Browsing by Author "Nair, A."

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    Articulated Robotic Arm for Feeding
    (Springer Science and Business Media Deutschland GmbH, 2023) Nair, A.; Rajendran, D.; Jacob, J.C.; Varghese, N.S.; Suvin, P.S.
    In today’s fast-paced world, disabled people are a large minority group, starved of services, mostly ignored by society, and live in isolation, segregation, poverty, charity and even pity. There are numerous forms of disabilities. The disability suffered by most persons includes mental disability, emotional, physical and cognitive. Perhaps the most overlooked effect of a disability that affects the motor functions of the limb is the reliance on other people for the completion of even simple tasks that ordinary people perform on a daily basis, like taking a shower, dressing up, brushing teeth, or even having a meal. This chips away at the self-worth of a disabled person and gnaws away at their confidence. Through our project, we aim to provide a solution to those with compromised motor functions. This project aims to develop a 4 DOF robotic manipulator that is able to map the facial structure of the user, and with a feeding device (spoon/fork) attached to its arm transfers adequate portion of food accurately into the user’s mouth without spillage through smooth motion, by incorporating Image Processing, Manipulator Kinematics and Machine Learning. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    Image Colorization Using GANs and Perceptual Loss
    (Institute of Electrical and Electronics Engineers Inc., 2020) Sankar, R.; Nair, A.; Abhinav, P.; Mothukuri, S.K.P.; Koolagudi, S.G.
    Image colorization is of great use for several applications, such as the restoration of old images, as well as enabling the storage of grayscale images, which take up less space, which can later be colorized. But this problem is hard since there exist many possible color combinations for a particular grayscale image. Recent developments have aimed to solve this problem using deep learning. But, for achieving good performance, they require highly processed inputs, along with additional elements, such as semantic maps. In this paper, an attempt has been made for generalizing the procedure of colorization using a conditional Deep Convolutional Generative Adversarial Network (DCGAN) by adding "Perceptual Loss". The network is trained over the CIFAR-100 dataset. The results of the proposed generative model with perceptual loss are compared with the existing state-of-the-art systems normal GAN model and U-Net Convolutional model. © 2020 IEEE.
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    Temperature- and SNR-based On Demand Routing Protocol for Underwater Communication
    (Institute of Electrical and Electronics Engineers Inc., 2021) Nair, A.; Chandavarkar, B.R.
    Underwater Communication comes with various obstacles which involves the interaction between acoustic signals and the physical features of the medium which include pH value, salinity and temperature. This paper focuses on surveying and proposing routing algorithms which takes into consideration of the temperature and well as the various factors that affect it within the medium. The paper proposes a new routing algorithm which will decide the next hop to be chosen based on the best Signal to Noise Ratio (SNR) value of its neighbours and it's study based on the effect temperature has on it. A model to calculate temperature has been developed. The SNR value being calculated considers physical parameters of the medium including pH, salinity and temperature. The routing algorithm is mathematically modelled and then simulated in UnetStack 3 for proof of correctness. © 2021 IEEE.