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In recent years, aircraft manufacturers have been making progress in the design of more efficient aircrafts to reduce the environmental footprint. To attain this target, aircrafts manufactures work on the replacement of the hydraulic and bleed systems for electrical systems leading to a “More Electrical Aircraft”. However, the expected mass gain is a challenge, as previous technologies have been developed and optimized for decades. The new electrical solutions need to be look into detail to be competitive with previous technologies. All degrees of freedom must be considered, that is, new technologies and architectures. In particular, an HVDC network that reduces the number of rectifier stages seems a promising solution. From the HVDC network, the different three phase AC loads will be supplied by a series of power generic inverters. As the power consumption of the different loads change during the flight mission, the same inverter is used to supply different loads. The connection between the inverters and the loads is managed by a matrix of contactors. The proposed solution also considers redundant configurations, thus increasing system robustness. The design of the innovative system is presented in this document. That is, determining the optimal trade-off between the number of power inverters and the nominal power of each generic inverter that will also impact the size of the matrix of contactors. However, to assess the combinatory problem, the mass of the different components as a function of the nominal power needs to be calculated. A design environment is therefore created to perform automatic and optimized design of power converters. The different components are described using a “direct modelling” approach and coded using “object-oriented” programming. The components are validated experimentally or by numerical simulations. The different models are coupled to an optimization environment and to a frequency solver allowing a fast calculation of the steady-state waveforms. The optimization environment performs the precise design of the different parts of the power inverter: heatsink, power module, DC filter and coupling inductor. The power inverter is designed for different values of nominal power and switching frequency. The optimization assesses as well the usage of different technologies. Finally, the results are used to determine the optimal trade-off between the number of inverters and the nominal power of each inverter using a heuristic algorithm.

Public-private partnerships (P3s) are increasingly used in jurisdictions across Canada to deliver public infrastructure and services. The need for new or redeveloped hospital infrastructure in particular has made this a leading avenue for P3 proliferation. The objective of this study is to analyze P3 policy and projects, principally in relation to the BC and Ontario provincial health sectors. The central arguments made are threefold: P3s are a unique form of accumulation by dispossession and public sector marketization; P3 projects are intrinsically unable to meet the promises made by proponents and carry several other negative social consequences beyond this; and P3 policy, though rooted in normative ideological assumptions and aspirations, is being normalized in BC and Ontario through the establishment of P3 enabling fields over the past decade. The concept of an ‘enabling field’ captures a constellation of new arrangements, notably capital planning procedures and legislative frameworks, supportive secondary reforms, and greater institutional support for privatization. Together these elements help routinize, institutionalize, and depoliticize P3 policy. Canada’s pioneering full spectrum P3 hospitals (where the private partner is charged with designing, building, operating, and financing the facility) are examined in detail: in BC, the Abbotsford Regional Hospital and Cancer Centre and the Gordon and Leslie Diamond Health Care Centre; and in Ontario, the Brampton Civic Hospital and the Royal Ottawa Hospital. These cases reveal the troubling results that policy normalization ignores: poor value for money and inadequate risk transfer, misleading claims of ‘on time and on budget’ delivery, an erosion of service quality and working conditions, and opaque partnership agreements that offer little by way of accountability and transparency. These findings challenge the assumptions and rhetoric of P3 proponents, and offer different examples of how dispossession and marketization manifest in the public health care system.

GNSSs are currently used in civil aviation to provide aircraft with position and velocity estimates from en-route to precision approach operations. Extending the use of GNSS to the guidance function during airport surface operations and under zero-visibility conditions remains a challenge. Indeed, during these operations, GNSS measurements may be affected by GNSS singular events, such as multipath or ionosphere anomalies. GNSS singular events may lead to unacceptable position errors in terms of accuracy and integrity for the zero-visibility guidance function. Current GNSS integrity monitoring systems are not designed to totally account for the GNSS singular event effects. The development of GNSS integrity monitoring systems designed to properly protect users from the singular event effects is essential to use GNSS for the guidance function under zero-visibility conditions. GNSS measurement error and integrity failure models are key inputs in the design of GNSS integrity monitoring systems. In this thesis, work has been mainly focused on the development of GNSS multipath measurement errors, on the assessment of the multipath impact on the GNSS-based position error, and on the development of GNSS multipath integrity failure models. For this matter, the dual frequency GPSL1C+GPSL5 and GalileoE1+GalileoE5a multipath pseudo- range error model adapted to airport navigation has been firstly proposed. Next, the impact of multipath on the GNSS-based position error has been assessed. To do so, a double constellation GPS+Galileo/IRS tight coupling algorithm based on a linearized Kalman filter has been selected. The theoretical and quantitative analysis of the impact of the GNSS multipath ranging errors on the horizontal position bias and on the covariance matrix of the horizontal position error have been proposed. Finally, a GNSS multipath integrity failure model has been proposed. The model describes the signature of the GNSS single multipath ranging failures, the factors influencing the signature as well as the occurrence model of these failures and their conditions of occurrence.

The term packing refers to the arrangement of multiple geometrical structures or shapes such as circles, squares, triangles, or polygons into a fixed and finite set of points. The geometric structures to be packed can also be trees and paths. Packing is also possible in a 3-dimensional space with geometric structures such as spheres, cylinders, and cubes. The concept of packing was introduced more than half a century ago. Since then, many researchers have studied the packing strategies of different geometric structures in different configurations of point-set. Packing strategies help to construct and arrange multiple geometric structures in a predetermined bounded space; hence, it can be classified as an optimization problem, as we are trying to allocate the optimal space for resources in a finite bounded space. The better the efficiency of the algorithm, the greater number of items that can be packed. Packing geometrical structures have applications in the storage, transportation, and transmission of objects in fields like automobile, aerospace, and naval industries. Since, in real-life scenarios, resources are finite, and space is limited; thus it raises the question, how to efficiently use limited space for accommodating multiple resources. However, packing multiple geometric structures can raise some design considerations. In our research, we have studied the packing of non-self-crossing, edge-disjoint plane spanning paths and have obtained some promising results. We further address some design considerations and provide a different approach on packing at least two non-self-crossing, edge-disjoint plane spanning paths into a point-set.

Hyperspectral (HS) imaging, which consists of acquiring a same scene in several hundreds of contiguous spectral bands (a three dimensional data cube), has opened a new range of relevant applications, such as target detection [MS02], classification [C.-03] and spectral unmixing [BDPD+12]. However, while HS sensors provide abundant spectral information, their spatial resolution is generally more limited. Thus, fusing the HS image with other highly resolved images of the same scene, such as multispectral (MS) or panchromatic (PAN) images is an interesting problem. The problem of fusing a high spectral and low spatial resolution image with an auxiliary image of higher spatial but lower spectral resolution, also known as multi-resolution image fusion, has been explored for many years [AMV+11]. From an application point of view, this problem is also important as motivated by recent national programs, e.g., the Japanese next-generation space-borne hyperspectral image suite (HISUI), which fuses co-registered MS and HS images acquired over the same scene under the same conditions [YI13]. Bayesian fusion allows for an intuitive interpretation of the fusion process via the posterior distribution. Since the fusion problem is usually ill-posed, the Bayesian methodology offers a convenient way to regularize the problem by defining appropriate prior distribution for the scene of interest. The aim of this thesis is to study new multi-band image fusion algorithms to enhance the resolution of hyperspectral image. In the first chapter, a hierarchical Bayesian framework is proposed for multi-band image fusion by incorporating forward model, statistical assumptions and Gaussian prior for the target image to be restored. To derive Bayesian estimators associated with the resulting posterior distribution, two algorithms based on Monte Carlo sampling and optimization strategy have been developed. In the second chapter, a sparse regularization using dictionaries learned from the observed images is introduced as an alternative of the naive Gaussian prior proposed in Chapter 1. instead of Gaussian prior is introduced to regularize the ill-posed problem. Identifying the supports jointly with the dictionaries circumvented the difficulty inherent to sparse coding. To minimize the target function, an alternate optimization algorithm has been designed, which accelerates the fusion process magnificently comparing with the simulation-based method. In the third chapter, by exploiting intrinsic properties of the blurring and downsampling matrices, a much more efficient fusion method is proposed thanks to a closed-form solution for the Sylvester matrix equation associated with maximizing the likelihood. The proposed solution can be embedded into an alternating direction method of multipliers or a block coordinate descent method to incorporate different priors or hyper-priors for the fusion problem, allowing for Bayesian estimators. In the last chapter, a joint multi-band image fusion and unmixing scheme is proposed by combining the well admitted linear spectral mixture model and the forward model. The joint fusion and unmixing problem is solved in an alternating optimization framework, mainly consisting of solving a Sylvester equation and projecting onto a simplex resulting from the non-negativity and sum-to-one constraints. The simulation results conducted on synthetic and semi-synthetic images illustrate the advantages of the developed Bayesian estimators, both qualitatively and quantitatively.

Since the development of the GPS, the global navigation satellite systems (GNSS) have been widely diversified: maintenance, modernization and deployment of new systems such as the European Galileo. In addition, the number of GNSS signals applications, based on the use of GNSS signals, is increasing. To meet these new challenges and requirements, GNSS receivers are constantly evolving. A new trend is the development of software receiver which processes the GNSS signal in a software way unlike hardware receiver, equipping our vehicles, smartphones, for example. This thesis is part of a common project between a laboratory and a company, consisting of the development of a software receiver tracking GPS L1 C/A and Galileo E1 OS. The more specific aim of the thesis is to study the acquisition, first signal processing which provides a rough estimation of the incoming signal parameters. This work focuses particularly the low power signals, an acquisition threshold is set at 27 dB-Hz considered as a representative of urban or degraded environments. It is important to note that the success of the acquisition of such signals should be at least 9 times out of 10, without any aid or knowledge of almanac or ephemeris. Initially, a solid theoretical study of the acquisition performance and sources of degradation is conducted. One of them is the bit transitions due to the presence of the navigation message and the secondary code on pilot component of the new signals. It is thus highlighted the need to use a Transition-Insensitive acquisition method. Secondly, an innovative method, the Double-Block Zero-Padding Transition-Insensitive (DBZPTI) is developed to permit efficiently the acquisition of Galileo E1 OS signal. It takes part in the development of the global acquisition strategy, which should provide an estimate of the Doppler frequency and code delay, fine and reliable, for a satisfactory signal tracking.

Texture characterization is a central element in many image processing applications. Texture analysis can be embedded in the mathematical framework of multifractal analysis, enabling the study of the fluctuations in regularity of image intensity and providing practical tools for their assessment, the coefficients or wavelet leaders. Although successfully applied in various contexts, multi fractal analysis suffers at present from two major limitations. First, the accurate estimation of multifractal parameters for image texture remains a challenge, notably for small sample sizes. Second, multifractal analysis has so far been limited to the analysis of a single image, while the data available in applications are increasingly multivariate. The main goal of this thesis is to develop practical contributions to overcome these limitations. The first limitation is tackled by introducing a generic statistical model for the logarithm of wavelet leaders, parametrized by multifractal parameters of interest. This statistical model enables us to counterbalance the variability induced by small sample sizes and to embed the estimation in a Bayesian framework. This yields robust and accurate estimation procedures, effective both for small and large images. The multifractal analysis of multivariate images is then addressed by generalizing this Bayesian framework to hierarchical models able to account for the assumption that multifractal properties evolve smoothly in the dataset. This is achieved via the design of suitable priors relating the dynamical properties of the multifractal parameters of the different components composing the dataset. Different priors are investigated and compared in this thesis by means of numerical simulations conducted on synthetic multivariate multifractal images. This work is further completed by the investigation of the potential benefit of multifractal analysis and the proposed Bayesian methodology for remote sensing via the example of hyperspectral imaging.

Cluster policy is at the crossroads as governments widely support the local agglomeration of companies and research institutes, but success or failure seems arbitrary for those ‘Silicon Somewheres’. Faced with limited proof of ‘what works’ for clusters, frustration is spreading among policy makers and stakeholders. The study offers a comparative analysis of high-technology clusters around the globe with the objective of finding a generalizable mechanism for making cluster policy successful. Interviews conducted with officials, researchers and industry stakeholders in Chicago, Copenhagen, Singapore and Vancouver reveal that networks have found a novel way of cluster support by having a ‘facilitator’. This network management institution or individual is able to connect and support cluster stakeholders while simultaneously linking them to government with the goal of better tailored policy and ultimately more successful innovative processes. The dissertation offers a new theoretical framework for investigating the facilitation mechanism based on intersecting science and technology policy, network management and innovation systems literature. The study highlights the fact that the key to success is the creation of capacity for networking (collaborative capacity) and identifying useful knowledge, knowledge gaps and future developments (absorptive capacity) rather than focusing solely on output. The central findings are threefold: Not only does facilitation exist in different parts of the world, it also enhances performance and varies depending on location. The variations include a government- and stakeholder-funded model in most European countries, an independent model in North America and finally, a government-financed model in Asia.