Signals

Deep learning for biomedical signal analysis is revolutionizing the analysis of complex physiological signals through the extraction of intricate patterns from physiological and wearable sensor data. This Special Issue brings together five articles that address cardiac, neural, muscular, and motion signals using complementary approaches across signal processing, machine learning, deep representation learning, and wearable sensing. Two contributions focus on ECG: one introduces a constraint-based method to suppress false positives in P/R/T peak detection, and the other one presents an interactive GUI for rapid R-peak correction. In the third study, an EEG biometrics system was developed that uses 3D brain mapping and autoencoder features, and the fourth study compares machine classifiers for athlete movement recognition from EMG. In the final study, wearable IMUs were used to improve jump height estimation via optimal sensor placement. We summarize these works and highlight themes such as physiological prior integration, human–AI interaction, and multi-sensor fusion. This Editorial concludes with the identification of future directions, including multi-modal modeling and adaptive learning in real-world settings.

Smart agriculture is a key application domain of the Fourth Industrial Revolution (4IR), where cyber–physical systems, the Internet of Things (IoT), and data-driven analytics converge to improve productivity, sustainability, and resource efficiency. In this context, large volumes of sensor data collected in the field form the basis for automated monitoring, decision support, and adaptive control across agricultural processes. However, the value of such data depends not only on sensing technologies and connectivity, but also on how data are generated, transferred, processed, and interpreted throughout their lifecycle. This paper is organized in two parts. The first part reports a narrative review of the literature, based on a documented search protocol, and provides an overview of sensor data and sensor data transmission networks in smart agriculture. It examines the main challenges, potentials, and limitations associated with IoT-based field deployments in 4IR environments, and consolidates them into ten open problems. These problems are ordered from the device layer to the socio-economic layer and span physical robustness, device security, rural connectivity, aerial sensing, interoperability, labelled-data scarcity, explainability, federated access control, data ownership, and cost. The second part responds to these open problems with the conceptualization of a layered architecture and a set of design proposals, covering hierarchical edge security, offline-first synchronization, semantic interoperability, explainable decision support, and farmer-centric data governance, that are grounded in established techniques and standards but have not been implemented or empirically validated in this work; they are intended as a design blueprint and a starting point for future prototyping and evaluation rather than as measured results. The paper is accordingly positioned as a conceptual, perspective-oriented contribution: it states the procedure by which the open problems were derived from the reviewed works, it states what the proposed integration is claimed to add over existing smart-agriculture architectures, and it sets out an evaluation plan that fixes the metrics and the baselines against which each proposed component would have to be tested. Throughout both parts, particular attention is given to the role of data integrity and provenance as foundational elements for trustworthy agricultural data, enabling reliable analysis, auditability, and long-term reuse. By synthesizing current approaches, open issues, and conceptual solution patterns, this paper aims to support the design of resilient, transparent, and future-ready smart agriculture systems aligned with the principles of the 4IR.

Accurate restoration and frequency tracking of non-stationary Doppler signals remain difficult under mixed and distribution-shifted noise. This study proposes EMD-THNet, a supervised one-dimensional framework that preserves the full 4096-sample resolution after offline empirical mode decomposition (EMD), combines a raw-waveform branch with a shared IMF encoder and energy-guided masked attention, and uses a hierarchical temporal encoder-decoder with waveform-restoration and instantaneous-frequency heads. The model was trained, validated, and tested using 96, 32, and 32 independently generated AM/FM windows sampled at 500 MHz. Robustness was further assessed through 1080 paired evaluations covering six input-SNR levels and six noise types. On the held-out synthetic set, EMD-THNet achieved an output SNR of 14.470 dB, an SNR gain of 12.296 dB (95% CI: 10.849–13.742 dB), and an SSIM of 0.8176. Six baselines were organized as classical filtering, Bayesian state estimation, and deep-learning temporal models. Relative to the strongest SNR-gain baseline in each group, EMD-THNet showed paired output-SNR advantages of 9.499, 4.676, and 2.389 dB, respectively (Holm-adjusted Wilcoxon p ≤ 7.68 × 10⁻⁸ for these three group-leading comparisons). On TSMS-Drone real I/Q waveforms with controlled added noise, zero-shot transfer yielded an SNR gain of 0.556 dB (95% CI: −0.316–1.416 dB), whereas adaptation using disjoint training distances yielded 10.241 dB (95% CI: 10.015–10.449 dB). These results demonstrate the effectiveness of EMD-THNet across the evaluated synthetic and semi-synthetic protocols and indicate its potential for robust Doppler-signal restoration and frequency tracking in future real-flight sensing applications.