Catálogo de publicaciones - libros

The development of an effective central model of action selection has already been reviewed in previous work. The central model has been set to resolve a foraging task with the use of heterogeneous behavioral modules. In contrast to collecting/depositing modules that have been hand-coded, modules related to exploring follow an evolutionary approach. However, in this paper we focus on the use of genetic algorithms for evolving the weights related to calculating the urgency for a behavior to be selected. Therefore, we aim to reduce the number of decisions made by a human designer when developing the neural substratum of a central selection mechanism.

Growing Functional Modules (GFM) is a recently introduced paradigm conceived to automatically generate an adaptive controller which consists of an architecture based on interconnected growing modules. When running, the controller is able to build its own representation of the environment through acting and sensing. Due to this deep-rooted interaction with the environment, robotics is, by excellence, the field of application. This paper describes a hardware architecture designed to satisfy the requirements of the GFM controller and presents the implementation of a simple mushroom shaped robot.

This paper proposes a novel fast protein structure alignment algorithm and its application. Because it is known that the functions of protein are derived from its structure, the method of measuring the structural similarities between two proteins can be used to infer their functional closeness. In this paper, we propose a 3D chain code representation for fast measuring the local geometric similarity of protein and introduce a backtracking algorithm for joining a similar local substructure efficiently. A 3D chain code, which is a sequence of the directional vectors between the atoms in a protein, represents a local similarity of protein. After constructing a pair of similar substructures by referencing local similarity, we perform the protein alignment by joining the similar substructure pair through a backtracking algorithm. This method has particular advantages over all previous approaches; our 3D chain code representation is more intuitive and our experiments prove that the backtracking algorithm is faster than dynamic programming in general case. We have designed and implemented a protein structure alignment system based on our protein visualization software (MoleView). These experiments show rapid alignment with precise results.

The main goal of this paper is to design an electromyogram (EMG) pattern classifier which is robust to muscular fatigue effects for human-machine interaction. When a user operates some machines such as a PC or a powered wheelchair using EMG-based interface, muscular fatigue is generated by sustained duration time of muscle contraction. Therefore, recognition rates are degraded by the muscular fatigue. In this paper, an important observation is addressed: the variations of feature values due to muscular fatigue effects are consistent for sustained duration time. From this observation, a robust pattern classifier was designed through the adaptation process of hyperboxes of Fuzzy Min-Max Neural Network. As a result, significantly improved performance is confirmed.

Breast cancer is one of the main causes of death in women and early diagnosis is an important means to reduce the mortality rate. The presence of microcalcification clusters are primary indicators of early stages of malignant types of breast cancer and its detection is important to prevent the disease. This paper proposes a procedure for the classification of microcalcification clusters in mammograms using sequential difference of gaussian filters (DoG) and three evolutionary artificial neural networks (EANNs) compared against a feedforward artificial neural network (ANN) trained with backpropagation. We found that the use of genetic algorithms (GAs) for finding the optimal weight set for an ANN, finding an adequate initial weight set before starting a backpropagation training algorithm and designing its architecture and tuning its parameters, results mainly in improvements in overall accuracy, sensitivity and specificity of an ANN, compared with other networks trained with simple backpropagation.

Ultrasound images are characterized by high level of speckle noise causing undefined contours and difficulties during the segmentation process. This paper presents a novel method to detect heart cavities in ultrasound images. The method is based on a Self Organizing Map and the use of the variance of images. Successful application of our approach to detect heart cavities on real images is presented.

This study aims to develop an effective measurement instrument and analysis method of gait stability, particularly focused on the motion of lower spine and pelvis during gait. Silicon micromechanical inertial instruments have been developed and body-attitude (pitch and roll) angles were estimated via closed-loop strapdown estimation filters, which results in improved accuracy of estimated attitude. Also, it is shown that the spectral analysis utilizing the Fast Fourier Transform (FFT) provides an efficient analysis method, which provides quantitative diagnoses for the gait stability. The results of experiments on various subjects suggest that the proposed system provides a simplified but an efficient tool for the evaluation of both gait stabilities and rehabilitation treatments effects.