Signals

This study presents a harmonic-based method for non-contact heart rate (HR) estimation from continuous-wave (CW) Doppler radar signals, validated across multiple species including humans and small animals (cat). Traditional frequency-domain methods struggle when the HR fundamental frequency is weak or overlaps with respiratory components. The proposed approach addresses this by identifying three higher-order HR harmonics (2nd, 3rd, and 4th) then reconstructing the HR fundamental frequency from their integer ratios (3/2, 4/3, 2/1). The algorithm processes 20-s sliding windows (1-s overlap) using bandpass filtering to remove respiratory components and HR fundamental while preserving higher harmonics, followed by Power Spectral Density (PSD) analysis. When a complete harmonic set cannot be found, the proposed algorithm switches to harmonic pair detection, enhancing robustness when one harmonic is absent or attenuated. Besides, an adaptive tolerance mechanism enables detection under non-ideal conditions. The method was validated using a public human dataset and an experimental cat dataset with varied positions (supine/prone) and anesthesia levels (1–3% isoflurane). For humans, the algorithm achieved HR Accuracy consistently above 98% with an average RMSE of 1.33 bpm (MAPE: 1.29%, MAE: 0.86 bpm) and Bland-Altman bias below 0.9 bpm. For the cat dataset, performance was even better with HR Accuracy remaining above 99%, an average RMSE of 0.39 bpm (MAPE: 0.22%, MAE: 0.30 bpm), and bias below 0.14 bpm.

Signal-to-noise ratio (SNR) estimation plays a crucial role in communication systems, directly impacting the quality and reliability of signal transmission. This paper proposes a novel deep learning framework aimed at enhancing the accuracy and robustness of SNR estimation. The framework converts received signals into time–frequency matrices as feature inputs, effectively capturing both temporal and spectral characteristics through time–frequency analysis. Extensive experimental results across an SNR range of −5 dB to 15 dB demonstrate that our method achieves a mean squared error (MSE) that closely approaches the theoretical Cramér–Rao bound (CRB), comparable to data-aided (DA) maximum likelihood methods. A quantitative analysis reveals that, even under challenging conditions, such as a low SNR of −5 dB, the model maintains superior accuracy with a mean absolute error (MAE) as low as 0.352, significantly outperforming traditional M2M4 and NDA estimators. The model’s performance was systematically evaluated in a wide range of scenarios, encompassing various signal modulation formats, upsampling factors, multipath fading channels, frequency offsets, phase shifts, and roll-off factors. The evaluation highlights its exceptional generalization capability and robustness, with high performance and stability maintained even in challenging and dynamic environments.

Modern manufacturing quality control demands intelligent, adaptive inspection systems capable of real-time contamination detection with minimal computational overhead. We present a five-agent vision framework for material-aware contamination detection in manufacturing environments. The system comprises: a Material Classification Agent that identifies contamination type (fiber, sand, or mixed), three Material-Specific Detection Agents, each employing patch-based CNNs optimized for their respective material with dynamic patch size selection (128 px, 256 px, 384 px), and an Adaptation Agent that monitors performance and eliminates consistently failing patch size configurations. This hierarchical architecture enables intelligent routing to specialized detectors and continuous refinement through performance-driven adaptation. The Material Classification Agent achieves 98% accuracy in contamination type identification. Material-specific agents demonstrate F1-scores of 0.968 (fiber), 0.977 (sand), and 0.977 (mixed) with real-time inference (2.40–11.11 ms per 512 × 512 image). The Adaptation Agent implements selective patch size elimination: configurations failing quality thresholds (F1 < 0.5) across multiple evaluation cycles are removed from the detection pipeline. On the synthetic test split used in this study, comparative evaluation against PatchCore, WinCLIP, and PaDiM shows 3–45× higher F1-scores with superior accuracy–latency trade-offs, validating the efficacy of specialized material-aware architectures for manufacturing contamination detection.