Francisco Maciá Pérez

A robust model of the neuronal regulator of the lower urinary tract based on artificial neural networks

The neuronal regulator of the lower urinary tract is a very complex nervous system that consists of a heterogeneous group of neuronal centres. We have developed a new system from a model based in a multi-agent system in which each neuronal centre corresponds with an agent. This system incorporates a heuristic in order to make it more robust in the presence of possible inconsistencies. The heuristic used is based on a neural network (orthogonal associative memory). Knowledge through training has been added to the system, using correct patterns of behaviour of the urinary tract and behaviour patterns resulting from dysfunctions in two neuronal centres as a minimum. The experiments prove that the model is robust and its functioning coincides with the behaviour of the biological system. This work fulfils the expectations of providing a model of the regulator system that allows breaking the problem into simple modules each with its own entity.

Model of integration and management for robotic functional components inspired by the human neuroregulatory system

This paper presents a model of integration and management for robotic functional components that make up the robotic control system. To that end, we use the human neuroregulatory system as the basis for the decomposition of tasks and actions behavior, and we rely on the SOA paradigm for the design of a distributed architecture that allows the viability of the system. This proposal will ensure a total decoupling between modules by promoting the reusability and features such as pattern-based design, while the system is fully distributed ensuring high flexibility, scalability, robustness and fault tolerance.

Modeling the Distributed Control of the Lower Urinary Tract Using a Multiagent System

In this article a model of the biological neuronal regulator system of the lower urinary tract is presented. The design and the implementation of the model has been carried out using distributed artificial intelligence, more specifically a system based on agents that carry out tasks of perception, deliberation and execution. The biological regulator is formed by neuronal centres. In the model, each agent is modeled so that its behaviour is similar to that of a neuronal centre. The use of the agent paradigm in the model confers it important properties: adaptability, distributed computing, modularity, synchronous or asynchronous functioning. This strategy also allows a complex systems approach formed by connected elements whose interaction is partially well-known. We have simulated and tested the model comparing results with clinical studies.

Mathematical modelling of the lower urinary tract

The lower urinary tract is one of the most complex biological systems of the human body as it involved hydrodynamic properties of urine and muscle. Moreover, its complexity is increased to be managed by voluntary and involuntary neural systems. In this paper, a mathematical model of the lower urinary tract it is proposed as a preliminary study to better understand its functioning. Furthermore, another goal of that mathematical model proposal is to provide a basis for developing artificial control systems. Lower urinary tract is comprised of two interacting systems: the mechanical system and the neural regulator. The latter has the function of controlling the mechanical system to perform the voiding process. The results of the tests reproduce experimental data with high degree of accuracy. Also, these results indicate that simulations not only with healthy patients but also of patients with dysfunctions with neurological etiology present urodynamic curves very similar to those obtained in clinical studies.

Modelling of dysfunctions in the neuronal control of the lower urinary tract

In this article a model of the regulator system of the lower urinary tract is presented discussing, in particular, the dysfunctions associated with the neurogenic bladder. The design and implementation of the model has been carried out using distributed artificial intelligence, more specifically a multi-agent system. Each agent is modelled so that its behaviour is similar to that of a neuronal centre. By means of this design, the behaviour of the neuronal regulator of the lower urinary tract is simulated using a model with a similar structure to the organisation of the biological system, conferring on it emergent properties. We compare the results obtained using the model in situations with several neurological dysfunctions with experimental results obtained from patients suffering from the analysed dysfunctions. The data, obtained using the model, are consistent with the existing real clinic studies in medicine related to the same dysfunctions.

An Agent Based Framework for Modelling Neuronal Regulators of the Biological Systems

The neuronal regulators of biological systems are very difficult to deal with since they present nonstructured problems. The agent paradigm can analyze this type of systems in a simple way. In this paper, a formal agent-based framework that incorporates aspects such as modularity, flexibility and scalability is presented. Moreover, it enables the modeling of systems that present distribution and emergence characteristics. The proposed framework provides a definition of a model for the neuronal regulator of the lower urinary tract. Several examples of the experiment have been carried out using the model as presented, and the results have been validated by comparing them with real data. The developed simulator can be used by specialists in research tasks, in hospitals and in the field of education.

Simulation of the Neuronal Regulator of the Lower Urinary Tract using a Multiagent System

In this paper, a model of the neuronal regulator of the lower urinary tract that regulates the micturition process is presented. A multiagent system has been used in which each agent models the behaviour of the different neuronal centres involved in the process. This model enables the distribution and emergence characteristics of neuronal regulation to be represented. Likewise, aspects such as modularity and flexibility that allow new advances in research into the nervous system to be incorporated into the model have also been taken into account. Based on the proposed model, a tool has been implemented which allows to simulate the functioning of the model showing the values related to urodymanic variables graphically. Several examples of the tests carried out with the model are presented in this paper and the results have been validated by comparing them with real data.

Architecture for image labelling in real conditions

A general model for the segmentation and labelling of acquired images in real conditions is proposed. These images could be obtained in adverse environmental conditions, such as faulty illumination, nonhomogeneous scale, etc. The system is based on surface identification of the objects in the scene using a database. This database stores features from series of each surface perceived with successive optical parameter values: the collection of each surface perceived at successive distances, and at successive illumination intensities, etc. We propose the use of non-specific descriptors, such as brightness histograms, which could be systematically used in a wide range of real situations and the simplification of database queries by obtaining context information. Self-organizing maps have been used as a basis for the architecture, in several phases of the process. Finally, we show an application of the architecture for labelling scenes obtained in different illumination conditions and an example of a deficiently illuminated outdoor scene.

System-on-chip design of the cortical-diencephalic centre of the lower urinary tract

This article presents the design of a field programmable gate array (FPGA)-based prototype of a system on chip (SoC) capable of behaving as one of the nerve centres comprising the neuroregulatory system in humans: the cortical-diencephalic nerve centre. The neuroregulatory system is a complex nerve system consisting of a heterogeneous group of nerve centres. These centres are distributed throughout the length of the spinal cord, are autonomous, communicate via interneurons, and govern and regulate the behaviour of multiple organs and systems in the human body. As a result of years of study of the functioning and composition of the neuroregulatory system of the lower urinary tract (LUT), the centres that regulate this system have been isolated. The objective of this study is to understand the individual functioning of each centre in order to create a general model of the neuroregulatory system that is capable of operating at the level of the actual nerve centre. This model represents an advancement of the current black box models that do not allow for isolated or independent treatment of system dysfunction. In this study, we re-visit our research into the viability of the hardware design of one of these centres-the cortical-diencephalic centre. We describe this hardware because functioning of the centre is completely configurable and programmable, which validates the design for other centres that comprise the neuroregulatory system. In this document, we succinctly present the formal model of the centre, propose a hardware design and an FPGA-based prototype, construct a testing and simulation environment to evaluate it and, lastly, analyse and contrast the results using data obtained from real patients, verifying that the functional behaviour fits that observed in humans.

Application of the Variable Precision Rough Sets Model to Estimate the Outlier Probability of Each Element

In a data mining process, outlier detection aims to use the high marginality of these elements to identify them by measuring their degree of deviation from representative patterns, thereby yielding relevant knowledge. Whereas rough sets (RS) theory has been applied to the field of knowledge discovery in databases (KDD) since its formulation in the 1980s; in recent years, outlier detection has been increasingly regarded as a KDD process with its own usefulness. The application of RS theory as a basis to characterise and detect outliers is a novel approach with great theoretical relevance and practical applicability. However, algorithms whose spatial and temporal complexity allows their application to realistic scenarios involving vast amounts of data and requiring very fast responses are difficult to develop. This study presents a theoretical framework based on a generalisation of RS theory, termed the variable precision rough sets model (VPRS), which allows the establishment of a stochastic approach to solving the problem of assessing whether a given element is an outlier within a specific universe of data. An algorithm derived from quasi-linearisation is developed based on this theoretical framework, thus enabling its application to large volumes of data. The experiments conducted demonstrate the feasibility of the proposed algorithm, whose usefulness is contextualised by comparison to different algorithms analysed in the literature.