A G RAMAKRISHNAN
Articles written in Sadhana
Volume 41 Issue 3 March 2016 pp 289-298
Particle swarm optimization (PSO) is used in several combinatorial optimization problems. In this work, particle swarms are used to solve quadratic programming problems with quadratic constraints. The central idea is to use PSO to move in the direction towards optimal solution rather than searching the entire feasibleregion. Binary classification is posed as a quadratically constrained quadratic problem and solved using the proposed method. Each class in the binary classification problem is modeled as a multidimensional ellipsoid to forma quadratic constraint in the problem. Particle swarms help in determining the optimal hyperplane or classification boundary for a data set. Our results on the Iris, Pima, Wine, Thyroid, Balance, Bupa, Haberman, and TAE datasets show that the proposed method works better than a neural network and the performance is close to that of a support vector machine
Volume 43 Issue 2 February 2018 Article ID 0015
Experiments performed by us using optical character recognizers (OCRs) show that the character level accuracy of the OCR reduces significantly with decrease in the spatial resolution of document images. There are real life scenarios, where high-resolution (HR) images are not available, where it is desirable toenhance the resolution of the low-resolution (LR) document image. In this paper, our objective is to construct a HR image, given a single LR binary image. The works reported in the literature mostly deal with super-resolutionof natural images, whereas we try to overcome the spatial resolution problem in document images. We have trained and obtained a novel convolutional model based on neural networks, which achieves significant improvement in terms of the peak-signal-to-noise ratio (PSNR) of the reconstructed HR images. Using parametric rectified linear units, mean PSNR improvements of 2.32, 4.38, 6.43 and 8.92 dB have been achieved over those of LR input images of 50, 75, 100 and 150 dots per inch (dpi) resolution and average word level accuracy of almost 43%, 45% and 57% on 75 dpi Tamil, English and Kannada images, respectively.
Volume 43 Issue 10 October 2018 Article ID 0153
Detection of transitions between broad phonetic classes in a speech signal has applications such as landmark detection and segmentation. The proposed hierarchical method detects silence to non-silence transitions, sonorant to non-sonorant transitions and vice-versa. The subset of the extrema (minimum or maximum amplitude samples) above a threshold, occurring between every pair of successive zero-crossings, is selected from each frame of the bandpass-filtered speech signal. Locations of the first and the last extrema lie on eitherside far away from the mid-point (reference) of a frame, if the speech signal belongs to a non-transition segment; else, one of these locations lies within a few samples from the reference, indicating a transition frame. Thetransitions are detected from the entire TIMIT database for clean speech and 93.6% of them are within a tolerance of 20 ms from the phone boundaries. Sonorant, unvoiced non-sonorant and silence classes and their respective onsets are detected with an accuracy of about 83.5% for the same tolerance with respect to the labelled TIMIT database as reference. The results are as good as, and in some aspects better than, the state-of the-art methods for similar tasks. The proposed method is also tested on the test set of the TIMIT database forrobustness with respect to white, babble and Schroeder noise, and about 90% of the transitions are detected within a tolerance of 20 ms at the signal to noise ratio of 5 dB. On NTIMIT database, 62.7% of the transitions are detected, and 63.5% of the sonorant onsets, within 20 ms tolerance.