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- NILM techniques for intelligent home energy management and ambient assisted living: a reviewPublication . Ruano, Antonio; Hernandez, Alvaro; Ureña, Jesus; Ruano, Maria; Garcia, JuanThe ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.
- A scalable and open source linear positioning system controllerPublication . Medeiros, M. C.; Fernandes, A. J. A.; Teixeira, C. A.; Ruano, M. GraçaThis paper is on the implementation of a dual axis positioning system controller. The system was designed to be used for space-dependent ultrasound signal acquisition problems, such as pressure field mapping. The work developed can be grouped in two main subjects: hardware and software. Each axis includes one stepper motor connected to a driver circuit, which is then connected to a processing unit. The graphical user interface is simple and clear for the user. The system resolution was computed as 127 mu m with an accuracy of 2.44 mu m. Although the target application is ultrasound signal acquisition, the controller can be applied to other devices that has up to four stepper motors. The application was developed as an open source software, thus it can be used or changed to fit different purposes.
- Cost/benefit criterion for selection of pulsed Doppler ultrasound spectral mean frequency and bandwidth estimatorsPublication . Ruano, M. Graça; Fish, P. J.A flexible selection criterion for spectral estimators based on the weighted statistical accuracy (benefit) of estimation of decisive spectral parameters under the constraint of low computational complexity (cost) is proposed. This new cost/benefit criterion also selects the model order for parametric spectral estimators - selecting model orders significantly lower than those determined by accepted criteria. The importance of different Doppler signal parameters (e.g., mean frequency and spectral bandwidth) and their accuracy of estimation is incorporated by the use of weighting factors. The use of this method with simulated Doppler signals led to the selection of the modified covariance Alt estimator. | A flexible selection criterion for spectral estimators based on the weighted statistical accuracy (benefit) of estimation of decisive spectral parameters under the constraint of low computational complexity (cost) is proposed. This new cost/benefit criterion also selects the model order for parametric spectral estimators - selecting model orders significantly lower than those determined by accepted criteria. The importance of different Doppler signal parameters (e.g., mean frequency and spectral bandwidth) and their accuracy of estimation is incorporated by the use of weighting factors. The use of this method with simulated Doppler signals led to the selection of the modified covariance Alt estimator.
- Evaluation of the influence of large temperature variations on the grey level content of B-mode imagesPublication . Alvarenga, A. V.; Teixeira, César; Ruano, M. Graça; Pereira, WagnerIn this work, the variation of the grey-level content of B-Mode images is assessed, when the medium is subjected to large temperature variations. The goal is to understand how the features obtained from the grey-level pattern can be used to improve the actual state-of-the-art methods for non-invasive temperature estimation (NITE). Herein, B-Mode images were collected from a tissue mimic phantom heated in a water bath. Entropy was extracted from image Grey-Level Co-occurrence Matrix, and then assessed for non-invasive temperature estimation. During the heating period, the average temperature varies from 27oC to 44oC, and entropy values were capable of identifying variations of 2.0oC. Besides, it was possible to quantify variations in the range from normal human body temperature (37oC) to critical values, as 41oC. Results are promising and encourage us to study the uncertainty associated to the experiment trying to improve the parameter sensibility.
- Time-variable blood flow averaged waveformsPublication . Leiria, A.; da Cruz Silva Andrade Madeira e Carvalho de Moura, Maria Margarida; Evans, D. H.; Ruano, M. GraçaThe computation of the average of random signals is addressed in this article. These averaging techniques are particularly relevant for Doppler blood flow clinical evaluation and research that usually accesses clinical indicators related to maximum and mean frequency, bandwidth and power variation over time. A novel technique is presented and compared to one previously described in the literature (Kitney and Giddens, "Analysis of blood velocity waveforms by phase shift averaging and autoregressive spectral estimation'', J. Biomech. Eng., 105, 398 - 404, 1983.). While the latter method is an iterative one, which sometimes does not converge, the former is a sequential technique, guaranteeing convergence. Clinical Doppler blood flow signals were averaged using both methods and the resulting waveforms were evaluated against the original signals, emphasising the impact on the relevant spectral parameters. The results obtained showed that, except for the maximum error in the computation of power variation over time, the errors produced are reduced by the proposed new algorithm.
- A method for sub-sample computation of time displacements between discrete signals based only on discrete correlation sequencesPublication . Teixeira, Cesar A.; Mendes, Luis; Graca Ruano, Maria; Pereira, Wagner C. A.In this paper, we propose a new method for sub-sample computation of time displacements between two sampled signals. The new algorithm is based on sampled auto- and cross-correlation sequences and takes into account only the sampled signals without the need for the customary interpolation and fitting procedures. The proposed method was evaluated and compared with other methods, in simulated and real signals. Four other methods were used for comparison: two based on cross-correlation plus fitting, one method based on spline fitting over the input signals, and another based on phase demodulation. With simulated signals, the proposed approach presented similar or better performance, concerning bias and variance, in almost all the tested conditions. The exception was signals with very low SNRs (<10 dB), for which the methods based on phase demodulation and spline fitting presented lower variances. Considering only the two methods based on cross-correlation, our approach presented improved results with signals with high and moderate noise levels. The proposed approach and other three out of the four methods used for comparison are robust in real data. The exception is the phase demodulation method, which may fail when applied to signals collected from real-world scenarios because it is very sensitive to phase changes caused by other oscillations not related to the main echoes. This paper introduced a new class of methods, demonstrating that it is possible to estimate sub-sample delay, based on discrete cross-correlations sequences without the need for interpolation or fitting over the original sampled signals. The proposed approach was robust when applied to real-world signals and presented a moderated computational complexity when compared to the other tested algorithms. Although the new method was tested using ultrasound signals, it can be applied to any time-series with observable events. (C) 2016 Elsevier Ltd. All rights reserved.
- A support vector machine seismic detector for early-warning applicationsPublication . Ruano, Antonio; Madureira, G.; Barros, O.; Khosravani, Hamid Reza; Ruano, M. Graça; Ferreira, P. M.This paper extends a Support Vector Machine (SVM) approach for the detection of seismic events, at the level of a seismic station. In previous works, it was shown that this approach produced excellent results, in terms of the Recall and Specificity measures, whether applied off-line or in a continuous scheme. The drawback was the time taken for achieving the detection, too large to be applied in a Early-Warning System (EWS). This paper shows that, by using alternative input features, a similar performance can be obtained, with a significant reduction in detection time. Additionally, it is experimentally proved that, whether off-line or in continuous operation, the best results are obtained when the SVM detector is trained with data originated from the respective seismic station.
- Métodos de soft computing para la estimación no invasiva de la temperatura en medios multicapa empleando ultrasonido retrodispersoPublication . Teixeira, C. A.; Pereira, W. C. A.; Ruano, M. Graça; Ruano, AntonioLa seguridad y eficacia de las terapias térmicas están ligadas con la determinación exacta de la temperatura, es por ello que la retroalimentacón de la temperatura en los métodos computacionales es de vital importancia.
- Seismic detection using support vector machinesPublication . Ruano, Antonio; Madureira, G.; Barros, O.; Khosravani, Hamid Reza; Ruano, M. Graça; Ferreira, P. M.This study describes research to design a seismic detection system to act at the level of a seismic station, providing a similar role to that of STA/LTA ratio-based detection algorithms. In a first step, Multi-Layer Perceptrons (MLPs) and Support Vector Machines (SVMs), trained in supervised mode, were tested. The sample data consisted of 2903 patterns extracted from records of the PVAQ station, one of the seismographic network’s stations of the Institute of Meteorology of Portugal (IM). Records’ spectral variations in time and characteristics were reflected in the input ANN patterns, as a set of values of power spectral density at selected frequencies. To ensure that all patterns of the sample data were within the range of variation of the training set, we used an algorithm to separate the universe of data by hyper-convex polyhedrons, determining in this manner a set of patterns that have a mandatory part of the training set. Additionally, an active learning strategy was conducted, by iteratively incorporating poorly classified cases in the training set. The proposed system best results, in terms of sensitivity and selectivity in the whole data ranged between 98% and 100%. These results compare very favourably with the ones obtained by the existing detection system, 50%, and with other approaches found in the literature. Subsequently, the system was tested in continuous operation for unseen (out of sample) data, and the SVM detector obtained 97.7% and 98.7% of sensitivity and selectivity, respectively. The classifier presented 88.4% and 99.4% of sensitivity and selectivity when applied to data of a different seismic station of IM. Due to the input features used, the average time taken for detection with this approach is in the order of 100 s. This is too long to be used in an early-warning system. In order to decrease this time, an alternative set of input features was tested. A similar performance was obtained, with a significant reduction in the average detection time (around 1.3 s). Additionally, it was experimentally proved that, whether off-line or in continuous operation, the best results are obtained when the SVM detector is trained with data originated from the respective seismic station.
- Intelligent non-invasive modeling of ultrasound-induced temperature in tissue phantomsPublication . Ferreira, R.; Ruano, M G; Ruano, AntonioRaising temperature of human cells (hyperthermia) is an ancient tool for tumor masses reduction and extinction, actually even before the existence of a molecular understanding of cancer cells. Hyperthermia is being increasingly used for patients' rehabilitation and oncological diseases' treatment but still constitutes a major driver for researching more efficient and reliable therapeutic usage aiming at outstanding patients wellbeing and socio-economic benefits. Efficient hyperthermia practice demands knowledge about the exact amount of heating required at a particular tissue location, as well as information concerning the spatial heating distribution. Both of these processes require accurate characterization. Until now, ultrasound heating treatments are being monitored by magnetic resonance imaging (MRI), recognized as being capable of achieving a 0.5 degrees C/cm(3) temperature resolution [1], thereby imposing a gold standard in this field. However, one can notice that MRI-based techniques, besides the inconvenient instrumental cost, obliges the presence of a team of expert clinicians and limits the hyperthermia ultrasound treatment area due to the space restrictions of an MRI examination procedure. This article introduces a novel noninvasive modelling approach of ultrasound-induced temperature propagation in tissues, to be used as a cost effective alternative to MRI monitoring of ultrasound therapeutic techniques, achieving a maximum temperature resolution of 0.26 degrees C/cm(3), clearly inferior to the MRI gold standard resolution of 0.5 degrees C/cm(3). In order to derive the model, and avoiding painful invasive in-vivo sampling, a phantom was employed, whose composition respects the human tissues' reaction to ultrasound beams. In contrast with previous works of the authors, in the present paper we study the possibility of using b-spline neural networks (BSNN) as reliable noninvasive estimator of temperature propagation in phantoms [2,3]. The proposed methodology achieves better results than previous approaches, does not require the use of an Imaging Ultrasound transducer and, as the proposed models are piecewise polynomial models, they can be easily inverted and used in closed-loop control of therapeutic ultrasound instruments. (C) 2016 Elsevier Ltd. All rights reserved.