Search results for " signal processing"

Reaction-Diffusion Network For Geometric Multiscale High Speed Image Processing

2010

International audience; In the framework of heavy mid-level processing for high speed imaging, a nonlinear bi-dimensional network is proposed, allowing the implementation of active curve algorithms. Usually this efficient type of algorithm is prohibitive for real-time image processing due to its calculus charge and the inadequate structure for the use of serial or parallel architectures. Another kind of implementation philosophy is proposed here, by considering the active curve generated by a propagation phenomenon inspired from biological modeling. A programmable nonlinear reaction-diffusion system is proposed under front control and technological constraints. Geometric multiscale processin…

Une architecture programmable de traitement des impulsions zéro-temps mort pour l'instrumentation nucléaire

2015

In the field of nuclear instrumentation, digital signal processing architectures have to deal with the poissonian characteristic of the signal, composed of random arrival pulses which requires current architectures to work in dataflow. Thus, the real-time needs implies losing pulses when the pulse rate is too high. Current architectures paralyze the acquisition of the signal during the pulse processing inducing a time during no signal can be processed, this is called the dead time. These issue have led current architectures to use dedicated solutions based on reconfigurable components such as FPGAs. The requirement of end users to implement a wide range of applications on a large number of …

Prise en compte de la technologie dans la quantification des biomarqueurs

2016

International audience; Part de la variance technologique dans la quantification des biomarqueurs. Exemple en protéomique avec la technologie MALDI-TOF

<title>Fast motion estimation based on spatio-temporal Gabor filters: parallel implementation on multi-DSP</title>

2000

The aim of our work is to implement a system of motion estimation in image sequences processing on DSP's: fast motion estimation based on Gabor spatio-temporal filters. Our approach consists to calculate optical flow using an energy-based method, named combined filtering which associates the energetic responses of Gabor spatio- temporal filters organized in triads. For this purpose, we applicate a technique developed by the Laboratory LIS in France, inspired from the architecture of Heeger. To reduce the computation time, we present also a parallel implementation of the algorithm on a multi-DSP architecture using SynDEx tool which is a programming environment to generate optimized distribut…

A Support Vector Machine Signal Estimation Framework

2018

Support vector machine (SVM) were originally conceived as efficient methods for pattern recognition and classification, and the SVR was subsequently proposed as the SVM implementation for regression and function approximation. Nowadays, the SVR and other kernel‐based regression methods have become a mature and recognized tool in digital signal processing (DSP). This chapter starts to pave the way to treat all the problems within the field of kernel machines, and presents the fundamentals for a simple, framework for tackling estimation problems in DSP using support vector machine SVM. It outlines the particular models and approximations defined within the framework. The chapter concludes wit…

Sensorless control of permanent magnet synchronous motors for wide speed range applications

2006

This paper deals with sensorless control of Interior Permanent Magnet Synchronous Motors (IPMS) based on the estimation of speed and rotor angular position. The above estimate is based on the injection of high frequency stator currents able to generate a signal similar to that generated by a resolver connected to the axis of the motor. A new digital algorithm has been designed to demodulate the above signal whose implementation can be carried out on the same DSP that processes the control algorithm. In this paper a new scheme of speed and angular position estimator is proposed and justified on the theoretic point of view. The experimental results here shown validate the effectiveness of the…

Electromagnetic scattering solutions for digital signal processing

2010

Positive L1 observer design for positive Switched systems

2014

Published version of an article in the journal: Circuits, Systems, and Signal Processing. Also available from the publisher at: http://dx.doi.org/10.1007/s00034-013-9737-6 This paper investigates the problem of L1 observer design for positive switched systems. Firstly, a new kind of positive L1 observer is proposed for positive switched linear delay-free systems with observable and unobservable subsystems. Based on the average dwell time approach, a sufficient condition is proposed to ensure the existence of the positive L1 observer. Under the condition obtained, the estimated error converges to zero exponentially, and the L1 -gain from the disturbance input to the estimated error is less t…

Joint Sub-Carrier and Power Allocation for Efficient Communication of Cellular UAVs

2020

| openaire: EC/H2020/857031/EU//5G!Drones Cellular networks are expected to be the main communication infrastructure to support the expanding applications of Unmanned Aerial Vehicles (UAVs). As these networks are deployed to serve ground User Equipment (UEs), several issues need to be addressed to enhance cellular UAVs’ services. In this paper, we propose a realistic communication model on the downlink, and we show that the Quality of Service (QoS) for the users is affected by the number of interfering BSs and the impact they cause. The joint problem of sub-carrier and power allocation is therefore addressed. Given its complexity, which is known to be NP-hard, we introduce a solution based …

Optimization of Linearized Belief Propagation for Distributed Detection

2020

In this paper, we investigate distributed inference schemes, over binary-valued Markov random fields, which are realized by the belief propagation (BP) algorithm. We first show that a decision variable obtained by the BP algorithm in a network of distributed agents can be approximated by a linear fusion of all the local log-likelihood ratios. The proposed approach clarifies how the BP algorithm works, simplifies the statistical analysis of its behavior, and enables us to develop a performance optimization framework for the BP-based distributed inference systems. Next, we propose a blind learning-adaptation scheme to optimize the system performance when there is no information available a pr…