Browsing by Author "Vashishtha, Shridhar"

Now showing 1 - 3 of 3
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    Control Algorithm Design for Transformable Unmanned Aerial Vehicle (UAV)
    (2022-12) Vashishtha, Shridhar
    The transformable Unmanned aerial vehicle (UAV) is a robot made in the Center for Distributed robotics at the University of Minnesota which can transform between a fixed wing and a quad-rotor state making it highly adjustable and usable. Its quad-rotor state can be used to fly the vehicle using energy stored in the batteries. Once the stored energy is close to being depleted, it can transition into its fixed wing state that can harness the solar energy. However, because the solar aerial robot is unmanned, it requires a control algorithm in the autopilot firmware to change the geometry during the flight. The control algorithm will be used to program the autopilot firmware to make the vehicle fly. This project’s goal is developing these control algorithms and test them on the transformable solar unmanned aerial vehicle (UAV).
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    Meta-Learning for Monitoring Environment Systems Across the Globe
    (2024-05-15) Vashishtha, Shridhar
    Data sparsity is a key challenge in monitoring climate because of the lack of quality data, problems in sensors, lack of historical data, or financial constraints in certain parts of the world. Thus, monitoring the environment using machine learning becomes a difficult task because classic machine learning algorithms’ main objective is to train a model that uses input features to learn classes. This paradigm requires huge datasets which makes it difficult to train models in tasks where data is sparse. Meta-learning, or learning-to-learn is a learning paradigm which provides an alternative methodology to classic machine learning algorithms. Meta-learning uses machine learning models in various learning episodes and uses this experience to learn in new learning environments. Thus, meta-learning can be used to monitor environment systems by training in scenarios where data is available and leveraging that information in data sparse tasks.
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    Multi-Modal Brain Tumor Segmentation Model to solve Mutual Inhibition between Modes
    (2023-12-19) Vashishtha, Shridhar
    Medical image segmentation has become a key research area in the machine learning community with brain tumor segmentation as one of the most challenging problems in the field. Brain tumor segmentation using machine learning models can help in diagnosing, treating, and monitoring of brain tumors which would significantly improve the medical care of patients. The aim of this research is to develop a network that could solve the problem of mutual inhibition in multi-modal image segmentation for brain tumors. Specifically, multi-modal image segmentation represents the true day-to-day scenario of brain tumor imaging which will be automated using machine learning networks. Contribution to the multi-modal brain tumor segmentation problem will allow for the fast detection and classification of brain tumors which will lead to improved medical care to patients.