Estimation of Channel Performance of Satellite Communication and Frequency Reader

ABSTRACT: National Remote Sensing Center (NRSC) receives data from different remote satellites like IRS-P6, IRS-P5, Cartosat-2, Cartosat-2a, etc., and processes it depending on the user requirements. The satellite data received in X band is in a particular data format. This data has to be frame synchronized using a special hardware. The receiver hardware setup must be ready at any time to make it ready it"s performance is to be tested continuously.The frequency with which satellite data is coming is also continuously tested . In the proposed project VHDL code has been developed for BER reader with differential encoding and decoding and frequency reader. The external frequency and number of errors in satellite data will be displayed on HP display devices. This project has been implemented and tested using the ALTERA EPLDs. This needs crystal oscillators, thumb wheel switches,7 segment display etc., must be programmed as per the requirement. The hardware required for this has been implemented on the wire-warp board

Assessment of Soil Fertility Status of Rayal Cheruvu Village in Ramachandrapuram Mandal of Tirupati District, Andhra Pradesh, India

One hundred soil samples from Rayal Cheruvu village in Ramachandrapuram mandal of Tirupati district, Andhra Pradesh were collected by using GPS to study soil fertility status. These samples were analysed for physico-chemical and chemical parameters. The results revealed that soils were slightly acidic to strongly alkaline in reaction and non-saline. Soil organic carbon was low to medium. The available nitrogen was low, the available phosphorus ranged from low to high and the available potassium ranged from medium to high. The fertility status was used to assess soil fertility constraints in the study area

Electricity Consumption Prediction Using Machine Learning

The use of electricity has a significant impact on the environment, energy distribution costs, and energy management since it directly impacts these costs. Long-standing techniques have inherent limits in terms of accuracy and scalability when it comes to predicting power usage. It is now feasible to properly anticipate power use using previous data thanks to improvements in machine learning techniques. In this paper, we provide a machine learning-based method for forecasting power use. In this study, we investigate a number of machine learning techniques, including linear regression, K Nearest Neighbours, XGBOOST, random forest, and artificial neural networks(ANN), to forecast power usage. Using historical electricity use data received from a power utility business, we trained and assessed these models. The data is a year’s worth of hourly power use that has been pre-processed to address outliers and missing numbers. Various assessment measures, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2), were used to assess the performance of the models [19]. The outcomes demonstrate that the suggested method may accurately forecast power use. The K Nearest Neighbours(KNN) model outperformed all others in terms of performance, with a 90.92% accuracy rate for predicting agricultural productio