Driven by the increasing demand for intelligent wearable electronics, pressure sensors have attracted substantial research interest. However, a pressure sensor that possesses both high sensitivity and wearable comfort for practical application in daily activities is still lacking. Herein, we design a fabric−elastomer hybrid pressure sensor that achieves a balance between sensing performance and comfort. In this well-designed sensor, medical gauze coated with silver nanowires acts as substrate to improve the comfort of the sensor, and an elastomer acts as an active sensing element to enhance the sensitivity of the sensor. The sensor exhibits exciting sensing performance, including a high sensitivity (58 kPa −1 , 0−0.5 kPa), long-term endurance (>27 500 cycles), a faster response speed (<27 ms), and an ultralow limit of detection (2.7 Pa). Additionally, by adopting a prestretchable medical bandage as the substrate, the resulting sensor is insensitive to tensile strain and can accurately detect pressure stimuli under complex conditions. Then, we verify the application of the sensor in different scenarios, such as sensing tiny objects, monitoring human physiological information, and recognizing body motion. Additionally, we integrate a 4 × 4 sensor array for spatial information monitoring to provide a proof of concept for future wearable electronics, especially intelligent medical diagnostic systems.

No abstract

No abstract

Automated machine learning (AutoML) usually involves several crucial components, such as Data Augmentation (DA) policy, Hyper-Parameter Optimization (HPO), and Neural Architecture Search (NAS). Although many strategies have been developed for automating these components in separation, joint optimization of these components remains challenging due to the largely increased search dimension and the variant input types of each component. Meanwhile, conducting these components in a sequence often requires careful coordination by human experts and may lead to sub-optimal results. In parallel to this, the common practice of searching for the optimal architecture first and then retraining it before deployment in NAS often suffers from low performance correlation between the search and retraining stages. An endto-end solution that integrates the AutoML components and returns a ready-to-use model at the end of the search is desirable. In view of these, we propose DHA, which achieves joint optimization of Data augmentation policy, Hyper-parameter and Architecture. Specifically, end-to-end NAS is achieved in a differentiable manner by optimizing a compressed lowerdimensional feature space, while DA policy and HPO are updated dynamically at the same time. Experiments show that DHA achieves state-of-the-art (SOTA) results on various datasets, especially 77.4% accuracy on ImageNet with cell based search space, which is higher than current SOTA by 0.5%. To the best of our knowledge, we are the first to efficiently and jointly optimize DA policy, NAS, and HPO in an end-to-end manner without retraining.