Compressed Skewed-Load Delay Test Generation Based on Evolution and Deterministic Initialization of Populations

The current design and manufacturing semiconductor technologies require to test the products against delay related defects. However, complex acpSOC require low-overhead testability methods to keep the test cost at an acceptable level. Skewed-load tests seem to be the appropriate way to test delay faults in these acpSOC because the test application requires only one storage element per scan cell. Compressed skewed-load test generator based on genetic algorithm is proposed for wrapper-based logic cores of acpSOC. Deterministic population initialization is used to ensure the highest achievable aclTDF coverage for the given wrapper and scan cell order. The developed method performs test data compression by generating test vectors containing already overlapped test vector pairs. The experimental results show high fault coverages, decreased test lengths and better scalability in comparison to recent methods

Scalable visual analytics over voluminous spatiotemporal data

2018 Fall.Includes bibliographical references.Visualization is a critical part of modern data analytics. This is especially true of interactive and exploratory visual analytics, which encourages speedy discovery of trends, patterns, and connections in data by allowing analysts to rapidly change what data is displayed and how it is displayed. Unfortunately, the explosion of data production in recent years has led to problems of scale as storage, processing, querying, and visualization have struggled to keep pace with data volumes. Visualization of spatiotemporal data pose unique challenges, thanks in part to high-dimensionality in the input feature space, interactions between features, and the production of voluminous, high-resolution outputs. In this dissertation, we address challenges associated with supporting interactive, exploratory visualization of voluminous spatiotemporal datasets and underlying phenomena. This requires the visualization of millions of entities and changes to these entities as the spatiotemporal phenomena unfolds. The rendering and propagation of spatiotemporal phenomena must be both accurate and timely. Key contributions of this dissertation include: 1) the temporal and spatial coupling of spatially localized models to enable the visualization of phenomena at far greater geospatial scales; 2) the ability to directly compare and contrast diverging spatiotemporal outcomes that arise from multiple exploratory "what-if" queries; and 3) the computational framework required to support an interactive user experience in a heavily resource-constrained environment. We additionally provide support for collaborative and competitive exploration with multiple synchronized clients

Optimisation of wind turbine blade structures using a genetic algorithm

The current diminution of fossil-fuel reserves, stricter environmental guidelines and the world’s ever-growing energy needs have directed to the deployment of alternative renewable energy sources. Among the many renewable energies, wind energy is one of the most promising and the fastest growing installed alternative-energy production technology. In order to meet the production goals in the next few decades, both significant increases in wind turbine installations and operability are required, while maintaining a profitable and competitive energy cost. As the size of the wind turbine rotor increases, the structural performance and durability requirements tend to become more challenging. In this sense, solving the wind turbine design problem is an optimization problem where an optimal solution is to be found under a set of design constraints and a specific target. Seen the world evolution towards the renewable energies and the beginning of an implementation of a local wind industry in Quebec, it becomes imperative to follow the international trends in this industry. Therefore, it is necessary to supply the designers a suitable decision tool for the study and design of optimal wind turbine blades. The developed design tool is an open source code named winDesign which is capable to perform structural analysis and design of composite blades for wind turbines under various configurations in order to accelerate the preliminary design phase. The proposed tool is capable to perform a Pareto optimization where optimal decisions need to be taken in the presence of trade-offs between two conflicting objectives: the annual energy production and the weight of the blade. For a given external blade shape, winDesign is able to determine an optimal composite layup, chord and twist distributions which either minimizes blade mass or maximizes the annual energy production while simultaneously satisfying design constraints. The newly proposed graphical tool incorporates two novel VCH and KGA techniques and is validated with numerical simulation on both mono-objective and multi-objective optimization problems

Design and Optimization of a 3-D Plasmonic Huygens Metasurface for Highly-Efficient Flat Optics

For miniaturization of future USAF unmanned aerial and space systems to become feasible, accompanying sensor components of these systems must also be reduced in size, weight and power (SWaP). Metasurfaces can act as planar equivalents to bulk optics, and thus possess a high potential to meet these low-SWaP requirements. However, functional efficiencies of plasmonic metasurface architectures have been too low for practical application in the infrared (IR) regime. Huygens-like forward-scattering inclusions may provide a solution to this deficiency, but there is no academic consensus on an optimal plasmonic architecture for obtaining efficient phase control at high frequencies. This dissertation asks the question: what are the ideal topologies for generating Huygens-like metasurface building blocks across a full 2π phase space? Instead of employing any a priori assumption of fundamental scattering topologies, a genetic algorithm (GA) routine was developed to optimize a “blank slate” grid of binary voxels inside a 3D cavity, evolving the voxel bits until a near-globally optimal transmittance (T) was attained at a targeted phase. All resulting designs produced a normalized T≥80 across the entire 2π range, which is the highest metasurface efficiency reported to-date for a plasmonic solution in the IR regime

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

Machine Learning-based Orchestration Solutions for Future Slicing-Enabled Mobile Networks

The fifth generation mobile networks (5G) will incorporate novel technologies such as network programmability and virtualization enabled by Software-Defined Networking (SDN) and Network Function Virtualization (NFV) paradigms, which have recently attracted major interest from both academic and industrial stakeholders. Building on these concepts, Network Slicing raised as the main driver of a novel business model where mobile operators may open, i.e., “slice”, their infrastructure to new business players and offer independent, isolated and self-contained sets of network functions and physical/virtual resources tailored to specific services requirements. While Network Slicing has the potential to increase the revenue sources of service providers, it involves a number of technical challenges that must be carefully addressed. End-to-end (E2E) network slices encompass time and spectrum resources in the radio access network (RAN), transport resources on the fronthauling/backhauling links, and computing and storage resources at core and edge data centers. Additionally, the vertical service requirements’ heterogeneity (e.g., high throughput, low latency, high reliability) exacerbates the need for novel orchestration solutions able to manage end-to-end network slice resources across different domains, while satisfying stringent service level agreements and specific traffic requirements. An end-to-end network slicing orchestration solution shall i) admit network slice requests such that the overall system revenues are maximized, ii) provide the required resources across different network domains to fulfill the Service Level Agreements (SLAs) iii) dynamically adapt the resource allocation based on the real-time traffic load, endusers’ mobility and instantaneous wireless channel statistics. Certainly, a mobile network represents a fast-changing scenario characterized by complex spatio-temporal relationship connecting end-users’ traffic demand with social activities and economy. Legacy models that aim at providing dynamic resource allocation based on traditional traffic demand forecasting techniques fail to capture these important aspects. To close this gap, machine learning-aided solutions are quickly arising as promising technologies to sustain, in a scalable manner, the set of operations required by the network slicing context. How to implement such resource allocation schemes among slices, while trying to make the most efficient use of the networking resources composing the mobile infrastructure, are key problems underlying the network slicing paradigm, which will be addressed in this thesis

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC