Asociación de la concentración de material particulado en Bogotá con infarto agudo de miocardio

Introducción: la contaminación atmosférica no solo tiene efectos sobre el sistema respiratorio sino también sobre el cardiovascular. El objetivo de este estudio es generar evidencia que permita establecer una asociación entre el infarto agudo del miocardio y la concentración de PM10 en el ambiente como un estudio preliminar para un grupo de pacientes en Bogotá. Metodología: la asociación entre la concentración del material particulado (en este caso PM10 medido en la estación más cercana del lugar reportado por el paciente) y el infarto agudo del miocardio se estableció utilizando el diseño case crossover. Se utilizó información de las historias clínicas de los pacientes con infarto agudo del miocardio que ingresaron al Servicio de Urgencias de la FSFB, y las concentraciones de PM10 medido en la estación más cercana al lugar de inicio de los síntomas de síndrome coronario agudo, reportado por el paciente. Resultados: se encontró que la asociación entre la concentración de PM10 y el diagnóstico de infarto agudo del miocardio es estadísticamente significativa teniendo en cuenta tres momentos de control: 2 horas antes del evento, 24 horas antes del evento y 48 horas antes del evento. Discusión: este estudio sugiere que las altas concentraciones de material particulado en el ambiente son un factor de riesgo para el desarrollo de infarto agudo del miocardio especialmente en personas con enfermedad coronaria subyacente. Con esta investigación se demuestra la importancia de generar acciones que disminuyan la contaminación de la ciudad y de esta forma proteger la salud de las personas.

Wireless sensor networks

This paper provides an introduction to Wireless Sensor Networks (WSN), their applications in the field of control engineering and elsewhere and gives pointers to future research needs. WSN are collections of stand-alone devices which, typically, have one or more sensors (e.g. temperature, light level), some limited processing capability and a wireless interface allowing communication with a base station. As they are usually battery powered, the biggest challenge is to achieve the necessary monitoring whilst using the least amount of power.

Design of a minimum variance multiple input-multiple output neuro self-tuning proportional-integral-derivative controller for non-linear dynamic systems

In this study a minimum variance neuro self-tuning proportional-integral-derivative (PID) controller is designed for complex multiple input-multiple output (MIMO) dynamic systems. An approximation model is constructed, which consists of two functional blocks. The first block uses a linear submodel to approximate dominant system dynamics around a selected number of operating points. The second block is used as an error agent, implemented by a neural network, to accommodate the inaccuracy possibly introduced by the linear submodel approximation, various complexities/uncertainties, and complicated coupling effects frequently exhibited in non-linear MIMO dynamic systems. With the proposed model structure, controller design of an MIMO plant with n inputs and n outputs could be, for example, decomposed into n independent single input-single output (SISO) subsystem designs. The effectiveness of the controller design procedure is initially verified through simulations of industrial examples.

On improving the performance in robust controllers for robot manipulators with parametric disturbances

This paper presents a new strategy for controlling rigid-robot manipulators in the presence of parametric uncertainties or un-modelled dynamics. The strategy combines an adaptation law with a well known robust controller proposed by Spong, which is derived using Lyapunov's direct method. Although the tracking problem of manipulators has been successfully solved with different strategies, there are some conditions under which their efficiency is limited. Specifically, their performance decreases when unknown loading masses or model disturbances are introduced. The aim of this work is to show that the proposed strategy performs better than existing algorithms, as verified with real-time experimental results with a Puma-560 robot. (c) 2006 Elsevier Ltd. All rights reserved.

Cultured neural networks

This article looks at the use of cultured neural networks as the decision-making mechanism of a control system. In this case biological neurons are grown and trained to act as an artificial intelligence engine. Such research has immediate medical implications as well as enormous potential in computing and robotics. An experimental system involving closed-loop control of a mobile robot by a culture of neurons has been successfully created and is described here. This article gives a brief overview of the problem area and ongoing research. Questions are asked as to where this will lead in the future.

Self-tuning proportional, integral and derivative controller based on genetic algorithm least squares

A self-tuning proportional, integral and derivative control scheme based on genetic algorithms (GAs) is proposed and applied to the control of a real industrial plant. This paper explores the improvement in the parameter estimator, which is an essential part of an adaptive controller, through the hybridization of recursive least-squares algorithms by making use of GAs and the possibility of the application of GAs to the control of industrial processes. Both the simulation results and the experiments on a real plant show that the proposed scheme can be applied effectively.

Cancellation of siren noise from two-way voice communication inside emergency vehicles

The development of an adaptive filter system, capable of reducing significantly the effect of siren noise within the cab of an emergency vehicle, is described. The system is capable of removing the siren noise picked up by the radio microphone inside the vehicle, without degrading the wanted voice signal, thus allowing the siren to be used at all times.

Applying the extended Kalman filter to systems described by nonlinear differential-algebraic equations

This paper describes a method for the state estimation of nonlinear systems described by a class of differential-algebraic equation models using the extended Kalman filter. The method involves the use of a time-varying linearisation of a semi-explicit index one differential-algebraic equation. The estimation technique consists of a simplified extended Kalman filter that is integrated with the differential-algebraic equation model. The paper describes a simulation study using a model of a batch chemical reactor. It also reports a study based on experimental data obtained from a mixing process, where the model of the system is solved using the sequential modular method and the estimation involves a bank of extended Kalman filters.

Implementation of robust hybrid feedback FxLMS active noise cancellation headset

Instability is a serious problem for acoustic Active Noise Cancellation (ANC) headsets as a result of large errors in estimating the transfer function of the plant. Typically this occurs when, for example, a wearer adjusts the headset. In this paper, the instability problem of adaptive ANC headset is addressed. To ensure stability of the whole system, we propose a hybrid solution consisting of an analog feedback loop parallel to the digital loop, and the role of the analog loop in stabilizing the headset is analyzed theoretically. Finally the methodology of implementing such a hybrid ANC headset is described in detail. The experiments carried out on the headset prototype show that the headset is robust under considerable fluctuations of the plant transfer characteristics, and has very good noise cancellation performance both for narrow-band and wide-band disturbances.

Dealing with complexity: an overview of the neural network approach

The problem of complexity is particularly relevant to the field of control engineering, since many engineering problems are inherently complex. The inherent complexity is such that straightforward computational problem solutions often produce very poor results. Although parallel processing can alleviate the problem to some extent, it is artificial neural networks (in various forms) which have recently proved particularly effective, even in dealing with the causes of the problem itself. This paper presents an overview of the current neural network research being undertaken. Such research aims to solve the complex problems found in many areas of science and engineering today.

Estimation of Gaussian process regression model using probability distance measures

A new class of parameter estimation algorithms is introduced for Gaussian process regression (GPR) models. It is shown that the integration of the GPR model with probability distance measures of (i) the integrated square error and (ii) Kullback–Leibler (K–L) divergence are analytically tractable. An efficient coordinate descent algorithm is proposed to iteratively estimate the kernel width using golden section search which includes a fast gradient descent algorithm as an inner loop to estimate the noise variance. Numerical examples are included to demonstrate the effectiveness of the new identification approaches.

On the optimum parameters of a device for harvesting energy from running and walking

This paper investigates the feasibility of using an energy harvesting device tuned such that its natural frequency coincides with higher harmonics of the input to capture energy from walking or running human motion more efficiently. The paper starts by reviewing the concept of a linear resonant generator for a tonal frequency input and then derives an expression for the power harvested for an input with several harmonics. The amount of power harvested is estimated numerically using measured data from human subjects. Assuming that the input is periodic, the signal is reconstructed using a Fourier series before being used in the simulation. It is found that although the power output depends on the input frequency, the choice of tuning the natural frequency of the device to coincide with a particular higher harmonic is restricted by the amount of damping that is needed to maximize the amount of power harvested, as well as to comply with the size limit of the device. It is also found that it is not feasible to tune the device to match the first few harmonics when the size of the device is small, because a large amount of damping is required to limit the motion of the mass.