Pulse Wave Propagating in Coplanar Waveguides (cPWS) on LTCC Substrates

The propagation of pulse waves in coplanar waveguides (CPWs) is investigated, and these CPWs are assumed to be fabricated on a single -layer low- temperature co-fired ceramic (LTCC) substrate. The input pulse wave can be a Gaussian pulse or a sinusoldally modulated Gaussian pulse. Based on the standard Galerkin 's method in the spectral domain, combined with fast Fourier transform (FFT), the pulse waveform and delay in CPWs are demonstrated and compared for a second plate, oriented orthogonally to the primary planar element, thus producing a crossed planar monopole (CPM), which is simpler to produce and has lower cost than a conical monopole. In this paper, further measurements have been made on this element

Copper(II) complexes of embelin and 2-aminobenzimidazole encapsulated in zeolite Y-potential as catalysts

Copper(II) complexes of two biologically important ligands, viz., embelin (2,5-dihydroxy-3-undecyl-2,5-cyclohexadien 1,4-dione) and 2-aminobenzimidazole were entrapped in the cages of zeolite Y by the flexible ligand method. The capability of these compounds in catalyzing the reduction of oxygen (industrially known as deoxo reaction) was explored and the results indicate an enhancement of the catalytic properties from that of the simple copper ion exchanged zeolite. These point to the ability of the ligands in enhancing the oxygen binding capability of the metal ion. Elemental analyses, Fourier transform infrared (FTIR), diffuse reflectance and EPR spectral studies, magnetic susceptibility measurements, TG, surface area analyses and powder X-ray diffraction studies were used in understanding the presence, composition and structure of the complexes inside the cages. The study also reveals the increased thermal and mechanical stability of the complexes as a result of encapsulation.

Spectral Analysis of Bounded Self-adjoint operators - A Linear Algebraic Approach

This thesis Entitled Spectral theory of bounded self-adjoint operators -A linear algebraic approach.The main results of the thesis can be classified as three different approaches to the spectral approximation problems. The truncation method and its perturbed versions are part of the classical linear algebraic approach to the subject. The usage of block Toeplitz-Laurent operators and the matrix valued symbols is considered as a particular example where the linear algebraic techniques are effective in simplifying problems in inverse spectral theory. The abstract approach to the spectral approximation problems via pre-conditioners and Korovkin-type theorems is an attempt to make the computations involved, well conditioned. However, in all these approaches, linear algebra comes as the central object. The objective of this study is to discuss the linear algebraic techniques in the spectral theory of bounded self-adjoint operators on a separable Hilbert space. The usage of truncation method in approximating the bounds of essential spectrum and the discrete spectral values outside these bounds is well known. The spectral gap prediction and related results was proved in the second chapter. The discrete versions of Borg-type theorems, proved in the third chapter, partly overlap with some known results in operator theory. The pure linear algebraic approach is the main novelty of the results proved here.

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