Petr Bilik

Non-Invasive Fetal Monitoring: A Maternal Surface ECG Electrode Placement-Based Novel Approach for Optimization of Adaptive Filter Control Parameters Using the LMS and RLS Algorithms

This paper is focused on the design, implementation and verification of a novel method for the optimization of the control parameters (such as step size μ and filter order N) of LMS and RLS adaptive filters used for noninvasive fetal monitoring. The optimization algorithm is driven by considering the ECG electrode positions on the maternal body surface in improving the performance of these adaptive filters. The main criterion for optimal parameter selection was the Signal-to-Noise Ratio (SNR). We conducted experiments using signals supplied by the latest version of our LabVIEW-Based Multi-Channel Non-Invasive Abdominal Maternal-Fetal Electrocardiogram Signal Generator, which provides the flexibility and capability of modeling the principal distribution of maternal/fetal ECGs in the human body. Our novel algorithm enabled us to find the optimal settings of the adaptive filters based on maternal surface ECG electrode placements. The experimental results further confirmed the theoretical assumption that the optimal settings of these adaptive filters are dependent on the ECG electrode positions on the maternal body, and therefore, we were able to achieve far better results than without the use of optimization. These improvements in turn could lead to a more accurate detection of fetal hypoxia. Consequently, our approach could offer the potential to be used in clinical practice to establish recommendations for standard electrode placement and find the optimal adaptive filter settings for extracting high quality fetal ECG signals for further processing. Ultimately, diagnostic-grade fetal ECG signals would ensure the reliable detection of fetal hypoxia.

A Phonocardiographic-Based Fiber-Optic Sensor and Adaptive Filtering System for Noninvasive Continuous Fetal Heart Rate Monitoring

This paper focuses on the design, realization, and verification of a novel phonocardiographic- based fiber-optic sensor and adaptive signal processing system for noninvasive continuous fetal heart rate (fHR) monitoring. Our proposed system utilizes two Mach-Zehnder interferometeric sensors. Based on the analysis of real measurement data, we developed a simplified dynamic model for the generation and distribution of heart sounds throughout the human body. Building on this signal model, we then designed, implemented, and verified our adaptive signal processing system by implementing two stochastic gradient-based algorithms: the Least Mean Square Algorithm (LMS), and the Normalized Least Mean Square (NLMS) Algorithm. With this system we were able to extract the fHR information from high quality fetal phonocardiograms (fPCGs), filtered from abdominal maternal phonocardiograms (mPCGs) by performing fPCG signal peak detection. Common signal processing methods such as linear filtering, signal subtraction, and others could not be used for this purpose as fPCG and mPCG signals share overlapping frequency spectra. The performance of the adaptive system was evaluated by using both qualitative (gynecological studies) and quantitative measures such as: Signal-to-Noise Ratio—SNR, Root Mean Square Error—RMSE, Sensitivity—S+, and Positive Predictive Value—PPV.

Testing of the voice communication in smart home care

This article is aimed to describe the method of testing the implementation of voice control over operating and technical functions of Smart Home Come. Custom control over operating and technical functions was implemented into a model of Smart Home that was equipped with KNX technology. A sociological survey focused on the needs of seniors has been carried out to justify the implementation of voice control into Smart Home Care. In the real environment of Smart Home Care, there are usually unwanted signals and additive noise that negatively affect the voice communication with the control system. This article describes the addition of a sophisticated system for filtering the additive background noise out of the voice communication with the control system. The additive noise significantly lowers the success of recognizing voice commands to control operating and technical functions of an intelligent building. Within the scope of the proposed application, a complex system based on fuzzy-neuron networks, specifically the ANFIS (Adaptive Neuro-Fuzzy Interference System) for adaptive suppression of unwanted background noises was created. The functionality of the designed system was evaluated both by subjective and by objective criteria (SSNR, DTW). Experimental results suggest that the studied system has the potential to refine the voice control of technical and operating functions of Smart Home Care even in a very noisy environment.

A ROBUST APPROACH FOR ACOUSTIC NOISE SUPPRESSION IN SPEECH USING ANFIS

Abstract The authors of this article deals with the implementation of a combination of techniques of the fuzzy system and artificial intelligence in the application area of non-linear noise and interference suppression. This structure used is called an Adaptive Neuro Fuzzy Inference System (ANFIS). This system finds practical use mainly in audio telephone (mobile) communication in a noisy environment (transport, production halls, sports matches, etc). Experimental methods based on the two-input adaptive noise cancellation concept was clearly outlined. Within the experiments carried out, the authors created, based on the ANFIS structure, a comprehensive system for adaptive suppression of unwanted background interference that occurs in audio communication and degrades the audio signal. The system designed has been tested on real voice signals. This article presents the investigation and comparison amongst three distinct approaches to noise cancellation in speech; they are LMS (least mean squares) and RLS (recursive least squares) adaptive filtering and ANFIS. A careful review of literatures indicated the importance of non-linear adaptive algorithms over linear ones in noise cancellation. It was concluded that the ANFIS approach had the overall best performance as it efficiently cancelled noise even in highly noise-degraded speech. Results were drawn from the successful experimentation, subjective-based tests were used to analyse their comparative performance while objective tests were used to validate them. Implementation of algorithms was experimentally carried out in Matlab to justify the claims and determine their relative performances.

CompactRIO embedded system in Power Quality Analysis

Electrical measurement department of VSB-Technical University has been involved for more than 14 years in research and development of power quality analyzer built on virtual instrumentation technology. PC-based power quality analyzer with National Instruments data acquisition board was designed and developed in this time frame. National Instruments LabVIEW is used as the development environment for all parts of power quality analyzer software running under MS Windows OS. Proved PC-based firmware was ported to new hardware platform for virtual instrumentation - National Instruments CompactRIO at the end of 2007. Platform change from PC to CompactRIO is not just code recompilation, but it brings up many needs for specific software redesigns. Paper describes how the monolithic executable for PC-based instruments was divided into three software layers to be ported on CompactRIO platform. The code for different parts of CompactRIO instrument is developed in a unified development environment no matter if the code is intended for FPGA, real-time processor of PC running Windows OS.

Realization of Prototype of a Low-Cost Bidirectional Communication System Through Fibreless Optics

The article deals with the design and realization of a prototype of an efficient Low-Cost, Low-Power, Low Complexity—hereinafter (L-CPC) bidirectional communication system for reading and configuration of inbuilt devices (e.g. devices for reading of water meters, power meters, gas meters, identification of property and other specific applications), which will comply with the requirements of no radio waves, reuses already existing components (NRW-RAEC). The goal of the authors was to realize a fibreless optical communication system that would be capable of efficiently replacing current systems (Wi-Fi, Bluetooth, ZigBee, IrDa etc.) at a fraction of their initial cost with an emphasis on L-CPC and NRW-RAEC. Within the development of the prototype, the authors were responsible for the hardware, firmware (ASM, C) and software components (C#) of the prototype. The functionality of the technical solution of the prototype was verified in a real-life application (reading of water meters). The original contribution of the presented article is the realization of a functional prototype including a patented technical solution and design of an intuitive UI. Real-life experiments have shown that the functional prototype designed by the authors can fully replace current systems at a fraction of their initial cost within the scope of the required parameters.

Modular system for distributed power quality monitoring

BK-ELCOM is a modular HW & SW platform for power quality monitoring and analysis based on virtual instrumentation technology. The analyzer of BK-ELCOM product line is based on PC hardware equipped by 16-bit NI data acquisition board running the instrument firmware fully written in LabVIEWtrade 8.2. Implemented algorithms follow the requirements of the latest power quality standards like IEC61000-4-30, IEC61000-4-15, IEC61000-4-7, EN 50160. Newly developed data storage format EDF (Extended Data Format) brings open platform for storing any type data for power quality analyzer. EDF brings also new possibilities for the postprocessing software package.

Adaptive noise suppression in voice communication using a neuro-fuzzy inference system

This paper describes the implementation of a combination of techniques of the fuzzy system and artificial intelligence in the application area of non-linear suppression of noise and interference. The structure used is called ANFIS (Adaptive Neuro Fuzzy Inference System). This system finds practical use mainly in audio telephone (mobile) communication in a noisy environment (transport, production halls, sports matches, etc.). Within the experiments carried out, the authors created, based on the ANFIS structure, a comprehensive system for the adaptive suppression of unwanted background interference that occurs in audio communication and which degrades the audio signal. The system designed has been tested on real voice signals. Noise cancellation performance of the algorithms has been compared by means of SSNR (Segmental Signal to Noise Ratio) and DTW (Dynamic Time Warping). Also processing durations of the algorithms are determined for evaluating the possibility of real time implementation. The results imply that a system using ANFIS has better experimental results than conventional systems built on adaptive algorithms of the LMS (Least Mean Squares) and RLS (Recursive Least Squares) families.

Measurement of voltage and current harmonics for frequencies up to 9 kHz according to IEC61000-4-7

The number of distributed energy sources (DER), the huge increase of loads with switching supplies and the massive use of compact saving lamps brings the need to measure and evaluate voltage and current up to 9kHz. Annex B of the latest update of the IEC 61000-4-7:2002/A1:2008 unifies the length of the basic time window for the evaluation of signal components up to 2kHz and for the evaluation of signal components in the range of 2kHz–9kHz. This fact, together with latest measurement technologies, allows the simultaneous evaluation of signal components in the entire range of 50Hz–9kHz, even for very small amplitudes of frequency components. This paper analyzes and describes the technical problems to be solved in order to meet the requirements of IEC 61000-4-7:2002/A1:2008. The paper presents the results of measurements performed on different loads with expected significant signal components in the frequency range of 2kHz–9kHz, such as PC power supply. Compatible levels for frequency components in the range of 2–9kHz are discussed.

Modelling of Fetal Hypoxic Conditions Based on Virtual Instrumentation

This article is dedicated to the design and implementation of a software simulator of the fetal ECG signal (fECG). Primarily, it aims at modelling the influence of physiological and pathological changes. Fetal electrocardiogram is a valuable carrier of information and its potential can be utilized in the diagnosis of hypoxic conditions. Analysis of real records and study of methods intended for obtaining the ECG signal in clinical practice resulted in creating a simulator of fECG waveforms based on virtual instrumentation. In a suitable user environment, the signal formed in this manner can be combined with physiological and pathological parameters that modify the signal. The respective changes can be observed and analysed with the help of graphical output elements of the user interface. The generator allows the user to model the transvaginal monitoring method (invasive), transabdominal monitoring method (non-invasive) and cardiotocograph (CTG) with ST analyser (STAN). Due to their morphology, the simulation results correlate with the established theoretical assumptions and approximate to real records.