Metrology and Measurement Systems

This overview paper presents and compares different methods traditionally used for estimating damped sinusoid parameters. Firstly, direct nonlinear least squares fitting the signal model in the time and frequency domains are described. Next, possible applications of the Hilbert transform for signal demodulation are presented. Then, a wide range of autoregressive modelling methods, valid for damped sinusoids, are discussed, in which frequency and damping are estimated from calculated signal linear self-prediction coefficients. These methods aim at solving, directly or using least squares, a matrix linear equation in which signal or its autocorrelation function samples are used. The Prony, Steiglitz-McBride, Kumaresan-Tufts, Total Least Squares, Matrix Pencil, Yule-Walker and Pisarenko methods are taken into account. Finally, the interpolated discrete Fourier transform is presented with examples of Bertocco, Yoshida, and Agrež algorithms. The Matlab codes of all the discussed methods are given. The second part of the paper presents simulation results, compared with the Cramér-Rao lower bound and commented. All tested methods are compared with respect to their accuracy (systematic errors), noise robustness, required signal length, and computational complexity.

Prior knowledge of the autocorrelation function (ACF) enables an application of analytical formalism for the unbiased estimators of variance s2a and variance of the mean s2a(xmacr;). Both can be expressed with the use of so-called effective number of observations neff. We show how to adopt this formalism if only an estimate {rk} of the ACF derived from a sample is available. A novel method is introduced based on truncation of the {rk} function at the point of its first transit through zero (FTZ). It can be applied to non-negative ACFs with a correlation range smaller than the sample size. Contrary to the other methods described in literature, the FTZ method assures the finite range 1 < neff ≤ n for any data. The effect of replacement of the standard estimator of the ACF by three alternative estimators is also investigated. Monte Carlo simulations, concerning the bias and dispersion of resulting estimators sa and sa(×), suggest that the presented formalism can be effectively used to determine a measurement uncertainty. The described method is illustrated with the exemplary analysis of autocorrelated variations of the intensity of an X-ray beam diffracted from a powder sample, known as the particle statistics effect.

In this paper, an algorithm that monitors the power system to detect and classify power quality events in real time is presented. The algorithm is able to detect events caused by waveform distortions and variations of the RMS values of the voltage. Detection of the RMS events is done by comparing the RMS values with certain thresholds, while detection of waveform distortions is made using an algorithm based on multiharmonic leasts-squares fitting.

The paper presents a methodology for parametric fault clustering in analog electronic circuits with the use of a self-organizing artificial neural network. The method proposed here allows fast and efficient circuit diagnosis on the basis of time and/or frequency response which may lead to higher production yield. A self-organizing map (SOM) has been applied in order to cluster all circuit states into possible separate groups. So, it works as a feature selector and classifier. SOM can be fed by raw data (data comes from the time or frequency response) or some pre-processing is done at first. The author proposes conversion of a circuit response with the use of e.g. gradient and differentiation. The main goal of the SOM is to distribute all single faults on a two-dimensional map without state overlapping. The method is aimed for the development stage because the tolerances of elements are not taken into account, however single but parametric faults are considered. Efficiency analyses of fault clustering have been made on several examples e.g. a Sallen-Key BPF and an ECG amplifier. Testing procedure is performed in time and frequency domains for the Sallen-Key BPF with limited number of test points i.e. it is assumed that only input and output pins are available. A similar procedure has been applied to a real ECG amplifier in the frequency domain. Results prove a high efficiency in acceptable time which makes the method very convenient (easy and quick) as a first test in the development stage.

This paper presents a novel strategy of fault classification for the analog circuit under test (CUT). The proposed classification strategy is implemented with the one-against-one Support Vector Machines Classifier (SVC), which is improved by employing a fault dictionary to accelerate the testing procedure. In our investigations, the support vectors and other relevant parameters are obtained by training the standard binary support vector machines. In addition, a technique of radial-basis-function (RBF) kernel parameter evaluation and selection is invented. This technique can find a good and proper kernel parameter for the SVC prior to the machine learning. Two typical analog circuits are demonstrated to validate the effectiveness of the proposed method.

To improve the estimation of active power, the possibility of estimating the amplitude square of a signal component using the interpolation of the squared amplitude discrete Fourier transform (DFT) coefficients is presented. As with an energy-based approach, the amplitude square can be estimated with the squared amplitude DFT coefficients around the component peak and a suitable interpolation algorithm. The use of the Hann window, for which the frequency spectrum is well known, and the three largest local amplitude DFT coefficients gives lower systematic errors in squared interpolated approach or in better interpolated squared approach than the energy-based approach, although the frequency has to be estimated in the first step. All investigated algorithms have almost the same noise propagation and the standard deviations are about two times larger than the Cramér-Rao lower bound.

This paper presents a piecewise line generalization algorithm (PG) based on shape characteristic analysis. An adaptive threshold algorithm is used to detect all corners, from which key points are selected. The line is divided into some segments by the key points and generalized piecewise with the Li-Openshaw algorithm. To analyze the performance, line features with different complexity are used. The experimental results compared with the DP algorithm and the Li-Openshaw algorithm show that the PG has better performance in keeping the shape characteristic with higher position accuracy.

The article presents the detection of gases using an infrared imaging Fourier-transform spectrometer (IFTS). The Telops company has developed the IFTS instrument HyperCam, which is offered as a short- or long-wave infrared device. The principle of HyperCam operation and methodology of gas detection has been shown in the paper, as well as theoretical evaluation of gas detection possibility. Calculations of the optical path between the IFTS device, cloud of gases and background have been also discussed. The variation of a signal reaching the IFTS caused by the presence of a gas has been calculated and compared with the reference signal obtained without the presence of a gas in IFTS's field of view. Verification of the theoretical result has been made by laboratory measurements. Some results of the detection of various types of gases has been also included in the paper.

Specimens of Si single crystals with different crystal orientation [100] and [110] were studied by Electro-Ultrasonic Spectroscopy (EUS) and Resonant Ultrasonic Spectroscopy (RUS). A silicon single crystal is an anisotropic crystal, so its properties are different in different directions in the material relative to the crystal orientation. EUS is based on interaction of two signals: an electric AC signal and an ultrasonic signal, which are working on different frequencies. The ultrasonic wave affects the charge carriers' transport in the structures and the intermodulation electrical signal which is created due to the interaction between the ultrasonic wave and charge carriers, is proportional to the density of structural defects. RUS enables to measure natural frequencies of free elastic vibrations of a simply shaped specimen by scanning a selected frequency range including the appropriate resonances of the measured specimens.

This paper presents the design and measurements of low-noise multichannel front-end electronics for recording extra-cellular neuronal signals using microelectrode arrays. The integrated circuit contains 64 readout channels and is fabricated in CMOS 180 nm technology. A single readout channel is built of an AC coupling circuit at the input, a low-noise preamplifier, a band-pass filter and a second amplifier. In order to reduce the number of output lines, the 64 analog signals from readout channels are multiplexed to a single output by an analog multiplexer. The chip is optimized for low noise and good matching performance and has the possibility of pass-band tuning. The low cut-off frequency can be tuned in the 1 Hz - 60 Hz range while the high cut-off frequency can be tuned in the 3.5 kHz - 15 kHz range. For the nominal gain setting at 44 dB and power dissipation per single channel of 220 μW, the equivalent input noise is in the range from 6 μV - 11 μV rms depending on the band-pass filter settings. The chip has good uniformity concerning the spread of its electrical parameters from channel to channel. The spread of the gain calculated as standard deviation to mean value is about 4.4% and the spread of the low cut-off frequency set at 1.6 Hz is only 0.07 Hz. The chip occupies 5×2.3 mm2 of silicon area. To our knowledge, our solution is the first reported multichannel recording system which allows to set in each recording channel the low cut-off frequency within a single Hz with a small spread of this parameter from channel to channel. The first recordings of action potentials from the thalamus of the rat under urethane anesthesia are presented.

Based on recent advances in non-linear analysis, the surface electromyography (sEMG) signal has been studied from the viewpoints of self-affinity and complexity. In this study, we examine usage of critical exponent analysis (CE) method, a fractal dimension (FD) estimator, to study properties of the sEMG signal and to deploy these properties to characterize different movements for gesture recognition. SEMG signals were recorded from thirty subjects with seven hand movements and eight muscle channels. Mean values and coefficient of variations of the CE from all experiments show that there are larger variations between hand movement types but there is small variation within the same type. It also shows that the CE feature related to the self-affine property for the sEMG signal extracted from different activities is in the range of 1.855~2.754. These results have also been evaluated by analysis-of-variance (p-value). Results show that the CE feature is more suitable to use as a learning parameter for a classifier compared with other representative features including root mean square, median frequency and Higuchi's method. Most p-values of the CE feature were less than 0.0001. Thus the FD that is computed by the CE method can be applied to be used as a feature for a wide variety of sEMG applications.

Systems of road traffic parameters measurement play a key role in the process of road traffic control, its supervision as well as in gathering and processing information for statistical purposes. Expectations of users of such systems mainly concern automation and provision of measurement continuity, possibility of selection of the measured road traffic parameters and high accuracy along with reliability of obtained results. In order to meet the requirements set for such systems, at the Department of Instrumentation and Measurement of the AGH University of Science and Technology in Cracow a new prototype system of road traffic parameters measurement - Traffic-1 - has been constructed. The innovativeness of the solution is manifested in the structure of the system that can be modified by the user adequately to current measurement needs and in the used algorithms of signals processing. The work contains a brief description of the constructed system with particular focus on the used innovations that are the result of many years of research work of the designers.

Laser triangulation is one of the machine vision measurement methods most commonly used in 3D quality control. However, considering its susceptibility to interference, it cannot be used in certain areas of industrial production e.g. very shiny surfaces. Thus, for the improvement of its applicability, a predictive algorithm of light profile segmentation was designed, where - as a result of using a'priori knowledge - the method becomes resistant to secondary reflexes.

The developed technique has been tested on selected parts with surfaces typical for the machine-building industry. The evaluation has been presented based on the surface representation (mapping) error analysis, using the difference between the obtained cloud of points and the nominal surface as processing data, as well as scatter of the discrete Gauss curvature.

Modern infrared cameras are constructed with two main types of infrared detectors: photon detectors and thermal detectors. Because of economic reasons, vast numbers of modern thermal cameras are constructed with the use of infrared microbolometric detectors which belong to the group of thermal detectors. Thermal detectors detect incident infrared radiation by measuring changes of temperature on the surface of a special micro-bridge structure. Thermal detectors, like microbolometric detectors on one hand should be sensitive to changing temperature to accurately measure incoming infrared radiation from the observed scene, on the other hand there are many other phenomena that change the temperature of the detector and influence the overall response of the detector. In order to construct an accurate infrared camera, there is a need to evaluate these phenomena and quantify their influence. In the article the phenomenon of self heating due to the operation of the readout circuit is analyzed on an UL 03 19 1 detector. The theoretical analysis is compared with the results of conducted measurements. Measurements with a type SC7900VL thermographic camera were performed to measure the thermodynamic behavior of the UL 03 19 1 detector array.

The paper describes the influence of the machining operation on a surface, which disturbs the projection of the tool profile in the form of its relative movements with respect to the object. The elements of the machine tool undergo constant wear during the machining process, it is therefore important to recognize the effects of their influence on the surface's irregularities. Amplitude-frequency analysis of lateral profiles has been used to evaluate and changes of turned lateral profiles. The results of simulation of radial and axial effects of the machine tool on surface and their spectral components were analyzed. Surfaces obtained in similar machining conditions on lathes operated in various time periods were analyzed spectrally. From the analysis of surface irregularity changes caused by disturbances in movements of the tool against the object, testifying the wear of main machine elements during its operation, the modulated, amplitude-frequency character of changes in surface irregularities of workpiece can be noticed.

Condition monitoring of machines working under non-stationary operations is one of the most challenging problems in maintenance. A wind turbine is an example of such class of machines. One of effective approaches may be to identify operating conditions and investigate their influence on used diagnostic features. Commonly used methods based on measurement of electric current, rotational speed, power and other process variables require additional equipment (sensors, acquisition cards) and software. It is proposed to use advanced signal processing techniques for instantaneous shaft speed recovery from a vibration signal. It may be used instead of extra channels or in parallel as signal verification.

Investigations on integration of optoelectronic components with LTCC (low temperature co-fired ceramics) microfluidic module are presented. Design, fabrication and characterization of the ceramic structure for optical absorbance is described as well. The geometry of the microfluidic channels has been designed according to results of the CFD (computational fluid dynamics) analysis. A fabricated LTCC-based microfluidic module consists of an U-shaped microchannel, two optical fibers and integrated light source (light emitting diode) and photodetector (light-to-voltage converter). Properties of the fabricated microfluidic system have been investigated experimentally. Several concentrations of potassium permanganate (KMnO4) in water were used for absorbance/transmittance measurements. The test has shown a linear detection range for various concentrations of heavy metal ions in distilled water. The fabricated microfluidic structure is found to be a very useful system in chemical analysis.