Catálogo de publicaciones - libros

The potential of thermal therapy’s applications improve with the development of accurate non-invasive time-spatial temperature models. These models should represent the non-linear tissue thermal behaviour and be capable of tracking temperature at both time-instant and spatial point. An in-vitro experiment was developed based on a gel phantom, heated by a therapeutic ultrasound (TUS) device emitting continuously. The heating process was monitored by an imaging ultrasound (IUS) transducer working in pulse-echo mode, placed perpendicularly to the TUS transducer. The IUS RF-lines and temperature values were collected 60 mm distant from the TUS transducer face. Three thermocouples were aligned along the IUS transducer axial direction and across the TUS transducer radial direction (1 cm spaced). Three different TUS intensities were applied. The non-invasive time-spatial evolutionary temperature models were created making use of radial basis functions neural networks (RBFNN). The neural network input information was: the propagation time-delay between RF-line echoes and the past temperature lags from three different medium locations and three different TUS intensities. A total of nine different operating situations were studied. The best RBFNN structures were automatically determined by a multiobjective genetic algorithm, due to the enormous number of possible structures. The RBFNN temperature models were evaluated with data never used in the models, neither at the training or structural selection phases. In order to precisely evaluate the model generalisation performance these data included the nine possible operating situations. The best model presents a maximum absolute error less than 0.5 degrees Celsius (gold-standard value for hyperthermia/diathermia applications). To be mentioned also that the best model presents low computational complexity enabling future real-time implementations. Concluding, a maximum absolute error below the gold-standard value pointed for hyperthermia/diathermia applications was attained. In addition, this methodology does not require a-priori determination of physical constants and mathematical simplifications required for analytical methodologies.

The quantitative and objective evaluation of spasticity has been desired in rehabilitation and orthopaedics where subjective evaluations such as modified Ashworth scale (MAS) were mostly used. The objective of this work is to develop the knee joint model for representing various pendulum trajectories and for quantifying the spasticity. Knee joint model included the extension and flexion muscles. The joint moment consisted of both the active moment from the stretch reflex and the passive moment from the viscoelastic joint properties. The stretch reflex was modeled as nonlinear feedback of muscle length and the muscle lengthening velocity, which is physiologically-feasible. Moreover, we modeled the spastic reflex as having dynamic threshold to account for the various pendulum trajectories of spastic patients. We determined the model parameters of three patients through minimization of error between experimental and simulated trajectories. The simulated joint trajectories closely matched with the experimental ones, which show the proposed model can predict pendulum motions of patients with different spastic severities. The predicted muscle force from spastic reflex appeared more frequently in the severe spastic patient, which indicates the dynamic threshold relaxes slowly in this patient as is manifested by the variation coefficient of dynamic threshold. The proposed method provides prediction of muscle force and intuitive and objective measure of spasticity and it is expected to be useful in quantitative assessment of spasticity.

A 3D heart model is proposed to simulate the effect of bundle branch blocks in human heart. This model consists of the ventricle model with the electrophysiological approximation of the cells. The computational approach to cardiac cells is based on the electrophysiological model of the human ventricular myocytes proposed by ten Tusscher et al. (2004). The 3D geometry of human heart tissue is discretized to a finite element mesh system for the simulation of electric wave propagation using a monodomain method. Purkinje fibers in ventricles are modeled based on an anatomical structure with simplified model of electric conduction. Using the proposed human heart model we simulated the electrophysiological effects of BBB (bundle branch block) in cardiac tissue. Computational results showed that the BBB induces the changes of electric wave conduction pattern and pseudo-ECG signal compared with normal heart.

Low level flight systems with possibilities to follow terrain expose pilots of agile combat aircraft to a new flight load. A new quality of the flight load is represented by domination of plus and minus gravitational acceleration changes. High level of pilot’s plus-minus Gz tolerance is an essential requirement to cope with low level flights in agile aircraft. It was proved that the sinusoidal profile during real flights can be used for pilots’ tolerance to plus and minus Gz load evaluation. Results of physiological responses to the gravitational load by means of sinusoidal flights demonstrated the negative influence of the so-called push-pull effect on the pilots’ basic tolerance to plus Gz acceleration. It was assessed pursuant to evaluation of continuous blood pressure and heart rate behavior. A testing method of push-pull effect in an LBNP examination was developed for this purpose.

We construct a pattern recognition system by modeling the structure of the visual cortex. The complexities of the visual cortex can be simplified by understanding that the neurons of this region are distinguished by the features of input image that each neuron detects. We propose a neural network(NN) model of the simple structure based on the function and structure of the visual cortex. Moreover, a lot of ideas of manufactured products with NN were proposed. One of issues for productization is uncertainty of the behavior of nonlinearity of NN. Accordingly, it is important to analyze the internal representation of NN. In this paper, we discuss the recognition and training mechanism of a NN model by use of Alopex algorithm. Alopex algorithm, which is an iterative and stochastic processing to minimize or maximize a cost function. processing to minimize or maximize a cost function. By this method, the receptive fields of the units in the output layer are obtained. We have proposed a four-layered feed-forward NN model for pattern recognition and analyzed the recognition mechanism as well as the performance of the model. We proposed a modified Alopex algorithm and calculated the receptive fields of the output unit. In the case of simple training character set, the receptive field changes according to the values of initial weight vectors. If the initial values are large, NN uses small amounts of input values for the classification. In contrast, if the initial values are small, NN uses whole input image. Moreover, it was seen that as the complexity of the set of training patterns increased the receptive field of the output unit changed. Smaller initial values of weight vector have advantage to get the more features. Alopex algorithm is an effective method to find the characteristics of images.

In order to improve new materials as middle ear prostheses, a method for evaluation of transfer function of these materials had to be developed. The measurements were performed in three stages. First, a mechanical middle ear model was designed which should simulate the mechanical and acoustical characteristics of intact middle ear. In this model different commercial prostheses and newly developed implants were measured by means of Laser Doppler Velocimetry (LDV). To control the stiffness of the system, we used Tympanometry as well as Multi-Frequency Tympanometry (MFT). In the second and third stages the same evaluations were followed in fresh human temporal bones as well as in animal experiments. The measurements in the mechanical middle ear model indicated a good comparability with the transfer function of the intact middle ear, particularly up to resonance frequency of the middle ear, which was about 1200-1500 Hz. The influences of mass and stiffness could be determined appropriately using the middle ear model. The measurements with different prostheses resulted in no significant variations in the transfer functions of commercial and new implants. The results of experiments on fresh temporal bones again showed no significant variation in the transfer functions of different prostheses. A median damping of about 15 to 20 dB particularly beyond the resonance frequency was measured using implanted prostheses relative to the transfer function of the intact ossicular chain. Also the results of MFT measurements in animals on the implanted and non-implanted side (300 days postoperative) will be presented.

Aim of this study is intended to develop a noninvasive methodology of assignment of arterial parameters of man. In a paper there are presented the method and preliminary results of arterial parameters. The parameters were assessed using a non-linear model based on electric analogy between a transmission line and arterial tree. Parameters of the model and also of the arterial tree were identified from signals measured non-invasively on a human body — phonocardiographic signals.

We propose a computational inference of transcriptional regulatory networks from transcription start sites identified by Cap Analysis of Gene Expression (CAGE). Inference of the regulatory networks is a challenging task, and is performed by analyzing observed dependencies of transcription levels. Binding of transcription factors to an upstream sequence of a gene enables transcription initiation from a specific genomic position. The position is called Transcription Start Site (TSS). Eukaryotes have multiple TSSs for a single gene, which reflect diversity in combinatorial regulation by transcription factors. By the CAGE high-throughput profiling genome-wide identification of activated TSSs under various experimental conditions yields CAGE transcriptome datasets. To distinguish transcription responses caused by the diversity in combinatorial regulations, inference that takes multiple TSSs into account will be more effective than the conventional ones using only transcription levels of microarray dataset. In this paper we report a feasibility study of inference of transcriptional regulatory network by using CAGE dataset. This is a preliminary study for inference that takes multiple TSSs into account. To perform inference of the regulatory networks, we used Bayesian network model which is appropriate to represent distribution of TSSs observed in CAGE dataset. Using the CAGE dataset published from the FANTOM3 project in RIKEN, we applied the model to inference of the regulatory networks of Mus musculus . For the purpose of the feasibility study, we compared inferred networks from the CAGE dataset with those from microarray dataset. Based on the comparative analysis, we confirmed that network inference by using the CAGE dataset is feasible like the case of conventional inferences by using microarray dataset. Based on the confirmation we discussed what we can reveal through the inference that takes into account multiple TSSs identified by a CAGE dataset.

In this study, we compared the observation experiment of a liquid-liquid interface with a simulation result about the three-phase micro channel used for an extraction reaction. As a result, each flow rate ratio greatly influences the formation of the three-phase flow. And the result of computing this multiphase flow model is corresponding to the experiment well, so the development of the unit chemistry operation model will become possible with the simulation in the future.

The primary aim of this study is to look into the feasibility of developing a mobile patient data management system using ASP .Net technology. It was envisioned that medical personnel, by using any WAP-enabled devices, will not be restricted to a specified location in order to retrieve, add, or edit patient data. The current system has achieved its main objectives of adding and editing patient demographical data, medical prescription, medical images and graphs. The system has been designed to be backward and forward compatible; ensuring unlimited module expansion in the future.