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The main aim of this study is to identify the shape variation of lungs which are affected by COVID-19 with the help of Hough Transform and Radon transform features. The dataset is taken from the kaggle open source. A total of 200 image samples are obtained using G-Power, by fixing the parameters such as effect size (d) is given 0.57, standard error rate is given as 0.05 and power is given as 0.80 respectively. These segmented images are used in this analysis for differentiation of normal and COVID subjects using the Radon transform for the shape features extraction. By using SPSS software the significance of the best algorithm is obtained. Shape features which are extracted by Hough transform and Radon transform algorithms are classified by using KNN and SVM classifiers. From the obtained results, the feature values of Radon moments are observed to be statistically significant for M15,7 (p=0.03) compared to Hough transformed moments M10,8 (p=0.06). The mean and standard values of M15,7 for inter (normal and COVID) subjects are (0.51±0.24, 0.10±0.05) for Radon normalised features. The mean and standard deviation values of M10,8 for inter subjects are (0.21±0.04, 0.18±0.03) for Hough normalised features. From the above mentioned values, it is clear that the COVID subjects have loss in shape of lungs due to abnormality. The accuracy and F1 score values of Radon transform are 100 and 100 using KNN and SVM classifiers respectively. Conclusion: Therefore, from the above analysis it is clearly observed that Radon transform provides better classification between normal and COVID subjects when compared to Hough transform. [ABSTRACT FROM AUTHOR]

The aim of this analysis is to estimate the texture deformation in lung CT images caused by COVID 19. For this analysis, a total of 176 sample sizes are obtained based on the given parameters. The input images are gathered from Github.org. This analysis comes in for the comparison between Local Binary Pattern and Gradient Descriptor Pattern and Gradient Local Ternary Pattern to analyze the textural changes in Computed Tomography (CT) lung Scans. To classify acquired characteristics, a K-NN and NN classifier are used. The feature values obtained using Gradient local ternary pattern are found to be statistically significant(p<0.05) compared to Local binary pattern and Gradient directional pattern. The significant values of Gradient local ternary pattern are GLTP86, GLTP70, GLTP82with mean values of normal (0.26, 0.19, 0.16) and COVID (0.12, 0.21, 0.13). GLTP acquired Area under curve(0.960), classification accuracy (0.838), F1-score(0.831), precision(0.848), and recall(0.838) are obtained using K-NN classifier. The most significant feature value of LBP and GDP are L11andG94which shows significant ability in differentiating normal(9.46) and COVID(0.38) of LBP and normal(10.80) and COVID(5.86) of GDP. As a result of the low LBP and GDP values, information in the CT images would be lost. The extracted textural features are consistent in demonstrating a substantial difference between normal and COVID CT scan images. [ABSTRACT FROM AUTHOR]

The aim of this analysis is to analyze the quantity of textural variations of lung CT scan images and extraction of features despite incidents of COVID-19. Essential input images for this analysis are acquired from a public domain database called Github.org. In this analysis, the comparison between Local Directional Texture Pattern (LDTP) and Local Directional Number Pattern (LDNP) to analyze the texture deformation of CT lung scans is carried out. Classification of normal and COVID subjects is carried out using Logistic Regression classifier. In COVID subjects textural variation is more despite tissue annihilation and in normal subjects textural variation is less due to the gentle surface of the lung. Feature values acquired using LDN are observed to be statistically significant (p<0.05) compared to LDTP values. LDN has obtained precision (0.891), F1-score (0.871), Recall (0.876), AUC (0.933), Classification Accuracy (0.876) are calculated using logistic regression classifier. Higher values of LDN of normal indicates that in COVID subjects there will be structural and intensity information loss despite the presence of COVID. LDN is tough against illumination, noise, time lapse variations. The extracted textural features are able to confine the significant difference in the normal and COVID CT scan images. [ABSTRACT FROM AUTHOR]

To estimate the texture deformation in lung CT images caused by COVID 19 using local binary pattern and Weber local descriptor Pattern in comparison with local gradient increasing pattern features. Materials and Methods: G-power tool is used to calculate the total number of samples required for the analysis. A total of 176 samples per group is obtained by fixing the standard error rate, effect size, and predefined power parameters in G power tool are set to 0.05, 0.3, and 0.80, respectively. Results: The feature values obtained using Local Gradient Increasing Pattern are found to exhibit the significance of 0.0001 (p<0.05) compared to local binary pattern and Weber Local Descriptor. Significant Local Gradient Increasing Patterns are LGIP11, LGIP19, LGIP23, with mean values of normal as 0.42, 0.48, and 0.45 respectively and COVID as 0.35, 0.39, and 0.41 respectively. Neural network classifier is found to achieve a high accuracy of 0.98 using LGIP features in differentiating normal and COVID subjects compared to logistic regression and K-NN classifier. Conclusion: Local gradient increasing patterns perform significantly better than the local binary pattern and Weber local descriptor in differentiating normal and COVID subjects. [ABSTRACT FROM AUTHOR]

Li1-xNixMgxFe2.5-xO4 ferrite system is synthesized from solid state reaction method (x = 0.00 to 0.25, each step is 0.05). The operational frequency is shifted to higher values with substituent concentration(x) in lithium ferrite. The natural resonance frequency has been observed to be enhanced 106 MHz to 1GHz for the concentration of x>0.10. Snoek product varies in proportion with Ms. Cation distribution is proposed from saturation magnetization and XRD studies, and verified from FTIR and electromagnetic properties. The variations of electromagnetic properties are explained by suggesting the transfer of Mg ions to tetrahedral site (x>0.10). The selected composition offered new possibilities in the solid state synthesis for enhancing the critical frequency. [ABSTRACT FROM AUTHOR]

Comparing the performance of triangular microstrip patch antennas on FR4 epoxy substrate with that of typical rectangular microstrip antennas while operating at 5.8 GHz is the goal of this research project. At a frequency of 5.8GHz, the HFSS tool was used to compare the gain and return loss of an innovative triangular microstrip patch antenna to that of a rectangular patch microstrip patch antenna on a FR4 epoxy substrate. This evaluation was carried out so that a conclusion could be drawn regarding which antenna configuration provided the better performance. Both antennas contributed a combined total of 32 samples that were gathered (i.eEach group has 16 samples). With the assistance of the G Power statistical programme, the size of the sample was determined with a degree of confidence equal to eighty percent. Both the gain (4.2525 dB) and the return loss (-14.5095 dB) of the innovative triangular microstrip patch antenna are superior than those of the standard rectangular microstrip patch antenna, which are, respectively, 3.8664 dB and-14.509 dB. (-15.7555 dB). The significance level reached by the use of SPSS is lower than 0.05. (0.035). Within the confines of this inquiry, the unique triangular microstrip patch antenna at 5.8GHz gives good gain (4.2525 dB) and return loss (1.1 dB) (-14.5095 dB). [ABSTRACT FROM AUTHOR]

This effort is being done in order to build a square patch antenna operating at 5.8 GHz that has a higher gain than the traditional rectangular microstrip patch antennas that are manufactured on a substrate made of FR4 epoxy. A square patch antenna on a FR4 epoxy substrate was constructed for use at 5.8 GHz. This was done so that the antenna's gain may be increased in comparison to that of rectangular microstrip patch antennas. Both the Materials and the Methods: The consequences of Novel Ideas in terms of money On a FR4 epoxy substrate, the HFSS programme compared the performance of a square microstrip patch antenna to that of a rectangle patch antenna while both antennas were operating at 5.8GHz. The two antennas produced a total of 32 samples when combined (i.e Each group has 16 samples). The statistical tool known as G Power is used in order to make an estimate of the sample size using a confidence interval of 80 percent. When compared to its rectangular counterpart, the innovative square microstrip patch antenna has a gain of 5.2371 and a return loss of 15.3903 dB that is much lower (-15.7555 dB). The p-value of 0.035 that was calculated using SPSS is lower than the critical value required for statistical significance (0.05). According to the findings of this research, the square microstrip patch antenna has a greater gain (5.2371dB) and a smaller return loss than the rectangular patch antenna while operating at 5.8GHz (-15.3903dB). [ABSTRACT FROM AUTHOR]

Cardiac arrest occurs as the heart muscle fails to contract properly, resulting in a sudden loss of blood supply. The ECG signal is one of the techniques for detecting cardiac electrical activity and is used to investigate heart block. In this paper different standardized work for early detection of cardiac arrest is described. Stages of ECG signal pre-processing involves denoised using digital filtering algorithms and extracting different features from clean ECG predicting cardiac arrest in early stage. Several other body parameters were also considered for this purpose. In this work denoising validation parameters were compared for showing effectiveness of the filtering algorithm and several body parameters and its implication on cardiac arrest was described. [ABSTRACT FROM AUTHOR]

Good and clean water are basic necessities for human beings for food, drinking and washing purpose. Various methods are available for converting salty water into pure drinking water. Desalination is one of the most popular salty water purification technologies. In this technology the pure water is produced without salt content from the salty water. When comparing with the various energy storage materials such as Cotton Cloths, Jute and Rubber mates. The Jute and Rubber mates bed materials gives more yield than other heat absorbing materials. The best slope of glass cover is 450 and the water depth of still is 0.050 m The maximum day yield for salty water with pebbles and gravels bed materials was 7420 ml/m2/day without thermocol insulation and the maximum day yield for salty water from Jute and Rubber mates bed material was 7650 ml/m2/day with thermocol insulation. And the maximum yield efficiency of solar still is 58%. [ABSTRACT FROM AUTHOR]