9,752 research outputs found

    Structured and flexible gray-box composition using invasive distributed patterns

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    ISBN = {ISSN: 1646-3692}International audienceThe evolution of complex distributed software systems often requires intricate composition operations in order to adapt or add functionalities, to react to unanticipated changes, or to apply performance improvements that cannot be modularized in terms of existing services and components. These evolutions often need controlled access to selected parts of the implementation, e.g., to manage exceptional situations and crosscutting within services and their compositions. However, existing composition techniques typically support only interface-level (black-box) composition or arbitrary access to the implementation (gray-box or white-box composition). In this paper, we present a structured approach to the composition of complex software systems that require invasive modifications. Concretely, we provide three contributions: (i) we present a small kernel composition language for structured gray-box composition using invasive distributed patterns; (ii) we motivate that gray-box composition approaches should be defined and evaluated in terms of the flexibility and control they provide, a notion of degrees of invasiveness is introduced to help assess this trade-off; (iii) we apply our approach to a new case study of evolution and evaluate it in the context of two previous studies involving two real-world software systems: benchmarking of grid algorithms with NASGrid and transactional replication with JBoss Cache. As a main result, we show that gray-box composition using invasive distributed patterns allows the declarative and modular definition of evolutions of real-world applications that need moderate to high degrees of invasive modifications

    Co-adaptive control strategies in assistive Brain-Machine Interfaces

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    A large number of people with severe motor disabilities cannot access any of the available control inputs of current assistive products, which typically rely on residual motor functions. These patients are therefore unable to fully benefit from existent assistive technologies, including communication interfaces and assistive robotics. In this context, electroencephalography-based Brain-Machine Interfaces (BMIs) offer a potential non-invasive solution to exploit a non-muscular channel for communication and control of assistive robotic devices, such as a wheelchair, a telepresence robot, or a neuroprosthesis. Still, non-invasive BMIs currently suffer from limitations, such as lack of precision, robustness and comfort, which prevent their practical implementation in assistive technologies. The goal of this PhD research is to produce scientific and technical developments to advance the state of the art of assistive interfaces and service robotics based on BMI paradigms. Two main research paths to the design of effective control strategies were considered in this project. The first one is the design of hybrid systems, based on the combination of the BMI together with gaze control, which is a long-lasting motor function in many paralyzed patients. Such approach allows to increase the degrees of freedom available for the control. The second approach consists in the inclusion of adaptive techniques into the BMI design. This allows to transform robotic tools and devices into active assistants able to co-evolve with the user, and learn new rules of behavior to solve tasks, rather than passively executing external commands. Following these strategies, the contributions of this work can be categorized based on the typology of mental signal exploited for the control. These include: 1) the use of active signals for the development and implementation of hybrid eyetracking and BMI control policies, for both communication and control of robotic systems; 2) the exploitation of passive mental processes to increase the adaptability of an autonomous controller to the user\u2019s intention and psychophysiological state, in a reinforcement learning framework; 3) the integration of brain active and passive control signals, to achieve adaptation within the BMI architecture at the level of feature extraction and classification

    EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications.

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    Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment. Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI applications. EEG-based intelligent BCI systems can facilitate continuous monitoring of fluctuations in human cognitive states under monotonous tasks, which is both beneficial for people in need of healthcare support and general researchers in different domain areas. In this review, we survey the recent literature on EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensating for the gaps in the systematic summary of the past five years. Specifically, we first review the current status of BCI and signal sensing technologies for collecting reliable EEG signals. Then, we demonstrate state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, to detect, monitor, and maintain human cognitive states and task performance in prevalent applications. Finally, we present a couple of innovative BCI-inspired healthcare applications and discuss future research directions in EEG-based BCI research

    Structured and flexible gray-box composition: Application to task rescheduling for grid benchmarking

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    International audienceThe evolution of complex distributed software systems often requires intricate composition operations in order to adapt or add functionalities, react to unanticipated changes to security policies, or do performance improvements, which cannot be modularized in terms of existing services or components. They often need controlled access to selected parts of the implementation, e.g., to manage exceptional situations and crosscutting within services and their compositions. However, existing composition techniques typically support only interface-level (black-box) composition or arbitrary access to the implementation (gray-box or white-box composition). In this paper, we present a more structured approach to the composition of complex software systems that require invasive accesses. Concretely, we provide two contributions, we (i) present a small kernel composition language for structured gray-box composition with explicit control mechanisms and a corresponding aspect-based implementation; (ii) present and compare evolutions using this approach to gray-box composition in the context of two real-world software systems: benchmarking of grid algorithms with NASGrid and transactional replication with JBoss Cache

    Aerospace medicine and biology: A continuing bibliography with indexes (supplement 352)

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    This bibliography lists 147 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during July 1991. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

    NOViSE: a virtual natural orifice transluminal endoscopic surgery simulator

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    Purpose: Natural Orifice Transluminal Endoscopic Surgery (NOTES) is a novel technique in minimally invasive surgery whereby a flexible endoscope is inserted via a natural orifice to gain access to the abdominal cavity, leaving no external scars. This innovative use of flexible endoscopy creates many new challenges and is associated with a steep learning curve for clinicians. Methods: We developed NOViSE - the first force-feedback enabled virtual reality simulator for NOTES training supporting a flexible endoscope. The haptic device is custom built and the behaviour of the virtual flexible endoscope is based on an established theoretical framework – the Cosserat Theory of Elastic Rods. Results: We present the application of NOViSE to the simulation of a hybrid trans-gastric cholecystectomy procedure. Preliminary results of face, content and construct validation have previously shown that NOViSE delivers the required level of realism for training of endoscopic manipulation skills specific to NOTES Conclusions: VR simulation of NOTES procedures can contribute to surgical training and improve the educational experience without putting patients at risk, raising ethical issues or requiring expensive animal or cadaver facilities. In the context of an experimental technique, NOViSE could potentially facilitate NOTES development and contribute to its wider use by keeping practitioners up to date with this novel surgical technique. NOViSE is a first prototype and the initial results indicate that it provides promising foundations for further development

    Aerospace medicine and biology: A continuing bibliography with indexes (supplement 359)

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    This bibliography lists 164 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during Jan. 1992. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance

    A Conceptual Framework for Adapation

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    This paper presents a white-box conceptual framework for adaptation that promotes a neat separation of the adaptation logic from the application logic through a clear identification of control data and their role in the adaptation logic. The framework provides an original perspective from which we survey archetypal approaches to (self-)adaptation ranging from programming languages and paradigms, to computational models, to engineering solutions
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