M.S. Woolfson

Feature selective validation (FSV) for validation of computational electromagnetics (CEM). part I-the FSV method

A goal for the validation of computational electromagnetics (CEM) is to provide the community with a simple computational method that can be used to predict the assessment of electromagnetic compatibility (EMC) data as it would be undertaken by individuals or teams of engineers. The benefits of being able to do this include quantifying the comparison of data that has hitherto only been assessed qualitatively, to provide the ability to track differences between model iterations, and to provide a means of capturing the variability and range of opinions of groups and teams of workers. The feature selective validation (FSV) technique shows great promise for achieving this goal. This paper presents a detailed analysis of the FSV method, setting it firmly in the context of previous comparison techniques; it suggests the relationship between validation of graphically presented data and the psychology of visual perception. A set of applicability tests to judge the effectiveness of computer-based CEM validation techniques is also proposed. This paper is followed by a detailed comparison with visual assessment, which is presented in Part II

Application of empirical mode decomposition to heart rate variability analysis.

The analysis of heart rate variability, involving changes in the autonomic modulation conditions, demands specific capabilities not provided by either parametric or non-parametric spectral estimation methods. Moreover, these methods produce time-averaged power estimates over the entire length of the record. Recently, empirical mode decomposition and the associated Hilbert spectra have been proposed for non-linear and non-stationary time series. The application of these techniques to real and simulated short-term heart rate variability data under stationary and non-stationary conditions is presented. The results demonstrate the ability of empirical mode decomposition to isolate the two main components of one chirp series and three signals simulated by the integral pulse frequency modulation model, and consistently to isolate at least four main components localised in the autonomic bands of 14 real signals under controlled breathing manoeuvres. In addition, within the short time-frequency range that is recognised for heart rate variability phenomena, the Hilbert amplitude component ratio and the instantaneous frequency representation are assessed for their suitability and accuracy in time-tracking changes in amplitude and frequency in the presence of non-stationary and non-linear conditions. The frequency tracking error is found to be less than 0.22% for two simulated signals and one chirp series.

Evaluation of frequency tracking methods

This paper presents an analysis of proposed methods for tracking the fundamental power frequency to see if they have the performance necessary to cope with the requirements of future protection and control equipment and are robust enough to cope with the more demanding nature of modern power system conditions. The analysis shows that the linear estimation of phases, decomposition of single phase into orthogonal components and discrete Fourier transform perform extremely well but they all suffer from a periodic error in the estimated frequency if it departs from the assumed frequency. This can be cancelled using a low pass filter although it would introduce delays and obscure any real oscillations in the fundamental frequency. Alternatively, averaging over three phases could be used but higher harmonics in the oscillation of the estimation would be present.

The effect of rotor design on sensorless speed estimation using rotor slot harmonics identified by adaptive digital filtering using the maximum likelihood approach

Adaptive digital filtering has been demonstrated as an effective technique for extracting a real-time, sensorless, speed signal from rotor slot harmonics (RSHs) embedded in the line current waveform of induction motor drives. It is known that sensorless speed estimation techniques using RSHs may exhibit poor performance with certain motor designs. This paper examines the reasons for that poor performance which reflects differences in the magnitude of the slot harmonic signals consequent upon rotor design. Experimental results for a 30 kW motor with 6 different rotors are presented. The significant parameters are the number of rotor slots, skew and the accuracy of construction. It is clearly shown how inferior performance can arise. Conversely, improvements in reliability of speed estimation and transient response can be obtained by recognising those aspects which provide an enhanced signal, by minimising the background noise of the inverter/machine, or by enhancing the adaptive filter. The recursive maximum likelihood technique is presented as an improved algorithm for tuning the digital filter which aids transient response and reliability of speed estimation. Real time, experimental transient performance is demonstrated for the different rotors used in this paper and the performance failure of a particular slot combination demonstrated.

Wavelet transform as a potential tool for ECG analysis and compression

The recently introduced wavelet transform is a member of the class of time-frequency representations which include the Gabor short-time Fourier transform and Wigner-Ville distribution. Such techniques are of significance because of their ability to display the spectral content of a signal as time elapses. The value of the wavelet transform as a signal analysis tool has been demonstrated by its successful application to the study of turbulence and processing of speech and music. Since, in common with these subjects, both the time and frequency content of physiological signals are often of interest (the ECG being an obvious example), the wavelet transform represents a particularly relevant means of analysis. Following a brief introduction to the wavelet transform and its implementation, this paper describes a preliminary investigation into its application to the study of both ECG and heart rate variability data. In addition, the wavelet transform can be used to perform multiresolution signal decomposition. Since this process can be considered as a sub-band coding technique, it offers the opportunity for data compression, which can be implemented using efficient pyramidal algorithms. Results of the compression and reconstruction of ECG data are given which suggest that the wavelet transform is well suited to this task.

Application of region-based segmentation and neural network edge detection to skin lesions

This paper proposes two approaches to the skin lesion image segmentation problem. The first is a mainly region-based segmentation method where an optimal threshold is determined iteratively by an isodata algorithm. The second method proposed is based on neural network edge detection and a rational Gaussian curve that fits an approximate closed elastic curve between the recognized neural network edge patterns. A quantitative comparison of the techniques is enabled by the use of synthetic lesions to which Gaussian noise is added. The proposed techniques are also compared with an established automatic skin segmentation method. It is demonstrated that for lesions with a range of different border irregularity properties the iterative thresholding method provides the best performance over a range of signal to noise ratios. Iterative thresholding technique is also demonstrated to have similar performance when tested on real skin lesions.

Dependence of inertial measurements of distance on accelerometer noise

An investigation is made into the errors in estimated position that are caused by noise and drift effects in stationary accelerometers. An analytical study is made into the effects of biases in the accelerometer data and the effects of changing the cut-off frequency in the anti-aliasing filter. The root mean square errors in position are calculated as a function of time and sampling frequency. A comparison is made between the theoretical results and experimental data taken from two commercial accelerometers. Recommendations are made regarding the calibration of accelerometers prior to their use in practical situations.

Interpretation of heart rate variability via detrended fluctuation analysis and αβ filter

Detrended fluctuation analysis (DFA), suitable for the analysis of nonstationary time series, has confirmed the existence of persistent long-range correlations in healthy heart rate variability data. In this paper, we present the incorporation of the alphabeta filter to DFA to determine patterns in the power-law behavior that can be found in these correlations. Well-known simulated scenarios and real data involving normal and pathological circumstances were used to evaluate this process. The results presented here suggest the existence of evolving patterns, not always following a uniform power-law behavior, that cannot be described by scaling exponents estimated using a linear procedure over two predefined ranges. Instead, the power law is observed to have a continuous variation with segment length. We also show that the study of these patterns, avoiding initial assumptions about the nature of the data, may confer advantages to DFA by revealing more clearly abnormal physiological conditions detected in congestive heart failure patients related to the existence of dominant characteristic scales.

A speed identifier for induction motor drives using real-time adaptive digital filtering

A novel sensorless speed identifier for real-time application in induction motor drives under steady-state and transient conditions is proposed. It is based on the calculation of rotor slot harmonic (RSH) frequencies using an adaptive digital filter. It outperforms other analog or spectrum-based RSH speed identifiers in terms of accuracy and speed of response. The new identifier measures the speed with less than 0.1% error by processing the stator current on a sample-by-sample basis. It is also capable of tracking speed transients of high slew rates with high accuracy. The authors believe this to be the first effective tracking of RSHs during transients ever reported. The proposed algorithm is computationally very efficient and requires only a single processor for its real-time implementation. Simulated and experimental data were used to validate the algorithm.

Flux position estimation in cage induction machines using synchronous HF injection and Kalman filtering

This paper proposes a new approach to the tracking of the flux position saliency of an induction machine by means of high-frequency signal injection in the synchronous rotating frame. The paper shows that existing synchronous pulsating injection methods perform poorly in the presence of secondary saliencies. A modified scheme is proposed in which a rotating HF field is established in the dq rotating reference frame. The scheme enhances the performance of harmonic elimination methods to reduce the effects of secondary saliencies. A new harmonic elimination method based on the Kalman filter is proposed in the paper. Experimental results are given showing the effectiveness of the harmonic elimination and the resulting tracking of the saturation saliency.