Measurement uncertainty budget of an interferometric flow velocity sensor

Flow rate measurements are a common topic for process monitoring in chemical engineering and food industry. To achieve the requested low uncertainties of 0:1% for flow rate measurements, a precise measurement of the shear layers of such flows is necessary. The Laser Doppler Velocimeter (LDV) is an established method for measuring local flow velocities. For exact estimation of the flow rate, the flow profile in the shear layer is of importance. For standard LDV the axial resolution and therefore the number of measurement points in the shear layer is defined by the length of the measurement volume. A decrease of this length is accompanied by a larger fringe distance variation along the measurement axis which results in a rise of the measurement uncertainty for the flow velocity (uncertainty relation between spatial resolution and velocity uncertainty). As a unique advantage, the laser Doppler profile sensor (LDV-PS) overcomes this problem by using two fan-like fringe systems to obtain the position of the measured particles along the measurement axis and therefore achieve a high spatial resolution while it still offers a low velocity uncertainty. With this technique, the flow rate can be estimated with one order of magnitude lower uncertainty, down to 0:05% statistical uncertainty.1 And flow profiles especially in film flows can be measured more accurately. The problem for this technique is, in contrast to laboratory setups where the system is quite stable, that for industrial applications the sensor needs a reliable and robust traceability to the SI units, meter and second. Small deviations in the calibration can, because of the highly position depending calibration function, cause large systematic errors in the measurement result. Therefore, a simple, stable and accurate tool is needed, that can easily be used in industrial surroundings to check or recalibrate the sensor. In this work, different calibration methods are presented and their in uences to the measurement uncertainty budget of the sensor is discussed. Finally, generated measurement results for the film flow of an impinging jet cleaning experiment are presented

Special Issue on Computational Ultrasound Imaging and Applications

[No abstract available

Rapid computational cell-rotation around arbitrary axes in 3D with multi-core fiber

Optical trapping is a vital tool in biology, allowing precise optical manipulation of nanoparticles, micro-robots, and cells. Due to the low risk of photodamage and high trap stiffness, fiber-based dual-beam traps are widely used for optical manipulation of large cells. Besides trapping, advanced applications like 3D refractive index tomography need a rotation of cells, which requires precise control of the forces, for example, the acting-point of the forces and the intensities in the region of interest (ROI). A precise rotation of large cells in 3D about arbitrary axes has not been reported yet in dual-beam traps. We introduce a novel dual-beam optical trap in which a multi-core fiber (MCF) is transformed to a phased array, using wavefront shaping and computationally programmable light. The light-field distribution in the trapping region is holographically controlled within 0.1 s, which determines the orientation and the rotation axis of the cell with small retardation. We demonstrate real-time controlled rotation of HL60 cells about all 3D axes with a very high degree of freedom by holographic controlled light through an MCF with a resolution close to the diffraction limit. For the first time, the orientation of the cell can be precisely controlled about all 3D axes in a dual-beam trap. MCFs provide much higher flexibility beyond the bulky optics, enabling lab-on-a-chip applications and can be easily integrated for applications like contactless cell surgery, refractive index tomography, cell-elasticity measurement, which require precise 3D manipulation of cells

Interferometric velocity measurements through a fluctuating interface using a Fresnel guide star-based wavefront correction system

To improve optical measurements, which are degraded by optical distortions, wavefront correction systems can be used. Generally, these systems evaluate a guide star in transmission. The guide star emits wellknown wavefronts, which sample the distortion by propagating through it. The system is able to directly measure the distortion and correct it. There are setups, where it is not possible to generate a guide star behind the distortion. Here, we consider a liquid jet with a radially open surface. A Mach–Zehnder interferometer is presented where both beams are stabilized through a fluctuating liquid jet surface with the Fresnel guide star (FGS) technique. The wavefront correction system estimates the beam path behind the surface by evaluating the incident beam angle and reflected beam angle of the Fresnel reflex with an observer to control the incident angle for the desired beam path. With this approach, only one optical access through the phase boundary is needed for the measurement, which can be traversed over a range of 250 μm with a significantly increased rate of valid signals. The experiment demonstrates the potential of the FGS technique for measurements through fluctuating phase boundaries, such as film flows or jets

Nonlinear microscopy using impulsive stimulated Brillouin scattering for high-speed elastography

The impulsive stimulated Brillouin microscopy promises fast, non-contact measurements of the elastic properties of biological samples. The used pump-probe approach employs an ultra-short pulse laser and a cw laser to generate Brillouin signals. Modeling of the microscopy technique has already been carried out partially, but not for biomedical applications. The nonlinear relationship between pulse energy and Brillouin signal amplitude is proven with both simulations and experiments. Tayloring of the excitation parameters on the biologically relevant polyacrylamide hydrogels outline sub-ms temporal resolutions at a relative precision of <1%. Brillouin microscopy using the impulsive stimulated scattering therefore exhibits high potential for the measurements of viscoelastic properties of cells and tissues

Intelligent self calibration tool for adaptive few-mode fiber multiplexers using multiplane light conversion

Space division multiplexing (SDM) is promising to enhance capacity limits of optical networks. Among implementation options, few-mode fibres (FMFs) offer high efficiency gains in terms of integratability and throughput per volume. However, to achieve low insertion loss and low crosstalk, the beam launching should match the fiber modes precisely. We propose an all-optical data-driven technique based on multiplane light conversion (MPLC) and neural networks (NNs). By using a phase-only spatial light modulator (SLM), spatially separated input beams are transformed independently to coaxial output modes. Compared to conventional offline calculation of SLM phase masks, we employ an intelligent two-stage approach that considers knowledge of the experimental environment significantly reducing misalignment. First, a single-layer NN called Model-NN learns the beam propagation through the setup and provides a digital twin of the apparatus. Second, another single-layer NN called Actor-NN controls the model. As a result, SLM phase masks are predicted and employed in the experiment to shape an input beam to a target output. We show results on a single-passage configuration with intensity-only shaping. We achieve a correlation between experiment and network prediction of 0.65. Using programmable optical elements, our method allows the implementation of aberration correction and distortion compensation techniques, which enables secure high-capacity long-reach FMF-based communication systems by adaptive mode multiplexing devices

AI-driven projection tomography with multicore fibre-optic cell rotation

Optical tomography has emerged as a non-invasive imaging method, providing three-dimensional insights into subcellular structures and thereby enabling a deeper understanding of cellular functions, interactions, and processes. Conventional optical tomography methods are constrained by a limited illumination scanning range, leading to anisotropic resolution and incomplete imaging of cellular structures. To overcome this problem, we employ a compact multi-core fibre-optic cell rotator system that facilitates precise optical manipulation of cells within a microfluidic chip, achieving full-angle projection tomography with isotropic resolution. Moreover, we demonstrate an AI-driven tomographic reconstruction workflow, which can be a paradigm shift from conventional computational methods, often demanding manual processing, to a fully autonomous process. The performance of the proposed cell rotation tomography approach is validated through the three-dimensional reconstruction of cell phantoms and HL60 human cancer cells. The versatility of this learning-based tomographic reconstruction workflow paves the way for its broad application across diverse tomographic imaging modalities, including but not limited to flow cytometry tomography and acoustic rotation tomography. Therefore, this AI-driven approach can propel advancements in cell biology, aiding in the inception of pioneering therapeutics, and augmenting early-stage cancer diagnostics.Comment: 15 pages, 6 figure