Comparative analysis of subjective/advanced objective technique of tropical cyclone intensity estimation

lkj & m".kdfVca/kh; pØokr dh rhozrk dk vkdyu djus ds fy, lewps fo’o esa O;kogkfjd :i ls mi;ksx dh tkus okyh M~oksjd rduhd esa mixzg ls izkIr fp=ksa dk mi;ksx fd;k tkrk gSA blesa O;ofLFkr laogu ds laca/k esa fo’ys"kd }kjk fd, x, foospu lfgr dqN izk;ksfxd ekunaMksa ds vk/kkj ij mixzg ls izkIr fp= ds iSVuZ dh igpku dh tkrh gSA fofHkUu fo’ys"k.k dsUnzksa }kjk fdlh ,d pØokr dk vkdyu djus esa gksus okyh fo"k;ijd foospu laca/kh folaxfr;k¡ daI;wVj ij vk/kkfjr ,yxksfjFe ds ek/;e ls de gqbZA bl la’kksf/kr rduhd dks fodflr fo"k;ijd M~oksjd rduhd ¼,- vks- Mh- Vh-½ dgk x;k vkSj ;g iw.kZ fodflr m".k dfVca/kh; pØokrksa ds fy, mi;qDr gSA bl 'kks/k&Ik= esa o"kZ 2004 esa vk, rhu m".kdfVca/kh; pØokrksa ds laca/k esa ,- vks- Mh- Vh- ds dk;Z & fu"iknu dk ewY;kdau fd;k x;k gSA buds rqYukRed fo’ys"k.k ls ;g irk pyk fd ,- vks- Mh- Vh- rduhd M~oksjd rduhd ds vk/kkj ij fd, x, pØokr dh rhozrk ds vkdyuksa] tks m".kdfVca/kh; fo’ys"k.k dsUnzksa ds mixzg ls izkIr fp=ksa ds fo’ys"kdksa }kjk O;kogkfjd :Ik ls rS;kj fd, x,] ds eqdkcys dh jghA Dvorak technique operationally used all over the world for estimating the tropical cyclone intensity is based on satellite observations. It involves image pattern recognition based on certain empirical rules along with the analyst interpretation of organized convection. The computer-based algorithm can minimize these subjective judgement discrepancies between different analysis centers estimating the same storm. This modified version is called Advanced Objective Dvorak Technique (AODT) and which is applicable for well-developed tropical cyclones. In this paper the performance of the AODT is evaluated on three cases of the year 2004 tropical cyclones. Comparative analysis indicates the technique to be competitive with, the Dvorak-based intensity estimates produced operationally by satellite analysts from tropical analysis centers.

Data Acquisition System and Pattern Image Generations for Hand Grip Device

Grip pattern is essential to understand how an object being held in hand. One of the solutions is to use the pressure sensing glove to capture the gripping pressure distributed on the surface of the palm. The objective of this project is to develop a data acquisition system for a gripping device that can capture the grip patterns when a person is gripping an object. The design comprises of Velostat sheet, rows, and columns of conductive threads, that are sandwiched and layered to form a glove with pressure sensor grids. Arduino is used to generate the signals for data acquisition and interface with the MATLAB program through serial communication. On the MATLAB, the sensor data are organized and represented in hand pattern color image. Voltage Divider Rule (VDR) was used in an experiment with different resistor values and the effect of the image patterns were observed. Another experiment has been designed to find out the grip consistency. The results show that resistor values 330ohm can cause the image pattern create noises. Meanwhile, 4.7kohm resistance value is sufficient to eliminate most of the noises made in the pattern images. In this paper, different grip images can be obtained from different grip activities, such as holding toothbrush, lifting dumbbell, and pressing syringe. Future works can be done in resolution improvement and grip pattern recognition.

Image Pattern Recognition in Spatial Data using Artificial Neural Network

This study predicts erosion based on the image patterns as the input data by using an ANN approach. Several simulations had been carried out to get the ANN parameter combination in producing the best accuracy through trials and errors. The results show that the accuracy of artificial neural network training is not influenced by the number of channels, namely the input dataset (erosion factors) and the dimensions of the data, but it is determined by changes in the network parameters. The best combination of parameters is 2 hidden layers, learning rate 0.001, Momentum 0.9, and RMS 0.0001 with an accuracy of 98.55%

An Automated Testing Framework for Gesture Recognition System using Dynamic Image Pattern Generation with Augmentation

In the field of information technology, the gesture recognition system plays a very essential role. As it has achieved vast importance, it is mandatory to test the recognition system to ensure the quality of the system by identifying the bugs in the software. In our research, we suggested a dynamic testing method for gesture recognition software. using dynamic image pattern generation with augmentation. The automated software testing framework is a set of processes to create new test cases for properly testing a image processing software. The research intention for generate automated testing cases by following a standard process which helps to increase the performance and efficiency of the gesture recognition system. We have built the framework to give proper testing and give result (accuracy and defect) for which gesture recognition system already in the market. our research, the team strongly following and adding two software testing standard. First one is ISO/IEC/IEEE/291129-3 to define the process for testing software. And the second one is ISO/IEC/IEEE/291129-5 to implement the techniques for software testing. We proposed this framework with major five parameters by noise, rotation, background, contrast, and scale. Which are the most use with every gesture recognition system. Our developed framework’s phase is used to generate new testing cases based on the existing gesture recognition system’s data. There are we work with five systems, commonly with the gesture recognition for experiments. We provide the testing report with total accuracy and defect by comparing existing well-known system’s data. At the final result, our system suggested an analysis report based on the testing result. And tell what are the improvement needs for the existing system to consider noised images or different scaled images to build a robust system. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 7, Dec 2020 P 42-50