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Vol. 13 No. 1 (2023): JITA - APEIRON

Siniša Tomić, Dalibor Drljača

Digitalization of Sound Using Pulse Code Modulation (PCM)

Original scientific paper

Abstract

This paper focuses on Pulse Code Modulation (PCM) as a technology for digitizing analog signals. PCM is a widely used technique that enables precise encoding and transmission of analog information through digital pulse signals. The basic principles of PCM are explained here. PCM converts an analog signal into a digital form through sampling, quantization, and encoding. Sampling refers to the conversion of a continuous analog signal into discrete samples at regular time intervals. Then, quantization is applied to round each sample to the nearest possible quantization value, reducing the continuous range of values to discrete levels. Afterward, each quantized sample is encoded into a digital form. PCM is commonly used in various communication systems as well as in digital audio processing. In communication systems, PCM enables reliable transmission of voice signals, music, and other audio content over digital networks. In digital audio processing, PCM is used for recording, playback, and manipulation of sound, enabling high-quality reproduction and precise processing.

Keywords: PCM, Pulse Code Modulation, Analog Signal Digitization, Sampling, Quantization, Pulse Signals.

Vol. 26 No. 2 (2023): JITA - APEIRON

Igor Shubinsky, Alexey Ozerov

Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures

Original scientific paper

Abstract

The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures.

Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.

Vol. 26 No. 2 (2023): JITA - APEIRON

Igor Shubinsky, Alexey Ozerov

Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures

Original scientific paper

Abstract

The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures.

Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.