SERS enhancement by aggregated Au colloids: effect of particle size

Aggregated Au colloids have been widely used as SERS enhancing media for many years but to date there has been no systematic investigation of the effect of the particle size on the enhancements given by simple aggregated Au colloid solutions. Previous systematic studies on isolated particles in solution or multiple particles deposited onto surfaces reported widely different optimum particle sizes for the same excitation wavelength and also disagreed on the extent to which surface plasmon absorption spectra were a good predictor of enhancement factors. In this work the spectroscopic properties of a range of samples of monodisperse Au colloids with diameters ranging from 21 to 146 nm have been investigated in solution. The UV/visible absorption spectra of the colloids show complex changes as a function of aggregating salt (MgSO4) concentration which diminish when the colloid is fully aggregated. Under these conditions, the relative SERS enhancements provided by the variously sized colloids vary very significantly across the size range. The largest signals in the raw data are observed for 46 nm colloids but correction for the total surface area available to generate enhancement shows that particles with 74 nm diameter give the largest enhancement per unit surface area. The observed enhancements do not correlate with absorbance at the excitation wavelength but the large differences between differently sized colloids demonstrate that even in the randomly aggregated particle assemblies studied here, inhomogeneous broadening does not mask the underlying changes due to differences in particle diameter.

A Lightweight Tool for Anomaly Detection in Cloud Data Centres

Cloud data centres are critical business infrastructures and the fastest growing service providers. Detecting anomalies in Cloud data centre operation is vital. Given the vast complexity of the data centre system software stack, applications and workloads, anomaly detection is a challenging endeavour. Current tools for detecting anomalies often use machine learning techniques, application instance behaviours or system metrics distribu- tion, which are complex to implement in Cloud computing environments as they require training, access to application-level data and complex processing. This paper presents LADT, a lightweight anomaly detection tool for Cloud data centres that uses rigorous correlation of system metrics, implemented by an efficient corre- lation algorithm without need for training or complex infrastructure set up. LADT is based on the hypothesis that, in an anomaly-free system, metrics from data centre host nodes and virtual machines (VMs) are strongly correlated. An anomaly is detected whenever correlation drops below a threshold value. We demonstrate and evaluate LADT using a Cloud environment, where it shows that the hosting node I/O operations per second (IOPS) are strongly correlated with the aggregated virtual machine IOPS, but this correlation vanishes when an application stresses the disk, indicating a node-level anomaly.

Supervised Aggregative Feature Extraction for Big Data Time Series Regression

In many applications, and especially those where batch processes are involved, a target scalar output of interest is often dependent on one or more time series of data. With the exponential growth in data logging in modern industries such time series are increasingly available for statistical modeling in soft sensing applications. In order to exploit time series data for predictive modelling, it is necessary to summarise the information they contain as a set of features to use as model regressors. Typically this is done in an unsupervised fashion using simple techniques such as computing statistical moments, principal components or wavelet decompositions, often leading to significant information loss and hence suboptimal predictive models. In this paper, a functional learning paradigm is exploited in a supervised fashion to derive continuous, smooth estimates of time series data (yielding aggregated local information), while simultaneously estimating a continuous shape function yielding optimal predictions. The proposed Supervised Aggregative Feature Extraction (SAFE) methodology can be extended to support nonlinear predictive models by embedding the functional learning framework in a Reproducing Kernel Hilbert Spaces setting. SAFE has a number of attractive features including closed form solution and the ability to explicitly incorporate first and second order derivative information. Using simulation studies and a practical semiconductor manufacturing case study we highlight the strengths of the new methodology with respect to standard unsupervised feature extraction approaches.