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Herstellung und Charakterisierung eines flexiblen kapazitiven Sensors für die Überwachung von kohlenfaservertärkten Polymeren
Get PDFThe following thesis deals with the modeling, development and technology of a miniaturized interdigital sensor on flexible substrates to measure the degree of curing during manufacturing of the carbon fiber reinforced plastics. The special feature of the sensor is a very thin flexible substrate. Despite the small thickness, the film of the polyimide has a very good mechanical, thermal and chemical stability. This reduces the foreign body effect and makes it possible to leave the sensor in the compound material for long term use. The FEM simulations are used to calculate the sensor capacitance in the air and the sensitivity of the sensors to different types. In the technological implementation, all microsystem technology processes are realized on SI wafers. At the end of the manufacturing process, the flexible sensor of the second generation can be peeled off back from the silicon substrate. After the sensor characterization with different media the sensors with isolated electrodes are embedded in to CFRP. During the curing process, the implemented interdigital sensors provide the necessary data for online process monitoring. The complete curing of the CFRP plate is confirmed by using differential scanning calorimetry analysis. To demonstrate the secondary use of the flexible interdigital sensor in the context of structural health monitoring is proved the water intake of the CFRP plate with integrated sensor
HCI, Solidarity Movements and the Solidarity Economy
Get PDFThe financial crisis and austerity politics in Europe has had a devastating impact on public services, social security and vulnerable populations. Greek civil society responded quickly by establishing solidarity structures aimed at helping vulnerable citizens to meet their basic needs and empower them to co-create an anti-austerity movement. While digital technology and social media played an important role in the initiation of the movement, it has a negligible role in the movement s on-going practices. Through embedded work with several solidarity structures in Greece, we have begun to understand the solidarity economy (SE) as an experiment in direct democracy and self-organization. Working with a range of solidarity structures we are developing a vision for a Solidarity HCI committed to designing to support personal, social and institutional transformation through processes of agonistic pluralism and contestation, where the aims and objectives of the SE are continuously re-formulated and put into practice
Machine Learning for Gait Classification
Get PDFMachine learning is a powerful tool for making predictions and has been widely used for solving various classification problems in last decades. As one of important applications of machine learning, gait classification focuses on distinguishing different gait patterns by investigating the quality of gait of individuals and categorizing them as belonging to particular classes. The most studied gait pattern classes are the normal gait patterns of healthy people, i.e., gait of people who do not have any gait disability caused by an illness or an injury, and the pathological gait of patients suffering from illnesses which cause gait disorders such as neurodegenerative diseases (NDDs). There has been significant research work trying to solve the gait classification problems using advanced machine learning techniques, as the results may be beneficial for the early detection of underlined NDDs and for the monitoring of the gait rehabilitation progress. Despite the huge development in the field of gait analysis and classification, there are still a number of challenges open to further research. One challenge is the optimization of applied machine learning strategies to achieve better classification results. Another challenge is to solve gait classification problems even in the case when only limited amount of data are available. Further, a challenge is the development of machine learning-based methods that could provide more precise results to evaluate the level of gait quality or gait disorder, in contrast of just classifying gait pattern as belonging to healthy or pathological gait. The focus of this thesis is on the development, implementation and evaluation of a novel and reliable solution for the complex gait classification problems by addressing the current challenges. This solution is presented as a classification framework that can be applied to different types of gait signals, such as lower-limbs joint angle signals, trunk acceleration signals, and stride interval signals. Developed framework incorporates a hybrid solution which combines two models to enhance the classification performance. In order to provide a large number of samples for training the models, a sample generation method is developed which could segments the gait signals into smaller fragments. Classification is firstly performed on the data sample level, and then the results are utilized to generate the subject-level results using a majority voting scheme. Besides the class labels, a confidence score is computed to interpret the level of gait quality. In order to significantly improve the gait classification performances, in this thesis a novel feature extraction methods are also proposed using statistical methods, as well as machine learning approaches. Gaussian mixture model (GMM), least square regression, and k-nearest neighbors (kNN) are employed to provide additional significant features. Promising classification results are achieved using the proposed framework and the extracted features. The framework is ultimately applied to the management of patients and their rehabilitation, and is proved to be feasible in many clinical scenarios, such as the evaluation of medication effect on Parkinsona s disease (PD) patientsa gait, the long-term gait monitoring of the hereditary spastic paraplegia (HSP) patient under physical therapy
Ground-based remote sensing of carbon dioxide and methane in the Arctic using Fourier-transform infrared spectrometry
Get PDFThe abundances of the atmospheric greenhouse gases carbon dioxide and methane are monitored across the globe by a network of ground-based solar absorption Fourier-transform infrared spectrometers, the Total Carbon Column Observing Network (TCCON). In this dissertation, the existing measurement time series of both gases, taken at the TCCON site in Ny-A lesund, Spitsbergen, has been augmented in two ways: extension to measurements during the polar night and the usage of middle-infrared spectra. In addition to the near-infrared TCCON measurements, spectra in the middle-infrared are routinely taken in Ny-A lesund. The newly obtained data from these spectra are compared to the standard TCCON retrieval. Additionally, in the high Arctic, measurements of solar absorption spectra are not possible in winter, because the Sun is permanently below the horizon. A new detector for measurements in the near-infrared is introduced and spectra were recorded between 2012 and 2016 to retrieve the column averaged dry-air mole fractions of CO2 and CH4. The lunar measurements were validated and compared to results from various reanalysis model simulations as well as in-situ measurements
A Flex-Rigid, Multi-Channel ECoG Microelectrode Array: Reliable Electrical Contact & Long-Term Stability in Saline
No full textAn Electrocorticography Micro-Electrode Array (ECoG MEA) is a promising signal-acquisition solution for weakly-invasive brain-computer interfaces. This PhD Thesis proposes a microfabrication scheme for a high-density ECoG MEA and investigates its long-term performance in saline. The ECoG device contains 124 circular electrodes of 100, 300 and 500 um diameters, situated on concentric hexagons (150 mm2 total recording area). The reference electrode, situated beside them, is to be bent to the array backside to become a skull-facing electrode (2.5 mm2 surface area). Metallization paths connect the electrodes to the assembly pads of 4 x 32 SMD Omnetics connectors. The ECoG device was realized as a polyimide-metal-polyimide stack on a silicon wafer. A DRIE process shaped a silicon interposer out of the carrier wafer, serving as mechanical platform for the assembly of fine-pitch electrical connectors. Electrical characterization was performed by means of electrochemical impedance spectroscopy. The electrode impedance scaled with electrode area. The strength of the solder joints was tested by means of pull tests. Electrical and mechanical tests revealed that removing the bottom polyimide from the solder-joint area enables a more reliable electrical contact. The array was implanted on the primary visual cortex (V1) of a macaque and recorded natural electrophysiological signals: the larger the electrode, the larger the signal. The skull-facing reference electrode provided signals of greater average Power Spectral Density (PSD) than common average referencing. However, the onset of parasitic short-circuits formation was encountered ca. 3-4 months after implantation. Accelerated soak tests were performed on planar-capacitor IDE structures to simulate the formation of parasitic short-circuits. The influence of curing, adhesion and sterilization on the water-barrier properties of polyimide and parylene coatings was monitored. Based on the results, a new ECoG MEA was fabricated and stored under accelerated soak conditions. The proposed ECoG MEA can be beneficial for the design, microfabrication and long-term stability of future flexible microdevices
Space-Borne Retrieval of Solar-Induced Plant Fluorescence and its Relationship to Photosynthetic Parameters
Get PDFStudies have shown that chlorophyll fluorescence is directly linked to the photosynthetic efficiency of plants. The excess absorbed energy by leaves which has not been used in photosynthesis is re-emitted to the environment, either as heat or fluorescence. Therefore, any potential stress in plants is technically visible through monitoring fluorescence and the Solar-Induced plant Fluorescence (SIF) can thus be monitored as an indicator for vegetation growth and health status. SIF is a broad band spectral feature exhibiting two maxima at about 680 and 740 nm respectively, also known as red and far-red SIF. In the recent decades, there have been several studies addressing SIF, its importance and approaches to measure its value over vegetated regions. Among several measurement approaches, satellite-based remote sensing of SIF is particularly valuable, since the covered (spatial) area can be explicitly larger than is the case with in-situ measurements. With current space-borne instruments, even a full global coverage is attainable within a few days. In the framework of this thesis, two novel methods have been developed, tested and utilized to retrieve SIF from hyper-spectral satellite measurements. In particular, the first developed method, makes use of the Fraunhofer absorption lines in the far-red spectral region (748.5 - 753 nm), to retrieve SIF via its in-filling effect on these absorption lines. However, the satellite-based remote sensing spectrometers, used in this work, typically exhibit an additive spectral feature, which is not fluorescence. This is often accompanying the actual SIF retrieval and can significantly deteriorate the results. To account for this effect, a correction method has been developed and is combined with the retrieval algorithm. The model-based sensitivity studies confirmed the feasibility of the method to disentangle SIF from this additive feature. Additionally, the potential influences of the atmospheric and measurement conditions on the retrieval results have been assessed. Finally, the method has been applied to ten years of SCIAMACHY data and the retrieved results have been mapped on seasonal base. On a global scale, the obtained values are between 0 to 4 mW ma 2 sra 1 nma 1 . In absence of large area ground based validation data, final judgment of the results obtained in the framework of this study, is not possible. Alternatively, comparison of the achieved results with those published by the US National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC) has been performed. Despite some differences, the comparison also exhibited close results, both qualitatively and quantitatively. It should be noted that comparisons among the retrievals provided by other research groups (not only GSFC) over the same spatial region is also variable depending on the instruments and methods utilized (ranging on average from a few tenths to more than 4 mW ma 2 sra 1 nma 1 ). To further assess the reliability of retrieved SIF, monthly average values have been compared to ground-based flux-tower measurements of Absorbed Photosynthetically Active Radiation by plants (APAR) and Gross Primary Production (GPP), for a time span of several years. The agreement between the seasonal trends of SIF and these parameters was significant. Although the main focus of this PhD work was on retrieving SIF in the far-red wavelength region using a spectral micro-window, there are clear scientific benefits in having an estimation over the full spectral emission range of SIF. Therefore, the second retrieval method, developed in the framework of this work, was to obtain the full spectrum of the emitted SIF by retrieving the leaf and canopy parameters, utilizing a combination of two radiative transfer models. The model-based studies showed the feasibility of the method to retrieve SIF with high accuracy. Moreover, the first results of applying this approach on GOME-2 measurements demonstrated promising outcomes. Examples of the fit quality and retrieved SIF over two different vegetation coverage types have been presented in this thesis, showing clear applicability of the method to retrieve SIF over its full spectral emission range and the potential to derive other vegetation parameters (e.g. Chlorophyll content of the leaves and the so-called leaf area index)
Interpretation of Natural-language Robot Instructions: Probabilistic Knowledge Representation, Learning, and Reasoning
Get PDFA robot that can be simply told in natural language what to do -- this has been one of the ultimate long-standing goals in both Artificial Intelligence and Robotics research. In near-future applications, robotic assistants and companions will have to understand and perform commands such as set the table for dinner'', make pancakes for breakfast'', or cut the pizza into 8 pieces.'' Although such instructions are only vaguely formulated, complex sequences of sophisticated and accurate manipulation activities need to be carried out in order to accomplish the respective tasks. The acquisition of knowledge about how to perform these activities from huge collections of natural-language instructions from the Internet has garnered a lot of attention within the last decade. However, natural language is typically massively unspecific, incomplete, ambiguous and vague and thus requires powerful means for interpretation. This work presents PRAC -- Probabilistic Action Cores -- an interpreter for natural-language instructions which is able to resolve vagueness and ambiguity in natural language and infer missing information pieces that are required to render an instruction executable by a robot. To this end, PRAC formulates the problem of instruction interpretation as a reasoning problem in first-order probabilistic knowledge bases. In particular, the system uses Markov logic networks as a carrier formalism for encoding uncertain knowledge. A novel framework for reasoning about unmodeled symbolic concepts is introduced, which incorporates ontological knowledge from taxonomies and exploits semantically similar relational structures in a domain of discourse. The resulting reasoning framework thus enables more compact representations of knowledge and exhibits strong generalization performance when being learnt from very sparse data. Furthermore, a novel approach for completing directives is presented, which applies semantic analogical reasoning to transfer knowledge collected from thousands of natural-language instruction sheets to new situations. In addition, a cohesive processing pipeline is described that transforms vague and incomplete task formulations into sequences of formally specified robot plans. The system is connected to a plan executive that is able to execute the computed plans in a simulator. Experiments conducted in a publicly accessible, browser-based web interface showcase that PRAC is capable of closing the loop from natural-language instructions to their execution by a robot
Case studies on the curriculum and pedagogy in chemistry and science education in Syria today
Get PDFAbstarct This dissertation intended to analyze the nature of the science teaching in Syria with a special emphasis on chemistry education (Chapter 1). It was operated through a triangle-based study of the intended curriculum, teachersa and studentsa views. The 10th grade Syrian intended chemistry curriculum has been analyzed as a case based on an analysis of the official grade-10 chemistry school textbook in comparison to six other chemistry textbooks from a purposeful sample of Arab countries, namely Algeria, Egypt, Kuwait, Palestine, Jordan and Saudi-Arabia. These countries are economically, societally, cultural and geographically diverse, but they all are part of the Arab world and use Arabic as a common language (Chapter 2). The second part of this study analyzed teachersa views on chemistry and physics teaching based on interviews. Instances of the interviews were the use of ICT, laboratory work, including the history of science in teaching, and the teachersa view on the national curriculum (Chapter 3). In a third step, grade-10 science studentsa attitudes and views towards science education were surveyed by interviews with respect to their possibilities for participation in the science classroom, practical work, out-of-school science education, and their current views on potential careers in science and technology (Chapter 4)
R/V MARIA S. MERIAN Cruise Report MSM57, Gas Hydrate Dynamics at the Continental Margin of Svalbard, Reykjavik - Longyearbyen - Reykjavik, 29 July - 07 September 2016.
Get PDFCoulomb interaction and phonons in doped semiconducting and metallic two-dimensional materials
Get PDFTwo-dimensional (2D) materials present a rapidly developing field of research with sometimes highly unusual and uniquely two-dimensional physics. Starting with graphene, many recent studies have investigated 2D materials, with results for properties encompassing such different topics as Dirac electrons, charge ordering, and superconductivity. To get closer to a predictive theory of the phases of 2D materials, this thesis systematically tackles the previously unclear problem of the Coulomb interaction and its influence on the electronic and many-body properties, with the focus on transition metal dichalcogenides (TMDCs). While this influence can be quite strong due to the low dimensionality and the corresponding reduced screening, there is so far no comprehensive understanding of the Coulomb interaction in 2D, its effects, and the possibilities for engineering it. Furthermore, the interplay between electron-electron interaction, electron-phonon interaction, and screening is far from being fully understood. The goal of this thesis is to improve on this by providing a description of the Coulomb interaction for the example of the TMDCs as well as to develop a material-systematic database on the basis of ab-initio calculations for electrons and phonons. We use Density Functional Theory to describe the electronic structure, Density Functional Perturbation Theory for the phonons and the electron-phonon interaction, and the Random Phase Approximation to obtain Coulomb matrix elements. In addition to both semiconducting and metallic TMDCs, we look at functionalized graphene C8H2. The first step is a quantification of the Coulomb interaction and the screening in the TMDCs along with calculations for the plasmonic spectra, which turn out to be highly susceptible to environment and doping. Secondly, we discuss the influence of the interaction on different many-body instabilities and find a small suppression of superconducting order in semiconducting TMDCs, depending again on doping and the dielectric environment, while the magnetic order in metallic TMDCs is enhanced. If we further include the phonons, we see that superconductivity is predicted to be a global phenomenon in the doped semiconducting TMDCs and C8H2, and that charge density waves at different wave vectors are supposedly occurring in all TMDCs