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We introduce the Satellite Neural Radiance Field (Sat-NeRF), a new end-to-end model for learning multi-view satellite photogrammetry in the wild. Sat-NeRF combines some of the latest trends in neural rendering with native satellite camera models, represented by rational polynomial coefficient (RPC) functions. The proposed method renders new views and infers surface models of similar quality to those obtained with traditional state-of-the-art stereo pipelines. Multi-date images exhibit significant changes in appearance, mainly due to varying shadows and transient objects (cars, vegetation). Robustness to these challenges is achieved by a shadow-aware irradiance model and uncertainty weighting to deal with transient phenomena that cannot be explained by the position of the sun. We evaluate Sat-NeRF using WorldView-3 images from different locations and stress the advantages of applying a bundle adjustment to the satellite camera models prior to training. This boosts the network performance and can optionally be used to extract additional cues for depth supervision., Accepted at CVPR EarthVision Workshop 2022

International audience; In this paper, a new local planning algorithm for urban autonomous driving is presented. Our main contribution is to define a fully algorithmic method, based on a geometrical representation of the environment, to compute predictive speed profiles on multiple paths, to ensure safety and comfort with respect to the scene and its predicted evolution. Simulation results are provided to evaluate the behaviour of the proposed algorithm on various scenarios. Those results show a good, comfortable and safe reaction of the vehicle to its static and dynamic environment with processing times compatible with real-time control. Environment Sensors Perception (sensor data fusion) Road network (HD map) Destination Global planning (Route planning in the road network) Decision (high-level semantic analysis) Local planning (generation of a local trajectory) Control Actuators Vehicle mission path useful data decision path D + Qlat static obstacles visibility limits dynamic obstacles predictions reference trajectory vehicle state

The dimming control is crucial for visible light communication (VLC) channels to maintain data transmission at different levels of lighting brightness. However, ensuring the dimming control while keeping a flicker-free channel remains a challenging task in VLC. Conventional techniques use the interleaving of compensation symbols (CS) applied on balanced codewords, usually obtained via run-length limited (RLL) codes. However, CS increases the redundancy used and are just thrown away during the decoding. Moreover, the interleaving step may increase the latency of the system. In this letter, new families of non-DC free RLL codes with variable weights are proposed. The dimming control can therefore be achieved without CS. Simulations stipulate that the proposed 4B7B code in FEC-coded channels achieve dimming ratios of 29% and 71% while improving on redundancy and error correction performance. For a 29% and 71% dimming ratios, at a BER of 10−4, gains in dB of 0.8 and 0.6 versus 3 and 2.6 are reported between the coded proposed RLL against the coded 1B2B and 4B6B with CS respectively.

The recursive projection-aggregation (RPA) decoder is a recently proposed near maximum likelihood (ML) decoder for Reed-Muller (RM) codes with low rates and short code lengths. However, the high computational complexity of RPA decoding is a major bottleneck for using RPA in applications that have a limited resource and energy budget. In this work, syndrome-based early stopping techniques as well as a scheduling scheme are proposed for the RPA decoder, which help in reducing the computational complexity while keeping similar decoding performance. Comparing to the baseline RPA decoder, the proposed techniques result in a 69−98% reduction in the average computational complexity for a target frame error rate (FER) of 10−5. Additionally, this work introduces hardware-friendly approximation functions to replace the RPA’s computationally expensive transcendental projection function.

Global water storage monitoring is often done using remote sensing to solve the problem of missing gauge station. Moreover, this information is very often not available to the public even when a measure station is available. The methods usually used are however often unpractical for small lakes, often require images that are costly or difficult to acquire (for example SAR images of sufficient resolution) or with a bad revisit time. In this paper we propose an automatic method for visible satellite images based on a precise tracking of shoreline. It uses bathymetry information of the lake when available but we also present different alternatives that still produce precise estimations. The method is applied to accurately track the water volume of lake of La Bultiere in 2019 using only PlanetScope images.

Reducing methane emissions is essential to tackle climate change. Here, we address the problem of detecting large methane leaks by using hyperspectral data from the satellite Sentinel-5P. By sampling Sentinel-5P spectral data at fine scale, we detect methane absorption features in the shortwave infrared wavelength range (SWIR). Our method involves two separate steps: i) background subtraction and ii) detection of local maxima in the negative logarithmic spectrum of each pixel. In the first step, we remove the impact of the albedo using albedo maps and the impact of the atmosphere by using a principal component analysis (PCA) over a time series of past observations. In the second step, we count for each pixel the number of local maxima that correspond to a subset of local maxima in the methane absorption spectrum. This counting method allows us to set up a statistical a contrario test that controls the false alarm rate of our detections.

Mid-infrared coherent light sources are of great interest for numerous applications ranging from spectroscopy, biomedicine and surgery, material processing, defense and security, as this spectral region contains the signatures of most basic molecules’ vibrations. Most of these applications require ultrafast lasers to avoid thermal effects e.g. in material processing and surgery or to optimize nonlinear processes, such as those involved in mid-IR supercontinuum generation. In recent years, considerable efforts have been directed towards the development of compact ultrafast sources based on rare earth doped fluoride-zirconate (ZBLAN) fibers. The best performances are obtained using ZBLAN fibers pumped with high power laser diodes around 980 or 1150 nm using, respectively, the 2.8 µm transition from the 4 I 11/2 to the 4 I 13/2 energy levels of erbium ions [1] or the 2.9 µm transition ( 5 I 6 → 5 I 7 ) of holmium-doped ZBLAN fibers [2] . A pulse energy as high as 7.6 nJ with 180 fs pulse duration and 37 kW peak power was then demonstrated [2] . However, these sources are based on the nonlinear polarization evolution technique which is highly sensitive to thermal or mechanical perturbation in its environment. The use of real saturable absorbers to initiate and stabilize the mode-locking regime allows to reduce this sensitivity to the environment in the picosecond regime [3] . Nonetheless, such sources deliver output beams with a highly unstable polarization state, which varies along with the perturbations. Here, we then report on the first demonstration of a passively mode-locked oscillator featuring a polarization maintaining erbium-doped ZBLAN fiber.

Learning Healthcare Systems are an emerging approach to healthcare research as translated into practice. For this purpose, a strong interconnection comes to be a necessity when dealing with healthcare services, research and knowledge transfer all at once. Practically, these connections imply that a routing protocol should guarantee anonymity to entities in compliance with both laws and ethical requirements while restricting the quantity of information obtainable had an entity been compromised. In order to bring more protection and meet all the requirements, a new message routing protocol is offered to allow the use of data access paths and to resist traffic analysis security threats. The protocol protects the addresses and roles pertaining to entities from any lurking malevolent minds by implementing proxies into a mix-network. Moreover, flows of synthetic datasets and contents identifiers are handled separately so as to curb any risk of re-identification. A model of this protocol is provided in the form of a multi-objective optimization problem, natively integrating objectives of minimizing both latency and entropy of the information exchanged. The assessment of this model shows that the constrained separation of data flows has a minimal impact on delay times, which not only reveals to be an acceptable compromise but also significantly increases security in data access.