Identification of a Time-Varying Curve in Spectrogram

Klejmova, Eva; Poměnková, Jitka

doi:10.13164/re.2017.0291

Cited by 10 publications

(7 citation statements)

References 31 publications

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“…(9) With this step, we have gained the adjusted TF matrices S AR and S ST F T making all three methods aligned. For the combination of methods we selected a simple multiplication (Klejmová and Poměnková 2017). We used combination of CWT and AR (S CW T,AR ) and the combination of CWT, AR and STFT (S CW T,AR,ST F T ):…”

Section: Combination Of Tf Methodsmentioning

confidence: 99%

Combination Of Time-Frequency Representations For Background Noise Suppression

Klejmova¹,

Poměnková²,

Blumenstein³

2017

ECMS 2017 Proceedings Edited by Zita Zoltay Paprika, Péter Horák, Kata Váradi, Péter Tamás Zwierczyk, Ágnes Vidovics-Dancs, Ján

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The aim of the paper is to propose approach for enhancement of time-frequency representation leading to the background noise suppression. The approach is based on combination of continuous wavelet analysis, timevarying autoregressive process and short time Fourier transform. By such combination we make the identification of important areas in the time-frequency representation easier. The proposed method is an alternative approach to significance tests which can be problematic in some cases. The performance of methods is presented on the gross domestic product of the United Kingdom and Group of 7. The results show that in the UK, oil crisis has a bigger impact compared to financial crisis, while from the perspectives of G7 countries, the impact of financial crisis was stronger. The obtained results can be also used for consequent econometric analysis which identify dependencies, relations, bilateral causalities or other economic aspects.

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Section: Combination Of Tf Methodsmentioning

confidence: 99%

Combination Of Time-Frequency Representations For Background Noise Suppression

Klejmova¹,

Poměnková²,

Blumenstein³

2017

ECMS 2017 Proceedings Edited by Zita Zoltay Paprika, Péter Horák, Kata Váradi, Péter Tamás Zwierczyk, Ágnes Vidovics-Dancs, Ján

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“…Figure 7 shows the time-frequency representation (spectrogram) [25] of the GNSS L1/E1 signal, where the jamming…”

Section: B Rfi Characterization In Gnss L-bandmentioning

confidence: 99%

An Open Architecture for Signal Monitoring and Recording Based on SDR and Docker Containers: A GNSS Use Case

Mehr¹,

Minetto²,

Dovis³

et al. 2023

Preprint

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<p>Signal monitoring and recording station architectures based on software-defined radio (SDR) have been proposed and implemented since several years. However, the large amount of data to be transferred, stored, and managed when high sampling frequency and high quantization depth are required, poses a limit to the performance, mostly because of the data losses during the data transfer between the front-end and the storage unit. To overcome these limitations, thus allowing a reliable, high-fidelity recording of the signals as required by some applications, a novel architecture named SMART (Signal Monitoring, Analysis and Recording Tool) based on the implementation of Docker containers directly on a Network Attached Storage (NAS) unit is presented. Such paradigms allow for a fully open-source system being more affordable and flexible than previous prototypes. The proposed architecture reduces computational complexity, increases efficiency, and provides a compact, cost-effective system that is easy to move and deploy. As a case study, this architecture is implemented to monitor RFIs on Global Navigation Satellite System (GNSS) L1/E1 and L5/E5 bands. The sample results show the benefits of a stable, long-term capture at a high sampling frequency to characterize the RFIs spectral signature effectively. </p>

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“…With this step, we obtain the adjusted TF matrices S ′ AR and S ′ FT by making all three methods aligned. The combination of methods is done by a simple multiplication (Klejmova & Pomenkova, 2017) and is called the 'enhanced TF picture'…”

Section: An Enhanced Spectrogram Modellingmentioning

confidence: 99%

Cyclicality in lending activity of Euro area in pre- and post- 2008 crisis: a local-adaptive-based testing of wavelets

Poměnková

Klejmova

Kučerová

2019

Baltic Journal of Economics

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The paper deals with the identification of time-frequency regions describing cyclicality of bank loans before, during and after the 2008 crisis via wavelets. We bring new methods and findings about the short and medium cycles of loans provided to corporates and households in the Euro Area in 2000-2017 using seasonally unadjusted monthly data. We have recognized an impact of the crisis on data volatility which further influences the type of significance testing of wavelet spectrograms. To avoid this influence we propose: (1) an adaptive spectrogram testing based on Torrence and Compo approach and (2) robustness analysis via enhanced spectrogram modelling tested by the MC simulations. Both cross-checked approaches prove the sensitivity of standard wavelet tests on data volatility. The results confirm the usability of the new approaches and show that the crisis in 2008 influenced the cyclical behaviour of both categories of economic sectors, but in a different way.

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Identification of a Time-Varying Curve in Spectrogram

Cited by 10 publications

References 31 publications

Combination Of Time-Frequency Representations For Background Noise Suppression

Combination Of Time-Frequency Representations For Background Noise Suppression

An Open Architecture for Signal Monitoring and Recording Based on SDR and Docker Containers: A GNSS Use Case

Cyclicality in lending activity of Euro area in pre- and post- 2008 crisis: a local-adaptive-based testing of wavelets

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