Classification et relations entre les traits fonctionnels des crustacés zooplanctoniques : de l’organisme à l’écosystème

Les écologistes reconnaissent depuis longtemps que les organismes sont soutenus par le flux, l’emmagasinage et le renouvellement d’énergie et de matériel de l’écosystème, puisqu’ils sont nécessaires au métabolisme biologique et à la construction de biomasse. L’importance des organismes dans la régularisation des processus écosystémiques est maintenant de plus en plus considérée. Situé au centre des chaînes trophiques aquatiques, le zooplancton influence les flux d’énergie et de matériel dans les écosystèmes. Plusieurs de leurs caractéristiques sont connues comme étant de bons indicateurs de leur effet sur l’environnement, notamment leur taille, contenu corporel et taux métabolique. La plupart de ces caractéristiques peuvent être appelées « traits fonctionnels ». Alors que l’emploi des traits devient de plus en plus populaire en écologie des communautés aquatiques, peu ont su utiliser cette approche afin de concrètement lier la structure des communautés zooplanctoniques aux processus écosystémiques. Dans cette étude, nous avons colligé les données provenant d’une grande variété de littérature afin de construire une base de données sur les traits du zooplancton crustacé contribuant directement ou indirectement aux flux de C, N et P dans les écosystèmes. Notre méta-analyse a permis d’assembler plus de 9000 observations sur 287 espèces et d’identifier par le fait même ce qu’il manque à nos connaissances. Nous avons examiné une série de corrélations croisées entre 16 traits, dont 35 étaient significatives, et avons exploré les relations entre les unités taxonomiques de même qu’entre les espèces marines et d’eaux douces. Notre synthèse a entre autres révélé des patrons significativement différents entre le zooplancton marin et dulcicole quant à leur taux de respiration et leur allométrie (masse vs. longueur corporelle). Nous proposons de plus une nouvelle classification de traits liant les fonctions des organismes à celles de l’écosystème. Notre but est d’offrir une base de données sur les traits du zooplancton, des outils afin de mieux lier les organismes aux processus écosystémiques et de stimuler la recherche de patrons généraux et de compromis entre les traits.

FPGA-based object detection using classification circuits

Dans l'apprentissage machine, la classification est le processus d’assigner une nouvelle observation à une certaine catégorie. Les classifieurs qui mettent en œuvre des algorithmes de classification ont été largement étudié au cours des dernières décennies. Les classifieurs traditionnels sont basés sur des algorithmes tels que le SVM et les réseaux de neurones, et sont généralement exécutés par des logiciels sur CPUs qui fait que le système souffre d’un manque de performance et d’une forte consommation d'énergie. Bien que les GPUs puissent être utilisés pour accélérer le calcul de certains classifieurs, leur grande consommation de puissance empêche la technologie d'être mise en œuvre sur des appareils portables tels que les systèmes embarqués. Pour rendre le système de classification plus léger, les classifieurs devraient être capable de fonctionner sur un système matériel plus compact au lieu d'un groupe de CPUs ou GPUs, et les classifieurs eux-mêmes devraient être optimisés pour ce matériel. Dans ce mémoire, nous explorons la mise en œuvre d'un classifieur novateur sur une plate-forme matérielle à base de FPGA. Le classifieur, conçu par Alain Tapp (Université de Montréal), est basé sur une grande quantité de tables de recherche qui forment des circuits arborescents qui effectuent les tâches de classification. Le FPGA semble être un élément fait sur mesure pour mettre en œuvre ce classifieur avec ses riches ressources de tables de recherche et l'architecture à parallélisme élevé. Notre travail montre que les FPGAs peuvent implémenter plusieurs classifieurs et faire les classification sur des images haute définition à une vitesse très élevée.

Développement d’un modèle de classification probabiliste pour la cartographie du couvert nival dans les bassins versants d’Hydro-Québec à l’aide de données de micro-ondes passives

Chaque jour, des décisions doivent être prises quant à la quantité d'hydroélectricité produite au Québec. Ces décisions reposent sur la prévision des apports en eau dans les bassins versants produite à l'aide de modèles hydrologiques. Ces modèles prennent en compte plusieurs facteurs, dont notamment la présence ou l'absence de neige au sol. Cette information est primordiale durant la fonte printanière pour anticiper les apports à venir, puisqu'entre 30 et 40% du volume de crue peut provenir de la fonte du couvert nival. Il est donc nécessaire pour les prévisionnistes de pouvoir suivre l'évolution du couvert de neige de façon quotidienne afin d'ajuster leurs prévisions selon le phénomène de fonte. Des méthodes pour cartographier la neige au sol sont actuellement utilisées à l'Institut de recherche d'Hydro-Québec (IREQ), mais elles présentent quelques lacunes. Ce mémoire a pour objectif d'utiliser des données de télédétection en micro-ondes passives (le gradient de températures de brillance en position verticale (GTV)) à l'aide d'une approche statistique afin de produire des cartes neige/non-neige et d'en quantifier l'incertitude de classification. Pour ce faire, le GTV a été utilisé afin de calculer une probabilité de neige quotidienne via les mélanges de lois normales selon la statistique bayésienne. Par la suite, ces probabilités ont été modélisées à l'aide de la régression linéaire sur les logits et des cartographies du couvert nival ont été produites. Les résultats des modèles ont été validés qualitativement et quantitativement, puis leur intégration à Hydro-Québec a été discutée.

Scoliosis curve type classification from 3D trunk image

Adolescent idiopathic scoliosis (AIS) is a deformity of the spine manifested by asymmetry and deformities of the external surface of the trunk. Classification of scoliosis deformities according to curve type is used to plan management of scoliosis patients. Currently, scoliosis curve type is determined based on X-ray exam. However, cumulative exposure to X-rays radiation significantly increases the risk for certain cancer. In this paper, we propose a robust system that can classify the scoliosis curve type from non invasive acquisition of 3D trunk surface of the patients. The 3D image of the trunk is divided into patches and local geometric descriptors characterizing the surface of the back are computed from each patch and forming the features. We perform the reduction of the dimensionality by using Principal Component Analysis and 53 components were retained. In this work a multi-class classifier is built with Least-squares support vector machine (LS-SVM) which is a kernel classifier. For this study, a new kernel was designed in order to achieve a robust classifier in comparison with polynomial and Gaussian kernel. The proposed system was validated using data of 103 patients with different scoliosis curve types diagnosed and classified by an orthopedic surgeon from the X-ray images. The average rate of successful classification was 93.3% with a better rate of prediction for the major thoracic and lumbar/thoracolumbar types.

Non invasive classification system of scoliosis curve types using least-squares support vector machines

Objective To determine scoliosis curve types using non invasive surface acquisition, without prior knowledge from X-ray data. Methods Classification of scoliosis deformities according to curve type is used in the clinical management of scoliotic patients. In this work, we propose a robust system that can determine the scoliosis curve type from non invasive acquisition of the 3D back surface of the patients. The 3D image of the surface of the trunk is divided into patches and local geometric descriptors characterizing the back surface are computed from each patch and constitute the features. We reduce the dimensionality by using principal component analysis and retain 53 components using an overlap criterion combined with the total variance in the observed variables. In this work, a multi-class classifier is built with least-squares support vector machines (LS-SVM). The original LS-SVM formulation was modified by weighting the positive and negative samples differently and a new kernel was designed in order to achieve a robust classifier. The proposed system is validated using data from 165 patients with different scoliosis curve types. The results of our non invasive classification were compared with those obtained by an expert using X-ray images. Results The average rate of successful classification was computed using a leave-one-out cross-validation procedure. The overall accuracy of the system was 95%. As for the correct classification rates per class, we obtained 96%, 84% and 97% for the thoracic, double major and lumbar/thoracolumbar curve types, respectively. Conclusion This study shows that it is possible to find a relationship between the internal deformity and the back surface deformity in scoliosis with machine learning methods. The proposed system uses non invasive surface acquisition, which is safe for the patient as it involves no radiation. Also, the design of a specific kernel improved classification performance.

A PVC plasticized membrane sensor for nickel ions

A new PVC membrane sensor, which is highly selective towards Ni (II) ions, has been developed using a thiophene-derivative Schiff base as the ionophore. The best performance was exhibited by the membrane having the composition percentage ratio of 5:3:61:31 (ionophore:NaTPB:DBP:PVC) (w=w), where NaTPB is the anion excluder, sodium tetraphenylborate and DBP is the plasticizing agent (dibutyl phthalate). The membrane exhibited a good Nernstian response for nickel ions over the concentration range of 1.0 10 1– 5.0 10 6M (limit of detection is 1.8 10 6 M) with a slope of 29.5 1.0mV per decade of activity. It has a fast response time of<20 s and can be used for a period of 4 months with good reproducibility. The sensor is suitable for use in aqueous solutions of a wide pH range of 3.2–7.9. The sensor shows high selectivity to nickel ions over a large number of mono-, bi- and trivalent cations. It has been successfully used as an indicator electrode in the potentiometric titration of nickel ions against EDTA and also for direct determination of nickel content in real samples – wastewater samples from electroplating industries and Indian chocolates.

A PVC Plasticized Membrane Sensor for Nickel Ions

A new PVC membrane sensor, which is highly selective towards Ni (II) ions, has been developed using a thiophene-derivative Schiff base as the ionophore. The best performance was exhibited by the membrane having the composition percentage ratio of 5:3:61:31 (ionophore:NaTPB:DBP:PVC) (w=w), where NaTPB is the anion excluder, sodium tetraphenylborate and DBP is the plasticizing agent (dibutyl phthalate). The membrane exhibited a good Nernstian response for nickel ions over the concentration range of 1.0 10 1– 5.0 10 6M (limit of detection is 1.8 10 6 M) with a slope of 29.5 1.0mV per decade of activity. It has a fast response time of<20 s and can be used for a period of 4 months with good reproducibility. The sensor is suitable for use in aqueous solutions of a wide pH range of 3.2–7.9. The sensor shows high selectivity to nickel ions over a large number of mono-, bi- and trivalent cations. It has been successfully used as an indicator electrode in the potentiometric titration of nickel ions against EDTA and also for direct determination of nickel content in real samples – wastewater samples from electroplating industries and Indian chocolates.

A PVC Plasticized Sensor for Ni(II) Ion Based on a Simple Ethylenediamine Derivative

new PVC membrane ion selective electrode which is highly selective towards Ni(II) ions was constructed using a Schiff base containing a binaphthyl moiety as the ionophore. The sensor exhibited a good Nernstian response for nickel ions over the concentration range 1.0 × 10–1 – 5.0 × 10–6 M with a lower limit of detection of 1.3 × 10–6 M. It has a fast response time and can be used for a period of 4 months with a good reproducibility. The sensor is suitable for use in aqueous solutions in a wide pH range of 3.6 – 7.4 and works satisfactorily in the presence of 25% (v/v) methanol or ethanol. The sensor shows high selectivity to nickel ions over a wide variety of cations. It has been successfully used as an indicator electrode in the potentiometric titration of nickel ions against EDTA and also for the direct determination of nickel content in real samples: effluent samples, chocolates and hydrogenated oils.

A Novel Potentiometric Sensor for the Determination of Nimesulide

A novel electrochemical sensor has been developed for the determination of nimesulide. The sensor is based on the NIM- molybdophosphoric acid (MPA) as the electroactive material in PVC matrix in presence of bis(2-ethyl hexyl) phthalate (BEP) as a plasticizer. The sensor showed a fast, stable, near Nernstian response for 1 × 10-2 –1 × 10-6 M NIM over the pH range 5 – 8 with a slope 55.6 ±0.5m V/decade and the response time is < 45 s. Selectivity coefficient data for some common ions show negligible interferences. The sensor was successfully applied for the determination of NIM in tablet and the results obtained are in good agreement with those obtained by the official method.

A fibre optic evanescent wave sensor for monitoring the rate of pulsed laser deposition of metal thin films

A novel sensing technique for the in situ monitoring of the rate of pulsed laser deposition (PLD) of metal thin films has been developed. This optical fibre based sensor works on the principle of the evanescent wave penetration of waveguide modes into the uncladded portion of a multimode fibre. The utility of this optical fibre sensor is demonstrated in the case of PLD of silver thin films obtained by a Q-switched Nd:YAG laser which is used to irradiate a silver target at the required conditions for the preparation of thin films. This paper describes the performance and characteristics of the sensor and shows how the device can be used as an effective tool for the monitoring of the deposition rate of silver thin films. The fibre optic sensor is very simple, inexpensive and highly sensitive compared with existing techniques for thin film deposition rate measurements

A fibre optic evanescent wave sensor for monitoring the rate of pulsed laser deposition of metal thin films

A novel sensing technique for the in situ monitoring of the rate of pulsed laser deposition (PLD) of metal thin films has been developed. This optical fibre based sensor works on the principle of the evanescent wave penetration of waveguide modes into the uncladded portion of a multimode fibre. The utility of this optical fibre sensor is demonstrated in the case of PLD of silver thin films obtained by a Q-switched Nd:YAG laser which is used to irradiate a silver target at the required conditions for the preparation of thin films. This paper describes the performance and characteristics of the sensor and shows how the device can be used as an effective tool for the monitoring of the deposition rate of silver thin films. The fibre optic sensor is very simple, inexpensive and highly sensitive compared with existing techniques for thin film deposition rate measurements.

A fibre optic evanescent wave sensor used for the detection of trace nitrites in water

A fibre optic technique for detecting trace amounts of nitrite compounds in water is described. The off-line fibre optic sensor outlined here is based on evanescent field absorption in a test solution formed by the reaction of nitrite compounds in water with suitable chemical reagents. A short unclad portion of a plastic clad silica fibre acts as the sensing region. The experimental results clearly establish the usefulness of the present technique for detecting very low concentrations of the order of 1 ppb (parts per billion) of nitrite compounds with a large dynamic range of 1–1000 ppb. Such a high sensitivity enables the present device to be used for measuring the nitrite content in drinking water.

A sensitive fibre optic pH sensor using multiple sol–gel coatings

The fabrication and characterization of a fibre optic pH sensor based on evanescent wave absorption is presented. The unclad portion of a multi-mode optical fibre is coated with a pH sensitive dye, which is immobilized by the sol–gel route. The sensitivity of the device has been found to increase when multiple sol–gel coatings are used as the sensing region. The dynamic range and the temporal response of the sensor are investigated for two different dyes, namely bromocresol purple and bromocresol green. The performance of the device is evaluated in terms of the results obtained during actual measurements.

A sensitive fibre optic pH sensor using multiple sol–gel coatings

The fabrication and characterization of a fibre optic pH sensor based on evanescent wave absorption is presented. The unclad portion of a multi-mode optical fibre is coated with a pH sensitive dye, which is immobilized by the sol–gel route. The sensitivity of the device has been found to increase when multiple sol–gel coatings are used as the sensing region. The dynamic range and the temporal response of the sensor are investigated for two different dyes, namely bromocresol purple and bromocresol green. The performance of the device is evaluated in terms of the results obtained during actual measurements

A sensitive fibre optic pH sensor using multiple sol–gel coatings

The fabrication and characterization of a fibre optic pH sensor based on evanescent wave absorption is presented. The unclad portion of a multi-mode optical fibre is coated with a pH sensitive dye, which is immobilized by the sol–gel route. The sensitivity of the device has been found to increase when multiple sol–gel coatings are used as the sensing region. The dynamic range and the temporal response of the sensor are investigated for two different dyes, namely bromocresol purple and bromocresol green. The performance of the device is evaluated in terms of the results obtained during actual measurements