Fricke S-duality in CHL models

open2siopenPersson, Daniel; Volpato, RobertoPersson, Daniel; Volpato, Robert

Signal processing methods for EEG data classification

Imperial Users onl

Vegetation and vegetation-environment relationships in a muskeg-fen near Thunder Bay, Ontario

This study takes place in an endangered peatland within the city of Thunder Bay Ontario. The study area, William Bog, is one of a few remaining peatlands in the Thunder Bay district which have developed on abandoned Minong phase lake basins on the north shore of Lake Superior. An Inventory of vascular plants, mosses, hepatics, and ground lichens reveals that the vascular flora is richer than the moss or hepatic flora and that ground lichens are rare. Vegetation zones identified in the study area are similar to communities described for peatlands in Ontario by Jeglum et.al. (1974). The study area is centered on a Carex spp. dominated graminoid fen which is bounded to the north and west by a conifer swamp, and to the east by a shrub rich treed bog. Ordination of vegetation data reveals that vegetation varies continuously from fen to swamp and from fen to bog. The nature and flow of groundwater is related to vegetation type such that within the fen, and to the north and west, vegetation can be classified as mlnerotrophic. East of the fen vegetation appears to be ombrotrophic in nature. The pH of both soil and water, calcium concentration , and conductance of water samples varies continuously along the vegetation gradients. This results in a corresponding environmental gradient which runs from strongly minerotrophic (fen) to weakly minerotrophic (conifer swamp) to the north and west, and from strongly minerotrophic (fen) to ombrotrophic (bog) in the east. William Bog exibits consistently higher and lower air temperatures when compared to the Thunder Bay Airport, 3 km SW, this peatland has a significantly shorter frost free period. Within the study area peats are coolest in ombrotrophic Sphagnum spp. hummocks east of the fen, and frost persists within these hummocks well into the growing season. West of the fen peats are warmer, likely the result of subsurface groundwater flow. There is no evidence of permafrost in the study area. The historical and evolutionary development of William Bog is based upon the lateral expansion of Phragmites communis marshes through paludification of the sandy lowland basin. This resulted in two developmental sequences which are based upon the flow of groundwater within the basin. Minerotrophic communities evolve where groundwater flow is concentrated. Ombrotrophic communities develop in drier sites where Sphagnum spp. growth elevates the surface above the influence of groundwater. Dynamics between these communities are based upon local climatic variations during the period following initial colonization of the site, and disturbance by wildlife. The proposed development of vegetation in William Bog appears to resemble sequences proposed by several peatland studies undertaken in northern Minnesota, southern Ontario, and southern Quebec

The Evolution of Economic Governance in EMU

This paper examines the benefits of co-ordination in EMU in a stylised manner and how these benefits have shaped the co-ordination framework in EMU. It then discusses in detail the co-ordination experience in four areas that are particularly important for the functioning of EMU: (i) fiscal policy co-ordination under the Stability and Growth Pact (SGP); (ii) the co-ordination of structural policies under the Lisbon Strategy for Growth and Jobs; (iii) the representation and co-ordination of euro-area positions in international financial fora; and (iv) the co-ordination of macroeconomic statistics. The thrust of the findings is that EMU's system of economic governance has, overall, proven fit for purpose. The current policy assignment to the institutions and instruments that govern the conduct of economic policy in EMU is sound, even though further progress is necessary in several areas, particularly as regards external representation.Governance, EMU, euro area, co-ordination, van den Noord, D�hring, Langedijk, Nogueira-Martins,Pench, Temprano-Arroyo, Thiel

Bio-driven control system for the rehabilitation hand device : a new approach

University of Technology Sydney. Faculty of Engineering and Information Technology.The myoelectric pattern recognition (M-PR) for hand rehabilitation devices has shown its efficacy in the laboratory environment. However, the performance of the M-PR in the clinical application is very poor. There is a big gap between the success of the laboratory experiment and the clinical application. The researchers found that the major cause of the gap was the robustness of the M-PR. Many aspects influence the robustness of the M-PR including the limb position, skin humidity, muscle fatigue, improvement in the muscle function, electrode shifts, and other clinical reasons. The aim of this thesis is to introduce novel M-PRs dealing with the robustness issues in real-time implementation. The goal was accomplished through the following actions. 1. Developing a new M-PR that can work well on the amputees and non-amputees. The proposed M-PR consists of time-domain and autoregressive features (TD-AR), spectral regression discriminant analysis (SRDA) as a feature reducer, and radial basis function extreme learning (RBF-ELM) as a classifier. The experimental results showed that the proposed system was able to detect the user’s intention with accuracy of roughly 99% on the able-bodied subjects and around 98% on the trans-radial amputees using six EMG channels. 2. Introducing new classifiers. The first classifier is adaptive wavelet extreme machine learning (AW-ELM). AW-ELM is the node-based ELM that can adapt to the changes that occur in the input. In general, AW-ELM could classify ten finger movements from two EMG channels with a good accuracy of 94.84 %. The second classifier is swarm radial basis extreme learning machine (SRBF-ELM). SRBF-ELM is a hybridization of particle swarm optimization (PSO) and the kernel-based ELM. The role of PSO is to optimize the kernel parameters. The last classifier is swarm wavelet extreme learning machine (SW-RBF-ELM). The role of the wavelet is to avoid PSO being trapped in local optima. The experiments have been done on the healthy subjects and amputees for both, SRBF-ELM and SW-RBF-ELM. On the healthy subjects, the accuracy of SW-RBF-ELM is 95.62 % while SRBF-ELM is 95.53 %. On the amputees, the SW-RBF-ELM achieved the average accuracy of 94.27 %, while SRBF-ELM produced the average accuracy of 92.55 %. 3. Developing a new feature projection and feature reduction called spectral regression extreme learning (SR-ELM). SR-ELM can enhance the class separability of the features to improve the classification performance. The experimental results showed that SR-ELM can work well on different classifiers and various numbers of classes with an average accuracy ranging from 95.67 % to 86.73 % 4. Developing a robust M-PR by involving the transient state of EMG signal along with the steady state of it in the real-time experiment. The classification accuracy is 90.46 % and 89.19 % on the offline and online classification, respectively. 5. Introducing a new myoelectric controller for the exoskeleton hand. The myoelectric controller consists of two main parts: the myoelectric pattern recognition (M-PR) and myoelectric non-pattern recognition (M-non-PR). In the system, RBF-ELM-R (radial basis extreme learning machine with a rejection mechanism) represents the M-PR, and the proportional controller represents the M-non-PR. The power actuated to the linear motors is proportional to the amplitude of the EMG signals. The experimental results showed that, in the offline experiment of 10 classes, the accuracy is around 90 % and 92 % for RBF-ELM and RBF-ELM-R, respectively. In the online experiment, the accuracy is about 89.22 % and 89.73 % for RBF-ELM and RBF-ELM-R, respectively. 6. Introducing an adaptive mechanism to the M-PR to adapt to changes in the characteristic of the electromyography (EMG) signal. The thesis proposes a new M-PR with online sequential extreme learning machine (OS-ELM) and OS-ELM with rejection (OS-ELM-R). The experimental results showed that the accuracy is around 89 % and 91 % for OS-ELM and OS-ELM-R on the first-day experiment