Guillermo Licea

A hybrid approach for image recognition combining type-2 fuzzy logic, modular neural networks and the Sugeno integral

In this paper, a hybrid approach for image recognition combining type-2 fuzzy logic, modular neural networks and the Sugeno integral is described. Interval type-2 fuzzy inference systems are used to perform edge detection and to calculate fuzzy densities for the decision process. A type-2 fuzzy system is used for edge detection, which is a pre-processing applied to the training data for better use in the neural networks. Another type-2 fuzzy system calculates the fuzzy densities necessary for the Sugeno integral, which is used to integrate results of the neural network modules. In this case, fuzzy logic is shown to be a good methodology to improve the results of a neural system facilitating the representation of the human perception. A comparative study is also made to verify that the proposed approach is better than existing approaches and improves the performance over type-1 fuzzy logic.

A New Method for Edge Detection in Image Processing Using Interval Type-2 Fuzzy Logic

Edges detection in digital images is a problem that has been solved by means of the application of different techniques from digital signal processing. Also the combination of some of these techniques with fuzzy inference system (FIS) has been applied. In this work a new FIS type-2 method is implemented for the detection of edges and the results of three different techniques for the same goal are compared.

Type-2 Fuzzy Logic for Improving Training Data and Response Integration in Modular Neural Networks for Image Recognition

The combination of Soft Computing techniques allows the improvement of intelligent systems with different hybrid approaches. In this work we consider two parts of a Modular Neural Network for image recognition, where a Type-2 Fuzzy Inference System (FIS 2) makes a great difference. The first FIS 2 is used for feature extraction in training data, and the second one to find the ideal parameters for the integration method of the modular neural network. Once again Fuzzy Logic is shown to be a tool that can help improve the results of a neural system, when facilitating the representation of the human perception.

Modular Neural Networks and Type-2 Fuzzy Logic for Face Recognition

In this paper we present a method for face recognition combining modular neural networks and two interval type-2 fuzzy inference systems (FIS 2) for face recognition. The first FIS 2 is used for edges detection in the training data, and the second one to find the ideal parameters for the Sugeno integral as a decision operator. Fuzzy logic is shown to be a tool that can help improve the results of a neural system facilitating the representation of the human perception.

Big five patterns for software engineering roles using an ANFIS learning approach with RAMSET

This paper proposes an ANFIS (Adaptive Network Based Fuzzy Inference System) Learning Approach where we have found patterns of personality types using Big Five Personality Tests for Software Engineering Roles in Software Development Project Teams as part of RAMSET (Role Assignment Methodology for Software Engineering Teams) methodology. An ANFIS model is applied to a set of role traits resulting from Big Five personality tests in our case studies obtaining a Takagi-Sugeno-Kang (TSK) Fuzzy Inference System (FIS) type model with rules that helps us recommend best suited roles for performing in software engineering teams.

Type-2 Fuzzy Systems for Improving Training Data and Decision Making in Modular Neural Networks for Image Recognition

In this paper we consider a Modular Neural Network combined with two Interval Type-2 Fuzzy Inference Systems (FIS 2) for image recognition. The first FIS 2 is used for edges detection in training data, and the second one to find the best parameters for the Sugeno Integral as decision operator. Once again Fuzzy Logic is shown to be a tool that can help improve the results of a neural system facilitating the representation of the human perception.

Experiences in software engineering courses using psychometrics with RAMSET

Lately Programming Psychology has opened up a vast area of study, where human, social and psychological factors of the programmer are studied in different computational areas. His behavior and how to relate with others are important aspects that influence performance of a developing team. In our daily work in education we have an obligation of shaping human resources to build a society with professionals participating in companies and corporations dedicated to industrial, social and economic development. Thus in searching for strategies to shape human resources and improve these group corporations we propose RAMSET a Role Assignment Methodology for Software Engineering Teams where we acknowledge the importance of relating personality with team roles, using sociometric techniques and psychometrics to aid in forming high performing teams for software development projects.

Teaching mobile and wireless information systems development in engineering courses

MADEE (Mobile Application Development and Execution Environment) is a platform that supports the development of small and middle size mobile and wireless information systems for handheld devices. MADEE allows a student to develop applications faster and easier than using conventional development tools. This study presents the results and experience obtained using MADEE to support the introduction of mobile and wireless information systems development concepts in the context of computer engineering courses.

Using MatLab's fuzzy logic toolbox to create an application for RAMSET in software engineering courses

Role Assignment Methodology for Software Engineering Teams (RAMSET) methodology relates personality, abilities, and software roles for building Software Engineering Teams, applying sociometric, and psychometric techniques. This paper presents the results and experience of applying RAMSETs software supporting tool developed under a fuzzy approach. This software facilitates the role assignment decision making process, which results in a choice of role selection for individuals in working team projects. It has been applied in Software Engineering Courses of our Computer Engineering Program with great success giving students a practical experience in learning objectives, functions, responsibilities, and tasks of a member in a specific role during the Software Engineering Process.

An experimental wireless sensor network applied in engineering courses

Wireless sensor networks (WSNs) are an emerging technology based on the progress of electrical and mechanical engineering, as well as computer science in the last decade. This paper presents our experiences in designing and developing a WSN using commercial‐off‐the‐shelf components and assembled in‐house. This WSN is used as a support tool for teaching in undergraduate engineering programs in Electronic and Computing, providing students a hands‐on experience with emphasis on embedded software design.