C. Dhanunjaya Naidu

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The present work is an attempt to develop a commercially viable and a robust character recognizer for Telugu texts. We aim at designing a recognizer which exploits the inherent characteristics of the Telugu Script. Our proposed method uses wavelet multiresolution analysis for the purpose extracting features and associative memory model to accomplish the(More)
This paper presents an electromagnetic wave propagation model of a human thorax and also an estimation of the propagation characteristics of the model for frequencies ranging from UHF to S-band. As the heart size varies in a cardiac cycle, the characteristic variation of the propagation parameters such as reflection coefficient, signal attenuation etc.,(More)
This paper provides a new technique for lossy multispectral images compression at very high data rates. Image compression is becoming more and more important, as new multispectral instruments are going to generate very high data rates due to the increased spatial and spectral resolutions. The Lossless compression does not provide a sufficient degree of data(More)
Multi spectral images are of high resolution which requires a lot of memory to store and transmit. In order to overcome these limitations compression is applied wherein the fine details of the image will be lost due to the poor quality of image after compression. To reconstruct back the same resolution with same quality involves the super resolution (SR)(More)
A new method for the enhancement of speech signals contaminated by speech-correlated noise, such as that in the output of a speech coder, is presented. This module is based on numerical speech processing algorithms which modelise the infected ear and generates the stimulus signals for the cilia cells (brain). The method is also based on constrained(More)
Generally, Speech enhancement aims to improve speech quality and intelligibility of a noise contaminated speech signal by using various signal processing approaches. Removal of a noise from a noisy speech is a common problem; already a vast research was carried out in earlier. However, due to the characteristics of various types of noises, the approaches(More)
Compressive Sensing is the present sampling paradigm which acquires only useful data, and hence reduces the acquisition time, eliminates the necessity of fast analog to digital converters and data compression stage, prior to transmission. Challenge lies in recovering the data from these few samples, which are far below Nyquist rate. Compressive sensing(More)
The continuous growth of mobile, desktop and wired and wireless digital communication technologies has made the extensive use of the text data unavoidable. The basic characteristics of text data like transmission rate, bandwidth, redundancy, bulk capacity and co-relation among text data makes basic compression algorithms mandatory. The research exploration(More)
This paper deals with the recognition of printed basic Telugu characters using the discrete curves and approximation string matching. The features are extracted from smoothed images, obtained after the thinning operation. As by only thinning, spines may arise which will affect the recognition of the character. The features are the discrete curves, specified(More)