Elastic Constants And Structural Parameters Of Selected Polycrystalline Ceramics By Ultrasonic Technique

The present work is an attempt to probe the elastic properties in some dielectric ceramics, by using ultrasonic pulse echo overlap technique. The base Ba6-xSm8+2xTi18O54 and Ca5Nb2TiO12 are very important dielectrics ceramics used for microwave communication as well as for substrate materials. Ultrasonic is one of the most widely used and powerful techniques to measure elastic properties of solids. The ultrasonic technique is nondestructive in nature and the measurements are relatively straightforward to perform. One unique advantantage of the ultrasonic technique is that both static and dynamic properties can be measured simultaneously. The velocity and attenuation coefficients of the ultrasonic waves propagating through a medium are related to the microscopic structure of the material and they provide valuable information about the structural changes in the system. Among the various ultrasonic techniques, the pulse echo overlap method is the most accurate and precise one. In the present case the decreased elastic properties of Cas-XMg,Nb2TiO12 and Cas-,ZnNb2TiO12 ceramics can be attributed to their mixture phases beyond x = 1. Moreover, the abrupt change in elastic properties observed for x >1 can also be correlated to the structural transformation of the materials from their phase pure form to mixture phases for higher extent of substitution of the concerned material . Ca4(ANb2Ti)012 (A = Mg, Zn) is the strongest compound with the maximum values for elastic properties . This could be due to the possible substitution of Mg/Zn ions with lesser radius [25] than Ca2+ in perovskite B-site of Ca(Cali4Nb2i4Tili4) O3 material to contribute more ordering and symmetry to the system [20]. All other compositions (x > 1) contain mixed-phases and for such mixed-phase samples, the mechanical properties are difficult to explain.

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.

Investigations on broadband planar monopole and slot radiators and their suitability for UWB applications

In recent years, there is a visible trend for products/services which demand seamless integration of cellular networks, WLANs and WPANs. This is a strong indication for the inclusion of high speed short range wireless technology in future applications. In this context UWB radio has a significant role to play as an extension/complement to existing cellular/access technology. In the present work, we have investigated two major types of wide band planar antennas: Monopole and Slot. Four novel compact broadband antennas, suitable for poratble applications, are designed and characterized, namely 1. Elliptical monopole 2. Inverted cone monopole 3. Koch fractal slot 4. Wide band slot The performance of these designs have been studied using standard simulation tools used in industry/academia and they have been experimentally verified. Antenna design guidelines are also deduced by accounting the resonances in each structure. In addition to having compact sized, high efficiency and broad bandwidth antennas, one of the major criterion in the design of impulse-UWB systems have been the transmission of narrow band pulses with minimum distortion. The key challenge is not only to design a broad band antenna with constant and stable gain but to maintain a flat group delay or linear phase response in the frequency domain or excellent transient response in time domain. One of the major contributions of the thesis lies in the analysis of the frequency and time-domain response of the designed UWB antennas to confirm their suitability for portable pulsed-UWB systems. Techniques to avoid narrowband interference by engraving narrow slot resonators on the antenna is also proposed and their effect on a nano-second pulse have been investigated.

Investigations on the Radiation Characteristics of Planar Printed UWB Antennas With Modified Ground Planes

A major challenge in the transmission of narrow pulses is the radiation characteristics of the antenna. Designing the front ends for UWB systems pose challenges compared to their narrow and wide band counterparts because in addition to having electrically small size, high efficiency and band width, the antenna has to have excellent transient response. The present work deals with the design of four novel antenna designs- Square Monopole, Semi-Elliptic Slot, Step and Linear Tapered slot - and an assay on their suitability in UWB Systems. Multiple resonances in the geometry are matched to UWB by redesigning the ground-patch interfaces. Techniques to avoid narrow band interference is proposed in the antenna level and their effect on a nano second pulse have also been investigated. The thesis proposes design guidelines to design the antenna on laminates of any permittivity and the analyzes are complete with results in the frequency and time domains.

Development and Evaluation of Blind Identification Techniques for Nonlinear Systems

Identification and Control of Non‐linear dynamical systems are challenging problems to the control engineers.The topic is equally relevant in communication,weather prediction ,bio medical systems and even in social systems,where nonlinearity is an integral part of the system behavior.Most of the real world systems are nonlinear in nature and wide applications are there for nonlinear system identification/modeling.The basic approach in analyzing the nonlinear systems is to build a model from known behavior manifest in the form of system output.The problem of modeling boils down to computing a suitably parameterized model,representing the process.The parameters of the model are adjusted to optimize a performanace function,based on error between the given process output and identified process/model output.While the linear system identification is well established with many classical approaches,most of those methods cannot be directly applied for nonlinear system identification.The problem becomes more complex if the system is completely unknown but only the output time series is available.Blind recognition problem is the direct consequence of such a situation.The thesis concentrates on such problems.Capability of Artificial Neural Networks to approximate many nonlinear input-output maps makes it predominantly suitable for building a function for the identification of nonlinear systems,where only the time series is available.The literature is rich with a variety of algorithms to train the Neural Network model.A comprehensive study of the computation of the model parameters,using the different algorithms and the comparison among them to choose the best technique is still a demanding requirement from practical system designers,which is not available in a concise form in the literature.The thesis is thus an attempt to develop and evaluate some of the well known algorithms and propose some new techniques,in the context of Blind recognition of nonlinear systems.It also attempts to establish the relative merits and demerits of the different approaches.comprehensiveness is achieved in utilizing the benefits of well known evaluation techniques from statistics. The study concludes by providing the results of implementation of the currently available and modified versions and newly introduced techniques for nonlinear blind system modeling followed by a comparison of their performance.It is expected that,such comprehensive study and the comparison process can be of great relevance in many fields including chemical,electrical,biological,financial and weather data analysis.Further the results reported would be of immense help for practical system designers and analysts in selecting the most appropriate method based on the goodness of the model for the particular context.

Fractal based techniques for classification of mammograms and identification of microcalcifications

After skin cancer, breast cancer accounts for the second greatest number of cancer diagnoses in women. Currently the etiologies of breast cancer are unknown, and there is no generally accepted therapy for preventing it. Therefore, the best way to improve the prognosis for breast cancer is early detection and treatment. Computer aided detection systems (CAD) for detecting masses or micro-calcifications in mammograms have already been used and proven to be a potentially powerful tool , so the radiologists are attracted by the effectiveness of clinical application of CAD systems. Fractal geometry is well suited for describing the complex physiological structures that defy the traditional Euclidean geometry, which is based on smooth shapes. The major contribution of this research include the development of • A new fractal feature to accurately classify mammograms into normal and normal (i)With masses (benign or malignant) (ii) with microcalcifications (benign or malignant) • A novel fast fractal modeling method to identify the presence of microcalcifications by fractal modeling of mammograms and then subtracting the modeled image from the original mammogram. The performances of these methods were evaluated using different standard statistical analysis methods. The results obtained indicate that the developed methods are highly beneficial for assisting radiologists in making diagnostic decisions. The mammograms for the study were obtained from the two online databases namely, MIAS (Mammographic Image Analysis Society) and DDSM (Digital Database for Screening Mammography.

Design and development of compact printed ultra wide band antennas

In recent years, there is a visible trend for products/services which demand seamless integration of cellular networks, WLANs and WPANs. This is a strong indication for the inclusion of high speed short range wireless technology in future applications. In this context UWB radio has a significant role to play as an extension/complement to existing cellular/access technology. In the present work, three major types of ultra wide band planar antennas are investigated: Monopole and Slot. Three novel compact UWB antennas, suitable for poratble applications, are designed and characterized, namely 1) Ground modified monopole 2) Serrated monopole 3) Triangular slot The performance of these designs have been studied using standard simulation tools used in industry/academia and they have been experimentally verified. Antenna design guidelines are also deduced by accounting the resonances in each structure. In addition to having compact sized, high efficiency and broad bandwidth antennas, one of the major criterion in the design of impulse-UWB systems have been the transmission of narrow band pulses with minimum distortion. The key challenge is not only to design a broad band antenna with constant and stable gain but to maintain a flat group delay or linear phase response in the frequency domain or excellent transient response in time domain. One of the major contributions of the thesis lies in the analysis of the frequency and timedomain response of the designed UWB antennas to confirm their suitability for portable pulsed-UWB systems. Techniques to avoid narrowband interference by engraving narrow slot resonators on the antenna is also proposed and their effect on a nano-second pulse have been investigated

Development of Techniques for the Automatic Extraction and Grade Detection of Glioma Tumors from Conventional Brain Magnetic Resonant Images

Cerebral glioma is the most prevalent primary brain tumor, which are classified broadly into low and high grades according to the degree of malignancy. High grade gliomas are highly malignant which possess a poor prognosis, and the patients survive less than eighteen months after diagnosis. Low grade gliomas are slow growing, least malignant and has better response to therapy. To date, histological grading is used as the standard technique for diagnosis, treatment planning and survival prediction. The main objective of this thesis is to propose novel methods for automatic extraction of low and high grade glioma and other brain tissues, grade detection techniques for glioma using conventional magnetic resonance imaging (MRI) modalities and 3D modelling of glioma from segmented tumor slices in order to assess the growth rate of tumors. Two new methods are developed for extracting tumor regions, of which the second method, named as Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA) can also extract white matter and grey matter from T1 FLAIR an T2 weighted images. The methods were validated with manual Ground truth images, which showed promising results. The developed methods were compared with widely used Fuzzy c-means clustering technique and the robustness of the algorithm with respect to noise is also checked for different noise levels. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and detection. Based on the thresholds of discriminant first order and gray level cooccurrence matrix based second order statistical features three feature sets were formulated and a decision system was developed for grade detection of glioma from conventional T2 weighted MRI modality.The quantitative performance analysis using ROC curve showed 99.03% accuracy for distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant glioma. The developed brain texture analysis techniques can improve the physician’s ability to detect and analyse pathologies leading to a more reliable diagnosis and treatment of disease. The segmented tumors were also used for volumetric modelling of tumors which can provide an idea of the growth rate of tumor; this can be used for assessing response to therapy and patient prognosis.

Faster techniques to evolve wavelet coefficients for better fingerprint image compression

In this article, techniques have been presented for faster evolution of wavelet lifting coefficients for fingerprint image compression (FIC). In addition to increasing the computational speed by 81.35%, the coefficients performed much better than the reported coefficients in literature. Generally, full-size images are used for evolving wavelet coefficients, which is time consuming. To overcome this, in this work, wavelets were evolved with resized, cropped, resized-average and cropped-average images. On comparing the peak- signal-to-noise-ratios (PSNR) offered by the evolved wavelets, it was found that the cropped images excelled the resized images and is in par with the results reported till date. Wavelet lifting coefficients evolved from an average of four 256 256 centre-cropped images took less than 1/5th the evolution time reported in literature. It produced an improvement of 1.009 dB in average PSNR. Improvement in average PSNR was observed for other compression ratios (CR) and degraded images as well. The proposed technique gave better PSNR for various bit rates, with set partitioning in hierarchical trees (SPIHT) coder. These coefficients performed well with other fingerprint databases as well.

Development of Directionally Adaptive Techniques for Single Image Super-Resolution

Super Resolution problem is an inverse problem and refers to the process of producing a High resolution (HR) image, making use of one or more Low Resolution (LR) observations. It includes up sampling the image, thereby, increasing the maximum spatial frequency and removing degradations that arise during the image capture namely aliasing and blurring. The work presented in this thesis is based on learning based single image super-resolution. In learning based super-resolution algorithms, a training set or database of available HR images are used to construct the HR image of an image captured using a LR camera. In the training set, images are stored as patches or coefficients of feature representations like wavelet transform, DCT, etc. Single frame image super-resolution can be used in applications where database of HR images are available. The advantage of this method is that by skilfully creating a database of suitable training images, one can improve the quality of the super-resolved image. A new super resolution method based on wavelet transform is developed and it is better than conventional wavelet transform based methods and standard interpolation methods. Super-resolution techniques based on skewed anisotropic transform called directionlet transform are developed to convert a low resolution image which is of small size into a high resolution image of large size. Super-resolution algorithm not only increases the size, but also reduces the degradations occurred during the process of capturing image. This method outperforms the standard interpolation methods and the wavelet methods, both visually and in terms of SNR values. Artifacts like aliasing and ringing effects are also eliminated in this method. The super-resolution methods are implemented using, both critically sampled and over sampled directionlets. The conventional directionlet transform is computationally complex. Hence lifting scheme is used for implementation of directionlets. The new single image super-resolution method based on lifting scheme reduces computational complexity and thereby reduces computation time. The quality of the super resolved image depends on the type of wavelet basis used. A study is conducted to find the effect of different wavelets on the single image super-resolution method. Finally this new method implemented on grey images is extended to colour images and noisy images

Development of 2-D MRT based Image Compression Techniques

The thesis explores the area of still image compression. The image compression techniques can be broadly classified into lossless and lossy compression. The most common lossy compression techniques are based on Transform coding, Vector Quantization and Fractals. Transform coding is the simplest of the above and generally employs reversible transforms like, DCT, DWT, etc. Mapped Real Transform (MRT) is an evolving integer transform, based on real additions alone. The present research work aims at developing new image compression techniques based on MRT. Most of the transform coding techniques employ fixed block size image segmentation, usually 8×8. Hence, a fixed block size transform coding is implemented using MRT and the merits and demerits are analyzed for both 8×8 and 4×4 blocks. The N2 unique MRT coefficients, for each block, are computed using templates. Considering the merits and demerits of fixed block size transform coding techniques, a hybrid form of these techniques is implemented to improve the performance of compression. The performance of the hybrid coder is found to be better compared to the fixed block size coders. Thus, if the block size is made adaptive, the performance can be further improved. In adaptive block size coding, the block size may vary from the size of the image to 2×2. Hence, the computation of MRT using templates is impractical due to memory requirements. So, an adaptive transform coder based on Unique MRT (UMRT), a compact form of MRT, is implemented to get better performance in terms of PSNR and HVS The suitability of MRT in vector quantization of images is then experimented. The UMRT based Classified Vector Quantization (CVQ) is implemented subsequently. The edges in the images are identified and classified by employing a UMRT based criteria. Based on the above experiments, a new technique named “MRT based Adaptive Transform Coder with Classified Vector Quantization (MATC-CVQ)”is developed. Its performance is evaluated and compared against existing techniques. A comparison with standard JPEG & the well-known Shapiro’s Embedded Zero-tree Wavelet (EZW) is done and found that the proposed technique gives better performance for majority of images