We use rigorous nonequilibrium thermodynamic arguments to establish that (i) the nonequilibrium entropy S(T_{0}) of any system is bounded below by the experimentally (calorimetrically) determined entropy S_{expt}(T_{0}), (ii) S_{expt}(T_{0}) is bounded below by the equilibrium or stationary state (such as the supercooled liquid) entropy S_{SCL}(T_{0}) and consequently (iii) S(T_{0}) cannot drop below S_{SCL}(T_{0}). It then follows that the residual entropy S_{R} is bounded below by the extrapolated S_{expt}(0)>S_{SCL}(0) at absolute zero. These results are very general and applicable to all nonequilibrium systems regardless of how far they are from their stationary states.
We consider an isolated system in an arbitrary state and provide a general formulation using first principles for an additive and non-negative statistical quantity that is shown to reproduce the equilibrium thermodynamic entropy of the isolated system. We further show that the statistical quantity represents the nonequilibrium thermodynamic entropy when the latter is a state function of nonequilibrium state variables; see text. We consider an isolated 1-d ideal gas and determine its non-equilibrium statistical entropy as a function of the box size as the gas expands freely isoenergetically, and compare it with the equilibrium thermodynamic entropy S_{0eq}. We find that the statistical entropy is less than S_{0eq} in accordance with the second law, as expected. To understand how the statistical entropy is different from thermodynamic entropy of classical continuum models that is known to become negative under certain conditions, we calculate it for a 1-d lattice model and discover that it can be related to the thermodynamic entropy of the continuum 1-d Tonks gas by taking the lattice spacing {\delta} go to zero, but only if the latter is state-independent. We discuss the semi-classical approximation of our entropy and show that the standard quantity S_{f}(t) in the Boltzmann's H-theorem does not directly correspond to the statistical entropy.
Machine Translation for Indian languages is an emerging research area. Transliteration is one such module that we design while designing a translation system. Transliteration means mapping of source language text into the target language. Simple mapping decreases the efficiency of overall translation system. We propose the use of stemming and part-of-speech tagging for transliteration. The effectiveness of translation can be improved if we use part-of-speech tagging and stemming assisted transliteration.We have shown that much of the content in Gujarati gets transliterated while being processed for translation to Hindi language.
The Braille system has been used by the visually impaired for reading and writing. Due to limited availability of the Braille text books an efficient usage of the books becomes a necessity. This paper proposes a method to convert a scanned Braille document to text which can be read out to many through the computer. The Braille documents are pre processed to enhance the dots and reduce the noise. The Braille cells are segmented and the dots from each cell is extracted and converted in to a number sequence. These are mapped to the appropriate alphabets of the language. The converted text is spoken out through a speech synthesizer. The paper also provides a mechanism to type the Braille characters through the number pad of the keyboard. The typed Braille character is mapped to the alphabet and spoken out. The Braille cell has a standard representation but the mapping differs for each language. In this paper mapping of English, Hindi and Tamil are considered.
In this paper, we use statistical texture features for handwritten and printed text classification. We primarily aim for word level classification in south Indian scripts. Words are first extracted from the scanned document. For each extracted word, statistical texture features are computed such as mean, standard deviation, smoothness, moment, uniformity, entropy and local range including local entropy. These feature vectors are then used to classify words via k-NN classifier. We have validated the approach over several different datasets. Scripts like Kannada, Telugu, Malayalam and Hindi i.e., Devanagari are primarily employed where an average classification rate of 99.26% is achieved. In addition, to provide an extensibility of the approach, we address Roman script by using publicly available dataset and interesting results are reported.