Multimode fibers (MMF) are promising candidates to increase the data rate while reducing the space required for optical fiber networks. However, their use is hampered by mode mixing and other effects, leading to speckled output patterns. This can be overcome by measuring the transmission matrix (TM) of a multimode fiber. In this contribution, a mode-selective excitation of complex amplitudes is performed with only one phase-only spatial light modulator. The light field propagating through the fiber is measured holographically and is analyzed by a rapid decomposition method. This technique requires a small amount of measurements N, which corresponds to the degree of freedom of the fiber. The TM determines the amplitude and phase relationships of the modes, which allows us to understand the mode scrambling processes in the MMF and can be used for mode division multiplexing.

The digital transition requires real-time control of complex systems with short loop time and low latency in various applications. Field Programmable Gate Arrays (FPGAs) are in principle capable to comply with this task but demand, on the other hand, a high programming effort. We propose an Field Programmable System on Chip (FPSoC) as a hybrid solution of an FPGA and a Central Processing Unit (CPU) on a single monolithic die to combine the strengths of both architectures. An FPSoC-based adaptive optical wavefront correction system is presented as a case study to correct camera images in real-time that are distorted by time-varying aberrations. While a short total loop time is achieved by interfacing the camera and a deformable mirror on a low level directly with the FPGA, all computationally non-intensive tasks are implemented on the CPU to keep the flexibility, reusability and development expense low. The system corrects the optical distortion of water surface waves with up to 3600 control cycles per second and spatially attenuates the distortion up to Zernike polynomial 14 with up to 150 Hz. The FPSoC-system enables fast spatiotemporal aberration correction in technical processes and offers a perspective for measuring complex flows through fluctuating interfaces.

Laser optical techniques are widely used for flow measurements as they offer a high spatial and velocity resolution. However, undisturbed optical access to the measurement volume is desired. In order to measure through a fluctuating phase boundary, we present the use of adaptive optics. In an experiment, we prove that the Fresnel reflex of a phase boundary can be used as a proper guide star for adaptive velocity measurements with a single optical access. Interferometric flow measurements through a fluctuating phase boundary have been accomplished by a Mach–Zehnder interferometer.

Aberrations degrade the accuracy of quantitative, imaging-based measurements, like particle image velocimetry (PIV). Adaptive optical elements can in principle correct the wavefront distortions, but are limited by their technical specifications. Here we propose an actuator-free correction based on a multiple-input deep convolutional neural network which uses an additional input from a wavefront sensor to correct time-varying distortions. It is applied for imaging flow velocimetry to conduct measurements through a fluctuating air-water phase boundary. Dataset for neural network is generated by an experimental setup with a deformable mirror. Correction performance of trained model is estimated in terms of image quality, which is improved significantly, and flow measurement results, where the errors induced by the distortion from fluctuating phase boundary can be corrected by 82 %. The technique has the potential to replace classical closed-loop adaptive optical systems where the performance of the actuators is not sufficient.

Interferometric velocity measurements are of great importance at flow investigations. However, the laser beams can be distorted at the interfaces between optical media of different refractive indices. Temporal fluctuations of these distortions will cause a deterioration of the laser interferometer signals. We have harnessed the power of programmable photonics devices to eliminate this signal deterioration. Non-invasive flow velocity measurements through a rapidly fluctuating media interface with large strokes of about 100 microns are presented. Our work represents a paradigm shift for interferometric velocity measurement techniques from using static to dynamic optical elements.