Advanced Image Acquisition, Processing Techniques and Applications

"Advanced Image Acquisition, Processing Techniques and Applications" is the first book of a series that provides image processing principles and practical software implementation on a broad range of applications. The book integrates material from leading researchers on Applied Digital Image Acquisition and Processing. An important feature of the book is its emphasis on software tools and scientific computing in order to enhance results and arrive at problem solution

Development of a Framework for the Analysis and Assessment of Daily Airport Operations

Tremendous progress has been made over the last two decades towards modernizing the National Airspace System (NAS) by way of technological advancements, and the introduction of procedures and policies that have maintained the safety of the United States airspace. However, as with any other system, there is a need to continuously address evolving challenges pertaining to the sustainment and resiliency of the NAS. One of these challenges involves efficiently analyzing and assessing daily airport operations for the identification of trends and patterns to inform better decision making so as to improve the efficiency and safety of airport operations. Efforts have been undertaken by stakeholders in the aviation industry to categorize airports as a means to facilitate the analysis of their operations. However, a comprehensive, repeatable, and robust approach for this very purpose is lacking. In addition, these efforts have not provided a means for stakeholders to assess the impacts and effectiveness of traffic management decisions and procedures on daily airport operations. Furthermore, an efficient and secure framework for extracting, processing, and storing the data needed for the analysis and assessment of daily airport operations is needed, as the current process employed by FAA analysts is manual, time-consuming, and prone to human error. Consequently, this dissertation addresses these gaps through a set of methodologies that 1) leverage unsupervised Machine Learning algorithms to categorize daily airport operations, 2) leverage a supervised Machine Learning algorithm to determine the category that subsequent daily airport operations belong to, 3) facilitate the comparison of similar and different daily airport operations for the identification of trends and patterns, 4) enable stakeholders to analyze and assess the impacts and effectiveness of traffic management decisions and procedures on daily airport operations, and 5) develop a framework to facilitate the efficient and secure extraction, processing and storage of data needed for the analysis and assessment of daily airport operations. The developed framework automates the flow of data from extraction through storage, and enables users to track the flow of data in real time. It also facilitates data provenance by logging the history of all processes and is equipped with the capability to log errors and their causes, and to notify analysts via email whenever they occur. In addition, it has the capacity to automatically extract, process, and store the data needed for the analysis and assessment of the daily operations of all airports in the NAS. Indeed, this framework will be one of the first of its kind to be deployed into the FAA's Enterprise Information Management platform and will serve as a template for leveraging cloud-based services and technologies to improve operations in the NAS. Finally, this framework will enable FAA analysts to analyze and assess daily airport operations in an efficient manner to facilitate the identification of trends and patterns for better decision making, which will lead to improved airport operational performance.Ph.D

Proceedings. 23. Workshop Computational Intelligence, Dortmund, 5. - 6. Dezember 2013

Dieser Tagungsband enthält die Beiträge des 23. Workshops Computational Intelligence des Fachausschusses 5.14 der VDI/VDE-Gesellschaft für Mess- und Automatisierungstechnik (GMA), der vom 5. - 6. Dezember 2013 in Dortmund stattgefunden hat. Im Fokus stehen Methoden, Anwendungen und Tools für Fuzzy-Systeme, Künstliche Neuronale Netze, Evolutionäre Algorithmen und Data-Mining-Verfahren

Advances in Modelling of Rainfall Fields

Rainfall is the main input for all hydrological models, such as rainfall–runoff models and the forecasting of landslides triggered by precipitation, with its comprehension being clearly essential for effective water resource management as well. The need to improve the modeling of rainfall fields constitutes a key aspect both for efficiently realizing early warning systems and for carrying out analyses of future scenarios related to occurrences and magnitudes for all induced phenomena. The aim of this Special Issue was hence to provide a collection of innovative contributions for rainfall modeling, focusing on hydrological scales and a context of climate changes. We believe that the contribution from the latest research outcomes presented in this Special Issue can shed novel insights on the comprehension of the hydrological cycle and all the phenomena that are a direct consequence of rainfall. Moreover, all these proposed papers can clearly constitute a valid base of knowledge for improving specific key aspects of rainfall modeling, mainly concerning climate change and how it induces modifications in properties such as magnitude, frequency, duration, and the spatial extension of different types of rainfall fields. The goal should also consider providing useful tools to practitioners for quantifying important design metrics in transient hydrological contexts (quantiles of assigned frequency, hazard functions, intensity–duration–frequency curves, etc.)

Proceedings of the NASA Workshop on Image Analysis

Three major topics of image analysis are addressed: segmentation, shape and texture analysis, and structural analysis

Part grouping for efficient process planning

A framework to provide automated part grouping has been investigated in order to overcome the limitations found in existing part grouping techniques. The work is targeted at: exploration of criteria for feature-based part grouping to make the process planning activity efficient; determination of the optimal number of part families in the part grouping process; development of an experimental hybrid process planning system (HYCAPP); investigation of the effects of improved part grouping on manufacturing cell design. The research work has explored the creation of a feature-based component data model and manufacturing system capability data model, and checked the limitations inherent in existing part grouping techniques i.e. part grouping: around methods; based on part geometry; based on machining processes; and based on machines. [Continues.

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse