Laboratory for Engineering Man/Machine Systems (LEMS): System identification, model reduction and deconvolution filtering using Fourier based modulating signals and high order statistics

Several important problems in the fields of signal processing and model identification, such as system structure identification, frequency response determination, high order model reduction, high resolution frequency analysis, deconvolution filtering, and etc. Each of these topics involves a wide range of applications and has received considerable attention. Using the Fourier based sinusoidal modulating signals, it is shown that a discrete autoregressive model can be constructed for the least squares identification of continuous systems. Some identification algorithms are presented for both SISO and MIMO systems frequency response determination using only transient data. Also, several new schemes for model reduction were developed. Based upon the complex sinusoidal modulating signals, a parametric least squares algorithm for high resolution frequency estimation is proposed. Numerical examples show that the proposed algorithm gives better performance than the usual. Also, the problem was studied of deconvolution and parameter identification of a general noncausal nonminimum phase ARMA system driven by non-Gaussian stationary random processes. Algorithms are introduced for inverse cumulant estimation, both in the frequency domain via the FFT algorithms and in the domain via the least squares algorithm

Technique for Measurement of Weld Resistance for AC Resistance Spot Welding via Instantaneous Phasor Measurement

The resistance measurement in the resistance spot welding (RSW), is an ongoing research topic. The high current flow during the welding process induces an electromagnetic field in the wires which are attached to the electrodes to measure tip voltage. This results an additional voltage drop which is proportional to the derivative of current. Also the presence of silicon controlled rectifier (SCR) in the welding power supply generates harmonics in both supply voltage and current. These issues together complicate the methods for resistance estimation. A set of simultaneous linear equations is derived for the on-line measurement of dynamic resistance and induced voltage constant by using the dynamic circuit analysis of weld setup. This can be solved to determine the weld resistance using instantaneous phasors measurements for the 1st, 3rd and 5th harmonics of current and measured voltage signals. The instantaneous phasor measurements for these desired harmonics are obtained by employing the following proposed method. In this thesis, a new method for the measurement of instantaneous phasor is proposed for the narrow band signals. The proposed algorithm is based on the internal model principle (IMP) defined for the cancellation of a sinusoidal disturbance signal. The IMP has two states, exhibiting the properties of being sinusoidal and orthogonal. The instantaneous values of IMP states are defined as real and imaginary components of a complex signal at each time instant. The instantaneous measurements of envelope and phase of a sinusoidal signal are determined from instantaneous values of complex signal by using arithmetic properties of complex numbers. In case of signal comprising of sum of sinusoids of different frequencies, the approach for obtaining instantaneous phasor for each sinusoidal component is presented by connecting multiple internal models in the parallel and open-loop configuration. The instantaneous phasor measurement of fundamental frequency signal is not only advantageous in detecting faults like short circuiting, harmonic distortion and frequency variations but it can also be applied to protect power system from these faults. In this work, the applicability of the proposed instantaneous phasor measurement algorithm is analyzed for scenarios of power disturbances due to the the harmonic distortion and decaying DC offset. The results are discussed and compared with few existing methods

Mitigation of Side-Effect Modulation in Optical OFDM VLC Systems

Side-effect modulation (SEM) has the potential to be a significant source of interference in future visible light communication (VLC) systems. SEM is a variation in the intensity of the light emitted by a luminaire and is usually a side-effect caused by the power supply used to drive the luminaires. For LED luminaires powered by a switched mode power supply, the SEM can be at much higher frequencies than that emitted by conventional incandescent or fluorescent lighting. It has been shown that the SEM caused by commercially available LED luminaires is often periodic and of low power. In this paper, we investigate the impact of typical forms of SEM on the performance of optical OFDM VLC systems; both ACO-OFDM and DCO-OFDM are considered. Our results show that even low levels of SEM power can significantly degrade the bit-error-rate performance. To solve this problem, an SEM mitigation scheme is described. The mitigation scheme is decision-directed and is based on estimating and subtracting the fundamental component of the SEM from the received signal. We describe two forms of the algorithm; one uses blind estimation while the other uses pilot-assisted estimation based on a training sequence. Decision errors, resulting in decision noise, limit the performance of the blind estimator even when estimation is based on very long signals. However, the pilot system can achieve more accurate estimations, thus better performance. Results are first presented for typical SEM waveforms for the case where the fundamental frequency of the SEM is known. The algorithms are then extended to include a frequency estimation step and the mitigation algorithm is shown also to be effective in this case

TFAW: wavelet-based signal reconstruction to reduce photometric noise in time-domain surveys

There have been many efforts to correct systematic effects in astronomical light curves to improve the detection and characterization of planetary transits and astrophysical variability. Algorithms like the Trend Filtering Algorithm (TFA) use simultaneously-observed stars to remove systematic effects, and binning is used to reduce high-frequency random noise. We present TFAW, a wavelet-based modified version of TFA. TFAW aims to increase the periodic signal detection and to return a detrended and denoised signal without modifying its intrinsic characteristics. We modify TFA's frequency analysis step adding a Stationary Wavelet Transform filter to perform an initial noise and outlier removal and increase the detection of variable signals. A wavelet filter is added to TFA's signal reconstruction to perform an adaptive characterization of the noise- and trend-free signal and the noise contribution at each iteration while preserving astrophysical signals. We carried out tests over simulated sinusoidal and transit-like signals to assess the effectiveness of the method and applied TFAW to real light curves from TFRM. We also studied TFAW's application to simulated multiperiodic signals, improving their characterization. TFAW improves the signal detection rate by increasing the signal detection efficiency (SDE) up to a factor ~2.5x for low SNR light curves. For simulated transits, the transit detection rate improves by a factor ~2-5x in the low-SNR regime compared to TFA. TFAW signal approximation performs up to a factor ~2x better than bin averaging for planetary transits. The standard deviations of simulated and real TFAW light curves are ~40x better than TFA. TFAW yields better MCMC posterior distributions and returns lower uncertainties, less biased transit parameters and narrower (~10x) credibility intervals for simulated transits. We present a newly-discovered variable star from TFRM.Comment: Accepted for publication by A&A. 13 pages, 16 figures and 5 table

Estimation of generalised frequency response functions

Volterra series theory has a wide application in the representation, analysis, design and control of nonlinear systems. A new method of estimating the Volterra kernels in the frequency domain is introduced based on a non-parametric algorithm. Unlike the traditional non-parametric methods using the DFT transformed input-output data, this new approach uses the time domain measurements directly to estimate the frequency domain response functions