New Trends in Artificial Intelligence: Applications of Particle Swarm Optimization in Biomedical Problems

Optimization is a process to discover the most effective element or solution from a set of all possible resources or solutions. Currently, there are various biological problems such as extending from biomolecule structure prediction to drug discovery that can be elevated by opting standard protocol for optimization. Particle swarm optimization (PSO) process, purposed by Dr. Eberhart and Dr. Kennedy in 1995, is solely based on population stochastic optimization technique. This method was designed by the researchers after inspired by social behavior of flocking bird or schooling fishes. This method shares numerous resemblances with the evolutionary computation procedures such as genetic algorithms (GA). Since, PSO algorithms is easy process to subject with minor adjustment of a few restrictions, it has gained more attention or advantages over other population based algorithms. Hence, PSO algorithms is widely used in various research fields like ranging from artificial neural network training to other areas where GA can be used in the system

Multipath channel identification by using global optimization in ambiguity function domain

Cataloged from PDF version of article.A new transform domain array signal processing technique is proposed for identification of multipath communication channels. The received array element outputs are transformed to delay-Doppler domain by using the cross-ambiguity function (CAF) for efficient exploitation of the delay-Doppler diversity of the multipath components. Clusters of multipath components can be identified by using a simple amplitude thresholding in the delay-Doppler domain. Particle swarm optimization (PSO) can be used to identify parameters of the multipath components in each cluster. The performance of the proposed PSO-CAF technique is compared with the space alternating generalized expectation maximization (SAGE) technique and with a recently proposed PSO based technique at various SNR levels. Simulation results clearly quantify the superior performance of the PSO-CAF technique over the alternative techniques at all practically significant SNR levels. (C) 2011 Elsevier B.V. All rights reserved

Parameter estimation in stochastic mammogram model by heuristic optimization techniques.

Contains fulltext : 50419.pdf (publisher's version ) (Closed access)The appearance of disproportionately large amounts of high-density breast parenchyma in mammograms has been found to be a strong indicator of the risk of developing breast cancer. Hence, the breast density model is popular for risk estimation or for monitoring breast density change in prevention or intervention programs. However, the efficiency of such a stochastic model depends on the accuracy of estimation of the model's parameter set. We propose a new approach-heuristic optimization-to estimate more accurately the model parameter set as compared to the conventional and popular expectation-maximization (EM) algorithm. After initial segmentation of a given mammogram, the finite generalized Gaussian mixture (FGGM) model is constructed by computing the statistics associated with different image regions. The model parameter set thus obtained is estimated by particle swarm optimization (PSO) and evolutionary programming (EP) techniques, where the objective function to be minimized is the relative entropy between the image histogram and the estimated density distributions. When our heuristic approach was applied to different categories of mammograms from the Mini-MIAS database, it yielded lower floor of estimation error in 109 out of 112 cases (97.3 %), and 101 out of 102 cases (99.0%), for the number of image regions being five and eight, respectively, with the added advantage of faster convergence rate, when compared to the EM approach. Besides, the estimated density model preserves the number of regions specified by the information-theoretic criteria in all the test cases, and the assessment of the segmentation results by radiologists is promising

Modelado dinámico del sistema respiratorio ante incrementos de demanda ventilatoria, enfermedades pulmonares y ventilación mecánica asistida

Respiratory diseases remain one of the leading causes of death and illness in Europe and worldwide. One of the most important is Chronic Obstructive Pulmonary Disease (COPD) associated mainly with chronic bronchitis and pulmonary emphysema. Patients with COPD during acute respiratory failure (ARF) require mechanical ventilation to assist or replace their lung function, where the selection of ventilatory mode and its configuration is an essential step for patient's treatment and recovery.The development of pathophysiological knowledge and technology has generated a wide variety of ventilation modes designed to increase alveolar ventilation, reduce respiratory work, improve the coupling between ventilation and perfusion and optimize oxygenation of arterial blood. In clinical practice, however, many of the benefits they provide are often unused because of: 1) the complexity and diversity of ventilatory modalities and ventilator brands, and 2) the lack of tools to assist in the proper selection and configuration of the ventilatory modes according to the specific characteristics of each patient.Several models of the respiratory system have been studied to enhance knowledge about the mechanism of ventilatory control that the system adopts in normal and pathological conditions and to predict its cardiorespiratory response. However, the connection between the respiratory control system and mechanical ventilators remains an open research field, since it is essential to know and predict properly the respiratory pattern and the parameters that affect it before to set up the ventilator.The main objective of this doctoral thesis is the developed and evaluation of new computational simulators that allow predicting appropriately the respiratory dynamic response of healthy subjects and respiratory patients under ventilatory demands and assisted mechanical ventilation.In this thesis, different models of the respiratory system are analyzed. Modifications in their modeling, adjustments in their parameters and comparative studies were performed in order to properly predict the response of the respiratory system in healthy and pathological subjects during increased ventilatory demand. In addition, a computational and interactive tool, based on a model that integrates the most relevant characteristics of the analyzed models and a model of a mechanical ventilator, has been developed to simulate the interaction between a respiratory patient and a mechanical ventilator.The main contributions of the thesis are:1) A new estimate of respiratory mechanical work with a better physiological meaning and whose minimization allows better prediction of the system control response. 2) A complete respiratory system model that properly predicts both transient and stationary response of a healthy subject under incremental ventilatory demands. This model uses an improved gas exchange and sensing respiratory plant and more appropriate optimization algorithms.3) A complete model of the respiratory system that adequately predicts the response of obstructive and restrictive lung diseases. This model incorporates the simplification of a well-known, detailed and complete respiratory mechanical plant that is approximated quadratically for its computational integration in the model of the previous healthy subject. Mechanical parameters of three submodels for each disease are also proposed. 5) A computer simulator with a friendly and interactive user's interface, which includes the previous analyzed models and a mechanical ventilator model. This tool, which has already been tested for usability, has been successfully used in courses for physicians, researchers and students. With all these tools, it is expected to provide resources that assist physicians in the configuration of mechanical ventilators and understanding the interaction patient-ventilator.Las enfermedades respiratorias son una de las causas principales de muerte y enfermedad en Europa y el mundo. Una de las más importantes es la Enfermedad Pulmonar Obstructiva Crónica (EPOC) asociada principalmente a la bronquitis crónica y al enfisema pulmonar. Los pacientes con EPOC durante una Insuficiencia Respiratoria Aguda (IRA) requieren ventilación mecánica para asistir o sustituir su función pulmonar, donde la selección y configuración del modo ventilatorio constituye un paso esencial durante el tratamiento y la recuperación del paciente. La evolución del conocimiento fisiopatológico y de la tecnología ha generado una gran variedad de modos de ventilación diseñados para aumentar la ventilación alveolar, reducir el trabajo respiratorio, mejorar el acoplamiento entre la ventilación y la perfusión y optimar la oxigenación de la sangre arterial. Sin embargo, en la práctica clínica suelen ser desaprovechados muchos de los beneficios que estos ofrecen debido a: 1) la complejidad y diversidad de modos ventilatorios y marcas de ventiladores, y 2) la falta de herramientas que ayuden a la selección y configuración adecuada de estos en función de las características específicas de cada paciente. Se han estudiado diversos modelos del sistema respiratorio para reforzar el conocimiento sobre el mecanismo de control ventilatorio que dicho sistema adopta en condiciones normales y patológicas. Sin embargo, la unión entre el sistema de control respiratorio y los ventiladores mecánicos sigue siendo un campo de investigación abierto, dado que antes de configurar el ventilador resulta fundamental conocer y predecir apropiadamente su patrón respiratorio y los parámetros que lo afectan. El objetivo principal de esta tesis es el desarrollo y evaluación de nuevos simuladores computacionales que permitan predecir apropiadamente la respuesta dinámica respiratoria de sujetos sanos y enfermos respiratorios ante demandas ventilatorias y ventilación mecánica asistida. En esta tesis diversos modelos del sistema respiratorio son analizados. Modificaciones en su modelado, ajustes en sus parámetros y estudios comparativos fueron realizados con el fin de predecir adecuadamente la respuesta del sistema respiratorio en sujetos sanos y patológicos durante demandas ventilatorias incrementadas. Además, una herramienta computacional, basada en un modelo que integra las características más relevantes de los modelos analizados y de un ventilador mecánico, ha sido desarrollada para simular la interacción paciente-ventilador. Las principales contribuciones de la tesis son: 1) Una nueva estimación del trabajo mecánico respiratorio con una mayor interpretación fisiológica y cuya minimización permite predecir mejor la respuesta del sistema de control. 2) Un modelo completo del sistema respiratorio que predice adecuadamente la respuesta tanto en régimen transitorio como estacionario de un sujeto sano ante demandas ventilatorias incrementales. Dicho modelo utiliza una planta respiratoria de intercambio y sensado de gases mejorada y algoritmos de optimización más apropiados. 3) Un modelo completo del sistema respiratorio que predice adecuadamente la respuesta de enfermedades pulmonares obstructivas y restrictivas. Dicho modelo incorpora la simplificación de una planta mecánica respiratoria conocida, detallada y completa que se aproxima cuadráticamente para su integración computacional en el modelo del sujeto sano anterior. Parámetros mecánicos de tres submodelos para cada enfermedad son también propuestos 5) Un simulador computacional con una interfaz amigable e interactiva, que incluye el modelo anterior de un paciente y de un ventilador mecánico. Dicha herramienta a la que ya se le han hecho pruebas de usabilidad, ha sido utilizada con éxito en cursos para médicos, investigadores y estudiantes. (...)Postprint (published version

Simple and Adaptive Particle Swarms

The substantial advances that have been made to both the theoretical and practical aspects of particle swarm optimization over the past 10 years have taken it far beyond its original intent as a biological swarm simulation. This thesis details and explains these advances in the context of what has been achieved to this point, as well as what has yet to be understood or solidified within the research community. Taking into account the state of the modern field, a standardized PSO algorithm is defined for benchmarking and comparative purposes both within the work, and for the community as a whole. This standard is refined and simplified over several iterations into a form that does away with potentially undesirable properties of the standard algorithm while retaining equivalent or superior performance on the common set of benchmarks. This refinement, referred to as a discrete recombinant swarm (PSODRS) requires only a single user-defined parameter in the positional update equation, and uses minimal additive stochasticity, rather than the multiplicative stochasticity inherent in the standard PSO. After a mathematical analysis of the PSO-DRS algorithm, an adaptive framework is developed and rigorously tested, demonstrating the effects of the tunable particle- and swarm-level parameters. This adaptability shows practical benefit by broadening the range of problems which the PSO-DRS algorithm is wellsuited to optimize

Simple and adaptive particle swarms

The substantial advances that have been made to both the theoretical and practical aspects of particle swarm optimization over the past 10 years have taken it far beyond its original intent as a biological swarm simulation. This thesis details and explains these advances in the context of what has been achieved to this point, as well as what has yet to be understood or solidified within the research community. Taking into account the state of the modern field, a standardized PSO algorithm is defined for benchmarking and comparative purposes both within the work, and for the community as a whole. This standard is refined and simplified over several iterations into a form that does away with potentially undesirable properties of the standard algorithm while retaining equivalent or superior performance on the common set of benchmarks. This refinement, referred to as a discrete recombinant swarm (PSODRS) requires only a single user-defined parameter in the positional update equation, and uses minimal additive stochasticity, rather than the multiplicative stochasticity inherent in the standard PSO. After a mathematical analysis of the PSO-DRS algorithm, an adaptive framework is developed and rigorously tested, demonstrating the effects of the tunable particle- and swarm-level parameters. This adaptability shows practical benefit by broadening the range of problems which the PSO-DRS algorithm is wellsuited to optimize.EThOS - Electronic Theses Online ServiceGBUnited Kingdo