An Electronic Workshop on the Performance Seeking Control and Propulsion Controlled Aircraft Results of the F-15 Highly Integrated Digital Electronic Control Flight Research Program

Flight research for the F-15 HIDEC (Highly Integrated Digital Electronic Control) program was completed at NASA Dryden Flight Research Center in the fall of 1993. The flight research conducted during the last two years of the HIDEC program included two principal experiments: (1) performance seeking control (PSC), an adaptive, real-time, on-board optimization of engine, inlet, and horizontal tail position on the F-15; and (2) propulsion controlled aircraft (PCA), an augmented flight control system developed for landings as well as up-and-away flight that used only engine thrust (flight controls locked) for flight control. In September 1994, the background details and results of the PSC and PCA experiments were presented in an electronic workshop, accessible through the Dryden World Wide Web (http://www.dfrc.nasa.gov/dryden.html) and as a compact disk

Ruggedized minicomputer hardware and software topics, 1981: Proceedings of the 4th ROLM MIL-SPEC Computer User's Group Conference

Presentations of a conference on the use of ruggedized minicomputers are summarized. The following topics are discussed: (1) the role of minicomputers in the development and/or certification of commercial or military airplanes in both the United States and Europe; (2) generalized software error detection techniques; (3) real time software development tools; (4) a redundancy management research tool for aircraft navigation/flight control sensors; (5) extended memory management techniques using a high order language; and (6) some comments on establishing a system maintenance scheme. Copies of presentation slides are also included

Designing the user experience of a spatiotemporal automated home heating system: a holistic design and implementation process

This research explores technological interventions to reduce energy use in the domestic sector, a notable contributor to the global energy footprint. In the UK elevated challenges associated with renovating an outdated, poorly performing housing stock render a search for alternatives to provide immediate energy saving at low cost. To solve this problem, this thesis takes a holistic design approach to designing and implementing a spatiotemporal heating solution, and aims to investigate experiences of comfort, thermal comfort concepts for automated home heating, users’ interactions and experiences of living with such a system in context, and the underlying utility of quasi-autonomous spatiotemporal home heating. The mixed-methods research process was employed to explore and answer four questions: 1) what is the context within which these home heating interfaces are used, 2) to what extent can spatiotemporal automated heating minimise energy use while providing thermal comfort, 3) how are different heating strategies experienced by users, and 4) How do visibility of feedback, and intelligibility affect the user experience related to understanding and control? Ideation techniques were used to explore the context within which the designs are used with regard to all factors and actors in play and resulted in a conceptual model of the context to be used as a UX design brief. This developed model used mismatches between users’ expectations and reality to indicate potential thermal comfort behaviour actions and mapped the factors within the home context that affected these mismatches. Potential user inclusion through participatory design provided stakeholder insight and interface designs concepts to be developed into prototypes. The results of a prototype probe study using these prototypes showed that intelligibility should not be an interface design goal in itself, but rather fit in with broader UX design agenda regarding data levels, context specificity, and timescales. Increased autonomy in the system was shown not to directly diminish the experience of control, but rather, control or the lack of originated from an alignment of expectations and reality. A quasi-autonomous spatiotemporal heating system design (including a novel heating control algorithm) was coupled with the design of a smartphone interface and the resultant system was deployed in a low-technology solution demonstrating the potential for academic studies to explore such automated systems in-situ in the intended environment over a long period of time. Assessment of the novel control algorithm in an emulated environment demonstrated its fitness for purpose in reducing the amount of energy required to provide adequate levels of thermal comfort (by a factor of seven compared with EnergyStar recommended settings for programmable thermostats), and that these savings can be increased by including occupants’ thermal preference as a variable in the control algorithm. Field deployment of that algorithm in a low-tech sensor-based heating system assessed the user experience of the automated heating system and its mobile application-based control interface, as well as demonstrated the user thermal comfort experience of two different heating strategies. The results highlighted the potential to utilise the lower energy-use “minimise discomfort” strategy without compromising user thermal comfort in comparison to a “maximise comfort” strategy. Diverse heating system use behaviours were also identified and conceptualised alongside users’ experiences in line with the developed conceptual model. A rich picture analysis of all previous findings was utilised to provide a model of the design space for home automated heating systems, and was used to draw interface design guidelines for a broader range of home automation control interfaces. The work presented here served as important first steps in demonstrating the importance of assessing UX of automated home heating systems in situ over elongated periods of time. Novel contributions of (i) conceptual model of automated systems’ domestic context and thermal comfort behaviours within, (ii) nudging this behaviour by selecting a “minimise discomfort” heating strategy over “maximise comfort”, (iii) using UX to influence user expectations and subsequently energy behaviour, and (iv) inclusion of thermal preference in domestic heating control algorithm were all resultant of examining naturally occurring behaviours in their natural setting. As such, they are important exploratory discoveries and require replication, but provide new research directions that would allow reduction of domestic energy use without compromise

Designing the user experience of a spatiotemporal automated home heating system: a holistic design and implementation process

This research explores technological interventions to reduce energy use in the domestic sector, a notable contributor to the global energy footprint. In the UK elevated challenges associated with renovating an outdated, poorly performing housing stock render a search for alternatives to provide immediate energy saving at low cost. To solve this problem, this thesis takes a holistic design approach to designing and implementing a spatiotemporal heating solution, and aims to investigate experiences of comfort, thermal comfort concepts for automated home heating, users’ interactions and experiences of living with such a system in context, and the underlying utility of quasi-autonomous spatiotemporal home heating. The mixed-methods research process was employed to explore and answer four questions: 1) what is the context within which these home heating interfaces are used, 2) to what extent can spatiotemporal automated heating minimise energy use while providing thermal comfort, 3) how are different heating strategies experienced by users, and 4) How do visibility of feedback, and intelligibility affect the user experience related to understanding and control? Ideation techniques were used to explore the context within which the designs are used with regard to all factors and actors in play and resulted in a conceptual model of the context to be used as a UX design brief. This developed model used mismatches between users’ expectations and reality to indicate potential thermal comfort behaviour actions and mapped the factors within the home context that affected these mismatches. Potential user inclusion through participatory design provided stakeholder insight and interface designs concepts to be developed into prototypes. The results of a prototype probe study using these prototypes showed that intelligibility should not be an interface design goal in itself, but rather fit in with broader UX design agenda regarding data levels, context specificity, and timescales. Increased autonomy in the system was shown not to directly diminish the experience of control, but rather, control or the lack of originated from an alignment of expectations and reality. A quasi-autonomous spatiotemporal heating system design (including a novel heating control algorithm) was coupled with the design of a smartphone interface and the resultant system was deployed in a low-technology solution demonstrating the potential for academic studies to explore such automated systems in-situ in the intended environment over a long period of time. Assessment of the novel control algorithm in an emulated environment demonstrated its fitness for purpose in reducing the amount of energy required to provide adequate levels of thermal comfort (by a factor of seven compared with EnergyStar recommended settings for programmable thermostats), and that these savings can be increased by including occupants’ thermal preference as a variable in the control algorithm. Field deployment of that algorithm in a low-tech sensor-based heating system assessed the user experience of the automated heating system and its mobile application-based control interface, as well as demonstrated the user thermal comfort experience of two different heating strategies. The results highlighted the potential to utilise the lower energy-use “minimise discomfort” strategy without compromising user thermal comfort in comparison to a “maximise comfort” strategy. Diverse heating system use behaviours were also identified and conceptualised alongside users’ experiences in line with the developed conceptual model. A rich picture analysis of all previous findings was utilised to provide a model of the design space for home automated heating systems, and was used to draw interface design guidelines for a broader range of home automation control interfaces. The work presented here served as important first steps in demonstrating the importance of assessing UX of automated home heating systems in situ over elongated periods of time. Novel contributions of (i) conceptual model of automated systems’ domestic context and thermal comfort behaviours within, (ii) nudging this behaviour by selecting a “minimise discomfort” heating strategy over “maximise comfort”, (iii) using UX to influence user expectations and subsequently energy behaviour, and (iv) inclusion of thermal preference in domestic heating control algorithm were all resultant of examining naturally occurring behaviours in their natural setting. As such, they are important exploratory discoveries and require replication, but provide new research directions that would allow reduction of domestic energy use without compromise

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective

New simulation techniques for energy aware cloud computing environments

In this thesis we propose a new simulation platform specifically designed for modelling cloud computing environments, its underlying architectures, and the energy consumed by hardware devices. The models that consists on servers are divided into the five basic subsystems: processing system, memory system, network system, storage system, and the power supply unit. Each one of these subsystems has been built including new strategies to simulate energy aware. On the top of these models, there have been deployed the virtualization models to simulate the hypervisor and its scheduling policies. In addition, the cloud manager, the core of the simulation platform, is responsible for the provisioning resources management policies. It design offers to researchers APIs, allowing to perform studies on scheduling policies of cloud computing systems. This simulation platform is aimed to model existent and new designs of cloud computing architectures, with a customizable environment to configure the energy consumption of different components. The main characteristics of this platform are flexibility, allowing a wide possibility of designs; scalability to study large environments; and to provide a good compromise between accuracy and performance. A validation process of the simulation platform has been reached by comparing results from real experiments, with results from simulation executions obtained by modelling the real experiments. Therefore, to evaluate the possibility to foresee the energy consumption of a real cloud environment, an experiment of deploying a model of a real application has been studied. Finally, scalability experiments has been performed to study the behaviour of the simulation platform with large scale environments experiments. The main aim of scalability tests, is to calculate both, the amount of time and memory needed to execute large simulations, depending on the size of the environment simulated, and the availability of hardware resources to execute them.En esta tesis se propone una nueva plataforma de simulación específicamente diseñada para modelar entornos de computación en la nube, sus arquitecturas subyacentes, y la energía consumida por los dispositivos hardware. Los modelos que constituyen los servidores se encuentran divididos en los cinco subsistemas básicos: sistema de procesamiento, sistema de memoria, sistema de almacenamiento, sistema de red, y fuente de alimentación. Cada uno de estos subsistemas ha sido modelado incluyendo nuevas estrategias para simular su consumo energético. Sobre estos modelos se despliegan los modelos de virtualización con la finalidad de simular el hipervisor y sus políticas de planificación. Además, se ha realizado el modelo del gestor de la nube, la pieza central de la plataforma de simulación y responsable de la gestión de las políticas de aprovisionamiento de recursos. Su diseño ofrece interfaces a los investigadores, permitiendo realizar sus estudios sobre políticas de planificación en entornos de computación en la nube. Los objetivos de esta plataforma de simulación son permitir el modelado de entornos existentes y nuevos diseños arquitectónicos de computación en la nube, con un entorno configurable que permita modificar valores de consumo energético de los distintos componentes. Las principales características de esta plataforma son su flexibilidad, permitiendo una amplia posibilidad de diseños; escalabilidad, para estudiar entornos con gran número de elementos; y proveer un buen compromiso entre la precisión de los resultados y su rendimiento. Se ha realizado el proceso de validación de la plataforma de simulación mediante la comparación de resultados de experimentos realizados en entornos reales, con los resultados de simulación obtenidos de modelar dichos entornos reales. Tras ello, se ha realizado una evaluación mostrando la capacidad de prever el consumo energético de un entorno de computación en la nube que modela una aplicación real. Finalmente, se han realizado experimentos para analizar la escalabilidad, con el fin de estudiar el comportamiento de la plataforma ante la simulación de entornos de gran escala. El principal objetivo de los test de escalabilidad consiste en calcular la cantidad de tiempo y de memoria necesarios para ejecutar simulaciones grandes, dependiendo del tamaño del entorno simulado, y de la disponibilidad de recursos físicos para ejecutarlas.This work has been partially funded under the grant TIN2013-41350-P of the Spanish Ministry of Economics and Competitiveness, the COST Action IC1305,”Network on Sustainable Ultrascale Computing (NESUS)”, ESTuDIo (TIN2012-36812-C02-01), SICOMORo-CM (S2013/ICE-3006), the SEPE (Servicio Público de Empleo Estatal) commonly known as INEM, my entire savings, and part from my parents.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: Félix García Carballeira.- Secretario: Jorge Enrique Pérez Martínez.- Vocal: Manuel Núñez Garcí

XSEDE: eXtreme Science and Engineering Discovery Environment Third Quarter 2012 Report

The Extreme Science and Engineering Discovery Environment (XSEDE) is the most advanced, powerful, and robust collection of integrated digital resources and services in the world. It is an integrated cyberinfrastructure ecosystem with singular interfaces for allocations, support, and other key services that researchers can use to interactively share computing resources, data, and expertise.This a report of project activities and highlights from the third quarter of 2012.National Science Foundation, OCI-105357

Proceedings of the 8th Annual Summer Conference: NASA/USRA Advanced Design Program

Papers presented at the 8th Annual Summer Conference are categorized as Space Projects and Aeronautics projects. Topics covered include: Systematic Propulsion Optimization Tools (SPOT), Assured Crew Return Vehicle Post Landing Configuration Design and Test, Autonomous Support for Microorganism Research in Space, Bioregenerative System Components for Microgravity, The Extended Mission Rover (EMR), Planetary Surface Exploration MESUR/Autonomous Lunar Rover, Automation of Closed Environments in Space for Human Comfort and Safety, Walking Robot Design, Extraterrestrial Surface Propulsion Systems, The Design of Four Hypersonic Reconnaissance Aircraft, Design of a Refueling Tanker Delivering Liquid Hydrogen, The Design of a Long-Range Megatransport Aircraft, and Solar Powered Multipurpose Remotely Powered Aircraft

Advanced Computational Methods for Oncological Image Analysis

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.