abstract: The marked increase in the inflow of remotely sensed data from satellites have trans- formed the Earth and Space Sciences to a data rich domain creating a rich repository for domain experts to analyze. These observations shed light on a diverse array of disciplines ranging from monitoring Earth system components to planetary explo- ration by highlighting the expected trend and patterns in the data. However, the complexity of these patterns from local to global scales, coupled with the volume of this ever-growing repository necessitates advanced techniques to sequentially process the datasets to determine the underlying trends. Such techniques essentially model the observations to learn characteristic parameters of data-generating processes and highlight anomalous planetary surface observations to help domain scientists for making informed decisions. The primary challenge in defining such models arises due to the spatio-temporal variability of these processes. This dissertation introduces models of multispectral satellite observations that sequentially learn the expected trend from the data by extracting salient features of planetary surface observations. The main objectives are to learn the temporal variability for modeling dynamic processes and to build representations of features of interest that is learned over the lifespan of an instrument. The estimated model parameters are then exploited in detecting anomalies due to changes in land surface reflectance as well as novelties in planetary surface landforms. A model switching approach is proposed that allows the selection of the best matched representation given the observations that is designed to account for rate of time-variability in land surface. The estimated parameters are exploited to design a change detector, analyze the separability of change events, and form an expert-guided representation of planetary landforms for prioritizing the retrieval of scientifically relevant observations with both onboard and post-downlink applications. / Dissertation/Thesis / Doctoral Dissertation Computer Engineering 2019

Frequency analysis is a vital tool when nding a well-suited probability distributionin order to predict extreme rainfall. The regional frequency approach have beenused for determination of homogeneous regions, using 11 sites in Skane, Sweden. Todescribe maximum annual daily rainfall, the Generalized Logistic (GLO), GeneralizedExtreme Value (GEV), Generalized Normal (GNO), Pearson Type III (PE3),and Generalized Pareto (GPA) distributions have been considered. The method ofL-moments have been used in order to nd parameter estimates for the candidatedistributions. Heterogeneity measures, goodness-of-t tests, and accuracy measureshave been executed in order to accurately estimate quantiles for 1-, 5-, 10-, 50- and100-year return periods. It was found that the whole province of Skane could beconsidered as homogeneous. The GEV distribution was the most consistent withthe data followed by the GNO distribution and they were both used in order toestimate quantiles for the return periods. The GEV distribution generated the mostprecise estimates with the lowest relative RMSE, hence, it was concluded to be thebest-t distribution for maximum annual daily rainfall in the province.

This master thesis gives an account for the numerous uncertainties that prevail one-dimensional hydraulic models and flood inundation maps, as well as suitable assessment methods for different types of uncertainties. A conducted uncertainty assessment on the river Voxnan in Sweden has been performed. The case study included the calibra-tion uncertainty in the spatially varying roughness coefficient and the boundary condi-tion uncertainty in the magnitude of a 100-year flood, in present and future climate conditions. By combining a scenario analysis, GLUE calibration method and Monte Carlo analysis, the included uncertainties with different natures could be assessed. Significant uncer-tainties regarding the magnitude of a 100-year flood from frequency analysis was found. The largest contribution to the overall uncertainty was given by the variance between the nine global climate models, emphasizing the importance of including projections from an ensemble of models in climate change studies. Furthermore, the study gives a methodological example on how to present uncertainty estimates visually in probabilistic flood inundation maps. The conducted method of how the climate change uncertainties, scenarios and models, were handled in frequency analysis is also suggested to be a relevant result of the study.

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第19273号 / 医博第4037号 / 新制||医||1011(附属図書館) / 32275 / 京都大学大学院医学研究科医学専攻 / (主査)教授 渡邉 大, 教授 村井 俊哉, 教授 高橋 淳 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Nonstationary signals are widely observed in many real-world applications, e.g., radar, sonar, radio astronomy, communication, acoustics, and vibration applications. Joint time-frequency (TF) domain representations provide a time-varying spectrum for their analyses, discrimination, and classifications. Nonstationary signals commonly exhibit sparse occupancy in the TF domain. In this dissertation, we incorporate such sparsity to enable robust TF analysis in impaired observing environments. In practice, missing data samples frequently occur during signal reception due to various reasons, e.g., propagation fading, measurement obstruction, removal of impulsive noise or narrowband interference, and intentional undersampling. Missing data samples in the time domain lend themselves to be missing entries in the instantaneous autocorrelation function (IAF) and induce artifacts in the TF representation (TFR). Compared to random missing samples, a more realistic and more challenging problem is the existence of burst missing data samples. Unlike the effects of random missing samples, which cause the artifacts to be uniformly spread over the entire TF domain, the artifacts due to burst missing samples are highly localized around the true instantaneous frequencies, rendering extremely challenging TF analyses for which many existing methods become ineffective. In this dissertation, our objective is to develop novel signal processing techniques that offer effective TF analysis capability in the presence of burst missing samples. We propose two mutually related methods that recover missing entries in the IAF and reconstruct high-fidelity TFRs, which approach full-data results with negligible performance loss. In the first method, an IAF slice corresponding to the time or lag is converted to a Hankel matrix, and its missing entries are recovered via atomic norm minimization. The second method generalizes this approach to reduce the effects of TF crossterms. It considers an IAF patch, which is reformulated as a low-rank block Hankel matrix, and the annihilating filter-based approach is used to interpolate the IAF and recover the missing entries. Both methods are insensitive to signal magnitude differences. Furthermore, we develop a novel machine learning-based approach that offers crossterm-free TFRs with effective autoterm preservation. The superiority and usefulness of the proposed methods are demonstrated using simulated and real-world signals. / Electrical and Computer Engineering

Non-stationary signals, particularly frequency modulated (FM) signals which arecharacterized by their time-varying instantaneous frequencies (IFs), are fundamental to radar, sonar, radio astronomy, biomedical applications, image processing, speech processing, and wireless communications. Time-frequency (TF) analyses of such signals provide two-dimensional mapping of time-domain signals, and thus are regarded as the most preferred technique for detection, parameter estimation, analysis and utilization of such signals. In practice, these signals are often received with compressed measurements as a result of either missing samples, irregular samplings, or intentional under-sampling of the signals. These compressed measurements induce undesired noise-like artifacts in the TF representations (TFRs) of such signals. Compared to random missing data, burst missing samples present a more realistic, yet a more challenging, scenario for signal detection and parameter estimation through robust TFRs. In this dissertation, we investigated the effects of burst missing samples on different joint-variable domain representations in detail. Conventional TFRs are not designed to deal with such compressed observations. On the other hand, sparsity of such non-stationary signals in the TF domain facilitates utilization of sparse reconstruction-based methods. The limitations of conventional TF approaches and the sparsity of non-stationary signals in TF domain motivated us to develop effective TF analysis techniques that enable improved IF estimation of such signals with high resolution, mitigate undesired effects of cross terms and artifacts and achieve highly concentrated robust TFRs, which is the goal of this dissertation. In this dissertation, we developed several TF analysis techniques that achieved the aforementioned objectives. The developed methods are mainly classified into two three broad categories: iterative missing data recovery, adaptive local filtering based TF approach, and signal stationarization-based approaches. In the first category, we recovered the missing data in the instantaneous auto-correlation function (IAF) domain in conjunction with signal-adaptive TF kernels that are adopted to mitigate undesired cross-terms and preserve desired auto-terms. In these approaches, we took advantage of the fact that such non-stationary signals become stationary in the IAF domain at each time instant. In the second category, we developed a novel adaptive local filtering-based TF approach that involves local peak detection and filtering of TFRs within a window of a specified length at each time instant. The threshold for each local TF segment is adapted based on the local maximum values of the signal within that segment. This approach offers low-complexity, and is particularly useful for multi-component signals with distinct amplitude levels. Finally, we developed knowledge-based TFRs based on signal stationarization and demonstrated the effectiveness of the proposed TF techniques in high-resolution Doppler analysis of multipath over-the-horizon radar (OTHR) signals. This is an effective technique that enables improved target parameter estimation in OTHR operations. However, due to high proximity of these Doppler signatures in TF domain, their separation poses a challenging problem. By utilizing signal self-stationarization and ensuring IF continuity, the developed approaches show excellent performance to handle multiple signal components with variations in their amplitude levels. / Electrical and Computer Engineering

The Swedish software industry is fast-growing and in needof competent personnel, the education system is on the frontline of producing qualified graduates to meet the job marketdemand. Reports and studies show there exists a gapbetween industry needs and what is taught in highereducation, and that there is an undefined skills shortageleading to recruitment failures. This study explored theindustry-education gap with a focus on higher vocationaleducation (HVE) through the use of natural languageprocessing (NLP) to ascertain the demands of the industryand what is taught in HVE. Using the authors' custom-madetool Vocational Education and Labour Market Analyser(VELMA), job ads and HVE curricula were collected fromthe Internet. Then analysed through the topic modellingprocess latent Dirichlet allocation (LDA) to classify lowerlevel keywords into cohesive categories for documentfrequency analysis. Findings show that a large number ofHVE programmes collaborate with the industry via indirectfinancing and that job ads written in Swedish consist, inlarger part, of inconsequential words compared to adswritten in English. Moreover, An industry demand withincloud and embedded technologies, security engineers andsoftware architects can be observed. Whereas, the findingsfrom HVE curricula point to a focus on educating webdevelopers and general object-oriented programminglanguages. While there are limitations in the topic modellingprocess, the authors conclude that there is a mismatchbetween what is taught in HVE programmes and industrydemand. The skills identified to be lacking in HVE wereassociated with cloud-, embedded-, and security-relatedtechnologies together with architectural disciplines. Theauthors recommend future work with a focus on improvingthe topic modelling process and including curricula fromgeneral higher education.

M. Tech. (Department of Metallurgical Engineering, Faculty of Engineering and Technology), Vaal University of Technology. / To meet the ever-increasing demand for maintenance of gear systems, industrial companies have traditionally depended on the shutdown of the machines before processing the fault diagnosis. Nowadays, online monitoring has proven to be effective in terms of machine state analysis and fault prediction. However, the application of such a technique in the analysis of combined multiple nonlinear faults is still a subject of research. The vibration signature of a coexisting nonlinear crack and pit in two-stage gear system is unknown, it can be regarded as one of the most difficult problems to extract and diagnose. Additionally, incorporating both a crack and a pit into numerical models is a time-consuming process that demands a breadth of mechanical understanding. Diagnostics of faulty gears, on the other hand, can be performed in the time and frequency domain or in the Time-Frequency domain, depending on the complexity of the vibration. Non-linear and non-stationary phenomena (Features) occur when repeated pitting and cracking faults occur, reducing the reliability of standard signal processing methods (Gear displacement and Fast Fourier Transform). This thesis solves each of these shortcomings by developing an eight-degree-of-freedom (DOF) gear model with a breathing crack and multiple pitted gear teeth. The identified spur-gear model enabled the investigation of the crack and pitting signatures and their effect on the ensuing vibrations independently of the action of other system components. Additionally, when pitting and cracking coexist, the study was conducted to determine the influence of such a failure on the transmission system. Theoretical results indicated that the presence of pitting and crack in the tooth of the gear resulted in a decrease in mesh stiffness. Additionally, the influence of the breathing pitting and crack results in material fatigue, which results in the generation of a random term in the vibration signal. To corroborate the acquired results, several experimental tests on a spur-gear test rig with a defined pit and crack size range were undertaken under a variety of conditions. In comparison to the presented methodologies, theoretical and experimental results indicate that 3D Frequency-RPM analysis is the most sensitive and resilient method for the early detection and identification of pit and crack faults. Furthermore, when crack or pit faults are studied individually, the STFT analysis yields interesting results. The feature analysis revealed that, when using the Time-Frequency technique, the crack and pit combination in a two-stage gear system is a priori more efficient than the other options.

Coastal regions are at risk of environmental threats. Flooding in coastal rivers is the result of intense precipitation which is triggered by climate change. Coastal watersheds are prone to losing significant amounts of sediment and nutrients because of the shorter transport pathway that drains directly into the coastal water. In this study, the hydrology, flood frequency, and water quality assessment of two coastal watersheds, Wolf River watershed (WRW) and Jourdan River watershed (JRW), were conducted using the Soil and Water Assessment Tool (SWAT). Since WRW and JRW are the main tributaries to fetch freshwater to Saint Louis Bay (SLB) of Western Mississippi Sound, an integrated approach to assess the influence of freshwater influx into the coastal water is also performed by coupling SWAT with hydrodynamic visual Environment Fluid Dynamics Code (v-EFDC). An auto-calibration tool, SWAT Calibration and Uncertainty Programs (SWAT-CUP) was used to calibrate and validate the flow, total suspended solids and mineral phosphorous for obtaining satisfactory statistical results. While comparing the flood frequency of historical, baseline and projected scenario in both watersheds, the results illustrated that using annual maximum series, 1% exceedance probability was the highest for WRW baseline scenario, whereas for JRW, 1% exceedance probability was the highest for projected scenario. The water quality assessment study of WRW and JRW suggested that ponds and wetlands were more effective in reducing TSS and riparian buffers were more effective in reducing MinP at the outlet of both the watersheds. The integrated approach of coupling SWAT-vEFDC model result indicated that major impact on water quality was observed at the location where the freshwater inflow into the SLB, and the impact was diminished while moving further along the Western Mississippi Sound. Overall, this study gives an insight for integrated coastal watershed management which includes prediction of future flood frequency, the application of best management practices for reducing sediment and nutrient load, and estimation of upstream watershed pollutant load draining along with runoff including its effect on the coastal water quality.

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