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Motion Estimation, Motion Compensation, Motion Vectors
Published in S. Merrill Weiss, Issues in Advanced Television Technology, 1996
Extensibility is a feature of a system design that allows the system to evolve with advances in the underlying technologies so that additional levels of performance can be implemented without rendering obsolete those existing products that conform to the basic requirements of the system. Extensibility is thus intended to make systems “future-proof’ to the extent possible. It means that the system must be structured so that devices built for the system can recognize information that they are able to interpret and use and can also ignore information that they are incapable of utilizing.
Future trends
Published in J A Clarke, Energy Simulation in Building Design, 2007
Behavioural inheritance also reduces coding and improves reliability since new classes gain access to their parent's code and so need only add the code for the extra functionality they provide. Extensibility is also assisted because new classes and variants of existing classes can be added without requiring any changes to existing classes.
Measuring the complexity of migration transition: an attempt using metrics
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2020
Harjot Kaur, Karanjeet Singh Kahlon
A metric suite for evaluating disparate agent-oriented architectures (Magarino, Cossentino, & Seidita, 2010) has been proposed to assess certain quality attributes like extensibility, modularity and complexity. The suite comprises metrics such as (Cohesion of a Module), (Cohesion of an architecture), (Coupling) for estimation of an extensibility attribute. For measuring modularity of an agent-oriented architecture, (Fan-in) and (Fan-out) have been added to the set of extensibility metrics. Furthermore, the complexity of an agent-oriented architecture can be estimated by using (Average of Common Architecture per Module), (Average of Components per Module), (Size), and (Average of Instances per Module) metrics. All these fore-mentioned set of metrics have been validated by authors using four problem domains and by modelling different case studies.
Earthquake damage assessment system for New Taipei City
Published in Journal of the Chinese Institute of Engineers, 2018
Ching-An Lee, Yu-Chi Sung, Chia-Chuan Hsu, Ming-De Lu, Kuang-Wu Chou
IronPython is an implementation of the programming language Python. As an interpreted language, Python is a high-level programming language for general-purpose programming. Its fast computational speed, simple syntax, and extensively integrated library favor engineering applications. Although EDAS uses a compiler framework for PGA and magnitude calculation, it employs a scripting language for the extensibility of damage estimation. To achieve extensibility in the subsequent research, the scripting language requires modification but without the need to alter the system core of EDAS. Because EDAS was developed using the .NET Framework, IronPython was selected as the core of the scripting language. IronPython is not only compatible with Python, but also supports the .NET Framework. Writing cumbersome programs is thus not required to expand the estimation functions of EDAS in subsequent research.WPF
Coupling sensor observation services and web processing services for online geoprocessing in water dam monitoring
Published in International Journal of Digital Earth, 2018
C. Stasch, B. Pross, B. Gräler, C. Malewski, C. Förster, S. Jirka
The benefits of using open standards for online geoprocessing are increased interoperability and re-usability of the offered services, see for example Hofer (2015) or Zhao, Foerster, and Yue (2012). This allows for flexible reuse of both, observation data sources as well as geoprocessing tools. In the TaMIS project, the water level information has been available in SOS before2 and is used in several external clients. Furthermore, water level measurements from federal agencies are also provided using SOS3 and can thus be easily integrated with the regional water level measurements. Other software, for example, the WPS ArcMap client (Pross et al. 2016), can access the functionalities via the WPS interface. Additional analysis functionality supporting the WPS interface can be easily integrated resulting in improved extensibility. Finally, using WPS interfaces also comes with the general advantage of distributed services in service-oriented architectures: The implementation of the actual processing is hidden behind the interface. It may be deployed on a simple Web server, as done in the TaMIS project, or in a grid or cloud infrastructure like demonstrated in Baranski (2008).