Zidek

Bio: Zidek is an academic researcher. The author has contributed to research in topics: Communications system & Adaptive filter. The author has an hindex of 1, co-authored 1 publications receiving 34 citations.

The real implementation of nlms channel equalizer into the system of software defined radio

Abstract: This paper deals with the usage of a combination of fuzzy system and artificial techniques, which are called adaptive neuro fuzzy inference system (ANFIS), in order to minimize the distance between the signal and SNR transmitting channel noise and then reduce the error rate of bit error rate (BER) transmission. The authors are focusing on a real implementation of ANFIS channel equalizer on software-defined radio (SDR) system working on PCI eXtensions for instrumentation (PXI) platform. This sophisticated modular measuring system consists of a vector signal generator RF VSG NI PXI-5670 and a vector signal analyzer RF VSA NI PXI-5661. Experimental results suggest that researched ANFIS equalizer embodies better BER values in comparison to commercially most common equalizers of the least mean square algorithm group. Moreover, the conducted experiments show that the usage of the SDR conception is very suitable for testing new principles in channel equalization field.

Testing of the voice communication in smart home care

Abstract: 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 system for improving the diagnostic quality of fetal electrocardiogram

Abstract: This article deals with utilisation of adaptive filtration for improvement of the diagnostic quality of fetal electrocardiogram as a modern tool used in prediction of intrapartum fetal hypoxia. In the case of the latest intrapartum monitoring method, the quality of the curve of fetal electrocardiogram has a major influence on the quality of the fetal monitoring. The aspect subjected to the diagnostic analysis is the ST interval of the ECG curve. In today's medical diagnostic technology this advanced method is only applied in internal monitoring systems. The authors of this study concentrate on external monitoring which, contrary to the classic internal monitoring, brings about a number of problems regarding the quality of recordings. Streszczenie. W artykule zaproponowano filtr adaptacyjny w celu poprawy jakości elektrokardiogramu plodu. Dotychczas stosowano taką metode do wewnetrznych systemow monitorowania. Autorzy rozszerzyli te metode takze na systemy zewnetrzne. (Metoda poprawy jakości diagnostyki przy wykorzystaniu elektrokardiogramu plodu).

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.

In-ear microphone speech quality enhancement via adaptive filtering and artificial bandwidth extension

Abstract: Bone and tissue conducted speech has been used in noisy environments to provide a relatively high signal-to-noise ratio signal. However, the limited bandwidth of bone and tissue conducted speech degrades the quality of the speech signal. Moreover in very noisy conditions, bandwidth extension of the bone and tissue conducted speech becomes problematic. In this paper, speech generated from bone and tissue conduction captured using an in-ear microphone is enhanced using adaptive filtering and a non-linear bandwidth extension method. Objective and subjective tests are used to evaluate the performance of the proposed techniques. Both evaluations show a statistically significant quality enhancement of the noisy in-ear microphone speech with ρ < 0.0001 after denoising and ρ < 0.01 after bandwidth extension.