Development of new scientific disciplines such as bioengineering and micro-nano engineering adopting nonconventional materials requests innovative methodologies that can accurately measure the mechanical properties of soft biological materials and characterize surface energy and adhesion properties of them, independent of measurement conditions. One of emerging methods to measure the deformation of materials under stress is digital image correlation (DIC) technique. As a noncontact strain measurement method, DIC has the advantages of prevention of experimental errors caused by the use of contact type sensors and of flexibility in its application to soft materials that are hard to be tested by conventional method. In the first part of the thesis, 2 dimensional and 3 dimensional DIC codes were developed and optimized, and then applied to two critical applications: 1) determining the stress-strain behaviour of polydimethylsiloxane (PDMS) sample, as a model soft material, using the optical images across large deformation region, and 2) detecting the stiffness variation within the gel mimicking the breast tumour using ultrasound images. The results of this study showed the capability of DIC as a strain sensor and suggested its potential as a diagnosing tool for the malignant lesion causing local stiffness variation. In the characterization of surface energy and adhesion properties of materials, two most common methods are contact angle measurement and JKR-type indentation test. In the second part of the thesis, the experimental set-up for these methods were developed and verified by using the PDMS in static (quasi equilibrium) state. From the dynamic tests, it showed its possible usage in studying adhesion hysteresis with respect to speed. The adhesion hysteresis was observed at high speed condition in both contact angle measurement and JKR-type indentation tests.

The collection of crop yield data has become much easier with the introduction of technologies such as the Global Positioning System (GPS), ground-based yield sensors, and Geographic Information Systems (GIS). This explosive growth and widespread use of spatial data has challenged the ability to derive useful spatial knowledge. In addition, outlier detection as one important pre-processing step remains a challenge because the technique and the definition of spatial neighbourhood remain non-trivial, and the quantitative assessments of false positives, false negatives, and the concept of region outlier remain unexplored. The overall aim of this study is to evaluate different spatial outlier detection techniques in terms of their accuracy and computational efficiency, and examine the performance of these outlier removal techniques in a site-specific management context. In a simulation study, unconditional sequential Gaussian simulation is performed to generate crop yield as the response variable along with two explanatory variables. Point and region spatial outliers are added to the simulated datasets by randomly selecting observations and adding or subtracting a Gaussian error term. With simulated data which contains known spatial outliers in advance, the assessment of spatial outlier techniques can be conducted as a binary classification exercise, treating each spatial outlier detection technique as a classifier. Algorithm performance is evaluated with the area and partial area under the ROC curve up to different true positive and false positive rates. Outlier effects in on-farm research are assessed in terms of the influence of each spatial outlier technique on coefficient estimates from a spatial regression model that accounts for autocorrelation. Results indicate that for point outliers, spatial outlier techniques that account for spatial autocorrelation tend to be better than standard spatial outlier techniques in terms of higher sensitivity, lower false positive detection rate, and consistency in performance. They are also more resistant to changes in the neighbourhood definition. In terms of region outliers, standard techniques tend to be better than spatial autocorrelation techniques in all performance aspects because they are less affected by masking and swamping effects. In particular, one spatial autocorrelation technique, Averaged Difference, is superior to all other techniques in terms of both point and region outlier scenario because of its ability to incorporate spatial autocorrelation while at the same time, revealing the variation between nearest neighbours. In terms of decision-making, all algorithms led to slightly different coefficient estimates, and therefore, may result in distinct decisions for site-specific management. The results outlined here will allow an improved removal of crop yield data points that are potentially problematic. What has been determined here is the recommendation of using Averaged Difference algorithm for cleaning spatial outliers in yield dataset. Identifying the optimal nearest neighbour parameter for the neighbourhood aggregation function is still non-trivial. The recommendation is to specify a large number of nearest neighbours, large enough to capture the region size. Lastly, the unbiased coefficient estimates obtained with Average Difference suggest it is the better method for pre-processing spatial outliers in crop yield data, which underlines its suitability for detecting spatial outlier in the context of on-farm research.

Video compression is an essential component of present-day applications and a decisive factor between the success or failure of a business model. There is an ever increasing demand to transmit larger number of superior-quality video channels into the available transmission bandwidth. Consumers are increasingly discerning about the quality and performance of video-based products and there is therefore a strong incentive for continuous improvement in video coding technology for companies to have market edge over its competitors. Even though processor speeds and network bandwidths continue to increase, a better video compression results in a more competitive product. This drive to improve video compression technology has led to a revolution in the last decade. In this thesis we addresses some of these data compression problems in a practical multimedia system that employ Hybrid video coding schemes. Typically Real life video signals show non-stationary statistical behavior. The statistics of these signals largely depend on the video content and the acquisition process. Hybrid video coding schemes like H264/AVC exploits some of the non-stationary characteristics but certainly not all of it. Moreover, higher order statistical dependencies on a syntax element level are mostly neglected in existing video coding schemes. Designing a video coding scheme for a video coder by taking into consideration these typically observed statistical properties, however, offers room for significant improvements in coding efficiency.In this thesis work a new frequency domain curve-fitting compression framework is proposed as an extension to H264 Context Adaptive Binary Arithmetic Coder (CABAC) that achieves better compression efficiency at reduced complexity. The proposed Curve-Fitting extension to H264 CABAC, henceforth called as CF-CABAC, is modularly designed to conveniently fit into existing block based H264 Hybrid video Entropy coding algorithms. Traditionally there have been many proposals in the literature to fuse surfaces/curve fitting with Block-based, Region based, Training-based (VQ, fractals) compression algorithms primarily to exploiting pixel- domain redundancies. Though the compression efficiency of these are expectantly better than DCT transform based compression, but their main drawback is the high computational demand which make the former techniques non-competitive for real-time applications over the latter. The curve fitting techniques proposed so far have been on the pixel domain. The video characteristic on the pixel domain are highly non-stationary making curve fitting techniques not very efficient in terms of video quality, compression ratio and complexity. In this thesis, we explore using curve fitting techniques to Quantized frequency domain coefficients. we fuse this powerful technique to H264 CABAC Entropy coding. Based on some predictable characteristics of Quantized DCT coefficients, a computationally in-expensive curve fitting technique is explored that fits into the existing H264 CABAC framework. Also Due to the lossy nature of video compression and the strong demand for bandwidth and computation resources in a multimedia system, one of the key design issues for video coding is to optimize trade-off among quality (distortion) vs compression (rate) vs complexity. This thesis also briefly studies the existing rate distortion (RD) optimization approaches proposed to video coding for exploring the best RD performance of a video codec. Further, we propose a graph based algorithm for Rate-distortion. optimization of quantized coefficient indices for the proposed CF-CABAC entropy coding.

The primary objective of this thesis is to develop a model for quantification of weather-related railway embankments hazards. The model for quantification of embankment hazards constitutes an essential component of a decision support system that is required for the management of railway embankment hazards. A model for the deterministic and probabilistic assessment of weather-related geo-hazards (W-GHA model) is proposed based on concepts of unsaturated soil mechanics and hydrology. The model combines a system of two-dimensional partial differential equations governing the thermo-hydro-mechanical behaviour of saturated/unsaturated soils and soil-atmosphere coupling equations. A Dynamic Programming algorithm for slope stability analysis (Safe-DP) was developed and incorporated into the W-GHA model. Finally, an efficient probabilistic and sensitivity analysis framework based on an alternative point estimate method was proposed. According to the W-GHA model framework, railway embankment hazards are assessed based on factors of safety and probabilities of failures computed using soil property variability and case scenarios. <p> A comprehensive study of unsaturated property variability is presented. A methodology for the characterization and assessment of unsaturated soil property variability is proposed. Appropriate fitting equations and parameter were selected. Probability density functions adequate for representing the unsaturated soil parameters studied were determined. Typical central tendency measures, variability measures, and correlation coefficients were established for the unsaturated soil parameters. The inherent variability of the unsaturated soil properties can be addressed using the probabilistic analysis framework proposed herein. <p> A large number of hypothetical railway embankments were analysed using the proposed model. The embankment analyses were undertaken in order to demonstrate the application of the proposed model and in order to determine the sensitivity of the factor of safety to the uncertainty in several input variables. The conclusions drawn from the sensitivity analysis study resulted in important simplifications of the W-GHA model. It was shown how unsaturated soil mechanics can be applied for the assessment of near ground surface stability hazards. The approach proposed in this thesis forms a protocol for application of unsaturated soil mechanics into geotechnical engineering practice. This protocol is based on predicted unsaturated soil properties and based on the use of case scenarios for addressing soil property uncertainty. Other classes of unsaturated soil problems will benefit from the protocol presented in this thesis.

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A wide range of ecological communities ranging from polar terrestrial to tropical marine environments are affectedby global climate change. Over the last century, atmospheric temperature has increased by an average of 0. 60 C andis expected to rise by 1.1- 6.40C over the next 100 years. This rising temperature has increased the intensity andfrequency of weather extremes due to which a large number of species are facing risk of extinction. Studies haveshown that species existing on lower latitude are more sensitive to temperature variability compared to speciesexisting on higher latitude but temperature is increasing rapidly in higher latitude compare to lower latitude. Thisuneven distribution of temperature sensitive species and warming rate has highlighted the need for combined studiesof temperature variability and sensitiveness of species to predict how the ecosystems will respond to increasinglyvariable climate. Using a generalized Rosenzweig-MacArthur model, I explored how temperature variability andsensitivity of species will affect the extinction risks of species and how the connectance and species-richness ofecological communities will govern this response. This study showed that the risk of extinction of species mostlydepends on their sensitivity to temperature deviation from the optimum value and level of temperature variability.Among these two, sensitivity of species to temperature deviation was most prominent factor affecting extinction risk.In this study, connectance did not show any effect on mean extinction risk and time taken by a certain proportion ofspecies to reach pre-defined extinction thresholds. But, species-richness showed some effect on mean extinction riskof species. It was found that risk of extinction of species in species-rich communities was higher compared tospecies-poor communities. Species-rich communities also took shorter time before they lost 1/6 of the species. Thepresent study also suggests a possible tipping point due to increasing temperature variability in near future. In furtherstudies, different sensitivity of species at different trophic levels and the possible evolution of sensitivity of speciesshould also be consider while predicting how ecological communities will respond to changing climate in the longrun.

The purpose of this thesis is see if national policies regarding aimed at combating climate change could work even if international ones, such as the Kyoto Protocol, are considered to fail. The question was if environmental policies could be included as an explanatory variable for the Environmental Kuznets Curve (EKC). The environmental polices of interest were the market-based instrument, environmental taxes.   First, the hypothesis whether or not Sweden fit the EKC theory is tested, and this proves to be the case when looking at the years 1800-1996. Second, a hypothesis was tested to see if environmental taxes can help decreasing carbon emission intensity further once a country has reached its turning point. Comparing Sweden to six other countries from the European Union show that this is the case, although environmental taxes must be one of many tools and cannot work alone.   The study shows that the role of taxes were significant, thus showing their importance for the work on climate change. It is also observed that national policies do work, while regional, or international, ones are harder to conduct. The importance of national policies is enhanced since they will be guiding countries when deciding whether or not to commit to international policies.

The purpose of this thesis is to examine how a depreciation in the exchange rate affects the trade balance in an economy over time. The outcomes of a depreciation are possible to analyze through the J-curve phenomenon that shows the relation between the exchange rate and the trade balance both in the short-run and the long-run. The data used in this thesis cover 39 countries and their quarterly changes in exchange rate between 1982 and 2005. The largest depreciation for each country during these years was detected and is the base for this research. In this thesis, focus is on the trade ratio rather than the trade balance for empirical purposes. The relation between the largest depreciations and its effect on the trade ratio are examined in two sets of regressions. The results show no evidence of a J-curve in neither one of the sets of regressions, even though the trade ratio is positively affected by the depreciation. When testing only for significantly large depreciations in the exchange rate the affect on the trade ratio is stronger, all else equal. According to the findings in this thesis, a depreciation in the real effective exchange rate causes the trade ratio to increase immediately and then decrease over time. The conclusion is that the findings are not in line with the J-curve phenomenon tested for; however, they support standard trade theory with the Marshall-Lerner condition being met i.e. a depreciation in the exchange rate will affect the trade balance positively.

Die Geometrie eines Druckbehälters wird unter Zuhilfenahme parametrischer Bezierkurven und durch die Integration einer externen Mathcad-Analyse in Creo hinsichtlich einer harmonischen, meridianen Spannungsverteilung optimiert.

This thesis introduces geometric dynamic active contours in the context of visual tracking, augmenting geometric curve evolution with physically motivated dynamics. Adding additional state information to an evolving curve lifts the curve evolution problem to space dimensions larger than two and thus forbids the use of classical level set techniques. This thesis therefore develops and explores level set methods for problems of higher codimension, putting an emphasis on the vector distance function based approach. This formalism is very general, it is interesting in its own right and still a challenging topic. Two different implementations for geometric dynamic active contours are explored: the full level set approach as well as a simpler partial level set approach. The full level set approach results in full topological flexibility and can deal with curve intersections in the image plane. However, it is computationally expensive. On the other hand the partial level set approach gives up the topological flexibility (intersecting curves cannot be represented) for increased computational efficiency. Contours colliding with different dynamic information (e.g., objects crossing in the image plane) will be merged in the partial level set approach whereas they will correctly traverse each other in the full level set approach. Both implementations are illustrated on synthetic and real examples. Compared to the traditional static curve evolution case, fundamentally different evolution behaviors can be obtained by propagating additional information along with every point on a curve.