Tamás Dabóczi

Multiparameter optimization of inverse filtering algorithms

This paper investigates inverse filtering of transient signals. The problem is ill-conditioned, which means that a small uncertainty in the measurement causes large deviations in the reconstructed signal. This amplified noise has to be suppressed at the price of bias in the estimation. The most difficult task is to find the optimal degree of noise reduction. Deconvolution algorithms are usually controlled by one or a few parameters. Several algorithms can be found in the literature to find the best setting of inverse filtering methods; however, usually methods with only one free parameter are handled. In this paper, an algorithm is proposed to optimize several parameters, on the basis of a spectral model. Multiparameter inverse filtering methods have the advantage that they can be better adapted to the measurement system, and to the noise and signal to be measured. The superiority of the proposed optimization method is demonstrated both on simulated and on experimental data.

Uncertainty of signal reconstruction in the case of jittery and noisy measurements

Time domain measurements are distorted by the measurement system if the bandwidth of the system is not sufficiently high compared to that of the signal to be measured. If the distortion is known the measured signal can be compensated for it (inverse filtering or deconvolution). Since the measurement is always corrupted by noise, the reconstruction is an estimation task, i.e., the reconstructed signal may vary depending on the actual noise record. Our aim is to investigate the errors related to the signal reconstruction, and to provide an error bound around the reconstructed time domain waveform. Based on their nature we can distinguish between systematic and stochastic errors. In this paper, we investigate the stochastic type of errors and suggest a method to calculate the uncertainty (variance) of the reconstruction. We developed a method for the calibration of high-speed sampling systems. Both stationary and jitter noises will be investigated.

Automatic testing of graphical user interfaces

A m Graphical User Interfaces me very difficuh io lesi, since resting requires simulation of fhe activity of a person. The paper presenfs an approach where “guided” random selection and aclivafion of fhe controls is perfomed Guidance is implemenfed on fhe bcrsis of aprobabilify tuble. The fechnical means to perform the lest is an acfion recorder (event recorder). Besides testing, fhis is a useful tool fo perform demonsfrations and selflguided infroduction to the CUI. The recorder has been implemented in MATLAB, and it is available on the WEB.

How to test graphical user interfaces

An approach for automatically testing GUIs in the MATLAB environment has been proposed. We developed a software tool that tests GUIs by simulating the user through an action recorder. We proposed a heuristic test procedure: providing random input to GUI, but guiding the randomness with predefined weights assigned to the user controls. The weights change during the testing process, as the controls are activated. The errors are collected for later investigation.

Offline Parameter Estimation of Permanent Magnet Sychronous Machines by means of LS Optimization

Industrial applications, especially automotive ones should be robust and cheap. Both properties can be improved by using model based state estimation. Sensor cost can be reduced if some signal values are calculated from the other, already measured signals or the robustness of the system can be increased by supervising the sensors by calculating their measurement value out of the existing signal values. Robustness and redundancy is extremely important considering drive-by-wire technology, where the physical connection between the steering wheel and the wheels of the vehicle is omitted. This paper reports advances in permanent magnet synchronous machine model identification. By measuring machine input voltages, output currents speed and using the least squares optimization method, internal parameters of the machine can be estimated. In the identification stage, the model excitation signals are the current values and the speed of the machine and the response signals are the input voltages. After having a properly identified model, the output currents and electrical torque of the machine can be calculated knowing the input voltages and the speed of the machine. Those current sensors can be either eliminated or supervised by the model based redundant information.

Improving INFORM calculation method on permanent magnet synchronous machines

This paper reports advances in sensorless rotor position estimation in motor control of electronic power assisted steering systems (EPAS). A systematic calculation error was discovered in the INFORM - a saliency based sensorless rotor position estimation method, - when it was applied to permanent magnet synchronous machines (PMSMs). A solution was found to correct the angle error of the measurement which was introduced by the PMSM.

Improving Motor Current Control Using Decoupling Technique

This paper reports advances in the design and development of decoupling method in motor control of electric power assisted steering (EPAS) systems. A motor model for permanent magnet synchronous machines (PMSM) is described. Implementation details, simulation and measurement results are also presented

Nonparametric identification assuming two noise sources: a deconvolution approach

Nonparametric identification of linear systems is investigated in this paper. Nonparametric identification is the estimation of the time record of the impulse response of the system. It is a deconvolution problem, i.e., inverse operation of the convolution of the impulse response and the excitation signal. The problem is ill posed, i.e., deconvolution amplifies the measurement noise to a great extent. The noise has to be suppressed with the price of a bias in the estimate. A tradeoff has to be found between the noisy and biased estimates. Because of the need for repeatability and to reduce the subjectivity, the level of noise reduction has to be set algorithmically. This paper introduces a method that optimizes the parameter(s) of deconvolution filters and, thus, controls the level of noise reduction. The proposed method assumes observation noise sources for both the measurement of the excitation signal and the system output.

A Far-Range Off-line Camera Calibration Method for Stereo Lane Detection Systems

A far-range camera calibration method for vision sensors used in automatic lane detection systems is investigated. Autonomous vehicle guidance based on stereo image acquisition requires the knowledge of the camera parameters, such as camera position, orientation, lens distortion, focal length etc. with high precision. Calibration is performed by placing markers in known positions in front of the vehicle holding the camera(s) and by detecting the location of their images in the acquired snapshots. The parameters are then estimated by minimizing the geometric error in the image. Most of the methods discovered in the literature do not consider errors in the measurement of marker locations. However, a precise marker arrangement for far-range calibration might be expensive to set up. This paper shows that imprecision in the 3D location of the markers can not be tolerated in some practical situations and in such cases, a different optimization is proposed in the calibration of the extrinsic camera parameters (camera position and orientation). A position estimation method is also shown to extract marker positions from far-range laser distance meter measurements. The estimated marker positions are corrected by considering aiming errors, as well. Parameter errors are also estimated. It will be shown that our new calibration and optimization method is expected to result a good accuracy compared to commercial methods.

Model based torque estimation of Permanent Magnet Synchronous Machines

This paper reports advances in model based machine torque estimation used in electronic power assisted steering systems. Using machine input voltages the permanent magnet synchronous machine internal states and the output torque can be estimated. This technique can be used to detect machine failures and also can be used to verify the failure of the machine current measurement sensors.