Survey of denoising,segmentation and classification of magnetic resonance imaging for prostate cancer

Multimedia Tools and Applications - Prostate cancer (PCa) has become the second most dreadful cancer in men after lung cancer. Traditional approaches used for treatment of PCa were manual, time...     

Quote examiner: verifying quoted images using web-based text similarity

Over the last few years, there has been a rapid growth in digital data. Images with quotes are spreading virally through online social media platforms. Misquotes found online often spread like a forest fire through social media, which highlights the lack of responsibility of the web users when circulating poorly cited quotes. Thus, it is important to authenticate the content contained in the images being circulated online. So, there is a need to retrieve the information within such textual images to verify quotes before its usage in order to differentiate a fake or misquote from an authentic one. Optical Character Recognition (OCR) is used in this paper, for converting textual images into readable text format, but none of the OCR tools are perfect in extracting information from the images accurately. In this paper, a method of post-processing on the retrieved text to improve the accuracy of the detected text from images has been proposed. Google Cloud Vision has been used for recognizing text from images. It has also been observed that using post-processing on the extracted text improved the accuracy of text recognition by 3.5% approximately. A web-based text similarity approach (URLs and domain name) has been used to examine the authenticity of the content of the quoted images. Approximately, 96.26% accuracy has been achieved in classifying quoted images as verified or misquoted. Also, a ground truth dataset of authentic site names has been created. In this research, images with quotes by famous celebrities and global leaders have been used. A comparative analysis has been performed to show the effectiveness of our proposed algorithm.

     

Characterization of PA-12 specimens fabricated by projection sintering at various sintering parameters

Large area projection sintering (LAPS) promises to be a new method in the field of additive manufacturing. Developed in the Mechanical Engineering Department, University of South Florida, LAPS uses long exposure times over a broad area of powder to fuse into dense, reproducible materials. In contrast, LS, a common powder-based additive manufacturing, uses a focused beam of light scanned quickly over the material. Local regions of concentrated high-energy bursts of light lead to higher peak temperatures and differing cooling dynamics and overall crystallinity. The mechanical properties of laser sintered specimens suffer because of uneven particle fusion. LAPS offers the capacity to fine-tune fusion properties through enhanced thermodynamic control of the heating and cooling profiles for sintering. Further research is required to identify the relationship between LAPS build settings and part properties to enable the fabrication of custom parts with desired properties. This study examines the influence of LAPS sintering parameters on chemical structures, crystallinity, mechanical, and thermal properties of polyamide-12 specimens using powder X-ray diffraction, Fourier transform infrared spectroscopy, differential scanning calorimetry, small-angle X-ray scattering, scanning electron microscopy, and microhardness testing. It was observed that higher crystallinity was imparted to specimens that were sintered for a shorter time and vice versa.     

An Authentication Based Approach for Prevention of Spectrum Sensing Data Falsification Attacks in Cognitive Radio Network

Wireless Personal Communications - This is the era of Intelligent Cognitive Radio Network (CRN) technology that provides the available spectrum with efficient utilization. Cognitive Radio (CR)...     

Comparative Study of the Different Variants of the DV-Hop Based Node Localization Algorithms for Wireless Sensor Networks

Wireless Personal Communications - Node localization is one of the essential services where sensor nodes in the wireless sensor network collaborate to provide location information of sensor nodes...     

Shear wave propagation in vertically heterogeneous viscoelastic layer over a micropolar elastic half-space

This article deals with the propagation of SH-wave in a vertically heterogeneous viscoelastic layer lying over a micropolar elastic half-space. Dispersion and damping equations are obtained analytically in closed form. Phase and damped velocities are computed numerically and depicted by means of a graph to exhibit the substantial effect of heterogeneity, viscoelasticity (internal friction), and micropolar parameter. As a special case of the problem, it is found that deduced dispersion relation is well in agreement to the classical-Love wave equation and damping equation vanishes identically for the isotropic case. Influence of micropolarity present in the medium of half-space is highlighted through comparative study.     

Effect of undulation on SH-wave propagation in corrugated magneto-elastic transversely isotropic layer

This article delves to study the effect of corrugated boundary surfaces on the propagation of horizontally-polarized shear waves (SH-waves) in a magnetoelastic transversely isotropic layer under a hydrostatic state of stress lying over an elastic half-space under gravity. A dispersion equation has been derived in closed-form and is found to be in good agreement to the classical Love-wave equation. The effect of magnetoelasticity, hydrostatic state of stress, gravity, corrugation, position parameter, and undulation on the phase velocity of the SH-wave has been identified. Numerical computation along with graphical demonstration has been carried out for cadmium, magnesium, and zinc materials of hexagonal symmetry to highlight some significant facts.     

Managing the degree of exfoliation and number of graphene layers using probe sonication approach

We have demonstrated a fast, versatile, and scalable approach to synthesize high-quality few layer graphene sheets with low defect ratio and high crystallinity produced from exfoliation of graphite flakes in DMF by using probe sonication. The effect of sonication time on degree of exfoliation and number of graphene layers has been fully investigated. The degree of exfoliation of graphene sheets as a function of sonication time has been successfully analyzed by XRD, UV-Vis spectroscopy, TEM, and BET studies. The morphological changes at different sonication times have also been observed by SEM. A structural and defect characterization of graphene sheets has been discussed in detail by Raman spectroscopic technique. The shift in position of 2D Raman band and its de-convolution provided information about formation of multi to few layer graphene sheets with sonication. Moreover, Raman results are highly consistent with TEM studies as per number of graphene layers is concerned.     

Empirical validation of object-oriented metrics for predicting fault proneness models

Empirical validation of software metrics used to predict software quality attributes is important to ensure their practical relevance in software organizations. The aim of this work is to find the relation of object-oriented (OO) metrics with fault proneness at different severity levels of faults. For this purpose, different prediction models have been developed using regression and machine learning methods. We evaluate and compare the performance of these methods to find which method performs better at different severity levels of faults and empirically validate OO metrics given by Chidamber and Kemerer. The results of the empirical study are based on public domain NASA data set. The performance of the predicted models was evaluated using Receiver Operating Characteristic (ROC) analysis. The results show that the area under the curve (measured from the ROC analysis) of models predicted using high severity faults is low as compared with the area under the curve of the model predicted with respect to medium and low severity faults. However, the number of faults in the classes correctly classified by predicted models with respect to high severity faults is not low. This study also shows that the performance of machine learning methods is better than logistic regression method with respect to all the severities of faults. Based on the results, it is reasonable to claim that models targeted at different severity levels of faults could help for planning and executing testing by focusing resources on fault-prone parts of the design and code that are likely to cause serious failures.