Search results for " Neural Network"
showing 10 items of 1232 documents
Kolmogorov Superposition Theorem and Its Application to Multivariate Function Decompositions and Image Representation
2008
International audience; In this paper, we present the problem of multivariate function decompositions into sums and compositions of monovariate functions. We recall that such a decomposition exists in the Kolmogorov's superposition theorem, and we present two of the most recent constructive algorithms of these monovariate functions. We first present the algorithm proposed by Sprecher, then the algorithm proposed by Igelnik, and we present several results of decomposition for gray level images. Our goal is to adapt and apply the superposition theorem to image processing, i.e. to decompose an image into simpler functions using Kolmogorov superpositions. We synthetise our observations, before …
Roughness evaluation of vine leaf by image processing
2013
International audience; The study of leaf surface roughness is very important in the domain of precision spraying. It is one of the parameters that allow to reduce costs and losses of phytosanitary prod- ucts and to improve the spray accuracy. Moreover, the leaf roughness is related to adhesion mechanisms of liquid on a surface. It can be used to define leaf nature surface (hy- drophilic/hydrophobic). The main goal of this study is thus to estimate and to follow the evolution of leaf roughness using image processing and computer vision. The develop- ment and application of computer vision for measurement of surface leaf roughness using artificial neural networks will be described. The syste…
Experimental and numerical enhancement of Vibrational Resonance in a neural circuit
2012
International audience; A neural circuit exactly ruled by the FitzHugh-Nagumo equations is excited by a biharmonic signal of frequencies f and F with respective amplitudes A and B. The magnitude spectrum of the circuit response is estimated at the low frequency driving f and presents a resonant behaviour versus the amplitude B of the high frequency. For the first time, it is shown experimentally that this Vibrational Resonance effect is much more pronounced when the two frequencies are multiple. This novel enhancement is also confirmed by numerical predictions. Applications of this nonlinear effect to the detection of weak stimuli are finally discussed.
A Neural Network Meta-Model and its Application for Manufacturing
2015
International audience; Manufacturing generates a vast amount of data both from operations and simulation. Extracting appropriate information from this data can provide insights to increase a manufacturer's competitive advantage through improved sustainability, productivity, and flexibility of their operations. Manufacturers, as well as other industries, have successfully applied a promising statistical learning technique, called neural networks (NNs), to extract meaningful information from large data sets, so called big data. However, the application of NN to manufacturing problems remains limited because it involves the specialized skills of a data scientist. This paper introduces an appr…
Incorporating depth information into few-shot semantic segmentation
2021
International audience; Few-shot segmentation presents a significant challengefor semantic scene understanding under limited supervision.Namely, this task targets at generalizing the segmentationability of the model to new categories given a few samples.In order to obtain complete scene information, we extend theRGB-centric methods to take advantage of complementary depthinformation. In this paper, we propose a two-stream deep neuralnetwork based on metric learning. Our method, known as RDNet,learns class-specific prototype representations within RGB anddepth embedding spaces, respectively. The learned prototypesprovide effective semantic guidance on the corresponding RGBand depth query ima…
Research and implementation of artificial neural networks models for high velocity oxygen fuel thermal spraying
2020
In the high velocity oxygen fuel (HVOF) spray process, the coating properties are sensitive to the characteristics of in-flight particles, which are mainly determined by the process parameters. Due to the complex chemical and thermodynamic reactions during the deposition procedure, obtaining a comprehensive multi-physical model or analytical analysis of the HVOF process is still a challenging issue. This study proposes to develop a robust methodology via artificial neural networks (ANN) to solve this problem for the HVOF sprayed NiCr-Cr3C2 coatings under different operating parameters.First, 40 sets of HVOF spray experiments were conducted and the coating properties were tested for analysis…
Event-Based Trajectory Prediction Using Spiking Neural Networks
2021
International audience; In recent years, event-based sensors have been combined with spiking neural networks (SNNs) to create a new generation of bio-inspired artificial vision systems. These systems can process spatio-temporal data in real time, and are highly energy efficient. In this study, we used a new hybrid event-based camera in conjunction with a multi-layer spiking neural network trained with a spike-timing-dependent plasticity learning rule. We showed that neurons learn from repeated and correlated spatio-temporal patterns in an unsupervised way and become selective to motion features, such as direction and speed. This motion selectivity can then be used to predict ball trajectory…
High third and second order non linearities of chalcogenide glasses and fibers for compact infrared non linear devices.
2008
Due to their intrinsic nature, chalcogenide glasses present attractive nonlinearities from third and second order, with values reaching between 10 and 1000 times those of silica. We present a study of their properties and their shaping with the purpose to reach efficient devices in the near-mid infrared.
Nonlinear Sculpturing of Optical Pulses in Fibre Systems
2019
The interplay among the effects of dispersion, nonlinearity and gain/loss in optical fibre systems can be efficiently used to shape the pulses and manipulate and control the light dynamics and, hence, lead to different pulse-shaping regimes [1,2]. However, achieving a precise waveform with various prescribed characteristics is a complex issue that requires careful choice of the initial pulse conditions and system parameters. The general problem of optimisation towards a target operational regime in a complex multi-parameter space can be intelligently addressed by implementing machine-learning strategies. In this paper, we discuss a novel approach to the characterisation and optimisation of …
The multiple facets of Cajal-Retzius neurons.
2021
ABSTRACTCajal-Retzius neurons (CRs) are among the first-born neurons in the developing cortex of reptiles, birds and mammals, including humans. The peculiarity of CRs lies in the fact they are initially embedded into the immature neuronal network before being almost completely eliminated by cell death at the end of cortical development. CRs are best known for controlling the migration of glutamatergic neurons and the formation of cortical layers through the secretion of the glycoprotein reelin. However, they have been shown to play numerous additional key roles at many steps of cortical development, spanning from patterning and sizing functional areas to synaptogenesis. The use of genetic l…