Platon Sovilj

Digital Stochastic Measurement of a Nonstationary Signal With an Example of EEG Signal Measurement

This paper presents a method of digital stochastic measurement (DSM) of nonstationary signals. The method is based on stochastic analog-to-digital (A/D) conversion and accumulation, with a hardware structure based on a field-programmable gate array and a low-resolution A/D converter. The characteristic of previous implementations of DSM was the measurement of stationary signal harmonics. This paper shows how DSM can be extended and also used when it is necessary to measure the time series of nonstationary signals. An electroencephalography signal is selected as an example of a real nonstationary signal, and its DSM is tested by simulations and experiments. Tests are done without adding noise and with adding a noise-varying signal-to-noise ratio (SNR) from 10 to -10 dB. The results of simulations and experiments are compared versus theory calculations, and the comparison confirms the theory. The presented method provides control of the measurement uncertainty even at low SNR values, by controlling the sample rate of the used A/D converter. This enables designers of measurement systems to choose fast A/D converters with low resolution to achieve higher measurement accuracy.

Stochastic Measurement of Power Grid Frequency Using a Two-Bit A/D Converter

This paper presents a new method for measurement of power grid frequency based on the use of three complementary strategies: stochastic A/D conversion, finite impulse response filtering and signal frequency estimation using zero-crossing detection. Although such an amalgamated concept offered great flexibility in choice of system parameters, this paper presents the variant with the two-bit A/D converter, which requires the simplest hardware. It is shown that this solution is highly robust against signal distortions, even when harmonic distortion factors have a value of 100%.

Concept of Stochastic Measurements in the Fourier Domain

This paper presents the concept of measurement of a crucial quantities in the public distribution network using the Fourier coefficients. The method for Fourier coefficients measurement is presented, as well as the instrument by which the measurement is performed. The accuracy of the measured coefficients is between 14 and 15 bits of FS.

Measurement uncertainty bounds of DSM method

Digital Stochastic Measurement (DSM) method provides Fourier coefficients by using dithered samples of the measured signal. In this paper we evaluate the uncertainty bounds of this method, and compare our results with the Cramer-Rao Lower Bound findings.

Measurement and processing of event-related brain potential records

The paper presents a system for measurement and processing of event-related brain potential records. ERP (event-related potentials) are described from point of view of its appearance and its significance in areas of cognitive neurosciences and neurophysiology. The system hardware is of modular structure, enabling easier changes of its elements. In the current version, software for stimulation and data processing implements oddball paradigm, with special attention to management of time intervals.

Reconstruction of an analog signal measured using two-bit stochastic digital measurement method

This paper considers the process of analog signal reconstruction measured by using a two-bit stochastic digital measurement method. The proposed process is tested by digital simulations on the example of a triangular signal. It is shown that for sampling frequency of 10.58 MHz and measurement interval of 1.28 s the precision (effective resolution) of the reconstructed signal is 12.18 bits. If one wants to achieve a more precise reconstruction, it is necessary to increase the measurement time interval and/or sampling frequency.

Proposal for extension of the standard paradigm of discrete digital measurement

The paper gives an elaborated proposal that the extended paradigm of discrete digital measurement, in addition to the standard sampling method (SSM), should also include stochastic digital measurement method (SDMM).

Multisensory platform based on NEC protocol

Multisensory signal acquisition represents one of the main concepts necessary to perform measurements in various industrial and consumer-oriented applications. This paper presents a development platform which provides a data acquisition from multiple sensors. The main module of the platform is the UNIDS-3 development board with PIC18F8520 microcontroller. Data acquisition from the sensors is performed on user demand by remote control. NEC protocol is implemented and IR receiver TSOP31238 is used. The measurement data are sent to a computer, which performs digital data processing, data visualization and data storage.

Measurement of definite integral of sinusoidal signal absolute value third power using digital stochastic method

In this paper a special case of digital stochastic measurement of the third power of definite integral of sinusoidal signals absolute value, using 2-bit AD converters is presented. This case of digital stochastic method had emerged from the need to measure power and energy of the wind. Power and energy are proportional to the third power of wind speed. Anemometer output signal is sinusoidal. Therefore an integral of the third power of sinusoidal signal is zero. Two approaches are proposed for the third power calculation of the wind speed signal. One approach is to use absolute value of sinusoidal signal (before AD conversion) for which there is no need of multiplier hardware change. The second approach requires small multiplier hardware change, but input signal remains unchanged. For the second approach proposed minimal hardware change was made to calculate absolute value of the result after AD conversion. Simulations have confirmed theoretical analysis. Expected precision of wind energy measurement of proposed device is better than 0,00051% of full scale.

Digital stochastic measurement of noised biomedical signals: Simulation versus experiment

An experimental hardware solution, which has been realized in order to verify the simulation model for digital stochastic measurement (DSM) of biomedical signals, is presented in this paper. The simulation has been realised using the Matlab program, and electrooculography (EOG) signal is used as an example of a real low nonstationary biomedical signal. Realized experimental hardware solution is based on a personal computer (PC) with additional microcontroller hardware for analogue signal processing. The experimental printed circuit board (PCB) has been designed with microcontroller (MCU) STM32F303C6T6. Tests are done with adding a noise-varying signal-to-noise ratio (SNR) from 10 to -10 dB. The experimental results are compared versus results of simulations, and the comparison confirms the simulation.