Any interaction of plants with phytopathogens involves the generation of various chemical molecules that are critical for activation of their defense machinery. One of the chemicals, salicylic acid (SA)induces systemic acquired resistance (SAR)in plants. The activation of SAR provides a broad-spectrum resistance against a wide range of related or unrelated pathogens. There has been considerable progress in the biochemical and molecular understanding of SAR activation in various plants. In addition, several chemicals including SA and its analogs are known to provide a direct or indirect defense against pathogens when applied to plants. Molecular mechanism of plant defense induced by synthetic chemical inducers is not very well understood. This review highlights the importance of salicylic acid and its most studied analog, Acibenzolar-S-methyl in inducing SAR and it also provides a description of other major chemical elicitors of plant defenses and their possible molecular mechanism.

Biotic and abiotic stresses are signifi cant factors limiting the production of food and other supporting materials required to sustain increasing world population. Plant health is directly related to human health and is increasingly becoming signifi cant and demands more attention towards limiting the damages caused by biotic and abiotic stresses. Signifi cant progress has been made towards our understanding of the processes, which mediate both biotic and abiotic stress signaling in plants. Signifi cant role is played by various plant hormones, e.g., salicylic acid (SA) and jasmonic acid (JA) in biotic stress and abscisic acid (ABA) in abiotic stress (Annu Rev Cell Dev Biol 28:489-521, 2012). Other hormones with minor role include the cytokinins (CK), auxins (indole 3 acetic acid. IAA), and the brassinosteroids (BR) (Annu Rev Cell Dev Biol 28:489-521, 2012). Cross talk between these plant hormones is signifi cant and may result in either synergistic or antagonistic effect on stress responses (Annu Rev Cell Dev Biol 28:489-521, 2012). In recent years, extensive research carried out in various laboratories has implicated cross talk between the ABA and the SA in abiotic stress response. This is signifi cant in light of SA being key player in biotic stress responses in plants. This review will discuss the role of SA in biotic and abiotic stress signaling and its cross talk with other hormones in mediating abiotic stress signaling in plants.

Salicylic acid (SA) is a key plant hormone that mediates host responses against microbial pathogens. Identification and characterization of SA-interacting/binding proteins is a topic which has always excited scientists studying microbial defense response in plants. It is likely that discovery of a true receptor for SA may greatly advance understanding of this important signaling pathway. SABP2 with its high affinity for SA was previously considered to be a SA receptor. Despite a great deal work we may still not have true a receptor for SA. It is also entirely possible that there may be more than one receptor for SA. This scenario is more likely given the diverse role of SA in various physiological processes in plants including, modulation of opening and closing of stomatal aperture, flowering, seedling germination, thermotolerance, photosynthesis, and drought tolerance. Recent identification of NPR3, NPR4 and NPR1 as potential SA receptors and α-ketoglutarate dehydrogenase (KGDHE2), several glutathione S transferases (GSTF) such as SA binding proteins have generated more interest in this field. Some of these SA binding proteins may have direct/indirect role in plant processes other than pathogen defense signaling. Development and use of new techniques with higher specificity to identify SA-interacting proteins have shown great promise and have resulted in the identification of several new SA interactors. This review focuses on SA interaction/binding proteins identified so far and their likely role in mediating plant defenses.

The agricultural sector is the backbone which supports the livelihoods and the economic development of nations across the globe. In consequence, the need for robust and continuous monitoring of agricultural crops is primordial to face the interlinked challenges of growth rate population, food security and climate change. Synthetic Aperture Radar (SAR) sensors have the powerful imaging capability of operating at almost all weather conditions, independent of day and night illumination. By penetrating through clouds and into the vegetation canopy, the incident radar signal interacts with the structural and dielectric properties of the vegetation and soil, thus providing critical information of the crop state, such as height, biomass, crop yield or leaf structure, which can help devise sustainable agricultural management practices. This is achieved by means of the Polarimetric SAR Interferometry (PolInSAR) technique, which by coherently combining interferometric SAR acquisitions at different polarization states allows for the retrieval of biophysical parameters of the vegetation. In this framework, this thesis focuses on the development of crop monitoring techniques that properly exploit satellite-based PolInSAR data. All the known InSAR and PolInSAR methodologies for this purpose have been analysed. The sensitivity of these data provided by the TanDEM-X bistatic system to both the physical parameters of the scene (height and structure of the plants, moisture and roughness of the soil) and the sensor configuration (polarization modes and observation geometry) is evaluated. The effect of different simplifications made in the physical model of the scene on the crop estimates is assessed. The interferometric sensitivity requirements to monitor a crop scenario are more demanding than others, such as forests. Steep incidences associated with the largest spatial baselines provided by the available data set lead to the most accurate estimates under all the different model assumptions. Shallower incidences, on the other hand, generally yield important errors due to their characteristic shorter spatial baselines. Through the methodologies proposed in this thesis, PolInSAR data have shown potential to refine current methods for the quantitative estimation of crop parameters. Results encourage to continue further research towards the objective of achieving operational crop monitoring applications. / Work supported by the Spanish Ministry of Science and Innovation, the State Agency of Research (AEI) and the European Funds for Regional Development (EFRD) under Project TEC2017-85244-C2-1-P. Noelia Romero-Puig received a grant from the Generalitat Valenciana and the European Social Fund (ESF) [ACIF/2018/204].

Cette thèse présente la méthode de détection de changements MIMOSA (Method for generalIzed Means Ordered Series Analysis). Cette nouvelle méthode permet de détecter automatiquement des changements entre couples ou séries temporelles d’images SAR. En effet, grâce aux moyennes temporelles, le nombre d’images en jeu n’importe plus puisque seulement deux moyennes différentes sont comparées de sorte à détecter les changements (par exemple moyenne géométrique et moyenne quadratique). De ce fait, les grand volumes de données disponibles de nos jours sont exploitables plus facilement puisque l’information utile est «résumée» dans les moyennes. Le seul paramètre de la détection est le taux de fausses alarmes obtenu dans le résultat, ce qui rend son analyse plus intuitive. Les cartes de changements fournies par MIMOSA sont de très bonne qualité en comparaison à celles fournies par d’autres méthodes. De nombreux tests ont été mis en place pour constater la robustesse de la méthode MIMOSA face aux problèmes les plus souvent rencontrés, comme une mauvaise calibration radiométrique, ou encore un mauvais recalage. Une interface graphique a de plus été développée autour de MIMOSA, incorporant de nombreux outils de préparation et traitement des données, ainsi que des outils d’analyse des résultats. / This PhD thesis presents the MIMOSA (Method for generalIzed Means Ordered Series Analysis) change detection methood. This new technique can automatically detect changes between SAR image pairs or within time series. Indeed, thanks to the temporeal means, the number of involved images doesn’t matters because only two different means are compared to detect the changes (for example, the geometric and quadratic means). Thus, large data volumes can be processed easily, since the useful information is condensed within the temporal means. The only change detection parameter is the false alarm rate that will be MIMOSA method are very good compared to other methods. Several tests have been performed in order to quantify the robustness of the method facing the most common problems, like image misregistration or radiometric calibration errors. A graphical user interface has also been developed for MIMOSA, including many useful tools to prepare and process SAR data, but also several analyse tools.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第18561号 / 工博第3922号 / 新制||工||1603(附属図書館) / 31461 / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 田村 正行, 准教授 須﨑 純一, 准教授 田中 賢治 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

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Das Radar mit synthetischer Apertur (Synthetic Aperture Radar - SAR) liefert eine quasi-fotographische Abbildung der beleuchteten Bodenoberfläche mit zusätzlichen Informationen, die von der gesendeten und empfangenen Polarisation der Wellen abhängig sind. Eine nützliche Anwendung der polarimetrischen SAR-Daten liegt bei der Klassifizierung der Bodenstruktur anhand der polarimetrischen Streuungseigenschaften. In diesem Zusammenhang beschäftigt sich die vorliegende Arbeit mit der Entwicklung und Untersuchung neuer polarimetrischen Fokussierungsfunktion für die SAR-Datenverarbeitung mit Hilfe der polarimetrischen Rückstreuungseigenschaft, die zu einer alternativen quantitativen Auswertung der polarimerischen SAR-Daten führen kann. Die physikalische Optik Approximation wird für die numerische Berechnung der rückgestreuten elektrischen Felder der kanonischen Ziele unter SAR-Geometrie unter Berücksichtigung der Polarisationslage verwendet. Aus den rückgestreuten elektrischen Felder werden die polarimetrischen Radarrückstreuquerschnitte berechnet. Ein SAR-Simulator wird zur Datenverarbeitung der E-SAR des DLR entwickelt. Der Ansatz des polarimetrischen Radarrückstreuquerschnittes ermöglicht die approximierte numerische Berechnung der Rückstreuungseigenschaften der kanonischen Ziele sowohl im kopolaren als auch im kreuzpolaren Polarisationsbetrieb. Bei der SAR-Datenverarbeitung werden die Rohdatensätze durch die Referenzfunktion eines Punktzieles in der Entfernungsrichtung verarbeitet. Bei der Azimutkompression werden die vier Referenzfunktionen, das heißt die Referenzfunktion eines Punktzieles, die polarimetrische Fokussierungsfunktion einer flachen Platte, die polarimetrische Fokussierungsfunktion eines Zweifach-Reflektors und die polarimetrische Fokussierungsfunktion eines Dreifach-Reflektors, eingesetzt. Die qunatitativen Auswertung der SAR-Daten werden anhand des Pauli-Zerlegungstheorems, der differentiellen Reflektivität und des linearen Depolarisationsverhältnises durchgeführt.

This research investigates lower-power lower-voltage analog circuit techniques suitable for wireless sensor applications. Wireless sensors have been used in a wide range of applications and will become ubiquitous with the revolution of internet of things (IoT). Due to the demand of low cost, miniature desirable size and long operating cycle, passive wireless sensors which don't require battery are more preferred. Such sensors harvest energy from energy sources in the environment such as radio frequency (RF) waves, vibration, thermal sources, etc. As a result, the obtained energy is very limited. This creates strong demand for low power, lower voltage circuits. The RF and analog circuits in the wireless sensor usually consume most of the power. This motivates the research presented in the dissertation. Specially, the research focuses on the design of a low power high efficiency regulator, low power Resistance to Digital Converter (RDC), low power Successive Approximation Register (SAR) Analog to Digital Converter (ADC) with parasitic error reduction and a low power low voltage Low Dropout (LDO) regulator. This dissertation includes a low power analog circuit design for the RFID wireless sensor which consists of the energy harvest circuits (an optimized rectifier and a regulator with high current efficiency) and a sensor measurement circuit (RDC), a single end sampling SAR ADC with no error induced by the parasitic capacitance and a digital loop LDO whose line and load variation response is improved. These techniques will boost the design of the wireless sensor and they can also be used in other similar low power design.

In this dissertation, three studies were done for radar and hyperspectral remote sensing applications using statistical techniques. The first study investigated a relationship between synthetic aperture radar backscatter and in situ soil properties for levee monitoring. A series of statistical analyses were performed to investigate potential correlations between three independent polarization channels of radar backscatter and various soil properties. The results showed a weak but considerable correlation between the cross-polarized (HV) radar backscatter coefficients and several soil properties. The second study performed effective statistical feature extraction for levee slide classification. Images about a levee are often very large, and it is difficult to monitor levee conditions quickly because of high computational cost and large memory requirement. Therefore, a time-efficient method to monitor levee conditions is necessary. The traditional support vector machine (SVM) did not work well on original radar images with three bands, requiring extraction of discriminative features. Gray level co-occurrence matrix is a powerful method to extract textural information from grey-scale images, but it may not be practical for a big data in terms of calculation time. In this study, very efficient feature extraction methods with spatial filtering were used, including a weighted average filter and a majority filter in conjunction with a nonlinear band normalization process. Feature extraction with these filters, along with normalized bands, yielded comparable results to gray level co-occurrence matrix with a much lower computational cost. The third study focused on the case when only a small number of ground truth labels were available for hyperspectral image classification. To overcome the difficulty of not having enough training samples, a semisupervised method was proposed. The main idea was to expand ground truth using a relationship between labeled and unlabeled data. A fast self-training algorithm was developed in this study. Reliable unlabeled samples were chosen based on SVM output with majority voting or weighted majority voting, and added to labeled data to build a better SVM classifier. The results showed that majority voting and weighted majority voting could effectively select reliable unlabeled data, and weighted majority voting yielded better performance than majority voting.