1,721,016 research outputs found
User-centric Networks Selection with Adaptive Data Compression for Smart Health
Full text linkThe increasing demand for intelligent and sustainable healthcare services has prompted the development of smart health systems. Rapid advances in wireless access technologies and in-network data reduction techniques can significantly assist in implementing such smart systems through providing seamless integration of heterogeneous wireless networks, medical devices, and ubiquitous access to data. Utilization of the spectrum across diverse radio technologies is expected to significantly enhance network capacity and quality of service (QoS) for emerging applications such as remote monitoring over mobile-health (m-health) systems. However, this imposes an essential need to develop innovative networks selection mechanisms that account for energy efficiency while meeting application quality requirements. In this context, this paper proposes an efficient networks selection mechanism with adaptive compression for improving medical data delivery over heterogeneous m-health systems. We consider different performance aspects, as well as networks characteristics and application requirements, so as to obtain an efficient solution that grasps the conflicting nature of the various users’ objectives and addresses their inherent tradeoffs. The proposed methodology advocates a user-centric approach towards leveraging heterogeneous wireless networks to enhance the performance of m-health systems. Simulation results show that our solution significantly outperforms state-of-the-art techniques
EEG-based Transceiver Design with Data Decomposition for Healthcare IoT Applications
Full text linkThe emergence of Internet of Things (IoT) applications and rapid advances in wireless communication technologies have motivated a paradigm shift in the development of viable applications such as mobile-health (m-health). These applications boost the opportunity for ubiquitous real-time monitoring using different data types such as electroencephalography (EEG), electrocardiography (ECG), etc. However, many remote monitoring applications require continuous sensing for different signals and vital signs, which result in generating large volumes of real time data that requires to be processed, recorded, and transmitted. Thus, designing efficient transceivers is crucial to reduce transmission delay and energy through leveraging data reduction techniques. In this context, we propose an efficient data-specific transceiver design that leverages the inherent characteristics of the generated data at the physical layer to reduce transmitted data size without significant overheads. The goal is to adaptively reduce the amount of data that needs to be transmitted in order to efficiently communicate and possibly store information, while maintaining the required application quality-of-service (QoS) requirements. Our results show the excellent performance of the proposed design in terms of data reduction gain, signal distortion, low complexity, and the advantages that it exhibits with respect to state-of-the-art techniques since we could obtain about 50% compression ratio at 0% distortion and sample error rate
Active Learning with Noisy Labelers for Improving Classification Accuracy of Connected Vehicles
Full text linkMachine learning has emerged as a promising paradigm for enabling connected, automated vehicles to autonomously cruise the streets and react to unexpected situations. Reacting to such situations requires accurate classification for uncommon events, which in turn depends on the selection of large, diverse, and high-quality training data. In fact, the data available at a vehicle (e.g., photos of road signs) may be affected by errors or have different levels of resolution and freshness. To tackle this challenge, we propose an active learning framework that, leveraging the information collected through onboard sensors as well as received from other vehicles, effectively deals with scarce and noisy data. Given the information received from neighboring vehicles, our solution: (i) selects which vehicles can reliably generate high-quality training data, and (ii) obtains a reliable subset of data to add to the training set by trading off between two essential features, i.e., quality and diversity. The results, obtained with different real-world datasets, demonstrate that our framework significantly outperforms state-of-the-art solutions, providing high classification accuracy with a limited bandwidth requirement for the data exchange between vehicles
Edge-based Compression and Classification for Smart Healthcare Systems: Concept, Implementation and Evaluation
Full text linkSmart healthcare systems require recording, transmitting and processing large volumes of multimodal medical data generated from different types of sensors and medical devices, which is challenging and may turn some of the remote health monitoring applications impractical. Moving computational intelligence to the net- work edge is a promising approach for providing efficient and convenient ways for continuous-remote monitoring. Implementing efficient edge-based classification and data reduction techniques are of paramount importance to enable smart health- care systems with efficient real-time and cost-effective remote monitoring. Thus, we present our vision of leveraging edge computing to monitor, process, and make au- tonomous decisions for smart health applications. In particular, we present and im- plement an accurate and lightweight classification mechanism that, leveraging some time-domain features extracted from the vital signs, allows for a reliable seizures detection at the network edge with precise classification accuracy and low com- putational requirement. We then propose and implement a selective data transfer scheme, which opts for the most convenient way for data transmission depending on the detected patient’s conditions. In addition to that, we propose a reliable energy-efficient emergency notification system for epileptic seizure detection, based on conceptual learning and fuzzy classification. Our experimental results assess the performance of the proposed system in terms of data reduction, classification accuracy, battery lifetime, and transmission delay. We show the effectiveness of our system and its ability to outperform conventional remote monitoring systems that ignore data processing at the edge by: (i) achieving 98.3% classification accuracy for seizures detection, (ii) extending battery lifetime by 60%, and (iii) decreasing average transmission delay by 90%
SSHealth: Toward Secure, Blockchain-enabled Healthcare Systems
Full text linkThe future of healthcare systems is being shaped by incorporating emerged technological innovations to drive new models for patient care. By acquiring, integrating, analyzing, and exchanging medical data at different system levels, new practices can be introduced, offering a radical improvement to healthcare services.
This paper presents a novel smart and secure Healthcare system (ssHealth), which, leveraging advances in edge computing and blockchain technologies, permits epidemics discovering, remote monitoring, and fast emergency response.
The proposed system also allows for secure medical data exchange among local healthcare entities, thus realizing the integration of multiple national and international entities and enabling the correlation of critical medical events for, e.g., emerging epidemics management and control. In particular, we develop a blockchain-based architecture and enable a flexible configuration thereof, which optimize medical data sharing between different health entities and fulfil the diverse levels of Quality of Service (QoS) that ssHealth may require.
Finally, we highlight the benefits of the proposed ssHealth system and possible directions for future research
25th May 2011 - Egyptian Minister for Scientific Research, Science and Technology A. Ezzat Salama signing the guest book with CERN Director-General R. Heuer and visiting CMS control centre with Collaboration Spokesperson G. Tonelli.
No full textHe visited the CMS control room on the Meyrin site with, from left, CMS spokesperson, Guido Tonelli, Alaa Awad, Fayum University, Hisham Badr, ambassador at the UN Geneva, and Maged Elsherbiny, president of the Scientific Research Academy
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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