INESC TEC Repository
Not a member yet
2130 research outputs found
Sort by
The effect of temporal aggregation on the estimation accuracy of time series models
Get PDFIn time series analysis, Autoregressive Moving Average (ARMA) models play a central role. Because of the importance of parameter estimation in ARMA modeling and since it is based on aggregate time series so often, we analyze the effect of temporal aggregation on estimation accuracy. We derive the relationships between the aggregate and the basic parameters and compute the actual values of the former from those of the latter in order to measure and compare their estimation accuracy. We run a simulation experiment that shows that aggregation seriously worsens estimation accuracy and that the impact increases with the order of aggregation
Learning Frameworks in a Social-Intensive Knowledge Environment - An Empirical Study
Get PDFApplication frameworks are a powerful technique for large-scale reuse, but require a considerable effort to understand them. Good documentation is costly, as it needs to address different audiences with disparate learning needs. When code and documentation prove insuficient, developers turn to their network of experts. Nevertheless, this proves difficult, mainly due to the lack of expertise awareness (who to ask), wasteful interruptions of the wrong people and unavailability ( either due to intrusion or time constraints). The DRIVER platform is a collaborative learning environment where framework users can, in a non-intrusive way, store and share their learning knowledge while following the best practices of framework understanding (patterns). Developed by the authors, it provides a framework documentation repository, mounted on a wiki, where the learning paths of the community of learners can be captured, shared, rated, and recommended. Combining these social activities, the DRIVER platform promotes collaborative learning, mitigating intrusiveness, unavailability of experts and loss of tacit knowledge. This paper presents the assessment of DRIVER using a controlled academic experiment that measured the performance, effectiveness and framework knowledge intake of MSc students. The study concluded that, especially for novice learners, the platform allows for a faster and more effective learning process
Heart rate variability metrics for fine-grained stress level assessment
No full textBackground and Objectives: In spite of the existence of a multitude of techniques that allow the estimation of stress from physiological indexes, its fine-grained assessment is still a challenge for biomedical engineering. The short-term assessment of stress condition overcomes the limits to stress characterization with long blocks of time and allows to evaluate the behaviour change in real-world settings and also the stress level dynamics. The aim of the present study was to evaluate time and frequency domain and nonlinear heart rate variability (HRV) metrics for stress level assessment using a short-time window. Methods: The electrocardiogram (ECG) signal from 14 volunteers was monitored using the Vital Jacketml while they performed the Trier Social Stress Test (TSST) which is a standardized stress-inducing protocol. Window lengths from 220 s to 50 s for HRV analysis were tested in order to evaluate which metrics could be used to monitor stress levels in an almost continuous way. Results: A sub-set of HRV metrics (AVNN, rMSSD, SDNN and pNN20) showed consistent differences between stress and non-stress phases, and showed to be reliable parameters for the assessment of stress levels in short-term analysis. Conclusions: The AVNN metric, using 50 s of window length analysis, showed that it is the most reliable metric to recognize stress level across the four phases of TSST and allows a fine-grained analysis of stress effect as an index of psychological stress and provides an insight into the reaction of the autonomic nervous system to stress
Multidimensional test coverage analysis: PARADIGM-COV tool
No full textCurrently, software tends to assume increasingly critical roles in our society so assuring its quality becomes ever more crucial. There are several tools and processes of software testing to help increase quality in virtually any type of software. One example is the so calledmodel-based testing (MBT) tools, that generate test cases from models. Pattern Based Graphical User Interface Testing (PBGT) is an example of a MBT new methodology that aims at systematizing and automating the Graphical User Interface (GUI) testing process. It is supported by a Tool (PBGT Tool) which provides an integrated modeling and testing environment for crafting test models based on User Interface Test Patterns (UITP) using a GUI modeling Domain Specific Language (DSL) called PARADIGM. Most of the MBT tools have a configuration phase, where test input data is provided manually by the tester, which influences the quality of the test suite generated. By adding coverage analysis to MBT tools, it is possible to give feedback and help the tester to define the configuration data needed to achieve the most valuable test suite as possible and, ultimately, contribute for increasing the quality of the software. This paper presents a multidimensional test coverage analysis approach and tool (PARADIGM-COV), developed in the context of the PBGT project, that produces coverage information both over the PARADIGM model elements and during test case execution (to identify the parts of the model that were actually exercised). It also presents a case study illustrating the benefits of having multidimensional analysis and assessing the overall test coverage approach
Short-term variability of gamma radiation at the ARM Eastern North Atlantic facility (Azores)
Get PDFThis work addresses the short-term variability of gamma radiation measured continuously at the Eastern North Atlantic (ENA) facility located in the Graciosa island (Azores, 39N; 28W), a fixed site of the Atmospheric Radiation Measurement programme (ARM). The temporal variability of gamma radiation is characterized by occasional anomalies over a slowly-varying signal. Sharp peaks lasting typically 2–4 h are coincident with heavy precipitation and result from the scavenging effect of precipitation bringing radon progeny from the upper levels to the ground surface. However the connection between gamma variability and precipitation is not straightforward as a result of the complex interplay of factors such as the precipitation intensity, the PBL height, the cloud's base height and thickness, or the air mass origin and atmospheric concentration of sub-micron aerosols, which influence the scavenging processes and therefore the concentration of radon progeny. Convective precipitation associated with cumuliform clouds forming under conditions of warming of the ground relative to the air does not produce enhancements in gamma radiation, since the drop growing process is dominated by the fast accretion of liquid water, resulting in the reduction of the concentration of radionuclides by dilution. Events of convective precipitation further contribute to a reduction in gamma counts by inhibiting radon release from the soil surface and by attenuating gamma rays from all gamma-emitting elements on the ground. Anomalies occurring in the absence of precipitation are found to be associated with a diurnal cycle of maximum gamma counts before sunrise decreasing to a minimum in the evening, which are observed in conditions of thermal stability and very weak winds enabling the build-up of near surface radon progeny during the night. © 2017 Elsevier Lt
Experimental low cost reflective type oximeter for wearable health systems
No full textThe advent of wearable technology is fundamental to the dissemination of wearable personal health monitoring devices. Recent developments of biomedical sensors have decreased the form factor and power consumption that can be worn on a permanent basis. This paper discusses a low cost reflective photoplethysmography (PPG) system using a dedicated integrated circuit (IC) solution as the core of a wearable health monitoring device. The measurement of two physiological indicators is performed, namely the pulse rate (HR) and the blood oxygen saturation (SpO(2)). The paper analyses in depth the PPG signals sensing architecture, guaranteeing high resolution measurements due to a delta-sigma analog to digital conversion unit. Post-processing digital filter operations are implemented to enhance low noise PPGs acquisition for physiological signals extraction. A complete system design is presented and a detailed evaluation is made in a real-time processing scenario. The test platform is completed with a PC based graphics application for on-line and off-line data analysis. Minimizing power dissipation is the main challenge in,a wearable design. However, it restrains PPG signal measurement sensitivity by lowering signal quality. Using the developed prototype power consumption, studies concerning the characterization of power consumption and signal quality over various working conditions are performed. Next, a performance merit figure is proposed as the main research contribution, which addresses the power consumption and signal quality trade-off subject. It aims to be used as an analysis for trade-offs between these two conflicting design criteria
On the implementation of a cloud-based computing test bench environment for prolog systems
Get PDFSoftware testing and benchmarking are key components of the software development process. Nowadays, a good practice in large software projects is the continuous integration (CI) software development technique. The key idea of CI is to let developers integrate their work as they produce it, instead of performing the integration at the end of each software module. In this paper, we extend a previous work on a benchmark suite for the YAP Prolog system, and we propose a fully automated test bench environment for Prolog systems, named Yet Another Prolog Test Bench Environment (YAPTBE), aimed to assist developers in the development and CI of Prolog systems. YAPTBE is based on a cloud computing architecture and relies on the Jenkins framework as well as a new Jenkins plugin to manage the underlying infrastructure. We present the key design and implementation aspects of YAPTBE and show its most important features, such as its graphical user interface (GUI) and the automated process that builds and runs Prolog systems and benchmarks. © 2017 by the authors
Vertical land motion and sea level change in Macaronesia
Get PDFThis study addresses long-term sea level variability in Macaronesia from a holistic perspective using all available instrumental records in the region, including a dense network of GPS continuous stations, tide gauges and satellite observations. A detailed assessment of vertical movement from GPS time series underlines the influence of the complex volcano-tectonic setting of the Macaronesian islands in local uplift/subsidence. Relative sea level for the region is spatially highly variable, ranging from -1.1 to 5.1 mm yr(-1). Absolute sea level from satellite altimetry exhibits consistent trends in the Macaronesia, with a mean value of 3.0 +/- 0.5 mm yr(-1). Typically, sea level trends from tide gauge records corrected for vertical movement using the estimates from GPS time series are lower than uncorrected estimates. The agreement between satellite altimetry and tide gauge trends corrected for vertical land varies substantially from island to island. Trends derived from the combination of GPS and tide gauge observations differ by less than 1 mm yr(-1) with respect to absolute sea level trends from satellite altimetry for 56 per cent of the stations, despite the heterogeneity in length of both GPS and tide gauge series, and the influence of volcanic-tectonic processes affecting the position of some GPS stations
Price forecasting of electricity markets in the presence of a high penetration of wind power generators
No full textPrice forecasting plays a vital role in the day-ahead markets. Once sellers and buyers access an accurate price forecasting, managing the economic risk can be conducted appropriately through offering or bidding suitable prices. In networks with high wind power penetration, the electricity price is influenced by wind energy; therefore, price forecasting can be more complicated. This paper proposes a novel hybrid approach for price forecasting of day-ahead markets, with high penetration of wind generators based on Wavelet transform, bivariate Auto-Regressive Integrated Moving Average (ARIMA) method and Radial Basis Function Neural Network (RBFN). To this end, a weighted time series for wind dominated power systems is calculated and added to a bivariate ARIMA model along with the price time series. Moreover, RBFN is applied as a tool to correct the estimation error, and particle swarm optimization (PSO) is used to optimize the structure and adapt the RBFN to the particular training set. This method is evaluated on the Spanish electricity market, which shows the efficiency of this approach. This method has less error compared with other methods especially when it considers the effects of large-scale wind generators. © 2017 by the authors