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Worldwide, glaucoma is a type of eye disease, which causes loss of vision by damaging optic nerves within the eye. Available glaucoma detection techniques are less accurate. This paper proposed a c...

In this paper, a novel single-feed dual-band circularly polarized half E-shaped microstrip patch antenna (HESMSA) array is proposed and experimentally studied. It consists of a 4 × 4 array realized...

This article presents the new nanoplasmonic branchline coupler (BLC) by using an edge coupled coplanar waveguide (CPW) with a finite length suspended backed conductor. The proposed coupler is designed by using the theoretical transmission line (TL) method, which is established based on the basic characteristics of the related CPW structures. For this purpose, the normalized propagation constant, propagation length, and characteristic impedance of the conductor backed coplanar waveguide (CBCPW) structure are studied and analyzed by applying a fullwave analysis using CAD simulation software tool CST microwave studio suite. Mainly, the coupling coefficient of the BLC can be controlled by changing the length of the backed conductor. In this article, the BLC is designed and analyzed by using a finite length backed conductor based on the theoretical TL method at a 3-dB coupling coefficient. This newly designed coupler can operate at two arbitrary optical frequency bands (O-band and L-band). Hence, it allows us to exchange many other couplers with their narrow bandwidths using this branchline coupler for subwavelength nanoscale wireless network applications and photonic integrated circuits (PICs).

The unnatural activities of brain due to seizure events are analysed by electroencephalogram (EEG) signals which are captured from the brain. In this work, a methodology is proposed to classify the seizure EEG signals. In the proposed method, a novel sparse spectrum based empirical wavelet transform (SS-EWT) is applied to decompose the EEG signal into coefficients. From the obtained SS-EWT coefficients, the cross-information potential and normalised energy are extracted as features. Then these features are ranked using the RELIEFF method to obtain significant features. After ranking, these features are fed into the k-nearest neighbour (k-NN) classifier to classify EEG signals corresponding to different brain activities. In this work, the first classification problem is the classification of the seizure (S), seizure-free (F), and normal (Z) EEG signals in which obtained classification accuracy (Acc) is 96.67 % . The second classification problem is the classification of S and Z EEG signals in which 100 % Acc is achieved by the proposed method.

In recent times, Social Networks became ubiquitous in our daily life. These networks can be used to obtain high-volume data related to any given event or topic. Twitter, one of the famous networks, became the wealthy supply of discussed topics. The users on Twitter express their point of views or opinions by tweets concerning different topics in a variety of fields. The data generated on Twitter can be extracted via Sentiment Analysis. In this proposed system, an artificial intelligence concept (NLP) is applied that understands the sentiment of extracted data and polarizes them into positive, negative, or neutral. These polarized data are then grouped based on their similarities and differences through a grouping algorithm. The data obtained is then represented graphically in form of a bar-chart. The information obtained from this text, an analysis based on emotions is used to create daily trends related to the event which is used to identify emerging patterns of this data or the current user sentiments related to the given event or topic

In this paper, a polymer matrix composite (PMC) which is widely used in Aerospace, automobile and other engineering applications, which is reinforced with fly ash is taken in different weight ratios for manufacturing of Fly wheel cup. The principle target of this paper is to determine the behavior of fly wheel cup which is manufactured by deep drawing process using polymer matrix composite (PMC). In order to find the behavior of finite element analysis technique is used to find the deformation and equivalent elastic strain. Deformation of Fly Wheel Cup was found in the range of 0.0017 m – 0.008 m, and the level of equivalent elastic strain was found in the range 0.0087 – 0.04. And compared the results with Mild Steel, which is presently in use.

In the present work, EEG signals of different classes are analysed in tunable-Q wavelet transform (TQWT) framework. The TQWT decomposes the EEG signals into subbands and arrange them into decreasing order of frequency. The nonlinearity of the EEG signals is assessed by computing the centered correntropy (CCE) from the obtained subbands, which is further used as a feature for classifying the different categories of EEG signals. In this work, EEG signals are categorised in two different classification problems. First category is seizure free and seizure (NF-S) classes, and the other one is the normal, seizure free and seizure (ZO-NF-S) classes. Features obtained from the EEG signals of these classes are fed to the input of three different classifiers namely, random forest classifier (RF), multilayer perceptron (MLP) classifier, and logistic regression (LR) classifier. For NF-S classes, we achieved 98.3% classification accuracy with RF classifier for signal length of 1000 samples. The obtained accuracy of classification is 98.2% for ZO-NF-S classes using MLP classifier when features are extracted from signal length of 1000 samples.

Background: Aortic root dimension varies on the basis of age, body surface area, ethnicity, and in pathological states such as bicuspid aortic valve and connective tissue disorders. Aim: This observational study has been done to determine aortic root dimension in normal Indian population. In this study, aortic root dimensions were measured by two-dimensional (2D) echocardiography in parasternal long axis (PLAX) view by inner edge to inner edge method. Materials and Methods: Clinically normal Indian population was prospectively recruited. All participants were asymptomatic. All echocardiographic measurements were done twice and image acquired by single observer on the same machine. Aortic root dimensions measurements were done with 2D echo in PLAX view at 4-level aortic annulus, sinus of Valsalva, sinotubular junction, and proximal ascending aorta. Results: Aortic annulus does not show any significant correlation with age. Age parameter shows a significant correlation with the ascending aorta and sinus, sino-tubular (ST) junction. Weight parameter shows a significant correlation with annulus, sinus, and ST junction. Height parameter shows a significant correlation with all aortic dimensions. Conclusion: Indian patient has different body surface area than the Western population; therefore, there is a need of standard normal aortogram dimension for normal Indian population.

Background: Thyroid abnormalities are common in chronic heart failure. Severity of heart failure rises by several fold in patients with thyroid dysfunction. Objectives: The purpose of this prospective study is to determine the correlation between low T3 syndrome and chronic heart failure with 2D echocardiography features & predicting the severity of chronic heart failure. Methods: In this descriptive, prospective cross sectional study, all patients who presented to the department of medicine with chronic heart failure during this study period of 12 months from January 2010-December 2011 in K.R.Hospital, Mysore were included. Patients were grouped into Low T3 chronic heart failure, hypothyroid chronic heart failure and chronic heart failure. Results: Mean age of low T3 chronic heart failure patients was higher than other two groups [60.50±6.15(SD) years, Systolic dysfunction on 2D Echo was more in low T3 dilated cardiomyopathy (20%), Diastolic dysfunction on 2D Echo was more in low T3 dilated cardiomyopathy group (30%), Pericardial effusion was seen in more number of patients with low T3 dilated cardiomyopathy (10%). Global hypokinesia was seen in more number of patients with low T3 dilated cardiomyopathy (30%). Segmental hypokinesia was seen in more number of patients with low T3 dilated cardiomyopathy (3%). Mean ejection fraction was seen in more number of patients with low T3 dilated cardiomyopathy [36.78±5.08 (SD) %]. Mean ejection fraction was lower in low T3 dilated cardiomyopathy [34.8±3.293 (SD) %].The high pulmonary artery systolic pressure was seen in more number of patients in low T3 dilated cardiomyopathy (70%). Conclusion: There is significant percentage of chronic heart failure patients having low T3 alone as biochemical parameter. It is important to recognize patients with chronic heart failure as it is associated with increased severity of heart failure. Asian Journal of Medical Science, Volume-5(3) 2014: 35-39 http://dx.doi.org/10.3126/ajms.v5i3.9522