Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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    IoT Based Joystick Controlled Pibot Using Socket Communication
    (Institute of Electrical and Electronics Engineers Inc., 2018) Radhika, K.A.; Raksha, B.L.; Sujatha, B.R.; Umesh, U.; Gangadharan, K.V.
    The Internet of things (IoT) and automated frameworks is a key driver of robotic development technology. In current technology, robots are controlled using Smartphone. In our approach Driving Force GT Joystick is used to control the robot wirelessly and which provide precise manual control on a robotic vehicle. Compared to web-controlled or Smartphone controlled Pibot, joystick controlled Pibot is more effective since it provides speed control. In this work, Raspberry Pi is used as a base controller to control robotic car called Pibot to work as real-time system and vehicle operations are controlled remotely at the ground station using USB joystick. The camera on the Pibot is used for live video streaming on a webpage using HTTP server for surveillance. The server-client socket communication (via strong Wi-Fi) is performed for controlling the vehicle in the remote station using python programming. © 2018 IEEE.
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    A QGIS Plug-in for Processing MODIS Data
    (Institute of Electrical and Electronics Engineers Inc., 2019) Aishwarya Hegde, A.; Umesh, U.; Shetty, A.
    In the past few decades number of Earth-observing satellites are continuously gathering information and only about 10 percent of the information is utilized by the users. With so much accessible information the researchers have not explored the datasets completely as there is absence of effective tool to process the information. MODIS data sensors have accessible data at various temporal and spatial resolutions. To productively use these datasets in open-source GIS programming like QGIS, there is a need to pre-process the dataset using a plug-in. The plug-in is built using python and PyQt interface for QGIS.The plug-in operates on MODIS Data (Terra/Aqua/Combined) computerizes and process the functionalities for MODIS products like MOD11, MOD09, MOD21. The processed datasets can be largely used in investigation of time series analysis for some earth resource application. © 2019 IEEE.
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    Automatizing the Khasra Maps Generation Process Using Open Source Software: QGIS and Python Coding Language
    (Springer Science and Business Media Deutschland GmbH, 2022) Sharma, R.; Beg, M.K.; Bhojaraja, B.E.; Umesh, P.
    Humans are trying to acquire a piece of land from the time they have come into existence. In modern era, the management of land and its ownership is taken up by the Land and Revenue Department of the State. In order to do that, they need maps with specific objectives, so that even a laymen can understand and use it. The process explained in this paper automate the process of map making after getting the digitized shapefile of the khasra (property identification number), as a single village is divided into numerous grids and it is a tedious work and can have lots of errors while doing it manually. So in order to do the process in swift manner and without having any errors, the process was developed using the Quantum Geographic Information System (QGIS) and Python. The proposed method involves making the use of models built in QGIS along with the Python console. It helps to run the whole process on its own with taking the required input parameters and storing the outputs in a specific folder designed for them. The requirement of the project was to do the same operations on a village file and to get the final khasra map from the village polygon file. Depending upon the village area and its dimensions, the numbers of grids for a particular village is decided and the same GIS tools need to be run on each grid files which make this process a tedious work and more prone to errors. By making use of the method suggested in the paper, all the work can be done error proof with the use of Python. The use of Python code helps to do work in just couple of seconds which would have taken days to complete. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    Electrical Load Forecasting for a Distribution Company Using Python Libraries
    (Springer Science and Business Media Deutschland GmbH, 2025) Halu, P.; Punekar, G.S.
    Electrical load forecasting is an indispensable concept in the planning, operation, and maintenance of the power grid. Forecasting electrical load is crucial from both a technical and financial perspective as it enhances the efficiency, dependability, safety, and stability of the power system. Additionally, it contributes to the reduction of operational costs associated with electricity generation and distribution. The present study discusses the electrical load forecasting of a distribution company in Karnataka. Predictive analysis is carried out using long short-term memory (LSTM)-based recurrent neural network (RNN) to forecast electrical load. As the load input data has missing values, the sequence is disturbed. Implications and analysis under such incomplete past data are analyzed. Libraries of Python programming language are used for electrical load forecasting. The outcome of the electrical load forecast shows that the training of RNN model fails with missing load data. Under such circumstances, the input missing data if replaced with ‘zero’ values resulted in huge error indices. The mean absolute percentage error (MAPE) in the present case is 5.06. This error could be further reduced by proper data management while training. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.