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Search results for: INSTRUMENTAL NOISE
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Discrete identification of continuous non-linear and non-stationary dynamical systems that is insensitive to noise correlation and measurement outliers
PublicationThe paper uses specific parameter estimation methods to identify the coefficients of continuous-time models represented by linear and non-linear ordinary differential equations. The necessary approximation of such systems in discrete time in the form of utility models is achieved by the use of properly tuned `integrating filters' of the FIR type. The resulting discrete-time descriptions retain the original continuous parameterization...
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Resistant to correlated noise and outliers discrete identification of continuous non-linear non-stationary dynamic objects
PublicationIn this article, specific methods of parameter estimation were used to identify the coefficients of continuous models represented by linear and nonlinear differential equations. The necessary discrete-time approximation of the base model is achieved by appropriately tuned FIR linear integral filters. The resulting discrete descriptions, which retain the original continuous parameterization, can then be identified using the classical...
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Resistant to correlated noise and outliers discrete identification of continuous non-linear non-stationary dynamic objects
PublicationIn this study, dedicated methods of parameter estimation were used to identify the coefficients of continuous models represented by linear and nonlinear differential equations. The necessary discrete-time approximation of the base model is achieved by appropriately tuned FIR linear integral filters. The resulting discrete descriptions, which retain the original continuous parameterization, can then be identified using the classical...
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Comparability of Raman Spectroscopic Configurations: A Large Scale Cross-Laboratory Study
PublicationThe variable configuration of Raman spectroscopic platforms is one of the major obstacles in establishing Raman spectroscopy as a valuable physicochemical method within real-world scenarios such as clinical diagnostics. For such real world applications like diagnostic classification, the models should ideally be usable to predict data from different setups. Whether it is done by training a rugged model with data from many setups...
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Deep-Learning-Based Precise Characterization of Microwave Transistors Using Fully-Automated Regression Surrogates
PublicationAccurate models of scattering and noise parameters of transistors are instrumental in facilitating design procedures of microwave devices such as low-noise amplifiers. Yet, data-driven modeling of transistors is a challenging endeavor due to complex relationships between transistor characteristics and its designable parameters, biasing conditions, and frequency. Artificial neural network (ANN)-based methods, including deep learning...
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Analysis of free‐air anomalies on the seaway of the Gulf of Gdańsk. A case study
PublicationIn this paper, we present an attempt to determine the accuracy of shipborne gravimetry for the needs of geoid determination. The shipborne gravity campaign, described in this article, is the beginning of a series of gravimetry measurements in the Polish Exclusive Economy Zone. The campaign was conducted in the area where the accuracy of geoid determination is crucial for the safety of navigation on numerous intersecting ships routes....
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Free-air anomaly grid on the Gulf of Gdańsk
Open Research DataWe present an attempt to determine the accuracy of shipborne gravimetry for the needs of geoid determination. The shipborne gravity campaign, is the beginning of a series of gravimetry measurements in the Polish Exclusive Economy Zone. The campaign was conducted in the area where the accuracy of geoid determination is crucial for the safety of navigation...