Publications

Attribution of Multivariate Extreme Events.
Yanira Guanche and Maha Shadaydeh and Miguel Mahecha and Joachim Denzler.
Climate Informatics Workshop. 2019.
[bibtex] [pdf] [abstract]

Causal Inference in Nonverbal Dyadic Communication with Relevant Interval Selection and Granger Causality.
Lea Müller and Maha Shadaydeh and Martin Thümmel and Thomas Kessler and Dana Schneider and Joachim Denzler.
International Conference on Computer Vision Theory and Applications (VISAPP). 2019.
[bibtex] [pdf] [abstract]

Nonlinear Causal Link Estimation under Hidden Confounding with an Application to Time-Series Anomaly Detection.
Violeta Teodora Trifunov and Maha Shadaydeh and Jakob Runge and Veronika Eyring and Markus Reichstein and Joachim Denzler.
German Conference on Pattern Recognition (GCPR). 2019.
[bibtex] [pdf] [abstract]

Time-Frequency Causal Inference Uncovers Anomalous Events in Environmental Systems.
Maha Shadaydeh and Joachim Denzler and Yanira Guanche Garcia and Miguel Mahecha.
German Conference on Pattern Recognition (GCPR). 2019.
[bibtex] [pdf] [abstract]

Causal Link Estimation under Hidden Confounding in Ecological Time Series.
Violeta Teodora Trifunov and Maha Shadaydeh and Jakob Runge and Veronika Eyring and Markus Reichstein and Joachim Denzler.
Climate Informatics Workshop. 2019.
[bibtex] [pdf] [abstract]

Registration of High Resolution SAR and Optical Satellite Imagery Using Fully Convolutional Networks.
Stefan Hoffmann and Clemens-Alexander Brust and Maha Shadaydeh and Joachim Denzler.
IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2019.
[bibtex] [pdf] [abstract]

Fetal ECG extraction exploiting joint sparse supports in a dual dictionary framework.
Furrukh Sana and Tarig Ballal and Maha Shadaydeh and Ibrahim Hoteit and Tareq Y. Al-Naffouri.
Biomedical Signal Processing and Control. pages 46 - 60. 2019.
[bibtex] [pdf] [web] [abstract]

Analyzing the Time Variant Causality in Ecological Time Series: A Time-Frequency Approach.
Maha Shadaydeh and Yanira Guanche Garcia and Miguel Mahecha and Markus Reichstein and Joachim Denzler.
International Conference on Ecological Informatics (ICEI). Pages 151-152. 2018.
[bibtex] [abstract]

Abstract: Attribution in ecosystems aims to identify the cause-effect relationships between the variables involved. The availability of high temporal resolution data along with the powerful computing platforms further enhance the capacity of data-driven methods in capturing the complex relationships between the variables of the underlying system. Time series of ecological variables most often contain different periodical components that can significantly mask the underling causality structure in time domain. This motivates the use of time-frequency processing techniques such as wavelet analysis or short time Fourier transform. In this study we present a time-frequency approach for causality analysis where the coupling between the variables is assumed to follow a locally time-invariant multivariate autoregressive (MVAR) model. We propose a sliding time window approach to examine the change of interactions, i.e. direction and strength of causality, between the different variables over seasons. The cause-effect relationships are extracted using the frequency domain representation of the MVAR Granger causality (MVAR-GC) [1,2] based on the generalized partial directed coherence (gPDC) [3]. We have first applied the proposed method to synthetic data to evaluate its sensitivity to different issues such as the selection of the model order, the sampling frequency, the absence of cause as well as the presence of non-linear coupling. The method is then applied to half-hourly meteorological observations and land flux eddy covariance data to investigate the causal-effect relationships between global radiation (Rg), air temperature (Tair), and the CO2 land fluxes: gross primary productivity (GPP), net ecosystem exchange (NEE) and ecosystem respiration (Reco). The results show that time-frequency analysis based on MVAR-GC has promising potential in identifying the time variant causality structure within these variables along with the main time delay between different cause- effect pairs. Further research work is currently going for the investigation of the selection criteria of the model order, the sampling frequency, and the size of the time window at different time scales of causality analysis. This study is carried out within the framework of the project BACI which in part aims at developing an attribution scheme for changes in ecosystem functioning and studying the impacts of these changes on biodiversity patterns.

Extreme anomaly event detection in biosphere using linear regression and a spatiotemporal MRF model.
Yanira Guanche Garcia and Maha Shadaydeh and Miguel Mahecha and Joachim Denzler.
Natural Hazards. pages 1-19. 2018.
[bibtex] [pdf] [web] [abstract]

Abstract: Detecting abnormal events within time series is crucial for analyzing and understanding the dynamics of the system in many research areas. In this paper, we propose a methodology to detect these anomalies in multivariate environmental data. Five biosphere variables from a preliminary version of the Earth System Data Cube have been used in this study: Gross Primary Productivity, Latent Energy, Net Ecosystem Exchange, Sensible Heat and Terrestrial Ecosystem Respiration. To tackle the spatiotemporal dependencies of the biosphere variables, the proposed methodology after preprocessing the data is divided into two steps: a feature extraction step applied to each time series in the grid independently, followed by a spatiotemporal event detection step applied to the obtained novelty scores over the entire study area. The first step is based on the assumption that the time series of each variable can be represented by an autoregressive moving average (ARMA) process, and the anomalies are those time instances that are not well represented by the estimated ARMA model. The Mahalanobis distance of the ARMA models multivariate residuals is used as a novelty score. In the second step, the obtained novelty scores of the entire study are treated as time series of images. Markov random fields (MRFs) provide an effective and theoretically well-established methodology for integrating spatiotemporal dependency into the classification of image time series. In this study, the classification of the novelty score images into three classes, intense anomaly, possible anomaly, and normal, is performed using unsupervised K-means clustering followed by multi-temporal MRF segmentation applied recursively on the images of each consecutive \(L \ge \) 1 time steps. The proposed methodology was applied to an area covering Europe and Africa. Experimental results and validation based on known historic events show that the method is able to detect historic events and also provides a useful tool to define sensitive regions.

Domain knowledge integration for causality analysis of carbon-cycle variables.
Violeta Teodora Trifunov, Maha Shadaydeh, Jakob Runge, Veronika Eyring, Markus Reichstein and Joachim Denzler.
AGU Fall Meeting 2018. 2018.
[bibtex] [abstract]

BACI: Towards a Biosphere Atmosphere Change Index - Detection of extreme events in the biosphere.
Yanira Guanche Garcia and Maha Shadaydeh and Miguel Mahecha and Markus Reichstein and Joachim Denzler. (2018)
[bibtex] [pdf] [web]

Causality analysis of ecological time series: a time-frequency approach.
Maha Shadaydeh and Yanira Guanche Garcia and Miguel Mahecha and Markus Reichstein and Joachim Denzler.
Climate Informatics Workshop 2018. 2018.
[bibtex] [pdf]

Image denoising via collaborative support-agnostic recovery.
Muzammil Behzad and Mudassir Masood and Tarig Ballal and Maha Shadaydeh and Tareq. Y. Al-Naffouri.
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Pages 1343-1347. 2017.
[bibtex] [web] [abstract]

Wetland mapping by fusion of airborne laser scanning and multi-temporal multispectral satellite imagery.
Maha Shadaydeh and Andras Zlinszky and Andrea Manno-Kovacs and Tamas Sziranyi.
International Journal of Remote Sensing. 38 (23): pages 7422-7440. 2017.
[bibtex] [pdf] [web] [abstract]

Biosphere Anomalies Detection by Regression Models.
Yanira Guanche and Maha Shadaydeh and Miguel Mahecha and Joachim Denzler.
Conference on Advances in Extreme Value Analysis and Application to Natural Hazards (EVAN). 2017.
[bibtex] [pdf]

Change Detection in Wetlands using Multi-layer Fusion Markov Random Field.
Maha Shadaydeh and Andrea Manno-Kovacs and Tamas Sziranyi.
What can Remote Sensing do for the Conservation of Wetlands? - 1st International Symposium. 2015.
[bibtex]

An improved mutual information similarity measure for registration of multi-modal remote sensing images.
Maha Shadaydeh and Tamas Sziranyi.
Proc. SPIE Remote Sensing. pages 9643 - 9643 - 7. 2015.
[bibtex] [web] [abstract]

Multilayer Markov Random Field models for change detection in optical remote sensing images.
Csaba Benedek and Maha Shadaydeh and Zoltan Kato and Tamas Sziranyi and Josiane Zerubia.
ISPRS Journal of Photogrammetry and Remote Sensing. pages 22-37. 2015.
[bibtex] [pdf] [web] [abstract]

An improved local similarity measure estimation for change detection in remote sensing images.
Maha Shadaydeh and Tamas Sziranyi.
International Conference on Aerospace Electronics and Remote Sensing Technology. Pages 234-238. 2014.
[bibtex] [web] [abstract]

Segmentation of Remote Sensing Images Using Similarity-Measure-Based Fusion-MRF Model.
Tamas Sziranyi and Maha Shadaydeh.
IEEE Geoscience and Remote Sensing Letters. 11 (9): pages 1544-1548. 2014.
[bibtex] [pdf] [web] [abstract]

Improved segmentation of a series of remote sensing images by using a fusion MRF model.
Tamas Sziranyi and Maha Shadaydeh.
International Workshop on Content-Based Multimedia Indexing (CBMI). Pages 137-142. 2013.
[bibtex] [web] [abstract]

Extraction of fetal ECG using adaptive Volterra filters.
Maha Shadaydeh and Yegui Xiao and Rabab K. Ward.
European Signal Processing Conference. Pages 1-5. 2008.
[bibtex] [pdf] [web] [abstract]

New narrowband active noise control systems with significantly less computational requirements.
Yegui Xiao and Maha Shadaydeh and Rabab K. Ward.
IEEE International Symposium on Circuits and Systems. Pages 41-44. 2008.
[bibtex] [web] [abstract]

Performance analysis of adaptive IIR notch filters based on least mean p-power error criterion.
Maha Shadaydeh and Masayuki Kawamata.
Proceedings of the International Symposium on Circuits and Systems (ISCAS). Pages 377-380. 2003.
[bibtex] [pdf] [web]

Steady State Analysis of the P-Power Algorithm for Constrained Adaptive IIR Notch Filter.
Maha Shadaydeh and Yegui Xiao and Masayuki Kawawata.
IEEE Midwest Symposium on Circuits and Systems (MWSCAS). Pages 664-667. 2002.
[bibtex] [abstract]

Bias Removal Algorithm for 2-D Equation Error Adaptive IIR Filters.
Maha Shadaydeh and Masayuki Kawamata.
Multidimensional Systems and Signal Processing. 10 (4): pages 429-441. 1999.
[bibtex] [pdf] [web] [abstract]

Steady State Analysis of Two-Dimensional LMS Adaptive Filters Using the Independence Assumption.
Maha Shadaydeh and Masayuki Kawamata.
IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences. E82-A (3): 1998.
[bibtex] [pdf]

Convergence Analysis of 2-D Doubly-Indexed Block LMS Adaptive Filters.
Maha Shadaydeh and Masayuki Kawawata.
IEEE International Workshop on Intelligent Signal Processing and Communication Systems. 1998.
[bibtex]

Identification of nonminimum phase FIR systems via fourth-order cumulants and genetic algorithm.
Maha Shadaydeh and Yegui Xiao and Yoshiaki Tadokoro.
Signal Processing. 60 (3): pages 339-347. 1997.
[bibtex] [pdf] [web] [abstract]

Abstract: Abstract In this paper, the problem of estimating the parameters of an FIR system from only the fourth-order cumulants of the noisy system output is considered. The FIR system is driven by a symmetric, independent, and identically distributed (i.i.d) non-Gaussian sequence. We propose a new formula called Weighted Overdetermined C(q, k) (WOC(q, k)) by extending the conventional C(q, k) formula. The optimal selection of the weights in WOC(q, k) is performed by using the Genetic Algorithm (GA) optimization method which minimizes a nonlinear error function based on the fourth-order cumulants alone. Simulations are provided to reveal the effectiveness and the superiority of this novel technique over the C(q, k) and other existing techniques. Zusammenfassung In diesem Artikel wird das Problem der Parameterschtzung eines FIR-Systems nur aus Kumulanten 4. Ordnung eines verrauschten Systemausganges betrachtet. Das FIR-System wird von einer symmetrisch, unabhngig identisch verteilten, nicht-gauschen Sequenz angesteuert. Wir schlagen eine neue Methode, Weighted Overdetermined C(q, k) (WOC(q, k)), als Erweiterung des konventionellen C(q, k)-Verfahrens vor. Die optimale Wahl der Gewichte in WOC(q, k) wird von einem genetischen Algorithmus (GA) bernommen, welcher einen nichtlinearen Fehlerterm lediglich anhand von Kumulanten 4. Ordnung minimiert. Simulationen zeigen die Effektivitt und bessere Qualitt dieser neuen Technik gegenber C(q, k) und anderen Techniken.Rsum Nous abordons dans cet article le problme de l'estimation des paramtres d'un systme FIR partir seulement des cumulants d'ordre quatre de la sortie bruite du systme. Le systme FIR est excit par une squence non-gaussienne distribution invariante, indpendante (i.i.d.) et symtrique. Nous proposons une formule nouvelle appele C(q, k) surdtermine pondre (WOC(q, k)) tendant la formule C(q, k) conventionnelle. La slection optimale des coefficients de pondration dans WOC(q, k) se fait l'aide d'une mthode d'optimisation par algorithme gntique (GA) qui minimise une fonction d'erreur non-linaire base sur les cumulants d'ordre quatre seuls. Des simulations sont fournies pour mettre en vidence l'efficience et la supriorit de cette technique nouvelle vis--vis du C(q, k) et d'autres techniques existantes.

Overdetermined C(q,k) formula using third and fourth order cumulants.
Yegui Xiao and Maha Shadaydeh and Yoshiaki Tadokoro.
Electronics Letters. 32 (6): pages 601-603. 1996.
[bibtex] [web] [abstract]