Progress on the development of a wearable ultrasonic bladder monitoring device is reported. The device is intended to help in the diagnosis of urinary incontinence of both young and elderly patients. We present all the components of the device - the textile band, electronics, as well as the early results of reconstruction algorithms.

The possibilities of identification of intraventricular haemorrhage with the aid of electrical impedance tomography are presented in this paper. On the base of the tomographic image of a human brain (see Fig. 1) a simplified two-dimensional mathematical model of the human head is proposed. The mathematical model is relatively poor, but the main purpose of this paper is to investigate the possibility and precision of the identification procedure. This task is particularly difficult because the object is hidden behind the skull, which becomes a low-conductivity screen when the scalp electrodes are used.

The eddy current nondestructive testing of conductive materials is well known problem. For some eddy current transducers for flaw detection the mathematical models were constructed and the inverse problem (IP) were formulated In order to solve those problems the artificial neural network approach, relatively new, was adopted. The public domain software was used to teach and test the network. It was proved that, a standard feedforward multilayered neural network is sufficient to identify the parameters of different kind defects with a satisfying accuracy. Neural network approach could be extinguished to impedance tomography and to eddy current tomography. Several examples illustrated abilities of proposed approach will be presented in the paper. Two artificial neural network reconstruction methods for electrical impedance tomography have been presented in this paper. The problem under study concerns the reconstruction of the conductivity distribution inside the investigated area using the information collected from the boundary. The first approach consists in ANN learning using electrical potential vectors, which were obtained from numerical solution of the forward problems. The second method using a standard feedforward multilayered neural network applies the circuit representation for the finite element discretization. Using the quadrilateral finite element, the neural network structure for EIT problem has been proposed. The advantages and disadvantages of both methods with respect to the classical approach have been discussed in details.