Browsing by Author "Sagar Bharadwaj, K.S."

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    CollabChain: Blockchain-backed trustless web-based volunteer computing platform
    (Springer Verlag service@springer.de, 2019) Sagar Bharadwaj, K.S.; Dharanikota, S.; Honawad, A.; Chandrasekaran, K.
    Volunteer computing is a distributed computing model in which individuals in possession of computing resources volunteer to provide them to a project. Owing to the availability of billions of computing devices all over the world, volunteer computing can help solve problems that are larger in scale even for supercomputers. However, volunteer computing projects are difficult to launch and deploy. These platforms also force volunteers to trust the authenticity of the project owner and to blindly accept credits allotted to their contribution by the project owner. As a result, very few high-profile trusted projects are able to sustain in this system. In this paper, we present an incentivized web-based volunteer computing platform that functions as a market place to buy and sell computing power. Launching a project on the system and contributing to an existing project happens over the browser without the need for a specialized software or hardware. We introduce the application of blockchain to remove the need to trust any other party in the system. We also present a prototype implementation and solve NP-Problems as examples using the proposed prototype. © 2019, Springer Nature Switzerland AG.
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    SolveIt: An Application for Automated Recognition and Processing of Handwritten Mathematical Equations
    (Institute of Electrical and Electronics Engineers Inc., 2018) Sagar Bharadwaj, K.S.; Bhat, V.; Krishnan, A.S.
    Solving mathematical equations is an integral part of most, if not all forms of scientific studies. Researchers usually go through an arduous process of learning the nuances and syntactic complexities of a mathematical tool in order to solve or process mathematical equations. In this paper, we present a mobile application that can process an image of a handwritten mathematical equation captured using the device's camera, recognise the equation, form the corresponding string that can be parsed by a computer algebraic system and display all possible solutions. We aim to make the whole experience of experimenting with equations very user friendly and to remove the hassle of learning a mathematical tool just for mathematical experimentation. We propose a novel machine learning approach to recognise handwritten mathematical symbols achieving a 99.2% cross validation percentage accuracy on the kaggle math symbol dataset with reduced symbols. The application covers useful features like simultaneous equation solving, graph plotting and simple arithmetic computations from images. Overall it is a very user friendly equation solver that can leverage the power of existing powerful math packages. © 2018 IEEE.