Using electroencephalography signals to control acoustical processing

Current hearing-assistive technology performs poorly in noisy multi-talker conditions. The goal of this thesis was to establish the feasibility of using EEG to guide acoustic processing in such conditions. To attain this goal, this research developed a model via the constructive research method, relying on literature review. Several approaches have revealed improvements in the performance of hearing-assistive devices under multi-talker conditions, namely beamforming spatial filtering, model-based sparse coding shrinkage, and onset enhancement of the speech signal. Prior research has shown that electroencephalography (EEG) signals contain information that concerns whether the person is actively listening, what the listener is listening to, and where the attended sound source is. This thesis constructed a model for using EEG information to control beamforming, model-based sparse coding shrinkage, and onset enhancement of the speech signal. The purpose of this model is to propose a framework for using EEG signals to control sound processing to select a single talker in a noisy environment containing multiple talkers speaking simultaneously. On a theoretical level, the model showed that EEG can control acoustical processing. An analysis of the model identified a requirement for real-time processing and that the model inherits the computationally intensive properties of acoustical processing, although the model itself is low complexity placing a relatively small load on computational resources. A research priority is to develop a prototype that controls hearing-assistive devices with EEG. This thesis concludes highlighting challenges for future research.

Predicting the performance of passenger ships using computer simulation

When designing a new passenger ship or naval vessel or modifying an existing design, how do we ensure that the proposed design is safe from an evacuation point of view? In the wake of major maritime disasters such as the Herald of Free Enterprise and the Estonia and in light of the growth in the numbers of high density, high-speed ferries and large capacity cruise ships, issues concerned with the evacuation of passengers and crew at sea are receiving renewed interest. In the maritime industry, ship evacuation models are now recognised by IMO through the publication of the Interim Guidelines for Evacuation Analysis of New and Existing Passenger Ships including Ro-Ro. This approach offers the promise to quickly and efficiently bring evacuation considerations into the design phase, while the ship is "on the drawing board" as well as reviewing and optimising the evacuation provision of the existing fleet. Other applications of this technology include the optimisation of operating procedures for civil and naval vessels such as determining the optimal location of a feature such as a casino, organising major passenger movement events such as boarding/disembarkation or restaurant/theatre changes, determining lean manning requirements, location and number of damage control parties, etc. This paper describes the development of the maritimeEXODUS evacuation model which is fully compliant with IMO requirements and briefly presents an example application to a large passenger ferry.

Neurorehabilitation of hand functions using brain computer interface

Introduction: Brain computer interface (BCI) is a promising new technology with possible application in neurorehabilitation after spinal cord injury. Movement imagination or attempted movement-based BCI coupled with functional electrical stimulation (FES) enables the simultaneous activation of the motor cortices and the muscles they control. When using the BCI- coupled with FES (known as BCI-FES), the subject activates the motor cortex using attempted movement or movement imagination of a limb. The BCI system detects the motor cortex activation and activates the FES attached to the muscles of the limb the subject is attempting or imaging to move. In this way the afferent and the efferent pathways of the nervous system are simultaneously activated. This simultaneous activation encourages Hebbian type learning which could be beneficial in functional rehabilitation after spinal cord injury (SCI). The FES is already in use in several SCI rehabilitation units but there is currently not enough clinical evidence to support the use of BCI-FES for rehabilitation. Aims: The main aim of this thesis is to assess outcomes in sub-acute tetraplegic patients using BCI-FES for functional hand rehabilitation. In addition, the thesis explores different methods for assessing neurological rehabilitation especially after BCI-FES therapy. The thesis also investigated mental rotation as a possible rehabilitation method in SCI. Methods: Following investigation into applicable methods that can be used to implement rehabilitative BCI, a BCI based on attempted movement was built. Further, the BCI was used to build a BCI-FES system. The BCI-FES system was used to deliver therapy to seven sub-acute tetraplegic patients who were scheduled to receive the therapy over a total period of 20 working days. These seven patients are in a 'BCI-FES' group. Five more patients were also recruited and offered equivalent FES quantity without the BCI. These further five patients are in a 'FES-only' group. Neurological and functional measures were investigated and used to assess both patient groups before and after therapy. Results: The results of the two groups of patients were compared. The patients in the BCI-FES group had better improvements. These improvements were found with outcome measures assessing neurological changes. The neurological changes following the use of the BCI-FES showed that during movement attempt, the activation of the motor cortex areas of the SCI patients became closer to the activation found in healthy individuals. The intensity of the activation and its spatial localisation both improved suggesting desirable cortical reorganisation. Furthermore, the responses of the somatosensory cortex during sensory stimulation were of clear evidence of better improvement in patients who used the BCI-FES. Missing somatosensory evoked potential peaks returned more for the BCI-FES group while there was no overall change in the FES-only group. Although the BCI-FES group had better neurological improvement, they did not show better functional improvement than the FES-only group. This was attributed mainly to the short duration of the study where therapies were only delivered for 20 working days. Conclusions: The results obtained from this study have shown that BCI-FES may induce cortical changes in the desired direction at least faster than FES alone. The observation of better improvement in the patients who used the BCI-FES is a good result in neurorehabilitation and it shows the potential of thought-controlled FES as a neurorehabilitation tool. These results back other studies that have shown the potential of BCI-FES in rehabilitation following neurological injuries that lead to movement impairment. Although the results are promising, further studies are necessary given the small number of subjects in the current study.

Impacto del sistema ERP Madan XL en la gestión financiera de la Empresa Inversiones CH Computer /

Trabajo de suficiencia profesional

Modelado orientado a objetos del sistema de control de la presión arterial en pacientes sometidos al tratamiento de hemodiálisis

Se ha realizado el modelado orientado a objetos del sistema de control cardiovascular en situaciones de diálisis aplicando una analogía eléctrica en el que se emplean componentes conectados mediante interconexiones. En este modelado se representan las ecuaciones diferenciales del sistema cardiovascular y del sistema de control barorreceptor así como las ecuaciones dinámicas del intercambio de fluidos y solutos del sistema hemodializador. A partir de este modelo se ha realizado experiencias de simulación en condiciones normales y situaciones de hemorragias, transfusiones de sangre y de ultrafiltración e infusión de fluido durante tratamiento de hemodiálisis. Los resultados obtenidos muestran en primer lugar la efectividad del sistema barorreceptor para compensar la hipotensión arterial inducida por los episodios de hemorragia y transfusión de sangre. En segundo lugar se muestra la respuesta del sistema de control ante diferentes tasas de ultrafiltración durante la hemodiálisis y se sugieren valores óptimos para la adecuada operación.

Sistema de conmutación y control de llamadas basado en OpenBTS y Asterisk

El presente trabajo empleó herramientas de hardware y software de licencia libre para el establecimiento de una estación base celular (BTS) de bajo costo y fácil implementación. Partiendo de conceptos técnicos que facilitan la instalación del sistema OpenBTS y empleando el hardware USRP N210 (Universal Software Radio Peripheral) permitieron desplegar una red análoga al estándar de telefonía móvil (GSM). Usando los teléfonos móviles como extensiones SIP (Session Initiation Protocol) desde Asterisk, logrando ejecutar llamadas entre los terminales, mensajes de texto (SMS), llamadas desde un terminal OpenBTS hacia otra operadora móvil, entre otros servicios.

Spatiotemporal neuromodulation therapies engaging muscle synergies improve motor control after spinal cord injury.

Electrical neuromodulation of lumbar segments improves motor control after spinal cord injury in animal models and humans. However, the physiological principles underlying the effect of this intervention remain poorly understood, which has limited the therapeutic approach to continuous stimulation applied to restricted spinal cord locations. Here we developed stimulation protocols that reproduce the natural dynamics of motoneuron activation during locomotion. For this, we computed the spatiotemporal activation pattern of muscle synergies during locomotion in healthy rats. Computer simulations identified optimal electrode locations to target each synergy through the recruitment of proprioceptive feedback circuits. This framework steered the design of spatially selective spinal implants and real-time control software that modulate extensor and flexor synergies with precise temporal resolution. Spatiotemporal neuromodulation therapies improved gait quality, weight-bearing capacity, endurance and skilled locomotion in several rodent models of spinal cord injury. These new concepts are directly translatable to strategies to improve motor control in humans.

An experimental study of adaptive control for evolutionary algorithms

International audience

EGCL: an extended G-Code Language with flow control, functions and mnemonic variables

In the context of computer numerical control (CNC) and computer aided manufacturing (CAM), the capabilities of programming languages such as symbolic and intuitive programming, program portability and geometrical portfolio have special importance -- They allow to save time and to avoid errors during part programming and permit code re-usage -- Our updated literature review indicates that the current state of art presents voids in parametric programming, program portability and programming flexibility -- In response to this situation, this article presents a compiler implementation for EGCL (Extended G-code Language), a new, enriched CNC programming language which allows the use of descriptive variable names, geometrical functions and flow-control statements (if-then-else, while) -- Our compiler produces low-level generic, elementary ISO-compliant Gcode, thus allowing for flexibility in the choice of the executing CNC machine and in portability -- Our results show that readable variable names and flow control statements allow a simplified and intuitive part programming and permit re-usage of the programs -- Future work includes allowing the programmer to define own functions in terms of EGCL, in contrast to the current status of having them as library built-in functions

Effect of Playing Interactive Computer Game on Distress of Insulin Injection Among Type 1 Diabetic Children

Background: Diabetic children and their families experience high level stress because of daily insulin injection. Objectives: This study was conducted to investigate the impact of an interactive computer game on behavioral distress due to insulin injection among diabetic children. Patients and Methods: In this clinical trial, thirty children (3-12 years) with type 1 diabetes who needed daily insulin injection were recruited and allocated randomly into two groups. Children in intervention groups received an interactive computer game and asked to play at home for a week. No special intervention was done for control group. The behavioral distress of groups was assessed before, during and after the intervention by Observational Scale of Behavioral Distress–Revised (OSBD-R). Results: Repeated measure ANOVA test showed no significantly difference of OSBD-R over time for control group (P = 0.08), but this changes is signification in the study group (P = 0.001). Comparison mean score of distress were significantly different between two groups (P = 0.03). Conclusions: According to the findings, playing interactive computer game can decrease behavioral distress induced by insulin injection in type 1 diabetic children. It seems this game can be beneficial to be used alongside other interventions.

AN ENERGY BASED MINIMUM-TIME OPTIMAL CONTROL OF DC-DC CONVERTERS

Time-optimal response is an important and sometimes necessary characteristic of dynamic systems for specific applications. Power converters are widely used in different electrical systems and their dynamic response will affect the whole system. In many electrical systems like microgrids or voltage regulators which supplies sensitive loads fast dynamic response is a must. Minimum time is the fastest converter to compensate the step output reference or load change. Boost converters as one of the wildly used power converters in the electrical systems are aimed to be controlled in optimal time in this study. Linear controllers are not able to provide the optimal response for a boost converter however they are still useful and functional for other applications like reference tracking or stabilization. To obtain the fastest possible response from boost converters, a nonlinear control approach based on the total energy of the system is studied in this research. Total energy of the system considers as the basis for developing the presented method, since it is easy and accurate to measure besides that the total energy of the system represents the actual operating condition of the boost converter. The detailed model of a boost converter is simulated in MATLAB/Simulink to achieve the time optimal response of the boost converter by applying the developed method. The simulation results confirmed the ability of the presented method to secure the time optimal response of the boost converter under four different scenarios.

STATE OF CHARGE BALANCING DROOP CONTROL

This thesis presents a load sharing method applied in a distributed micro grid system. The goal of this method is to balance the state-of-charge (SoC) of each parallel connected battery and make it possible to detect the average SoC of the system by measuring bus voltage for all connected modules. In this method the reference voltage for each battery converter is adjusted by adding a proportional SoC factor. Under such setting the battery with a higher SoC will output more power, whereas the one with lower SoC gives out less. Therefore the higher SoC battery will use its energy faster than the lower ones, and eventually the SoC and output power of each battery will converge. And because the reference voltage is related to SoC status, the information of the average SoC in this system could be shared for all modules by measuring bus voltage. The SoC balancing speed is related to the SoC droop factors. This SoC-based load sharing control system is analyzed in feasibility and stability. Simulations in MATLAB/Simulink are presented, which indicate that this control scheme could balance the battery SoCs as predicted. The observation of SoC sharing through bus voltage was validated in both software simulation and hardware experiments. It could be of use to non-communicated distributed power system in load shedding and power planning.

REAL TIME FUZZY CONTROLLER FOR QUADROTOR STABILITY CONTROL

In this report, we develop an intelligent adaptive neuro-fuzzy controller by using adaptive neuro fuzzy inference system (ANFIS) techniques. We begin by starting with a standard proportional-derivative (PD) controller and use the PD controller data to train the ANFIS system to develop a fuzzy controller. We then propose and validate a method to implement this control strategy on commercial off-the-shelf (COTS) hardware. An analysis is made into the choice of filters for attitude estimation. These choices are limited by the complexity of the filter and the computing ability and memory constraints of the micro-controller. Simplified Kalman filters are found to be good at estimation of attitude given the above constraints. Using model based design techniques, the models are implemented on an embedded system. This enables the deployment of fuzzy controllers on enthusiast-grade controllers. We evaluate the feasibility of the proposed control strategy in a model-in-the-loop simulation. We then propose a rapid prototyping strategy, allowing us to deploy these control algorithms on a system consisting of a combination of an ARM-based microcontroller and two Arduino-based controllers. We then use a combination of the code generation capabilities within MATLAB/Simulink in combination with multiple open-source projects in order to deploy code to an ARM CortexM4 based controller board. We also evaluate this strategy on an ARM-A8 based board, and a much less powerful Arduino based flight controller. We conclude by proving the feasibility of fuzzy controllers on Commercial-off the shelf (COTS) hardware, we also point out the limitations in the current hardware and make suggestions for hardware that we think would be better suited for memory heavy controllers.

DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments.

Leveraging Symbiotic Relationships for Emulation of Computer Networks

The lack of analytical models that can accurately describe large-scale networked systems makes empirical experimentation indispensable for understanding complex behaviors. Research on network testbeds for testing network protocols and distributed services, including physical, emulated, and federated testbeds, has made steady progress. Although the success of these testbeds is undeniable, they fail to provide: 1) scalability, for handling large-scale networks with hundreds or thousands of hosts and routers organized in different scenarios, 2) flexibility, for testing new protocols or applications in diverse settings, and 3) inter-operability, for combining simulated and real network entities in experiments. This dissertation tackles these issues in three different dimensions. First, we present SVEET, a system that enables inter-operability between real and simulated hosts. In order to increase the scalability of networks under study, SVEET enables time-dilated synchronization between real hosts and the discrete-event simulator. Realistic TCP congestion control algorithms are implemented in the simulator to allow seamless interactions between real and simulated hosts. SVEET is validated via extensive experiments and its capabilities are assessed through case studies involving real applications. Second, we present PrimoGENI, a system that allows a distributed discrete-event simulator, running in real-time, to interact with real network entities in a federated environment. PrimoGENI greatly enhances the flexibility of network experiments, through which a great variety of network conditions can be reproduced to examine what-if questions. Furthermore, PrimoGENI performs resource management functions, on behalf of the user, for instantiating network experiments on shared infrastructures. Finally, to further increase the scalability of network testbeds to handle large-scale high-capacity networks, we present a novel symbiotic simulation approach. We present SymbioSim, a testbed for large-scale network experimentation where a high-performance simulation system closely cooperates with an emulation system in a mutually beneficial way. On the one hand, the simulation system benefits from incorporating the traffic metadata from real applications in the emulation system to reproduce the realistic traffic conditions. On the other hand, the emulation system benefits from receiving the continuous updates from the simulation system to calibrate the traffic between real applications. Specific techniques that support the symbiotic approach include: 1) a model downscaling scheme that can significantly reduce the complexity of the large-scale simulation model, resulting in an efficient emulation system for modulating the high-capacity network traffic between real applications; 2) a queuing network model for the downscaled emulation system to accurately represent the network effects of the simulated traffic; and 3) techniques for reducing the synchronization overhead between the simulation and emulation systems.