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.

Spiking neural network-based target tracking control for autonomous mobile robots

In this paper, a target tracking controller based on spiking neural network is proposed for autonomous robots. This controller encodes the preprocessed environmental and target information provided by CCD cameras, encoders and ultrasonic sensors into spike trains, which are integrated by a three-layer spiking neural network (SNN). The outputs of SNN are generated based on the competition between the forward/backward neuron pair corresponding to each motor, with the weights evolved by the Hebbian learning. The application to target tracking of a mobile robot in unknown environment verifies the validity of the proposed controller.

Multi-surface sliding control of MIMO autonomous flight systems

In this paper, we present a hardware in the loop simulation of our proposed multi-surface sliding control (MSSC) for trajectory tracking of 6 degrees of freedom (6-DOF) inertia coupled aerial vehicles with multiple inputs and multiple outputs (MIMO). Using MSSC on MIMO autonomous flight systems creates confluent control that can account for both matched and mismatched uncertainties, system disturbances and excitation in internal dynamics. The control law is implemented on an onboard computer and is validated though Hardware-In-the-Loop (HIL) simulations, between the hardware and the flight simulator X-Plane, which simulates the unmanned aircraft dynamics, sensors, and actuators. Simulation results are presented to validate the analysis.

Assessment of a six-week computer-based remediation program for social cognition in chronic schizophrenia

Background: Programs to remediate cognitive deficits have shown promising results in schizophrenia, but remediation of social cognition deficits is less well understood. Social cognitive deficits may cause more disability than the widely recognized neurocognitive deficits, suggesting that this is an area worthy of further investigation. Aim: Implement and evaluate a brief computerized cognitive remediation program designed to improve memory, attention, and facial affect recognition (FAR) in outpatients with chronic schizophrenia.

Methods: Baseline assessments of FAR and of clinical, cognitive, and psychosocial functioning were completed on 20 males with schizophrenia enrolled in an outpatient rehabilitation program at the Shanghai Mental Health Center (the intervention group) and on 20 males with schizophrenia recruited from among regular outpatients at the Center (the control group). Both groups received treatment as usual, but the intervention group also completed an average of 12.7 sessions of a computer-based remediation program for neurocognitive, social, and FAR functioning over a 6-week period. The baseline measures were repeated in both groups at the end of the 6-week trial.

Results: There were no statistically significant differences in the changes in clinical symptoms (assessed by the Positive and Negative Syndrome Scale, PANSS) or cognitive measures (assessed using the Hong Kong List Learning Test and the Letter-Number Sequencing Task) between the intervention and control groups over the 6-week trial, but there were modest improvements on the PANSS for the intervention group between baseline and after the intervention. There was a significantly greater improvement in the social functioning measure (the Personal and Social Performance scale, PSP) in the intervention group than in the control group. The pre-post change in the total facial recognition score in the intervention group was statistically significant (paired t-test=-2.60, p=0.018), and there was a statistical trend of a greater improvement in facial recognition in the intervention group than in the control group (F(1,37)=2.93; p=0.092).

Conclusions: Integration of FAR training with a short, computer-administrated cognitive remediation program may improve recognition of facial emotions by individuals with schizophrenia, and, thus, improve their social functioning. But more work on developing the FAR training modules and on testing them in larger, more diverse samples will be needed before this can be recommended as a standard part of cognitive remediation programs.

An investigation into using SAP-PS as a multidimensional project control system (MPCS)

This paper investigates the effectiveness of using a corporate enterprise resource planning (ERP) system as a multi-dimensional project control system (MPCS) to monitor and control the work performed on projects, meet the needs and expectations of the project managers and support the requirements of other key stakeholders. A qualitative approach i.e. case study interviews and literature review accompanied by a quantitative computer system validation test approach was deployed. The results from this study suggest that the corporate ERP system is effective at monitoring and controlling the project stakeholder success criteria within a fully integrated environment. The system does however need to be setup and configured for the purpose of MPCS. This study contributes to the field by providing empirical evidence that corporate ERP systems are likely one of the only systems truly capable of solving the age old problem of how to expand the traditional singular dimensional approaches commonly used in project control, thus multiple control dimensions are integrated with each other and other business systems to form a multi-dimensional project control system.

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.