An Alternative Approach to Obtain a New Gain in Step-Size of LMS Filters Dealing with Periodic Signals

Partial updates (PU) of adaptive filters have been successfully applied in different contexts to lower the computational costs of many control systems. In a PU adaptive algorithm, only a fraction of the coefficients is updated per iteration. Particularly, this idea has been proved as a valid strategy in the active control of periodic noise consisting of a sum of harmonics. The convergence analysis carried out here is based on the periodic nature of the input signal, which makes it possible to formulate the adaptive process with a matrix-based approach, the periodic least-mean-square (P-LMS) algorithm In this paper, we obtain the upper bound that limits the step-size parameter of the sequential PU P-LMS algorithm and compare it to the bound of the full-update P-LMS algorithm. Thus, the limiting value for the step-size parameter is expressed in terms of the step-size gain of the PU algorithm. This gain in step-size is the quotient between the upper bounds ensuring convergence in the following two scenarios: first, when PU are carried out and, second, when every coefficient is updated during every cycle. This step-size gain gives the factor by which the step-size can be multiplied so as to compensate for the convergence speed reduction of the sequential PU algorithm, which is an inherently slower strategy. Results are compared with previous results based on the standard sequential PU LMS formulation. Frequency-dependent notches in the step-size gain are not present with the matrix-based formulation of the P-LMS. Simulated results confirm the expected behavior

Performance Analysis of Shrinkage Linear Complex-Valued LMS Algorithm

The shrinkage linear complex-valued least mean squares (SL-CLMS) algorithm with a variable step size overcomes the conflicting issue between fast convergence and low steady-state misalignment. To the best of our knowledge, the theoretical performance analysis of the SL-CLMS algorithm has not been presented yet. This letter focuses on the theoretical analysis of the excess mean square error transient and steady-state performance of the SL-CLMS algorithm. Simulation results obtained for identification scenarios show a good match with the analytical results

The Application of Adaptive Linear and N on-Linear Filters to Fringe Order Identification in White-Light Interferometry Systems

Conventional optical interferometry systems driven by highly coherent light sources have a very short unambiguous operating range, a direct consequence of the flatness of the interference fringes visibility profile at the output of the system. The range can be extended by using a white-light interferometer (WU), which is driven by a low-coherence source and produces a Gaussian visibility profile with a unique maximum in correspondence of the central fringe. Due to system and/or measurement noise, however, the position of the maximum (from which an accurate measurement of the measurand - displacement, temperature, pressure, flow, etc. - can be derived) is not easily detectable, and can lead to large measurement errors. This is especially true in a multiplexing scheme, where the source power is distributed evenly among various sensors, with a corresponding drop in the overall signal-to-noise ratio. The inclusion of a signal processing scheme at the receiver end is thus a necessity. As the fringe pattern at the output of a WLI system is basically a noisy sine wave amplitude modulated by a Gaussian envelope, it can be classified as a non-stationary, narrow-band, linear but non-Gaussian signa\. So far, no attempt has been made to apply digital filtering techniques, as understood in the signal processing community, to the output signal of a WLI system. This thesis constitutes a first step in that direction. Since the only measurable information given by the system is contained in the output signal, the system is modelled as a "black box" driven by the system and measurement noise processes and containing an unknown set of parameters. Standard least squares techniques can then be applied to estimate the parameters of the model, as is usually done in the field of system identification when only noisy output measurements are available. It is shown that identification of the model parameters is equivalent to finding a set of coefficients for an inverse filter which takes the WU signal at its input and delivers the unknown noise process at the output. The non-stationarity of the signal is accounted for by allowing for time variations of the model parameters; this justifies the use of adaptive filters with time-varying coefficients. A new central fringe identification scheme is proposed, based on a modification of the standard least mean square (LMS) adaptive filtering algorithm in combination with amplitude thresholding of the fringe pattern. The new scheme is shown to offer considerable improvement in the identification rate when tested against current schemes over comparable operating ranges, while retaining the computational simplicity and operational speed of the standard LMS. Its performance is also shown to be largely independent of the step-size parameter controlling the rate of convergence and tracking in the standard LMS, which is known to be the main obstacle for a successful application of the algorithm in a practical setting. The non-Gaussianity of the signal is explored and an attempt is made to apply higher-order statistics (HOS) algorithms to central fringe identification. The effectiveness of Gaussianity tests on pilot Gaussian data is seen to depend not only on the number and length of records available but, perhaps more importantly, on the bandwidth of the process. Violation of the stationarity assumption is shown to lead to mis-classification of a seemingly non-Gaussian signal into a Gaussian one, as the visibility profile may alter the distribution of the underlying sinusoid making it appear Gaussian, even when beam diffraction and wavefront aberrations combine to produce a nonGaussian profile. HOS-based adaptive algorithms may still be of some benefit, however, if processing is confined to that region of the fringe pattern where sufficient non-Gaussianity is allowed to develop. Non-linear adaptive filters based on the Volterra theories are finally applied to compensate for possible non-linearities introduced by mismatches in optical components, chromatic aberrations, and analogue-to-digital converters. It is shown that although a Volterra filter is able to reproduce the low-amplitude distortions of the fringe pattern better than a linear filter does, the identification rate does not improve. Reasons are given for such behaviour

Compensation numérique pour convertisseur large bande hautement parallélisé.

Time-interleaved analog-to-digital converters (TIADC) seem to be the holy grail of analog-to-digital conversion. Theoretically, their sampling speed can be increased, very simply, by duplicating the sub-converters. The real world is different because mismatches between the converters strongly reduce the TIADC performance, especially when trying to push forward the sampling speed, or the resolution of the converter. Using background digital mismatch calibration can alleviate this limitation. The first part of the thesis is dedicated to studying the sources and effects of mismatches in a TIADC. Performance metrics such as the SNDR and the SFDR are derived as a function of the mismatch levels. In the second part, new background digital mismatch calibration techniques are presented. They are able to reduce the offset, gain, skew and bandwidth mismatch errors. The mismatches are estimated by using the statistical properties of the input signal and digital filters are used to reconstruct the correct output samples. In the third part, a 1.6 GS/s TIADC circuit, implementing offset, gain and skew mismatch calibration, demonstrates a reduction of the mismatch spurs down to a level of -70 dBFS, up to an input frequency of 750 MHz. The circuit achieves the lowest level of mismatches among TIADCs in the same frequency range, with a reasonable power and area, in spite of the overhead caused by the calibration.Les convertisseurs analogique-numérique à entrelacement temporel (TIADC) semblent être une solution prometteuse dans le monde de la conversion analogique-numérique. Leur fréquence d’échantillonnage peut théoriquement être augmentée en augmentant le nombre de convertisseurs en parallèle. En réalité, des désappariements entre les convertisseurs peuvent fortement dégrader les performances, particulièrement à haute fréquence d’échantillonnage ou à haute résolution. Ces défauts d’appariement peuvent être réduits en utilisant des techniques de calibration en arrière-plan. La première partie de cette thèse est consacrée à l’étude des sources et effets des différents types de désappariements dans un TIADC. Des indicateurs de performance tels que le SNDR ou la SFDR sont exprimés en fonction du niveau des désappariements. Dans la deuxième partie, des nouvelles techniques de calibration sont proposées. Ces techniques permettent de réduire les effets des désappariements d’offset, de gain, d’instant d’échantillonnage et de bande passante. Les désappariements sont estimés en se basant sur des propriétés statistiques du signal et la reconstruction des échantillons de sortie se fait en utilisant des filtres numériques. La troisième partie démontre les performance d’un TIADC fonctionnant a une fréquence d’échantillonnage de 1.6 GE/s et comprenant les calibration d’offset, de gain et d’instant d’échantillonnage proposées. Les raies fréquentielles dues aux désappariements sont réduites à un niveau de -70dBc jusqu’à une fréquence d’entrée de 750 MHz. Ce circuit démontre une meilleure correction de désappariements que des circuits similaires récemment publiés, et ce avec une augmentation de puissance consommée et de surface relativement faible

Mathematical Models for Planning and Controlling Air Quality; Proceedings of an IIASA Workshop, October 1979

Air-quality management problems fall into three main classes: it is difficult to obtain a reliable picture of all the physicochemical processes involved, comprehensive assessments of the costs and benefits of alternative control strategies are not easily made, and the technology for pollution abatement is not yet well established. Various mathematical or formal management models do exist but the overall impact of modeling on decision making has so far been relatively small. The first aim of the IIASA Workshop on which this volume is based was to bridge the gap between air-quality modeling and management. As described in the ten papers in Part One, Workshop participants examined the goals actually pursued by decision makers, the potential role of mathematical models in air-quality management, and the extent to which modeling has been used in real situations in a number of countries. The Workshop's second aim, reported in the eight papers in Part Two, was to consider the unusual strategy of real-time emission control. An extended description of the IIASA case study of the Venetian Lagoon area was presented, together with contributions on real-time forecast and control schemes in operation in Japan and Italy