NAS Integration: CST and Air Traffic Insertion The Way Ahead

Differences in handling aircraft vs. spacecraft in ATM Challenges for CST ATM & STM Integration CST Integration in Airspace ERAU and DLR Collaboration EUROCONTROL - Network Manager DLR Remote Tower solutions for Spaceports ATM Integration of Space Vehicles in Europe / Germany SESAR Requirements & SWIM Spacecraft Flight Planning and Executio

Ultra-compact tunable fiber laser for coherent anti-Stokes Raman imaging

This work describes the construction of an ultra-compact narrowband fiber laser source for coherent anti-Stokes Raman scattering microscopy of Raman tags, that is, for addressing Raman resonances of deuterated molecules and alkyne tags in the spectral range from 2080 to 2220 cm−1. A narrowband and fast electronically tunable cw seed source based on a semiconductor optical amplifier (SOA) emitting around 1335 nm has been employed to seed four-wave mixing (FWM) in an endlessly single mode fiber (ESM) pumped by a ps pulse duration Yb-fiber laser. A conversion efficiency of 50% is demonstrated. This compact fiber optical parametric amplifier (FOPA) has been used to perform coherent anti-Stokes Raman imaging experiments of crystalline deuterated palmitic acid

Computational tissue staining of non-linear multimodal imaging using supervised and unsupervised deep learning

Hematoxylin and Eosin (H&E) staining is the 'gold-standard' method in histopathology. However, standard H&E staining of high-quality tissue sections requires long sample preparation times including sample embedding, which restricts its application for 'real-time' disease diagnosis. Due to this reason, a label-free alternative technique like non-linear multimodal (NLM) imaging, which is the combination of three non-linear optical modalities including coherent anti-Stokes Raman scattering, two-photon excitation fluorescence and second-harmonic generation, is proposed in this work. To correlate the information of the NLM images with H&E images, this work proposes computational staining of NLM images using deep learning models in a supervised and an unsupervised approach. In the supervised and the unsupervised approach, conditional generative adversarial networks (CGANs) and cycle conditional generative adversarial networks (cycle CGANs) are used, respectively. Both CGAN and cycle CGAN models generate pseudo H&E images, which are quantitatively analyzed based on mean squared error, structure similarity index and color shading similarity index. The mean of the three metrics calculated for the computationally generated H&E images indicate significant performance. Thus, utilizing CGAN and cycle CGAN models for computational staining is beneficial for diagnostic applications without performing a laboratory-based staining procedure. To the author's best knowledge, it is the first time that NLM images are computationally stained to H&E images using GANs in an unsupervised manner

Semantic segmentation of non-linear multimodal images for disease grading of inflammatory bowel disease: A segnet-based application

Non-linear multimodal imaging, the combination of coherent anti-stokes Raman scattering (CARS), two-photon excited fluorescence (TPEF) and second harmonic generation (SHG), has shown its potential to assist the diagnosis of different inflammatory bowel diseases (IBDs). This label-free imaging technique can support the ‘gold-standard’ techniques such as colonoscopy and histopathology to ensure an IBD diagnosis in clinical environment. Moreover, non-linear multimodal imaging can measure biomolecular changes in different tissue regions such as crypt and mucosa region, which serve as a predictive marker for IBD severity. To achieve a real-time assessment of IBD severity, an automatic segmentation of the crypt and mucosa regions is needed. In this paper, we semantically segment the crypt and mucosa region using a deep neural network. We utilized the SegNet architecture (Badrinarayanan et al., 2015) and compared its results with a classical machine learning approach. Our trained SegNet mod el achieved an overall F1 score of 0.75. This model outperformed the classical machine learning approach for the segmentation of the crypt and mucosa region in our study

In vivo coherent anti-Stokes Raman scattering microscopy reveals vitamin A distribution in the liver

Here we present a microscope setup for coherent anti-Stokes Raman scattering (CARS) imaging, devised to specifically address the challenges of in vivo experiments. We exemplify its capabilities by demonstrating how CARS microscopy can be used to identify vitamin A (VA) accumulations in the liver of a living mouse, marking the positions of hepatic stellate cells (HSCs). HSCs are the main source of extracellular matrix protein after hepatic injury and are therefore the main target of novel nanomedical strategies in the development of a treatment for liver fibrosis. Their role in the VA metabolism makes them an ideal target for a CARS-based approach as they store most of the body's VA, a class of compounds sharing a retinyl group as a structural motive, a moiety that is well known for its exceptionally high Raman cross section of the C=C stretching vibration of the conjugated backbone