Training deep learning models for photoplethysmography(PPG)-based cuff-less blood pressure estimation often requires a substantial amount of labeled data collected through sophisticated medical instruments, posing significant challenges in practical applications. To address this issue, we propose Physiological Knowledge-Aware Contrastive Learning (PhysCL), a novel approach designed to reduce the dependence on labeled PPG data while improving blood pressure estimation accuracy. Specifically, PhysCL tackles the semantic consistency problem in contrastive learning by introducing a knowledge-aware augmentation bank, which generates positive physiological signal pairs using knowledge-based constraints during the contrastive pair generation. Additionally, we propose a contrastive feature reconstruction method to enhance feature diversity and prevent model collapse through feature re-sampling and re-weighting. We evaluate PhysCL on data from 106 subjects across the MIMIC III, MIMIC IV, and UQVS datasets under cross-dataset validation settings, comparing it against state-of-the-art contrastive learning methods and blood pressure estimation models. PhysCL achieves an average mean absolute error of 9.5/5.9 mmHg (systolic/diastolic) across the three datasets, using only 2% labeled data combined with 98% unlabeled data for pre-training and 5 samples for personalization, which represents a 6.2% /4.3% improvement, respectively, over the current best supervised methods. The ablation study provides further convincing evidence that the unlabeled data can be utilized to improve the existing cuff-less blood pressure estimation models and shed light on unsupervised contrastive learning for physiological signals.

In this article, a power-on-reset (POR) circuit with accurate trigger-point voltage and ultralow typical quiescent current for emerging nonvolatile memory (NVM) is presented. To keep the trigger-point voltage from the effect of process, voltage, and temperature (PVT) and supply ramp rate variations, low-cost current generators and a current comparator are adopted with brown-out detection (BOD). A protection circuit is utilized for correct operations in different BOD events. Meanwhile, delay blocks are utilized to generate reliable pulse signals that are less influenced by temperature and supply ramp rate. The simulation results show that the trigger-point voltage is 2.08 V with a temperature coefficient (TC) of 81.8 ppm/$^{\circ}$C and a deviation to the ramp time of 9.91% for a wide ramp time range from 10$\mu $s to dc. In addition, the reset duration time of the POR pulse is also reliable. The proposed POR circuit designed in the 55-nm CMOS process consumes only 0.65-nA typical quiescent current and 59$\times$146$\mu $m area, which is suitable for emerging NVM systems.

In this article, a low-cost quadruple-node-upsets resilient latch (LCQRL) design is proposed. To meet the high-reliability demands of safety-critical applications, the latch integrates nine soft-error-interceptive modules (SIMs) to form robust feedback loops, ensuring complete resilience to quadruple-node upsets (QNUs). Each Sim comprises ten CMOS transistors and a clocked inverter. Notably, C-element (CE) and dual interlocked storage cell (DICE) modules are not employed in this circuit, resulting in a small area and low power consumption. The simulation results verify the complete QNU self-recoverability and cost-effectiveness of this design. Compared with the existing radiation-hardened QNU resilient latches, the LCQRL latch demonstrates significant improvements in area, power consumption, and area-power–delay product (APDP) by 47.8%, 63%, and 75.5%, respectively. Furthermore, it exhibits low sensitivity to process, voltage, and temperature (PVT) variations.

For low-semantic sensor signals from human activity recognition (HAR), contrastive learning (CL) is essential to implement novel applications or generic models without manual annotation, which is a high-performance self-supervised learning (SSL) method. However, CL relies heavily on data augmentation for pairwise comparisons. Especially for low semantic data in the HAR area, conducting good performance augmentation strategies in pretext tasks still rely on manual attempts lacking generalizability and flexibility. To reduce the augmentation burden, we propose an end-to-end auto-augmentation contrastive learning (AutoCL) method for wearable-based HAR. AutoCL is based on a Siamese network architecture that shares the parameters of the backbone and with a generator embedded to learn auto-augmentation. AutoCL trains the generator based on the representation in the latent space to overcome the disturbances caused by noise and redundant information in raw sensor data. The architecture empirical study indicates the effectiveness of this design. Furthermore, we propose a stop-gradient design and correlation reduction strategy in AutoCL to enhance encoder representation learning. Extensive experiments based on four wide-used HAR datasets demonstrate that the proposed AutoCL method significantly improves recognition accuracy compared with other SOTA methods.