Williams, Robert Charles
No description available.
Measurement of fractal structure in the human population distribution and the implications for telecommunications networksAppleby, Stephen C. January 1995 (has links)
No description available.
Computer Aided Design of VLSI algorithms for digital signal processing based on the Residue Number SystemEl-Menhawy, A. El-H. January 1987 (has links)
No description available.
Xu, Ping Josephine, Allgor, Russell, Graves, Stephen C.
At the time of a customer order, the e-tailer assigns the order to one or more of its order fulfillment centers, and/or to drop shippers, so as to minimize procurement and transportation costs, based on the available current information. However this assignment is necessarily myopic as it cannot account for all future events, such as subsequent customer orders or inventory replenishments. We examine the potential benefits from periodically re-evaluating these real-time order-assignment decisions. We construct near-optimal heuristics for the re-assignment for a large set of customer orders with the objective to minimize the total number of shipments. We investigate how best to implement these heuristics for a rolling horizon, and discuss the effect of demand correlation, customer order size, and the number of customer orders on the nature of the heuristics. Finally, we present potential saving opportunities by testing the heuristics on sets of order data from a major e-tailer. / Singapore-MIT Alliance (SMA)
Models and solution approaches for intermodal and less-than-truckload network design with load consolidationsAgrahari, Homarjun 15 May 2009 (has links)
Logistics and supply chain problems arising in the context of intermodal transportation and less-than-truckload (LTL) network design typically require commodities to be consolidated and shipped via the most economical route to their destinations. Traditionally, these problems have been modelled using network design or hub-and- spoke approaches. In a network design problem, one is given the network and flow requirements between the origin and destination pairs (commodities), and the objective is to route the flows over the network so as to minimize the sum of the fixed charge incurred in using arcs and routing costs. However, there are possible benefits, due to economies-of-scale in transportation, that are not addressed in standard network design models. On the other hand, hub location problems are motivated by potential economies-of-scale in transportation costs when loads are consolidated and shipped together over a completely connected hub network. However, in a hub location problem, the assignment of a node to a hub is independent of the commodities originating at, or destined to, this node. Such an indiscriminate assignment may not be suitable for all commodities originating at a particular node because of their different destinations. Problems arising in the area of LTL transportation, intermodal transportation and package routing generally have characteristics such as economies- of-scale in transportation costs in addition to the requirement of commodity-based routing. Obviously, the existing network design and hub location-based models are not directly suitable for these applications. In this dissertation, we investigate the development of models and solution algorithms for problems in the areas of LTL and intermodal transportation as well as in the freight forwarders industry. We develop models and solution methods to address strategic, tactical and operational level decision issues and show computational results. This research provides new insights into these application areas and new solution methods therein. The solution algorithms developed here also contribute to the general area of discrete optimization, particularly for problems with similar characteristics.
To have a well functioning and tailored network based on priorities and requirements is an important part of most modern companies. Network technicians that design these networks have very important tasks because many people rely on the solutions they chose to help them achieve many of their daily tasks. This study focused on what you should think about when designing a network for a customer. Two simulated companies were created and modeled from real-world references. From their priorities and requirements a network proposal was created, suiting each company. The companies were presented, along with an interview where the questions were used to gain the information necessary to reveal the clients needs. The answers were used as underlying motivation to what products and solutions were used to create the network proposals. Different approaches on what is the most suitable for each company are discussed and hopefully these can be of use when designing networks in the future.
Since the introduction of the Internet and the realisation of its potential companies have either transformed their operation or are in the process of doing so. It has been observed, that developments in I.T., telecommunications and the Internet have boosted the number of enterprises engaging into e-commerce, e-business and virtual enterprising. These trends are accompanied by re-shaping, transformation and changes in an enterprise's boundaries. The thesis gives an account of the research into the area of dynamic enterprise modelling and provides a modelling methodology that allows different roles and business models to be tested and evaluated without the risk associated with committing to a change.
Monitoring network design and identitication of unknown groundwater pollution sources using a feedback based linked simulation-optimization methodologyChadalavada, Sreenivasulu January 2009 (has links)
Australia has a widespread and significant incidence of land and water contamination, which can lead to economic, trade, ecosystem and human health impacts. Over the past 20 years the problem has been growing and there is also a growing realization of the extent of the problem. The installation of monitoring network is pivotal for understanding the groundwater hydraulics and subsurface contamination. At the same time the process is expensive. The systematic study of the subsurface system with the available scanty data regarding the groundwater flow and the subsurface contamination can help us to arrive at the optimum monitoring network design for effective site characterization.
Feedforward artificial neural networks (FANNs), which have been successfully applied to various image processing tasks, are particularly suitable for image subsampling due to their high processing speed. However, the performance of FANNs in image subsampling, which depends on both the FANN topology and the FANN training algorithm, has not been acceptable so far. High performance image subsampling is important in many systems, such as subband decomposition systems, and scalable image and video processing systems. This thesis addresses the design of FANNs with application to image subsampling. More specifically, we focus on both the topological design of FANNs and the training algorithm, so that efficient FANN structures, yielding good performance in image subsampling, are obtained. That is, we aim at obtaining compact FANNs that yield good subsampled versions of the original images, such that if reconstructed, they are as close as possible to the original images. Moreover, we aim at obtaining better performance-speed tradeoffs than those of the traditional lowpass filtering and subsampling methods. First, we propose a design method for FANNs, which leads to compact tridiagonally symmetrical feedforward neural networks (TS—FANNs). Next, in order to address the problem of artifacts that generally appear in the reconstructed images after FANN-based subsampling, we propose a training method for FANNs. When applied to first-order (FOS) and multi-stage first-order (MFOS) image subsampling, the FANNs trained using our method outperform the traditional lowpass filtering and subsampling (LPFS) method, without requiring pre- or post-processing stages. Motivated by our observation that the computational demands of the MFOS process increase approximately linearly with the image size, we then combine the proposed methods and evaluate the performance-complexity tradeoffs of the resulting TS-FANNs in FOS and MFOS. We show that our TS-FANNs-based subsampling has important advantages over subsampling methods based on fully connected FANNs (FC—FANNs) and LPFS, such as significantly reduced computational demands, and the same, or better, quality of the resulting images. The main contributions of this thesis consist of a method for FANN design with tridiagonal symmetry constraints, a training algorithm for FANNs applied to image subsampling, the design and evaluation of the performance-speed tradeoffs of FC—FANNs in image subsampling, and the design and evaluation of the performancespeed tradeoffs of TS—FANNs in image subsampling. The FANN performance in image subsampling is evaluated objectively (using the peak signal-to-noise ratios), subjectively (by visual examination of the subsampled and of the reconstructed images), and in the context of a video coding application. The speed and memory demands of the designed FANN structures are evaluated in terms of the subsampling time and the number of FANN parameters, respectively. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate
Leveraging Hydrological Models in Conjunction with Multi-Objective Optimization Based Methods to Design Streamflow Monitoring NetworksUrsulak, Jacob January 2020 (has links)
Hydrometric data provides forcing data inputs to run hydrological models and observed output time-series to facilitate the calibration and validation process. Hydrometric monitoring networks are often designed without considering the innate relationship between data collection, model set-up, and model application. This research compares the relative effectiveness of a previously established model-based network design strategy to a newly proposed method. The traditional design method identifies a set of Pareto-optimal networks using intermediate entropy-based design objectives, facilitated by the dual entropy multi-objective optimization (DEMO) tool, and then applies models as a post-processing mechanism. Streamflow time-series from networks initially identified by DEMO are used to calibrate two semi-distributed rainfall-runoff models. The calibration process enables a reassessment for non-dominance based on the primary network design objectives, which are maximizing model performance at manually defined flood sensitive catchment outlets and minimizing network size. The newly proposed alternative method embeds the hydrological models and their calibration process into the optimization algorithm, resulting in direct optimization based on the primary design objectives. Both techniques were applied to design networks in two large western Canadian watersheds. Bubble maps are presented to illustrate variations in the spatial distribution of optimal solution sets, with respect to both model performance at flood sensitive catchments and individual station selection frequency, for all design scenarios. Results indicate the newly proposed method provides superior results regardless of network size and that trends in the spatial distribution of optimal networks are highly case-specific. The proposed methodology can be readily adapted to address a wide variety of design applications by varying the models and model performance criteria used in the design process. The findings from this research can be used to guide future network design projects when the proposed network is intended to support one or more model-based applications. / Thesis / Master of Civil Engineering (MCE) / Engineers in the field of water resources monitor hydrometric data to maintain a record of historical conditions that can be used to guide future designs and decisions. Hydrometric data are collected from monitoring networks that should be optimally designed to ensure measurements are gathered efficiently. One of the main applications of hydrometric data are to run and calibrate hydrological models. Therefore, hydrological models can be integrated into the network design process to design monitoring networks that enhance model performance while considering the intended model application. This research introduces a new model-based approach for designing streamflow monitoring networks and compares the relative effectiveness of the new technique to previously established methods. Results indicate the new process provides superior results but is also more computationally demanding. The newly proposed methodology is adaptable and can be used to facilitate user-directed designs of hydrometric monitoring networks for a wide variety of engineering applications.
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