Compact Frequency and Bandwidth Reconfigurable Microwave Filter

This paper details the construction and working of a compound reconfigurable filter capable of frequency and bandwidth reconfiguration in the frequency range from 2 to 3 GHz. The switching between frequency and bandwidth reconfiguration is inherited by two PIN diodes. Bandwidth tuning is facilitated by tuning two transmission zeros individually using varactor diodes, giving flexibility in reconfiguring the upper and lower pass edges. The two transmission zeros are obtained using simple concentric square loop resonators. The maximum bandwidth obtained is 1.5 times the minimum bandwidth offered by the filter. Hence the filter can be used for dynamic bandwidth allocation. This prototype is fabricated and validated in real-time. The simulated and measured results are analogous to each other.

     

Computational completeness of simple semi-conditional insertion–deletion systems of degree (2,1)

Natural Computing - Insertion–deletion (or ins–del for short) systems are simple models of bio-inspired computing. They are well studied in formal language theory, especially regarding...     

An automatic segmentation algorithm for 3D cell cluster splitting using volumetric confocal images

With the rapid advance of three-dimensional (3D) confocal imaging technology, more and more 3D cellular images will be available. Segmentation of intact cells is a critical task in automated image analysis and quantification of cellular microscopic images. One of the major complications in the automatic segmentation of cellular images arises due to the fact that cells are often closely clustered. Several algorithms are proposed for segmenting cell clusters but most of them are 2D based. In other words, these algorithms are designed to segment 2D cell clusters from a single image. Given 2D segmentation methods developed, they can certainly be applied to each image slice with the 3D cellular volume to obtain the segmented cell clusters. Apparently, in such case, the 3D depth information with the volumetric images is not really used. Often, 3D reconstruction is conducted after the individualized segmentation to build the 3D cellular models from segmented 2D cellular contours. Such 2D native process is not appropriate as stacking of individually segmented 2D cells or nuclei do not necessarily form the correct and complete 3D cells or nuclei in 3D. This paper proposes a novel and efficient 3D cluster splitting algorithm based on concavity analysis and interslice spatial coherence. We have taken the advantage of using the 3D boundary points detected using higher order statistics as an input contour for performing the 3D cluster splitting algorithm. The idea is to separate the touching or overlapping cells or nuclei in a 3D native way. Experimental results show the efficiency of our algorithm for 3D microscopic cellular images.     

On embedding subclasses of height-balanced trees in hypercubes

A height-balanced tree is a rooted binary tree T in which for every vertex v∈V(T), the heights of the subtrees, rooted at the left and right child of v, differ by at most one; this difference is called the balance factor of v. These trees are extensively used as data structures for sorting and searching. We embed several subclasses of height-balanced trees of height h in Q_h+1 under certain conditions. In particular, if a tree T is such that the balance factor of every vertex in the first three levels is arbitrary (0 or 1) and the balance factor of every other vertex is zero, then we prove that T is embeddable in its optimal hypercube with dilation 1 or 2 according to whether it is balanced or not.     

Prediction of Cardiovascular Disease Using Machine Learning Technique—A Modern Approach

Cardio Vascular disease (CVD), involving the heart and blood vessels is one of the most leading causes of death throughout the world. There are several risk factors for causing heart diseases like sedentary lifestyle, unhealthy diet, obesity, diabetes, hypertension, smoking and consumption of alcohol, stress, hereditary factory etc. Predicting cardiovascular disease and improving and treating the risk factors at an early stage are of paramount importance to save the precious life of a human being. At present, the highly stressful life with bad lifestyle activities causes heart disease at a very young age. The main aim of this research is to predict the premature heart disease based on machine learning algorithms. This paper deals with a novel approach using the machine learning algorithm for predicting the cardiovascular disease at the premature stage itself. Support Vector Machine (SVM) is used for segregating the CVD patients based on their symptoms and medical observation. The experimentation results by using the proposed method will facilitate the medical practitioners to provide suitable treatment for the patients on time. A sophisticated model has been developed with the current approach to examine the various stages of CVD and the performance metrics used have given effective and fruitful results as compared to other machine learning techniques.     

Virtual reality prototyping of bio-molecules

Modelling, sharing and transmission of three-dimensional (3D) graphics data of biomolecules are essential in many bio visualization tasks from collaborative research and education to molecular simulation and drug discovery. In the current paper, modelling and representing of bio-molecular structure for virtual and physical rapid prototyping is presented. Our aim is to devise a uniform solution for visualizing, browsing, interacting and prototyping of bio-molecules in various environments including internet, immersive virtual reality (VR), and rapid manufacturing. To do so, we use non uniform rational B-spline surfaces (NURBS) to represent protein secondary structure and surface structure. NURBS protein structures are then tessellated to form bio-molecular graphics models. Their triangular mesh representation is next extracted from their scene graph. A geometric optimization process is followed to make data compatible for their formatting in compact and consistent VR standard to support protein internet browsing, protein VR visualization, protein 3D rapid prototyping and crystal sub-surface laser engraving.     

Adaptive-weighted cubic B-spline using lookup tables for fast and efficient axial resampling of 3D confocal microscopy images

Confocal laser scanning microscopy has become a most powerful tool to visualize and analyze the dynamic behavior of cellular molecules. Photobleaching of fluorochromes is a major problem with confocal image acquisition that will lead to intensity attenuation. Photobleaching effect can be reduced by optimizing the collection efficiency of the confocal image by fast z-scanning. However, such images suffer from distortions, particularly in the z dimension, which causes disparities in the x, y, and z directions of the voxels with the original image stacks. As a result, reliable segmentation and feature extraction of these images may be difficult or even impossible. Image interpolation is especially needed for the correction of undersampling artifact in the axial plane of three-dimensional images generated by a confocal microscope to obtain cubic voxels. In this work, we present an adaptive cubic B-spline-based interpolation with the aid of lookup tables by deriving adaptive weights based on local gradients for the sampling nodes in the interpolation formulae. Thus, the proposed method enhances the axial resolution of confocal images by improving the accuracy of the interpolated value simultaneously with great reduction in computational cost. Numerical experimental results confirm the effectiveness of the proposed interpolation approach and demonstrate its superiority both in terms of accuracy and speed compared to other interpolation algorithms.