physics 201

Statistical stability and continuity of SRB entropy for systems with Gibbs-Markov structures

We present conditions on families of diffeomorphisms that guarantee statistical stability and SRB entropy continuity. They rely on the existence of horseshoe-like sets with infinitely many branches and variable return times. As an application we consider the family of Henon maps within the set of Benedicks-Carleson parameters