Conference Papers

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

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Author: search.filters.author.Singh, M.Subject: search.filters.subject.CNN

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    A Study on Depth Estimation from Single Image Using Neural Networks
    (Institute of Electrical and Electronics Engineers Inc., 2022) Shree, R.; Madagaonkar, S.B.; Singh, M.; Chandra, M.T.A.; Rathnamma, M.V.; Venkataramana, V.; Chandrasekaran, K.
    Depth estimation is fundamental in upcoming technology advancements like scene understanding, robot vision, intelligent driver assistance systems, and many new technologies. Estimating the depth of objects from a viewport can be achieved using various mathematical, geometrical, and stereo concepts, but the process is unaffordable and erroneous. Depth estimation from a single can be accurately done using neural networks. Although this is a challenging task, researchers around the globe have published various works. The works include different neural network standards like CNN, GANs, Encoder-Decoder. The paper analyses and examines famous works in this field of study. Later in the paper, a comparative survey of depth estimation approaches using neural networks is done. © 2022 IEEE.
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    Complex Aware Transformer-CNN for Refractive Index Prediction in Plasmonic Waveguide
    (Institute of Electrical and Electronics Engineers Inc., 2025) Chaurasia, A.R.; Marwade, V.; Singh, M.
    Estimating the effective refractive index of a plasmonic waveguide with high precision is essential for various photonic applications. Traditional analytical and numerical methods often involve extensive computational methods. Deep learning-based approaches have shown promise in improving both accuracy and efficiency. This paper presents a deep learning-based approach for effective refractive index estimation using a hybrid Complex Aware Transformer-Convolutional Neural Network (CAT-CNN) model utilizing convolutional feature extraction, transformer-based attention mechanisms, and squeeze-and-excitation blocks to improve predictive accuracy. Trained on a dataset of plasmonic waveguide parameters at a fixed frequency of 193.2 THz, the model achieves a combined testing R2 score of 0.99978, demonstrating high precision in predicting the real and imaginary parts of the effective refractive index. Our results demonstrate that CAT-CNN achieves state-of-the-art performance in terms of prediction accuracy and computational efficiency. The proposed model has significant implications for the design of high-performance plasmonic sensors and integrated photonic devices. © 2025 IEEE.