Complex extreme learning machine applications in terahertz pulsed signals feature sets

This paper presents a novel approach to the automatic classification of very large data sets composed of terahertz pulse transient signals, highlighting their potential use in biochemical, biomedical, pharmaceutical and security applications. Two different types of THz spectra are considered in the classification process. Firstly a binary classification study of poly-A and poly-C ribonucleic acid samples is performed. This is then contrasted with a difficult multi-class classification problem of spectra from six different powder samples that although have fairly indistinguishable features in the optical spectrum, they also possess a few discernable spectral features in the terahertz part of the spectrum. Classification is performed using a complex-valued extreme learning machine algorithm that takes into account features in both the amplitude as well as the phase of the recorded spectra. Classification speed and accuracy are contrasted with that achieved using a support vector machine classifier. The study systematically compares the classifier performance achieved after adopting different Gaussian kernels when separating amplitude and phase signatures. The two signatures are presented as feature vectors for both training and testing purposes. The study confirms the utility of complex-valued extreme learning machine algorithms for classification of the very large data sets generated with current terahertz imaging spectrometers. The classifier can take into consideration heterogeneous layers within an object as would be required within a tomographic setting and is sufficiently robust to detect patterns hidden inside noisy terahertz data sets. The proposed study opens up the opportunity for the establishment of complex-valued extreme learning machine algorithms as new chemometric tools that will assist the wider proliferation of terahertz sensing technology for chemical sensing, quality control, security screening and clinic diagnosis. Furthermore, the proposed algorithm should also be very useful in other applications requiring the classification of very large datasets.

Electrochemical deposition of praseodymium oxide on tin-doped indium oxide as a thin sensing film

Fabrication of a thin praseodymium oxide film is of great technological interest in sensor, semiconducting, and ceramic industries. It is shown for the first time that an ultrathin layer of praseodymium oxide can be deposited on tin-doped indium oxide surface (ITO) by applying a negative sweeping voltage (cathodic electrodeposition) to the aqueous solution containing Pr(NO3)(3) and H2O2 using cyclic voltammetry, followed by annealing the film at 500 S C for 1 h. X-ray diffraction suggested that the predominant phase of the film is Pr6O11 and atomic force microscopy and scanning electron microscopy characterizations indicated that this film is assembled with a monolayer coverage of spherical praseodymium oxide nanoparticles packed closely on the ITO surface. AC impedance measurements of the thin Pr6O11 film on ITO also revealed that the composite material displays a much higher electrical conductivity compared to the pure ITO. As a result, the material could suitably be used as a new chemical sensor. (c) 2006 The Electrochemical Society.

Targeting Staphylococcus aureus quorum sensing with non-peptidic small molecule inhibitors

A series of 3-oxo-C12-HSL, tetramic acid and tetronic acid analogues was synthesized to gain insights into the structural requirements for quorum sensing inhibition in Staphylococcus aureus. Compounds active against agr were non-competitive inhibitors of the auto-inducing peptide (AIP)-activated AgrC receptor, by altering the activation efficacy of the cognate AIP-1. They appeared to act as negative allosteric modulators and are exemplified by 3-tetradecanoyltetronic acid 17 which reduced nasal cell colonization and arthritis in a murine infection model.