Digging input-driven pushdown automata

Input-driven pushdown automata (IDPDA) are pushdown automata where the next action on the pushdown store (push, pop, nothing) is solely governed by the input symbol. Nowadays such devices are usually defined such that popping from the empty pushdown does not block the computation but continues it with empty pushdown. Here, we consider IDPDAs that have a more balanced behavior concerning pushing and popping. Digging input-driven pushdown automata (DIDPDA) are basically IDPDAs that, when forced to pop from the empty pushdown, dig a hole of the shape of the popped symbol in the bottom of the pushdown. Popping further symbols from a pushdown having a hole at the bottom deepens the current hole furthermore. The hole can only be filled up by pushing symbols previously popped. We study the impact of the new behavior of DIDPDAs on their power and compare their capacities with the capacities of ordinary IDPDAs and tinput-driven pushdown automata which are basically IDPDAs whose input may be preprocessed by length-preserving finite state transducers. It turns out that the capabilities are incomparable. We address the determinization of DIDPDAs and their descriptional complexity, closure properties, and decidability questions.

System for Identifying and Correcting Invalid Words in the Devanagari Script for Text to Speech Engine

The Text To Speech (TTS) system takes text as an input and generates speech as an output. If input text is incorrect then overall quality of speech output may degrade. The main aim of the proposed system is to provide correct input text to the TTS. The system takes Unicode word as an input, identifies invalid word and corrects it by inserting, deleting or updating characters of the word. In this system, the State Machine is used to identify and correct invalid word in the Devanagari script which in turn is based on rules. Rules are developed for converting character to input symbol. Actions and States are identified for State Machine. Finally, the state transition table is developed for validation and correction of word. Using this system, incorrect words of the Devanagari script can be corrected to valid words (word contains all the valid Devanagari syllables) based on Devanagari script grammar. Since, all Devanagari characters are not present in Hindi language; this system will correct these nonHindi characters to Hindi

System for Identifying and Correcting Invalid Words in the Devanagari Script for Text to Speech Engine

The Text To Speech (TTS) system takes text as an input and generates speech as an output. If input text is incorrect then overall quality of speech output may degrade. The main aim of the proposed system is to provide correct input text to the TTS. The system takes Unicode word as an input, identifies invalid word and corrects it by inserting, deleting or updating characters of the word. In this system, the State Machine is used to identify and correct invalid word in the Devanagari script which in turn is based on rules. Rules are developed for converting character to input symbol. Actions and States are identified for State Machine. Finally, the state transition table is developed for validation and correction of word. Using this system, incorrect words of the Devanagari script can be corrected to valid words (word contains all the valid Devanagari syllables) based on Devanagari script grammar. Since, all Devanagari characters are not present in Hindi language; this system will correct these nonHindi characters to Hindi.

Universal Hypergraphic Automata Representation by Autonomous Input Symbols

Hypergraphic automata are automata with state sets and input symbol sets being hypergraphs which are invariant under actions of transition and output functions. Universally attracting objects of a category of hypergraphic automata are automata Atm(H1,H2). Here,H1is a state hypergraph,H2is classified as an output symbol hypergraph, andS= EndH1× Hom(H1,H2) is an input symbol semigroup. Such automata are called universal hypergraphic automata. The input symbol semigroupSof such an automaton Atm(H1,H2) is an algebra of mappings for such an automaton. Semigroup properties are interconnected with properties of the algebraic structure of the automaton. Thus, we can study universal hypergraphic automata with the help of their input symbol semigroups. In this paper, we investigated a representation problem of universal hypergraphic automata in their input symbol semigroup. The main result of the current study describes a universal hypergraphic automaton as a multiple-set algebraic structure canonically constructed from autonomous input automaton symbols. Such a structure is one of the major tools for proving relatively elementary definability of considered universal hypergraphic automata in a class of semigroups in order to analyze interrelation of elementary characteristics of universal hypergraphic automata and their input symbol semigroups. The main result of the paper is the solution of this problem for universal hypergraphic automata for effective hypergraphs withp-definable edges. It is an important class of automata because such an algebraic structure variety includes automata with state sets and output symbol sets represented by projective or affine planes, along with automata with state sets and output symbol sets divided into equivalence classes. The article is published in the authors' wording.

A Discussion of Finite State Machine in String Search Based on Function & Class FSM Implementations

The simplest type of computing machine that is worth considering is called a ‘finite state machine’. As it happens, the finite state machine is also a useful approach to many problems in software architecture, only in this case you don’t build one you simulate it. Essentially a finite state machine consists of a number of states – finite naturally! When a symbol, a character from some alphabet say, is input to the machine it changes state in such a way that the next state depends only on the current state and the input symbol. Notice that this is more sophisticated than you might think because inputting the same symbol doesn’t always produce the same behaviour or result because of the change of state. A few examples based on the C++ implementation of the finite state algorithm based on the function & class objects is presented.

An Efficient Primitive-Based Method to Recognize Online Sketched Symbols with Autocompletion

We present a new structural method of sketched symbol recognition, which aims to recognize a hand-drawn symbol before it is fully completed. It is invariant to scale, stroke number, and order. We also present two novel descriptors to represent the spatial distribution between two primitives. One is invariant to rotation and the other is not. Then a symbol is represented as a set of descriptors. The distance between the input symbol and the template one is calculated based on the assignment problem. Moreover, a fast nearest neighbor (NN) search algorithm is proposed for recognition. The method achieves a satisfactory recognition rate in real time.