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Journal Details
Format
Journal
eISSN
1335-8871
First Published
07 Mar 2008
Publication timeframe
6 times per year
Languages
English

Search

Volume 17 (2017): Issue 3 (June 2017)

Journal Details
Format
Journal
eISSN
1335-8871
First Published
07 Mar 2008
Publication timeframe
6 times per year
Languages
English

Search

6 Articles
access type Open Access

New Discrete Fibonacci Charge Pump Design, Evaluation and Measurement

Published Online: 15 Jun 2017
Page range: 100 - 107

Abstract

Abstract

This paper focuses on the practical aspects of the realisation of Dickson and Fibonacci charge pumps. Standard Dickson charge pump circuit solution and new Fibonacci charge pump implementation are compared. Both charge pumps were designed and then evaluated by LTspice XVII simulations and realised in a discrete form on printed circuit board (PCB). Finally, the key parameters as the output voltage, efficiency, rise time, variable power supply and clock frequency effects were measured.

Keywords

  • Dickson charge pump
  • Fibonacci charge pump
  • voltage gain
  • output series resistance
  • rise time
  • efficiency
access type Open Access

Application of Monte Carlo Method for Evaluation of Uncertainties of ITS-90 by Standard Platinum Resistance Thermometer

Published Online: 15 Jun 2017
Page range: 108 - 116

Abstract

Abstract

Evaluation of uncertainties of the temperature measurement by standard platinum resistance thermometer calibrated at the defining fixed points according to ITS-90 is a problem that can be solved in different ways. The paper presents a procedure based on the propagation of distributions using the Monte Carlo method. The procedure employs generation of pseudo-random numbers for the input variables of resistances at the defining fixed points, supposing the multivariate Gaussian distribution for input quantities. This allows taking into account the correlations among resistances at the defining fixed points. Assumption of Gaussian probability density function is acceptable, with respect to the several sources of uncertainties of resistances. In the case of uncorrelated resistances at the defining fixed points, the method is applicable to any probability density function. Validation of the law of propagation of uncertainty using the Monte Carlo method is presented on the example of specific data for 25 Ω standard platinum resistance thermometer in the temperature range from 0 to 660 °C. Using this example, we demonstrate suitability of the method by validation of its results.

Keywords

  • The law of propagation of uncertainty
  • Monte Carlo method
  • the International Temperature Scale of 1990 (ITS-90)
  • Standard Platinum Resistance Thermometer
access type Open Access

Stationary Wavelet-based Two-directional Two-dimensional Principal Component Analysis for EMG Signal Classification

Published Online: 15 Jun 2017
Page range: 117 - 124

Abstract

Abstract

Discrete wavelet transform (WT) followed by principal component analysis (PCA) has been a powerful approach for the analysis of biomedical signals. Wavelet coefficients at various scales and channels were usually transformed into a one-dimensional array, causing issues such as the curse of dimensionality dilemma and small sample size problem. In addition, lack of time-shift invariance of WT coefficients can be modeled as noise and degrades the classifier performance. In this study, we present a stationary wavelet-based two-directional two-dimensional principal component analysis (SW2D2PCA) method for the efficient and effective extraction of essential feature information from signals. Time-invariant multi-scale matrices are constructed in the first step. The two-directional two-dimensional principal component analysis then operates on the multi-scale matrices to reduce the dimension, rather than vectors in conventional PCA. Results are presented from an experiment to classify eight hand motions using 4-channel electromyographic (EMG) signals recorded in healthy subjects and amputees, which illustrates the efficiency and effectiveness of the proposed method for biomedical signal analysis.

Keywords

  • Wavelet transform
  • principal component analysis
  • feature extraction
  • pattern classification
  • electromyographic signal
access type Open Access

The Application of Vibration Accelerations in the Assessment of Average Friction Coefficient of a Railway Brake Disc

Published Online: 15 Jun 2017
Page range: 125 - 134

Abstract

Abstract

Due to their wide range of friction characteristics resulting from the application of different friction materials and good heat dissipation conditions, railway disc brakes have long replaced block brakes in many rail vehicles. A block brake still remains in use, however, in low speed cargo trains. The paper presents the assessment of the braking process through the analysis of vibrations generated by the components of the brake system during braking. It presents a possibility of a wider application of vibroacoustic diagnostics (VA), which aside from the assessment of technical conditions (wear of brake pads) also enables the determination of the changes of the average friction coefficient as a function of the braking onset speed. Vibration signals of XYZ were measured and analyzed. The analysis of the results has shown that there is a relation between the values of the point measures and the wear of the brake pads.

Keywords

  • Vibration
  • signal
  • diagnostic
  • railway disc brake
  • coefficient of friction
access type Open Access

Identification and Adjustment of Guide Rail Geometric Errors Based on BP Neural Network

Published Online: 15 Jun 2017
Page range: 135 - 144

Abstract

Abstract

The relative positions between the four slide blocks vary with the movement of the table due to the geometric errors of the guide rail. Consequently, the additional load on the slide blocks is increased. A new method of error measurement and identification by using a self-designed stress test plate was presented. BP neural network model was used to establish the mapping between the stress of key measurement points on the test plate and the displacements of slide blocks. By measuring the stress, the relative displacements of slide blocks were obtained, from which the geometric errors of the guide rails were converted. Firstly, the finite element model was built to find the key measurement points of the test plate. Then the BP neural network was trained by using the samples extracted from the finite element model. The stress at the key measurement points were taken as the input and the relative displacements of the slide blocks were taken as the output. Finally, the geometric errors of the two guide rails were obtained according to the measured stress. The results show that the maximum difference between the measured geometric errors and the output of BP neural network was 5 μm. Therefore, the correctness and feasibility of the method were verified.

Keywords

  • Guide rail geometric error
  • stress
  • the test plate
  • finite element model
  • BP neural network
access type Open Access

Magnetic Resonance Super-resolution Imaging Measurement with Dictionary-optimized Sparse Learning

Published Online: 15 Jun 2017
Page range: 145 - 152

Abstract

Abstract

Magnetic Resonance Super-resolution Imaging Measurement (MRIM) is an effective way of measuring materials. MRIM has wide applications in physics, chemistry, biology, geology, medical and material science, especially in medical diagnosis. It is feasible to improve the resolution of MR imaging through increasing radiation intensity, but the high radiation intensity and the longtime of magnetic field harm the human body. Thus, in the practical applications the resolution of hardware imaging reaches the limitation of resolution. Software-based super-resolution technology is effective to improve the resolution of image. This work proposes a framework of dictionary-optimized sparse learning based MR super-resolution method. The framework is to solve the problem of sample selection for dictionary learning of sparse reconstruction. The textural complexity-based image quality representation is proposed to choose the optimal samples for dictionary learning. Comprehensive experiments show that the dictionary-optimized sparse learning improves the performance of sparse representation.

Keywords

  • Magnetic resonance imaging measurement
  • sparse learning
  • dictionary learning
  • super-resolution imaging
6 Articles
access type Open Access

New Discrete Fibonacci Charge Pump Design, Evaluation and Measurement

Published Online: 15 Jun 2017
Page range: 100 - 107

Abstract

Abstract

This paper focuses on the practical aspects of the realisation of Dickson and Fibonacci charge pumps. Standard Dickson charge pump circuit solution and new Fibonacci charge pump implementation are compared. Both charge pumps were designed and then evaluated by LTspice XVII simulations and realised in a discrete form on printed circuit board (PCB). Finally, the key parameters as the output voltage, efficiency, rise time, variable power supply and clock frequency effects were measured.

Keywords

  • Dickson charge pump
  • Fibonacci charge pump
  • voltage gain
  • output series resistance
  • rise time
  • efficiency
access type Open Access

Application of Monte Carlo Method for Evaluation of Uncertainties of ITS-90 by Standard Platinum Resistance Thermometer

Published Online: 15 Jun 2017
Page range: 108 - 116

Abstract

Abstract

Evaluation of uncertainties of the temperature measurement by standard platinum resistance thermometer calibrated at the defining fixed points according to ITS-90 is a problem that can be solved in different ways. The paper presents a procedure based on the propagation of distributions using the Monte Carlo method. The procedure employs generation of pseudo-random numbers for the input variables of resistances at the defining fixed points, supposing the multivariate Gaussian distribution for input quantities. This allows taking into account the correlations among resistances at the defining fixed points. Assumption of Gaussian probability density function is acceptable, with respect to the several sources of uncertainties of resistances. In the case of uncorrelated resistances at the defining fixed points, the method is applicable to any probability density function. Validation of the law of propagation of uncertainty using the Monte Carlo method is presented on the example of specific data for 25 Ω standard platinum resistance thermometer in the temperature range from 0 to 660 °C. Using this example, we demonstrate suitability of the method by validation of its results.

Keywords

  • The law of propagation of uncertainty
  • Monte Carlo method
  • the International Temperature Scale of 1990 (ITS-90)
  • Standard Platinum Resistance Thermometer
access type Open Access

Stationary Wavelet-based Two-directional Two-dimensional Principal Component Analysis for EMG Signal Classification

Published Online: 15 Jun 2017
Page range: 117 - 124

Abstract

Abstract

Discrete wavelet transform (WT) followed by principal component analysis (PCA) has been a powerful approach for the analysis of biomedical signals. Wavelet coefficients at various scales and channels were usually transformed into a one-dimensional array, causing issues such as the curse of dimensionality dilemma and small sample size problem. In addition, lack of time-shift invariance of WT coefficients can be modeled as noise and degrades the classifier performance. In this study, we present a stationary wavelet-based two-directional two-dimensional principal component analysis (SW2D2PCA) method for the efficient and effective extraction of essential feature information from signals. Time-invariant multi-scale matrices are constructed in the first step. The two-directional two-dimensional principal component analysis then operates on the multi-scale matrices to reduce the dimension, rather than vectors in conventional PCA. Results are presented from an experiment to classify eight hand motions using 4-channel electromyographic (EMG) signals recorded in healthy subjects and amputees, which illustrates the efficiency and effectiveness of the proposed method for biomedical signal analysis.

Keywords

  • Wavelet transform
  • principal component analysis
  • feature extraction
  • pattern classification
  • electromyographic signal
access type Open Access

The Application of Vibration Accelerations in the Assessment of Average Friction Coefficient of a Railway Brake Disc

Published Online: 15 Jun 2017
Page range: 125 - 134

Abstract

Abstract

Due to their wide range of friction characteristics resulting from the application of different friction materials and good heat dissipation conditions, railway disc brakes have long replaced block brakes in many rail vehicles. A block brake still remains in use, however, in low speed cargo trains. The paper presents the assessment of the braking process through the analysis of vibrations generated by the components of the brake system during braking. It presents a possibility of a wider application of vibroacoustic diagnostics (VA), which aside from the assessment of technical conditions (wear of brake pads) also enables the determination of the changes of the average friction coefficient as a function of the braking onset speed. Vibration signals of XYZ were measured and analyzed. The analysis of the results has shown that there is a relation between the values of the point measures and the wear of the brake pads.

Keywords

  • Vibration
  • signal
  • diagnostic
  • railway disc brake
  • coefficient of friction
access type Open Access

Identification and Adjustment of Guide Rail Geometric Errors Based on BP Neural Network

Published Online: 15 Jun 2017
Page range: 135 - 144

Abstract

Abstract

The relative positions between the four slide blocks vary with the movement of the table due to the geometric errors of the guide rail. Consequently, the additional load on the slide blocks is increased. A new method of error measurement and identification by using a self-designed stress test plate was presented. BP neural network model was used to establish the mapping between the stress of key measurement points on the test plate and the displacements of slide blocks. By measuring the stress, the relative displacements of slide blocks were obtained, from which the geometric errors of the guide rails were converted. Firstly, the finite element model was built to find the key measurement points of the test plate. Then the BP neural network was trained by using the samples extracted from the finite element model. The stress at the key measurement points were taken as the input and the relative displacements of the slide blocks were taken as the output. Finally, the geometric errors of the two guide rails were obtained according to the measured stress. The results show that the maximum difference between the measured geometric errors and the output of BP neural network was 5 μm. Therefore, the correctness and feasibility of the method were verified.

Keywords

  • Guide rail geometric error
  • stress
  • the test plate
  • finite element model
  • BP neural network
access type Open Access

Magnetic Resonance Super-resolution Imaging Measurement with Dictionary-optimized Sparse Learning

Published Online: 15 Jun 2017
Page range: 145 - 152

Abstract

Abstract

Magnetic Resonance Super-resolution Imaging Measurement (MRIM) is an effective way of measuring materials. MRIM has wide applications in physics, chemistry, biology, geology, medical and material science, especially in medical diagnosis. It is feasible to improve the resolution of MR imaging through increasing radiation intensity, but the high radiation intensity and the longtime of magnetic field harm the human body. Thus, in the practical applications the resolution of hardware imaging reaches the limitation of resolution. Software-based super-resolution technology is effective to improve the resolution of image. This work proposes a framework of dictionary-optimized sparse learning based MR super-resolution method. The framework is to solve the problem of sample selection for dictionary learning of sparse reconstruction. The textural complexity-based image quality representation is proposed to choose the optimal samples for dictionary learning. Comprehensive experiments show that the dictionary-optimized sparse learning improves the performance of sparse representation.

Keywords

  • Magnetic resonance imaging measurement
  • sparse learning
  • dictionary learning
  • super-resolution imaging

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