no code implementations • 15 Dec 2023 • Anusha A. S., Pradeep Kumar G., A. G. Ramakrishnan
The study reported herein attempts to understand the neural mechanisms engaged in the conscious control of breathing and breath-hold.
no code implementations • 26 Oct 2023 • Anand Sharma, A. G. Ramakrishnan
Hindi character datasets used for training and testing the developed classifier consist of samples of handwritten characters from 96 different character classes.
no code implementations • 12 Oct 2023 • Anand Sharma, A. G. Ramakrishnan
These properties are used to extract sub-units from Hindi ideal online characters.
no code implementations • 5 Sep 2023 • Anand Sharma, A. G. Ramakrishnan
The character datasets used for training and testing the classifiers consist of online handwritten samples of 96 different Hindi characters.
no code implementations • 19 Mar 2020 • Jerrin Thomas Panachakel, A. G. Ramakrishnan, T. V. Ananthapadmanabha
The recent advances in the field of deep learning have not been fully utilised for decoding imagined speech primarily because of the unavailability of sufficient training samples to train a deep network.
no code implementations • 19 Mar 2020 • Jerrin Thomas Panachakel, A. G. Ramakrishnan
This paper proposes a novel approach that uses deep neural networks for classifying imagined speech, significantly increasing the classification accuracy.
no code implementations • 13 Mar 2020 • Jerrin Thomas Panachakel, Nandagopal Netrakanti Vinayak, Maanvi Nunna, A. G. Ramakrishnan, Kanishka Sharma
This work proposes improvements in the electroencephalogram (EEG) recording protocols for motor imagery through the introduction of actual motor movement and/or somatosensory cues.
no code implementations • 12 Feb 2019 • Ram Krishna Pandey, Souvik Karmakar, A. G. Ramakrishnan, Nabagata Saha
These modifications help the network learn additional information from the gradient and Laplacian of the images.
1 code implementation • 6 Dec 2018 • Ram Krishna Pandey, K Vignesh, A. G. Ramakrishnan, Chandrahasa B
This gives rise to the problem of binary document image super-resolution (BDISR).
no code implementations • 25 Aug 2018 • Ram Krishna Pandey, Nabagata Saha, Samarjit Karmakar, A. G. Ramakrishnan
With the recent advancement in the deep learning technologies such as CNNs and GANs, there is significant improvement in the quality of the images reconstructed by deep learning based super-resolution (SR) techniques.
no code implementations • 16 Jul 2018 • Ram Krishna Pandey, Samarjit Karmakar, A. G. Ramakrishnan
In this work, we have investigated various style transfer approaches and (i) examined how the stylized reconstruction changes with the change of loss function and (ii) provided a computationally efficient solution for the same.
no code implementations • 23 May 2018 • Ram Krishna Pandey, Aswin Vasan, A. G. Ramakrishnan
We propose a computationally efficient architecture that learns to segment lesions from CT images of the liver.
no code implementations • 23 May 2018 • Ram Krishna Pandey, A. G. Ramakrishnan
We propose a novel architecture that learns an end-to-end mapping function to improve the spatial resolution of the input natural images.
no code implementations • 30 Jan 2017 • Ram Krishna Pandey, A. G. Ramakrishnan
The problem involves quality improvement before passing it to a properly trained OCR to get accurate recognition of the text.
no code implementations • 16 Sep 2016 • T. V. Ananthapadmanabha, A. G. Ramakrishnan
Using a known speaker-intrinsic normalization procedure, formant data are scaled by the reciprocal of the geometric mean of the first three formant frequencies.
no code implementations • 16 Jun 2015 • T. V. Ananthapadmanabha, A. G. Ramakrishnan, Shubham Sharma
An objective critical distance (OCD) has been defined as that spacing between adjacent formants, when the level of the valley between them reaches the mean spectral level.
no code implementations • 23 Jul 2014 • Deepak Kumar, A. G. Ramakrishnan
The approach of particle swarms is an example for interior point methods in optimization as an iterative technique.