Abstract: A method for accurately tracking and controlling robots includes capturing a first image of a first matrix code labeled on a first mobile robot at a first time by one or more stationery cameras, wherein the first matrix code is encoded with an identification that uniquely identifies the first mobile robot, correcting a tilt of the first image to produce a first corrected image that shows the first matrix code in a substantially rectangular or square shape, extracting the identification to uniquely identify the first mobile robot, calculating a first position of the first mobile robot based on the first corrected image by a computer system, and controlling a movement of the first mobile robot based at least in part on the identification and the first position of the first mobile robot.

Abstract: A method for accurately tracking and controlling robots includes capturing a first image of a first matrix code labeled on a first mobile robot at a first time by one or more stationery cameras, wherein the first matrix code is encoded with an identification that uniquely identifies the first mobile robot, correcting a tilt of the first image to produce a first corrected image that shows the first matrix code in a substantially rectangular or square shape, extracting the identification to uniquely identify the first mobile robot, calculating a first position of the first mobile robot based on the first corrected image by a computer system, and controlling a movement of the first mobile robot based at least in part on the identification and the first position of the first mobile robot.

Abstract: A method for accurately tracking and controlling robots includes capturing a first image of a first matrix code labeled on a first mobile robot at a first time by one or more stationery cameras, wherein the first matrix code is encoded with an identification that uniquely identifies the first mobile robot, correcting a tilt of the first image to produce a first corrected image that shows the first matrix code in a substantially rectangular or square shape, extracting the identification to uniquely identify the first mobile robot, calculating a first position of the first mobile robot based on the first corrected image by a computer system, and controlling a movement of the first mobile robot based at least in part on the identification and the first position of the first mobile robot.

Abstract: A method for encoding a multi-lingual text in a matrix code symbol includes receiving a multi-lingual text comprising Latin-based characters in a Latin-based language and non-Latin-based characters in a non-Latin-based language, converting the non-Latin-based characters in the multi-lingual text to index values to produce a pseudo text, and encoding the pseudo text in a matrix-code symbol. The conversion is based on a predefined mapping that converts the Unicode values of the non-Latin-based characters to index values having fewer digits than the corresponding Unicode values of the non-Latin-based characters.

Abstract: Project management systems and methods to streamline project planning, task management, time management, and other similar applications. The method includes filing a request to start a project on a project management system. The method further includes receiving notice that role handlers have been assigned to a plurality of tasks required to complete the project. The method further includes performing at least one task of the plurality of tasks. The method further includes receiving notice that the at least one task has been reviewed by at least one of the assigned role handlers.

Abstract: A method for accurately tracking and controlling robots includes capturing a first image of a first matrix code labeled on a first mobile robot at a first time by one or more stationery cameras, wherein the first matrix code is encoded with an identification that uniquely identifies the first mobile robot, correcting a tilt of the first image to produce a first corrected image that shows the first matrix code in a substantially rectangular or square shape, extracting the identification to uniquely identify the first mobile robot, calculating a first position of the first mobile robot based on the first corrected image by a computer system, and controlling a movement of the first mobile robot based at least in part on the to identification and the first position of the first mobile robot.

Abstract: A method for automatically recognizing Arabic text includes building an Arabic corpus comprising Arabic text files written in different writing styles and ground truths corresponding to each of the Arabic text files, storing writing-style indices in association with the Arabic text files, digitizing an Arabic word to form an array of pixels, dividing the Arabic word into line images, forming a text feature vector from the line images, training a Hidden Markov Model using the Arabic text files and ground truths in the Arabic corpus in accordance with the writing-style indices, and feeding the text feature vector into a Hidden Markov Model to recognize the Arabic words.

Abstract: A method for preparing a multi-lingual personal identification card includes receiving a multi-lingual text comprising Latin-based characters in a Latin-based language and non-Latin-based characters in a non-Latin-based language. The multi-lingual text includes the name of the holder of the personal identification card in the Latin-based language and the non-Latin-based language. The method converts the non-Latin-based characters in the multi-lingual text to index values to produce a pseudo text. The conversion is based on a predefined mapping that converts the Unicode values of the non-Latin-based characters to index values having fewer digits than the corresponding Unicode values of the non-Latin-based characters. The method receives vector data for a personal image which includes a facial image, a finger print, or a combination of both of the holder of the personal identification card, and encodes the pseudo text and the vector data in the matrix-code symbol.

Abstract: A method for automatically recognizing Arabic text includes building an Arabic corpus comprising Arabic text files written in different writing styles and ground truths corresponding to each of the Arabic text files, storing writing-style indices in association with the Arabic text files, digitizing an Arabic word to form an array of pixels, dividing the Arabic word into line images, forming a text feature vector from the line images, training a Hidden Markov Model using the Arabic text files and ground truths in the Arabic corpus in accordance with the writing-style indices, and feeding the text feature vector into a Hidden Markov Model to recognize the Arabic words.

Abstract: A method for automatically recognizing Arabic text includes building an Arabic corpus comprising Arabic text files written in different writing styles and ground truths corresponding to each of the Arabic text files, storing writing-style indices in association with the Arabic text files, digitizing a line of Arabic characters to form an array of pixels, dividing the line of the Arabic characters into line images, forming a text feature vector from the line images, training a Hidden Markov Model using the Arabic text files and ground truths in the Arabic corpus in accordance with the writing-style indices, and feeding the text feature vector into a Hidden Markov Model to recognize the line of Arabic characters.

Abstract: Project management systems and methods to streamline project planning, task management, time management, and other similar applications. The method includes filing a request to start a project on a project management system. The method further includes receiving notice that role handlers have been assigned to a plurality of tasks required to complete the project. The method further includes performing at least one task of the plurality of tasks. The method further includes receiving notice that the at least one task has been reviewed by at least one of the assigned role handlers.

Abstract: The invention generally relates to IT, and more particularly, to a method and system to achieve project-based diffusion of IT innovations. In accordance with aspects of the invention, the system comprises three integrated components. The first component is directed to the project management stages taken by an organization to deploy new technology innovations. This in turn consists of four stages: visioning, matching vision, deployment, and evaluation and improvement. The second component is directed to knowledge barriers to IT innovation diffusion. The third component is directed to critical success factors (CSF) of IT innovation implementations. In one embodiment the system includes of a Program Management Module (PMM) to implement corporate strategy for project management and to transform the organization into a learning one. In further embodiments, system is explained in detail using an example of ERP implementation.

Abstract: A method for encoding a multi-lingual text in a matrix code symbol includes receiving a multi-lingual text comprising Latin-based characters in a Latin-based language and non-Latin-based characters in a non-Latin-based language, converting the non-Latin-based characters in the multi-lingual text to index values to produce a pseudo text, and encoding the pseudo text in a matrix-code symbol. The conversion is based on a predefined mapping that converts the Unicode values of the non-Latin-based characters to index values having fewer digits than the corresponding Unicode values of the non-Latin-based characters.

Abstract: A method for encoding a multi-lingual text in a matrix code symbol includes receiving a multi-lingual text comprising Latin-based characters in a Latin-based language and non-Latin-based characters in a non-Latin-based language, converting the non-Latin-based characters in the multi-lingual text to index values to produce a pseudo text, and encoding the pseudo text in a matrix-code symbol. The conversion is based on a predefined mapping that converts the Unicode values of the non-Latin-based characters to index values having fewer digits than the corresponding Unicode values of the non-Latin-based characters.

Abstract: A method for automatically recognizing Arabic text includes building an Arabic corpus comprising Arabic text files written in different writing styles and ground truths corresponding to each of the Arabic text files, storing writing-style indices in association with the Arabic text files, digitizing a line of Arabic characters to form an array of pixels, dividing the line of the Arabic characters into line images, forming a text feature vector from the line images, training a Hidden Markov Model using the Arabic text files and ground truths in the Arabic corpus in accordance with the writing-style indices, and feeding the text feature vector into a Hidden Markov Model to recognize the line of Arabic characters.

Abstract: A method for facilitating texting in a non-Latin-based language includes storing mapping between a non-Latin-based language and a Latin-based language in a computer storage. The characters in the non-Latin-based language are mapped to a first group of characters in the Latin-based language. The diacritics in the non-Latin-based language are mapped to a second group of characters in the Latin-based language. The characters in the non-Latin-based language in a texting message are converted into in the coded text comprising characters in the Latin-based language according to the mapping. The texting message having the coded text is transmitted from a first communication device to a second communication device. The characters of the Latin-based language in the coded text are converted back into the characters in the non-Latin-based language according to the mapping to allow a user to read the text message in the non-Latin-based language on the second communication device.

Abstract: A method for automatically recognizing Arabic text includes digitizing a line of Arabic characters to form a two-dimensional array of pixels each associated with a pixel value, wherein the pixel value is expressed in a binary number, dividing the line of the Arabic characters into a plurality of line images, defining a plurality of cells in one of the plurality of line images, wherein each of the plurality of cells comprises a group of adjacent pixels, serializing pixel values of pixels in each of the plurality of cells in one of the plurality of line images to form a binary cell number, forming a text feature vector according to binary cell numbers obtained from the plurality of cells in one of the plurality of line images, and feeding the text feature vector into a Hidden Markov Model to recognize the line of Arabic characters.

Abstract: A method for automatically recognizing Arabic text includes digitizing a line of Arabic characters to form a two-dimensional array of pixels each associated with a pixel value, wherein the pixel value is expressed in a binary number, dividing the line of the Arabic characters into a plurality of line images, defining a plurality of cells in one of the plurality of line images, wherein each of the plurality of cells comprises a group of adjacent pixels, serializing pixel values of pixels in each of the plurality of cells in one of the plurality of line images to form a binary cell number, forming a text feature vector according to binary cell numbers obtained from the plurality of cells in one of the plurality of line images, and feeding the text feature vector into a Hidden Markov Model to recognize the line of Arabic characters.

Abstract: Project management systems and methods to streamline project planning, task management, time management, and other similar applications. The method includes filing a request to start a project on a project management system. The method further includes receiving notice that role handlers have been assigned to a plurality of tasks required to complete the project. The method further includes performing at least one task of the plurality of tasks. The method further includes receiving notice that the at least one task has been reviewed by at least one of the assigned role handlers.

Abstract: A method for preparing a multi-lingual personal identification card includes receiving a multi-lingual text comprising Latin-based characters in a Latin-based language and non-Latin-based characters in a non-Latin-based language. The multi-lingual text includes the name of the holder of the personal identification card in the Latin-based language and the non-Latin-based language. The method converts the non-Latin-based characters in the multi-lingual text to index values to produce a pseudo text. The conversion is based on a predefined mapping that converts the Unicode values of the non-Latin-based characters to index values having fewer digits than the corresponding Unicode values of the non-Latin-based characters. The method receives vector data for a personal image which includes a facial image, a finger print, or a combination of both of the holder of the personal identification card, and encodes the pseudo text and the vector data in the matrix-code symbol.