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Understanding the causality behind the observational data is of great importance to a lot of real world applications, e.g., the improvement of Quality of Service. Non-Gaussianity has been exploited in numerous causal discovery methods for observational linear acyclic data. Transforming non-Gaussianity into indirect metrics is a conventional solution employed by existing methods, although this usually...
This paper uses high-frequency intraday electricity prices from the EPEX market to estimate and forecast realised volatility. Variation is broken down into jump and continuous components using quadratic variation theory. Then several heterogeneous autoregressive models are estimated for the logarithmic and standard deviation transformations. GARCH structures are included in the error terms of the...
This paper presents the results of testing a Phasor Measurement Unit (PMU) data-based mode estimation application deployed within a decentralized architecture using a real-time test platform. By extending the author's previous work in [1], this paper shows that a decentralized architecture is effective in detecting local modes which are less observable in presence of other dominant modes when estimated...
In this paper, we propose a tracking mechanism for the residual sampling clocking offset (SCO) with corresponding pilot and auxiliary pilot arrangements for filter bank multi-carrier (FBMC) offset QAM (OQAM) baseband receiver in the 60 GHz band. This work can effectively compensate the non-ideal effects on the high-band subcarriers while using more data subcarrier for increasing bandwidth efficiency...
This paper presents a hardware realisation of a novel ECG baseline drift removal that preserves the ECG signal integrity. The microcontroller implementation detects the fiducial markers of the ECG signal and the baseline wander estimation is achieved through a weighted piecewise linear interpolation. This estimated drift is then removed to recover a “clean” ECG signal without significantly distorting...
In this work we developed a simulation model for spatial coordinates estimation in a photogrammetric system. We analyzed how the initial measurement errors of pixel coordinates on the sensor of a digital camera and the scene geometry influence the result errors of the 3D coordinates measured by the photogrammetric system. Our findings demonstrate the expected system's accuracy for industrial applications...
For speed sensorless control at zero electrical stator frequency inherent machine saliencies need to be exploited. One way to obtain saliency information is to use the voltage step excitation of the inverter and evaluate the current reaction by estimating the derivative of the current from the measured current slope. The initial transient oscillation of the current slope prohibits estimation of the...
Marine robots and unmanned surface vehicles will increasingly be deployed in rivers and riverine environments. The structure produced by flowing waters may be exploited for purposes of estimation, planning, and control. This paper adopts a widely acknowledged model for the geometry of watercourse channels, namely sine-generated curves, as a basis for estimators that predict the shape of the yet unseen...
The normal approximation and Monte Carlo simulation methods are widely used in the metrology to evaluate the expanded uncertainty, whereby the latter method is known to be the most robust and reliable. In some cases, however, (e.g., when the probability distribution is not known a priori) different frameworks may be desired as an alternative to the aforementioned techniques. One of them is commonly...
Regression-Discontinuity Design is a non-experimental method to estimate the impacts of social welfare programs in situations where the treatment assignment is determined by whether an observed variable (running variable) is above or below a known cutoff point. The main idea behind RDD is that individuals whose running variable is just above or just below the cutoff are similar, and so, any differences...
We develop a linear parameter-varying (LPV) spectral decomposition method, based on least-squares estimation and kernel expansions. Statistical properties of the estimator are analyzed and verified in simulations. The method is linear in the parameters, applicable to both the analysis and modeling problems and is demonstrated on both simulated signals as well as measurements of the torque in an electrical...
We consider two close ways of linearization for sublinear operator that takes compact convex values. The first way consists in a representation of given multioperator by the family of so called basis selectors that are single-valued linear bounded operators. The second way consists in linear extension of given multioperator from its values on some Hamel basis. Every of the ways above leads to its...
The SLAM problem is known to have a special property that when robot orientation is known, estimating the history of robot poses and feature locations can be posed as a standard linear least squares problem. In this work, we develop a SLAM framework that uses relative feature-to-feature measurements to exploit this structural property of SLAM. Relative feature measurements are used to pose a linear...
Nowadays, face recognition systems are going to widespread in many fields of application, from automatic user login for financial activities and access to restricted areas, to surveillance for improving security in airports and railway stations, to cite a few. In such scenarios, several architectures based on both 2D image analysis and 3D reconstruction are investigated and proposed in literature...
This paper extends the idea of Universum learning to regression problems. We propose new Universum-SVM formulation for regression problems that incorporates a priori knowledge in the form of additional data samples. These additional data samples, or Universum samples, belong to the same application domain as the training samples, but they follow a different distribution. Several empirical comparisons...
Nearly all existing estimations of the central subspace in regression take the frequentist approach. However, when the predictors fall naturally into a number of groups, these frequentist methods treat all predictors indiscriminately and can result in loss of the group-specific relation between the response and the predictors. In this article, we propose a Bayesian solution for dimension reduction...
Fast error concealment algorithms play an important role in image and video signal processing. Within this paper, a novel and highly accelerated Frequency Selective Extrapolation adaption is introduced for rapid image error concealment. The state-of-the-art complex-valued Frequency Selective Extrapolation runs with fixed parameters, e.g., a constant number of iterations, so far. In this paper, the...
In the process of curve fitting, the unknown relationship between the data sampling rate and the frequency of the measured signal as well as signal's frequency fluctuations can cause remarkable error. In this paper, a sine curve fitting algorithm with frequency precise estimation is proposed to solve this problem. Firstly, ellipse fitting algorithm is used to estimate the measured signal frequency...
Our aim is to evaluate fundamental parameters from the analysis of the electromagnetic spectra of stars. We may use 103–105 spectra; each spectrum being a vector with 102–104 coordinates. We thus face the so-called “curse of dimensionality”. We look for a method to reduce the size of this data-space, keeping only the most relevant information. As a reference method, we use principal component analysis...
The process of mining comprises of supervised learning and unsupervised learning. It includes various approaches out of which data classification is one of the beneficial and constructive methods. This paper explores the effective functioning of the whole process. There are several cases in classification where the important data is missed during the process. It can hence be concluded that the process...
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