Search results for "Networking & Telecommunications"
showing 10 items of 962 documents
New Optimization and Security Approaches to Enhance the Smart Grid Performance and Reliability
2016
International audience; Nowadays, the Smart Grid (SG) is becoming smarter thanks to the integration of different information and communication technologies to enhance the reliability and efficiency of the power grid. However, several issues should be met to ensure high SG performance. Among these issues, we cite the problem of electric vehicles (EVs) integration into the SG to avoid electricity intermittence due to the important load that EVs can create. Another issue is the SG communication network security that can be attempted by malicious intruders in order to create damages and make the power grid instable. In this context, we propose at a first level a Bayesian game-theory model that …
Towards a methodology for semantic and context-aware mobile learning
2013
International audience; Internet and mobile devices open the way towards mobile learning (m-learning), offering new opportunities to extend learning beyond the traditional teacher-led classroom. M-learning is not only any form of teaching or studying that takes place when the user interacts with a mobile device. It is more than just using a mobile device to access resources and communicate with others. It should take account of the constant mobile situation of the learner. The challenge here is to exploit this continually changing situation with a system that can dynamically recognize and adapt educational resources and services to the "context" in which the learner operates (localization, …
An Impulse Response Model for the 60 Ghz Channel Based on Spectral Techniques of alpha-stable Processes
2007
International audience; In order to make realistic simulations of the radio propagation mechanism in ultra-wide band channels, an appropriate model is needed. In this paper we propose a new technique to model the impulse response of the 60 Ghz channel. This new approach is based on the spectral analysis of alpha-stable processes. Our new model presents many advantages: firstly, the channel is characterized only by a one deterministic function (spectral density) in the place of four parameters. Secondly, the estimations procedure deals directly with the measured transfer functions which avoids loosing information in data pretreatment. Finally, an estimation of the spectral measure permits to…
A 1.3 megapixel FPGA-based smart camera for high dynamic range real time video
2013
International audience; A camera is able to capture only a part of a high dynamic range scene information. The same scene can be fully perceived by the human visual system. This is true especially for real scenes where the difference in light intensity between the dark areas and bright areas is high. The imaging technique which can overcome this problem is called HDR (High Dynamic Range). It produces images from a set of multiple LDR images (Low Dynamic Range), captured with different exposure times. This technique appears as one of the most appropriate and a cheap solution to enhance the dynamic range of captured environments. We developed an FPGA-based smart camera that produces a HDR liv…
Quadratic Objective Functions for Dichromatic Model Parameters Estimation
2017
International audience; In this paper, we present a novel method to estimate dichromatic model parameters from a single color image. Estimation of reflectance, shading and specularity has many applications such as shape recovery, specularity removal and facilitates classical image processing and computer vision tasks such as segmentation or classification. Our method is based on two successive and independent constrained quadratic programming steps to recover the parameters of the model. Compared to recent methods, our approach has the advantage to transform a complex inverse problem into two parralelizable optimization steps that are much easier to solve. We have compared our method with r…
A new minimum trees-based approach for shape matching with improved time computing : application to graphical symbols recognition
2010
Recently we have developed a model for shape description and matching. Based on minimum spanning trees construction and specifics stages like the mixture, it seems to have many desirable properties. Recognition invariance in front shift, rotated and noisy shape was checked through median scale tests related to GREC symbol reference database. Even if extracting the topology of a shape by mapping the shortest path connecting all the pixels seems to be powerful, the construction of graph induces an expensive algorithmic cost. In this article we discuss on the ways to reduce time computing. An alternative solution based on image compression concepts is provided and evaluated. The model no longe…
Noise estimation from digital step-model signal
2013
International audience; This paper addresses the noise estimation in the digital domain and proposes a noise estimator based on the step signal model. It is efficient for any distribution of noise because it does not rely only on the smallest amplitudes in the signal or image. The proposed approach uses polarized/directional derivatives and a nonlinear combination of these derivatives to estimate the noise distribution (e.g., Gaussian, Poisson, speckle, etc.). The moments of this measured distribution can be computed and are also calculated theoretically on the basis of noise distribution models. The 1D performances are detailed, and as our work is mostly dedicated to image processing, a 2D…
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…
A Mobile Computing Framework for Pervasive Adaptive Platforms
2012
International audience; Ubiquitous computing is now the new computing trend, such systems that interact with their environment require self-adaptability. Bioinspiration is a natural candidate to provide the capability to handle complex and changing scenarios. This paper presents a programming framework dedicated to pervasive platforms programming. This bioinspired and agentoriented framework has been developed within the frame of the PERPLEXUS European project that is intended to provide support for bioinspiration-driven system adaptability. This framework enables the platform to adapt itself to application requirements at high-level while using hardware acceleration at node level. The resu…
Embedded multi-spectral image processing for real-time medical application
2016
International audience; The newly introduced Kubelka-Munk Genetic Algorithm (KMGA) is a promising technique for the assessment of skin lesions from multi-spectral images. Using five skin parameter maps such as concentration or epidermis/dermis thickness, this method combines the Kubelka-Munk Light-Tissue interaction model and Genetic Algorithm optimization process to produce a quantitative measure of cutaneous tissue. Up to the present, variant improved KMGA implementations have been successfully realized using the recent parallel computing techniques. However, all these achievements are based on the multi-core CPUs. This results in a quite high cost and low practicability for the hardware …