Publications by authors named "Jiluan Liu"
Article Synopsis
- Wearable verbal language servers enhance interactions between humans and machines using advanced acoustic vibration sensors that can pick up sound signals.
- By integrating hydrogen bonding with protein micro-fibers and ionic hydrogel, the sensors achieve improved responsiveness, stability, and pressure sensitivity, with notable specs like a 0.6 ms response time and a detection limit of 0.12 Pa.
- Utilizing a 1D convolutional neural network for signal processing, these sensors can accurately recognize acoustic signals, achieving a high accuracy rate of 98.2%, making them ideal for applications in biometric authentication and human-computer interaction.
Traditional methods under sparse view for reconstruction of photoacoustic tomography (PAT) often result in significant artifacts. Here, a novel image to image transformation method based on unsupervised learning artifact disentanglement network (ADN), named PAT-ADN, was proposed to address the issue. This network is equipped with specialized encoders and decoders that are responsible for encoding and decoding the artifacts and content components of unpaired images, respectively.
View Article and Find Full Text PDFSignificance: Photoacoustic imaging is an emerging imaging modality that combines the high contrast of optical imaging and the high penetration of acoustic imaging. However, the strong focusing of the laser beam in optical-resolution photoacoustic microscopy (OR-PAM) leads to a limited depth-of-field (DoF).
Aim: Here, a volumetric photoacoustic information fusion method was proposed to achieve large volumetric photoacoustic imaging at low cost.