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Deep learning-based image de-raining using discrete Fourier transformation / Prasen Kumar Sharma in The Visual Computer, vol 37 n° 8 (August 2021)  

Public [article]  inThe Visual Computer > vol 37 n° 8 (August 2021) . - pp 2083 - 2096 Titre : Deep learning-based image de-raining using discrete Fourier transformation Type de document : Article/Communication Auteurs : Prasen Kumar Sharma, Auteur ; Sathisha Basavaraju, Auteur ; Arijit Sur, Auteur Année de publication : 2021 Article en page(s) : pp 2083 - 2096 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] bruit (théorie du signal) [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] décomposition d'image [Termes IGN] filtrage du bruit [Termes IGN] pluie [Termes IGN] transformation de Fourier Résumé : (auteur) Single image rain streak removal is a well-explored topic in the field of computer vision. The de-raining problem is modeled as an image decomposition task where a rainy image is decomposed into rain-free background image and rain streek map. Unlike most of the existing de-raining methods, this paper attempts to decompose the rainy image in the frequency domain. The idea is inspired by pseudo-periodic characteristics of the noise signal (here the rain streaks) which leave some traces in the frequency domain, and the same can be utilized to predict the noise signal. In this paper, a deep learning-based rain streak prediction model is proposed which learns in discrete Fourier transform Oppenheim and Schafer (Discrete-Time Signal Processing, Prentice Hall, Upper Saddle River, 1989) domain. To the best of our knowledge, this is the first approach where compressed domain coefficients are directly used as input to a deep convolutional neural network. The proposed model has been tested on publicly available synthetic datasets Fu et al. (in: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. https://doi.org/10.1109/CVPR.2017.186, Yang et al. (in: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. https://doi.org/10.1109/CVPR.2017.183), Yeh et al. (in: 2015 IEEE International Conference on Consumer Electronics-Taiwan, 2015. https://doi.org/10.1109/ICCE-TW.2015.7216999) and results are found to be comparable with the state of the art methods in the spatial domain. The presented analysis and study have an obvious indication to extend transform domain input to train the deep learning architecture especially image de-noising like problems. Numéro de notice : A2021-597 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-020-01971-w Date de publication en ligne : 16/09/2020 En ligne : https://doi.org/10.1007/s00371-020-01971-w Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98226 [article]

Applied signal processing / Sadasivan Puthusserypady (2021)  

Public Titre : Applied signal processing Type de document : Guide/Manuel Auteurs : Sadasivan Puthusserypady, Auteur Editeur : Boston, Delft : Now publishers Année de publication : 2021 Collection : *NowOpen* Importance : 550 p. ISBN/ISSN/EAN : 978-1-68083-979-1 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal [Termes IGN] convolution (signal) [Termes IGN] filtrage du signal [Termes IGN] modulation d'amplitude [Termes IGN] modulation de fréquence [Termes IGN] série de Fourier [Termes IGN] signal aléatoire [Termes IGN] transformation de Fourier [Termes IGN] transformation de Hilbert Résumé : (éditeur) Being an inter-disciplinary subject, Signal Processing has application in almost all scientific fields. Applied Signal Processing tries to link between the analog and digital signal processing domains. Since the digital signal processing techniques have evolved from its analog counterpart, this book begins by explaining the fundamental concepts in analog signal processing and then progresses towards the digital signal processing. This will help the reader to gain a general overview of the whole subject and establish links between the various fundamental concepts. While the focus of this book is on the fundamentals of signal processing, the understanding of these topics greatly enhances the confident use as well as further development of the design and analysis of digital systems for various engineering and medical applications. Applied Signal Processing also prepares readers to further their knowledge in advanced topics within the field of signal processing. Note de contenu : 1- Introduction 2- Power and Energy 3- Fourier series 4- Fourier transform 5- Complex signals 6- Analog systems 7- Sampling and digital signals 8- Transform of discrete time signals 9- Fourier spectra of discrete-time signals 10- Digital systems 11- Implementation of digital systems 12- Discrete Fourier transform 13- Fast Fourier transform 14- Design of digital filters 15- Random signals 16- Modulation 17- Power Spectrum Estimation Numéro de notice : 28562 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Manuel de cours DOI : 10.1561/9781680839791 En ligne : http://dx.doi.org/10.1561/9781680839791 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97593

Wavelet theory / Somayeh Mohammady (2021)  

Public Titre : Wavelet theory Type de document : Monographie Auteurs : Somayeh Mohammady, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2021 Importance : 398 p. Format : 19 x 27 cm ISBN/ISSN/EAN : 978-1-83881-955-2 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] filtrage du bruit [Termes IGN] ondelette de Haar [Termes IGN] traitement du signal [Termes IGN] transformation de Fourier [Termes IGN] transformation en ondelettes Résumé : (éditeur) The wavelet is a powerful mathematical tool that plays an important role in science and technology. This book looks at some of the most creative and popular applications of wavelets including biomedical signal processing, image processing, communication signal processing, Internet of Things (IoT), acoustical signal processing, financial market data analysis, energy and power management, and COVID-19 pandemic measurements and calculations. The editor’s personal interest is the application of wavelet transform to identify time domain changes on signals and corresponding frequency components and in improving power amplifier behavior. Note de contenu : 1- Time Frequency Analysis of Wavelet and Fourier Transform 2- Wavelet Theory: Applications of the Wavelet 3- Wavelet Theory and Application in Communication and Signal Processing 4- Wavelet Based Multicarrier Modulation (MCM) Systems: PAPR Analysis 5- Wavelets for EEG Analysis 6- Ultra-High Performance and Low-Cost Architecture of Discrete Wavelet Transforms 7- Fault Detection, Diagnosis, and Isolation Strategy in Li-Ion Battery Management Systems of HEVs Using 1-D Wavelet Signal Analysis 8- Industrial IoT Using Wavelet Transform 9- Wavelet Transform for Signal Processing in Internet-of-Things (IoT) 10- The Discrete Quincunx Wavelet Packet Transform 11- Uncertainty and the Oracle of Market Returns: Evidence from Wavelet Coherence Analysis 12- Case Study: Coefficient Training in Paley-Wiener Space, FFT, and Wavelet Theory 13- Wavelet Filter Banks Using Allpass Filters 14- A Wavelet Threshold Function for Treatment of Partial Discharge Measurements 15- Use of Daubechies Wavelets in the Representation of Analytical Functions 16- Higher Order Haar Wavelet Method for Solving Differential Equations 17- COVID-19 Outbreak and Co-Movement of Global Markets: Insight from Dynamic Wavelet Correlation Analysis Numéro de notice : 28316 Affiliation des auteurs : non IGN Thématique : IMAGERIE/MATHEMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.87895 En ligne : https://doi.org/10.5772/intechopen.87895 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98235

Estimation of frequency and duration of ionospheric disturbances over Turkey with IONOLAB-FFT algorithm / Secil Karatay in Journal of geodesy, vol 94 n° 9 (September 2020)  

Public [article]  inJournal of geodesy > vol 94 n° 9 (September 2020) . - n° 89 Titre : Estimation of frequency and duration of ionospheric disturbances over Turkey with IONOLAB-FFT algorithm Type de document : Article/Communication Auteurs : Secil Karatay, Auteur Année de publication : 2020 Article en page(s) : n° 89 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] fréquence [Termes IGN] oscillation [Termes IGN] perturbation ionosphérique [Termes IGN] récepteur bifréquence [Termes IGN] récepteur GPS [Termes IGN] teneur totale en électrons [Termes IGN] transformation rapide de Fourier [Termes IGN] Turquie Résumé : (auteur) One of the more common methods of observation of variability of the Earth’s ionosphere is based on total electron content (TEC) estimated from ground-based dual-frequency Global Positioning System (GPS) receivers. Variations in solar, geomagnetic and seismic activity cause depletions or enhancements in the ionospheric electron concentrations that can be detected as disturbances. Some of these disturbances have wave-like characteristics, where frequency of oscillation can be used to identify and classify the disturbance. In this study, the frequency of such periodic disturbances is estimated using a fast Fourier transform (FFT)-based method, namely IONOLAB-FFT, in the spectral domain. IONOLAB-FFT, which was initially developed to be used on slant TEC (STEC), is modified to be applied to TEC in the local zenith direction of the receiver. The algorithm is tested using literature data on disturbances generated by a geomagnetic activity, a solar flare, a medium-scale traveling ionospheric disturbance (MSTID), a large-scale TID (LSTID) and an earthquake. An accordance with these known disturbances is observed in running IONOLAB-FFT, and the main frequencies and durations of the disturbances are estimated. IONOLAB-FFT method is applied to TEC computed from Turkish Permanent GPS Network (TNPGN-Active) which lies in mid-latitude region to detect the any wave-like oscillations, sudden disturbances and other irregularities during December, March, June and September months for 2010, 2011 and 2012 years. It is observed that a large number of the estimated frequencies are accumulated between 0.08 and 0.14 MHz corresponding to periods of 3.5 h to 2 h. The significant frequencies are grouped less than 0.28 MHz. A large number of the durations of the oscillations are between 425 and 550 min in 2010, 300 and 550 min in 2011 and 350 and 400 min in 2012. The longest duration (around 800 min: 13.33 h) is observed in December months, and the shortest duration (around 2 h) is observed in September months. Numéro de notice : A2020-541 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-020-01416-1 Date de publication en ligne : 31/08/2020 En ligne : https://doi.org/10.1007/s00190-020-01416-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95743 [article]

An analytic expression for the phase noise of the goldstein–werner filter / Scott Hensley in IEEE Transactions on geoscience and remote sensing, vol 57 n° 9 (September 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 57 n° 9 (September 2019) . - pp 6499 - 6516 Titre : An analytic expression for the phase noise of the goldstein–werner filter Type de document : Article/Communication Auteurs : Scott Hensley, Auteur Année de publication : 2019 Article en page(s) : pp 6499 - 6516 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] bruit thermique [Termes IGN] corrélation temporelle [Termes IGN] densité spectrale de puissance [Termes IGN] filtrage du bruit [Termes IGN] filtre de Goldstein [Termes IGN] image radar moirée [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] phase [Termes IGN] pouvoir de résolution spectrale [Termes IGN] rapport signal sur bruit [Termes IGN] transformation de Fourier Résumé : (auteur) Interferogram filtering for noise reduction is a key to many radar interferometric applications. Repeat pass radar interferometry often uses data with less than ideal correlation levels resulting from either long spatial or temporal baselines or changes between observations leading to high levels of temporal correlation. To maximize the utility of such pairs filtering the interferogram to get maximal noise reduction is often needed. One technique that has proved quite useful in the geophysical community is power spectral or Goldstein–Werner filtering of the interferogram whereby a power-weighted version of the Fourier transform is used to enhance fringe visibility. Although this paper defining the filter briefly touched upon the spatial resolution and noise reduction induced by the filter, it did not provide a useful formula for predicting the phase noise after filtering. This paper derives a formula for the phase noise obtained from power spectral filtering albeit under the restriction of several simplifying assumptions to make the problem analytically tractable. In particular, it is assumed that the interferometric phase is locally well approximated by a linear phase ramp with nonlinear phase perturbations small in a spectral energy sense compared to the linear term. Numéro de notice : A2019-343 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2906549 Date de publication en ligne : 25/04/2019 En ligne : http://doi.org/10.1109/TGRS.2019.2906549 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93378 [article]

The possibility of measuring the dynamic response of structures using non-contact geodetic method / Bostjan Kovacic in Geodetski vestnik, vol 63 n° 1 (March - May 2019)  

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FFT swept filtering: a bias-free method for processing fringe signals in absolute gravimeters / Petr Křen in Journal of geodesy, vol 93 n° 2 (February 2019)  

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Signaux et systèmes / André Quinquis (2019)

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Physics of oscillations and waves / Arnt Inge Vistnes (2018)  

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The open data HELI-DEM DTM for the western alpine area: computation and publication / L. Biagi in Applied geomatics, vol 8 n° 3-4 (December 2016)  

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A computational introduction to digital image processing / Alasdair McAndrew (2016)

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Introduction to partial differential equations / David Borthwick (2016)  

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Comparison among three harmonic analysis techniques on the sphere and the ellipsoid / Hussein Abd-Elmotaal in Journal of applied geodesy, vol 8 n° 1 (April 2014)  

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Introduction to partial differential equations / Peter J. Olver (2014)  

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Signal Processing / Charles L. Byrne (2014)  

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