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Aime Lay-Ekuakille
Researcher at University of Salento
Publications - 275
Citations - 3476
Aime Lay-Ekuakille is an academic researcher from University of Salento. The author has contributed to research in topics: Signal & Computer science. The author has an hindex of 27, co-authored 257 publications receiving 2760 citations. Previous affiliations of Aime Lay-Ekuakille include University of Zagreb.
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Journal ArticleDOI
Empirical Mode Decomposition vs. Wavelet Decomposition for the Extraction of Respiratory Signal From Single-Channel ECG: A Comparison
Domenico Labate,Fabio La Foresta,Gianluigi Occhiuto,Francesco Carlo Morabito,Aime Lay-Ekuakille,P. Vergallo +5 more
TL;DR: Two techniques of decomposition of the ECG signal into suitable bases of functions are proposed, such as the empirical mode decomposition (EMD) and the wavelet analysis, and performance achieved by applying these algorithms to extract the respiratory waveform shape from single-channel ECG is presented.
Journal ArticleDOI
Multimodal Medical Image Sensor Fusion Framework Using Cascade of Wavelet and Contourlet Transform Domains
TL;DR: A two-stage multimodal fusion framework using the cascaded combination of stationary wavelet transform (SWT) and non sub-sampled Contourlet Transform (NSCT) domains for images acquired using two distinct medical imaging sensor modalities is presented.
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An IoT Cloud System for Traffic Monitoring and Vehicular Accidents Prevention Based on Mobile Sensor Data Processing
Antonio Celesti,Antonino Galletta,Lorenzo Carnevale,Maria Fazio,Aime Lay-Ekuakille,Massimo Villari +5 more
TL;DR: This paper discusses an IoT Cloud system for traffic monitoring and alert notification based on OpenGTS and MongoDB, and proves that the system provides acceptable response times that allows drivers to receive alert messages in useful time so as to avoid the risk of possible accidents.
Journal ArticleDOI
Entropy Index in Quantitative EEG Measurement for Diagnosis Accuracy
Aime Lay-Ekuakille,P. Vergallo,G. Griffo,Francesco Conversano,Sergio Casciaro,Shabana Urooj,Vikrant Bhateja,Antonio Trabacca +7 more
TL;DR: The research proposes a multidimensional approach with a combined use of decimated signal diagonalization (DSD) as basis from which it is possible to work by finding appropriate signal windows for revealing expected information and overcoming signal processing limitations encountered in quantitative EEG.
Journal ArticleDOI
Deep ConvLSTM With Self-Attention for Human Activity Decoding Using Wearable Sensors
TL;DR: A deep neural network architecture that not only captures the spatio-temporal features of multiple sensor time-series data but also selects, learns important time points by utilizing a self-attention mechanism is proposed.