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Multimode fiber (MMF) imaging aided by machine learning holds promise for numerous applications, including medical endoscopy. A key challenge for this technology is the sensitivity of modal transmission characteristics to environmental perturbations. Here, we show experimentally that an MMF imaging scheme based on a neural network (NN) can achieve results that are significantly robust to thermal perturbations. For example, natural images are successfully reconstructed as the MMF's temperature is varied by up to 50$^{\circ}$C relative to the training scenario, despite substantial variations in the speckle patterns caused by thermal changes. A dense NN with a single hidden layer is found to outperform a convolutional NN suitable for standard computer vision tasks. In addition, we demonstrate that NN parameters can be used to understand the MMF properties by reconstructing the approximate transmission matrices, and we show that the image reconstruction accuracy is directly related to the temperature dependence of the MMF's transmission characteristics., Comment: 11 pages, 5 figures

Cerebrovascular disease often requires multiple imaging modalities for accurate diagnosis, treatment, and monitoring. Computed Tomography Angiography (CTA) and Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) are two common non-invasive angiography techniques, each with distinct strengths in accessibility, safety, and diagnostic accuracy. While CTA is more widely used in acute stroke due to its faster acquisition times and higher diagnostic accuracy, TOF-MRA is preferred for its safety, as it avoids radiation exposure and contrast agent-related health risks. Despite the predominant role of CTA in clinical workflows, there is a scarcity of open-source CTA data, limiting the research and development of AI models for tasks such as large vessel occlusion detection and aneurysm segmentation. This study explores diffusion-based image-to-image translation models to generate synthetic CTA images from TOF-MRA input. We demonstrate the modality conversion from TOF-MRA to CTA and show that diffusion models outperform a traditional U-Net-based approach. Our work compares different state-of-the-art diffusion architectures and samplers, offering recommendations for optimal model performance in this cross-modality translation task.

We demonstrate heavy and hyper doping of ZnO by a combination of gallium (Ga) ion implantation using a focused ion beam (FIB) system and post-implantation laser annealing. Ion implantation allows for the incorporation of impurities with nearly arbitrary concentrations, and the laser-annealing process enables dopant activation close to or beyond the solid-solubility limit of Ga in ZnO. We achieved heavily doped ZnO:Ga with free-carrier concentrations of ~10^21 cm^(-3), resulting in a plasma wavelength of 1.02 um, which is substantially shorter than the telecommunication wavelength of 1.55 um. Thus, our approach enables the control of the plasma frequency of ZnO from the far infrared down to 1.02 um, providing a promising plasmonic material for applications in this regime., Comment: Main text + supplementary

Although deep reinforcement learning has recently been very successful at learning complex behaviors, it requires a tremendous amount of data to learn a task. One of the fundamental reasons causing this limitation lies in the nature of the trial-and-error learning paradigm of reinforcement learning, where the agent communicates with the environment and progresses in the learning only relying on the reward signal. This is implicit and rather insufficient to learn a task well. On the contrary, humans are usually taught new skills via natural language instructions. Utilizing language instructions for robotic motion control to improve the adaptability is a recently emerged topic and challenging. In this paper, we present a meta-RL algorithm that addresses the challenge of learning skills with language instructions in multiple manipulation tasks. On the one hand, our algorithm utilizes the language instructions to shape its interpretation of the task, on the other hand, it still learns to solve task in a trial-and-error process. We evaluate our algorithm on the robotic manipulation benchmark (Meta-World) and it significantly outperforms state-of-the-art methods in terms of training and testing task success rates. Codes are available at \url{https://tumi6robot.wixsite.com/million}.

Modern LiDAR sensors find increasing use in safety-critical applications. Therefore, highly accurate modeling of the system's behavior under demanding environmental conditions is necessary. In this paper, we present a modular structure to accurately simulate the amplified raw detector signal of a direct time-of-flight LiDAR system for coaxial transmitter-receiver optics. Our model describes, a measurement system based on standard optical components and a detector able of converting single photons to an electrical signal. To verify the model's predictions, single-point measurements for targets of different reflectivity at defined distances were performed. Statistical analysis shows an R-squared value greater than 0.990 for simulated and measured signal amplitude levels. Noise modeling shows good accordance with the performed measurements for different target irradiance levels. The presented results have a guiding significance in the modeling of the complex signal processing chain of LiDAR systems, as it enables the prediction of key parameters of the system early in the development process. Hence, unnecessary costs by design flaws can be mitigated. The modular structure allows easy adaption for arbitrary LiDAR systems.

A strain-insensitive temperature sensor based on multimode interference using standard multimode fibers (MMFs) is proposed according to the comprehensive study of the characteristics of the MMFs. The temperature and strain dependences on the core diameter, numerical aperture (NA), and the length of the MMF section in the single-mode--multimode--single-mode (SMS) fiber structure are investigated experimentally. The results indicate that the larger core diameter of the MMF leads to higher temperature sensitivity but lower strain sensitivity (absolute values); the higher NA does not influence the temperature sensitivity but results in higher absolute value of strain sensitivity; the longer MMF section brings lower temperature sensitivity but does not have an impact on strain sensitivity. These findings also contribute to the theoretical analysis of the length dependence in the SMS fiber sensors. Besides, the results of the characterization study show that the strain sensitivity is relatively low, which brings a possibility to develop a strain-insensitive temperature sensor. The proposed sensor is used for temperature sensing while the strain is constantly applied from 0 to 1100 $\mu\epsilon$ with steps of 100 $\mu\epsilon$. The measured results are consistent with the comprehensive study. The mean temperature sensitivity is 6.14 pm/$^{\circ}$C with a standard deviation of 0.39 pm/$^{\circ}$C, which proves that the proposed temperature sensor exhibits good stability and is insensitive to strain. We expect that these results will provide a profound guideline to fiber sensors based on multimode interference.

We demonstrate spatial modification of the optical properties of thin-film metal oxides, zinc oxide and vanadium dioxide as representatives, using a commercial focused ion beam (FIB) system. Using a Ga+ FIB and thermal annealing, we demonstrated variable doping of a band semiconductor, zinc oxide (ZnO), achieving carrier concentrations from 10^18 cm-3 to 10^20 cm-3. Using the same FIB without subsequent thermal annealing, we defect-engineered a correlated semiconductor, vanadium dioxide (VO2), locally modifying its insulator-to-metal transition (IMT) temperature by range of ~25 degrees C. Such area-selective modification of metal oxides by direct writing using a FIB provides a simple, mask-less route to the fabrication of optical structures, especially when multiple or continuous levels of doping or defect density are required., Comment: Main text + supplementary. Updated manuscript with some more science