P.P. Kanjilal

Abstract: The extraction of fetal electrocardiogram (ECG) from the composite maternal ECG signal obtained from the abdominal lead is discussed. The proposed method employs singular value decomposition (SVD) and analysis based on the singular value ratio (SVR) spectrum. The maternal ECG (M-ECG) and the fetal ECG (F-ECG) components are identified in terms of the SV-decomposed modes of the appropriately configured data matrices, and elimination of the M-ECG and determination of F-ECG are achieved through selective separation of the SV-decomposed components. The unique feature of the method is that only one composite maternal ECG signal is required to determine the P-ECG component. The method is numerically robust and computationally efficient.

Abstract: This book is about prediction and control of processes which can be expressed by discrete-time models (i.e. the characteristics vary in some way with time). The aim of the book is to provide a unified and comprehensive coverage of the principles, perspectives and methods of adaptive prediction, which is used by scientists and researchers in a wide variety of disciplines.

Abstract: This paper presents a new concept of decomposition of a signal into component periodic waveforms. The singular value decomposition (SVD) is used for the detection of periodicity and separation of the component signals. The signal is configured for the sequential extraction of successively weaker components with different period lengths. The approach enjoys the numerical stability associated with SVD. >

Abstract: Implicit self-tuners based on single-stage cost-function minimization schemes require exact knowledge of the dead-time of the controlled plant. Explicit pole-placement self-tuners on the other hand, though robust against variations of the time-delay, are very sensitive to the assumptions made about the model order of the plant; wrong assumptions may result in very poor performance. An explicit method based on minimization of a multistage cost-function and a CARIMA plant model formulated in state-space is presented. The method extends the design features of the generalized minimum variance scheme to the multistage case; these can be used to tailor the closed-loop response of the system. The approach is shown to be robust against wrong a priori assumptions made about the plant dead-time or order, thus maintaining good servo and disturbance-rejection properties (see Part II).

Abstract: Qualitative assessment of the overall clinical status of the subject and characterization of complex cardiovascular dynamics from digital blood volume pulsations measured noninvasively using a photo-plethysmographic device is addressed. A novel concept is employed to detect the dominant nonsinusoidal periodicity embedded in the data series and to extract the associated periodic component. The detection and the extraction of periodic component is performed with moving window to accommodate the variations of the physiological oscillations. The covariance matrix formed by the gradually varying pattern is used as a simple measure of qualitative assessment. Further, the characterization of the underlying system in the light of nonlinear dynamical analysis is also presented. The stable subjects are shown to behave as a low-dimensional system whereas the diseased subjects exhibit comparatively high dimensional activity.

Abstract: 1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Abstract: Photoplethysmography (PPG) is a simple and low-cost optical technique that can be used to detect blood volume changes in the microvascular bed of tissue. It is often used non-invasively to make measurements at the skin surface. The PPG waveform comprises a pulsatile ('AC') physiological waveform attributed to cardiac synchronous changes in the blood volume with each heart beat, and is superimposed on a slowly varying ('DC') baseline with various lower frequency components attributed to respiration, sympathetic nervous system activity and thermoregulation. Although the origins of the components of the PPG signal are not fully understood, it is generally accepted that they can provide valuable information about the cardiovascular system. There has been a resurgence of interest in the technique in recent years, driven by the demand for low cost, simple and portable technology for the primary care and community based clinical settings, the wide availability of low cost and small semiconductor components, and the advancement of computer-based pulse wave analysis techniques. The PPG technology has been used in a wide range of commercially available medical devices for measuring oxygen saturation, blood pressure and cardiac output, assessing autonomic function and also detecting peripheral vascular disease. The introductory sections of the topical review describe the basic principle of operation and interaction of light with tissue, early and recent history of PPG, instrumentation, measurement protocol, and pulse wave analysis. The review then focuses on the applications of PPG in clinical physiological measurements, including clinical physiological monitoring, vascular assessment and autonomic function.

Abstract: : This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series. Two approaches to feature selection are used. First, a subset enumeration method is used to determine which financial indicators are most useful for aiding in prediction of the S&P 500 futures daily price. The candidate indicators evaluated include RSI, Stochastics and several moving averages. Results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages. The second approach to feature selection is calculation of individual saliency metrics. A new decision boundary-based individual saliency metric, and a classifier independent saliency metric are developed and tested. Ruck's saliency metric, the decision boundary based saliency metric, and the classifier independent saliency metric are compared for a data set consisting of the RSI and Stochastics indicators as well as delayed closing price values. The decision based metric and the Ruck metric results are similar, but the classifier independent metric agrees with neither of the other metrics. The nine most salient features, determined by the decision boundary based metric, are used to train a neural network and the results are presented and compared to other published results. (AN)

Abstract: The original GMV self-tuner was later extended to provide a general framework which included feedforward compensation and user-chosen polynomials with detuned model-reference, optimal Smith predictor and load-disturbance tailoring objectives. This paper adds similar refinements to the GPC algorithm which are illustrated by a set of simulations. The relationship between GPC and LQ designs is investigated to show the computational advantage of the new approach. The roles of the output and control horizons are explored for processes with nonminimum-phase, unstable and variable dead-time models. The robustness of the GPC approach to model over- and under-parameterization and to fast sampling rates is demonstrated by further simulations. An appendix derives stability results showing that certain choices of control and output horizons in GPC lead to cheap LQ, “mean-level”, state-dead-beat and pole-placement controllers.

Abstract: De-noising and extraction of the weak signature are crucial to fault prognostics in which case features are often very weak and masked by noise. The wavelet transform has been widely used in signal de-noising due to its extraordinary time-frequency representation capability. In this paper, the performance of wavelet decomposition-based de-noising and wavelet filter-based de-noising methods are compared based on signals from mechanical defects. The comparison result reveals that wavelet filter is more suitable and reliable to detect a weak signature of mechanical impulse-like defect signals, whereas the wavelet decomposition de-noising method can achieve satisfactory results on smooth signal detection. In order to select optimal parameters for the wavelet filter, a two-step optimization process is proposed. Minimal Shannon entropy is used to optimize the Morlet wavelet shape factor. A periodicity detection method based on singular value decomposition (SVD) is used to choose the appropriate scale for the wavelet transform. The signal de-noising results from both simulated signals and experimental data are presented and both support the proposed method.