Having mapped the effective operable landscape for PCAIN-based methods with the goal of obtaining the maximal PCAIN effectiveness and highest possible SNR, we propose an adaptive framework (Figure 5D) for such fine-tuning of the parameters as required by the application of interest.
To meet such a goal, this paper presents a Self-Adaptive Framework for Concurrency Architectures (SAFCA) that consists of multiple well-documented architectural patterns in addition to monitoring and adaptive capabilities.
In this work, we therefore propose a scalable run-time self-adaptive framework for MPSoC systems that addresses these problems, thereby considerably improving the system efficiency.
We develop an adaptive sensing framework for tracking time-varying fields using a wireless sensor network.
Behavioural reaction norms are set within an evolutionary adaptive framework designed to account for two key parameters of traits (1) personality (defined in a BRN as the average behavioural response across contexts) and behavioural plasticity (representing the flexible expression of behaviours associated with a trait as a context changes) (Dingemanse et al., 2010).
In this paper, we develop a direct adaptive control framework for adaptive stabilization of the MIMO nonlinear uncertain systems, which can be represented as discrete-time normal form with input-to-state zero dynamics.
This paper presents a self-configuring adaptive framework optimizing resource utilization for scientific applications on top of Cloud technologies.
The adaptive framework is elaborated for a steady-state genetic algorithm (SSGA) to control nine parameters.
The adaptive framework is utilized for configuring the resource layers and for self-optimizing a specific job regarding runtime and resource utilization.
The adaptive framework provides a modular reference implementation for adaptively optimizing the utility with respect to differing objectives (resource utilization, runtime).
