Laser Spectroscopy: TDLAS instrumentation and NIR analysis of some organic materials

Near-infrared spectroscopy can be a workhorse technique for materials analysis in industries such as agriculture, pharmaceuticals, chemicals and polymers. A near-infrared spectrum represents combination bands and overtone bands that are harmonics of absorption frequencies in the mid-infrared. Near-infrared absorption includes a combination-band region immediately adjacent to the mid-infrared and three overtone regions. All four near-infrared regions contain "echoes" of the fundamental mid-infrared absorptions. For example, vibrations in the mid-infrared due to the C-H stretches will produce four distinct bands in each of the overtone and combination regions. As the bands become more removed from the fundamental frequencies they become more widely separated from their neighbors, more broadened and are dramatically reduced in intensity. Because near-infrared bands are much less intense, more of the sample can be used to produce a spectra and with near-infrared, sample preparation activities are greatly reduced or eliminated so more of the sample can be utilized. In addition, long path lengths and the ability to sample through glass in the near-infrared allows samples to be measured in common media such as culture tubes, cuvettes and reaction bottles. This is unlike mid-infrared where very small amounts of a sample produce a strong spectrum; thus sample preparation techniques must be employed to limit the amount of the sample that interacts with the beam. In the present work we describe the successful the fabrication and calibration of a linear high resolution linear spectrometer using tunable diode laser and a 36 m path length cell and meuurement of a highly resolved structure of OH group in methanol in the transition region A v =3. We then analyse the NIR spectrum of certain aromatic molecules and study the substituent effects using local mode theory

Studies on pulp propogation in single mode optical fibres

Studies on pulse propagation in single mode optical fibers have attracted interest from a wide area of science and technology as they have laid down the foundation for an in-depth understanding of the underlying physical principles, especially in the field of optical telecommunications. The foremost among them is discovery of the optical soliton which is considered to be one of the most significant events of the twentieth century owing to its fantastic ability to propagate undistorted over long distances and to remain unaflected after collision with each other. To exploit the important propertia of optical solitons, innovative mathematical models which take into account proper physical properties of the single mode optical fibers demand special attention. This thesis contains a theoretical analysis of the studies on soliton pulse propagation in single mode optical fibers.

Spectral clustering independent component analysis for tissue classification from brain MRI

A spectral angle based feature extraction method, Spectral Clustering Independent Component Analysis (SC-ICA), is proposed in this work to improve the brain tissue classification from Magnetic Resonance Images (MRI). SC-ICA provides equal priority to global and local features; thereby it tries to resolve the inefficiency of conventional approaches in abnormal tissue extraction. First, input multispectral MRI is divided into different clusters by a spectral distance based clustering. Then, Independent Component Analysis (ICA) is applied on the clustered data, in conjunction with Support Vector Machines (SVM) for brain tissue analysis. Normal and abnormal datasets, consisting of real and synthetic T1-weighted, T2-weighted and proton density/fluid-attenuated inversion recovery images, were used to evaluate the performance of the new method. Comparative analysis with ICA based SVM and other conventional classifiers established the stability and efficiency of SC-ICA based classification, especially in reproduction of small abnormalities. Clinical abnormal case analysis demonstrated it through the highest Tanimoto Index/accuracy values, 0.75/98.8%, observed against ICA based SVM results, 0.17/96.1%, for reproduced lesions. Experimental results recommend the proposed method as a promising approach in clinical and pathological studies of brain diseases

Flexure-shear Analysis of Concrete Beam Reinforced with GFRP bars

This paper gives the details of flexure-shear analysis of concrete beams reinforced with GFRP rebars. The influence of vertical reinforcement ratio, longitudinal reinforcement ratio and compressive strength of concrete on shear strength of GFRP reinforced concrete beam is studied. The critical value of shear span to depth ratio (a/d) at which the mode of failure changes from flexure to shear is studied. The fail-ure load of the beam is predicted for various values of a/d ratio. The prediction show that the longitudinally FRP reinforced concrete beams having no stirrups fail in shear for a/d ratio less than 9.0. It is expected that the predicted data is useful for structural engineers to design the FRP reinforced concrete members.

Raman and infrared spectral analysis of thallium niobyl phosphates: Tl2NbO2PO4, Tl3NaNb4O9(PO4)2 and TlNbOP2O7

Raman and infrared spectra of Tl2NbO2PO4, Tl3NaNb4O9(PO4)2 and TlNbOP2O7 are reported. The observed bands are assigned in terms of vibrations of NbO6 octahedra and PO4 tetrahedra in the first two compounds and in terms of NbO6 octahedra and P2O7 4− anion in the third compound. The NbO6 octahedra in all the title compounds are found to be corner-shared and distorted. The higher wavenumber values of the ν1 (NbO6) mode and other stretching modes indicate that the NbO6 octahedra in them are distorted in the order TlNbOP2O7 > Tl2NbO2PO4 > Tl3NaNb4O9(PO4)2. The splitting of the ν3 (PO4) mode indicates that PO4 tetrahedra is distorted more in Tl2NbO2PO4 than in Tl3NaNb4O9(PO4)2. The symmetry of P2O7 4− anion in TlNbOP2O7 is lowered. Bands indicate that the P–O–P bridge in the above compound has a bent P–O–P bridge configuration

Analysis of Stochastic Volatility Sequences Generated by Product Autoregressive Models

The classical methods of analysing time series by Box-Jenkins approach assume that the observed series uctuates around changing levels with constant variance. That is, the time series is assumed to be of homoscedastic nature. However, the nancial time series exhibits the presence of heteroscedasticity in the sense that, it possesses non-constant conditional variance given the past observations. So, the analysis of nancial time series, requires the modelling of such variances, which may depend on some time dependent factors or its own past values. This lead to introduction of several classes of models to study the behaviour of nancial time series. See Taylor (1986), Tsay (2005), Rachev et al. (2007). The class of models, used to describe the evolution of conditional variances is referred to as stochastic volatility modelsThe stochastic models available to analyse the conditional variances, are based on either normal or log-normal distributions. One of the objectives of the present study is to explore the possibility of employing some non-Gaussian distributions to model the volatility sequences and then study the behaviour of the resulting return series. This lead us to work on the related problem of statistical inference, which is the main contribution of the thesis