Digital FIR Hilbert Transformers: Fundamentals and Efficient Design Methods

Romero, David Ernesto Troncoso; Dolecek, Gordana Jovanovic

doi:10.5772/46451

Cited by 15 publications

(5 citation statements)

References 29 publications

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“…They are provided with indices 1 -signal reflected from a sheet of metal, 2 -signal reflected from a structure in an area without a crack, and 4 -a virtual signal created by the GeoVizy-2020 program. Two more signals (3 and 5) are the result of applying the Hilbert transform [30] to signals 2 and 4, respectively. The GeoVizy-2020 software allows one to process data in a semi-automatic mode.…”

Section: The Proposed Problem-solving Methodsmentioning

confidence: 99%

Nondestructive diagnostics and detection of subsurface cracks in non-rigid pavements with GPR

Batrakov¹,

Batrakova²,

Urdzik³

et al. 2021

Diagnostyka

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The results of laboratory experiments that show the capabilities of modern georadars are presented. Analysis of GPR capabilities allowed to propose a new approach to solving the actual problem of detecting subsurface cracks. The proposed method for recording pulse signals and data processing is based on algorithms and software previously developed by the authors. With the help of this method and the experiments carried out, the possibility of agreed solution to the problem of choosing the optimal parameters of the GPR pulsed signals was shown. To confirm the effectiveness of the developed signal processing methods, a series of laboratory experiments were carried out. The new version of the program "GeoVizy-2020" made it possible to increase the efficiency of processing complex impulse signals, as well as to realize the possibility of calculating the quantitative values of the most important parameters of the road pavement layers -the relative permittivity and thickness. Also, to check the adequacy of the results obtained, a method was used, which is based on the procedure for direct measurement of the real layer thickness in laboratory and field conditions and comparison of the data obtained with the results of numerical simulation. As a result, evidence is presented of the advantages of using pulsed signals for assessing layer parameters, searching for subsurface cracks and processing the results of surveys of road surfaces is presented.

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Section: The Proposed Problem-solving Methodsmentioning

confidence: 99%

Nondestructive diagnostics and detection of subsurface cracks in non-rigid pavements with GPR

Batrakov¹,

Batrakova²,

Urdzik³

et al. 2021

Diagnostyka

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“…where F is the Fourier transform and the multiplier −j = e −jπ/2 is the equivalent of a 90 • phase shift. Consequently, the complex analytic signal has a one-sided spectrum as the negative frequency image is canceled out [27], [28]. Furthermore, the instantaneous envelope, phase and frequency can be extracted from the complex time-domain signal [26].…”

Section: Signal Dynamic Modelmentioning

confidence: 99%

Characterization of Non-Stationary Signals in Electric Grids: A Functional Dictionary Approach

Karpilow

Derviškadić²,

Frigo

et al. 2022

IEEE Trans. Power Syst.

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With the expanding role of converter-interfaced distributed energy resources, modern power grids are evolving towards low-inertia networks that are increasingly vulnerable to extreme dynamics. Consequently, advanced signal processing techniques are needed to accurately characterize measured signals in power systems during non-stationary conditions. However, as advocated by recent literature, state-of-the-art phasor estimation methods are unable to sufficiently capture the broadband nature of these signal dynamics since they rely on a quasisteady state, single tone model. Inspired by previous work by the authors, this paper proposes a signal processing method that uses a dictionary of kernels, modeling common signal dynamics, to compress time-domain information into a few coefficients. The identified signal model and the extracted coefficients capture the broadband spectrum of typical power system signal dynamics and allow for an improved reconstruction of the measured signal.

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“…Digital Hilbert Transforms are a special class of all-pass digital filter whose characteristic is to introduce a π/2 radians phase shift of the input signal [7]. This transformation can be represented as a digital filter or a convolutional operator as seen in the equation 9 and 10 [7], which represents the Hilbert kernel and the frequency response respectively.…”

Section: 3hilbert Transformmentioning

confidence: 99%

A Review of Mathematical Transforms for Phase-Locked Loops Control

Plech¹,

Silva²,

Silva³

2022

Blucher Engineering Proceedings

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The mathematical transforms used in Phase-Locked Loops (PLLs) play a crucial role in their performance and functioning. Depending on the format of the control loop's input signal, noise level requirements and other requirements such as available computational power, one must choose which topology best suits a given application. This article will address the most used transforms and filters in the most common PLL topologies, such as the Clarke Transform, the Park Transform, the Second-Order Generalized Integrator (SOGI) filter and the Hilbert Transform. Finally, a comparison of the advantages and disadvantages of each of these methods is discussed.

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Digital FIR Hilbert Transformers: Fundamentals and Efficient Design Methods

Cited by 15 publications

References 29 publications

Nondestructive diagnostics and detection of subsurface cracks in non-rigid pavements with GPR

Nondestructive diagnostics and detection of subsurface cracks in non-rigid pavements with GPR

Characterization of Non-Stationary Signals in Electric Grids: A Functional Dictionary Approach

A Review of Mathematical Transforms for Phase-Locked Loops Control

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