Dielectric properties of certain biological materials at microwave frequencies.

In the medical field, microwaves play a larger role for treatment than diagnosis. For the detection of diseases by microwave methods, it is essential to know the dielectric properties of biological materials. For the present study, a cavity perturbation technique was employed to determine the dielectric properties of these materials. Rectangular cavity resonators were used to measure the complex permittivity of human bile, bile stones, gastric juice and saliva. The measurements were carried out in the S and J bands. It is observed that normal and infected bile have different dielectric constant and loss tangent. Dielectric constant of infected bile and gastric juice varies from patient to patient. Detection and extraction of bile stone with possible method of treatment is also discussed.

Dielectric properties of ionomers at microwave frequencies

Ionic polymers (ionomers) with interesting characteristics are emerging as important commercial polymers. Ionomers have the unique ability to behave as cross-linked materials at ambient temperatures and to melt and flow at elevated temperatures like thermoplastics. The complex permittivity and conductivity of a class of ionomers at microwave frequencies are determined using the cavity perturbation technique and the results are presented.

Process profiling using frequencies of system calls

In this paper we discuss our research in developing general and systematic method for anomaly detection. The key ideas are to represent normal program behaviour using system call frequencies and to incorporate probabilistic techniques for classification to detect anomalies and intrusions. Using experiments on the sendmail system call data, we demonstrate that we can construct concise and accurate classifiers to detect anomalies. We provide an overview of the approach that we have implemented

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.

Comments on Simple and Accurate Formula for the Resonant Frequency of the Equilateral Triangular Microstrip Patch Antenna

Antennas and Propagation, IEEE Transactions on,VOL 48,issue 4,pp 636