Real-time parameter estimation of dynamic temperature and humidity models for greenhouse adaptive climate control

A real-time parameter estimator for the climate discrete-time dynamic models of a greenhouse located at the North of Portugal are presented. The experiments showed that the second order models identified for the air temperature and humidity achieve a close agreement between simulated and experimantal data.

Real-time parameter estimation of dynamic temperature models for greenhouse environmental control

For a greenhouse located at UTAD-University, the methods used to estimate in real-time the parameters of the inside air temperature model will be described. The structure and the parameters of the climate discrete-time dynamic model were previously identified using data acquired during two different periods of the year.

On the assessment of time-shift variations from backscattered ultrasound for large temperature changes in biological phantoms

This work reports the assessment of time-shifts (TS) from backscattered ultrasound (BSU) signals when large temperature variations (up to 15 degrees C) were induced in a gel-based phantom. The results showed that during cooling temperature is linear with TS at a rate of approximately 74 ns/degrees C. However during a complete heating/cooling cycle, the relation is highly non-linear. This can be explained by the fact that during cooling the temperature distribution is more uniform. Another problem to report is that TS is very sensitive to external movements.

Soft-computing techniques applied to artificial tissue temperature estimation

Tese dout., Engenharia electrónica e computação - Processamento de sinal, Universidade do Algarve, 2008

Open source data sensing software for ultrasonic non-invasive temperature estimation

In this paper, an open source solution for measurement of temperature and ultrasonic signals (RF-lines) is proposed. This software is an alternative to the expensive commercial data acquisition software, enabling the user to tune applications to particular acquisition architectures. The collected ultrasonic and temperature signals were used for non-invasive temperature estimation using neural networks. The existence of precise temperature estimators is an essential point aiming at the secure and effective applica tion of thermal therapies in humans. If such estimators exist then effective controllers could be developed for the therapeutic instrumentation. In previous works the time-shift between RF-lines echoes were extracted, and used for creation of neural networks estimators. The obtained estimators successfully represent the temperature in the time-space domain, achieving a maximum absolute error inferior to the threshold value defined for hyperthermia/diathermia applications.

Model comparison for temperature estimation inside buildings

This paper presents a comparison between a physical model and an artificial neural network model (NN) for temperature estimation inside a building room. Despite the obvious advantages of the physical model for structure optimisation purposes, this paper will test the performance of neural models for inside temperature estimation. The great advantage of the NN model is a big reduction of human effort time, because it is not needed to develop the structural geometry and structural thermal capacities and to simulate, which consumes a great human effort and great computation time. The NN model deals with this problem as a “black box” problem. We describe the use of the Radial Basis Function (RBF), the training method and a multi-objective genetic algorithm for optimisation/selection of the RBF neural network inputs and number of neurons.

A soft-computing approach for non-invasive temperature estimation

The domain of thermal therapies applications can be improved with the development of accurate non-invasive timespatial temperature models. These models should represent the non-linear tissue thermal behaviour and be capable of tracking temperature at both time-instant and spatial position. If such estimators exist then efficient controllers for the therapeutic instrumentation could be developed, and the desired safety and effectiveness reached.

A time frequency approach to blind deconvolution in multipath underwater channels

Blind deconvolution is studied in the underwater acoustic channel context, by time-frequency (TF) processing. The acoustic propagation environment is modelled by ray tracing and mathematically described by a multipath propagation channel. Representation of the received signal by means of a signal-dependent TF distribution (radially Gaussian kernel distribution) allowed to visualize the resolved replicas of the emitted signal, while signi cantly attenuating the inherent interferences of classic quadratic TF distributions. The source signal instantaneous frequency estimation was the starting point for both source and channel estimation. Source signature estimation was performed by either TF inversion, based on the Wigner-Ville distribution of the received signal, or a subspace- -based method. The channel estimate was obtained either via a TF formulation of the conventional matched- lter, or via matched- - ltering with the previously obtained source estimate. A shallow water realistic scenario is considered, comprising a 135-m depth water column and an acoustic source located at 90-m depth and 5.6-km range from the receiver. For the corresponding noiseless simulated data, the quality of the best estimates was 0.856 for the source signal, and 0.9664 and 0.9996 for the amplitudes and time-delays of the impulse response, respectively. Application of the proposed deconvolution method to real data of the INTIMATE '96 sea trial conduced to source and channel estimates with the quality of 0.530 and 0.843, respectively. TF processing has proved to remove the typical ill-conditioning of single sensor deterministic deconvolution techniques.

Tissue temperature estimation with pulse-echo in blood flow presence

Aiming at time-spatial characterization of tissue temperature when ultrasound is applied for thermal therapeutic proposes two experiments were developed considering gel-based phantoms, one of them including an artificial blood vessel. The blood vessel was mimicking blood flow in a common carotid artery. For each experiment phantoms were heated by a therapeutic ultrasound (TU) device emitting different intensities (0.5, 1, 1.5, 1.8 W/cm2). Temperature was monitored by thermocouples and estimated through imaging ultrasound transducer's signals within specific special points inside the phantom. The temperature estimation procedure was based on temporal echo-shifts (TES), computed based on echo-shifts collected through image ultrasound (IU) transducer. Results show that TES is a reliable non-invasive method of temperature estimation, regardless the TU intensities applied. Presence of a pulsatile blood flow vessel in the focal point of TU transducer reduces thermal variation in more than 50%, also affecting the temperature variation in the surrounding area. In other words, vascularized tissues require longer ultrasound thermal therapeutic sessions or higher TU intensities and inclusion of IU in the therapeutic procedure enables non-invasive monitoring of temperature. © 2013 IEEE.