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Nuno Pombo

Publications and source records attributed to Nuno Pombo.

8 recordsLinked to original sources

ANADEF: A Nested-Permutation Alarm for Dual-Parameter Earthquake Forecasting

Spatially resolved stress proxies and rate-based seismicity models are increasingly combined for regional earthquake forecasting, yet formally testing their non-redundancy remains largely unaddressed. We present the Nested-Permutation Alarm for Dual-Parameter Earthquake Forecasting (ANADEF) pipeline, integrating a stress-sensitive Gutenberg--Richter $b$-value field, estimated via a penalized 2D B-spline inversion, with a stationary background rate ($\mu$) from space--time ETAS stochastic declustering. Applied to the Zagros Fold--Thrust Belt using an 18-year catalog ($n=40{,}731$, $M_{\mathrm{N}}\geq1.5$, 2006--2024) under a two-stage protocol with non-overlapping training (2006--2014) and target (2015--2024) windows, the model achieves, for $M_w\geq5.0$ ($N=55$), a retrospective Area Skill Score $S=0.69$ (95\% CI: 0.64--0.73), reducing alarmed area from $\tau\approx0.38$ ($\mu$-only) to $\tau\approx0.28$ while retaining hit rate $\nu=92.7\%$; since thresholds are calibrated on the evaluation catalog, these are in-sample, not out-of-sample, estimates. A nested permutation procedure re-optimizing thresholds within each null realization tests whether $b$-value adds information beyond $\mu$, absorbing the optimization bias. Significance is null-model dependent: cell-wise randomization yields $p=0.012$, while a conservative spatial-structure-preserving null gives weaker, non-significant evidence ($p=0.057$)---incremental stress information is suggestive but not unambiguously established. Calibrated thresholds were frozen and applied to updated 2015--2024 fields, generating an unvalidated, forward-looking spatial alarm template for 2025--2029. These results establish a reproducible, statistically transparent methodology for testing, rather than assuming, complementarity between stress-sensitive and rate-based predictors, offering a candidate operational template pending prospective validation.

physics.geo-ph

A Cluster-Based Opposition Differential Evolution Algorithm Boosted by a Local Search for ECG Signal Classification

Electrocardiogram (ECG) signals, which capture the heart's electrical activity, are used to diagnose and monitor cardiac problems. The accurate classification of ECG signals, particularly for distinguishing among various types of arrhythmias and myocardial infarctions, is crucial for the early detection and treatment of heart-related diseases. This paper proposes a novel approach based on an improved differential evolution (DE) algorithm for ECG signal classification for enhancing the performance. In the initial stages of our approach, the preprocessing step is followed by the extraction of several significant features from the ECG signals. These extracted features are then provided as inputs to an enhanced multi-layer perceptron (MLP). While MLPs are still widely used for ECG signal classification, using gradient-based training methods, the most widely used algorithm for the training process, has significant disadvantages, such as the possibility of being stuck in local optimums. This paper employs an enhanced differential evolution (DE) algorithm for the training process as one of the most effective population-based algorithms. To this end, we improved DE based on a clustering-based strategy, opposition-based learning, and a local search. Clustering-based strategies can act as crossover operators, while the goal of the opposition operator is to improve the exploration of the DE algorithm. The weights and biases found by the improved DE algorithm are then fed into six gradient-based local search algorithms. In other words, the weights found by the DE are employed as an initialization point. Therefore, we introduced six different algorithms for the training process (in terms of different local search algorithms). In an extensive set of experiments, we showed that our proposed training algorithm could provide better results than the conventional training algorithms.

cs.NE

Identifying Packet Loss and Reordering Packets in Keyed UDP Transmissions

The User Datagram Protocol (UDP) and other similar protocols send the application data from the source machine to the destination machine inside segments, without foreseeing nor allowing for any type of control on the transmission or success metrics. These protocols are very convenient for e.g. real time data transmission. But when the reliability of the transmitted data is critical, other protocols termed as connection-oriented, allow for full control of the data transmission process, assuring that the received data is an exact copy of the transmitted data, e.g. the case of the Transmission Control Protocol (TCP). To sustain the increased functionality and features of the connection-oriented protocol, a set of mechanisms is implemented based on some specific fields of the segment header. These mechanisms result in a significant overhead in terms of the increased number of transmitted packets. This may further translate into significant delays, because of the additional number of switching and routing tasks, and eventually, because of more complex communications procedures, such as e.g. transmission window resizing, and of course, acknowledgement and sequence numbers updating. The two extremes of these communication modalities, one that has no control at all, and the other one that allows for full control, have resulted in the creation of an intermediate protocol that allows for a limited degree of knowledge on how successful a transmission was, and even for an eventual reordering of the segments that arrive out of sequence. This paper presents simulation results that confirm the efficiency of the new almost-reliable UDP protocol, termed Keyed User Datagram Protocol (or KUDP) for transmission of data that includes the ability to identify which packets were lost and to reorder packets that were received out-of-sequence, and points future tasks to be pursued in this research.

cs.NI

An Efficient Machine Learning-based Elderly Fall Detection Algorithm

Falling is a commonly occurring mishap with elderly people, which may cause serious injuries. Thus, rapid fall detection is very important in order to mitigate the severe effects of fall among the elderly people. Many fall monitoring systems based on the accelerometer have been proposed for the fall detection. However, many of them mistakenly identify the daily life activities as fall or fall as daily life activity. To this aim, an efficient machine learning-based fall detection algorithm has been proposed in this paper. The proposed algorithm detects fall with efficient sensitivity, specificity, and accuracy as compared to the state-of-the-art techniques. A publicly available dataset with a very simple and computationally efficient set of features is used to accurately detect the fall incident. The proposed algorithm reports and accuracy of 99.98% with the Support Vector Machine(SVM) classifier.

cs.LG

A Multiple Data Source Framework for the Identification of Activities of Daily Living Based on Mobile Device Data

Most mobile devices include motion, magnetic, acoustic, and location sensors. They allow the implementation of a framework for the recognition of Activities of Daily Living (ADL) and its environments, composed by the acquisition, processing, fusion, and classification of data. This study compares different implementations of artificial neural networks, concluding that the obtained results were 85.89% and 100% for the recognition of standard ADL. Additionally, for the identification of standing activities with Deep Neural Networks (DNN) respectively, and 86.50% for the identification of the environments with Feedforward Neural Networks. Numerical results illustrate that the proposed framework can achieve robust performance from the data fusion of off-the-shelf mobile devices.

cs.CY

Pattern Recognition Techniques for the Identification of Activities of Daily Living using Mobile Device Accelerometer

This paper focuses on the recognition of Activities of Daily Living (ADL) applying pattern recognition techniques to the data acquired by the accelerometer available in the mobile devices. The recognition of ADL is composed by several stages, including data acquisition, data processing, and artificial intelligence methods. The artificial intelligence methods used are related to pattern recognition, and this study focuses on the use of Artificial Neural Networks (ANN). The data processing includes data cleaning, and the feature extraction techniques to define the inputs for the ANN. Due to the low processing power and memory of the mobile devices, they should be mainly used to acquire the data, applying an ANN previously trained for the identification of the ADL. The main purpose of this paper is to present a new method implemented with ANN for the identification of a defined set of ADL with a reliable accuracy. This paper also presents a comparison of different types of ANN in order to choose the type for the implementation of the final method. Results of this research probes that the best accuracies are achieved with Deep Learning techniques with an accuracy higher than 80%.

cs.CY

User Environment Detection with Acoustic Sensors Embedded on Mobile Devices for the Recognition of Activities of Daily Living

The detection of the environment where user is located, is of extreme use for the identification of Activities of Daily Living (ADL). ADL can be identified by use of the sensors available in many off-the-shelf mobile devices, including magnetic and motion, and the environment can be also identified using acoustic sensors. The study presented in this paper is divided in two parts: firstly, we discuss the recognition of the environment using acoustic sensors (i.e., microphone), and secondly, we fuse this information with motion and magnetic sensors (i.e., motion and magnetic sensors) for the recognition of standing activities of daily living. The recognition of the environments and the ADL are performed using pattern recognition techniques, in order to develop a system that includes data acquisition, data processing, data fusion, and artificial intelligence methods. The artificial intelligence methods explored in this study are composed by different types of Artificial Neural Networks (ANN), comparing the different types of ANN and selecting the best methods to implement in the different stages of the system developed. Conclusions point to the use of Deep Neural Networks (DNN) with normalized data for the identification of ADL with 85.89% of accuracy, the use of Feedforward neural networks with non-normalized data for the identification of the environments with 86.50% of accuracy, and the use of DNN with normalized data for the identification of standing activities with 100% of accuracy.

cs.SD

Data Fusion on Motion and Magnetic Sensors embedded on Mobile Devices for the Identification of Activities of Daily Living

Several types of sensors have been available in off-the-shelf mobile devices, including motion, magnetic, vision, acoustic, and location sensors. This paper focuses on the fusion of the data acquired from motion and magnetic sensors, i.e., accelerometer, gyroscope and magnetometer sensors, for the recognition of Activities of Daily Living (ADL) using pattern recognition techniques. The system developed in this study includes data acquisition, data processing, data fusion, and artificial intelligence methods. Artificial Neural Networks (ANN) are included in artificial intelligence methods, which are used in this study for the recognition of ADL. The purpose of this study is the creation of a new method using ANN for the identification of ADL, comparing three types of ANN, in order to achieve results with a reliable accuracy. The best accuracy was obtained with Deep Learning, which, after the application of the L2 regularization and normalization techniques on the sensors data, reports an accuracy of 89.51%.

cs.CY