Abstract: We demonstrate concepts and results of a field trial for a flexible-rate passive optical network (FLCS-PON), which delivers bitrates up to 100 Gbit/s and allows for adaptations in the transmission method to match the users' channel conditions and optimize throughput. FLCS-PON builds on top of the hardware ecosystem that will be developed for ITU-T 50 Gbit/s PON and employs three new ingredients: optical network unit (ONU) grouping, flexible modulation format, and flexible forward error correction (FEC) code rate. Together, these techniques take advantage of the optical distribution network (ODN) statistics to realize a system capable of more than twofold throughput increase compared to the upcoming 50 Gbit/s PON, but still able to support a full array of deployed fiber edge cases, which are problematic for legacy PONs. In this paper we explain the concepts behind enabling techniques of FLCS-PON. We then report on a field trial over a deployed fiber infrastructure, using a system consisting of one FLCS-PON OLT and two ONUs. We report both pre- and post-forward-error-correction (post-FEC) performance of our system, demonstrating achievable net bitrate over an operator's fiber infrastructure. We realize a downlink transmission at double the speed of ITU-T 50 Gbit/s PON for ONUs exhibiting lower optical path loss (OPL), while simultaneously continue to support ONUs at high OPLs. We additionally realize a record-high 31.5 dB loss budget for 100 Gbit/s transmission using a direct-detection ONU with an optical preamplifier.

Abstract: Electron tomography is an essential imaging technique for the investigation of morphology and 3D structure of nanomaterials. This method, however, suffers from well-known missing wedge artifacts due to a restricted tilt range, which limits the objectiveness, repeatability and efficiency of quantitative structural analysis. Discrete tomography represents one of the promising reconstruction techniques for materials science, potentially capable of delivering higher fidelity reconstructions by exploiting the prior knowledge of the limited number of material compositions in a specimen. However, the application of discrete tomography to practical datasets remains a difficult task due to the underlying challenging mathematical problem. In practice, it is often hard to obtain consistent reconstructions from experimental datasets. In addition, numerous parameters need to be tuned manually, which can lead to bias and non-repeatability. In this paper, we present the application of a new
iterative reconstruction technique, named TVR-DART, for discrete electron tomography. The technique is capable of consistently delivering reconstructions with significantly reduced missing wedge artifacts for a variety of challenging data and imaging conditions, and can automatically estimate its key parameters. We describe the principles of the technique and apply it to datasets from three different types of samples acquired under diverse imaging modes. By further reducing the available tilt range and number of projections, we show that the
proposed technique can still produce consistent reconstructions with minimized missing wedge artifacts. This new development promises to provide the electron microscopy community with an easy-to-use and robust tool for high-fidelity 3D characterization of nanomaterials.