L'eau fait partie intégrante de la vie sur notre planète et joue un rôle important dans les études pour évaluer l'impact du changement climatique. La recherche des ressources en eau est donc très importante pour la communauté scientifique du "climat" non seulement en surveillant de près le budget régional et mondial des ressources en eau, mais aussi pour comprendre les changements dans la fréquence et l'intensité des événements météorologiques ponctuels. Cela est particulièrement vrai pour les phénomènes météorologiques extrêmes, qui ont de grands impacts socio-économiques. L'impact des tempêtes tropicales plus ou plus intenses, des méga-neiges ou des coupes de poussières est l'un des principaux domaines de la recherche climatique. Le but de mon travail de recherche est de fournir des moyens d'évaluer l'impact des changements climatiques sur les ressources en eau et de trouver des outils flexibles permettant une gestion durable de l'eau. Des études récentes ont montré que l'on peut tirer parti des ondes émises en continu par les constellations du système mondial de navigation par satellite (GNSS) pour mesurer l'humidité du sol. Cette technique de télédétection opportuniste, connue sous le nom de réflectométrie GNSS (GNSS-R), consiste à comparer l'interférence des ondes réfléchies par le sol et celles provenant directement des satellites. Dans ma thèse, je me suis concentré sur la base GNSS-R sur le rapport signal-sur-bruit (SNR) enregistré par un récepteur GNSS conventionnel avec une unique antenne pour récupérer les variations d'humidité du sol. Beaucoup d'études ont montré l'efficacité de la méthode sur les sols argileux, et j'ai démontré dans ma thèse qu'elle était tout aussi efficace sur les sols sableux à condition d'appliquer la méthode du déroulement de phase. Cette méthode que je propose a été appliquée avec succès pour déterminer les variations locales d'humidité du sol: (1) 100% du sable dans le terrain de jeu de volleyball (Toulouse, France); et (2) >85% de sable dans la zone critique sahélienne de Dahra (Sénégal). En outre, la mesure précise et continue des niveaux d'eau des rivières est un élément important de la gestion des ressources en eau, afin d'obtenir une estimation continue du débit de la rivière dans le monde. La précision de la technique GNSS-R pour l'altimétrie fluviale est son pas d'échantillonnage élevé permet de suivre les événements hydrologiques extrêmes. Deux méthodes, les moindres carrés et la technique "Larson", ont été appliquées avec succès pour déterminer les variations des hauteurs au Vietnam: (1) sur la fleuve Rouge (21 ° 2'44.04 "N, 105 ° 51'48.86" E) où les événements et changements morphologiques associés aux événements hydrologiques (tempête tropicale) en 2016; et (2) sur le delta du Mékong (9 ° 31'38.63 "N, 106 ° 12'2.01" E) où les eaux continentales interagissent avec les eaux océanique. Mon travail montre que le GNSS-R est une alternative puissante et un complément significatif aux techniques actuelles de mesure de la gestion des ressources en eau en établissant un lien entre les différentes résolutions temporelles et spatiales actuellement obtenues par les outils conventionnels (in-situ capteurs, télédétection radar, etc.). Cette technique présente le grand avantage d'être basic sur le réseau pérenne du constatation GNSS et peut donc être utilisé sur n'importe quelle station GNSS. Par conséquent, en implantant une chaîne de traitement SNR, on peut automatiquement suivre les variations des environnementaux fondamentaux, i.e. la hauteur de la rivière, la pente locale de la surface de l'eau, les zones inondées, les variations d'humidité du sol et même la hauteur de végétation. / Water is an integral part of life on our planet and it plays an important role in climatic changes. Water resources research is, therefore, very important for the climate communities to not only closely monitor the regional and global water supply budget, but to also understand changes in frequency of occurrence and strength of individual weather events. This is especially true for extreme weather events, which have great societal and economic impacts. Whether we will have more or more intense tropical storms, mega-snow events, or dust-bowls in the near or far future climate. This is one of the key focus areas of climate research. The aim of my PhD work is to provide some answers to assess the impact of future climate change threats on water resources. And we are trying to find the adaptive tools needed for sustainable water resources management. In an effort to optimize water resource management, it is crucial to improve soil moisture situation awareness. With the advent of remote sensing, soil moisture is systematically monitored at the global scale but at the expense of the temporal and/or spatial resolution. Recent studies suggested to take advantage of continuously emitted waves by the Global Navigation Satellite System (GNSS) constellations, to retrieve soil moisture. This opportunistic remote sensing technique, known as GNSS Reflectometry (GNSS-R), consists in comparing the interference of reflected waves by the ground and those which come directly from satellites. In my thesis, I focused on GNSS-R technique base on the signal-to-noise ratio (SNR) recorded by conventional GNSS receiver with single antenna to retrieve soil moisture variations. Previous studies show the efficiency of this methodology for clay soil and I demonstrate for the first time, it's efficiency for sandy soil using Unwrapping phase method. This method that I propose has been successfully applied to determine local soil moisture variations of : (1) 100% of sand in Volleyball playground (Toulouse, France); and (2) >85% of sand in the Sahelian critical zone of Dahra (Senegal). In addition, accurate and continuous measurement of river water levels is an important element in water resource management, to obtain an ongoing estimate of the river's flow around the world. The accuracy of GNSS-R technique for river altimetry is useful for detection of extreme hydrological events and to show the competition between continental and oceanic water near coastal area. The two methods, Least Square and "Larson" methods, has been successfully applied to determine local variations in Vietnam of: (1) the Red river (21°02'44.04"N, 105°51'48.86"E) to identify flood events and morphological changes associated to the hydrological events (tropical storm) in 2016; and (2) the Mekong river delta (9°31'38.63"N, 106°12'2.01"E) where continental water interacts with oceanic water. My work shows that GNSS-R is a powerful alternative and a significant complement to the current measurement techniques for managing water resource by establishing a link between the different temporal and spatial resolutions currently achieved by conventional tools (in-situ sensors, remote sensing radar, etc.). This technique has a great advantage based on already-developed and sustainable GNSS satellites networks and can be applied to any GNSS geodetic station. Therefore, by installing a processing chain of the SNR acquisitions, we are able to monitor various environmental parameters i.e. height river, local slope of water surface, flooded areas, soil moisture variations and even vegetation/plant height.

近年來，田口方法的應用推廣與發展已經蔚為一股風潮，企業界許多設計工程師、生產現場的技術人員均普遍地使用田口方法，將品質設計於產品與生產製程之中，期能在商場上持續發展，並佔一席之地。然而，在以往田口方法的應用實例中，參數設計之因子水準的選取大都仰賴工程師憑著經驗來決定，但是經驗常會造成實驗的偏差，有鑒於此，本論文除了針對田口方法的使用做進一步的研究，並提出一套根據統計理論所推導出來的方法來協助工程師，使其在參數設計時對各因子水準的決定有一參考的依據，同時，為使其廣泛應用，本研究亦考慮到其它工業界常用的分配，而不再只侷限於常態分配。此外，本論文又針對SN比與損失函數之間的關係做進一步的討論，以使工業界能更深一層體會田口方法的優點及參數設計的目的。最後，本論文則研究非常態分配對損失函數及SN比估計值的影響情形，並表列各不同分配在α值給定之下，應如何決定樣本數(sample size)值，以使常態分配的假設更為合理。

近年來，隨著消費意識的提升，食品業者正面臨最嚴格的考驗，品質 產 生變異，將會反應在銷售量上，嚴重影響企業的經營。而目前台灣食品 造業者，有許多廠家仍停留在經驗掛帥、老式的檢驗品質階段，對於源流 瑊z的觀念仍不甚了解，致使品質提昇的效率不盡理想。有鑑於此，本研 究B用田口玄一博士生產線外品質工程理念架構，靈活搭配各種工具（如 系統洈k、直交表），以使田口方法更臻完善。同時利用S/N比（signal- to-Noes Ratio）變異數分析兩種方法進行資料解析，提供食品製造業者 最佳生ㄡ捰X。最後，乃以本人所親自參與之食品製造業品質改善研究實 驗為例，憭什ㄟ筆嗾膋熊痕G報告外，另詳述記載寶貴的實驗經歷，可進 一步提供相鶻t商作為改善品質的借鏡。

Orthogonal frequency division multiplexing (OFDM) modulator with redundancy has been adopted in many wireless communication systems for higher data rate transmissions. The introduced redundancy at the transmitter allows us to overcome serious inter-block interference (IBI) problems due to highly dispersive channel. However, the selection of redundancy length will affect the system performance and spectral efficiency, and is highly dependent on the length of channel impulse response. In this thesis, based on the pseudorandom postfix (PRP) OFDM scheme we propose a novel block-based OFDM transceiver framework. Since in the PRP-OFDM system the PRP can be employed for semi-blind channel estimation with order-one statistics of the received signal. Hence, for sufficient redundancy case the PRP-OFDM system with the Bayesian decision feedback equalizer (DFE) is adopted for suppressing the IBI and ISI simultaneously. However, for the insufficient redundancy case (the length of redundancy is less than the order of channel), we first propose a modified scheme for channel estimation. To further reduce the complexity of receiver, the maximum shortening signal-to-noise-ratio time domain equalizer (MSSNR TEQ) with the Bayesian DFE is developed for suppressing the IBI and ISI, separately. That is, after knowing the channel state information (CSI) and removing the effect of IBI with MSSNR TEQ, the Bayesian DFE is applied for eliminating the ISI. Via computer simulation, we verify that performance improvement, in terms of bit error rate (BER), compared with the conventional block-based minimum mean square error (MMSE)-DFE can be achieved.

Orthogonal frequency division multiplexing (OFDM) is the major technique for next generation wireless communication system because of its high spectral efficiency. In addition, multiple-input multiple-output (MIMO) technique is usually used to further increase system capacity. There are two major coding schemes adopted in MIMO-OFDM systems, i.e. space-time block code (STBC) and space-frequency block code (SFBC). This thesis investigates the orthogonal-space-frequency block code OFDM (OSFBC-OFDM) system. In SFBC-OFDM systems, the channel frequency response is usually assumed to be the same for adjacent subcarriers. However, this assumption is not valid in frequency-selective fading environment. Therefore, the orthogonality of code structure is destroyed, leading to substantial increase in interference and significant decrease in system performance. This thesis proposes a receiver equalizer which adopts an interference cancellation (IC) mechanism to maximize the signal to interference plus noise ratio (SINR). Both the Lagrange multiplier method and eigenvalue method are adopted in the interference cancellation. Simulation experiments are conducted to verify the system performance and results demonstrate that the SINR performance is dramatically improved.

Different diversity techniques such as Maximal-Ratio Combining (MRC), Equal-Gain Combining (EGC) and Selection Combining (SC) are described and analyzed. Two branches (N=2) diversity systems that are used for pre-detection combining have been investigated and computed. The statistics of carrier to noise ratio (CNR) and carrier to interference ratio (CIR) without diversity assuming Rayleigh fading model have been examined and then measured for diversity systems. The probability of error (p_e) vs CNR and (p_e) versus CIR have also been obtained. The fading dynamic range of the instantaneous CNR and CIR is reduced remarkably when diversity systems are used [1]. For a certain average probability of error, a higher valued average CNR and CIR is in need for non-diversity systems [1]. But a smaller valued of CNR and CIR are compared to diversity systems. The overall conclusion is that maximal-ratio combining (MRC) achieves the best performance improvement compared to other combining methods. Diversity techniques are very useful to improve the performance of high speed wireless channel to transmit data and information. The problems which considered in this thesis are not new but I have tried to organize, prove and analyze in new ways.

To understand how the components of a complex system like the biological cell interact and regulate each other, we need to collect data for how the components respond to system perturbations. Such data can then be used to solve the inverse problem of inferring a network that describes how the pieces influence each other. The work in this thesis deals with modelling the cell regulatory system, often represented as a network, with tools and concepts derived from systems biology. The first investigation focuses on network sparsity and algorithmic biases introduced by penalised network inference procedures. Many contemporary network inference methods rely on a sparsity parameter such as the L1 penalty term used in the LASSO. However, a poor choice of the sparsity parameter can give highly incorrect network estimates. In order to avoid such poor choices, we devised a method to optimise the sparsity parameter, which maximises the accuracy of the inferred network. We showed that it is effective on in silico data sets with a reasonable level of informativeness and demonstrated that accurate prediction of network sparsity is key to elucidate the correct network parameters. The second investigation focuses on how knowledge from association networks can be transferred to regulatory network inference procedures. It is common that the quality of expression data is inadequate for reliable gene regulatory network inference. Therefore, we constructed an algorithm to incorporate prior knowledge and demonstrated that it increases the accuracy of network inference when the quality of the data is low. The third investigation aimed to understand the influence of system and data properties on network inference accuracy. L1 regularisation methods commonly produce poor network estimates when the data used for inference is ill-conditioned, even when the signal to noise ratio is so high that all links in the network can be proven to exist for the given significance. In this study we elucidated some general principles for under what conditions we expect strongly degraded accuracy. Moreover, it allowed us to estimate expected accuracy from conditions of simulated data, which was used to predict the performance of inference algorithms on biological data. Finally, we built a software package GeneSPIDER for solving problems encountered during previous investigations. The software package supports highly controllable network and data generation as well as data analysis and exploration in the context of network inference. / <p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 4: Manuscript.</p><p> </p>

L‐glutamate is associated with several neurological disorders; thus, monitoring fast dynamics of L‐glutamate is of great importance in the field of neuroscience. Electrode miniaturization demanded by many applications leads to reduced surface area and decreased amounts of immobilized enzymes on coated electrodes. As a result, lower signal‐to‐noise ratios are observed for oxidase‐enzyme based sensors. To increase the signal‐to‐noise ratio we have developed a process to fabricate micro‐ and nano‐ structures on the microelectrode surface. Localized surface‐plasmon resonances (SPR) has been extensively used to design label‐free biosensors that can monitor receptor‐ligand interactions. A major challenge with localized SPR sensors is that they remain highly susceptible to interference because they respond to both solution refractive index changes and surface binding of the target analyte. The key concept introduced in the present work is the exploitation of transverse and longitudinal resonance modes of nanorod arrays to differentiate between bulk refractive index changes and surface interactions. The transverse bulk sensitivity of the localized SPR sensor (107 nm/RIU) remains competitive with typical single mode gold nanosphere SPR sensors. The figure of merit for the device’s cross‐sensitivity (1.99) is comparable to that of typical wavelength‐interrogated propagating SPR sensors with self referencing.

Thesis (Ph.D.); Submitted to the University of California, Berkeley, CA (US); 24 Nov 2004. / Published through the Information Bridge: DOE Scientific and Technical Information. "LBNL--56751" McGuire, John Andrew. USDOE Director. Office of Science. Office of Basic Energy Sciences. Materials Science and Engineering Division (US) 11/24/2004. Report is also available in paper and microfiche from NTIS.

abstract: A dual-channel directional digital hearing aid (DHA) front-end using a fully differential difference amplifier (FDDA) based Microphone interface circuit (MIC) for a capacitive Micro Electro Mechanical Systems (MEMS) microphones and an adaptive-power analog font end (AFE) is presented. The Microphone interface circuit based on FDDA converts the capacitance variations into voltage signal, achieves a noise of 32 dB SPL (sound pressure level) and an SNR of 72 dB, additionally it also performs single to differential conversion allowing for fully differential analog signal chain. The analog front-end consists of 40dB VGA and a power scalable continuous time sigma delta ADC, with 68dB SNR dissipating 67u¬W from a 1.2V supply. The ADC implements a self calibrating feedback DAC, for calibrating the 2nd order non-linearity. The VGA and power scalable ADC is fabricated on 0.25 um CMOS TSMC process. The dual channels of the DHA are precisely matched and achieve about 0.5dB gain mismatch, resulting in greater than 5dB directivity index. This will enable a highly integrated and low power DHA / Dissertation/Thesis / Ph.D. Electrical Engineering 2011