The Shareholders’ Derivative-Claim Exception to the Attorney-Client Privilege

The use of an over-parametrized state-space model for system identification has some clear advantages: A single model structure covers the entire class of multivariable systems up to a given order. The over-parametrization also leads to the possibility to choose a numerically stable parametrization. During the parametric optimization the gradient calculations constitute the main computational part of the algorithm. Consequently using more than theminimal number of parameters requiredslows down thealgorithm. However, we show that for any chosen (over)-parametrization it is possible to reduce the gradientcalculations to the minimal amount by constructing the parameter subspace which is orthonormal to the tangent space of the manifold representing equivalent models

Income From Separate Property: Towards a Theoretical Foundation

The characterization of the rents, issues and profits from separate property brought into or acquired during marriage is discussed. There has been no comprehensive treatment of this issue in community property case law and literature in recent years

Auto-Calibration of Co-located Uniform Linear Array Antennas

An algorithm for auto-calibration of a group of co-located uniform linear array antennas is presented. If the number of signal sources are known and, for at least one array, the ratio of the gains between two consecutive antenna elements is known, the individual unknown antenna gains can be estimated. The method is based on determining the antenna calibration parameters such that a matrix built from the array snapshots has a given rank. A numerical example illustrates the performance of the method. The numerical results suggest that the method is consistent in SNR

A Multivariate Local Rational Modeling Approach for Detection of Structural Changes in Test Vehicles

A data driven structural change detection method is described and evaluated where the data are acceleration and force measurements from a mechanical structure in the form of a vehicle. By grouping the measured signals as inputs and outputs an hypothesized MIMO linear dynamic relation between the inputs and outputs is assumed. It is assumed that baseline data are available to build statistical models for the estimated frequency function of the baseline system at selected frequencies. When new data is available, the monitoring algorithm re-estimates the non-parametric frequency function and uses a test statistic based on the statistical distance to detect possible change. To generate the frequency function estimates a non-parametric MIMO frequency function estimator based on the local rational model (LRM) method is developed. A statistical analysis of the proposed test statistic shows that it has an F-distribution for data from the baseline case. The method is evaluated on simulated data from a high fidelity full scale vehicle simulation generating both baseline data and data from a structurally changed vehicle. In the evaluation, the frequency response functions were estimated by the non-parametric LRM method, the parametric ARX estimate and the non-parametric ETFE. The results show that all three methods can detect the structural change while the LRM method is more robust with respect to the selection of the hyperparameters. Copyright (C) 2021 The Authors

Optical flow estimation on image sequences with differently exposed frames

Optical flow (OF) methods are used to estimate dense motion information between consecutive frames in image sequences. In addition to the specific OF estimation method itself, the quality of the input image sequence is of crucial importance to the quality of the resulting flow estimates. For instance, lack of texture in image frames caused by saturation of the camera sensor during exposure can significantly deteriorate the performance. An approach to avoid this negative effect is to use different camera settings when capturing the individual frames. We provide a framework for OF estimation on such sequences that contain differently exposed frames. Information from multiple frames are combined into a total cost functional such that the lack of an active data term for saturated image areas is avoided. Experimental results demonstrate that using alternate camera settings to capture the full dynamic range of an underlying scene can clearly improve the quality of flow estimates. When saturation of image data is significant, the proposed methods show superior performance in terms of lower endpoint errors of the flow vectors compared to a set of baseline methods. Furthermore, we provide some qualitative examples of how and when our method should be used

Introduction aux modèles espace-état et au filtre de Kalman

Nous détaillons ici les principaux concepts et problèmes liés aux modèles espace-état, ainsi que leurs applications. Nous présentons d’abord ces modèles dans leur généralité. Ensuite, nous explicitons les algorithmes utilisés afin de procéder à l’estimation par le maximum de vraisemblance, c’est-à-dire fondamentalement le filtre de Kalman et l’algorithme EM. Nous considérons enfin quatre applications : les décompositions tendance-cycle, l’extraction d’indicateurs coïncidents d’activité, l’estimation d’un taux de chômage d’équilibre pouvant varier avec le temps (TV-Nairu) et l’évaluation du contenu informatif de la courbe des taux sur l’inflation future

On robust optical flow estimation on image sequences with differently exposed frames using primal-dual optimization

Optical flow methods are used to estimate pixelwise motion information based on consecutive frames in image sequences. The image sequences traditionally contain frames that are similarly exposed. However, many real-world scenes contain high dynamic range content that cannot be captured well with a single exposure setting. Such scenes result in certain image regions being over- or underexposed, which can negatively impact the quality of motion estimates in those regions. Motivated by this, we propose to capture high dynamic range scenes using different exposure settings every other frame. A framework for OF estimation on such image sequences is presented, that can straightforwardly integrate techniques from the state-of-the-art in conventional OF methods. Different aspects of robustness of OF methods are discussed, including estimation of large displacements and robustness to natural illumination changes that occur between the frames, and we demonstrate experimentally how to handle such challenging flow estimation scenarios. The flow estimation is formulated as an optimization problem whose solution is obtained using an efficient primal–dual method

Variational Optical Flow Estimation for Images with Spectral and Photometric Sensor Diversity

Motion estimation of objects in image sequences is an essential computer vision task. To this end, optical flow methods compute pixel-level motion, with the purpose of providing low-level input to higher-level algorithms and applications. Robust flow estimation is crucial for the success of applications, which in turn depends on the quality of the captured image data. This work explores the use of sensor diversity in the image data within a framework for variational optical flow. In particular, a custom image sensor setup intended for vehicle applications is tested. Experimental results demonstrate the improved flow estimation performance when IR sensitivity or flash illumination is added to the system

A Unified Subspace Classification Framework Developed for Diagnostic System Using Microwave Signal

Subspace learning is widely used in many signal processing and statistical learning problems where the signal is assumably generated from a low dimensional space. In this paper, we present a unified classifier including several concepts from different subspace techniques, such as PCA, LRC, LDA, GLRT, etc. The objective is to project the original signal (usually of high dimension) into a smaller subspace with 1) within-class data structure preserved and 2) between-class-distance enhanced. A novel classification technique called Maximum Angle Subspace Classifier (MASC) is presented to achieve these purposes. To compensate for the computational complexity and non-convexity of MASC, an approximation is proposed as a trade-off between the classification performance and the computational issue. The approaches are applied to the problem of classifying high dimensional frequency measurements from a microwave based diagnostic system and results are compared with existing methods

A Subspace Learning Algorithm For Microwave Scattering Signal Classification With Application To Wood Quality Assessment

A classification algorithm based on a linear subspace model has been developed and is presented in this paper. To further improve the classification results, the full linear subspace of each class is split into subspaces with lower dimensions and characterized by local coordinates constructed from automatically selected training data. The training data selection is implemented by optimizations with least squares constraints or L1 regularization. The working application is to determine the quality in wooden logs using microwave signals [1]. The experimental results are shown and compared with classical method