Real-time, Software-Defined, GPU-Based Receiver Field Trial

We demonstrate stable real-time operation of a software-defined, GPU-based receiver over a metropolitan network. Massive parallelization is exploited for implementing direct-detection and coherent Kramers-Kronig detection in real time at 2 and 1 GBaud, respectively.Comment: Accepted for presentation at the European Conference on Optical Communications (ECOC) 202

Revisiting the Sample Adaptive Offset post-filter of VVC with Neural-Networks

The Sample Adaptive Offset (SAO) filter has been introduced in HEVC to reduce general coding and banding artefacts in the reconstructed pictures, in complement to the De-Blocking Filter (DBF) which reduces artifacts at block boundaries specifically. The new video compression standard Versatile Video Coding (VVC) reduces the BD-rate by about 36% at the same reconstruction quality compared to HEVC. It implements an additional new in-loop Adaptive Loop Filter (ALF) on top of the DBF and the SAO filter, the latter remaining unchanged compared to HEVC. However, the relative performance of SAO in VVC has been lowered significantly. In this paper, it is proposed to revisit the SAO filter using Neural Networks (NN). The general principles of the SAO are kept, but the a-priori classification of SAO is replaced with a set of neural networks that determine which reconstructed samples should be corrected and in which proportion. Similarly to the original SAO, some parameters are determined at the encoder side and encoded per CTU. The average BD-rate gain of the proposed SAO improves VVC by at least 2.3% in Random Access while the overall complexity is kept relatively small compared to other NN-based methods

Beyond multi-view deconvolution for inherently-aligned fluorescence tomography

In multi-view fluorescence microscopy, each angular acquisition needs to be aligned with care to obtain an optimal volumetric reconstruction. Here, instead, we propose a neat protocol based on auto-correlation inversion, that leads directly to the formation of inherently aligned tomographies. Our method generates sharp reconstructions, with the same accuracy reachable after sub-pixel alignment but with improved point-spread-function. The procedure can be performed simultaneously with deconvolution further increasing the reconstruction resolution

Real-time transmission of geometrically-shaped signals using a software-defined GPU-based optical receiver

A software-defined optical receiver is implemented on an off-the-shelf commercial graphics processing unit (GPU). The receiver provides real-time signal processing functionality to process 1 GBaud minimum phase (MP) 4-, 8-, 16-, 32-, 64-, 128-ary quadrature amplitude modulation (QAM) as well as geometrically shaped (GS) 8- and 128-QAM signals using Kramers-Kronig (KK) coherent detection. Experimental validation of this receiver over a 91 km field-deployed optical fiber link between two Tokyo locations is shown with detailed optical signal-to-noise ratio (OSNR) investigations. A net data rate of 5 Gbps using 64-QAM is demonstrated.</p

Size Dependent Transport of Floating Plastics Modeled in the Global Ocean

Plastic has been detected in the ocean in most locations where scientists have looked for it. While ubiquitous in the environment, plastic pollution is heterogeneous, and plastics of varying composition, shape, and size accumulate differently in the global ocean. Many physical and biological processes influence the transport of plastics in the marine environment. Here we focus on physical processes and how they can naturally sort floating plastics at the ocean surface and within its interior. We introduce a new open-source GPU-accelerated numerical model, ADVECT, which simulates the three-dimensional dispersal of large arrays of modelled ocean plastics with varying size, shape, and density. We use this model to run a global simulation and find that buoyant particles are sorted in the ocean according to their size, both at the surface due to wind-driven drift and in the water column due to their rising velocity. Finally, we compare our findings with recent literature reporting the size distribution of plastics in the ocean and discuss which observations can and cannot be explained by the physical processes encoded in our model

Model Simplification for Efficient Collision Detection in Robotics

Motion planning for industrial robots is a computationally intensive task due to the massive number of potential motions between any two configurations. Calculating all possibilities is generally not feasible. Instead, many motion planners sample a sub-set of the available space until a viable solution is found. Simplifying models to improve collision detection performance, a significant component of motion planning, results in faster and more capable motion planners. Several approaches for simplifying models to improve collision detection performance have been presented in the literature. However, many of them are sub-optimal for an industrial robotics application due to input model limitations, accuracy sacrifices, or the probability of increasing false negatives during collision queries. This thesis focuses on the development of model simplification approaches optimised for industrial robotics applications. Firstly, a new simplification approach, the Bounding Sphere Simplification (BSS), is presented that converts triangle-mesh inputs to a collection of spheres for efficient collision and distance queries. Additionally, BSS removes small features and generates an output model less prone to false negatives