Bayesian algorithms for multiple target tracking with binary sensors on particle filter

In this paper, we propose an approximate Bayesian computation approach to perform a multiple target tracking within a binary sensor network. The nature of the binary sensors (\emph{getting closer - moving away} information) do not allow the use of the classical tools (e.g. Kalman Filter, Particle Filer), because the exact likelihood is intractable. To overcome this, we use the particular feature of the likelihood-free algorithms to produce an efficient multiple target tracking methodology.

Hybrid Qualitative and Quantitative Diagnosis Approach

Parametric faults are detected and isolated using parameter tracking algorithms based on optimization algorithms or filtering techniques (e.g., Kalman filter, particle filter). Online, simultaneous tracking of all parametric faults can fails since there may be too many combinations of parameter values that explain the observed behavior. Hence, a correct diagnosis solution is not obtained. An alternative in the single fault case is to track separately each parametric fault in parallel and choose the one that best explains the observed behavior according to some chosen metric (e.g., mean square error). This approach is feasible but computationally expensive, since there may be too many tracking algorithms running in parallel. We propose using analytic redundancy relations (ARRs) to reduce the number of parametric faults that are tracked simultaneously. ARRs qualitatively point to a set of possible explanations but usually require a large number of sensors to achieve good isolability of fault causes. They induce a fault signature matrix (FSM) that can be derived offline. The parameter tracking algorithms will be instantiated for the faults in the set of possible explanations produced by the ARRs. By combining ARRs with online parameter tracking algorithms we can obtain a good tradeoff between computational effort and fault isolability. We demonstrate our approach by diagnosing faults in a rectifier circuit.

Data assimilation method to de-noise and de-filter particle image velocimetry data

We present a variational data assimilation method in order to improve the accuracy of velocity fields $\tilde{\boldsymbol{v}}$, that are measured using particle image velocimetry (PIV). The method minimises the space–time integral of the difference between the reconstruction $\boldsymbol{u}$ and $\tilde{\boldsymbol{v}}$, under the constraint, that $\boldsymbol{u}$ satisfies conservation of mass and momentum. We apply the method to synthetic velocimetry data, in a two-dimensional turbulent flow, where realistic PIV noise is generated by computationally mimicking the PIV measurement process. The method performs optimally when the assimilation integration time is of the order of the flow correlation time. We interpret these results by comparing them to one-dimensional diffusion and advection problems, for which we derive analytical expressions for the reconstruction error.

Development and testing of an online method to measure ambient fine particulate reactive oxygen species (ROS) based on the 2',7'-dichlorofluorescin (DCFH) assay

An online, semi-continuous instrument to measure fine particle (PM2.5) reactive oxygen species (ROS) was developed based on the fluorescent probe 2'7'-dichlorofluorescin (DCFH). Parameters that influence probe response were first characterized to develop an optimal method for use in a field instrument. The online method used a mist chamber scrubber to collect total (gas plus particle) ROS components (ROSt) alternating with gas phase ROS (ROSg) by means of an inline filter. Particle phase ROS (ROSp) was determined by the difference between ROSt and ROSg. The instrument was deployed in urban Atlanta, Georgia, USA, and at a rural site during various seasons. Concentrations from the online instrument generally agreed well with those from an intensive filter measurement of ROSp. Concentrations of the ROSp measurements made with this instrument were lower than reported in other studies, often below the instrument's average limit of detection (0.15 nmol H2O2 equivalents m−3). Mean ROSp concentrations were 0.26 nmol H2O2 equivalents m−3 at the Atlanta urban sites compared to 0.14 nmol H2O2 equivalents m−3 at the rural site.