Here, in this article an improved MD coding based on adaptive temporal sampling is proposed to make sure the decoder can work correctly when scene changes.
Network-Oriented Modelling based on adaptive temporal causal networks provides a unified approach to model and analyse dynamics and adaptivity of various processes, including mental and social interaction processes.
To address dynamics well, Network-Oriented Modelling based on adaptive temporal causal networks has been developed [1, 2, 10].
The Network-Oriented Modelling approach based on adaptive temporal causal networks, as described here (see also [1, 10]), provides a dynamic modelling approach that enables a modeller to design high-level conceptual model representations in the form of cyclic graphs (or connection matrices).
We propose optimizations based on adaptive techniques.
This paper presents a novel video enhancement system based on an adaptive spatio-temporal connective (ASTC) noise filter and an adaptive piecewise mapping function (APMF).
An adaptive threshold based on the temporal variations of this absolute difference allows detecting motion.
In this study, we introduce a novel framework where in both functional connectivity (FC) based on instantaneous temporal correlation and effective connectivity (EC) based on causal influence in brain networks are used as features in an SVM classifier.
This argument cuts both ways, in the sense that it entails that the causal order is the temporal order (contra the arguments from time travel, simultaneous causation, and physics), but also entails that the causal order cannot be based on the temporal order on pain of circularity.
We used the actual BXD genotypes and generated traits based on four models: single causal locus, two epistatic causal loci, three additive causal loci, and three epistatic loci.
We further improve robustness to lighting conditions by introducing a new adaptive filter based on temporal local scale normalization.
