986 resultados para real-time data


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Embedded real-time applications increasingly present high computation requirements, which need to be completed within specific deadlines, but that present highly variable patterns, depending on the set of data available in a determined instant. The current trend to provide parallel processing in the embedded domain allows providing higher processing power; however, it does not address the variability in the processing pattern. Dimensioning each device for its worst-case scenario implies lower average utilization, and increased available, but unusable, processing in the overall system. A solution for this problem is to extend the parallel execution of the applications, allowing networked nodes to distribute the workload, on peak situations, to neighbour nodes. In this context, this report proposes a framework to develop parallel and distributed real-time embedded applications, transparently using OpenMP and Message Passing Interface (MPI), within a programming model based on OpenMP. The technical report also devises an integrated timing model, which enables the structured reasoning on the timing behaviour of these hybrid architectures.

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The foreseen evolution of chip architectures to higher number of, heterogeneous, cores, with non-uniform memory and non-coherent caches, brings renewed attention to the use of Software Transactional Memory (STM) as an alternative to lock-based synchronisation. However, STM relies on the possibility of aborting conflicting transactions to maintain data consistency, which impacts on the responsiveness and timing guarantees required by real-time systems. In these systems, contention delays must be (efficiently) limited so that the response times of tasks executing transactions are upperbounded and task sets can be feasibly scheduled. In this paper we defend the role of the transaction contention manager to reduce the number of transaction retries and to help the real-time scheduler assuring schedulability. For such purpose, the contention management policy should be aware of on-line scheduling information.

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Wireless Sensor Networks (WSNs) are highly distributed systems in which resource allocation (bandwidth, memory) must be performed efficiently to provide a minimum acceptable Quality of Service (QoS) to the regions where critical events occur. In fact, if resources are statically assigned independently from the location and instant of the events, these resources will definitely be misused. In other words, it is more efficient to dynamically grant more resources to sensor nodes affected by critical events, thus providing better network resource management and reducing endto- end delays of event notification and tracking. In this paper, we discuss the use of a WSN management architecture based on the active network management paradigm to provide the real-time tracking and reporting of dynamic events while ensuring efficient resource utilization. The active network management paradigm allows packets to transport not only data, but also program scripts that will be executed in the nodes to dynamically modify the operation of the network. This presumes the use of a runtime execution environment (middleware) in each node to interpret the script. We consider hierarchical (e.g. cluster-tree, two-tiered architecture) WSN topologies since they have been used to improve the timing performance of WSNs as they support deterministic medium access control protocols.

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Behavioral biometrics is one of the areas with growing interest within the biosignal research community. A recent trend in the field is ECG-based biometrics, where electrocardiographic (ECG) signals are used as input to the biometric system. Previous work has shown this to be a promising trait, with the potential to serve as a good complement to other existing, and already more established modalities, due to its intrinsic characteristics. In this paper, we propose a system for ECG biometrics centered on signals acquired at the subject's hand. Our work is based on a previously developed custom, non-intrusive sensing apparatus for data acquisition at the hands, and involved the pre-processing of the ECG signals, and evaluation of two classification approaches targeted at real-time or near real-time applications. Preliminary results show that this system leads to competitive results both for authentication and identification, and further validate the potential of ECG signals as a complementary modality in the toolbox of the biometric system designer.

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Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia Electrotécnica e de Computadores

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The accuracy of the Navigation Satellite Timing and Ranging (NAVSTAR) Global Positioning System (GPS) measurements is insufficient for many outdoor navigation tasks. As a result, in the late nineties, a new methodology – the Differential GPS (DGPS) – was developed. The differential approach is based on the calculation and dissemination of the range errors of the GPS satellites received. GPS/DGPS receivers correlate the broadcasted GPS data with the DGPS corrections, granting users increased accuracy. DGPS data can be disseminated using terrestrial radio beacons, satellites and, more recently, the Internet. Our goal is to provide mobile platforms within our campus with DGPS data for precise outdoor navigation. To achieve this objective, we designed and implemented a three-tier client/server distributed system that establishes Internet links with remote DGPS sources and performs campus-wide dissemination of the obtained data. The Internet links are established between data servers connected to remote DGPS sources and the client, which is the data input module of the campus-wide DGPS data provider. The campus DGPS data provider allows the establishment of both Intranet and wireless links within the campus. This distributed system is expected to provide adequate support for accurate (submetric) outdoor navigation tasks.

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Hyperspectral instruments have been incorporated in satellite missions, providing large amounts of data of high spectral resolution of the Earth surface. This data can be used in remote sensing applications that often require a real-time or near-real-time response. To avoid delays between hyperspectral image acquisition and its interpretation, the last usually done on a ground station, onboard systems have emerged to process data, reducing the volume of information to transfer from the satellite to the ground station. For this purpose, compact reconfigurable hardware modules, such as field-programmable gate arrays (FPGAs), are widely used. This paper proposes an FPGA-based architecture for hyperspectral unmixing. This method based on the vertex component analysis (VCA) and it works without a dimensionality reduction preprocessing step. The architecture has been designed for a low-cost Xilinx Zynq board with a Zynq-7020 system-on-chip FPGA-based on the Artix-7 FPGA programmable logic and tested using real hyperspectral data. Experimental results indicate that the proposed implementation can achieve real-time processing, while maintaining the methods accuracy, which indicate the potential of the proposed platform to implement high-performance, low-cost embedded systems, opening perspectives for onboard hyperspectral image processing.

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Recent embedded processor architectures containing multiple heterogeneous cores and non-coherent caches renewed attention to the use of Software Transactional Memory (STM) as a building block for developing parallel applications. STM promises to ease concurrent and parallel software development, but relies on the possibility of abort conflicting transactions to maintain data consistency, which in turns affects the execution time of tasks carrying transactions. Because of this fact the timing behaviour of the task set may not be predictable, thus it is crucial to limit the execution time overheads resulting from aborts. In this paper we formalise a FIFO-based algorithm to order the sequence of commits of concurrent transactions. Then, we propose and evaluate two non-preemptive and one SRP-based fully-preemptive scheduling strategies, in order to avoid transaction starvation.

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Real-time monitoring applications may be used in a wireless sensor network (WSN) and may generate packet flows with strict quality of service requirements in terms of delay, jitter, or packet loss. When strict delays are imposed from source to destination, the packets must be delivered at the destination within an end-to-end delay (EED) hard limit in order to be considered useful. Since the WSN nodes are scarce both in processing and energy resources, it is desirable that they only transport useful data, as this contributes to enhance the overall network performance and to improve energy efficiency. In this paper, we propose a novel cross-layer admission control (CLAC) mechanism to enhance the network performance and increase energy efficiency of a WSN, by avoiding the transmission of potentially useless packets. The CLAC mechanism uses an estimation technique to preview packets EED, and decides to forward a packet only if it is expected to meet the EED deadline defined by the application, dropping it otherwise. The results obtained show that CLAC enhances the network performance by increasing the useful packet delivery ratio in high network loads and improves the energy efficiency in every network load.

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The observational method in tunnel engineering allows the evaluation in real time of the actual conditions of the ground and to take measures if its behavior deviates considerably from predictions. However, it lacks a consistent and structured methodology to use the monitoring data to adapt the support system in real time. The definition of limit criteria above which adaptation is required are not defined and complex inverse analysis procedures (Rechea et al. 2008, Levasseur et al. 2010, Zentar et al. 2001, Lecampion et al. 2002, Finno and Calvello 2005, Goh 1999, Cui and Pan 2012, Deng et al. 2010, Mathew and Lehane 2013, Sharifzadeh et al. 2012, 2013) may be needed to consistently analyze the problem. In this paper a methodology for the real time adaptation of the support systems during tunneling is presented. In a first step limit criteria for displacements and stresses are proposed. The methodology uses graphics that are constructed during the project stage based on parametric calculations to assist in the process and when these graphics are not available, since it is not possible to predict every possible scenario, inverse analysis calculations are carried out. The methodology is applied to the “Bois de Peu” tunnel which is composed by two tubes with over 500 m long. High uncertainty levels existed concerning the heterogeneity of the soil and consequently in the geomechanical design parameters. The methodology was applied in four sections and the results focus on two of them. It is shown that the methodology has potential to be applied in real cases contributing for a consistent approach of a real time adaptation of the support system and highlight the importance of the existence of good quality and specific monitoring data to improve the inverse analysis procedure.

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First published online: December 16, 2014.

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Hospitals have multiple data sources, such as embedded systems, monitors and sensors. The number of data available is increasing and the information are used not only to care the patient but also to assist the decision processes. The introduction of intelligent environments in health care institutions has been adopted due their ability to provide useful information for health professionals, either in helping to identify prognosis or also to understand patient condition. Behind of this concept arises this Intelligent System to track patient condition (e.g. critic events) in health care. This system has the great advantage of being adaptable to the environment and user needs. The system is focused in identifying critic events from data streaming (e.g. vital signs and ventilation) which is particularly valuable for understanding the patient’s condition. This work aims to demonstrate the process of creating an intelligent system capable of operating in a real environment using streaming data provided by ventilators and vital signs monitors. Its development is important to the physician because becomes possible crossing multiple variables in real-time by analyzing if a value is critic or not and if their variation has or not clinical importance.

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BACKGROUND: HSV-1 and HSV-2 cause CNS infections of dissimilar clinico-pathological characteristics with prognostic and therapeutic implications. OBJECTIVES: To validate a type-specific real-time PCR that uses MGB/LNA Taqman probes and to review the virologico-clinical data of 25 eligible patients with non-neonatal CNS infections. RESULTS: This real-time PCR was evaluated against conventional PCR (26 CSF and 20 quality controls), and LightCycler assay (51 mucocutaneous, 8 CSF and 32 quality controls) and culture/immunofluorescence (75 mucocutaneous) to assess typing with independent methods. Taqman real-time PCR detected 240 HSV genomes per ml CSF, a level appropriate for the management of patients, and provided unambiguous typing for the 104 positive (62 HSV-1 and 42 HSV-2) out the 160 independent clinical samples tested. HSV type diagnosed by Taqman real-time PCR predicted final diagnosis (meningitis versus encephalitis/meningoencephalitis, p<0.001) in 24/25 patients at time of presentation, in contrast to clinical evaluation. CONCLUSIONS: Our real-time PCR, as a sensitive and specific means for type-specific HSV diagnosis, provided rapid prognostic information for patient management.

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Monitoring of T-cell responses in genital mucosa has remained a major challenge because of the absence of lymphoid aggregates and the low abundance of T cells. Here we have adapted to genital tissue a sensitive real-time reverse transcription-PCR (TaqMan) method to measure induction of gamma interferon (IFN-gamma) mRNA transcription after 3 h of antigen-specific activation of CD8 T cells. For this purpose, we vaccinated C57BL/6 mice subcutaneously with human papillomavirus type 16 L1 virus-like particles and monitored the induction of CD8 T cells specific to the L1(165-173) H-2D(b)-restricted epitope. Comparison of the responses induced in peripheral blood mononuclear cells and lymph nodes (LN) by L1-specific IFN-gamma enzyme-linked immunospot assay and TaqMan determination of the relative increase in L1-specific IFN-gamma mRNA induction normalized to the content of CD8b mRNA showed a significant correlation, despite the difference in the readouts. Most of the cervicovaginal tissues could be analyzed by the TaqMan method if normalization to glyceraldehyde-3-phosphate dehydrogenase mRNA was used and a significant L1-specific IFN-gamma induction was found in one-third of the immunized mice. This local response did not correlate with the immune responses measured in the periphery, with the exception of the sacral LN, an LN draining the genital mucosa, where a significant correlation was found. Our data show that the TaqMan method is sensitive enough to detect antigen-specific CD8 T-cell responses in the genital mucosa of individual mice, and this may contribute to elaborate effective vaccines against genital pathogens.

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Background: Both brucellosis and tuberculosis are chronic-debilitating systemic granulomatous diseases with a high incidence in many countries in Africa, Central and South America, the Middle East and the Indian subcontinent. Certain focal complications of brucellosis and extrapulmonary tuberculosis are very difficult to differentiate clinically, biologically and radiologically. As the conventional microbiological methods for the diagnosis of the two diseases have many limitations, as well as being time-consuming, multiplex real time PCR (M RT-PCR) could be a promising and practical approach to hasten the differential diagnosis and improve prognosis. Methodology/Principal Findings: We designed a SYBR Green single-tube multiplex real-time PCR protocol targeting bcsp31 and the IS711 sequence detecting all pathogenic species and biovars of Brucella genus, the IS6110 sequence detecting Mycobacterium genus, and the intergenic region senX3-regX3 specifically detecting Mycobacterium tuberculosis complex. The diagnostic yield of the M RT-PCR with the three pairs of resultant amplicons was then analyzed in 91 clinical samples corresponding to 30 patients with focal complications of brucellosis, 24 patients with extrapulmonary tuberculosis, and 36 patients (Control Group) with different infectious, autoimmune or neoplastic diseases. Thirty-five patients had vertebral osteomyelitis, 21 subacute or chronic meningitis or meningoencephalitis, 13 liver or splenic abscess, eight orchiepididymitis, seven subacute or chronic arthritis, and the remaining seven samples were from different locations. Of the three pairs of amplicons (senX3-regX3+ bcsp3, senX3-regX3+ IS711 and IS6110+ IS711) only senX3-regX3+ IS711 was 100% specific for both the Brucella genus and M. tuberculosis complex. For all the clinical samples studied, the overall sensitivity, specificity, and positive and negative predictive values of the M RT-PCR assay were 89.1%, 100%, 85.7% and 100%, respectively, with an accuracy of 93.4%, (95% CI, 88.3—96.5%). Conclusions/Significance: In this study, a M RT-PCR strategy with species-specific primers based on senX3-regX3+IS711 sequences proved to be a sensitive and specific test, useful for the highly efficient detection of M. tuberculosis and Brucella spp in very different clinical samples. It thus represents an advance in the differential diagnosis between some forms of extrapulmonary tuberculosis and focal complications of brucellosis.