Quadratic Predictor based Differential Encoding and Decoding of Speech Signals

Modeling nonlinear systems using Volterra series is a century old method but practical realizations were hampered by inadequate hardware to handle the increased computational complexity stemming from its use. But interest is renewed recently, in designing and implementing filters which can model much of the polynomial nonlinearities inherent in practical systems. The key advantage in resorting to Volterra power series for this purpose is that nonlinear filters so designed can be made to work in parallel with the existing LTI systems, yielding improved performance. This paper describes the inclusion of a quadratic predictor (with nonlinearity order 2) with a linear predictor in an analog source coding system. Analog coding schemes generally ignore the source generation mechanisms but focuses on high fidelity reconstruction at the receiver. The widely used method of differential pnlse code modulation (DPCM) for speech transmission uses a linear predictor to estimate the next possible value of the input speech signal. But this linear system do not account for the inherent nonlinearities in speech signals arising out of multiple reflections in the vocal tract. So a quadratic predictor is designed and implemented in parallel with the linear predictor to yield improved mean square error performance. The augmented speech coder is tested on speech signals transmitted over an additive white gaussian noise (AWGN) channel.

Design, Development and Realization of Quadratic Volterra Filters for Selected Applications

The basic concepts of digital signal processing are taught to the students in engineering and science. The focus of the course is on linear, time invariant systems. The question as to what happens when the system is governed by a quadratic or cubic equation remains unanswered in the vast majority of literature on signal processing. Light has been shed on this problem when John V Mathews and Giovanni L Sicuranza published the book Polynomial Signal Processing. This book opened up an unseen vista of polynomial systems for signal and image processing. The book presented the theory and implementations of both adaptive and non-adaptive FIR and IIR quadratic systems which offer improved performance than conventional linear systems. The theory of quadratic systems presents a pristine and virgin area of research that offers computationally intensive work. Once the area of research is selected, the next issue is the choice of the software tool to carry out the work. Conventional languages like C and C++ are easily eliminated as they are not interpreted and lack good quality plotting libraries. MATLAB is proved to be very slow and so do SCILAB and Octave. The search for a language for scientific computing that was as fast as C, but with a good quality plotting library, ended up in Python, a distant relative of LISP. It proved to be ideal for scientific computing. An account of the use of Python, its scientific computing package scipy and the plotting library pylab is given in the appendix Initially, work is focused on designing predictors that exploit the polynomial nonlinearities inherent in speech generation mechanisms. Soon, the work got diverted into medical image processing which offered more potential to exploit by the use of quadratic methods. The major focus in this area is on quadratic edge detection methods for retinal images and fingerprints as well as de-noising raw MRI signals

Occurrence of filamentous algae and soil characteristics in the paddy fields of Kuttanad and Kole lands of Kerala

The present work is the study of filamentous algae in the paddy fields of Kuttanad and Kole lands of Kerala. This investigation was initiated by sampling of filamentous algae in Kuttanad during December 2010 to February 2011. A second phase of sampling was done from November 2011 to February 2012. The sampling periodicity corresponded to the crop growth starting from field preparation through sowing, and continued till the harvest. Sampling locations were selected from the active paddy cultivation regions of the six agronomic zones of Kuttanad. The numbers of sampling locations were proportional to the area of each zone. Algae of the Kole lands were collected during from October 2011 to January 2012. It was observed that blue-green algae dominated in both Kuttanad and Kole lands. Thirty two species of blue-green algae and eight species of green algae were identified from Kuttanad. The highest number of algal species was observed from Kayal lands in Kuttanad throughout the cropping season. Among the thirty two species of blue-green algae twenty five species are nonheterocystous and seven species are heterocystous. Twenty eight species of blue-green and six species of green algae were identified from Kole lands, and highest number of species was observed in Palakkal throughout the cropping season. Among the twenty eight species of blue-green algae collected from Kole lands twenty one species are non-heterocystous, and only seven species are heterocystous filamentous algae. Blooms of Spirogyra were observed during the second phase of sampling in Kuttanad and also in the Kole lands. The results of the germination study revealed that the extract of Spirogyra sp. inhibited seed germination and reduced seedling vigour. The growth of the treated seedlings was evaluated by pot experiments. The results clearly showed that Spirogyra sp. can negatively affect the seed germination, seedling vigour, and the yield of rice.