Search results for "recognition"
showing 10 items of 3607 documents
A new hydrogen bonding motif involved in self-recognition in the solid state by functionalised macrocycles
2011
Self-recognition within the crystal lattices of three functionalised macrocycles results in the formation of arrays of remarkably similar hermaphroditic pairs of macrocycles. In the case of two of the macrocycles containing acylhydrazine substituents, a hitherto unknown recognition pattern is found in the interaction of the hydrazine moiety with crown-ether oxygen atoms.
Crystallographic snapshots of host–guest interactions in drugs@metal–organic frameworks: towards mimicking molecular recognition processes
2018
We report a novel metal–organic framework (MOF) featuring functional pores decorated with hydroxyl groups derived from the natural amino acid L-serine, which is able to establish specific interactions of different natures, strengths and directionalities with organic molecules of technological interest, i.e. ascorbic acid, pyridoxine, bupropion and 17-β-estradiol, based on their different sizes and chemical natures. The ability of 1 to distinctly organize guest molecules within its channels, through the concomitant effect of different directing supramolecular host–guest interactions, enables gaining unique insights, by means of single-crystal X-ray crystallography, into the host–guest intera…
Systematic Investigation of Resorcin[4]arene-Based Cavitands as Affinity Materials on Quartz Crystal Microbalances.
2017
Resorcin[4]arene cavitands are well-known supramolecular hosts, and their outstanding guest-binding abilities in solution have been studied in detail in recent decades. In a systematic approach, different resorcin[4]arene cavitands and container molecules are characterized as affinity materials for gravimetric sensing using high-fundamental-frequency quartz crystal microbalances. Analysis of their affinity toward a series of various analytes reveals a remarkable dependence of both selectivity and sensitivity on the shape, accessibility, and size of the cavity, along with their supramolecular interactions with the host molecules.
Application of Rigidity-Controlled Supramolecular Affinity Materials for the Gravimetric Detection of Hazardous and Illicit Compounds.
2016
The combination of an (-)-isosteviol-derived building block and 9,9'-spirobifluorene or tetraphenylmethane generated highly potent new affinity materials for the detection of volatile organic compounds (VOCs). Comparison of their affinity behaviour with different core structures showed remarkable influence on selectivity and sensitivity due to structural rigidity and their pre-organization. Their unique supramolecular properties were investigated in an affinity assay using high fundamental frequency quartz crystal microbalances.
Optimized Class-Separability in Hyperspectral Images
2016
International audience; Image visualization techniques are mostly based on three bands as RGB color composite channels for human eye to characterize the scene. This, however, is not effective in case of hyper-spectral images (HSI) because they contain dozens of informative spectral bands. To eliminate redundancy of spectral information among these bands, dimensionality reduction (DR) is applied while at the same trying to retain maximum information. In this paper, we propose a new method of information-preserved hyper-spectral satellite image visualization that is based on fusion of unsupervised band selection techniques and color matching function (CMF) stretching. The results show consist…
Statistical retrieval of atmospheric profiles with deep convolutional neural networks
2019
Abstract Infrared atmospheric sounders, such as IASI, provide an unprecedented source of information for atmosphere monitoring and weather forecasting. Sensors provide rich spectral information that allows retrieval of temperature and moisture profiles. From a statistical point of view, the challenge is immense: on the one hand, “underdetermination” is common place as regression needs to work on high dimensional input and output spaces; on the other hand, redundancy is present in all dimensions (spatial, spectral and temporal). On top of this, several noise sources are encountered in the data. In this paper, we present for the first time the use of convolutional neural networks for the retr…
Multi-phase classification by a least-squares support vector machine approach in tomography images of geological samples
2016
Abstract. Image processing of X-ray-computed polychromatic cone-beam micro-tomography (μXCT) data of geological samples mainly involves artefact reduction and phase segmentation. For the former, the main beam-hardening (BH) artefact is removed by applying a best-fit quadratic surface algorithm to a given image data set (reconstructed slice), which minimizes the BH offsets of the attenuation data points from that surface. A Matlab code for this approach is provided in the Appendix. The final BH-corrected image is extracted from the residual data or from the difference between the surface elevation values and the original grey-scale values. For the segmentation, we propose a novel least-squar…
Convolutional Neural Networks for Cloud Screening: Transfer Learning from Landsat-8 to Proba-V
2018
Cloud detection is a key issue for exploiting the information from Earth observation satellites multispectral sensors. For Proba-V, cloud detection is challenging due to the limited number of spectral bands. Advanced machine learning methods, such as convolutional neural networks (CNN), have shown to work well on this problem provided enough labeled data. However, simultaneous collocated information about the presence of clouds is usually not available or requires a great amount of manual labor. In this work, we propose to learn from the available Landsat −8 cloud masks datasets and transfer this learning to solve the Proba-V cloud detection problem. CNN are trained with Landsat images adap…
Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks
2020
Interest in drone solutions in forestry applications is growing. Using drones, datasets can be captured flexibly and at high spatial and temporal resolutions when needed. In forestry applications, fundamental tasks include the detection of individual trees, tree species classification, biomass estimation, etc. Deep neural networks (DNN) have shown superior results when comparing with conventional machine learning methods such as multi-layer perceptron (MLP) in cases of huge input data. The objective of this research is to investigate 3D convolutional neural networks (3D-CNN) to classify three major tree species in a boreal forest: pine, spruce, and birch. The proposed 3D-CNN models were emp…
Estimating Missing Information by Cluster Analysis and Normalized Convolution
2018
International audience; Smart city deals with the improvement of their citizens' quality of life. Numerous ad-hoc sensors need to be deployed to know humans' activities as well as the conditions in which these actions take place. Even if these sensors are cheaper and cheaper, their installation and maintenance cost increases rapidly with their number. We propose a methodology to limit the number of sensors to deploy by using a standard clustering technique and the normalized convolution to estimate environmental information whereas sensors are actually missing. In spite of its simplicity, our methodology lets us provide accurate assesses.