Pattern Analysis Techniques for the Recognition of Unconstrained Handwritten Malayalam Character Images

The objective of the study is to develop a hand written character recognition system that could recognisze all the characters in the mordern script of malayalam language at a high recognition rate

Median filtering: structure analysis and application

Median filtering is a simple digital non—linear signal smoothing operation in which median of the samples in a sliding window replaces the sample at the middle of the window. The resulting filtered sequence tends to follow polynomial trends in the original sample sequence. Median filter preserves signal edges while filtering out impulses. Due to this property, median filtering is finding applications in many areas of image and speech processing. Though median filtering is simple to realise digitally, its properties are not easily analysed with standard analysis techniques,

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.

Development of a Fully Automated Image Analysis Method for High Density cDNA and array CGH Microarray Based Genomic Studies

In this thesis, different techniques for image analysis of high density microarrays have been investigated. Most of the existing image analysis techniques require prior knowledge of image specific parameters and direct user intervention for microarray image quantification. The objective of this research work was to develop of a fully automated image analysis method capable of accurately quantifying the intensity information from high density microarrays images. The method should be robust against noise and contaminations that commonly occur in different stages of microarray development.

Spatial and Temporal Variability of some Meteorological Parameters over Tropical Indian Ocean

The objective of the present study is to understand the spatial and temporal variability of sea surface temperature(SST), precipitable water, zonal and meridional components of wind stress over the tropical Indian Ocean to understand the different scales of variability of these features of Indian Ocean. Empirical Orthogonal Function (EOF) and wavelet analysis techniques are utilized to understand the standing oscillations and multi scale oscillations respectively. The study has been carried out over Indian Ocean and South Indian Ocean. For the present study, NCEP/NCAR(National Center for Environmental Prediction National Center for Atmospheric Research) reanalyzed daily fields of sea surface temperature, zonal and meridional surface wind components and precipitable water amount during 1960-1998 are used. The principle of EOF analysis and the methodology used for the analysis of spatial and temporal variance modes.

Xanthate Accelerators for Low Temperature Curing of Natural Rubber

ABSTRACT: Zinc salts of ethyl, isopropyl, and butyl xanthates were prepared in the laboratory. They were purified by reprecipitation and were characterized by IR, NMR, and thermogravimetric analysis techniques. The melting points were also determined. The rubber compounds with different xanthate accelerators were cured at temperatures from 30 to 150°C. The sheets were molded and properties such as tensile strength, tear strength, crosslink density, elongation at break, and modulus at 300% elongation were evaluated. The properties showed that all three xanthate accelerators are effective for room temperature curing.

Selected Cardiovascular Studies Based on Photoplethysmography Technique

Photoplethysmography (PPG) is a simple and inexpensive optical technique that can be used to detect blood volume changes in the microvascular bed of tissues. There has been a resurgence of interest in the technique in recent years, driven by the demand for low cost, simple and portable technology for the primary care and community based clinical settings and the wide availability of low cost and small semiconductor components, and the advancement of computer-based pulse wave analysis techniques. The present research work deals with the design of a PPG sensor for recording the blood volume pulse signals and carry out selected cardiovascular studies based on these signals. The interaction of light with tissue, early and recent history of PPG, instrumentation, measurement protocol and pulse wave analysis are also discussed in this study. The effect of aging, mild cold exposure, and variation in the body posture on the PPG signal have been experimentally studied.

Data Security in Fault Tolerant Hard Real Time Systems- Use of Time Dependant Multiple Random Cipher Code

The present research problem is to study the existing encryption methods and to develop a new technique which is performance wise superior to other existing techniques and at the same time can be very well incorporated in the communication channels of Fault Tolerant Hard Real time systems along with existing Error Checking / Error Correcting codes, so that the intention of eaves dropping can be defeated. There are many encryption methods available now. Each method has got it's own merits and demerits. Similarly, many crypt analysis techniques which adversaries use are also available.

Geochemistry of transition, non-transition and rare-earth elements in the surficial sediments of continental shelf of Kerala and an annex to the Cochin estuarine system

The metals present in the surface sediments have high demand on a global perspective, and the main reservoir of these elements is believed to be the ocean floor. A lot of studies on metals are going on throughout the world for its quantification and exploitation. Even though, some preliminary attempts have been made in selected areas for the quantitative study of metals in the western continental shelf of India, no comprehensive work has been reported so far. The importance of this study also lies on the fact that there has not been a proper evaluation of the impact of the Great Tsunami of 2004 on the coastal areas of the south India. In View of this, an attempt has been made to address the seasonal distribution, behavior and mechanisms which control the deposition of metals in the sediments of the western continental shelf and Cochin Estuary, an annex to this coastal marine region.Surface sediment samples were collected seasonally from two subenvironemnts of southwest coast of India, (continental shelf of Kerala and Cochin estuarine system), to estimate the seasonal distribution and geochemical behavior of non-transition, transition, rare-earth elements, Th and U. Bottom water samples were also taken from each station, and analysed for temperature, salinity and dissolved oxygen, hence the response of redox sensitive elements to oxygen minimum zone can be addressed. In addition, other sedimentary parameters such as sand, silt, clay fractions, CaCO3 and organic carbon content were also estimated to evaluate the control factors on level of metals present in the sediment. The study used different environmental data analysis techniques to evaluate the distribution and behavior of elements during different seasons. This includes environmental parameters such as elemental normalisation, enrichment factor, element excess, cerium and europium anomalies and authigenic uranium.

Photometric and fundamental plane studies of galaxies

Determining the morphological parameters that describe galaxies has always been a challenging task. The studies on the correlations between different photometric as well as spectroscopic parameters of the galaxies help in understanding their structure, properties of the stars and gas which constitute the galaxy, the various physical and chemical processes which determine the properties, and galaxy formation and evolution. In the last few decades, the advent of Charge Coupled Devices (CCDs) and near infrared arrays ha\·e provided quick and reliable digitized data acquisition, in the optical and near infrared bands. This has provided an avalanche of data, which can be processed using sophisticated image analysis techniques to obtain information about the morphology of galaxies. The photometric analysis performed in this thesis involve the extraction of structural parameters of early type gala.xies imaged in the near infrared K (2.2ttm) band, obtaining correlations between these, parameters and using them to constrain the large scale properties of galaxi,~s.

Investigations On A Microstrip Excited Rectangular Dielectric Resonator Antenna

The thesis relates to the investigations carried out on Rectangular Dielectric Resonator Antenna configurations suitable for Mobile Communication applications. The main objectives of the research are to: - numerically compute the radiation characteristics of a Rectangular DRA - identify the resonant modes - validate the numerically predicted data through simulation and experiment 0 ascertain the influence of the geometrical and material parameters upon the radiation behaviour of the antenna ° develop compact Rectangular DRA configurations suitable for Mobile Communication applications Although approximate methods exist to compute the resonant frequency of Rectangular DRA’s, no rigorous analysis techniques have been developed so far to evaluate the resonant modes. In this thesis a 3D-FDTD (Finite Difference Time Domain) Modeller is developed using MATLAB® for the numerical computation of the radiation characteristics of the Rectangular DRA. The F DTD method is a powerful yet simple algorithm that involves the discretimtion and solution of the derivative form of Maxwell’s curl equations in the time domain.

Classification of Mammograms and DW T based Detection of Microcalcification

Cancer treatment is most effective when it is detected early and the progress in treatment will be closely related to the ability to reduce the proportion of misses in the cancer detection task. The effectiveness of algorithms for detecting cancers can be greatly increased if these algorithms work synergistically with those for characterizing normal mammograms. This research work combines computerized image analysis techniques and neural networks to separate out some fraction of the normal mammograms with extremely high reliability, based on normal tissue identification and removal. The presence of clustered microcalcifications is one of the most important and sometimes the only sign of cancer on a mammogram. 60% to 70% of non-palpable breast carcinoma demonstrates microcalcifications on mammograms [44], [45], [46].WT based techniques are applied on the remaining mammograms, those are obviously abnormal, to detect possible microcalcifications. The goal of this work is to improve the detection performance and throughput of screening-mammography, thus providing a ‘second opinion ‘ to the radiologists. The state-of- the- art DWT computation algorithms are not suitable for practical applications with memory and delay constraints, as it is not a block transfonn. Hence in this work, the development of a Block DWT (BDWT) computational structure having low processing memory requirement has also been taken up.

Characterization and FTIR spectral studies of human urinary stones from Southern India

Urinary stones resected from urinary bladders of patients hailing from Kollam district of Kerala State, India were analyzed by SEM, XRD and by thermal analysis techniques. The analytical results indicate that, stones have different composition, i.e., calcium phosphate, calcium phosphate hydroxide and sodium calcium carbonate. Infrared spectral studies also reveal the presence of phosphates or carbonates in these samples. Further, IR spectral investigations have revealed that amorphous carbonated species are occupied in PO4 sites in calcium phosphate type stone and OH sites in calcium phosphate hydroxide sample. Thermal studies of these samples also reveal that, carbon dioxide is released from carbonated samples upon heating which is related to amount of carbon content and bond strength. Crystals with defects and irregular morphology are grown inside the urinary bladder due to variation in crystal growth conditions

Speech Analysis using Modern Techniques of Nonlinear Dynamics

Medical fields requires fast, simple and noninvasive methods of diagnostic techniques. Several methods are available and possible because of the growth of technology that provides the necessary means of collecting and processing signals. The present thesis details the work done in the field of voice signals. New methods of analysis have been developed to understand the complexity of voice signals, such as nonlinear dynamics aiming at the exploration of voice signals dynamic nature. The purpose of this thesis is to characterize complexities of pathological voice from healthy signals and to differentiate stuttering signals from healthy signals. Efficiency of various acoustic as well as non linear time series methods are analysed. Three groups of samples are used, one from healthy individuals, subjects with vocal pathologies and stuttering subjects. Individual vowels/ and a continuous speech data for the utterance of the sentence "iruvarum changatimaranu" the meaning in English is "Both are good friends" from Malayalam language are recorded using a microphone . The recorded audio are converted to digital signals and are subjected to analysis.Acoustic perturbation methods like fundamental frequency (FO), jitter, shimmer, Zero Crossing Rate(ZCR) were carried out and non linear measures like maximum lyapunov exponent(Lamda max), correlation dimension (D2), Kolmogorov exponent(K2), and a new measure of entropy viz., Permutation entropy (PE) are evaluated for all three groups of the subjects. Permutation Entropy is a nonlinear complexity measure which can efficiently distinguish regular and complex nature of any signal and extract information about the change in dynamics of the process by indicating sudden change in its value. The results shows that nonlinear dynamical methods seem to be a suitable technique for voice signal analysis, due to the chaotic component of the human voice. Permutation entropy is well suited due to its sensitivity to uncertainties, since the pathologies are characterized by an increase in the signal complexity and unpredictability. Pathological groups have higher entropy values compared to the normal group. The stuttering signals have lower entropy values compared to the normal signals.PE is effective in charaterising the level of improvement after two weeks of speech therapy in the case of stuttering subjects. PE is also effective in characterizing the dynamical difference between healthy and pathological subjects. This suggests that PE can improve and complement the recent voice analysis methods available for clinicians. The work establishes the application of the simple, inexpensive and fast algorithm of PE for diagnosis in vocal disorders and stuttering subjects.