Object joint detection and tracking using adaptive multiple motion models

This paper deals with the problem of detecting objects that may switch between different motion models. In order to accurately detect these moving objects taking into account possible changing motion models, we propose an adaptive multi-motion model in the joint detection and tracking (JDT) framework. The proposed technique differs from the existing JDT-based methods mainly in two ways. First we express the solution in the JDT framework via a formulation in the multiple motion model setting. Second, we introduce a new motion model prediction function which exploits the correlation between the motion model and object kinematic state. Experiments on both synthetic and real videos demonstrate that the JDT method employing the proposed adaptive multi-motion model can detect objects more accurately than the existing peer methods when objects change their motion models.     

Synthesis and characterisation of monomethine cyanine dyes based on N-bridgehead heterocycles

In this paper, the synthesis and spectral properties of novel asymmetrical and symmetrical monomethine cyanine dyes are described. Reaction of phenylpyrazolones and oxazolones with acetyl thiourea and/or thiosemicarbazide afforded acetyl intermediate compounds which were then reacted with metal divalent chlorides, followed by reaction with N -methyl hetetrocyclic quaternary salts, to give asymmetrical monomethine cyanine dyes. Reaction of the 5-amino derivative of the intermediate compounds with acetaldehyde followed by reaction with N -methyl hetetrocyclic quaternary salts produced the symmetrical monomethine cyanine dyes. These new compounds were characterised with elemental analyses, visible absorption and infrared spectrometry, and ¹H/¹³C NMR and mass spectroscopy. The correlation between the colour and structure of these dyes was also investigated.     

An Analytical Model for Estimating Cloud Resources of Elastic Services

In the cloud, ensuring proper elasticity for hosted applications and services is a challenging problem and far from being solved. To achieve proper elasticity, the minimal number of cloud resources that are needed to satisfy a particular service level objective (SLO) requirement has to be determined. In this paper, we present an analytical model based on Markov chains to predict the number of cloud instances or virtual machines (VMs) needed to satisfy a given SLO performance requirement such as response time, throughput, or request loss probability. For the estimation of these SLO performance metrics, our analytical model takes the offered workload, the number of VM instances as an input, and the capacity of each VM instance. The correctness of the model has been verified using discrete-event simulation. Our model has also been validated using experimental measurements conducted on the Amazon Web Services cloud platform.     

Performance evaluation of classification trees for building detection from aerial images and LiDAR data: a comparison of classification trees models

This study assesses the performance of three classification trees (CT) models (entropy, gain ratio and gini) for building detection by the fusion of airborne laser scanner data and multispectral aerial images. Data from four study areas with different sensors and scene characteristics were used to assess the performance of the models. The process of performance evaluation is based on four criteria: model validation and testing, classification accuracies, relative importance of input variables, as well as transferability of CT derived from one data set to another. The LiDAR point clouds were filtered to generate a digital terrain model (DTM) based on the orthogonal polynomials, and then a digital surface model (DSM) and the normalized digital surface model (nDSM) were generated. A set of 26 uncorrelated feature attributes were derived from the original aerial images, LiDAR intensity image, DSM and nDSM. Finally, the three CT models were used to classify buildings, trees, roads and ground from aerial images, LiDAR data and the generated attributes, with the most accurate average classifications of 95% being achieved. The entropy splitting algorithm proved to be a preferable algorithm for building detection from aerial images and LiDAR data.     

A Gaussian process-based RRT planner for the exploration of an unknown and cluttered environment with a UAV

A new framework which adopts a rapidly-exploring random tree (RRT) path planner with a Gaussian process (GP) occupancy map is developed for the navigation and exploration of an unknown but cluttered environment. The GP map outputs the probability of occupancy given any selected query point in the continuous space and thus makes it possible to explore the full space when used in conjunction with a continuous path planner. Furthermore, the GP map-generated path is embedded with the probability of collision along the path which lends itself to obstacle avoidance. Finally, the GP map-building algorithm is extended to include an exploration mission considering the differential constraints of a rotary unmanned aerial vehicle and the limitation arising from the environment. Using mutual information as an information-theoretic measure, an informative path which reduces the uncertainty of the environment is generated. Simulation results show that GP map combined with RRT planner can achieve the 3D navigation and exploration task successfully in unknown and complex environments.     

Decentralized Coordinated Tracking with Mixed Discrete–Continuous Decisions

In this paper, we study the problem of dynamically positioning a team of mobile robots for target tracking. We treat the coordination of mobile robots for target tracking as a joint team optimization to minimize uncertainty in target state estimates over a fixed horizon. The optimization is inherently a function of both the positioning of robots in continuous space and the assignment of robots to targets in discrete space. Thus, the robot team must make decisions over discrete and continuous variables. In contrast to methods that decouple target assignments and robot positioning, our approach avoids the strong assumption that a robot's utility for observing a target is independent of other robots’ observations. We formulate the optimization as a mixed integer nonlinear program and apply integer relaxation to develop an approximate solution in decentralized form. We demonstrate our coordinated multirobot tracking algorithm both in simulation and using a pair of mobile robotic sensor platforms to track moving pedestrians. Our results show that coupling target assignment and robot positioning realizes coordinated behaviors that are not possible with decoupled methods.     

A pseudo-spectral method with volume penalisation for magnetohydrodynamic turbulence in confined domains

We present a Fourier pseudo-spectral method for solving the resistive magnetohydrodynamic equations of incompressible flow in confined domains. A volume penalisation method allows to take into account boundary conditions and the geometry of the domain. A code validation is presented for the z-pinch test case. Numerical simulations of decaying MHD turbulence in two space dimensions show spontaneous spin-up of the flow in non-axisymmetric geometries, which is reflected by the generation of angular momentum. First results of decaying MHD turbulence in a cylinder illustrate the potential of the new method for three-dimensional simulations.     

Optimizing the Reliability of Streaming Applications Under Throughput Constraints

Mapping a pipelined application onto a distributed and parallel platform is a challenging problem. The problem becomes even more difficult when multiple optimization criteria are involved, and when the target resources are heterogeneous (processors and communication links) and subject to failures. This paper investigates the problem of mapping pipelined applications, consisting of a linear chain of stages executed in a pipeline way, onto such platforms. The objective is to optimize the reliability under a performance constraint, i.e., while guaranteeing a threshold throughput. In order to increase reliability, we replicate the execution of stages on multiple processors. We compare interval mappings, where the application is partitioned into intervals of consecutive stages, with general mappings, where stages may be partitioned without any constraint, thereby allowing a better usage of processors and communication network capabilities. However, the price to pay for general mappings is a dramatic increase in the problem complexity. We show that computing the period of a given general mapping is an NP-complete problem, and we give polynomial bounds to determine a (conservative) approximated value. On the contrary, the period of an interval mapping obeys a simple formula, and we provide an optimal dynamic programming algorithm for the bi-criteria interval mapping problem on homogeneous platforms. On the more practical side, we design a set of efficient heuristics, and we compare the performance of interval and general mapping strategies through extensive simulations.     

Evaluation using online support-vector-machines and fuzzy reasoning. Application to condition monitoring of speeds rolling process

A method for process condition monitoring and evaluation, which combines the online support vector machine (SVM) regression and the fuzzy sets methods, is proposed. To account for the time dependence, the proposed approach is based on moving windows in order to take into account the past and new data for the model’s adaptation. The fuzzy analysis is then applied to the generated residual data to give an evaluation of the condition monitoring. The proposed approach is applied to hot rolling for constructing a complementary condition monitoring system, which permits an online quality evaluation in the rolling process. Simulation results based on residual data show that the new approach is easily implementable.     

Architectures for Cooperative Airborne Simultaneous Localisation and Mapping

This paper develops active Simultaneous Localisation And Mapping (SLAM) trajectory control strategies for multiple cooperating Unmanned Aerial Vehicles (UAVs) for tasks such as surveillance and picture compilation in Global Positioning System (GPS)-denied environments. Each UAV in the team uses inertial sensor and terrain sensor information to simultaneously localise the UAV while building a point feature map of the surrounding terrain, where map information is shared between vehicles over a data fusion network. Multi-vehicle active SLAM control architectures are proposed that actively plan the trajectories and motions of each of the vehicles in the team based on maximising information in the localisation and mapping estimates. We demonstrate and compare an ideal, centralised architecture, where a central planning node chooses optimal actions for each UAV, and a coordinated, decentralised architecture, where UAVs make their own control decisions based on common shared map information. The different architectures involve varying degrees of complexity and optimality through differing communications and computational requirements. Results are presented using a three-UAV team in a six-degree of freedom multi-UAV simulator.