Conference Papers

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

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    Active Decoupling Control for a Planetary Coaxial Helicopter Using Force Feedback
    (IEEE Computer Society help@computer.org, 2018) Balakrishnan, B.; Shamrao; Aditya, R.; Narendra Nath, S.; Narayana, S.; Prasad, R.V.
    Drones developed for interplanetary space missions require full autonomy of operations including safe landing and hovering due to the delay in communication. For operation in low atmospheric densities, coaxial helicopters are best suited and they are capable of handling manoeuvres due to their small footprints and ease of operation. However, the dynamics of the helicopter is coupled in lateral axes which need to be compensated for precise control. The present solutions include vision-based tracking in order to decouple the dynamics, which needs additional hardware. In this paper, a decoupling controller is presented that employs an accelerometer-based force feedback system for measuring the undesired forces in off-axis which does not need any additional hardware. The simulation results indicate that the force feedback methodology is very effective in controlling the off-axis drift of the coaxial helicopter. © 2018 IEEE.
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    Stress Detection with Machine Learning and Deep Learning using Multimodal Physiological Data
    (Institute of Electrical and Electronics Engineers Inc., 2020) Bobade, P.; Vani, M.
    Stress is a common part of everyday life that most people have to deal with on various occasions. However, having long-term stress, or a high degree of stress, will hinder our safety and disrupt our normal lives. Detecting mental stress earlier can prevent many health problems associated with stress. When a person gets stressed, there are notable shifts in various bio-signals like thermal, electrical, impedance, acoustic, optical, etc., by using such bio-signals stress levels can be identified. This paper proposes different machine learning and deep learning techniques for stress detection on individuals using multimodal dataset recorded from wearable physiological and motion sensors, which can prevent a person from various stress-related health problems. Data of sensor modalities like three-axis acceleration (ACC), electrocardiogram (ECG), blood volume pulse (BVP), body temperature (TEMP), respiration (RESP), electromyogram (EMG) and electrodermal activity (EDA) are for three physiological conditions - amusement, neutral and stress states, are taken from WESAD dataset. The accuracies for three-class (amusement vs. baseline vs. stress) and binary (stress vs. non-stress) classifications were evaluated and compared by using machine learning techniques like K-Nearest Neighbour, Linear Discriminant Analysis, Random Forest, Decision Tree, AdaBoost and Kernel Support Vector Machine. Besides, simple feed forward deep learning artificial neural network is introduced for these three-class and binary classifications. During the study, by using machine learning techniques, accuracies of up to 81.65% and 93.20% are achieved for three-class and binary classification problems respectively, and by using deep learning, the achieved accuracy is up to 84.32% and 95.21% respectively. © 2020 IEEE.