Novel dual transient temperature modulation technique for multi-vapour detection

Here we present a novel signal processing technique for a square wave thermally-modulated carbon black/polymer composite chemoresistor. The technique consists of only two mathematical operations: summing the off-transient and on-transient conductance signals; and subtracting the steady-state conductance signal. A single carbon black/polyvinylpyrrolidone composite chemo -resistor was fabricated and used to demonstrate the validity of the technique. Classification of water, methanol and ethanol vapours was successfully performed using only the peak time of the resultant curves. Quantification of the different vapours was also possible using the height of the peaks, because it was linearly proportional to concentration. This technique does not require zero-gas calibration and thus is superior to previously reported methods. ©2009 IEEE.

Bayesian Online Changepoint Detection

Changepoints are abrupt variations in the generative parameters of a data sequence. Online detection of changepoints is useful in modelling and prediction of time series in application areas such as finance, biometrics, and robotics. While frequentist methods have yielded online filtering and prediction techniques, most Bayesian papers have focused on the retrospective segmentation problem. Here we examine the case where the model parameters before and after the changepoint are independent and we derive an online algorithm for exact inference of the most recent changepoint. We compute the probability distribution of the length of the current ``run,'' or time since the last changepoint, using a simple message-passing algorithm. Our implementation is highly modular so that the algorithm may be applied to a variety of types of data. We illustrate this modularity by demonstrating the algorithm on three different real-world data sets.