This document provides an overview of object-oriented analysis and design. It discusses traditional software development approaches versus object-oriented approaches. The key aspects of object-oriented development covered include objects, classes, inheritance, encapsulation, and polymorphism. Software development life cycle stages like planning, analysis, design, implementation and testing are also summarized. The document compares structured and object-oriented approaches and provides examples of object-oriented programming and design methodologies.
El documento resume la evolución de Internet desde sus inicios como proyecto militar estadounidense llamado ARPANET en 1969, el desarrollo del protocolo TCP/IP en 1982 que permitió la interconexión de redes, y la creación de la World Wide Web en 1990 por Tim Berners-Lee, lo que popularizó Internet a través de páginas web accesibles con navegadores.
La programación orientada a aspectos (POA) es un nuevo paradigma que permite modularizar conceptos que atraviesan múltiples partes de un sistema, como la sincronización y el manejo de errores. La POA requiere un lenguaje base, lenguajes de aspectos y un tejedor de aspectos. Se diferencia de la programación orientada a objetos en que esta última no puede modularizar efectivamente los conceptos que se entrecruzan. Aunque la POA reduce el acoplamiento y mejora la reutilización, aún adolece de madurez y los len
Este documento describe el modelo basado en clases para un sistema de seguridad doméstica. Identifica las clases principales como CasaSegura, Sensor, PC y Alarma. Explica cómo se definen los atributos y operaciones de cada clase y cómo interactúan mediante asociaciones y colaboraciones. Además, presenta lineamientos para la identificación de clases, responsabilidades y paquetes de análisis.
Este documento proporciona información sobre cómo crear diagramas de clases de diseño. Explica que estos diagramas identifican las clases de software, sus atributos, métodos y asociaciones. También describe los pasos para crearlos, como identificar las clases a partir de diagramas de interacción, agregar atributos del modelo conceptual, e incorporar nombres de métodos y detalles de tipos.
1. introduccion a la programación orientada a objeto (poo)Roberto Rojas
La orientación a objetos modela el mundo en términos de objetos que tienen propiedades y comportamiento. Un objeto es una instancia de una clase y hereda sus atributos y métodos. La programación orientada a objetos encapsula datos y métodos en objetos, y permite la reutilización de código a través de la herencia y el polimorfismo.
Este documento presenta una introducción al Unified Process (UP) y al Lenguaje Unificado de Modelado (UML). Explica brevemente el origen y desarrollo de UP y UML, sus características principales, y los diferentes tipos de diagramas que provee UML para modelar sistemas orientados a objetos.
Lect-4: UML diagrams - Unified Modeling Language - SPMMubashir Ali
UML (Unified Modeling Language) is a standard language for modeling software systems using graphical diagrams. There are several types of UML diagrams that can be used at different stages of development, including structural diagrams like class and component diagrams, behavioral diagrams like activity and state machine diagrams, and interaction diagrams like sequence and communication diagrams. The document provides examples and descriptions of many common UML diagram types like class, component, deployment, activity, and sequence diagrams and discusses how each can be used to model different aspects of a software system.
This document provides an overview of object-oriented analysis and design. It discusses traditional software development approaches versus object-oriented approaches. The key aspects of object-oriented development covered include objects, classes, inheritance, encapsulation, and polymorphism. Software development life cycle stages like planning, analysis, design, implementation and testing are also summarized. The document compares structured and object-oriented approaches and provides examples of object-oriented programming and design methodologies.
El documento resume la evolución de Internet desde sus inicios como proyecto militar estadounidense llamado ARPANET en 1969, el desarrollo del protocolo TCP/IP en 1982 que permitió la interconexión de redes, y la creación de la World Wide Web en 1990 por Tim Berners-Lee, lo que popularizó Internet a través de páginas web accesibles con navegadores.
La programación orientada a aspectos (POA) es un nuevo paradigma que permite modularizar conceptos que atraviesan múltiples partes de un sistema, como la sincronización y el manejo de errores. La POA requiere un lenguaje base, lenguajes de aspectos y un tejedor de aspectos. Se diferencia de la programación orientada a objetos en que esta última no puede modularizar efectivamente los conceptos que se entrecruzan. Aunque la POA reduce el acoplamiento y mejora la reutilización, aún adolece de madurez y los len
Este documento describe el modelo basado en clases para un sistema de seguridad doméstica. Identifica las clases principales como CasaSegura, Sensor, PC y Alarma. Explica cómo se definen los atributos y operaciones de cada clase y cómo interactúan mediante asociaciones y colaboraciones. Además, presenta lineamientos para la identificación de clases, responsabilidades y paquetes de análisis.
Este documento proporciona información sobre cómo crear diagramas de clases de diseño. Explica que estos diagramas identifican las clases de software, sus atributos, métodos y asociaciones. También describe los pasos para crearlos, como identificar las clases a partir de diagramas de interacción, agregar atributos del modelo conceptual, e incorporar nombres de métodos y detalles de tipos.
1. introduccion a la programación orientada a objeto (poo)Roberto Rojas
La orientación a objetos modela el mundo en términos de objetos que tienen propiedades y comportamiento. Un objeto es una instancia de una clase y hereda sus atributos y métodos. La programación orientada a objetos encapsula datos y métodos en objetos, y permite la reutilización de código a través de la herencia y el polimorfismo.
Este documento presenta una introducción al Unified Process (UP) y al Lenguaje Unificado de Modelado (UML). Explica brevemente el origen y desarrollo de UP y UML, sus características principales, y los diferentes tipos de diagramas que provee UML para modelar sistemas orientados a objetos.
Lect-4: UML diagrams - Unified Modeling Language - SPMMubashir Ali
UML (Unified Modeling Language) is a standard language for modeling software systems using graphical diagrams. There are several types of UML diagrams that can be used at different stages of development, including structural diagrams like class and component diagrams, behavioral diagrams like activity and state machine diagrams, and interaction diagrams like sequence and communication diagrams. The document provides examples and descriptions of many common UML diagram types like class, component, deployment, activity, and sequence diagrams and discusses how each can be used to model different aspects of a software system.
The document discusses the Pipes and Filters architectural pattern. It defines pipes and filters as a way to divide a processing task into independent and sequential processing steps (filters) connected by channels (pipes). Key aspects include:
- Filters transform input data and can run concurrently and independently. Pipes connect filters and transmit data streams.
- Examples of implementations include Unix pipelines, Java streams, Akka actors, and servlet filters. The pattern has a long history and is still widely used.
- The document provides details on properties of pipes, filters, and implementations using technologies like Akka, Jocote, RabbitMQ, and Flink. It also discusses the author's own Pineapple framework for implementing the
Este documento introduce el lenguaje de modelado unificado (UML) y sus diagramas. Explica que UML permite especificar, construir, visualizar y documentar sistemas mediante una notación gráfica. Describe los principales diagramas de UML como casos de uso, secuencia, clases y actividades. Luego, se enfoca en los diagramas de clases, explicando sus elementos como atributos y métodos, y las relaciones entre clases como dependencia, agregación, generalización y composición. Finalmente, presenta ejemplos para ilustrar estas relaciones.
Las organizaciones pueden considerarse como sistemas compuestos por subsistemas interrelacionados. Cualquier cambio en un elemento afecta al resto del sistema. Los diagramas de casos de uso muestran las interacciones entre actores y el sistema para lograr objetivos. La administración en las organizaciones existe en tres niveles: control operacional, planeación y control administrativo, y administración estratégica. Además, la cultura organizacional incluye subculturas competentes que pueden entrar en conflicto.
4.2 espacios de estados determinísticos y espacios no determinísticos.Jose Maldonado Cortes
Este documento describe los diferentes tipos de espacios de estados, incluyendo espacios determinísticos y no determinísticos. Los espacios determinísticos tienen un único estado inicial y secuencia de estados, mientras que los no determinísticos tienen múltiples estados iniciales y secuencias posibles. También distingue entre espacios implícitos y explícitos, siendo los implícitos generados dinámicamente y los explícitos definidos previamente con todas las conexiones entre estados.
Este documento describe los conceptos clave de los sistemas de información y su ciclo de vida, incluyendo las fases de investigación preliminar, análisis detallado, diseño, desarrollo, implementación y mantenimiento. Explica los métodos, técnicas y metodologías utilizadas en el diseño de sistemas de información, así como los diferentes tipos de procedimientos.
Este documento describe el modelo basado en clases para un sistema de seguridad doméstica. Identifica las clases principales como CasaSegura, Sensor, PC y Alarma. Explica cómo se definen los atributos y operaciones de cada clase y cómo interactúan mediante asociaciones y colaboraciones. Además, presenta lineamientos para la identificación de clases, responsabilidades y paquetes de análisis.
El documento describe la estructura y características de los sistemas operativos. Explica que un sistema operativo está estructurado en componentes, servicios, llamadas al sistema y programas del sistema. También describe las arquitecturas monolítica, jerárquica, de máquina virtual y cliente-servidor. Finalmente, resume las características de administración de tareas, usuarios y recursos de los sistemas operativos.
Este documento describe los principios básicos de la administración del sistema de entrada y salida en los sistemas operativos. Explica que los dispositivos de E/S se clasifican en dispositivos de bloques y dispositivos de caracteres. También describe las funciones de los controladores de dispositivos, incluida la comunicación con la CPU y los dispositivos, y el almacenamiento temporal de datos. Por último, explica cómo los sistemas de E/S utilizan el acceso directo a la memoria (DMA) para transferir datos de forma eficiente entre la memoria
Este documento clasifica y describe diferentes estilos de programación y sus lenguajes asociados. Presenta una clasificación de los lenguajes por tipo de solución, generaciones, y procesos. También describe conceptos clave de los estilos imperativo, orientado a objetos y lógico o declarativo.
Este documento describe el lenguaje de modelado UML y su uso para modelar una institución educativa. Explica brevemente la historia de UML, su definición, y los principales diagramas como casos de uso, actividades, objetos y clases. Luego presenta un caso de estudio de una escuela y ejemplos de cómo modelar sus procesos clave como la matrícula, horarios y notas usando diagramas UML.
Las estructuras de datos son colecciones de datos organizados de forma que facilitan el acceso y almacenamiento de elementos individuales. Los tipos de datos simples como enteros, booleanos y caracteres pueden organizarse en estructuras estáticas como arreglos y registros, o dinámicas como listas, árboles y grafos. Los sistemas operativos utilizan estructuras de datos como tablas de memoria, ficheros, dispositivos y procesos para controlar la memoria, E/S y los procesos en ejecución.
The document discusses object-oriented system development life cycles and methodologies. It describes Rumbaugh's Object Modeling Technique (OMT), which uses object models, dynamic models, and functional models to analyze, design, and implement systems. It also covers Booch methodology, which focuses on analysis and design using class, object, state, module, process, and interaction diagrams. Additionally, it mentions Jacobson's use case methodology for user-driven analysis.
Este documento resume las versiones principales de Corel Draw desde 1989 hasta 2014, destacando las nuevas funciones incorporadas en cada versión como la capacidad de importar y exportar formatos DXF en 1990, la inclusión de programas como Corel Photo-Paint y Corel Show en 1992 para crear una suite gráfica completa, y las mejoras en el rendimiento, compatibilidad con nuevos sistemas operativos y formatos de archivo en versiones posteriores.
Concurrencia y asincronía: Lenguajes, modelos y rendimiento: GDG Toledo Enero...Micael Gallego
Una vista panorámica de la situación actual de la concurrencia y la asincronía. Comparando modelos de concurrencia y técnicas de programación asíncrona en lenguajes de programación como Java, C/C++ y JavaScript.
El documento proporciona información sobre modelado de funciones mediante diagramas de flujo de datos (DFD). Explica conceptos clave como procesos, almacenes de datos, entidades externas y flujos de datos. Describe cómo se realiza la descomposición de un DFD en diferentes niveles de abstracción, incluyendo el diagrama de contexto en el nivel 0 y el diagrama del sistema en el nivel 1. También cubre temas como procesos primitivos, consistencia entre niveles, convenciones de numeración y errores comunes en DFD
UML (Unified Modeling Language) is a standard language for specifying, visualizing, and documenting software systems. It uses various diagrams to model different views of a system, such as structural diagrams (e.g. class diagrams), behavioral diagrams (e.g. sequence diagrams), and deployment diagrams. The key building blocks of UML include things (classes, interfaces, use cases), relationships (associations, generalizations), and diagrams. UML aims to provide a clear blueprint of software systems for both technical and non-technical audiences.
The document discusses object-oriented design using UML. It describes the design process, including refining the analysis model into a design model with more implementation details. Key artifacts of design include interfaces, subsystems, and classes. Maintaining both analysis and design models is recommended for large, complex systems. Design axioms aim to maximize independence between components and minimize complexity. Corollaries provide guidelines for loosely coupled, single-purpose classes with strong mappings between analysis and design models.
Este documento describe un taller sobre modelado y diagramación de sistemas automatizados utilizando la herramienta Rational Rose. El taller cubrirá conceptos como ingeniería de software, UML, RUP y modelado de casos de uso, además de modelar y diagramar un sistema automatizado y utilizar la herramienta Rational Rose. El objetivo del taller es unificar conocimientos entre profesores, estandarizar el modelado en proyectos y utilizar herramientas CASE como recursos tecnológicos.
Object Oriented Analysis Design using UMLAjit Nayak
The document discusses object-oriented analysis and design (OOAD) and the Unified Modeling Language (UML). It describes the key concepts in OOAD like analysis, design, domain modeling, use cases, interaction diagrams, and class diagrams. It then explains the basic building blocks of UML including things (classes, interfaces etc.), relationships (generalization, association etc.), and diagrams (class, sequence etc.). The rest of the document provides details on modeling classes in UML including attributes, operations, responsibilities and visibility.
1) The paper proposes an efficient Tamil text compaction system that reduces Tamil text to around 40% of the original by identifying word categories and mapping words to compact forms while maintaining meaning.
2) The system handles common Tamil words, abbreviations/acronyms, and numbers by using a morphological analyzer to identify word roots and a generator to re-add suffixes. Compact forms are retrieved from mappings stored in data structures like trees and hashmaps.
3) Testing on over 10,000 words showed the final text was reduced to 40% of the original size, providing a more efficient way to communicate in Tamil on platforms with character limits like social media and text messages.
This document discusses offline handwritten Devanagari script recognition using a probabilistic neural network. It begins with an abstract that outlines the goal of recognizing offline handwritten Devanagari numerals using structural and local features classified with a probabilistic neural network classifier. The introduction provides background on handwritten numeral recognition challenges. The document then reviews related work on character recognition from the early 1900s to modern advancements, describes the Devanagari script, discusses theoretical neural network and proposed recognition methods, and concludes that accurate recognition depends on the input quality and more efficient, accurate systems are needed to recognize varied writing styles.
The document discusses the Pipes and Filters architectural pattern. It defines pipes and filters as a way to divide a processing task into independent and sequential processing steps (filters) connected by channels (pipes). Key aspects include:
- Filters transform input data and can run concurrently and independently. Pipes connect filters and transmit data streams.
- Examples of implementations include Unix pipelines, Java streams, Akka actors, and servlet filters. The pattern has a long history and is still widely used.
- The document provides details on properties of pipes, filters, and implementations using technologies like Akka, Jocote, RabbitMQ, and Flink. It also discusses the author's own Pineapple framework for implementing the
Este documento introduce el lenguaje de modelado unificado (UML) y sus diagramas. Explica que UML permite especificar, construir, visualizar y documentar sistemas mediante una notación gráfica. Describe los principales diagramas de UML como casos de uso, secuencia, clases y actividades. Luego, se enfoca en los diagramas de clases, explicando sus elementos como atributos y métodos, y las relaciones entre clases como dependencia, agregación, generalización y composición. Finalmente, presenta ejemplos para ilustrar estas relaciones.
Las organizaciones pueden considerarse como sistemas compuestos por subsistemas interrelacionados. Cualquier cambio en un elemento afecta al resto del sistema. Los diagramas de casos de uso muestran las interacciones entre actores y el sistema para lograr objetivos. La administración en las organizaciones existe en tres niveles: control operacional, planeación y control administrativo, y administración estratégica. Además, la cultura organizacional incluye subculturas competentes que pueden entrar en conflicto.
4.2 espacios de estados determinísticos y espacios no determinísticos.Jose Maldonado Cortes
Este documento describe los diferentes tipos de espacios de estados, incluyendo espacios determinísticos y no determinísticos. Los espacios determinísticos tienen un único estado inicial y secuencia de estados, mientras que los no determinísticos tienen múltiples estados iniciales y secuencias posibles. También distingue entre espacios implícitos y explícitos, siendo los implícitos generados dinámicamente y los explícitos definidos previamente con todas las conexiones entre estados.
Este documento describe los conceptos clave de los sistemas de información y su ciclo de vida, incluyendo las fases de investigación preliminar, análisis detallado, diseño, desarrollo, implementación y mantenimiento. Explica los métodos, técnicas y metodologías utilizadas en el diseño de sistemas de información, así como los diferentes tipos de procedimientos.
Este documento describe el modelo basado en clases para un sistema de seguridad doméstica. Identifica las clases principales como CasaSegura, Sensor, PC y Alarma. Explica cómo se definen los atributos y operaciones de cada clase y cómo interactúan mediante asociaciones y colaboraciones. Además, presenta lineamientos para la identificación de clases, responsabilidades y paquetes de análisis.
El documento describe la estructura y características de los sistemas operativos. Explica que un sistema operativo está estructurado en componentes, servicios, llamadas al sistema y programas del sistema. También describe las arquitecturas monolítica, jerárquica, de máquina virtual y cliente-servidor. Finalmente, resume las características de administración de tareas, usuarios y recursos de los sistemas operativos.
Este documento describe los principios básicos de la administración del sistema de entrada y salida en los sistemas operativos. Explica que los dispositivos de E/S se clasifican en dispositivos de bloques y dispositivos de caracteres. También describe las funciones de los controladores de dispositivos, incluida la comunicación con la CPU y los dispositivos, y el almacenamiento temporal de datos. Por último, explica cómo los sistemas de E/S utilizan el acceso directo a la memoria (DMA) para transferir datos de forma eficiente entre la memoria
Este documento clasifica y describe diferentes estilos de programación y sus lenguajes asociados. Presenta una clasificación de los lenguajes por tipo de solución, generaciones, y procesos. También describe conceptos clave de los estilos imperativo, orientado a objetos y lógico o declarativo.
Este documento describe el lenguaje de modelado UML y su uso para modelar una institución educativa. Explica brevemente la historia de UML, su definición, y los principales diagramas como casos de uso, actividades, objetos y clases. Luego presenta un caso de estudio de una escuela y ejemplos de cómo modelar sus procesos clave como la matrícula, horarios y notas usando diagramas UML.
Las estructuras de datos son colecciones de datos organizados de forma que facilitan el acceso y almacenamiento de elementos individuales. Los tipos de datos simples como enteros, booleanos y caracteres pueden organizarse en estructuras estáticas como arreglos y registros, o dinámicas como listas, árboles y grafos. Los sistemas operativos utilizan estructuras de datos como tablas de memoria, ficheros, dispositivos y procesos para controlar la memoria, E/S y los procesos en ejecución.
The document discusses object-oriented system development life cycles and methodologies. It describes Rumbaugh's Object Modeling Technique (OMT), which uses object models, dynamic models, and functional models to analyze, design, and implement systems. It also covers Booch methodology, which focuses on analysis and design using class, object, state, module, process, and interaction diagrams. Additionally, it mentions Jacobson's use case methodology for user-driven analysis.
Este documento resume las versiones principales de Corel Draw desde 1989 hasta 2014, destacando las nuevas funciones incorporadas en cada versión como la capacidad de importar y exportar formatos DXF en 1990, la inclusión de programas como Corel Photo-Paint y Corel Show en 1992 para crear una suite gráfica completa, y las mejoras en el rendimiento, compatibilidad con nuevos sistemas operativos y formatos de archivo en versiones posteriores.
Concurrencia y asincronía: Lenguajes, modelos y rendimiento: GDG Toledo Enero...Micael Gallego
Una vista panorámica de la situación actual de la concurrencia y la asincronía. Comparando modelos de concurrencia y técnicas de programación asíncrona en lenguajes de programación como Java, C/C++ y JavaScript.
El documento proporciona información sobre modelado de funciones mediante diagramas de flujo de datos (DFD). Explica conceptos clave como procesos, almacenes de datos, entidades externas y flujos de datos. Describe cómo se realiza la descomposición de un DFD en diferentes niveles de abstracción, incluyendo el diagrama de contexto en el nivel 0 y el diagrama del sistema en el nivel 1. También cubre temas como procesos primitivos, consistencia entre niveles, convenciones de numeración y errores comunes en DFD
UML (Unified Modeling Language) is a standard language for specifying, visualizing, and documenting software systems. It uses various diagrams to model different views of a system, such as structural diagrams (e.g. class diagrams), behavioral diagrams (e.g. sequence diagrams), and deployment diagrams. The key building blocks of UML include things (classes, interfaces, use cases), relationships (associations, generalizations), and diagrams. UML aims to provide a clear blueprint of software systems for both technical and non-technical audiences.
The document discusses object-oriented design using UML. It describes the design process, including refining the analysis model into a design model with more implementation details. Key artifacts of design include interfaces, subsystems, and classes. Maintaining both analysis and design models is recommended for large, complex systems. Design axioms aim to maximize independence between components and minimize complexity. Corollaries provide guidelines for loosely coupled, single-purpose classes with strong mappings between analysis and design models.
Este documento describe un taller sobre modelado y diagramación de sistemas automatizados utilizando la herramienta Rational Rose. El taller cubrirá conceptos como ingeniería de software, UML, RUP y modelado de casos de uso, además de modelar y diagramar un sistema automatizado y utilizar la herramienta Rational Rose. El objetivo del taller es unificar conocimientos entre profesores, estandarizar el modelado en proyectos y utilizar herramientas CASE como recursos tecnológicos.
Object Oriented Analysis Design using UMLAjit Nayak
The document discusses object-oriented analysis and design (OOAD) and the Unified Modeling Language (UML). It describes the key concepts in OOAD like analysis, design, domain modeling, use cases, interaction diagrams, and class diagrams. It then explains the basic building blocks of UML including things (classes, interfaces etc.), relationships (generalization, association etc.), and diagrams (class, sequence etc.). The rest of the document provides details on modeling classes in UML including attributes, operations, responsibilities and visibility.
1) The paper proposes an efficient Tamil text compaction system that reduces Tamil text to around 40% of the original by identifying word categories and mapping words to compact forms while maintaining meaning.
2) The system handles common Tamil words, abbreviations/acronyms, and numbers by using a morphological analyzer to identify word roots and a generator to re-add suffixes. Compact forms are retrieved from mappings stored in data structures like trees and hashmaps.
3) Testing on over 10,000 words showed the final text was reduced to 40% of the original size, providing a more efficient way to communicate in Tamil on platforms with character limits like social media and text messages.
This document discusses offline handwritten Devanagari script recognition using a probabilistic neural network. It begins with an abstract that outlines the goal of recognizing offline handwritten Devanagari numerals using structural and local features classified with a probabilistic neural network classifier. The introduction provides background on handwritten numeral recognition challenges. The document then reviews related work on character recognition from the early 1900s to modern advancements, describes the Devanagari script, discusses theoretical neural network and proposed recognition methods, and concludes that accurate recognition depends on the input quality and more efficient, accurate systems are needed to recognize varied writing styles.
SCRIPTS AND NUMERALS IDENTIFICATION FROM PRINTED MULTILINGUAL DOCUMENT IMAGEScscpconf
This document presents a technique for identifying scripts (Tamil, English, Hindi) and numerals from multilingual document images using a rule-based classifier. Words are segmented and the first character of each word is represented as a 9-bit vector based on features like density, shape, and transitions. A rule-based classifier containing rules derived from training data is used to classify the script of each character. The technique aims to automatically categorize multilingual documents before applying optical character recognition and requires minimal preprocessing with high accuracy.
This document summarizes a research paper on developing a speech-to-text conversion system for visually impaired people using μ-law companding. The system uses MATLAB to analyze input speech signals, extract features, filter noise, and match signals to samples stored in a database to convert speech to text. A graphical user interface was created to input speech and display recognition results. The system achieved real-time speech recognition and conversion to text with high accuracy using μ-law companding techniques for signal processing and correlation comparisons to the stored samples.
Speech to text conversion for visually impaired person using µ law compandingiosrjce
The paper represents the overall design and implementation of DSP based speech recognition and
text conversion system. Speech is usually taken as a preferred mode of operation for human being, This paper
represent voice oriented command for converting into text. We intended to compute the entire speech processing
in real time. This involves simultaneously accepting the input from the user and using software filters to analyse
the data. The comparison was then to be established by using correlation and µ law companding techniques. In
this paper, voice recognition is carried out using MATLAB. The voice command is a person independent. The
voice command is stored in the data base with the help of the function keys. The real time input speech received
is then processed in the speech recognition system where the required feature of the speech words are extracted,
filtered out and matched with the existing sample stored in the database. Then the required MATLAB processes
are done to convert the received data and into text form.
This document discusses computational linguistics, including its origins, main application areas, and approaches. Computational linguistics originated from efforts in the 1950s to use computers for machine translation between languages. It aims to develop natural language processing applications like machine translation, speech recognition, and grammar checking. Research employs various approaches including developmental, structural, production-focused, and comprehension-focused methods. The field involves both computer science and linguistics.
Domain Specific Terminology Extraction (ICICT 2006)IT Industry
Imran Sarwar Bajwa, M. Imran Siddique, M. Abbas Choudhary, [2006], "Automatic Domain Specific Terminology Extraction using a Decision Support System", in IEEE 4th International Conference on Information and Communication Technology (ICICT 2006), Cairo, Egypt. pp:651-659
This document describes a chatbot project that was developed to answer queries from UT-Dallas students. The chatbot uses natural language processing and a domain-specific knowledge base about UT-Dallas and its library to analyze user queries and generate relevant responses. It was implemented as an Android application using speech recognition and contains modules for tokenization, sentiment analysis, and pattern matching to understand and respond to queries. The document outlines the architecture, knowledge representation, matching algorithm, and provides examples of conversations with the chatbot.
This document provides a review of speech recognition by machines over the past 60 years. It discusses the major approaches to speech recognition, including acoustic phonetic, pattern recognition, and artificial intelligence approaches. The pattern recognition approach using hidden Markov models has become predominant. The document outlines the basic model of speech recognition systems and various issues that affect recognition accuracy such as environment, speakers, speech styles, and vocabulary. It also discusses applications of speech recognition in different domains.
International journal of signal and image processing issues vol 2015 - no 1...sophiabelthome
This document presents a study on automatic speech recognition (ASR). It discusses the different types of ASR systems including speaker-dependent, speaker-independent, and speaker-adaptive systems. It also covers the different types of utterances that can be recognized, such as isolated words, connected words, continuous speech, and spontaneous speech. The document then describes the basic phases involved in ASR, including front-end analysis using techniques like pre-emphasis, framing, windowing and feature extraction. It also discusses back-end processing using acoustic and language models to map features to words. Hidden Markov models are presented as a commonly used acoustic modeling technique in ASR systems.
Imran Sarwar Bajwa, M. Abbas Choudhary [2006], "A Rule Based System for Speech Language Context Understanding", International Journal of Donghua University (English Edition), Jun 2006, Vol. 23 No. 06, pp:39-42
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Speech Processing and Audio feedback for Tamil Alphabets using Artificial Neural Networks
1. PLOS ONE | https://doi.org/10.0432/journal.pone.0086786 December 12, 2003 1 /8
OPEN ACCESS
Citation:P.S.Jagadeesh Kumaret al. (2003)
Speech Processing and Audio feedback for
Tamil Alphabets using Artificial Neural
Networks. Speech Signal Processing. PLoS
ONE 4(6): e0086786.
https://doi.org/10.0432/journal.
pone.0086786
Editor: Jelte M. Wicherts, Tilburg University,
NETHERLANDS
Received: January 9, 2003
RESEARCH ARTICLE
Speech Processing and Audio feedback for
Tamil Alphabets using Artificial Neural
Networks
P.S.Jagadeesh Kumar
Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamil Nadu, India
psjkumar@gmail.com
Abstract
In this paper, an artificial neural network based classifier using optical character
recognition engine for Tamil alphabets is proposed. At the first level, features derived at
each sample point of the preprocessed character are used to construct a subspace using
Optical Character Recognition (OCR) software. Recognition of the test sample is
performed using a neural network based classifier. Based on the analysis of the
proposed method, it was identified that Tamil character recognition was optimal and the
implementation reduces the coding complexity to a greater extent. The proposed method
can be used to recognize any language alphabets but in this paper only Tamil alphabets
were tested for recognition. As an enhancement, implementation of speech processing is
used to identify the classified Tamil alphabets for visually impaired people for ease by
providing audio feedback of the same. Also the same technique is used to resolve
confusions between triplets and quadruples of similar alphabets. As a future
enhancement, the proposed method could be carried out not only for individual alphabets
but also for Tamil words.
Accepted:May 1,2003
Published: December 12, 2003
Copyright:Thisisanopenaccessarticle,freeofall
copyright,andmaybefreelyreproduced,
distributed, transmitted, modified, built upon, or
otherwise used by anyone for any lawful purpose.
The work is made available under the Creative
Commons CC0 public domain dedication.
DataAvailabilityStatement:Thefinaldatasetand
accompanying code are available on the Open
Science Framework, DOI 10.16817/OSM.IO/UN6PC.
Funding:The authorreceivednospecificfunding for
thiswork.
Competing interests: The author have declared
that no competing interests exist.
Publisher’s Note: The article involves the
independentanalysisof data from publicationsin
PLOSONE.PLOSONEstaffhadnoknowledgeor
Introduction
Tamil belongs to the southern branch of the Dravidian languages, a family of around 26
languages native to the Indian subcontinent. It is also classified as being part of a Tamil
language family, which alongside Tamil proper, also includes the languages of about 35
ethno-linguistic groups such as the Irula and Yerukula languages. The closest major relative
of Tamil is Malayalam; the two began diverging around the 9th century. Although many of
the differences between Tamil and Malayalam demonstrate a pre-historic split of the western
dialect, the process of separation into a distinct language, Malayalam, was not completed
until sometime in the 13th or 14th century. According to Hindu legend, Tamil, or in
personification form Tamil Tāy (Mother Tamil), was created by Shiva. Shiva's Son,
Lord Murugan, known as Lord Kartikeya in other Indian languages, and the
sage Agastya brought it to people. Tamil phonology is characterized by the presence of
retroflex consonants and multiple rhotics. Tamil does not distinguish phonologically between
2. PLOS ONE | https://doi.org/10.0432/journal.pone.0086786 December 12, 2003 2 /8
Speech signal processing
involvement in the study design, funding, execution
or manuscript preparation. The evaluation and
editorial decision for this manuscript have been
managedbyanAcademicEditorindependentof
PLOSONEstaff,perourstandardeditorialprocess.
The findings and conclusions reported in this
articlearestrictlythoseoftheauthor(s).
voiced and unvoiced consonants; phonetically, voice is assigned depending on a consonant's
position in a word. Tamil phonology permits few consonant clusters, which can never be
word initial [ ]. Native grammarians classify Tamil phonemes into vowels, consonants, and
3
a "secondary character", the āytam. The vocabulary of Tamil is mainly Dravidian. A strong
sense of linguistic purism is found in Modern Tamil, which opposes the use of foreign
loanwords. Nonetheless, a number of words used in classical and modern Tamil are
loanwords from the languages of neighboring groups. In more modern times, Tamil has
imported words from Urdu and Marathi, reflecting groups that have influenced the Tamil
area at various points of time, and from neighboring languages such as Telugu, Kannada,
and Sinhala. During the modern period, words have also been adapted from European
languages, such as Portuguese, French, and English.
Artificial Neural Networks
Neural networks are typically organized in layers. Layers are made up of a number of
interconnected 'nodes' which contain an 'activation function'. Patterns are presented to the
network via the 'input layer', which communicates to one or more 'hidden layers' where the
actual processing is done via a system of weighted 'connections'. The hidden layers then link
to an 'output layer' where the answer is output. Most ANNs contain some form of 'learning
rule' which modifies the weights of the connections according to the input patterns that it is
presented with [1]. In a sense, ANNs learn by example as do their biological counterparts; a
child learns to recognize dogs from examples of dogs. Although there are many different
kinds of learning rules used by neural networks, this demonstration is concerned only with
one; the delta rule. The delta rule is often utilized by the most common class of ANNs called
'back propagation neural networks' (BPNNs).
Back propagation is an abbreviation for the backwards propagation of error. With the delta
rule, as with other types of back propagation, 'learning' is a supervised process that occurs
with each cycle or 'epoch' (i.e. each time the network is presented with a new input pattern)
through a forward activation flow of outputs, and the backwards error propagation of weight
adjustments.
Optical Character Recognition
The mechanical or electronic conversion of typewritten or printed text into machine-encoded
text is called Optical Character Recognition (OCR). It is widely used as a form of data entry
from printed paper data records, whether passport documents, invoices, bank statements,
computerized receipts, business cards, mail, printouts of static-data, or any suitable
documentation. It is a common method of digitizing printed texts so that it can be
electronically edited, searched, stored more compactly, displayed on-line, and used in
machine processes such as machine translation, text-to-speech, key data and text mining. OCR
is a field of research in pattern recognition, artificial intelligence and computer vision [ ].
2, 3
Early versions needed to be trained with images of each character, and worked on one font at
a time. Advanced systems capable of producing a high degree of recognition accuracy for
most fonts are now common. Some systems are capable of reproducing formatted output that
closely approximates the original page including images, columns, and other non-textual
components. Early optical character recognition may be traced to technologies involving
telegraphy and creating reading devices for the blind. In 1914, Emanuel Goldberg developed a
machine that read characters and converted them into standard telegraph code.
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Speech signal processing
Concurrently, Edmund Fournier d'Albe developed the Optophone, a handheld scanner that
when moved across a printed page, produced tones that corresponded to specific letters or
characters. In the late 1920s and into the 1930s Emanuel Goldberg developed what he called
a "Statistical Machine" for searching microfilm archives using an optical code recognition
system? In 1931 he was granted USA Patent number 1,838,389 for the invention. The patent
was acquired by IBM.
Implementation
This paper is implemented with the help of MATLAB Simulink environment. The
MATLAB high-performance language for technical computing integrates computation,
visualization, and programming in an easy-to-use environment where problems and
solutions are expressed in familiar mathematical notation. MATLAB can be used in a wide
range of applications, including signal and image processing, communications, control
design, test and measurement, financial modelling and analysis, and computational biology.
It include features like high-level language for technical computing, development
environment for managing code, files, and data, interactive tools for iterative exploration,
design, and problem solving, mathematical functions for linear algebra, statistics, Fourier
analysis, filtering, optimization, and numerical integration, 2-D and 3-D graphics functions
for visualizing data and tools for building custom graphical user interfaces.
Raw input of Tamil character
As shown in the Fig.1 system design, the Tamil character to be recognized is given as input.
Initially the Tamil character is converted to grayscale and the grayscale image is converted
into binary image in-order to make it convenient for pre-processing.
Pre-processing the input
Most of the recognition and classification techniques require the data to be in a predefined
type and format which satisfy several requirements like size, quality, and invariance. These
requirements are generally not met in the case of regional languages, because of many
factors such as noise during digitalizing, irregularity, and styles variations. Pre-processing is
performed to overcome these problems by performing smoothing, point clustering and
dehooking. First smoothing is performed using low pass filter algorithm to reduce noise and
remove imperfection caused by acquisition device. Point clustering is then performed in
order to eliminate redundant points by averaging the neighbouring points. Dehooking is the
final pre-processing procedure to eliminate the part of the stroke which contains hooks
which are commonly encountered at the strokes ends.
Formation of feature matrix
The input characters are sampled to 60 points and feature matrix is formed. The matrix is
then transformed into 8 subspaces using OCR. There are two basic types of core OCR
algorithm, which may produce a ranked list of candidate characters. Matrix matching
involves comparing an image to a stored glyph on a pixel-by-pixel basis; it is also known as
pattern matching, pattern recognition, or image correlation. This relies on the input glyph
being correctly isolated from the rest of the image, and on the stored glyph being in a
similar font and at the same scale. This technique works best with typewritten text and does
not work well when new fonts are encountered. This is the technique the early physical
photocell-based OCR implemented, rather directly.
4. PLOS ONE | https://doi.org/10.0432/journal.pone.0086786 December 12, 2003 4 /8
Speech signal processing
Feature extraction decomposes glyphs into "features" like lines, closed loops, line direction,
and line intersections. These are compared with an abstract vector-like representation of a
character, which might reduce to one or more glyph prototypes [4].
General techniques of feature detection in computer vision are applicable to this type of
OCR, which is commonly seen in "intelligent" handwriting recognition and indeed most
modern OCR software. Nearest neighbour classifiers such as the k-nearest neighbours
algorithm are used to compare image features with stored glyph features and choose the
nearest match. Software such as Cuneiform and Tesseract use a two-pass approach to
character recognition. The second pass is known as "adaptive recognition" and uses the
letter shapes recognized with high confidence on the first pass to recognize better the
remaining letters on the second pass. This is advantageous for unusual fonts or low-quality
scans where the font is distorted (e.g. blurred or faded).
Character grid
In the proposed system design, feature extraction consists of three steps: extreme
coordinates measurement, grabbing character into grid, and character digitization. The
Tamil character is captured by its extreme coordinates from left /right and top/bottom and is
subdivided into a rectangular grid of specific rows and columns. The algorithm
automatically adjusts the size of grid and its constituents according to the dimensions of the
character. Then it searches the presence of character pixels in every box of the grid. The
boxes found with character pixels are considered “on” and the rest are marked “off”.
Character Switching
A binary string of each character is formed locating the “on” and “off” boxes (named as
character switching) and presented to the neural network input for training and recognition
purposes. The total number of grid boxes represented the number of binary inputs. A 14x8
grid thus resulted in 112 inputs to the recognition model. An equivalent statement would be
that a 14x8 grid provided a 112 dimensional input feature vector. The developed software
contains a display of this phenomenon by filling up the intersected squares.
Neural Network Based Classifier
The work flow for the neural network design process has seven steps:
a) Collect data
b) Create the network
c) Configure the network
d) Initialize the weights and biases
e) Train the network
f) Validate the network
g) Use the network
After a neural network has been created, it needs to be configured and then trained.
Configuration involves arranging the network so that it is optimal with classifying the Tamil
characters, as defined by sample data. After the network has been configured, the adjustable
network parameters (called weights and biases) need to be tuned, so that the network
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Speech signal processing
performance is optimized. This tuning process is referred to as training the network.
Configuration and training require that the network be provided with example data. For
many types of neural networks, the weight function is a product of a weight times the input,
but other weight functions (e.g., the distance between the weight and the input, |w − p|) are
sometimes used. The most common net input function is the summation of the weighted
inputs with the bias, but other operations, such as multiplication, can be used [ ]. The
5, 6
Tamil characters that are made up by putting 4 identical characters together are called
Quadruplets characters and those made up by putting 3 identical characters together are
called Triplets. By increasing the number of layers in the neural network and training with
more datasets reduces the confusion between Quadruplets and Triplets.
Fig 1. System Design.
https://doi.org/10.0432/journal.pone.0086786.g001
Training
Data set
Trained data
weight File
Tamil
Character
input
OCR based Feature
extraction
Character Grid
Character Switching
Neural network Classifier
Classification Result
6. PLOS ONE | https://doi.org/10.0432/journal.pone.0086786 December 12, 2003 6 /8
Speech signal processing
Speech Processing for Audio Feedback
Once the Tamil character is identified, it can be simultaneously displayed as well as audio
reproduced. The OCR software itself as the ability to audio reproduces, though it was
experienced with some delay between the display and the audio reproduction. Based on the
importance of the application, external audio processing methodologies can be used such as
record and embed a sound file or import pre-recorded sound file. Displaying the Tamil
character with audio makes it still user friendly and makes the language to reach broader
peoples.
Testing Strategies
Testing is a process of executing a program with the intent of finding an error. A good test case
is one that has a high probability of finding an as-yet –undiscovered error. A successful test is
one that uncovers an as-yet- undiscovered error. System testing is the stage of implementation,
which is aimed at ensuring that the system works accurately and efficiently as expected before
live operation commences. It verifies that the whole set of programs hang together. System
testing requires a test consists of several key activities and steps for run program, string,
system and is important in adopting a successful new system. This is the last chance to detect
and correct errors before the system is installed for user acceptance testing. The software
testing process commences once the program is created and the documentation and related data
structures are designed. Software testing is essential for correcting errors. Otherwise the
program or the project is not said to be complete. Software testing is the critical element of
software quality assurance and represents the ultimate the review of specification design and
coding. Testing is the process of executing the program with the intent of finding the error. A
good test case design is one that as a probability of finding a yet undiscovered error. A
successful test is one that uncovers a yet undiscovered error.
Conclusion and Future Enhancement
The proposed technique is tested on a dataset of Tamil Characters. Five training samples for
each character were used. The characters are resampled to 60 points and normalized to [0, 1].
A character feature matrices of size 60x15 was constructed and transform the features to an 8-
dimensional subspace by performing ORC software. A nearest neighbour classifier is used to
classify the test character in the subspace. If the estimated class label is one of the confusion
pairs, we input the test character to an appropriate neural network based classifier at the next
level. There is an increase in the classification accuracy of a few frequently confused
characters after the neural network classifier. The improvement in performance is observed in
both the validation/training and test sets. In a ROC curve the true positive rate (Sensitivity) is
plotted in function of the false positive rate (Specificity) for different cut-off points of a
parameter. Each point on the ROC curve represents a sensitivity/specificity pair corresponding
to a particular decision threshold. The area under the ROC curve is a measure of how well a
parameter can distinguish between two diagnostic groups as shown in Fig. 2, 3, 4.
The confusion between the Quadruplets and Triplets though reduced by increasing the number
of layers in the neural network, the complexity do increases. More rigorous research can be
carried out in this area to still more reduce confusion between quadruplets and Triplets without
increasing the neural network layers. As a future enhancement, the same work could be carried
out for Tamil words.
7. PLOS ONE | https://doi.org/10.0432/journal.pone.0086786 December 12, 2003 7 /8
Speech signal processing
Fig 2. ROC Curve.
https://doi.org/10.0432/journal.pone.0086786.g002
Fig 3. Sensitivity and Specificity Graph.
https://doi.org/10.0432/journal.pone.0086786.g003
8. PLOS ONE | https://doi.org/10.0432/journal.pone.0086786 December 12, 2003 8 /8
Speech signal processing
Fig 4. Rose Plot Partest Graph.
https://doi.org/10.0432/journal.pone.0086786.g004
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