Engineering

Our goal is to enable rapid production of static and dynamic object models from natural language description of problems. Rapid modeling is achieved through automation of analysis tasks. This automation captures the cognitive schemes analysts use to build their models of the world through the use of a precise methodology. The methodology is based on the use of proposed technique called role posets, and a semi-natural language (called 4W).

Our goal is to enable rapid production of static and dynamic object models from natural language description of problems. Rapid modeling is achieved through automation of analysis tasks. This automation captures the cognitive schemes analysts use to build their models of the world through the use of a precise methodology. The methodology is based on the use of proposed technique called role posets, and a semi-natural language (called 4W). First versions of this tool were used as prototypes to produce early design artifacts for very small (toy) problems. Current version has been successfully used as an educational tool in object oriented software engineering courses. We present the tool with its new complete features and results of its application in learning process. Original problem statements are automatically translated to 4W language. The produced sentences then, are analyzed with role posets to produce static model views. Finally the 4W sentences are used to generate dynamic views of the problem. This set of methods maximizes analysis process agility, promotes reusability and constitutes a valuable tool in the learning process of object thinking. The prototype tool: GOOAL (Graphic Object Oriented Analysis Laboratory) receives a natural language (NL) description of a problem and produces the object models taking decisions sentence by sentence. The user realizes the consequences of the analysis of every sentence in real time. Unique features of this tool are the underlying methodology and the production of dynamic object models.

Our goal is to enable rapid production of static and dynamic object models from natural language description of problems. Rapid modeling is achieved through automation of analysis tasks. This automation captures the cognitive schemes analysts use to build their models of the world through the use of a precise methodology. The methodology is based on the use of proposed technique called role posets, and a semi-natural language (called 4W).

The development of quality software is a basic requirement that must be observed. Measuring software is a tool that allows the development of quality software for its entire life cycle. For software measurement, software metrics are used, among other techniques, which allow us to obtain a numerical value from a software product. There are two problems with these measurements: a value obtained can have different meanings depending on the project and what is desired as a result from the measurement, and the other problem is that the number and type of measurements is limited by the capabilities of the used tool. This paper presents a promising solution to the problem above by presenting a technique with which users can obtain any desired metrics and apply them to code in any programming language.

In this document we present a methodology for movement pattern recognition from arm-forearm myoelectric signals, starting off from the design and implementation of an electromyography (EMG) instrumentation system, considering the Surface EMG for the Non Invasive Assessment of Muscles (SENIAM) rules. Signal processing and characterization techniques were applied using the pass-band Butterworth digital filter and fast Fourier transform (FFT). Artificial neural networks (ANN) such as backpropagation and radial basis function (RBF) were used for the pattern recognition or classification of the EMG signals. The best results were obtained using the RBF ANN, achieving an average accuracy of 98%.

de la posición cráneo-cervical en pacientes con clases esqueletales II y III. Int. J. Morphol., 33(2):415-419, 2015. RESUMEN: La posición cráneo-cervical representa un factor importante en el diagnostico morfológico de discrepancias óseas, articulares y miofuncionales. En base a las diversas clases esqueletales se observan diferencias en la ubicación de puntos craneométricos que resultan determinantes en el diagnóstico del equilibrio ortostático del cráneo con la porción cervical de la columna vertebral. El objetivo de este estudio fue evaluar y comparar la posición cráneo-cervical en clases esqueletales II y III. Se recolectaron 114 radiogra-fías laterales de cráneo, se analizaron y compararon los puntos craneométricos por medio de cefalometría con la Técnica de Rocabado. Los resultados muestran diferencias estadísticamente significativas en las posiciones craneales para cada clase esquelética tanto en distancias como rotación entre cráneo y porción cervical de la columna vertebral.