Non-invasive Studies on Age Related Parameters Using a Blood Volume Pulse Sensor

A non-invasive technique is implemented to measure a parameter which is closely related to the distensibility of large arteries, using the second derivative of the infrared photoplethysmographic waveform. Thirty subjects within the age group of 20-61 years were involved in this pilot study. Two new parameters, namely the area of the photoplethysmographic waveform under the systolic peak, and the ratio of the time delay between the systolic and the diastolic peaks and the time period of the waveform ( T/T) were studied as a function of age. It was found that while the parameter which is supposed to be a marker of distensibility of large arteries and T /T values correlate negatively with age, the area under the systolic peak correlates positively with age. The results suggest that the derived parameters could provide a simple, non-invasive means for studying the changes in the elastic properties of the vascular system as a function of age.

Photothermal beam deflection for non-destructive evaluation of semiconductor thin films

Non-destructive testing (NDT) is the use of non-invasive techniques to determine the integrity of a material, component, or structure. Engineers and scientists use NDT in a variety of applications, including medical imaging, materials analysis, and process control.Photothermal beam deflection technique is one of the most promising NDT technologies. Tremendous R&D effort has been made for improving the efficiency and simplicity of this technique. It is a popular technique because it can probe surfaces irrespective of the size of the sample and its surroundings. This technique has been used to characterize several semiconductor materials, because of its non-destructive and non-contact evaluation strategy. Its application further extends to analysis of wide variety of materials. Instrumentation of a NDT technique is very crucial for any material analysis. Chapter two explores the various excitation sources, source modulation techniques, detection and signal processing schemes currently practised. The features of the experimental arrangement including the steps for alignment, automation, data acquisition and data analysis are explained giving due importance to details.Theoretical studies form the backbone of photothermal techniques. The outcome of a theoretical work is the foundation of an application.The reliability of the theoretical model developed and used is proven from the studies done on crystalline.The technique is applied for analysis of transport properties such as thermal diffusivity, mobility, surface recombination velocity and minority carrier life time of the material and thermal imaging of solar cell absorber layer materials like CuInS2, CuInSe2 and SnS thin films.analysis of In2S3 thin films, which are used as buffer layer material in solar cells. The various influences of film composition, chlorine and silver incorporation in this material is brought out from the measurement of transport properties and analysis of sub band gap levels.The application of photothermal deflection technique for characterization of solar cells is a relatively new area that requires considerable attention.The application of photothermal deflection technique for characterization of solar cells is a relatively new area that requires considerable attention. Chapter six thus elucidates the theoretical aspects of application of photothermal techniques for solar cell analysis. The experimental design and method for determination of solar cell efficiency, optimum load resistance and series resistance with results from the analysis of CuInS2/In2S3 based solar cell forms the skeleton of this chapter.

Automatic Detection and Classification of Glioma Tumors using Statistical Features

The characterization and grading of glioma tumors, via image derived features, for diagnosis, prognosis, and treatment response has been an active research area in medical image computing. This paper presents a novel method for automatic detection and classification of glioma from conventional T2 weighted MR images. Automatic detection of the tumor was established using newly developed method called Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA).Statistical Features were extracted from the detected tumor texture using first order statistics and gray level co-occurrence matrix (GLCM) based second order statistical methods. Statistical significance of the features was determined by t-test and its corresponding p-value. A decision system was developed for the grade detection of glioma using these selected features and its p-value. The detection performance of the decision system was validated using the receiver operating characteristic (ROC) curve. The diagnosis and grading of glioma using this non-invasive method can contribute promising results in medical image computing

Catalysis by Enzymes immobilized on Tuned Mesoporous Silica

Mesoporous silica nanoparticles provide a non-invasive and biocompatible delivery platform for a broad range of applications in therapeutics, pharmaceuticals and diagnosis. Additionally, mesoporous silica materials can be synthesized together with other nanomaterials to create new nanocomposites, opening up a wide variety of potential applications. The ready functionalization of silica materials makes them ideal candidates for bioapplications and catalysis. These properties of mesoporous silica like high surface areas, large pore volumes and ordered pore networks allow them for higher loading of drugs or biomolecules. Comparative studies have been made to evaluate the different procedures; much of the research to date has involved quick exploration of new methods and supports. Requirements for different enzymes may vary, and specific conditions may be needed for a particular application of an immobilized enzyme such as a highly rigid support. In this endeavor, mesoporous silica materials having different pore size were synthesized and easily modified with active functional groups and were evaluated for the immobilization of enzymes. In this work, Aspergillus niger glucoamylase, Bovine liver catalase, Candida rugosa lipase were immobilized onto support by adsorption and covalent binding. The structural properties of pure and immobilized supports are analyzed by various characterization techniques and are used for different reactions of industrial applications.

Study on period search methods of variable stars: Application to ASAS and CRTS databases

Study on variable stars is an important topic of modern astrophysics. After the invention of powerful telescopes and high resolving powered CCD’s, the variable star data is accumulating in the order of peta-bytes. The huge amount of data need lot of automated methods as well as human experts. This thesis is devoted to the data analysis on variable star’s astronomical time series data and hence belong to the inter-disciplinary topic, Astrostatistics. For an observer on earth, stars that have a change in apparent brightness over time are called variable stars. The variation in brightness may be regular (periodic), quasi periodic (semi-periodic) or irregular manner (aperiodic) and are caused by various reasons. In some cases, the variation is due to some internal thermo-nuclear processes, which are generally known as intrinsic vari- ables and in some other cases, it is due to some external processes, like eclipse or rotation, which are known as extrinsic variables. Intrinsic variables can be further grouped into pulsating variables, eruptive variables and flare stars. Extrinsic variables are grouped into eclipsing binary stars and chromospheri- cal stars. Pulsating variables can again classified into Cepheid, RR Lyrae, RV Tauri, Delta Scuti, Mira etc. The eruptive or cataclysmic variables are novae, supernovae, etc., which rarely occurs and are not periodic phenomena. Most of the other variations are periodic in nature. Variable stars can be observed through many ways such as photometry, spectrophotometry and spectroscopy. The sequence of photometric observa- xiv tions on variable stars produces time series data, which contains time, magni- tude and error. The plot between variable star’s apparent magnitude and time are known as light curve. If the time series data is folded on a period, the plot between apparent magnitude and phase is known as phased light curve. The unique shape of phased light curve is a characteristic of each type of variable star. One way to identify the type of variable star and to classify them is by visually looking at the phased light curve by an expert. For last several years, automated algorithms are used to classify a group of variable stars, with the help of computers. Research on variable stars can be divided into different stages like observa- tion, data reduction, data analysis, modeling and classification. The modeling on variable stars helps to determine the short-term and long-term behaviour and to construct theoretical models (for eg:- Wilson-Devinney model for eclips- ing binaries) and to derive stellar properties like mass, radius, luminosity, tem- perature, internal and external structure, chemical composition and evolution. The classification requires the determination of the basic parameters like pe- riod, amplitude and phase and also some other derived parameters. Out of these, period is the most important parameter since the wrong periods can lead to sparse light curves and misleading information. Time series analysis is a method of applying mathematical and statistical tests to data, to quantify the variation, understand the nature of time-varying phenomena, to gain physical understanding of the system and to predict future behavior of the system. Astronomical time series usually suffer from unevenly spaced time instants, varying error conditions and possibility of big gaps. This is due to daily varying daylight and the weather conditions for ground based observations and observations from space may suffer from the impact of cosmic ray particles. Many large scale astronomical surveys such as MACHO, OGLE, EROS, xv ROTSE, PLANET, Hipparcos, MISAO, NSVS, ASAS, Pan-STARRS, Ke- pler,ESA, Gaia, LSST, CRTS provide variable star’s time series data, even though their primary intention is not variable star observation. Center for Astrostatistics, Pennsylvania State University is established to help the astro- nomical community with the aid of statistical tools for harvesting and analysing archival data. Most of these surveys releases the data to the public for further analysis. There exist many period search algorithms through astronomical time se- ries analysis, which can be classified into parametric (assume some underlying distribution for data) and non-parametric (do not assume any statistical model like Gaussian etc.,) methods. Many of the parametric methods are based on variations of discrete Fourier transforms like Generalised Lomb-Scargle peri- odogram (GLSP) by Zechmeister(2009), Significant Spectrum (SigSpec) by Reegen(2007) etc. Non-parametric methods include Phase Dispersion Minimi- sation (PDM) by Stellingwerf(1978) and Cubic spline method by Akerlof(1994) etc. Even though most of the methods can be brought under automation, any of the method stated above could not fully recover the true periods. The wrong detection of period can be due to several reasons such as power leakage to other frequencies which is due to finite total interval, finite sampling interval and finite amount of data. Another problem is aliasing, which is due to the influence of regular sampling. Also spurious periods appear due to long gaps and power flow to harmonic frequencies is an inherent problem of Fourier methods. Hence obtaining the exact period of variable star from it’s time series data is still a difficult problem, in case of huge databases, when subjected to automation. As Matthew Templeton, AAVSO, states “Variable star data analysis is not always straightforward; large-scale, automated analysis design is non-trivial”. Derekas et al. 2007, Deb et.al. 2010 states “The processing of xvi huge amount of data in these databases is quite challenging, even when looking at seemingly small issues such as period determination and classification”. It will be beneficial for the variable star astronomical community, if basic parameters, such as period, amplitude and phase are obtained more accurately, when huge time series databases are subjected to automation. In the present thesis work, the theories of four popular period search methods are studied, the strength and weakness of these methods are evaluated by applying it on two survey databases and finally a modified form of cubic spline method is intro- duced to confirm the exact period of variable star. For the classification of new variable stars discovered and entering them in the “General Catalogue of Vari- able Stars” or other databases like “Variable Star Index“, the characteristics of the variability has to be quantified in term of variable star parameters.

Oxidation kinetics of nickel nano crystallites obtained by controlled thermolysis of diaquabis(ethylenediamine) nickel(II) nitrate

The metal complex, [Ni(en)2(H2O)2](NO3)2 (en = ethylenediamine), was decomposed in a static furnace at 200 C by autogenous decomposition to obtain phase pure metallic nickel nanocrystallites. The nickel metal thus obtained was studied by XRD, IR spectra, SEM and CHN analysis. The nickel crystallites are in the nanometer range as indicated by XRD studies. The IR spectral studies and CHN analyses show that the surface is covered with a nitrogen containing species. Thermogravimetric mass gain shows that the product purity is high (93%). The formed nickel is stable and resistant to oxidation up to 350 C probably due to the coverage of nitrogen containing species. Activation energy for the oxidation of the prepared nickel nanocrystallites was determined by non-isothermal methods and was found to depend on the conversion ratio. The oxidation kinetics of the nickel crystallites obeyed a Johnson–Mehl–Avrami mechanism probably due to the special morphology and crystallite strain present on the metal.

Studies on Soft Bycatch Reduction Devices for Selective Trawling

Trawling, though an efficient method of fishing, is known to be one of the most non-selective methods of fish capture. The bulk of the wild caught penaeid shrimps landed in India are caught by trawling.In addition to shrimps, the trawler fleet also catches considerable amount of non-shrimp resources. The term bycatch means that portion of the catch other than target species caught while fishing, which are either retained or discarded. Bycatch discards is a serious problem leading to the depletion of the resources and negative impacts on biodiversity. In order to minimize this problem, trawling has to be made more selective by incorporating Bycatch Reduction Devices (BRDs). There are several advantages in using BRDs in shrimp trawling. BRDs reduce the negative impacts of shrimp trawling on marine community. Fishers could benefit economically from higher catch value due to improved catch quality, shorter sorting time, lower fuel costs, and longer tow duration. Adoption of BRDs by fishers would forestall criticism by conservation groups against trawling.

Development of active microwave imaging techniques for applications in mammography

Breast cancer is the most common non - skin malignancy in women and a leading cause of female morality. A potentially important strategy for reducing this menace is the detection at an early stage . The invention of non-invasive and non-ionizing microwave technique, to reveal the internal structure of biological objects was a break through in the field of medical diagnostics. Electrical properties of biological tissues and their interaction with electromagmetic waves have direct impact on human life. This thesis focuses on theoretical and experimental investigations of active microwave imaging techniques for breast cancer detection.