Search

We describe a fiber laser interferometer that has been designed with the purpose of non-invasive continuous fetal heart rate monitoring. The measuring principle is based on the measurement of the arterial wave of a fetus from the arterial pulse wave of a pregnant patient, so the application requires the measurement of arterial pulse waves with very high resolution. The main characteristics of the specific application interferometer are: First, it is based on a common path topology in order to simplify the design of the non-contact optical probe to place on the body of the patient. Second, it uses a low power semiconductor laser at a safe operating wavelength of 1,55 μm. Third, a phase modulation with digital demodulation scheme is used in order to increase range and resolution simultaneously. The resulting interferometer provides up to 1 nm resolution and one millimeter range. Results showing arterial pulse waves measured on the wrist and a finger nail of a patient are presented.

In this paper we propose a method based on blind-source-separation of acoustic emissions of partial discharges in power equipment. The goal is to build an on-line monitoring system for automatic detection and localization of partial discharges and defects in the insulation. We have built a test bech to generate acoustic signals similar to those produced by partial discharges inside a propagating media and a data acquisition system with multiple acoustic detectors. We show that the INFOMAX algorithm used for blind-source-separation is able to decouple at least two different sources of partial discharges from the signal of two acoustic detector located at fixed positions.

On-line identification and classification of voltage and current disturbances in power systems are important tasks in the power quality monitoring and protection of power systems. Some power quality disturbances are non-stationary and transitory while other are steady-state variations that distort the voltage signal. One, two or more different power quality disturbances may appear at the same time. In this paper we propose a power quality monitoring system that employs Independent Component Analysis algorithm that is able to decouple multiple simultaneous power quality disturbances, and Support Vector Machines for identification the occurrence of a disturbance. We also show the first steps towards embedding the proposed system on an FPGA for online power quality monitoring.

We present a successful design for a high-performance, low-resource-consuming hardware for Support Vector Classification and Support Vector Regression. The system has been implemented on a low cost FPGA device and exploits the advantages of parallel processing to compute the feed forward phase in support vector machines. In this paper we show that the same hardware can be used for classification problems and regression problems, and we show satisfactory results on an image recognition problem by SV multiclass classification and on a function estimation problem by SV regression.

We describe the hardware implementation of a Hamming classifier using an optoelectronic architecture. It is composed of two layers, the first layer is an optoelectronic matrix-vector multiplier based on the optical broadcast architecture; it is a novel architecture composed of a set of electronic neurons that receive the input sequentially by means of an optical broadcast interconnection. The second layer is an electronic winner take all circuit. The main characteristic of the system is that it is readily scalable in speed and size to large numbers of pixel neurons. We will describe the optoelectronic architecture, the hardware implementation of a prototype and evaluation of its performance characteristics.

In this paper we propose of a novel hardware electronic-optoelectronic neural network processor for vision applications. The architecture of the proposed neuroprocessor is based on a hybrid optic and optoelectronic implementation of the system. Some neural operations, like interconnection weight storage and assignment, are done in the electronic domain while the interconnection between processing elements is done optically. By this way we exploit the communication strength of optics and the computational strength of electronics in an optical fashion. The main characteristics of the architecture are that it is fully interconnected, the interconnections are fully programmable, it avoids optical alignment problems, and it is readily scalable to large numbers of pixel neurons. We will describe the architecture, the hardware implementation of a first prototype and its functionality for pattern recognition applications. The neural network models we have implemented on our neuroprocessor have been a basic logic functions operator, a Hopfield network and the matching scores layer of a Hamming network.