Flexible tactile sensors are garnering substantial interest for various promising applications, including artificial intelligence, prosthetics, healthcare monitoring, and human–machine interactions (HMI). However, it still remains a critical challenge in developing high‐resolution tactile sensors without involving high‐cost and complicated manufacturing processes. Herein, a flexible high‐resolution triboelectric sensing array (TSA) for self‐powered real‐time tactile sensing is developed through a facile, mask‐free, high‐efficient, and environmentally friendly laser direct writing technique. A 16 × 16 pixelated TSA with a resolution of 8 dpi based on patterned laser‐induced graphene (LIG) electrodes (7 Ω sq−1) is fabricated by the complementary intersection overlapping between upper and lower aligned semicircular electrode arrays. With the especially patterning design, the complexity of TSA and the number of data channels is reduced. Meanwhile, the TSA platform exhibits excellent durability and synchronicity and enables the achievement of real‐time visualization of multipoint touch, sliding, and tracking motion trajectory without power consumption. Furthermore, a smart wireless controlled HMI system, composed of a 9‐digital arrayed touch panel based on a LIG‐patterned triboelectric nanogenerator, is constructed to control personal electronics wirelessly. Consequently, the self‐powered TSA as a promising platform demonstrates great potential for an active real‐time tactile sensing system, wireless controlled HMI, security identification and, many others.

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PurposeQuantification of carotid plaques has been shown to be important for assessing as well as monitoring the progression and regression of carotid atherosclerosis. Various metrics have been proposed and methods of measurements ranging from manual tracing to automated segmentations have also been investigated. Of those metrics, quantification of carotid plaques by measuring vessel‐wall‐volume (VWV) using the segmented media‐adventitia (MAB) and lumen‐intima (LIB) boundaries has been shown to be sensitive to temporal changes in carotid plaque burden. Thus, semi‐automatic MAB and LIB segmentation methods are required to help generate VWV measurements with high accuracy and less user interaction.MethodsIn this paper, we propose a semiautomatic segmentation method based on deep learning to segment the MAB and LIB from carotid three‐dimensional ultrasound (3DUS) images. For the MAB segmentation, we convert the segmentation problem to a pixel‐by‐pixel classification problem. A dynamic convolutional neural network (Dynamic CNN) is proposed to classify the patches generated by sliding a window along the norm line of the initial contour where the CNN model is fine‐tuned dynamically in each test task. The LIB is segmented by applying a region‐of‐interest of carotid images to a U‐Net model, which allows the network to be trained end‐to‐end for pixel‐wise classification.ResultsA total of 144 3DUS images were used in this development, and a threefold cross‐validation technique was used for evaluation of the proposed algorithm. The proposed algorithm‐generated accuracy was significantly higher than the previous methods but with less user interactions. Comparing the algorithm segmentation results with manual segmentations by an expert showed that the average Dice similarity coefficients (DSC) were 96.46 ± 2.22% and 92.84 ± 4.46% for the MAB and LIB, respectively, while only an average of 34 s (vs 1.13, 2.8 and 4.4 min in previous methods) was required to segment a 3DUS image. The interobserver experiment indicated that the DSC was 96.14 ± 1.87% between algorithm‐generated MAB contours of two observers' initialization.ConclusionsOur results showed that the proposed carotid plaque segmentation method obtains high accuracy and repeatability with less user interactions, suggesting that the method could be used in clinical practice to measure VWV and monitor the progression and regression of carotid plaques.

International audienceThe spontaneous flapping of a flag in a steady flow can be used to power an output circuit usingpiezoelectric elements positioned at its surface. Here, we study numerically the effect of inductive circuits on the dynamics of this fluid-solid-electric system and on its energy-harvesting efficiency. In particular, a destabilization of the system is identified, leading to energy harvesting at lower flow velocities. Also, a frequency lock-in between the flag and the circuit is shown to significantly enhance the system’s harvesting efficiency. These results suggest promising efficiency enhancements of such flow-energy harvesters through the output circuit optimization