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Both standards are compared in terms of delay, reliability, scalability, and mobility support in the context of various application requirements.
Concerning ILS design, it will be influenced by various application requirements such as location network scalability, energy efficiency, and location accuracy [1].
Experimental results show that dynamic switching between MAC strategies is possible with minimal receive chain overhead, while meeting the various application requirements (reliability and low-energy consumption).
To address the aforementioned flexibility constraints of MapReduce, we propose an architecture called Flexible Architecture for Cluster Evolution (FACE) which is both language-independent and platform-independent. FACE allows a MapReduce cluster to be designed to match various application requirements by customizing data partitioning, localization, and processing procedures.
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Actually, many applications robustness to data quality issues is relatively unknown, and various applications' requirements and sensitivities on data quality still worth to be further investigated [8, 25].
To meet the application requirements, various design and fabrication methods are adopted to produce highly porous composites.
They enable distance, performance, and flexibility to meet different requirements for various application environments.
Flexible and configurable design of GNet makes it suitable to requirements in various application circumstances.
The programmable full-well capacity and signal-to-noise ratio can be traded off against noise performance for various application-specific performance requirements.
These results show that in order to limit coding redundancy, should be defined in a limited range, which can be flexibly controlled according to the requirement of various application systems.
Additionally, the instruction code can be dynamically reconfigured in various modes to adapt to different application requirements.
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