Specialization in pyramidal cell structure in the sensory-motor cortex of the vervet monkey (Cercopethicus pygerythrus)

Recent studies have revealed systematic differences in the pyramidal cell structure between functionally related cortical areas of primates. Trends for a parallel in pyramidal cell structure and functional complexity have been reported in visual, somatosensory, motor, cingulate and prefrontal cortex in the macaque monkey cortex. These specializations in structure have been interpreted as being fundamental in determining cellular and systems function, endowing circuits in these different cortical areas with different computational power. In the present study we extend our initial finding of systematic specialization of pyramidal cell structure in sensory-motor cortex in the macaque monkey [Cereb Cortex 12 (2002) 1071] to the vervet monkey. More specifically, we investigated pyramidal cell structure in somatosensory and motor areas 1/2, 5, 7, 4 and 6. Neurones in fixed, flat-mounted, cortical slices were injected intracellularly with Lucifer Yellow and processed for a light-stable 3,3'-diaminobenzidine reaction product. The size of, number of branches in, and spine density of the basal dendritic arbors varied systematically such that there was a trend for increasing complexity in arbor structure with progression through 1/2, 5 and 7. In addition, cells in area 6 were larger, more branched, and more spinous than those in area 4. (c) 2005 IBRO. Published by Elsevier Ltd. All rights reserved.

Reduced variability of postural strategy prevents normalization of motor changes induced by back pain: A risk factor for chronic trouble?

Variability is fundamental to biological systems and is important in posturomotor learning and control. Pain induces a protective postural strategy, although variability is normally preserved. If variability is lost, does the normal postural strategy return when pain stops? Sixteen subjects performed arm movements during control trials, when the movement evoked back pain and then when it did not. Variability in the postural strategy of the abdominal muscles and pain-related cognitions were evaluated. Only those subjects for whom pain induced a reduction in variability of the postural strategy failed to return to a normal strategy when pain stopped. They were also characterized by their pain-related cognitions. Ongoing perception of threat to the back may exert tighter evaluative control over variability of the postural strategy.

Knowledge based improvement:simulation and artificial intelligence for identifying and improving human decision-making in an operations systems

The performance of most operations systems is significantly affected by the interaction of human decision-makers. A methodology, based on the use of visual interactive simulation (VIS) and artificial intelligence (AI), is described that aims to identify and improve human decision-making in operations systems. The methodology, known as 'knowledge-based improvement' (KBI), elicits knowledge from a decision-maker via a VIS and then uses AI methods to represent decision-making. By linking the VIS and AI representation, it is possible to predict the performance of the operations system under different decision-making strategies and to search for improved strategies. The KBI methodology is applied to the decision-making surrounding unplanned maintenance operations at a Ford Motor Company engine assembly plant.

Interactions within heterogeneous systems of uncontrolled rectifiers for aircraft electrical power systems

There is an increase in the use of multi-pulse, rectifier-fed motor-drive equipment on board more-electric aircraft. Motor drives with feedback control appear as constant power loads to the rectifiers, which can cause instability of the DC filter capacitor voltage at the output of the rectifier. This problem can be exacerbated by interactions between rectifiers that share a common source impedance. In order that such a system can be analysed, there is a need for average, dynamic models of systems of rectifiers. In this study, an efficient, compact method for deriving the approximate, linear, large-signal, average models of two heterogeneous systems of rectifiers, which are fed from a common source impedance, is presented. The models give insight into significant interaction effects that occur between the converters, and that arise through the shared source impedance. First, a 6-pulse and doubly wound, transformer-fed, 12-pulse rectifier system is considered, followed by a 6-pulse and autotransformer-fed, 12-pulse rectifier system. The system models are validated against detailed simulations and laboratory prototypes, and key characteristics of the two system types are compared.

An advanced real-time predictive maintenance framework for large scale machine systems

This thesis introduces and develops a novel real-time predictive maintenance system to estimate the machine system parameters using the motion current signature. Recently, motion current signature analysis has been addressed as an alternative to the use of sensors for monitoring internal faults of a motor. A maintenance system based upon the analysis of motion current signature avoids the need for the implementation and maintenance of expensive motion sensing technology. By developing nonlinear dynamical analysis for motion current signature, the research described in this thesis implements a novel real-time predictive maintenance system for current and future manufacturing machine systems. A crucial concept underpinning this project is that the motion current signature contains infor­mation relating to the machine system parameters and that this information can be extracted using nonlinear mapping techniques, such as neural networks. Towards this end, a proof of con­cept procedure is performed, which substantiates this concept. A simulation model, TuneLearn, is developed to simulate the large amount of training data required by the neural network ap­proach. Statistical validation and verification of the model is performed to ascertain confidence in the simulated motion current signature. Validation experiment concludes that, although, the simulation model generates a good macro-dynamical mapping of the motion current signature, it fails to accurately map the micro-dynamical structure due to the lack of knowledge regarding performance of higher order and nonlinear factors, such as backlash and compliance. Failure of the simulation model to determine the micro-dynamical structure suggests the pres­ence of nonlinearity in the motion current signature. This motivated us to perform surrogate data testing for nonlinearity in the motion current signature. Results confirm the presence of nonlinearity in the motion current signature, thereby, motivating the use of nonlinear tech­niques for further analysis. Outcomes of the experiment show that nonlinear noise reduction combined with the linear reverse algorithm offers precise machine system parameter estimation using the motion current signature for the implementation of the real-time predictive maintenance system. Finally, a linear reverse algorithm, BJEST, is developed and applied to the motion current signature to estimate the machine system parameters.

Cognitive systems associated with the hippocampus of the human brain and their role in behaviour and neurodegenerative disease

Cognitive systems research involves the synthesis of ideas from natural and artificial systems in the analysis, understanding, and design of all intelligent systems. This chapter discusses the cognitive systems associated with the hippocampus (HC) of the human brain and their possible role in behaviour and neurodegenerative disease. The hippocampus (HC) is concerned with the analysis of highly abstract data derived from all sensory systems but its specific role remains controversial. Hence, there have been three major theories concerning its function, viz., the memory theory, the spatial theory, and the behavioral inhibition theory. The memory theory has its origin in the surgical destruction of the HC, which results in severe anterograde and partial retrograde amnesia. The spatial theory has its origin in the observation that neurons in the HC of animals show activity related to their location within the environment. By contrast, the behavioral inhibition theory suggests that the HC acts as a ‘comparator’, i.e., it compares current sensory events with expected or predicted events. If a set of expectations continues to be verified then no alteration of behavior occurs. If, however, a ‘mismatch’ is detected then the HC intervenes by initiating appropriate action by active inhibition of current motor programs and initiation of new data gathering. Understanding the cognitive systems of the hippocampus in humans may aid in the design of intelligent systems involved in spatial mapping, memory, and decision making. In addition, this information may lead to a greater understanding of the course of clinical dementia in the various neurodegenerative diseases in which there is significant damage to the HC.

Experiments in time:exploring the components of motor timing behaviour in dyslexia

This investigation aimed to pinpoint the elements of motor timing control that are responsible for the increased variability commonly found in children with developmental dyslexia on paced or unpaced motor timing tasks (Chapter 3). Such temporal processing abilities are thought to be important for developing the appropriate phonological representations required for the development of literacy skills. Similar temporal processing difficulties arise in other developmental disorders such as Attention Deficit Hyperactivity Disorder (ADHD). Motor timing behaviour in developmental populations was examined in the context of models of typical human timing behaviour, in particular the Wing-Kristofferson model, allowing estimation of the contribution of different timing control systems, namely timekeeper and implementation systems (Chapter 2 and Methods Chapters 4 and 5). Research examining timing in populations with dyslexia and ADHD has been inconsistent in the application of stimulus parameters and so the first investigation compared motor timing behaviour across different stimulus conditions (Chapter 6). The results question the suitability of visual timing tasks which produced greater performance variability than auditory or bimodal tasks. Following an examination of the validity of the Wing-Kristofferson model (Chapter 7) the model was applied to time series data from an auditory timing task completed by children with reading difficulties and matched control groups (Chapter 8). Expected group differences in timing performance were not found, however, associations between performance and measures of literacy and attention were present. Results also indicated that measures of attention and literacy dissociated in their relationships with components of timing, with literacy ability being correlated with timekeeper variance and attentional control with implementation variance. It is proposed that these timing deficits associated with reading difficulties are attributable to central timekeeping processes and so the contribution of error correction to timing performance was also investigated (Chapter 9). Children with lower scores on measures of literacy and attention were found to have a slower or failed correction response to phase errors in timing behaviour. Results from the series of studies suggest that the motor timing difficulty in poor reading children may stem from failures in the judgement of synchrony due to greater tolerance of uncertainty in the temporal processing system.

Characteristics of motor resonance predict the pattern of flash-lag effects for biological motion

Background - When a moving stimulus and a briefly flashed static stimulus are physically aligned in space the static stimulus is perceived as lagging behind the moving stimulus. This vastly replicated phenomenon is known as the Flash-Lag Effect (FLE). For the first time we employed biological motion as the moving stimulus, which is important for two reasons. Firstly, biological motion is processed by visual as well as somatosensory brain areas, which makes it a prime candidate for elucidating the interplay between the two systems with respect to the FLE. Secondly, discussions about the mechanisms of the FLE tend to recur to evolutionary arguments, while most studies employ highly artificial stimuli with constant velocities. Methodology/Principal Finding - Since biological motion is ecologically valid it follows complex patterns with changing velocity. We therefore compared biological to symbolic motion with the same acceleration profile. Our results with 16 observers revealed a qualitatively different pattern for biological compared to symbolic motion and this pattern was predicted by the characteristics of motor resonance: The amount of anticipatory processing of perceived actions based on the induced perspective and agency modulated the FLE. Conclusions/Significance - Our study provides first evidence for an FLE with non-linear motion in general and with biological motion in particular. Our results suggest that predictive coding within the sensorimotor system alone cannot explain the FLE. Our findings are compatible with visual prediction (Nijhawan, 2008) which assumes that extrapolated motion representations within the visual system generate the FLE. These representations are modulated by sudden visual input (e.g. offset signals) or by input from other systems (e.g. sensorimotor) that can boost or attenuate overshooting representations in accordance with biased neural competition (Desimone & Duncan, 1995).

Small-signal input admittance characteristics of a brushless DC motor drive

To carry out stability studies on more electric systems in which there is a preponderance of motor drive equipment, input admittance expressions are required for the individual pieces of equipment. In this paper the techniques of averaging and small-signal linearisation will be used to derive a simple input admittance model for a low voltage, trapezoidal back EMF, brushless, DC motor drive system.

Flexible fault-tolerant topology for switched reluctance motor drives

Switched reluctance motor (SRM) drives are one competitive technology for traction motor drives. This paper proposes a novel and flexible SRM fault-tolerant topology with fault diagnosis, fault tolerance, and advanced control functions. The converter is composed of a single-phase bridge and a relay network, based on the traditional asymmetrical half-bridge driving topology. When the SRM-driving system is subjected to fault conditions including open-circuit and short-circuit faults, the proposed converter starts its fault-diagnosis procedure to locate the fault. Based on the relay network, the faulty part can be bypassed by the single-phase bridge arm, while the single-phase bridge arm and the healthy part of the converter can form a fault-tolerant topology to sustain the driving operation. A fault-tolerant control strategy is developed to decrease the influence of the fault. Furthermore, the proposed fault-tolerant strategy can be applied to three-phase 12/8 SRM and four-phase 8/6 SRM. Simulation results in MATLAB/Simulink and experiments on a three-phase 12/8 SRM and a four-phase 8/6 SRM validate the effectiveness of the proposed strategy, which may have significant economic implications in traction drive systems.

Project finance in Hungarian electricity sector. Effect of feed-in tariff system’s change onto power plant investments

The research analyses the former and the current status of the small gas-motor power plant investments in the Hungarian energy sector. It discusses the development of project financing in the segment and the major changes and effects of new regulations and subsidy-policy implemented in 2010. The objective of this paper is to present the results of an empirical research of the so called GCHP projects, and to draw conclusion concerning how classic project financing conditions were present and changed during the last decade, and how regulation affected the current and future financial status of these projects.

New tools for cognitive and motor rehabilitation: development and clinical validation

Nervous system disorders are associated with cognitive and motor deficits, and are responsible for the highest disability rates and global burden of disease. Their recovery paths are vulnerable and dependent on the effective combination of plastic brain tissue properties, with complex, lengthy and expensive neurorehabilitation programs. This work explores two lines of research, envisioning sustainable solutions to improve treatment of cognitive and motor deficits. Both projects were developed in parallel and shared a new sensible approach, where low-cost technologies were integrated with common clinical operative procedures. The aim was to achieve more intensive treatments under specialized monitoring, improve clinical decision-making and increase access to healthcare. The first project (articles I – III) concerned the development and evaluation of a web-based cognitive training platform (COGWEB), suitable for intensive use, either at home or at institutions, and across a wide spectrum of ages and diseases that impair cognitive functioning. It was tested for usability in a memory clinic setting and implemented in a collaborative network, comprising 41 centers and 60 professionals. An adherence and intensity study revealed a compliance of 82.8% at six months and an average of six hours/week of continued online cognitive training activities. The second project (articles IV – VI) was designed to create and validate an intelligent rehabilitation device to administer proprioceptive stimuli on the hemiparetic side of stroke patients while performing ambulatory movement characterization (SWORD). Targeted vibratory stimulation was found to be well tolerated and an automatic motor characterization system retrieved results comparable to the first items of the Wolf Motor Function Test. The global system was tested in a randomized placebo controlled trial to assess its impact on a common motor rehabilitation task in a relevant clinical environment (early post-stroke). The number of correct movements on a hand-to-mouth task was increased by an average of 7.2/minute while the probability to perform an error decreased from 1:3 to 1:9. Neurorehabilitation and neuroplasticity are shifting to more neuroscience driven approaches. Simultaneously, their final utility for patients and society is largely dependent on the development of more effective technologies that facilitate the dissemination of knowledge produced during the process. The results attained through this work represent a step forward in that direction. Their impact on the quality of rehabilitation services and public health is discussed according to clinical, technological and organizational perspectives. Such a process of thinking and oriented speculation has led to the debate of subsequent hypotheses, already being explored in novel research paths.

Motor de recomendación construido sobre implicaciones

Este Trabajo Fin de Grado (TFG) tiene como objetivo la creación de un framework para su uso en sistemas de recomendación. Se ha realizado por dos personas en la modalidad de trabajo en equipo. Las tareas de este TFG están divididas en dos partes, una realizada conjuntamente y la otra de manera individual. La parte conjunta se centra en construir un sistema que sea capaz de, a partir de comentarios y opiniones sobre puntos de interés (POIs) y haciendo uso de la herramienta de procesamiento de lenguaje natural AlchemyAPI, construir contextos formales y contextos formales multivaluados. Para crear este último es necesario hacer uso de ontologías. El context formal multivaluado es el punto de partida de la segunda parte (individual), que consistirá en, haciendo uso del contexto multivaluado, obtener un conjunto de dependencias funcionales mediante la implementación en Java del algoritmo FDMine. Estas dependencias podrán ser usados en un motor de recomendación. El sistema se ha implementado como una aplicación web Java EE versión 6 y una API para trabajar con contextos formales multivaluados. Para el desarrollo web se han empleado tecnologías actuales como Spring y jQuery. Este proyecto se presenta como un trabajo inicial en el que se expondrán, además del sistema construido, diversos problemas relacionados con la creacion de conjuntos de datos validos. Por último, también se propondrán líneas para futuros TFGs.

Analysis of the impact of stationary energy storage systems in trolleybus grids using Simulink-based modelling

The voltage profile of the catenary between traction substations (TSSs) is affected by the trolleybus current intake and by its position with respect to the TSSs: the higher the current requested by the bus and the further the bus from the TSSs, the deeper the voltage drop. When the voltage drops below 500V, the trolleybus is forced to decrease its consumption by reducing its input current. This thesis deals with the analysis of the improvements that the installation of an BESS produces in the operation of a particularly loaded FS of the DC trolleybus network of the city of Bologna. The stationary BESS is charged by the TSSs during off-peak times and delivers the stored energy when the catenary is overloaded alleviating the load on the TSSs and reducing the voltage drops. Only IMC buses are considered in the prospect of a future disposal of all internal combustion engine vehicles. These trolleybuses cause deeper voltage drops because they absorb enough current to power their traction motor and recharge the on board battery. The control of the BESS aims to keep the catenary voltage within the admissible voltage range and makes sure that all physical limitations are met. A model of FS Marconi Trento Trieste is implemented in Simulink environment to simulate its daily operation and compare the behavior of the trolleybus network with and without BESS. From the simulation without BESS, the best location of the energy storage system is deduced, and the battery control is tuned. Furthermore, from the knowledge of the load curve and the battery control trans-characteristic, it is formulated a prediction of the voltage distribution at BESS connection point. The prediction is then compared with the simulation results to validate the Simulink model. The BESS allows to decrease the voltage drops along the catenary, the Joule losses and the current delivered by the TSSs, indicating that the BESS can be a solution to improve the operation of the trolleybus network.

Motor Unit Spike Trains Recontruction from sEMG Signals for Gesture Classification on a Low-Power Processing Platform

Hand gesture recognition based on surface electromyography (sEMG) signals is a promising approach for the development of intuitive human-machine interfaces (HMIs) in domains such as robotics and prosthetics. The sEMG signal arises from the muscles' electrical activity, and can thus be used to recognize hand gestures. The decoding from sEMG signals to actual control signals is non-trivial; typically, control systems map sEMG patterns into a set of gestures using machine learning, failing to incorporate any physiological insight. This master thesis aims at developing a bio-inspired hand gesture recognition system based on neuromuscular spike extraction rather than on simple pattern recognition. The system relies on a decomposition algorithm based on independent component analysis (ICA) that decomposes the sEMG signal into its constituent motor unit spike trains, which are then forwarded to a machine learning classifier. Since ICA does not guarantee a consistent motor unit ordering across different sessions, 3 approaches are proposed: 2 ordering criteria based on firing rate and negative entropy, and a re-calibration approach that allows the decomposition model to retain information about previous sessions. Using a multilayer perceptron (MLP), the latter approach results in an accuracy up to 99.4% in a 1-subject, 1-degree of freedom scenario. Afterwards, the decomposition and classification pipeline for inference is parallelized and profiled on the PULP platform, achieving a latency < 50 ms and an energy consumption < 1 mJ. Both the classification models tested (a support vector machine and a lightweight MLP) yielded an accuracy > 92% in a 1-subject, 5-classes (4 gestures and rest) scenario. These results prove that the proposed system is suitable for real-time execution on embedded platforms and also capable of matching the accuracy of state-of-the-art approaches, while also giving some physiological insight on the neuromuscular spikes underlying the sEMG.