Books

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Rodríguez-Jorge, R. (2025). Signal Processing and Machine Learning for Innovation Engineering: Understanding grey box and neural models. OmniaScience. DOI: 10.3926/oms.418

Abstract

This book presents recent advances in signal processing and artificial neural network (ANN) applications aimed at driving innovation in engineering. The proposed developments constitute advanced IT solutions for research, data access and knowledge management, built primarily on Internet of Things (IoT)-assisted architectures.

The techniques described integrate signal processing with wireless neural-network implementations. Several signal-processing, feature-selection and feature-extraction approaches are explored, including band-pass filters combined with numerical derivatives, the Hilbert transform, adaptive thresholding, moving-average filters, autocorrelation functions, wavelet transforms and the Hilbert–Huang transform.

For feature selection, methods such as feature normalization and the False Nearest Neighbor (FNN) technique are examined, while principal component analysis (PCA) is applied for feature extraction. The book also presents real-time tests of IoT-assisted architectures using acquired signals, focusing on the accuracy and performance of ANN models in tasks such as prediction, modelling, control, monitoring and classification.

The applications discussed include fault diagnosis, arrhythmia classification, electrocardiogram (ECG) analysis for global health monitoring, auto-scaling systems and anomaly detection. Overall, this work offers a comprehensive overview of how integrating signal processing and ANN techniques within IoT frameworks can advance intelligent engineering systems.