Adaptive optics for structured illumination microscopy based on deep learning.

Structured illumination microscopy (SIM) is widely used in biological imaging for its high resolution, fast imaging speed, and simple optical setup. However, when imaging thick samples, the structured illumination patterns in SIM will suffer from optical aberrations, leading to a serious deterioration in resolution. Therefore, it is necessary to reconstruct structured illumination patterns with high quality and efficiency in deep tissue imaging.

Wavefront reconstruction based on deep transfer learning for microscopy.

The application of machine learning in wavefront reconstruction has brought great benefits to real-time, non-invasive, deep tissue imaging in biomedical research. However, due to the diversity and heterogeneity of biological tissues, it is difficult to train the dataset with a unified model. In general, the utilization of some unified models will result in the specific sample falling outside the training set, leading to low accuracy of the machine learning model in some real applications.

Aberration corrections of doughnut beam by adaptive optics in the turbid medium.

The doughnut beam is a spatially structured beam which has been widely used in super-resolution microscopy, laser trapping and so on. However, when it passes through thick scattering medium, aberrations will seriously affect its performance. Currently, adaptive optics (AO) has become one of the most powerful tools to compensate aberrations.

Machine learning based adaptive optics for doughnut-shaped beam.

The doughnut-shaped beam has been widely applied in the field of super-resolution microscopic imaging, micro-nanostructure lithography, ultra-high-density storage, and laser trapping. However, how to maintain the doughnut-shaped focus inside the scattering medium becomes a challenge, due to the wavefront aberrations. Here we demonstrate a machine learning based adaptive optics method to recover the doughnut-shaped focus with high speed.

Large field of view correction by using conjugate adaptive optics with multiple guide stars.

Adaptive optics has been widely used in the optical microscopy to recover high-resolution images deep into the sample. However, the corrected field of view (FOV) with a single correction is generally limited, which seriously restricts the imaging speed. In this article, we demonstrate a high-speed wavefront correction method by using the conjugate adaptive optical correction with multiple guide stars (CAOMG) based on the coherent optical adaptive technique.

Optimization for imaging through scattering media for confocal microscopes with divided elliptical apertures.

We develop a confocal system equipped with optimal elliptical apertures to improve axial point spread function and signal-to-background ratio (SBR) for different detector sizes. By adjusting the parameters of the elliptical apertures, the axial half width at half-maximum can be reduced to 4.986 (described in optical coordinates) and SBR can be improved to 0.

A novel on-line spatial-temporal k-anonymity method for location privacy protection from sequence rules-based inference attacks.

Analyzing large-scale spatial-temporal k-anonymity datasets recorded in location-based service (LBS) application servers can benefit some LBS applications. However, such analyses can allow adversaries to make inference attacks that cannot be handled by spatial-temporal k-anonymity methods or other methods for protecting sensitive knowledge. In response to this challenge, first we defined a destination location prediction attack model based on privacy-sensitive sequence rules mined from large scale anonymity datasets.

Location Prediction Based on Transition Probability Matrices Constructing from Sequential Rules for Spatial-Temporal K-Anonymity Dataset.

Spatial-temporal k-anonymity has become a mainstream approach among techniques for protection of users' privacy in location-based services (LBS) applications, and has been applied to several variants such as LBS snapshot queries and continuous queries. Analyzing large-scale spatial-temporal anonymity sets may benefit several LBS applications. In this paper, we propose two location prediction methods based on transition probability matrices constructing from sequential rules for spatial-temporal k-anonymity dataset.