Implementation of a neural network-based electromyographic control system for a printed robotic hand

3D printing has revolutionized the manufacturing process reducing costs and time, but only when combined with robotics and electronics, this structures could develop their full potential. In order to improve the available printable hand designs, a control system based on electromyographic (EMG) signals has been implemented, so that different movement patterns can be recognized and replicated in the bionic hand in real time. This control system has been developed in Matlab/ Simulink comprising EMG signal acquisition, feature extraction, dimensionality reduction and pattern recognition through a trained neural-network. Pattern recognition depends on the features used, their dimensions and the time spent in signal processing. Finding balance between this execution time and the input features of the neural network is a crucial step for an optimal classification.Ingeniería Biomédic

Instance-based Learning with Prototype Reduction for Real-Time Proportional Myocontrol: A Randomized User Study Demonstrating Accuracy-preserving Data Reduction for Prosthetic Embedded Systems

This work presents the design, implementation and validation of learning techniques based on the kNN scheme for gesture detection in prosthetic control. To cope with high computational demands in instance-based prediction, methods of dataset reduction are evaluated considering real-time determinism to allow for the reliable integration into battery-powered portable devices. The influence of parameterization and varying proportionality schemes is analyzed, utilizing an eight-channel-sEMG armband. Besides offline cross-validation accuracy, success rates in real-time pilot experiments (online target achievement tests) are determined. Based on the assessment of specific dataset reduction techniques' adequacy for embedded control applications regarding accuracy and timing behaviour, Decision Surface Mapping (DSM) proves itself promising when applying kNN on the reduced set. A randomized, double-blind user study was conducted to evaluate the respective methods (kNN and kNN with DSM-reduction) against Ridge Regression (RR) and RR with Random Fourier Features (RR-RFF). The kNN-based methods performed significantly better (p<0.0005) than the regression techniques. Between DSM-kNN and kNN, there was no statistically significant difference (significance level 0.05). This is remarkable in consideration of only one sample per class in the reduced set, thus yielding a reduction rate of over 99% while preserving success rate. The same behaviour could be confirmed in an extended user study. With k=1, which turned out to be an excellent choice, the runtime complexity of both kNN (in every prediction step) as well as DSM-kNN (in the training phase) becomes linear concerning the number of original samples, favouring dependable wearable prosthesis applications

Functional organisation of behavioural inhibitory control mechanisms in cortico-basal ganglia circuitry: implications for stimulant use disorder.

The neural and psychological mechanisms of inhibitory control processes were investigated, focusing on the cortico-basal ganglia circuits in rats and humans. These included behavioural flexibility, ‘waiting’ and ‘stopping’ impulsivity and involved serial spatial reversal learning task in rodents, and in humans, premature responses in the Monetary Incentive Delay (MID) task and the stop-signal reaction time task. Chapter 2 and Chapter 3 focus on individual differences in behavioural flexibility in rats while Chapter 4, Chapter 5 and Chapter 6 consider how inhibitory control mechanisms are affected by the psychostimulant drug cocaine in both rats and humans. As reported in Chapter 2, systemic modulation of monoaminergic transmission by monoamine oxidase A (MAO-A) inhibitors enhanced reversal learning performance, selectively by decreasing the lose-shift probability, thereby implicating a role for dopamine, serotonin and noradrenaline in facilitating learning from negative feedback. Resting state functional magnetic resonance imaging (fMRI) revealed enhanced functional connectivity of the orbitofrontal and motor cortices as a correlate of flexible reversal learning performance, consistent with elevated levels of monoamines in these region (Chapter 3). Having clarified the mechanisms underlying behavioural flexibility in rats, Chapter 4 reports that escalation of intravenous cocaine self-administration induces behavioural inflexibility in rats even after a relatively short period of cocaine intake. Computational models, including a reinforced and Bayesian learner, revealed a lack of exploitation of the learned response-outcome relationships in cocaine-exposed rats. Chapter 5 focused on impulse control in human volunteers, identifying the striatal and cingulo-opercular networks as substrates of impulsive, premature responding in healthy 4 volunteers, stimulant-dependent individuals and their unaffected siblings. Loss of impulse control was elicited by different incentives for drug-free participants as opposed to drug users. Drug cues elicited striatal activation and increased premature responses in the stimulant-dependent group compared with the control group. In contrast, the ventral striatum was linked to incentive specific activation to reward anticipation. Task-based fMRI demonstrated that interactions between dorsal striatum and cingulo-opercular “cold cognition” networks underlie failures of impulse control in the control, at-risk and stimulant-dependent groups. However, whereas the cingulo-opercular networks were associated with premature responding in all groups, the reward system was activated specifically by the drug incentive cues in the stimulant group, and by monetary incentive cues in the drug-free groups. Chapter 6 presents evidence that corticostriatal functional and effective connectivity in an overlapping network that includes the anterior cingulate and inferior frontal cortices as well as motor cortex, the subthalamic nucleus and dorsal striatum, is critical to stopping impulse control in both control and cocaine individuals. No stopping efficiency impairments were observed in the cocaine-dependent group. Nevertheless, lower structural corticostriatal connectivity measured using diffusion MRI was associated with response execution impairments in cocaine participants performing a stop-signal reaction time task. Further, response execution was rescued by the selective noradrenaline reuptake inhibitor atomoxetine, which also increased corticostriatal effective connectivity. Finally, increased impulsivity and behavioural inflexibility seen in stimulant use disorder in Chapter 5 and Chapter 4, respectively, were not observed in the endophenotype at risk for developing stimulant abuse but were rather a consequence of stimulant abuse. These results further clarify the monoaminergic substrates of behavioural flexibility and specify the neural and computational impairments in inhibitory control induced by stimulant dependence.Pinsent Darwin Studentship from the Dept of Physiology, Development and Neuroscienc

Efficient Learning Machines

Computer scienc

Solar power satellite. Concept evaluation. Activities report. Volume 2: Detailed report

Comparative data are presented among various design approaches to thermal engine and photovoltaic SPS (Solar Power System) concepts, to provide criteria for selecting the most promising systems for more detailed definition. The major areas of the SPS system to be examined include solar cells, microwave power transmission, transportation, structure, rectenna, energy payback, resources, and environmental issues

Instance-based learning with prototype reduction for real-time proportional myocontrol: a randomized user study demonstrating accuracy-preserving data reduction for prosthetic embedded systems

This work presents the design, implementation and validation of learning techniques based on the kNN scheme for gesture detection in prosthetic control. To cope with high computational demands in instance-based prediction, methods of dataset reduction are evaluated considering real-time determinism to allow for the reliable integration into battery-powered portable devices. The influence of parameterization and varying proportionality schemes is analyzed, utilizing an eight-channel-sEMG armband. Besides offline cross-validation accuracy, success rates in real-time pilot experiments (online target achievement tests) are determined. Based on the assessment of specific dataset reduction techniques' adequacy for embedded control applications regarding accuracy and timing behaviour, Decision Surface Mapping (DSM) proves itself promising when applying kNN on the reduced set. A randomized, double-blind user study was conducted to evaluate the respective methods (kNN and kNN with DSM-reduction) against Ridge Regression (RR) and RR with Random Fourier Features (RR-RFF). The kNN-based methods performed significantly better (p < 0.0005) than the regression techniques. Between DSM-kNN and kNN, there was no statistically significant difference (significance level 0.05). This is remarkable in consideration of only one sample per class in the reduced set, thus yielding a reduction rate of over 99% while preserving success rate. The same behaviour could be confirmed in an extended user study. With k=1, which turned out to be an excellent choice, the runtime complexity of both kNN (in every prediction step) as well as DSM-kNN (in the training phase) becomes linear concerning the number of original samples, favouring dependable wearable prosthesis applications

Low Power Digital Filter Implementation in FPGA

Digital filters suitable for hearing aid application on low power perspective have been developed and implemented in FPGA in this dissertation. Hearing aids are primarily meant for improving hearing and speech comprehensions. Digital hearing aids score over their analog counterparts. This happens as digital hearing aids provide flexible gain besides facilitating feedback reduction and noise elimination. Recent advances in DSP and Microelectronics have led to the development of superior digital hearing aids. Many researchers have investigated several algorithms suitable for hearing aid application that demands low noise, feedback cancellation, echo cancellation, etc., however the toughest challenge is the implementation. Furthermore, the additional constraints are power and area. The device must consume as minimum power as possible to support extended battery life and should be as small as possible for increased portability. In this thesis we have made an attempt to investigate possible digital filter algorithms those are hardware configurable on low power view point. Suitability of decimation filter for hearing aid application is investigated. In this dissertation decimation filter is implemented using ‘Distributed Arithmetic’ approach.While designing this filter, it is observed that, comb-half band FIR-FIR filter design uses less hardware compared to the comb-FIR-FIR filter design. The power consumption is also less in case of comb-half band FIR-FIR filter design compared to the comb-FIR-FIR filter. This filter is implemented in Virtex-II pro board from Xilinx and the resource estimator from the system generator is used to estimate the resources. However ‘Distributed Arithmetic’ is highly serial in nature and its latency is high; power consumption found is not very low in this type of filter implementation. So we have proceeded for ‘Adaptive Hearing Aid’ using Booth-Wallace tree multiplier. This algorithm is also implemented in FPGA and power calculation of the whole system is done using Xilinx Xpower analyser. It is observed that power consumed by the hearing aid with Booth-Wallace tree multiplier is less than the hearing aid using Booth multiplier (about 25%). So we can conclude that the hearing aid using Booth-Wallace tree multiplier consumes less power comparatively. The above two approached are purely algorithmic approach. Next we proceed to combine circuit level VLSI design and with algorithmic approach for further possible reduction in power. A MAC based FDF-FIR filter (algorithm) that uses dual edge triggered latch (DET) (circuit) is used for hearing aid device. It is observed that DET based MAC FIR filter consumes less power than the traditional (single edge triggered, SET) one (about 41%). The proposed low power latch provides a power saving upto 65% in the FIR filter. This technique consumes less power compared to previous approaches that uses low power technique only at algorithmic abstraction level. The DET based MAC FIR filter is tested for real-time validation and it is observed that it works perfectly for various signals (speech, music, voice with music). The gain of the filter is tested and is found to be 27 dB (maximum) that matches with most of the hearing aid (manufacturer’s) specifications. Hence it can be concluded that FDF FIR digital filter in conjunction with low power latch is a strong candidate for hearing aid application

The Foundations of Infinite-Dimensional Spectral Computations

Spectral computations in infinite dimensions are ubiquitous in the sciences. However, their many applications and theoretical studies depend on computations which are infamously difficult. This thesis, therefore, addresses the broad question, “What is computationally possible within the field of spectral theory of separable Hilbert spaces?” The boundaries of what computers can achieve in computational spectral theory and mathematical physics are unknown, leaving many open questions that have been unsolved for decades. This thesis provides solutions to several such long-standing problems. To determine these boundaries, we use the Solvability Complexity Index (SCI) hierarchy, an idea which has its roots in Smale's comprehensive programme on the foundations of computational mathematics. The Smale programme led to a real-number counterpart of the Turing machine, yet left a substantial gap between theory and practice. The SCI hierarchy encompasses both these models and provides universal bounds on what is computationally possible. What makes spectral problems particularly delicate is that many of the problems can only be computed by using several limits, a phenomenon also shared in the foundations of polynomial root-finding as shown by McMullen. We develop and extend the SCI hierarchy to prove optimality of algorithms and construct a myriad of different methods for infinite-dimensional spectral problems, solving many computational spectral problems for the first time. For arguably almost any operator of applicable interest, we solve the long-standing computational spectral problem and construct algorithms that compute spectra with error control. This is done for partial differential operators with coefficients of locally bounded total variation and also for discrete infinite matrix operators. We also show how to compute spectral measures of normal operators (when the spectrum is a subset of a regular enough Jordan curve), including spectral measures of classes of self-adjoint operators with error control and the construction of high-order rational kernel methods. We classify the problems of computing measures, measure decompositions, types of spectra (pure point, absolutely continuous, singular continuous), functional calculus, and Radon--Nikodym derivatives in the SCI hierarchy. We construct algorithms for and classify; fractal dimensions of spectra, Lebesgue measures of spectra, spectral gaps, discrete spectra, eigenvalue multiplicities, capacity, different spectral radii and the problem of detecting algorithmic failure of previous methods (finite section method). The infinite-dimensional QR algorithm is also analysed, recovering extremal parts of spectra, corresponding eigenvectors, and invariant subspaces, with convergence rates and error control. Finally, we analyse pseudospectra of pseudoergodic operators (a generalisation of random operators) on vector-valued $l^p$ spaces. All of the algorithms developed in this thesis are sharp in the sense of the SCI hierarchy. In other words, we prove that they are optimal, realising the boundaries of what digital computers can achieve. They are also implementable and practical, and the majority are parallelisable. Extensive numerical examples are given throughout, demonstrating efficiency and tackling difficult problems taken from mathematics and also physical applications. In summary, this thesis allows scientists to rigorously and efficiently compute many spectral properties for the first time. The framework provided by this thesis also encompasses a vast number of areas in computational mathematics, including the classical problem of polynomial root-finding, as well as optimisation, neural networks, PDEs and computer-assisted proofs. This framework will be explored in the future work of the author within these settings

A nano-tensile testing system for studying nanostructures inside an electron microscope:design, characterization and application

Mechanical properties of nanostructures could be remarkably different from their bulk counterparts owing to scale effects, which have attracted considerable research interest in recent years. However, nanomechanics studies are hindered by the difficulties of conducting well-instrumented mechanical testing. The objective of this thesis is to develop a novel tensile stage that can be used to probe mechanical properties of universal one-dimensional (1D) nanostructures, like nanowires and nanotubes, inside a scanning/transmission electron microscope (SEM/TEM). The main challenges of performing tensile tests at the nanoscale are: (1) specimen alignment and fixation on the tensile stage; (2) application and measurement of tensile force with nano-Newton resolution; (3) measurement of specimen elongation with nanometer resolution. Previous studies have shown that micro-electromechanical system (MEMS) technology combined with advanced microscopy (e.g. SEM and TEM) provides promising perspectives to address these challenges. Two types of nano-tensile stages, fabricated in a silicon on insulator (SOI) wafer, were developed in this thesis, which consisted of a comb-drive actuator and either a differential capacitive force sensor or a double clamped beam force sensor. The optimized comb-drive actuators could output an in-plane force of about 210 µN at a drive voltage of 120 V, and the force sensors achieved resolutions of better than 50 nN. Individual 1D nanostructures were placed on the MEMS device by in-situ nanomanipulations and fixed at their two ends via focused electron beam induced deposition (FEBID). A strategy of modifying device topography, e.g. in the form of trenches or pillars, was proposed to facilitate the specimen preparation by in-situ manipulation that could achieve a high yield of about 80%. The mechanical testing function of the developed micro devices was demonstrated by tensile tests on individual Co and Si nanowires (NWs) inside an SEM. The average apparent Young's modulus, tensile strength and fracture strain of the electrochemically deposited Co NWs were measured to be (75.3±14.6) GPa, (1.6±0.4) GPa and (2.2±0.6) %, respectively. The measured Young's modulus is significantly lower than that of Co in the bulk form (209 GPa), which is likely caused by structural defects (e.g. pores) and surface effects (e.g. surface contaminations and surface oxide layers). The phosphorous-doped SiNWs grown bottom up by the vapor-liquid-solid (VLS) technique showed an average Young's modulus of (170.0±2.4) GPa and a tensile strength larger than 8.3 GPa. This finding confirms that materials strength increases as their sizes scale down. The top down electroless chemically etched Si NWs show a tensile strength of 5.4 GPa. The developed MEMS devices and experimental techniques enable an alternative way of in-situ nanomechanical characterization based on electron microscopy. The design methodology and learning presented in this thesis would be useful to develop nano-tensile stages of other configurations with more advanced functions