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Vol. 1 No. 2 (2011): JITA - APEIRON

Aleksej Avramović, Patricio Bulić, Zdenka Babić

Digital Signal Processing Applications with Iterative Logarithmic Multipliers

Original scientific paper

DOI: https://doi.org/10.7251/JIT1102083A

Abstract

Many digital signal processing applications demand a huge number of multiplications, which are time, power and area consuming. But input data is often corrupted with noise, which means that a few least significant bits do not carry usable information and do not need to be processed. Therefore, approximate multiplication does not affect application efficiency when approximation error is less than noise introduced during data acquisition. This fact enables usage of faster and less power-consuming algorithms that is important in many cases where processing includes convolution, integral transformations, distance computations etc. This paper discusses logarithm-based approximate multipliers and squarers, their characteristics and digital signal processing applications based on approximate multiplications. Our iterative multipliers and squarers contain arbitrary series of basic blocks that involves only adders and shifters; therefore, it is not power and time consuming and enables achieving arbitrary accuracy. It was shown that proposed approximate multipliers and squarers can be used in several signal processing applications without decreasing of application efficiency.

Keywords: Approximate multiplication, Digital signal processing.

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.