Optical micro-phase-shift dropvolume in a diffractive deep neural network.

To provide a desirable number of parallel subnetworks as required to reach a robust inference in an active modulation diffractive deep neural network, a random micro-phase-shift dropvolume that involves five-layer statistically independent dropconnect arrays is monolithically embedded into the unitary backpropagation, which does not require any mathematical derivations with respect to the multilayer arbitrary phase-only modulation masks, even maintaining the nonlinear nested characteristic of neural networks, and generating an opportunity to realize a structured-phase encoding within the dropvolume. Further, a drop-block strategy is introduced into the structured-phase patterns designed to flexibly configure a credible macro-micro phase dropvolume allowing for convergence. Concretely, macro-phase dropconnects concerning fringe griddles that encapsulate sparse micro-phase are implemented.

Optical random micro-phase-shift DropConnect in a diffractive deep neural network.

The formulation and training of unitary neural networks is the basis of an active modulation diffractive deep neural network. In this Letter, an optical random phase DropConnect is implemented on an optical weight to manipulate a jillion of optical connections in the form of massively parallel sub-networks, in which a micro-phase assumed as an essential ingredient is drilled into Bernoulli holes to enable training convergence, and malposed deflections of the geometrical phase ray are reformulated constantly in epochs, allowing for enhancement of statistical inference. Optically, the random micro-phase-shift acts like a random phase sparse griddle with respect to values and positions, and is operated in the optical path of a projective imaging system.

Optical random phase dropout in a diffractive deep neural network.

Unitary learning is a backpropagation (BP) method that serves to update unitary weights in fully connected deep complex-valued neural networks, meeting a prior unitary in an active modulation diffractive deep neural network. However, the square matrix characteristic of unitary weights in each layer results in its learning belonging to a small-sample training, which produces an almost useless network that has a fairly poor generalization capability. To alleviate such a serious over-fitting problem, in this Letter, optical random phase dropout is formulated and designed.

Phase-shift determination for a 4 × 4 intelligent photonic neural network with compatible learning.

Intelligent photonic circuits (IPCs) tuned with an appropriate phase-shift vector could enable a photonic intelligent matrix possibly implemented in multiple neural layers for a task-oriented topologies. A photonic Mach-Zehnder Interferometer (MZI) is a fundamental photonic component in IPCs, whose matrix representation could be broadcasted into an arbitrary matrix that is equipped with an optimized phase-shift vector. The initialized MZIs' phases are tentatively probed between analytical elements and a digital weight matrix that is learned from samples with efficient compatible learning for complex-valued neural networks.

Mucilaginibacter yixingensis sp. nov., isolated from vegetable soil.

A Gram-reaction-negative, aerobic, non-motile, non-spore-forming, rod-shaped bacterium, designated YX-36T, was isolated from a vegetable plot in Yixing, Jiangsu province, China. The strain grew at 15-37 °C (optimally at 37 °C), at pH 6.0-9.

Robust self-calibration three-dimensional shape measurement in fringe-projection photogrammetry.

Commonly, fringe-projection photogrammetry involves two independent stages: system calibration and measurement. The measurement accuracy largely depends on the calibration procedure. However, the results of system calibration may be unstable in different occasions.

Flexible geometrical calibration for fringe-reflection 3D measurement.

System geometrical calibration is a challenging task in fringe-reflection 3D measurement because the fringe displayed on the LCD screen does not lie within the camera's field of view. Commonly, a flat mirror with markers can accomplish system geometrical calibration. However, the position of the markers must be precisely located by photogrammetry in advance.

Three-dimensional shape measurement of aspheric mirrors with fringe reflection photogrammetry.

Three-dimensional (3D) shape measurement of an aspheric mirror with fringe reflection photogrammetry involves three steps: correspondence matching, triangulation, and bundle adjustment. Correspondence matching is realized by absolute phase tracking and triangulation is computed by the intersection of reflection and incidence rays. The main contribution in this paper is constraint bundle adjustment for carefully dealing with lens distortion in the process of ray intersection, as compared to the well-known grating reflection photogrammetry.

Fringe inverse videogrammetry based on global pose estimation.

Fringe inverse videogrammetry based on global pose estimation is presented to measure a three-dimensional (3D) coordinate. The main components involve an LCD screen, a tactile probe equipped with a microcamera, and a portable personal computer. The LCD is utilized to display fringes, a microcamera is installed on the tactile probe, and the 3D coordinate of the center of the probe tip can be calculated through the microcamera's pose.

Multiple-image optical encryption: an improved encoding approach.

An improved encoding approach to multiple-image optical encryption based on a cascaded phase retrieval algorithm (CPRA) is proposed. The system consists of several stages of a standard 4-f correlator, in which the keys are not only the phase mask pairs produced by CPRA but also the phase distribution of the output plane of the front stage. The security and the capacity of the system are also discussed.