Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

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Table Of Integrals Series And Products

Recently, to ensure the reliability and safety of modern large-scale industrial processes, data-driven methods have been receiving considerably increasing attention, particularly for the purpose of process monitoring. However, great challenges are also met under different real operating conditions by using the basic data-driven methods. In this paper, widely applied data-driven methodologies suggested in the literature for process monitoring and fault diagnosis are surveyed from the application point of view. The major task of this paper is to sketch a basic data-driven design framework with necessary modifications under various industrial operating conditions, aiming to offer a reference for industrial process monitoring on large-scale industrial processes.

A Review on Basic Data-Driven Approaches for Industrial Process Monitoring

This paper provides an overview of the recent developments in data-based techniques focused on modern industrial applications. As one of the hottest research topics for complicated processes, the data-based techniques have been rapidly developed over the past two decades and widely used in numerous industrial sectors nowadays. The core of data-based techniques is to take full advantage of the huge amounts of available process data, aiming to acquire the useful information within. Compared with the well-developed model-based approaches, data-based techniques provide efficient alternative solutions for different industrial issues under various operating conditions. The main objective of this paper is to review and summarize the recent achievements in data-based techniques, especially for complicated industrial applications, thus providing a referee for further study on the related topics both from academic and practical points of view. This paper begins with a brief evolutionary overview of data-based techniques in the last two decades. Then, the methodologies only based on process measurements and the model-data integrated techniques will be further introduced. The recent developments for modern industrial applications are, respectively, presented mainly from perspectives of monitoring and control. The new trends of data-based technique as well as potential application fields are finally discussed.

Data-Based Techniques Focused on Modern Industry: An Overview

Space-Time Block Coding for Wireless Communications

In three-phase systems using unconditional maximum likelihood estimation

Cette etude traite de l'estimation de la frequence fondamentale dans les reseaux electriques. Nous proposons trois estimateurs de la frequence fondamentale exploitant conjointement la structure harmonique et la nature multidimensionnelle du signal. Les resultats de simulation montrent que ces techniques permettent d'obtenir une estimation precise de la frequence fondamentale meme lorsque la longueur du signal a analyser reste tres faible

https://hal.archives-ouvertes.fr/hal-00863015/document

Analyse Spectrale Paramétrique dans un contexte Smart-Grid

La reconnaissance aveugle des parametres d'une communication est une thematique importante dans le contexte des recepteurs auto-configurants et de la guerre electronique. Actuellement, peu de recherches se focalisent sur la reconnaissance aveugle du codage spatio-temporel utilise dans les communications sans fils multi-emetteurs. Pour caracteriser ces codages, nous proposons une nouvelle fonction de contraste basee sur des proprietes matricielles remarquables de statistiques d'ordre 2. L'utilisation de cette fonction de contraste en entree d'un classifieur SVM permet la reconnaissance automatique du codage. Les performances de notre algorithme sont evaluees pour la reconnaissance de 3 types de codage (multiplexage spatial, codage d'Alamouti et codage OTSBC3 de rendement 1/2).

http://documents.irevues.inist.fr/bitstream/handle/2042/17489/GRETSI_2007_249.pdf;sequence=1

Reconnaissance aveugle de codages OSTBC basée sur les propriétés matricielles des statistiques d'ordre 2

This paper proposes a parametric-based network architecture for joint channel estimation and data detection in communications systems with hardware impairments. This architecture is composed of a data-augmented layer, a custom soft thresholding function, and several linear layers modeling the effect of channel effects and hardware impairments. In the proposed network, the soft thresholding function softly constrains the detected data to be within the considered constellation. The latter depends only on one one parameter that is optimized during training. The benefit of the proposed approach is illus-trated through a communication chain corrupted by multiple impairments and noises.

/pdf/paramnet-a-multi-layer-parametric-network-for-joint-channel-2ekflgfb.pdf

Vincent Choqueuse

Papers

In three-phase systems using unconditional maximum likelihood estimation

Analyse Spectrale Paramétrique dans un contexte Smart-Grid

Reconnaissance aveugle de codages OSTBC basée sur les propriétés matricielles des statistiques d'ordre 2

ParamNet: A Multi-Layer Parametric Network for Joint Channel Estimation and Symbol Detection

Blind Modulation Recognition for MIMO communications