The non-contact current measurement method with magnetic sensors has become a subject of research. Unfortunately, magnetic sensors fail to distinguish the interested magnetic field from nearby interference and suffer from gauss white noise due to the intrinsic noise of the sensor and external disturbance. In this paper, a novel adaptive filtering-based current reconstruction method with a magnetic sensor array is proposed. Interference-rejection methods based on two classic algorithms, the least-mean-square (LMS) and recursive-least-square (RLS) algorithms, are compared when used in the parallel structure and regular triangle structure of three-phase system. Consequently, the measurement range of RLS-based algorithm is wider than that of LMS-based algorithm. The results of carried out simulations and experiments show that RLS-based algorithms can measure currents with an error of around 1%. Additionally, the RLS-based algorithm can filter the gauss white noise whose magnitude is within 10% of the linear magnetic field range of the sensor.
The article presents measurement results of prototype integrated circuits for acquisition and processing of images in real time. In order to verify a new concept of circuit solutions of analogue image processors, experimental integrated circuits were fabricated. The integrated circuits, designed in a standard 0.35 μm CMOS technology, contain the image sensor and analogue processors that perform low-level convolution-based image processing algorithms. The prototype with a resolution of 32 × 32 pixels allows the acquisition and processing of images at high speed, up to 2000 frames/s. Operation of the prototypes was verified in practice using the developed software and a measurement system based on a FPGA platform.
An array consisting of four commercial gas sensors with target specifications for hydrocarbons, ammonia, alcohol, explosive gases has been constructed and tested. The sensors in the array operate in the dynamic mode upon the temperature modulation from 350°C to 500°C. Changes in the sensor operating temperature lead to distinct resistance responses affected by the gas type, its concentration and the humidity level. The measurements are performed upon various hydrogen (17-3000 ppm), methane (167-3000 ppm) and propane (167-3000 ppm) concentrations at relative humidity levels of 0-75%RH. The measured dynamic response signals are further processed with the Discrete Fourier Transform. Absolute values of the dc component and the first five harmonics of each sensor are analysed by a feed-forward back-propagation neural network. The ultimate aim of this research is to achieve a reliable hydrogen detection despite an interference of the humidity and residual gases.