Constrained LMS for Dynamic Flow Networks

In this era of climate change, there is a growing need to offer adaptive learning algorithms in the optimisation of natural resources. These resources are typically optimised by evolutionary algorithms. However, evolutionary algorithms (EAs) are no longer adequate due to the ‘drift’ component introduced by environmental factors such as flash flooding. We therefore propose a novel constrained Least Mean Squares (LMS) algorithm for the optimisation of flow networks. For rigor, we provide a stability analysis of our adaptive algorithm, which enables us to interpret the physical meaning of the network at equilibrium. We evaluate our proposed method against genetic algorithm (GA), the most common evolutionary algorithm. The results are promising: not only the proposed constrained LMS has a performance advantage over GA, but its computational cost is significantly lower making it more suitable for real-time applications

Statistical Mechanics and Information-Theoretic Perspectives on Complexity in the Earth System

Peer reviewedPublisher PD

Statistical mechanics and information-theoretic perspectives on complexity in the Earth system

This review provides a summary of methods originated in (non-equilibrium) statistical mechanics and information theory, which have recently found successful applications to quantitatively studying complexity in various components of the complex system Earth. Specifically, we discuss two classes of methods: (i) entropies of different kinds (e.g., on the one hand classical Shannon and R´enyi entropies, as well as non-extensive Tsallis entropy based on symbolic dynamics techniques and, on the other hand, approximate entropy, sample entropy and fuzzy entropy); and (ii) measures of statistical interdependence and causality (e.g., mutual information and generalizations thereof, transfer entropy, momentary information transfer). We review a number of applications and case studies utilizing the above-mentioned methodological approaches for studying contemporary problems in some exemplary fields of the Earth sciences, highlighting the potentials of different techniques

Combined brain connectivity and cooperative sensor networks for modelling movement related cortical activities.

The elucidation of the brain’s anatomical and functional organisation during specific tasks is a challenging field in modern brain research. There is also a growing interest in the field of brain connectivity and its relation to specific motor and mental tasks, as well as neurodegenerative diseases like Parkinson’s and Alzheimer’s. In this thesis, a novel approach for modelling motor tasks is proposed. This approach combines diffusion adaptation and brain connectivity measures in order to build models which describe complex tasks through time and space. In particular, an S-transform based measure is introduced to estimate the connectivity on single-trial basis. The connectivity values, corresponding to different frequency bands across time, are effectively coupled with diffusion adaptation. The diffusion strategy exploits the time-space characteristics in a distributed and collaborated manner, and leads to an enhanced model for motor or mental tasks. Specifically, the imaginary part of S-transform coherency is introduced as an EEG connectivity measure. The performance improvement over the existing connectivity measures on a single-trial basis is demonstrated. Moreover, diffusion Kalman filtering is used as it performs well for nonstationary problems like this. This novel method is tested on various scenarios. Initially, its performance is demonstrated for simulated datasets which are based on realistic scenarios. Then, the method is applied to two datasets of real data. The first set of experiments includes a complex motor task of clockwise and anticlockwise hand movement and the second set includes a multi-modal dataset acquired from Parkinson’s patients. The results show that the connectivity enhanced modelling outperforms the simple case where connectivity information is ignored, and can build a robust task-related model