Self-similar pulse compression in a tapered Pb-silicate photonic crystal fiber at 2 µm.

We report a 2-µm all-fiber nonlinear pulse compressor based on a tapered Pb-silicate photonic crystal fiber (PCF), which is capable of achieving large compression with low pedestal energy. A tapered Pb-silicate photonic crystal fiber with increased nonlinear coefficients is proposed for achieving self-similar pulse compression (SSPC) at 2 µm. The dynamic evolution of the fundamental order soliton is numerically analyzed based on the designed tapered fiber.

Antiresonant fiber structures based on swarm intelligence design.

In this work, we obtained a new, to the best of our knowledge, structure of anti-resonant fiber (ARF) by an adaptive particle swarm optimization (PSO) algorithm. Different from the prior method of stacking elemental parts and optimizing parameters through experience or algorithm, we decompose some classic structures into points and optimize the positions of these points through swarm intelligence. The fiber structure is reconstructed by interpolation, and some new structures with low confinement loss (CL) and high higher order mode extinction ratio (HOMER) are obtained.

Construction of Tongue Image-Based Machine Learning Model for Screening Patients with Gastric Precancerous Lesions.

Screening patients with precancerous lesions of gastric cancer (PLGC) is important for gastric cancer prevention. The accuracy and convenience of PLGC screening could be improved with the use of machine learning methodologies to uncover and integrate valuable characteristics of noninvasive medical images related to PLGC. In this study, we therefore focused on tongue images and for the first time constructed a tongue image-based PLGC screening deep learning model (AITongue).

Decoding multilevel relationships with the human tissue-cell-molecule network.

Understanding the biological functions of molecules in specific human tissues or cell types is crucial for gaining insights into human physiology and disease. To address this issue, it is essential to systematically uncover associations among multilevel elements consisting of disease phenotypes, tissues, cell types and molecules, which could pose a challenge because of their heterogeneity and incompleteness. To address this challenge, we describe a new methodological framework, called Graph Local InfoMax (GLIM), based on a human multilevel network (HMLN) that we established by introducing multiple tissues and cell types on top of molecular networks.

Imaging for Small UAV-Borne FMCW SAR.

Unmanned aerial vehicle borne frequency modulated continuous wave synthetic aperture radars are attracting more and more attention due to their low cost and flexible operation capacity, including the ability to capture images at different elevation angles for precise target identification. However, small unmanned aerial vehicles suffer from large trajectory deviation and severe range-azimuth coupling due to their simple navigational control and susceptibility to air turbulence. In this paper, we utilize the squint minimization technique to reduce this coupling while simultaneously eliminating intra-pulse motion-induced effects with an additional spectrum scaling.

MIMO radar 3D imaging based on combined amplitude and total variation cost function with sequential order one negative exponential form.

In inverse synthetic aperture radar (ISAR) imaging, a target is usually regarded as consist of a few strong (specular) scatterers and the distribution of these strong scatterers is sparse in the imaging volume. In this paper, we propose to incorporate the sparse signal recovery method in 3D multiple-input multiple-output radar imaging algorithm. Sequential order one negative exponential (SOONE) function, which forms homotopy between 1 and 0 norms, is proposed to measure the sparsity.

Sparse array 3-D ISAR imaging based on maximum likelihood estimation and CLEAN technique.

Large 2-D sparse array provides high angular resolution microwave images but artifacts are also induced by the high sidelobes of the beam pattern, thus, limiting its dynamic range. CLEAN technique has been used in the literature to extract strong scatterers for use in subsequent signal cancelation (artifacts removal). However, the performance of DFT parameters estimation based CLEAN algorithm for the estimation of the signal amplitudes is known to be poor, and this affects the signal cancelation.