Étude préliminaire sur l'incorporation de liants dans un aliment composé pour poisson d'élevage en Côte d'Ivoire

Artificial feeding constitutes a very important aspect for intensive fish culture. Then, the physical properties, particularly water stability of feed, wider influence the fish production. By trying to find some answers to these problems, a preliminary study on water stability has been conducted at the LAYO aquaculture research station. This work consisted in incorporating different types of binders available in tropical countries in fish pellets of 3.5 mm and 6.0 mm diameters in order to identify an efficient binder allowing the improvement of the water stability of feed. From this experiment, it comes out that the use of wheat flour (A1) gives excellent quality pellets followed by cassava flour residue (A3). Moreover, this study shows that it exists an influence of the diameter on the crumbling of the pellets.

Toxicité des sulfures dissous chez le poisson-chat africain, Heterobranchus longifilis (Valenciennes, 1840)

Biological tests were carried out using static bioassay systems to determine the median lethal concentration of total sulfide (H2S, HS, S2) for African catfish (Heterobranchus longifilis, Valenciennes, 1840) at fry and juvenile stages.

Croissance et détermination de l'âge par lecture d'écailles d'un poisson plat de Côte d'Ivoire, Cynoglossus canariensis (Steind. 1882)

Cynoglossus canariensis has a very rapid growth. The rate of the males is 0,36 and the female one is 0,32. The asymptotic size is 55,0cm for the females and 50,5cm for the males. Females and males younger than three years (40cm), which represent 90 per cent of the Côte d'Ivoire stock have a similar growth, so the average equation: Lt=53,5 (1-e -0,34(t+1)) will be used.

BOLTZMANN MACHINES FOR SPEECH RECOGNITION.

Boltzmann machines offer a new and exciting approach to automatic speech recognition, and provide a rigorous mathematical formalism for parallel computing arrays. In this paper we briefly summarize Boltzmann machine theory, and present results showing their ability to recognize both static and time-varying speech patterns. A machine with 2000 units was able to distinguish between the 11 steady-state vowels in English with an accuracy of 85%. The stability of the learning algorithm and methods of preprocessing and coding speech data before feeding it to the machine are also discussed. A new type of unit called a carry input unit, which involves a type of state-feedback, was developed for the processing of time-varying patterns and this was tested on a few short sentences. Use is made of the implications of recent work into associative memory, and the modelling of neural arrays to suggest a good configuration of Boltzmann machines for this sort of pattern recognition.

Enhanced poisson sum representation for alpha-stable processes

In this paper we present Poisson sum series representations for α-stable (αS) random variables and a-stable processes, in particular concentrating on continuous-time autoregressive (CAR) models driven by α-stable Lévy processes. Our representations aim to provide a conditionally Gaussian framework, which will allow parameter estimation using Rao-Blackwellised versions of state of the art Bayesian computational methods such as particle filters and Markov chain Monte Carlo (MCMC). To overcome the issues due to truncation of the series, novel residual approximations are developed. Simulations demonstrate the potential of these Poisson sum representations for inference in otherwise intractable α-stable models. © 2011 IEEE.

Multiple pitch estimation using non-homogeneous poisson processes

Novel statistical models are proposed and developed in this paper for automated multiple-pitch estimation problems. Point estimates of the parameters of partial frequencies of a musical note are modeled as realizations from a non-homogeneous Poisson process defined on the frequency axis. When several notes are combined, the processes for the individual notes combine to give a new Poisson process whose likelihood is easy to compute. This model avoids the data-association step of linking the harmonics of each note with the corresponding partials and is ideal for efficient Bayesian inference of unknown multiple fundamental frequencies in a signal. © 2011 IEEE.