The Use of High-Speed Cameras as a Tool for the Characterization of Raindrops in Splash Laboratory Studies

Measuring the characteristics of raindrops is essential for different processes studies. There have been many methods used throughout history to measure raindrops. In recent years, automatic image recognition and processing systems have been used with high-speed cameras to characterize rainfall by obtaining the spectrum of droplet sizes and their speeds and thus being able to use this technology to calibrate rainfall simulators. In this work, two phases were carried out: in the first one, individual drops with terminal speeds of different sizes were measured and processed both in speed and in shape with a high-speed camera; and in the second phase, a calibration procedure was designed but in multidrop images, determining the characteristics of the drops produced by a rain simulator. According to results, the real shape of each drop depending on the size was determined, from round to ovaloid shapes, and the terminal velocity of water drops with different sizes was measured. Based on the rain images used to calibrate a rainfall simulator, it was observed that, with a higher intensity of rain, the drops produced were smaller, which contrasts with real rain, in which just the opposite happens. This calibration evaluates their resemblance to reality, calculates the real kinetic energy of the rain they produce and see if they can be used to model events in nature.

Convolution Based Neural Network Model to Prevent Diseases Classification and Prediction Using Deep Learning

The farmer is a backbone to nation, but majority of the cultivated crops in india affecting by various diseases at various stages of its cultivation. Recent research works shows that diseases are not providing accurate results and few identifying but not providing optimized solutions to the system. In proposed work, the recent developments of Artificial intelligence through Deep Learning show that AIR (Automatic Image Recognition systems) using CNN algorithm models can be very beneficial in such scenarios. The Rice leaf diseases images related dataset is not easily available to automate , so that we have created our own trained data set which is small in size hence we have used transfer learning to develop our Proposed model which supports deep learning models. The Proposed CNN architecture illustrated based on VGG-16 model and it is trained, tested on given dataset collected from rice fields and the internet. The accuracy of the proposed model is moderately accurate with 92.46%.

Towards enhanced nanoindentation by image recognition

The Oliver–Pharr method is maybe the most established method to determine a material’s Young’s modulus and hardness. However, this method has a number of requirements that render it more challenging for hard and stiff materials. Contact area and frame stiffness have to be calibrated for every tip, and the surface contact has to be accurately identified. The frame stiffness calibration is particularly prone to inaccuracies since it is easily affected, e.g., by sample mounting. In this study, we introduce a method to identify Young’s modulus and hardness from nanoindentation without separate area function and frame stiffness calibrations and without surface contact identification. To this end, we employ automatic image recognition to determine the contact area that might be less than a square micrometer. We introduce the method and compare the results to those of the Oliver–Pharr method. Our approach will be demonstrated and evaluated for nanoindentation of Si, a hard and stiff material, which is challenging for the proposed method. Graphic Abstract

Using Neural Natural Network for Image Recognition in Bioinformatics

Automatic image recognition is very useful in bioinformatics. This article presents a novel technique to recognize the characters in the number plate automatically by using connected component analysis (CCA), artificial neural network (ANN) and neural natural network (Triple N). The preprocessing steps, Sobel edge detection technique and CCA are applied to the captured image of the vehicle to obtain character images. ANN technique can be used over these images to recognize the characters of the image in bioinformatics. The preprocessing steps are used to remove the noise and to enhance the image for recognizing the characters effectively. After performing the preprocessing steps, the edge detection technique and CCA are carried out to separate the character images from the whole image which can be recognized using ANN. These text characters can be compared with database to find authentication of vehicle, identifying the owner of the vehicle, penalty bill generation, etc.

Optical Analysis of the Internal Void Structure in Polymer Membranes for Gas Separation

Global warming by greenhouse gas emissions is one of the main threats of our modern society, and efficient CO2 capture processes are needed to solve this problem. Membrane separation processes have been identified among the most promising technologies for CO2 capture, and these require the development of highly efficient membrane materials which, in turn, requires detailed understanding of their operation mechanism. In the last decades, molecular modeling studies have become an extremely powerful tool to understand and anticipate the gas transport properties of polymeric membranes. This work presents a study on the correlation of the structural features of different membrane materials, analyzed by means of molecular dynamics simulation, and their gas diffusivity/selectivity. We propose a simplified method to determine the void size distribution via an automatic image recognition tool, along with a consolidated Connolly probe sensing of space, without the need of demanding computational procedures. Based on a picture of the void shape and width, automatic image recognition tests the dimensions of the void elements, reducing them to ellipses. Comparison of the minor axis of the obtained ellipses with the diameters of the gases yields a qualitative estimation of non-accessible paths in the geometrical arrangement of polymeric chains. A second tool, the Connolly probe sensing of space, gives more details on the complexity of voids. The combination of the two proposed tools can be used for a qualitative and rapid screening of material models and for an estimation of the trend in their diffusivity selectivity. The main differences in the structural features of three different classes of polymers are investigated in this work (glassy polymers, superglassy perfluoropolymers and high free volume polymers of intrinsic microporosity), and the results show how the proposed computationally less demanding analysis can be linked with their selectivities.

Game Running Performance and Fitness in Women’s Futsal

The study aimed to verify possible associations between game-play running performance and outcomes derived from fitness (running) tests in female futsal players. Sixteen women professional elite futsal players from a 1st division league team (19.2±2 years-old, 4.3±2.1 years of experience) participated. Firstly, a graded incremental treadmill test was adopted to determine maximal oxygen uptake (VO2max). Following 72 h of laboratory protocol, players were asked to perform a repeated-sprint test on a court (8×40 m with two 180° change-of-directions). Twenty-four hours after, players participated in a one-off friendly game (two 20-min half-times). A computerized automatic image recognition software (DVIDEOW; 30 Hz) allowed to determine game running performance variables. Fatigue index and best time in the court test and VO2max and its attached speed derived from laboratory-based test showed significant moderate-to-moderately high correlations (r=− 0.59–0.76; p<0.05) with some game running performance outputs, notably related to high-intensity running. In conclusion, the present study provided initial evidence on associations between two fitness tests and one-off game running performance in female futsal. Information derived from the work potentially help conditioning professionals working with female futsal athletes gain awareness about some properties of common testing tools.