System identification, time series analysis and forecasting:The Captain Toolbox handbook.

CAPTAIN is a MATLAB compatible toolbox for non stationary time series analysis, system identification, signal processing and forecasting, using unobserved components models, time variable parameter models, state dependent parameter models and multiple input transfer function models. CAPTAIN also includes functions for true digital control

Hard Metal Production by ERS: Processing Parameter Roles in Final Properties

Cemented carbide is a hard composite material, used widely in a variety of industries. The value of the global tungsten carbide market is expected to grow by 4.4% (compound annual growth rate) from 2017 to 2022. One of the main markets is in metal cutting and wear parts, where small pieces (or inserts), a few grams in weight, are used. Field-assisted sintering technique (FAST) technologies allow for the production of small blanks in a single step from powder, which are near final dimensions. Production cycles are very short. In this paper, one of the FAST processes, the ERS technology, is applied to obtain WC10Co parts. A review of the process variable effects on the final properties of the parts is accomplished. Final properties of a range of conventionally produced inserts are obtained, using 100 MPa compacting pressure, 80 MA/m2 of current density, and processing times of around 800 ms.This research was funded by EU, grant number FoF.NMP.2013-10 608729 (7th Framework Programme) EFFIPRO

Visual parameter optimisation for biomedical image processing

Background: Biomedical image processing methods require users to optimise input parameters to ensure high quality output. This presents two challenges. First, it is difficult to optimise multiple input parameters for multiple input images. Second, it is difficult to achieve an understanding of underlying algorithms, in particular, relationships between input and output. Results: We present a visualisation method that transforms users’ ability to understand algorithm behaviour by integrating input and output, and by supporting exploration of their relationships. We discuss its application to a colour deconvolution technique for stained histology images and show how it enabled a domain expert to identify suitable parameter values for the deconvolution of two types of images, and metrics to quantify deconvolution performance. It also enabled a breakthrough in understanding by invalidating an underlying assumption about the algorithm. Conclusions: The visualisation method presented here provides analysis capability for multiple inputs and outputs in biomedical image processing that is not supported by previous analysis software. The analysis supported by our method is not feasible with conventional trial-and-error approaches

Distributed Constrained Recursive Nonlinear Least-Squares Estimation: Algorithms and Asymptotics

This paper focuses on the problem of recursive nonlinear least squares parameter estimation in multi-agent networks, in which the individual agents observe sequentially over time an independent and identically distributed (i.i.d.) time-series consisting of a nonlinear function of the true but unknown parameter corrupted by noise. A distributed recursive estimator of the \emph{consensus} + \emph{innovations} type, namely $\mathcal{CIWNLS}$, is proposed, in which the agents update their parameter estimates at each observation sampling epoch in a collaborative way by simultaneously processing the latest locally sensed information~(\emph{innovations}) and the parameter estimates from other agents~(\emph{consensus}) in the local neighborhood conforming to a pre-specified inter-agent communication topology. Under rather weak conditions on the connectivity of the inter-agent communication and a \emph{global observability} criterion, it is shown that at every network agent, the proposed algorithm leads to consistent parameter estimates. Furthermore, under standard smoothness assumptions on the local observation functions, the distributed estimator is shown to yield order-optimal convergence rates, i.e., as far as the order of pathwise convergence is concerned, the local parameter estimates at each agent are as good as the optimal centralized nonlinear least squares estimator which would require access to all the observations across all the agents at all times. In order to benchmark the performance of the proposed distributed $\mathcal{CIWNLS}$ estimator with that of the centralized nonlinear least squares estimator, the asymptotic normality of the estimate sequence is established and the asymptotic covariance of the distributed estimator is evaluated. Finally, simulation results are presented which illustrate and verify the analytical findings.Comment: 28 pages. Initial Submission: Feb. 2016, Revised: July 2016, Accepted: September 2016, To appear in IEEE Transactions on Signal and Information Processing over Networks: Special Issue on Inference and Learning over Network

Teaching Signal Processing to the Medical Profession

Knowledge of signal processing is very important for medical students. A medical signal may be used for monitoring, constructing an image, or for extracting the numerical quantity of a parameter. This information forms a basis for medical decisions. However, the processing of the signal may lead to distortion and an incorrect interpretation. The present article describes an educational practical for first year medical students. It uses the electrocardiogram, which can be obtained easily, as a convenient example of a medical signal. The practical was developed at the VU University Amsterdam and summarizes the elementary concepts of signal processing