Blood pressure estimation through pulse wave analysis (PWA) aims to establish the relationship between features of pulse waveforms and blood pressure. This study is the first to investigate the connection between features of carotid artery diameter waveforms and variations in blood pressure, as well as to develop a blood pressure estimation model based on these features. A dataset was constructed from 14 subjects, with data collected across various physiological states and time points. For each subject, carotid artery diameter waveforms were measured using ultrasound, while synchronous blood pressure data were recorded with a reference device. A total of 52 morphological features were extracted from the diameter waveforms and their first and second derivatives. The influence of different models and feature combinations on blood pressure estimation was analyzed using various machine learning approaches. Ultimately, optimal models were developed for each subject to dynamic blood pressure fluctuations. On independent test data where blood pressure fluctuations exceeded 25 mmHg, the mean absolute error (MAE) of the estimates was 3.3 ± 4.1 mmHg. Even after a period of two days or more, the models remained effective, yielding a MAE of 4.2 ± 5.3 mmHg.

Energy detection is widely used in cooperative spectrum sensing because it requires little prior information about the primary signal, but its performance is sensitive to node-dependent noise power and the signal-to-noise ratio (SNR). This paper proposes a dynamic dual-threshold method under bounded noise power uncertainty. For each sensing node, the local energy statistic is modeled under the idle and occupied hypotheses, and a Bayes-optimal one-sided threshold is evaluated for every admissible noise power value in a multiplicative interval. The lower and upper thresholds are defined as the minimum and maximum of these candidate thresholds. Observations outside the interval are transmitted as one-bit hard decisions, whereas uncertain region observations are normalized, uniformly quantized, and combined at the fusion center by equal gain fusion. Controlled simulations at a matched global false alarm probability show that a well-calibrated fixed dual-threshold benchmark can be competitive near its design point, while node-specific dynamic adaptation becomes advantageous as the uncertainty level increases. The uncertain region reporting probability rises with the uncertainty bound, making the robustness–reporting tradeoff explicit. The results support dynamic threshold adaptation under moderate or relatively large noise power uncertainty and clarify its associated communication cost.

Existing metasurface-based radar countermeasures commonly manipulate echoes from metasurface-covered regions, whereas protecting exposed scattering centers that cannot accommodate metasurfaces remains challenging. This paper presents a multi-region time-coding metasurface (MRTCM) architecture for coherent target cancellation and deceptive jamming. The proposed system uses multiple independently controlled metasurface regions distributed over available platform surfaces. Each region generates zeroth-order and higher-order harmonic components through periodic phase modulation, and these components coherently combine with echoes from uncovered areas. The zeroth-order component is invariant under cyclic shifts of the coding sequence, enabling stable cancellation during multi-pulse coherent processing without locking radar-pulse arrival to the start of the coding cycle. Higher-order harmonics can shift the apparent range of the metasurface response and support cancellation at range-separated cells, but require accurate timing and synchronization. Numerical simulations demonstrate more than 40 dB suppression in high-resolution range profiles and validate the cancellation behavior under moving-target detection processing. The MRTCM architecture therefore provides a theoretically grounded framework for electromagnetic protection when full metasurface coverage is impractical.