CMOS indoor light energy harvesting system for wireless sensing applications

Dissertação para obtenção do Grau de Doutor em Engenharia Electrotécnica e de Computadores

Control system for actuation and sensing in digital microfluidics devices

Digital Microfluidics (DMF) is a second generation technique, derived from the conventional microfluidics that instead of using continuous liquid fluxes, it uses only individual droplets driven by external electric signals. In this thesis a new DMF control/sensing system for visualization, droplet control (movement, dispensing, merging and splitting) and real time impedance measurement have been developed. The software for the proposed system was implemented in MATLAB with a graphical user interface. An Arduino was used as control board and dedicated circuits for voltage switching and contacts were designed and implemented in printed circuit boards. A high resolution camera was integrated for visualization. In our new approach, the DMF chips are driven by a dual-tone signal where the sum of two independent ac signals (one for droplet operations and the other for impedance sensing) is applied to the electrodes, and afterwards independently evaluated by a lock-in amplifier. With this new approach we were able to choose the appropriated amplitudes and frequencies for the different proposes (actuation and sensing). The measurements made were used to evaluate the real time droplet impedance enabling the knowledge of its position and velocity. This new approach opens new possibilities for impedance sensing and feedback control in DMF devices.

Land cover mapping analysis and urban growth modeling using remote sensing techniques

The rapid growth of big cities has been noticed since 1950s when the majority of world population turned to live in urban areas rather than villages, seeking better job opportunities and higher quality of services and lifestyle circumstances. This demographic transition from rural to urban is expected to have a continuous increase. Governments, especially in less developed countries, are going to face more challenges in different sectors, raising the essence of understanding the spatial pattern of the growth for an effective urban planning. The study aimed to detect, analyse and model the urban growth in Greater Cairo Region (GCR) as one of the fast growing mega cities in the world using remote sensing data. Knowing the current and estimated urbanization situation in GCR will help decision makers in Egypt to adjust their plans and develop new ones. These plans should focus on resources reallocation to overcome the problems arising in the future and to achieve a sustainable development of urban areas, especially after the high percentage of illegal settlements which took place in the last decades. The study focused on a period of 30 years; from 1984 to 2014, and the major transitions to urban were modelled to predict the future scenarios in 2025. Three satellite images of different time stamps (1984, 2003 and 2014) were classified using Support Vector Machines (SVM) classifier, then the land cover changes were detected by applying a high level mapping technique. Later the results were analyzed for higher accurate estimations of the urban growth in the future in 2025 using Land Change Modeler (LCM) embedded in IDRISI software. Moreover, the spatial and temporal urban growth patterns were analyzed using statistical metrics developed in FRAGSTATS software. The study resulted in an overall classification accuracy of 96%, 97.3% and 96.3% for 1984, 2003 and 2014’s map, respectively. Between 1984 and 2003, 19 179 hectares of vegetation and 21 417 hectares of desert changed to urban, while from 2003 to 2014, the transitions to urban from both land cover classes were found to be 16 486 and 31 045 hectares, respectively. The model results indicated that 14% of the vegetation and 4% of the desert in 2014 will turn into urban in 2025, representing 16 512 and 24 687 hectares, respectively.

Analyzing Geospatial patterns of syrian refugee flows in southeastern Turkey by use of remote sensing and complementary data

Crisis-affected communities and global organizations for international aid are becoming increasingly digital as consequence geotechnology popularity. Humanitarian sector changed in profound ways by adopting new technical approach to obtain information from area with difficult geographical or political access. Since 2011, turkey is hosting a growing number of Syrian refugees along southeastern region. Turkish policy of hosting them in camps and the difficulty created by governors to international aid group expeditions to get information, made such international organizations to investigate and adopt other approach in order to obtain information needed. They intensified its remote sensing approach. However, the majority of studies used very high-resolution satellite imagery (VHRSI). The study area is extensive and the temporal resolution of VHRSI is low, besides it is infeasible only using these sensors as unique approach for the whole area. The focus of this research, aims to investigate the potentialities of mid-resolution imagery (here only Landsat) to obtain information from region in crisis (here, southeastern Turkey) through a new web-based platform called Google Earth Engine (GEE). Hereby it is also intended to verify GEE currently reliability once the Application Programming Interface (API) is still in beta version. The finds here shows that the basic functions are trustworthy. Results pointed out that Landsat can recognize change in the spectral resolution clearly only for the first settlement. The ongoing modifications vary for each case. Overall, Landsat demonstrated high limitations, but need more investigations and may be used, with restriction, as a support of VHRSI.

UAV - based imagery processing using structure from motion and remote sensing technology

With the recent advances in technology and miniaturization of devices such as GPS or IMU, Unmanned Aerial Vehicles became a feasible platform for a Remote Sensing applications. The use of UAVs compared to the conventional aerial platforms provides a set of advantages such as higher spatial resolution of the derived products. UAV - based imagery obtained by a user grade cameras introduces a set of problems which have to be solved, e. g. rotational or angular differences or unknown or insufficiently precise IO and EO camera parameters. In this work, UAV - based imagery of RGB and CIR type was processed using two different workflows based on PhotoScan and VisualSfM software solutions resulting in the DSM and orthophoto products. Feature detection and matching parameters influence on the result quality as well as a processing time was examined and the optimal parameter setup was presented. Products of the both workflows were compared in terms of a quality and a spatial accuracy. Both workflows were compared by presenting the processing times and quality of the results. Finally, the obtained products were used in order to demonstrate vegetation classification. Contribution of the IHS transformations was examined with respect to the classification accuracy.

Evaluation of geostatistical techniques to estimate the spatial distribution of aboveground biomass in the Amazon rainforest using high-resolution remote sensing data

ABSTRACT The spatial distribution of forest biomass in the Amazon is heterogeneous with a temporal and spatial variation, especially in relation to the different vegetation types of this biome. Biomass estimated in this region varies significantly depending on the applied approach and the data set used for modeling it. In this context, this study aimed to evaluate three different geostatistical techniques to estimate the spatial distribution of aboveground biomass (AGB). The selected techniques were: 1) ordinary least-squares regression (OLS), 2) geographically weighted regression (GWR) and, 3) geographically weighted regression - kriging (GWR-K). These techniques were applied to the same field dataset, using the same environmental variables derived from cartographic information and high-resolution remote sensing data (RapidEye). This study was developed in the Amazon rainforest from Sucumbíos - Ecuador. The results of this study showed that the GWR-K, a hybrid technique, provided statistically satisfactory estimates with the lowest prediction error compared to the other two techniques. Furthermore, we observed that 75% of the AGB was explained by the combination of remote sensing data and environmental variables, where the forest types are the most important variable for estimating AGB. It should be noted that while the use of high-resolution images significantly improves the estimation of the spatial distribution of AGB, the processing of this information requires high computational demand.

Multifunctional Ti–Me (Me=Al, Cu) thin film systems for biomedical sensing devices

Ti-Me binary intermetallic thin films based on a titanium matrix doped with increasing amounts of Me (Me = Al, Cu) were prepared by magnetron sputtering (under similar conditions), aiming their application in biomedical sensing devices. The differences observed on the composition and on the micro(structural) features of the films, attributed to changes in the discharge characteristics, were correlated with the electrical properties of the intermetallic systems (Ti-Al and Ti-Cu). For the same Me exposed areas placed on the Ti target (ranging from 0.25 cm2 to 20 cm2) the Cu content increased from 3.5 at.% to 71.7 at.% in the Ti-Cu system and the Al content, in Ti-Al films, ranged from 11 to 45 at.%. The structural characterization evidenced the formation of metastable Ti-Me intermetallic phases for Al/Ti atomic ratios above 0.20 and for Cu/Ti ratios above 0.25. For lower Me concentrations, the effect of the α-Ti(Me) structure domains the overall structure. With the increase amount of the Me into Ti structure a clear trend for amorphization was observed. For both systems it was observed a significant decrease of the electrical resistivity with increasing Me/Ti atomic ratios (higher than 0.5 for Al/Ti atomic ratio and higher than 1.3 for Cu/Ti atomic ratio). Although similar trends were observed in the resistivity evolution for both systems, the Ti-Cu films presented lower resistivity values in comparison to Ti-Al system.

Development of sensing systems for environmental monitoring

This project focused on the investigation and the development of a chemical sensing system for the determination of chromium Cr6+ and a bio-reactor followed by electrochemical detection at a glassy carbon electrode, for the determination of organochlorine compounds. The conjugation of Cr6+ with 1,5-diphenylcarbazide was studied at various types of electrodes such as glassy carbon, ultra-trace epoxy-graphite, chemically or un-modified carbon-paste and dropping-mercury. The cyclic voltammetric behaviour of the complex was also investigated. In addition, the possibility of developing a chemical sensor, Le. an electrochemical probe capable of sensing Cr6+ through its complexation with 1,5-diphenylacarbazide was studied. The conjugations of l-chloro-2,4-dinitrobenzene, 2,4-dichloronitrobenzene and ethacrynic, which are electrophilic organochlorine compounds, with reduced glutathione, were studied in order to test the bioreactor developed, based on the immobilisation of glutathione s-transferase. This was carried out at different types of electrodes such as glassy-carbon, gold, silver, platinum, epoxy-graphite, hangingmercury, and ferrocene-modified rotating-disc electrodes.

A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.

Structured output SVM for remote sensing image classification

In the recent years, kernel methods have revealed very powerful tools in many application domains in general and in remote sensing image classification in particular. The special characteristics of remote sensing images (high dimension, few labeled samples and different noise sources) are efficiently dealt with kernel machines. In this paper, we propose the use of structured output learning to improve remote sensing image classification based on kernels. Structured output learning is concerned with the design of machine learning algorithms that not only implement input-output mapping, but also take into account the relations between output labels, thus generalizing unstructured kernel methods. We analyze the framework and introduce it to the remote sensing community. Output similarity is here encoded into SVM classifiers by modifying the model loss function and the kernel function either independently or jointly. Experiments on a very high resolution (VHR) image classification problem shows promising results and opens a wide field of research with structured output kernel methods.

Recombinant " Physcomitrella patens " as a sensitive tool to study heat sensing and heat-shock signal transduction in plants

SUMMARY When exposed to heat stress, plants display a particular set of cellular and molecular responses, such as chaperones expression, which are highly conserved in all organisms. In chapter 1, I studied the ability of heat shock genes to become transiently and abundantly induced under various temperature regimes. To this aim, I designed a highly sensitive heat-shock dependent conditional gene expression system in the moss Physcomitrella patens, using the soybean heatinducible promoter (hsp17.3B). Heat-induced expression of various reporter genes was over three orders of magnitude, in tight correlation with the intensity and duration of the heat treatments. By performing repeated heating/cooling cycles, a massive accumulation of recombinant proteins was obtained. Interestingly, the hsp17.3B promoter was also activated by specific organic chemicals. Thus, in chapter 2, I took advantage of the extreme sensitivity of this promoter to small temperature variations to further address the role of various natural and organic chemicals and develop a plant based-bioassay that can serve as an early warning indicator of toxicity by pollutants and heavy metals. A screen of several organic pollutants from textile and paper industry showed that chlorophenols as well as sulfonated anthraquinones elicited a heat shock like response at noninducing temperatures. Their effects were synergistically amplified by mild elevated temperatures. In contrast to standard methods of pollutant detection, this plant-based biosensor allowed to monitor early stress-responses, in correlation with long-term toxic effect, and to attribute effective toxicity thresholds for pollutants, in a context of varying environmental cues. In chapter 3, I deepened the study of the primary mechanism by which plants sense mild temperature variations and trigger a cellular signal leading to the heat shock response. In addition to the above described heat-inducible reporter line, I generated a P. patens transgenic line to measure, in vivo, variations of cytosolic calcium during heat treatment, and another line to monitor the role of protein unfolding in heat-shock sensing and signalling. The heat shock signalling pathway was found to be triggered by the plasma membrane, where temperature up shift specifically induced the transient opening of a putative high afimity calcium channel. The calcium influx triggered a signalling cascade leading to the activation of the heat shock genes, independently on the presence of misfolded proteins in the cytoplasm. These results strongly suggest that changes in the fluidity of the plasma membrane are the primary trigger of the heatshocksignalling pathway in plants. The present thesis contributes to the understanding of the basic mechanism by which plants perceive and respond to heat and chemical stresses. This may contribute to developing appropriate better strategies to enhance plant productivity under the increasingly stressful environment of global warming. RÉSUME Les plantes exposées à des températures élevées déclenchent rapidement des réponses cellulaires qui conduisent à l'induction de gènes codant pour les heat shock proteins (HSPs). En fonction de la durée d'exposition et de la vitesse à laquelle la température augmente, les HSPs sont fortement et transitoirement induites. Dans le premier chapitre, cette caractéristique aété utilisée pour développer un système inductible d'expression de gènes dans la mousse Physcomitrella patens. En utilisant plusieurs gènes rapporteurs, j'ai montré que le promoteur du gène hsp17.3B du Soja est activé d'une manière. homogène dans tous les tissus de la mousse proportionnellement à l'intensité du heat shock physiologique appliqué. Un très fort taux de protéines recombinantes peut ainsi être produit en réalisant plusieurs cycles induction/recovery. De plus, ce promoteur peut également être activé par des composés organiques, tels que les composés anti-inflammatoires, ce qui constitue une bonne alternative à l'induction par la chaleur. Les HSPs sont induites pour remédier aux dommages cellulaires qui surviennent. Étant donné que le promoteur hsp17.3B est très sensible à des petites augmentations de température ainsi qu'à des composés chimiques, j'ai utilisé les lignées développées dans le chapitre 1 pour identifier des polluants qui déclenchent une réaction de défense impliquant les HSPs. Après un criblage de plusieurs composés, les chlorophénols et les antraquinones sulfonés ont été identifiés comme étant activateurs du promoteur de stress. La détection de leurs effets a été réalisée seulement après quelques heures d'exposition et corrèle parfaitement avec les effets toxiques détectés après de longues périodes d'exposition. Les produits identifiés montrent aussi un effet synergique avec la température, ce qui fait du biosensor développé dans ce chapitre un bon outil pour révéler les effets réels des polluants dans un environnement où les stress chimiques sont combinés aux stress abiotiques. Le troisième chapitre est consacré à l'étude des mécanismes précoces qui permettent aux plantes de percevoir la chaleur et ainsi de déclencher une cascade de signalisation spécifique qui aboutit à l'induction des gènes HSPs. J'ai généré deux nouvelles lignées afin de mesurer en temps réel les changements de concentrations du calcium cytosolique ainsi que l'état de dénaturation des protéines au cours du heat shock. Quand la fluidité de la membrane augmente après élévation de la température, elle semble induire l'ouverture d'un canal qui permet de faire entrer le calcium dans les cellules. Ce dernier initie une cascade de signalisation qui finit par activer la transcription des gènes HSPs indépendamment de la dénaturation de protéines cytoplasmiques. Les résultats présentés dans ce chapitre montrent que la perception de la chaleur se fait essentiellement au niveau de la membrane plasmique qui joue un rôle majeur dans la régulation des gènes HSPs. L'élucidation des mécanismes par lesquels les plantes perçoivent les signaux environnementaux est d'une grande utilité pour le développement de nouvelles stratégies afin d'améliorer la productivité des plantes soumises à des conditions extrêmes. La présente thèse contribue à décortiquer la voie de signalisation impliquée dans la réponse à la chaleur.