Strategies for neural networks in ballistocardiography with a view towards hardware implementation

A thesis submitted for the degree of Doctor of Philosophy at the University of LutonThe work described in this thesis is based on the results of a clinical trial conducted by the research team at the Medical Informatics Unit of the University of Cambridge, which show that the Ballistocardiogram (BCG) has prognostic value in detecting impaired left ventricular function before it becomes clinically overt as myocardial infarction leading to sudden death. The objective of this study is to develop and demonstrate a framework for realising an on-line BCG signal classification model in a portable device that would have the potential to find pathological signs as early as possible for home health care. Two new on-line automatic BeG classification models for time domain BeG classification are proposed. Both systems are based on a two stage process: input feature extraction followed by a neural classifier. One system uses a principal component analysis neural network, and the other a discrete wavelet transform, to reduce the input dimensionality. Results of the classification, dimensionality reduction, and comparison are presented. It is indicated that the combined wavelet transform and MLP system has a more reliable performance than the combined neural networks system, in situations where the data available to determine the network parameters is limited. Moreover, the wavelet transfonn requires no prior knowledge of the statistical distribution of data samples and the computation complexity and training time are reduced. Overall, a methodology for realising an automatic BeG classification system for a portable instrument is presented. A fully paralJel neural network design for a low cost platform using field programmable gate arrays (Xilinx's XC4000 series) is explored. This addresses the potential speed requirements in the biomedical signal processing field. It also demonstrates a flexible hardware design approach so that an instrument's parameters can be updated as data expands with time. To reduce the hardware design complexity and to increase the system performance, a hybrid learning algorithm using random optimisation and the backpropagation rule is developed to achieve an efficient weight update mechanism in low weight precision learning. The simulation results show that the hybrid learning algorithm is effective in solving the network paralysis problem and the convergence is much faster than by the standard backpropagation rule. The hidden and output layer nodes have been mapped on Xilinx FPGAs with automatic placement and routing tools. The static time analysis results suggests that the proposed network implementation could generate 2.7 billion connections per second performance

Optical Tweezers as a Micromechanical Tool for Studying Defects in 2D Colloidal Crystals

This paper reports on some new results from the analyses of the video microscopy data obtained in a prior experiment on two-dimensional (2D) colloidal crystals. It was reported previously that optical tweezers can be used to create mono- and di-vacancies in a 2D colloidal crystal. Here we report the results on the creation of a vacancy-interstitial pair, as well as tri-vacancies. It is found that the vacancy-interstitial pair can be long-lived, but they do annihilate each other. The behavior of tri-vacancies is most intriguing, as it fluctuates between a configuration of bound pairs of dislocations and that of a locally amorphous state. The relevance of this observation to the issue of the nature of 2D melting is discussed.Comment: 6 pages, 4 figure

Rapid characterization of microscopic two-level systems using Landau-Zener transitions in a superconducting qubit

This is the published version. Copyright 2015 American Institute of PhysicsWe demonstrate a fast method to detect microscopic two-level systems in a superconducting phase qubit. By monitoring the population leak after sweeping the qubit bias flux, we are able to measure the two-level systems that are coupled with the qubit. Compared with the traditional method that detects two-level systems by energy spectroscopy, our method is faster and more sensitive. This method supplies a useful tool to investigate two-level systems in solid-state qubits

Demonstration of Geometric Landau-Zener Interferometry in a Superconducting Qubit

Geometric quantum manipulation and Landau-Zener interferometry have been separately explored in many quantum systems. In this Letter, we combine these two approaches to study the dynamics of a superconducting phase qubit. We experimentally demonstrate Landau-Zener interferometry based on the pure geometric phases in this solid-state qubit. We observe the interference caused by a pure geometric phase accumulated in the evolution between two consecutive Landau-Zener transitions, while the dynamical phase is canceled out by a spin-echo pulse. The full controllability of the qubit state as a function of the intrinsically robust geometric phase provides a promising approach for quantum state manipulation.Comment: 5 pages + 3 pages supplemental Materia