917 resultados para Complementary filter
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we demonstrate a polarization switchable, single/ multi-wavelength fiber ring laser based on an intra-cavity all fiber Lyot filter. The laser can operate at single-, multi-wavelength by adjusting polarization controller, and givessingle polarization output. © 2015 OSA.
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Inverted repeat gene structures designed for silencing functional genes have been widely used both in academic and applied research. The correct orientations of such structures are usually validated with restriction analysis and/or sequencing. We speculated that the inverted repeat nature of such constructs can be shown by a simple PCR reaction with a single forward primer. To test this hypothesis five different constructs were established from grapevine sequences in a hairpin-intron style silencing system. We were able to amplify the appropriate products in each case. Thus a forward-primed PCR alone may be sufficient to prove the inverted repeat nature of the desired constructs.
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Mammalian C3 is a pivotal complement protein, encoded for by a single gene. In some vertebrate species multiple C3 isoforms are products of different C3 genes. The goal of this study was to determine whether multiple genes encode for shark C3. A protocol was developed for the isolation of mRNA from shark blood for the isolation of C3 cDNA clones. RT-PCR amplification of mRNA, using sense (GCGEQNM) and antisense (TWLTAYV) primers encoding conserved regions of human C3, yielded 21 clones. The C3-like clones isolated shared 97% similarity with each other and 40% similarity to human C3. RACE-PCR amplification of shark liver RNA, using gene specific primers, yielded products ranging from 1800bp to 3000bp. Deduced amino acid sequence, corresponding to 408bp of the 1800bp fragment, was obtained which showed 51% similarity to human C3. These results suggest that nurse shark C3 might be encoded for by more than one gene. ^
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Airborne Light Detection and Ranging (LIDAR) technology has become the primary method to derive high-resolution Digital Terrain Models (DTMs), which are essential for studying Earth's surface processes, such as flooding and landslides. The critical step in generating a DTM is to separate ground and non-ground measurements in a voluminous point LIDAR dataset, using a filter, because the DTM is created by interpolating ground points. As one of widely used filtering methods, the progressive morphological (PM) filter has the advantages of classifying the LIDAR data at the point level, a linear computational complexity, and preserving the geometric shapes of terrain features. The filter works well in an urban setting with a gentle slope and a mixture of vegetation and buildings. However, the PM filter often removes ground measurements incorrectly at the topographic high area, along with large sizes of non-ground objects, because it uses a constant threshold slope, resulting in "cut-off" errors. A novel cluster analysis method was developed in this study and incorporated into the PM filter to prevent the removal of the ground measurements at topographic highs. Furthermore, to obtain the optimal filtering results for an area with undulating terrain, a trend analysis method was developed to adaptively estimate the slope-related thresholds of the PM filter based on changes of topographic slopes and the characteristics of non-terrain objects. The comparison of the PM and generalized adaptive PM (GAPM) filters for selected study areas indicates that the GAPM filter preserves the most "cut-off" points removed incorrectly by the PM filter. The application of the GAPM filter to seven ISPRS benchmark datasets shows that the GAPM filter reduces the filtering error by 20% on average, compared with the method used by the popular commercial software TerraScan. The combination of the cluster method, adaptive trend analysis, and the PM filter allows users without much experience in processing LIDAR data to effectively and efficiently identify ground measurements for the complex terrains in a large LIDAR data set. The GAPM filter is highly automatic and requires little human input. Therefore, it can significantly reduce the effort of manually processing voluminous LIDAR measurements.
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The Solid Oxide Fuel Cell (SOFC) is a class of fuel cells that is capable of generating very high levels of power at high temperatures. SOFCs are used for stationary power generation and as Combined Heat and Power (CHP) systems. In spite of all the beneficial features of the SOFC, the propagation of ripple currents, due to nonlinear loads, is a challenging problem, as it interferes with the physical operation of the fuel cell. The purpose of this thesis is to identify the cause of ripples and attempt to eliminate or reduce the ripple propagation through the use of Active Power Filters (APF). To this end, a systematic approach to modeling the fuel cell to account for its nonlinear behavior in the presence of current ripples is presented. A model of a small fuel cell power system which consists of a fuel cell, a DC-DC converter, a single-phase inverter and a nonlinear load is developed in MATLAB/Simulink environment. The extent of ripple propagation, due to variations in load magnitude and frequency, are identified using frequency spectrum analysis. In order to reduce the effects of ripple propagation, an APF is modeled to remove ripples from the DC fuel cell current. The emphasis of this thesis is based on the idea that small fuel cell systems cannot implement large passive filters to cancel the effects of ripple propagation and hence, the compact APF topology effectively protects the fuel cell from propagating ripples and improves its electrical performance.
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Peer reviewed
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Peer reviewed
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Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.
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A 21.6 Gbit/s 1.78 bit/s/Hz OFDM signal is transmitted over 50 Km of fiber without using DSP in the transmitter or the receiver. The synchronization scheme only requires one PLL to synchronize all the subcarriers.
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This dissertation contributes to the rapidly growing empirical research area in the field of operations management. It contains two essays, tackling two different sets of operations management questions which are motivated by and built on field data sets from two very different industries --- air cargo logistics and retailing.
The first essay, based on the data set obtained from a world leading third-party logistics company, develops a novel and general Bayesian hierarchical learning framework for estimating customers' spillover learning, that is, customers' learning about the quality of a service (or product) from their previous experiences with similar yet not identical services. We then apply our model to the data set to study how customers' experiences from shipping on a particular route affect their future decisions about shipping not only on that route, but also on other routes serviced by the same logistics company. We find that customers indeed borrow experiences from similar but different services to update their quality beliefs that determine future purchase decisions. Also, service quality beliefs have a significant impact on their future purchasing decisions. Moreover, customers are risk averse; they are averse to not only experience variability but also belief uncertainty (i.e., customer's uncertainty about their beliefs). Finally, belief uncertainty affects customers' utilities more compared to experience variability.
The second essay is based on a data set obtained from a large Chinese supermarket chain, which contains sales as well as both wholesale and retail prices of un-packaged perishable vegetables. Recognizing the special characteristics of this particularly product category, we develop a structural estimation model in a discrete-continuous choice model framework. Building on this framework, we then study an optimization model for joint pricing and inventory management strategies of multiple products, which aims at improving the company's profit from direct sales and at the same time reducing food waste and thus improving social welfare.
Collectively, the studies in this dissertation provide useful modeling ideas, decision tools, insights, and guidance for firms to utilize vast sales and operations data to devise more effective business strategies.
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Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.