Magnetoresistive angle position sensors are, beside Hall effect sensors, especially suitable for usage within servo systems due to their reliability, longevity, and resilience to unfavorable environmental conditions. The proposed distributed method for self-calibration of magnetoresistive angular position sensor uses the data collected during the highest allowed speed shaft movement for the identification of the measurement process model parameters. Data acquisition and initial data processing have been realized as a part of the control process of the servo system, whereas the identification of the model parameters is a service of an application server. The method of minimizing of the sum of algebraic distances of the sensor readings and the parametrized model is employed for the identification of parameters of linear compensation, whereas the average shaft rotation speed has been used as a high precision reference for the identification of parameters of harmonic compensation. The proposed method, in addition to a fast convergence, provides for the increase in measurement accuracy for an order of magnitude. Experimentally obtained measurement uncertainty was better than 0.5°, with the residual variance less than 0.02°, comparable to the sensor resolution.

A terminal for remote control of charging stations for electric vehicles (EV) powered by renewable energy has been presented in this paper. This terminal enables remote control of EV chargers, smart batteries, smart electricity meters, fiscal cash registers (FCR), as well as remote control of renewable energy sources and other devices within the station. This terminal also makes charging stations more accessible to electric vehicles users, to electricity distribution companies, to electricity suppliers, to tax administrations, and finally to users and owners of charging stations. Therefore, communication and control with all these devices and systems is integrated in one device. Realization of hardware and software of such a terminal has also been described in this paper. The net result of development and commercialization of terminals would encourage an increase in the use of electric vehicles powered by energy from renewable sources, which would cause a decrease in the level of air pollution and all negative effects it causes in the future. Different categories of this device are considered. Moreover, although it is a device with embedded software, a very advanced method was used, that is, a model-driven development method, which enables fast and more efficient development and maintenance of the device. The results of the application of this method to the terminal for remote control of fiscal cash registers are provided. They were compared with the results of the development of the terminal for remote control of smart meters without applying this method. A simulation of the development of the terminal for remote control of the station is also provided. The presented method can be used in the future for faster and better-quality development of embedded software.

This paper presents a unified model of magnetoresistive angular position sensors, applicable to sensors based on the three major sensor technologies: Anisotropic Magnetoresistance, Giant Magnetoresistance, and Tunnel Magnetoresistance magnetoresistive elements. Theoretical models of the three different magnetoresistive elements, including manufacturing tolerances, have been derived first. The models are then employed to derive a realistic model of a magnetoresistive measurement bridge. In order to construct a model of a magnetoresistive magnetic field direction sensor, besides bridge models, a novel model of a free ferromagnetic layer has been developed. Finally, a unified magnetoresistive angular position sensor model has been obtained by combining the magnetic field direction sensor and a simple model of a permanent magnet. The experimental results confirmed the validity of the proposed model.

Increasing the resolution of digital images and the frame rate of video sequences leads to an increase in the amount of required logical and memory resources necessary for digital image and video decompression. Therefore, the development of new hardware architectures for digital image decoder with a reduced amount of utilized logical and memory resources become a necessity. In this paper, a digital image decoder for efficient hardware implementation, has been presented. Each block of the proposed digital image decoder has been described. Entropy decoder, decoding probability estimator, dequantizer and inverse subband transformer (parts of the digital image decoder) have been developed in such way which allows efficient hardware implementation with reduced amount of utilized logic and memory resources. It has been shown that proposed hardware realization of inverse subband transformer requires 20% lower memory capacity and uses less logic resources compared with the best state-of-the-art realizations. The proposed digital image decoder has been implemented in a low-cost FPGA device and it has been shown that it requires at least 32% less memory resources in comparison to the other state-of-the-art decoders which can process high-definition frame size. The proposed solution also requires effectively lower memory size than state-of-the-art architectures which process frame size or tile size smaller than high-definition size. The presented digital image decoder has maximum operating frequency comparable with the highest maximum operating frequencies among the state-of-the-art solutions.