The application potential of wearable electronics in the healthcare field has been of great interest over the past several decades. Flexible and wearable devices based on skin-friendly soft elastic materials can be snugly attached to the surface of human skin, so that a series of vital health information such as wrist pulse, body temperature, and blood glucose can be extracted and analyzed to help the patient maintain physical fitness. Here, we outlined the most common types of wearable electronics for monitoring human health information, including force sensors, temperature sensors, physiological biochemical sensors, and multifunctional sensors. Their general working principles and structural innovations are reviewed. Then, we discussed two functional modules that make the wearable sensors more applicable in real life—self-powered module and signal processing module. The challenges and future research directions are also proposed to develop wearable electronics for monitoring human health information.

In the domain of automatic visual inspection for miniature capacitor quality control, the task of accurately detecting defects presents a formidable challenge. This challenge stems primarily from the small size and limited sample availability of defective micro-capacitors, which leads to issues such as reduced detection accuracy and increased false-negative rates in existing inspection methods. To address these challenges, this paper proposes an innovative approach employing an enhanced ‘you only look once’ version 8 (YOLOv8) architecture specifically tailored for the intricate task of micro-capacitor defect inspection. The merging of the bidirectional feature pyramid network (BiFPN) architecture and the simplified attention module (SimAM), which greatly improves the model’s capacity to recognize fine features and feature representation, is at the heart of this methodology. Furthermore, the model’s capacity for generalization was significantly improved by the addition of the weighted intersection over union (WISE-IOU) loss function. A micro-capacitor surface defect (MCSD) dataset comprising 1358 images representing four distinct types of micro-capacitor defects was constructed. The experimental results showed that our approach achieved 95.8% effectiveness in the mean average precision (mAP) at a threshold of 0.5. This indicates a notable 9.5% enhancement over the original YOLOv8 architecture and underscores the effectiveness of our approach in the automatic visual inspection of miniature capacitors.

In this work, we irradiated a high-definition (HD) industrial camera based on a commercial-off-the-shelf (COTS) CMOS image sensor (CIS) with Cobalt-60 gamma-rays. All components of the camera under test were fabricated without radiation hardening, except for the lens. The irradiation experiments of the HD camera under biased conditions were carried out at 1.0, 10.0, 20.0, 50.0 and 100.0 Gy/h. During the experiment, we found that the tested camera showed a remarkable degradation after irradiation and differed in the dose rates. With the increase of dose rate, the same target images become brighter. Under the same dose rate, the radiation effect in bright area is lower than that in dark area. Under different dose rates, the higher the dose rate is, the worse the radiation effect will be in both bright and dark areas. And the standard deviations of bright and dark areas become greater. Furthermore, through the progressive degradation analysis of the captured image, experimental results demonstrate that the attenuation of signal to noise ratio (SNR) versus radiation time is not obvious at the same dose rate, and the degradation is more and more serious with increasing dose rate. Additionally, the decrease rate of SNR at 20.0, 50.0 and 100.0 Gy/h is far greater than that at 1.0 and 10.0 Gy/h. Even so, we confirm that the HD industrial camera is still working at 10.0 Gy/h during the 8 h of measurements, with a moderate decrease of the SNR (5 dB). The work is valuable and can provide suggestion for camera users in the radiation field.

This paper presents YOLO-FIX, an improved intelligent detection model based on YOLOv11, designed to identify glue line defects in mobile phone frames. The model addresses the challenges of complex glue line morphology, background interference, and illumination transformation. YOLO-FIX enhances the extraction of local and global features to optimize the detection accuracy by integrating advanced attention mechanisms and multi-scale feature fusion modules specifically Deformable Large-Kernel Attention (De-formable-LSKA) and Mamba-Like Linear Attention (MLLA). Experimental evaluations demonstrate that YOLO-FIX achieves a mean Average Precision (mAP50) of 95.2%, an 8.6% improvement over the baseline YOLOv11 model while maintaining a real-time detection speed of 189 FPS. It effectively identifies five common defect types: broken glue, wall climbing, glue dropping, single-tip wall climbing, and collapsed glue, showcasing exceptional robustness and generalization across varying production environments. These results affirm YOLO-FIX as a highly accurate and efficient solution for automated defects in industrial applications.