Investigation of the performance of multi-input multi-output detectors based on deep learning in non-Gaussian environments

The next generation of wireless cellular communication networks must be energy efficient, extremely reliable, and have low latency, leading to the necessity of using algorithms based on deep neural networks (DNN) which have better bit error rate (BER) or symbol error rate (SER) performance than traditional complex multi-antenna or multi-input multi-output (MIMO) detectors. This paper examines deep neural networks and deep iterative detectors such as OAMP-Net based on information theory criteria such as maximum correntropy criterion (MCC) for the implementation of MIMO detectors in non-Gaussian environments, and the results illustrate that the proposed method has better BER or SER performance

Production and characterization of algae extract from Chlamydomonas reinhardtii

Background: Algae offer many advantages as biofuel sources including: high growth rates, high lipid content, the ability to grow on non-agricultural land, and the genetic versatility to improve strains rapidly and produce co-products. Research is ongoing to make algae biofuels a more financially attractive energy option; however, it is becoming evident that the economic viability of algae-based fuels may hinge upon high-value co-products. This work evaluated the feasibility of using a co-product, algae extract, as a nutrient source in cell culture media. Results: Algae extract prepared from autolysed Chlamydomonas reinhardtii was found to contain 3.0% protein, 9.2% total carbohydrate, and 3.9% free \u3b1-amino acid which is similar to the nutrient content of commercially available yeast extract. The effects of algae extract on the growth andmetabolism of laboratory strains of Escherichia coli and Saccharomyces cerevisiae were tested by substituting algae extract for yeast extract in LB and YPAD growth media recipes. Complex laboratory media supplemented with algae extract instead of yeast extract showed markedly improved effects on the growth and metabolism of common laboratory microorganisms in all cases except ethanol production rates in yeast. Conclusions: This study showed that algae extract derived from C. reinhardtii is similar, if not superior, to commercially available yeast extract in nutrient content and effects on the growth and metabolism of E. coli and S. cerevisiae. Bacto\u2122 yeast extract is valued at USD $0.15\u20130.35 per gram, if algae extract was sold at similar prices, it would serve as a high-value co-product in algae-based fuel processes

Algorithmes et modeles pour l'estimation de retards non stationnaires

Available from INIST (FR), Document Supply Service, under shelf-number : T 83062 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueSIGLEFRFranc

Etude fonctionnelle des recepteurs de la galanine et de leur couplage aux proteines G dans le pancreas endocrine et le cerveau de rat

SIGLEAvailable from INIST (FR), Document Supply Service, under shelf-number : TD 81139 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Limits of the performance of the information of a direct-detection channel using classical and non-classical states of light

International audienc

Methodes efficientes pour l'estimation d'un retard non stationnaire

Nous proposons un ensemble de méthodes permettant une estimation efficace d'un retard non-stationnaire à partir de l'observation d'un signal et d'une version retardée et bruitée. Une première méthode fournit un estimateur au sens du Maximum de Vraisemblance (MV) des coefficients du retard dans une base de fonctions donnée. L'équation du MV obtenue itérativement d'après l'observation des signaux sur un horizon fixé. Une deuxième classe de méthodes, utilisant un formalisme du type "filtrage de Kalman", fournit un estimateur récursif en temps, soit des paramètres du modèle précédent, soit du retard lui-même. Nous montrons l'équivalence de ces deux méthodes. Des simulations confirment les bonnes performances obtenues en présence de retard rapidement variable et avec un niveau de bruit appréciable

Second-order statistical properties of quantum chaotic beams

International audienc

DIRECTED EVOLUTION: SELECTION OF THE HOST ORGANISM

Directed evolution has become a well-established tool for improving proteins and biological systems. A critical aspect of directed evolution is the selection of a suitable host organism for achieving functional expression of the target gene. To date, most directed evolution studies have used either Escherichia coli or Saccharomyces cerevisiae as a host; however, other bacterial and yeast species, as well as mammalian and insect cell lines, have also been successfully used. Recent advances in synthetic biology and genomics have opened the possibility of expanding the use of directed evolution to new host organisms such as microalgae. This review focuses on the different host organisms used in directed evolution and highlights some of the recent directed evolution strategies used in these organisms