Abstract: Provided are a method and apparatus for evaluating anatomical adaptation of an implantable orthopedic medical device. The method includes: obtaining an anatomical adaptation region of a to-be-evaluated bone with the implantable orthopedic medical device; determining a baseline of the anatomical adaptation region and a baseline of the implantable orthopedic medical device for position registration, and conducting the position registration for the anatomical adaptation region and the implantable orthopedic medical device based on the baselines; and evaluating adhesion of the to-be-evaluated bone to the implantable orthopedic medical device by calculating a distance from each point on an inner curved surface of the implantable orthopedic medical device to the anatomical adaptation region.

Abstract: Provided are a method and an apparatus for implementing batch extraction of human anatomical feature parameters. The method includes: obtaining a to-be-measured sample; generating an average model; calculating a point correspondence of a spatial location formed by the average model and each to-be-measured sample; measuring each to-be-measured sample on the average model; and outputting feature parameter data of each to-be-measured sample in batch. Calculation time is reduced by combining feature point extraction with affine transformation, and corresponding points are calculated through a non-rigid registration method to achieve batch and quick measurement.

Abstract: A multipath deep diffractive neural network comprises a first optical path for performing a first task and a second optical path for performing a second task. The second task is different than the first task. The multipath deep diffractive neural network further comprises an overlap optical path where the first optical path and the second optical path overlap. The multipath deep diffractive neural network comprises one or more optical elements that are configured to create a multipath optical neural network that performs a plurality of different tasks using multi-task machine learning.

Abstract: The techniques described herein relate to methods and apparatus for interferometric modulation. An apparatus includes an interferometric device comprising a first optical path and a second optical path, and at least one Franz-Keldysh (FK) modulator disposed in either the first optical path or the second optical path of the interferometric device. The interferometric device receives input light, wherein a first portion of the input light travels along the first optical path of the interferometric device, and a second portion of the input light travels along the second optical path of the interferometric device. The FK modulator modulates an intensity of either the first portion of the input light or the second portion of the input light.

Abstract: A method for preparing a film of aligned rod-like nanostructures or nanotubes comprises preparing a solution that comprises rod-like nanostructures or nanotubes, wherein the rod-like nanostructures or the nanotubes are well-dispersed, and performing vacuum filtration of the solution through a filtration membrane, wherein the vacuum filtration produces a film on the filtration membrane where the rod-like nanostructures or the nanotubes are aligned. The well-dispersed individual rod-like nanostructures or nanotubes may be separately suspended in the solution. The concentration of rod-like nanostructures or nanotubes may be below a threshold value and/or the filtration speed may be as slow as possible. Where a surfactant is utilized to aid dispersion, the surfactant concentration may be below a critical micelle concentration (CMC).