Object-based fusion for urban tree species classification from hyperspectral,panchromatic and nDSM data

This study aims at identifying the best object-based fusion strategy that takes advantage of the complementarity of several heterogeneous airborne data sources for improving the classification of 15 tree species in an urban area (Toulouse, France). The airborne data sources are: hyperspectral Visible Near-Infrared (160 spectral bands, spatial resolution of 0.4 m) and Short-Wavelength Infrared (256 spectral bands, 1.6 m), panchromatic (14 cm), and a normalized Digital Surface Model (12.5 cm). Object-based feature and decision level fusion strategies are proposed and compared when applied to a reference site where the species are previously identified during ground truth collection. This allows the best fusion strategy to be selected with a view to introducing the method in an automatic process (tree crown delineation and species classification) on a test site, independent of the reference site used for learning. In particular, a decision level fusion is selected: based on the Support Vector Machine algorithm, Visible Near-Infrared and Short-Wavelength Infrared classifications use Minimum Noise Fraction components at the original spatial resolution, whereas panchromatic and normalized Digital Surface Model classifications use, respectively, Haralick’s and structural features computed at the object scale. After the computation of a decision profile for each source at the object level based on the classification algorithms’ membership probabilities, these decision profiles are combined and a decision rule is applied to predict the species. Focusing on the reference site, the Visible Near-Infrared exhibits the best performances with F-score values higher than 60% for 13 species out of 15. The Short-Wavelength Infrared is the most powerful for three species with F-score greater than 60% for seven common species with the Visible Near-Infrared. The panchromatic and normalized Digital Surface Model contribute marginally. The best fusion strategy (decision fusion) does not improve significantly the overall accuracy with 77% (kappa = 74%) against 75% (kappa = 72%) for the Visible Near-Infrared but in general, it improves the results for cases where complementarities have been observed. When applied to the test site and assessed for the two majority species (Tilia tomentosa and Platanus x hispanica), the selected approach gives consistent results with an overall accuracy of 63% against 55% for the Visible Near-Infrared.     

RTAB‐Map as an open‐source lidar and visual simultaneous localization and mapping library for large‐scale and long‐term online operation

Distributed as an open‐source library since 2013, real‐time appearance‐based mapping (RTAB‐Map) started as an appearance‐based loop closure detection approach with memory management to deal with large‐scale and long‐term online operation. It then grew to implement simultaneous localization and mapping (SLAM) on various robots and mobile platforms. As each application brings its own set of constraints on sensors, processing capabilities, and locomotion, it raises the question of which SLAM approach is the most appropriate to use in terms of cost, accuracy, computation power, and ease of integration. Since most of SLAM approaches are either visual‐ or lidar‐based, comparison is difficult. Therefore, we decided to extend RTAB‐Map to support both visual and lidar SLAM, providing in one package a tool allowing users to implement and compare a variety of 3D and 2D solutions for a wide range of applications with different robots and sensors. This paper presents this extended version of RTAB‐Map and its use in comparing, both quantitatively and qualitatively, a large selection of popular real‐world datasets (e.g., KITTI, EuRoC, TUM RGB‐D, MIT Stata Center on PR2 robot), outlining strengths, and limitations of visual and lidar SLAM configurations from a practical perspective for autonomous navigation applications.     

Einfluss der elastischen Nachgiebigkeit auf die Kraft beim tauchenden Kegelradschleifen

Forschung im Ingenieurwesen - Aufgrund steigender Anforderungen an den Wirkungsgrad und die Geräuschanregung von Zahnradgetrieben ist die Hartbearbeitung von Zahnrädern für viele...     

About the control of semiconducting properties of chromia: investigation using photoelectrochemistry and orientation mapping in a TEM

Abstract

The semiconducting properties of chromia, studied by photoelectrochemistry (PEC), are varied by oxidizing pure Cr as a function of temperature and oxygen activity. At 800 °C and a p(O2) of 10-14 atm, a single n-chromia is observed, while at 900 °C and a p(O2) of 10-12 atm a n- & p-layer is obtained. For intermediate conditions, an insulating stoichiometric Cr2O3 is identified at 850 °C for a p(O2) of 10-13 atm. The TEM investigation reveals a duplex morphology for every scales: an equiaxed (resp. columnar) morphology has been developed in the internal (resp. external) part. Between these two subscales, a textured chromia layer has been identified as the first layer to form. Finally, the association of TEM and PEC techniques permits the identification of major point defects. It is revealed that the morphology is only linked to the growth direction: anionic (resp. cationic) growth leads to equiaxed (resp. columnar) grains.     

Engineered Ferritin for Magnetogenetic Manipulation of Proteins and Organelles Inside Living Cells

Magnetogenetics is emerging as a novel approach for remote‐controlled manipulation of cellular functions in tissues and organisms with high spatial and temporal resolution. A critical, still challenging issue for these techniques is to conjugate target proteins with magnetic probes that can satisfy multiple colloidal and biofunctional constraints. Here, semisynthetic magnetic nanoparticles are tailored based on human ferritin coupled to monomeric enhanced green fluorescent protein (mEGFP) for magnetic manipulation of proteins inside living cells. This study demonstrates efficient delivery, intracellular stealth properties, and rapid subcellular targeting of those magnetic nanoparticles via GFP–nanobody interactions. By means of magnetic field gradients, rapid spatial reorganization in the cytosol of proteins captured to the nanoparticle surface is achieved. Moreover, exploiting efficient nanoparticle targeting to intracellular membranes, remote‐controlled arrest of mitochondrial dynamics using magnetic fields is demonstrated. The studies establish subcellular control of proteins and organelles with unprecedented spatial and temporal resolution, thus opening new prospects for magnetogenetic applications in fundamental cell biology and nanomedicine.     

Fused Benzothiadiazole: A Building Block for n‐Type Organic Acceptor to Achieve High‐Performance Organic Solar Cells

Narrow bandgap n‐type organic semiconductors (n‐OS) have attracted great attention in recent years as acceptors in organic solar cells (OSCs), due to their easily tuned absorption and electronic energy levels in comparison with fullerene acceptors. Herein, a new n‐OS acceptor, Y5, with an electron‐deficient‐core‐based fused structure is designed and synthesized, which exhibits a strong absorption in the 600–900 nm region with an extinction coefficient of 1.24 × 10⁵ cm^?1, and an electron mobility of 2.11 × 10^?4 cm² V^?1 s^?1. By blending Y5 with three types of common medium‐bandgap polymers (J61, PBDB‐T, and TTFQx‐T1) as donors, all devices exhibit high short‐circuit current densities over 20 mA cm^?2. As a result, the power conversion efficiency of the Y5‐based OSCs with J61, TTFQx‐T1, and PBDB‐T reaches 11.0%, 13.1%, and 14.1%, respectively. This indicates that Y5 is a universal and highly efficient n‐OS acceptor for applications in organic solar cells.     

Detection of unanticipated faults for autonomous underwater vehicles using online topic models

For robots to succeed in complex missions, they must be reliable in the face of subsystem failures and environmental challenges. In this paper, we focus on autonomous underwater vehicle (AUV) autonomy as it pertains to self‐perception and health monitoring, and we argue that automatic classification of state‐sensor data represents an important enabling capability. We apply an online Bayesian nonparametric topic modeling technique to AUV sensor data in order to automatically characterize its performance patterns, then demonstrate how in combination with operator‐supplied semantic labels these patterns can be used for fault detection and diagnosis by means of a nearest‐neighbor classifier. The method is evaluated using data collected by the Monterey Bay Aquarium Research Institute's Tethys long‐range AUV in three separate field deployments. Our results show that the proposed method is able to accurately identify and characterize patterns that correspond to various states of the AUV, and classify faults at a high rate of correct detection with a very low false detection rate.     

Laser-Empowered Random Metasurfaces for White Light Printed Image Multiplexing

Printed image multiplexing based on the design of metasurfaces has attracted much interest in the past decade. Optical switching between different images displayed directly on the metasurface is performed by altering the parameters of the incident light such as polarization, wavelength, or incidence angle. When using white light, only two-image multiplexing is implemented with polarization switching. Such metasurfaces are made of nanostructures perfectly controlled individually, which provide high-resolution pixels but small images and involve long fabrication processes. Here, it is demonstrated that laser processing of nanocomposites offers a versatile low-cost, high-speed method with large area processing capabilities for controlling the statistical properties of random metasurfaces, allowing up to three-image multiplexing under white light illumination. By independently controlling absorption and interference effects, colors in reflection and transmission can be varied independently yielding two-image multiplexing under white light. Using anisotropy of plasmonic nanoparticles, a third image can be multiplexed and revealed through polarization changes. The design strategy, the fundamental properties, and the versatility of implementation of these laser-empowered random metasurfaces are discussed. The technique, applied on flexible substrate, can find applications in information encryption or functional switchable optical devices, and offers many advantages for visual security and anticounterfeiting.     

A multi-sequences MRI deep framework study applied to glioma classfication

Multimedia Tools and Applications - Glioma is one of the most important central nervous system tumors, ranked 15th in the most common cancer for men and women. Magnetic Resonance Imaging (MRI)...