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Programmable optical devices provide performance enhancement and flexibility to spatial multiplexing systems enabling transmission of tributaries in high-order eigenmodes of spatially-diverse transmission media, like multimode fiber (MMF). Wavefront shaping with spatial light modulators (SLMs) facilitates scalability of the transmission media by allowing for channel diagonalization and quasi-single-input single-output operation. Programmable mode multiplexing configurations like multi-plane light conversion (MPLC) utilise the SLM and offer the potential to simultaneously launch an arbitrary subset of spatial tributaries in any N-mode MMF. Such programmable optical processor would enable the throughput of space-division multiplexing (SDM) systems to be progressively increased by addressing a growing number of tributaries over one MMF and in this way meet a growing traffic demand - similarly to the wavelength-division multiplexing evolution path. Conventionally, MPLC phasemasks are calculated using the wavefront matching algorithm (WMA). However, this method does not exploit the full potential of programmable mode multiplexers. We show, that computer-generated hologram algorithms like direct search enable significant improvement compared to the traditional WMA-approach. Such gains are enabled by tailored cost functions with dynamic constraints concerning insertion loss as well as mode extinction ratio. We show that average mode extinction ratio can be greatly improved by as much as 15 dB at the expense of insertion loss deterioration of < 3 dB. One particular feature of programmable mode multiplexers is the adaptability to optimised transmission functions. Besides conventional LP modes transmission, we employ our approach on Schmidt modes, which are spatial eigenchannels with minimum crosstalk derived from a measured transmission matrix.

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 figures

Quantitative phase imaging (QPI) through multi-core fibers (MCFs) has been an emerging in vivo label-free endoscopic imaging modality with minimal invasiveness. However, the computational demands of conventional iterative phase retrieval algorithms have limited their real-time imaging potential. We demonstrate a learning-based MCF phase imaging method, that significantly reduced the phase reconstruction time to 5.5 ms, enabling video-rate imaging at 181 fps. Moreover, we introduce an innovative optical system that automatically generated the first open-source dataset tailored for MCF phase imaging, comprising 50,176 paired speckle and phase images. Our trained deep neural network (DNN) demonstrates robust phase reconstruction performance in experiments with a mean fidelity of up to 99.8\%. Such an efficient fiber phase imaging approach can broaden the applications of QPI in hard-to-reach areas., Comment: 5 pages. 5 figures

A novel MIMO-DSP for space-division multiplexing over multimode fibres is proposed. A principal modes approach is shown to provide two-fold benefits: over 13 times channel memory reduction while minimising the number of optical front-ends needed to detect a subset of the spatial domain., Comment: Submitted to 2022 European Conference on Optical Communication (ECOC)

Multimode fibers hold great promise to advance data rates in optical communications but come with the challenge to compensate for modal crosstalk and mode-dependent losses, resulting in strong distortions. The holographic measurement of the transmission matrix enables not only correcting distortions but also harnessing these effects for creating a confidential data connection between legitimate communication parties, Alice and Bob. The feasibility of this physical-layer-security-based approach is demonstrated experimentally for the first time on a multimode fiber link to which the eavesdropper Eve is physically coupled. Once the proper structured light field is launched at Alice's side, the message can be delivered to Bob, and, simultaneously, the decipherment for an illegitimate wiretapper Eve is destroyed. Within a real communication scenario, we implement wiretap codes and demonstrate confidentiality by quantifying the level of secrecy. Compared to an uncoded data transmission, the amount of securely exchanged data is enhanced by a factor of 538. The complex light transportation phenomena that have long been considered limiting and have restricted the widespread use of multimode fiber are exploited for opening new perspectives on information security in spatial multiplexing communication systems., Comment: 14 pages, 8 figures

Quantitative phase imaging (QPI) is a label-free technique providing both morphology and quantitative biophysical information in biomedicine. However, applying such a powerful technique to in vivo pathological diagnosis remains challenging. Multi-core fiber bundles (MCFs) enable ultra-thin probes for in vivo imaging, but current MCF imaging techniques are limited to amplitude imaging modalities. We demonstrate a computational lensless microendoscope that uses an ultra-thin bare MCF to perform quantitative phase imaging of biomedical samples with up to 1 {\mu}m lateral resolution and nanoscale axial resolution. The incident complex light field at the measurement side is precisely reconstructed from a single-shot far-field speckle pattern at the detection side, enabling digital focusing and 3D volumetric reconstruction without any mechanical movement. The accuracy of the quantitative phase reconstruction is validated by imaging the phase target and hydrogel beads through the MCF. With the proposed imaging modality, 3D imaging of human cancer cells is achieved through the ultra-thin fiber endoscope, promising widespread clinical applications., Comment: 16pages, 6 figures

Reliable and secure communication is an important aspect of modern fiber optic communication. In this work we consider a multi-mode fiber (MMF) channel wiretapped by an eavesdropper. We assume the transmitter knows the legitimate channel, but statistical knowledge of the eavesdropper's channel only. We propose a transmission scheme with artificial noise (AN) for such a channel. In particular, we formulate the corresponding optimization problem which aims to maximize the average secrecy rate and develop an algorithm to solve it. We apply this algorithm to actual measured MMF channels. As real fiber measurements show, for a 55 mode MMF we can achieve positive average secrecy rates with the proper use of AN. Furthermore, the gain compared to standard precoding and power allocation schemes is illustrated., Comment: 5 pages, 2 figures

The use of multimode fibers offers advantages in the field of communication technology in terms of transferable information density and information security. For applications using physical layer security or mode division multiplexing, the complex transmission matrix must be known. To measure the transmission matrix, the individual modes of the multimode fiber are excited sequentially at the input and a mode decomposition is performed at the output. Mode decomposition is usually performed using digital holography, which requires the provision of a reference wave and leads to high efforts. To overcome these drawbacks, a neural network is proposed, which performs mode decomposition with intensity-only camera recordings of the multimode fiber facet. Due to the high computational complexity of the problem, this approach was usually limited to a number of 6 modes. In this work, it could be shown for the first time that by using a DenseNet with 121 layers it is possible to break through the hurdle of 6 modes. The advancement is demonstrated by a mode decomposition with 10 modes experimentally. The training process is based on synthetic data. The proposed method is quantitatively compared to the conventional approach with digital holography. In addition, it is shown that the network can perform mode decomposition on a 55-mode fiber, which also supports modes unknown to the neural network. The smart detection using a DenseNet opens new ways for the application of multimode fibers in optical communication networks for physical layer security., Comment: 17 pages

Inverse precoding algorithms in multimode fiber based communication networks are used to exploit mode dependent losses on the physical layer. This provides an asymmetry between legitimate (Bob) and unlegitimate (Eve) receiver of messages resulting in a significant SNR advantage for Bob. In combination with dynamic mode channel changes, Eve has no chance to reconstruct a sent message even in a worst case scenario in which she is almighty. This is the first time, Physical Layer Security in a fiber optical network is investigated on the basis of measured transmission matrices. These results show that messages can be sent securely with conventional communication techniques. Translating the task of securing data from software to hardware represents the potential of a scientific paradigm shift. The introduced technique is a step towards the development of cyber physical systems.

Diffraction-limited focusing deep into biological tissue is challenging due to spherical aberrations that lead to a broadening of the focal spot particularly in axial direction. While the diffraction-limit can be restored employing aberration correction with a deformable mirror or spatial light modulator, a bulky optical setup results due to the required beam-folding. We propose a bi actor adaptive lens, that enables axial scanning and at the same time correction of specimen induced spherical aberrations while offering a compact setup. Using the bi-actor lens in a confocal microscope, we show diffraction limited axial scanning up to \SI{340}{\micro m} deep inside a phantom specimen. Applying this technique for in-vivo measurements of zebrafish embryos with reporter gene-driven fluorescence in the thyroid gland reveals substructures of thyroid follicles.