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Found 17 papers, 1 papers with code
Brain-scale Theta Band Functional Connectivity As A Signature of Slow Breathing and Breath-hold Phases
The study reported herein attempts to understand the neural mechanisms engaged in the conscious control of breathing and breath-hold.
A Classifier Using Global Character Level and Local Sub-unit Level Features for Hindi Online Handwritten Character Recognition
Hindi character datasets used for training and testing the developed classifier consist of samples of handwritten characters from 96 different character classes.
Structural analysis of Hindi online handwritten characters for character recognition
These properties are used to extract sub-units from Hindi ideal online characters.
Histograms of Points, Orientations, and Dynamics of Orientations Features for Hindi Online Handwritten Character Recognition
The character datasets used for training and testing the classifiers consist of online handwritten samples of 96 different Hindi characters.
A Novel Deep Learning Architecture for Decoding Imagined Speech from EEG
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.
Decoding Imagined Speech using Wavelet Features and Deep Neural Networks
This paper proposes a novel approach that uses deep neural networks for classifying imagined speech, significantly increasing the classification accuracy.
An Improved EEG Acquisition Protocol Facilitates Localized Neural Activation
This work proposes improvements in the electroencephalogram (EEG) recording protocols for motor imagery through the introduction of actual motor movement and/or somatosensory cues.
Improving Facial Emotion Recognition Systems Using Gradient and Laplacian Images
These modifications help the network learn additional information from the gradient and Laplacian of the images.
Binary Document Image Super Resolution for Improved Readability and OCR Performance
This gives rise to the problem of binary document image super-resolution (BDISR).
MSCE: An edge preserving robust loss function for improving super-resolution algorithms
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.
Computationally Efficient Approaches for Image Style Transfer
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.
Segmentation of Liver Lesions with Reduced Complexity Deep Models
We propose a computationally efficient architecture that learns to segment lesions from CT images of the liver.
A hybrid approach of interpolations and CNN to obtain super-resolution
We propose a novel architecture that learns an end-to-end mapping function to improve the spatial resolution of the input natural images.
Language Independent Single Document Image Super-Resolution using CNN for improved recognition
The problem involves quality improvement before passing it to a properly trained OCR to get accurate recognition of the text.
Intrinsic normalization and extrinsic denormalization of formant data of vowels
Using a known speaker-intrinsic normalization procedure, formant data are scaled by the reciprocal of the geometric mean of the first three formant frequencies.
Significance of the levels of spectral valleys with application to front/back distinction of vowel sounds
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.
Quadratically constrained quadratic programming for classification using particle swarms and applications
The approach of particle swarms is an example for interior point methods in optimization as an iterative technique.
