A Software Defined Radio based UHF Digital Ground Receiver System for Flying Object using LabVIEW

This study demonstrates the design and implementation of a software defined radio based digital ground receiver system using LabVIEW. In flight testing centre, command transmission system is used to transmit specific commands to execute some operation inside the flight vehicle. One ground receiver system is needed to monitor the transmitted command and monitor the presence of the command in air. The newly implemented ground receiver system consists of FPGA, RTOS and general processing unit. The analog to digital conversion and RF down conversions are carried out in high speed PCI extension for instrumentation express cards. The communication algorithms, digital down conversion are implemented in FPGAs. The communication system uses digital demodulation and decoding scheme and realised by NI PXI-7966R with Xilinx Virtex 5, SXT, FPGA. The performance of the receiver system has been analysed by linearity measurement of pre-amplifier Gain, Noise figure, frequency, power and also measurement of sensitivity. The results show successful implementation of the ground receiver system

Land use/land cover classification using machine learning models

An ensemble model has been proposed in this work by combining the extreme gradient boosting classification (XGBoost) model with support vector machine (SVM) for land use and land cover classification (LULCC). We have used the multispectral Landsat-8 operational land imager sensor (OLI) data with six spectral bands in the electromagnetic spectrum (EM). The area of study is the administrative boundary of the twin cities of Odisha. Data collected in 2020 is classified into seven land use classes/labels: river, canal, pond, forest, urban, agricultural land, and sand. Comparative assessments of the results of ten machine learning models are accomplished by computing the overall accuracy, kappa coefficient, producer accuracy and user accuracy. An ensemble classifier model makes the classification more precise than the other state-of-the-art machine learning classifiers