Mikrokontrolleriohjaus led-ryhmälle

Luonnonvarojen ehtyminen ja ympäristön saastuminen on luonut kysyntää uusille, energiaa säästäville ja ympäristöystävällisille teknologioille. Valaistuksessa tällainen teknologia on led-tekniikka. Led-tekniikalla on useita etuja verrattuna kilpaileviin tekniikoihin kuten pitkä elinikä, ympäristöystävällisyys ja mekaaninen kestävyys. Ledejä käytetään nykyään laajalti erilaisissa erikoissovelluksissa, erityisesti jos vaatimuksena on valon värillisyys. Näihin päiviin asti ledien hinta ja heikko valontuotto ovat rajoittaneet led-valaisimien yleistymistä hehkulamppujen ja muiden valaisintyyppien korvaajina. Tekniikan nopea kehittyminen on tehnyt led-tekniikasta varteenotettavan vaihtoehdon myös yleisvalaistukseen. Uusimpien valkoisten ledien valotehokkuus on 2 - 5 -kertainen hehkulamppuun verrattuna. Led-tekniikassa on vielä paljon käyttämätöntä potentiaalia, tulevaisuudessa päästäneen 10 - 15 -kertaiseen valotehokkuuteen hehkulamppuun verrattuna. Työssä suunnitellaan mikrokontrolleripohjainen ohjausjärjestelmä valkoista valoa tuottavalle led-valaisimelle, jonka värisävyä ja kirkkautta käyttäjä voi säätää. Valkoinen valo synnytetään sekoittamalla neljän erivärisen led-rivin valoa. Mikrokontrolleri ohjaa kutakin led-riviä väriensekoitusteoriaan perustuen. Mikrokontrolleriohjaus huomioi myös ledien optisten ominaisuuksien muutokset lämpötilan suhteen. Mikrokontrolleriohjauksen suorituskyky todetaan käytännön mittauksilla.

Feromônios associados aos coleópteros-praga de produtos armazenados

One strategy to overcome risks of insecticide-based control in agriculture is to use semiochemicals. In the case of pheromones, these specific compounds can be applied in traps to detect and monitor the occurrence, abundance and distribution of insect pests. Reliable detection helps to time insecticide sprays, to decide the quantity of insecticide that will be used and the place where it will be applied. This manuscript aims to give an overview of the pheromones associated to coleopteran pests in stored products, and their utilization in integrated pest management.

Fraktioinnin tehostaminen lajittelussa

Painelajittimet ovat yleisimpiä hiokkeen lajitteluun käytettyjä lajittimia. Painelajittimien suorituskyky on parantunut viimeisten vuosikymmenien aikana niin paljon, että pyörrepuhdistuksesta on pääosin voitu luopua osana hiokkeen lajittelua. Painelajittimen erinomaisuus perustuu siihen, että sillä voidaan erottaa massasta hyvinkin erilaisia epäpuhtauksia. Nykyään vallitsevia painelajittelun trendejä ovat sakeuden nosto, energiankulutuksen vähentäminen sekä eri fraktioiden erottumisen tehostuminen. Kaikilla pyritään vähentämään vedenkäyttöä ja parantamaan massan ominaisuuksia jatkoprosesseja silmälläpitäen. Tässä työssä tarkasteltiin painelajittimen roottorin kierrosnopeuden vaikutusta akseptimassan laatuun. Luodaan malli freeness-pudotukselle lajittimen yli. Sekä tarkastellaan myös automaatioon pohjautuvan säädön käyttöönottoa lajittimen akseptimassan freenesvaihteluiden tasaamiseksi. Toisaalta tehdään myös suppea selvitys lajittimien energiankulutuksista erilaisilla roottorin pyörimisnopeuksilla. Tuotantokäytössä oleviin lajittimiin asennettujen invertterisäätöjen ja koepisteistä saatujen tulosten avulla voidaan todeta, että laskemalla roottorin pyörimisnopeutta voidaan lajittimessa tapahtuvaa freenespudotusta kasvattaa. Samalla saavutetaan hyötyjä myös muissa massan ominaisuuksissa, kuten vetolujuudessa. Automaattisäädön käyttöönotolla saavutetaan huomattavasti pienempi hajonta akseptimassan freeneksessä. Rejektilinjan lajittimissa hajonta lähes puolittui ja päälinjan lajittimissa päästiin noin kolmanneksen parannukseen. Energian kulutuksia tutkittaessa huomataan, että roottorin kierrosnopeutta alentamalla voidaan merkittävästi vähentää lajittimien energian kulutusta. Parhaassa tapauksessa voidaan säästää neljännes lajittelun energiakustannuksissa.

Hitsausvirtalähteen ohjaus lämmöntuonnin ja jatkuvan jäähtymisen S-käyrän perusteella

Lämmöntuonnilla on oleellinen vaikutus hitsausliitoksen ominaisuuksiin, koska se vaikuttaa liitoksen jäähtymisnopeuteen, jolla on puolestaan suuri vaikutus jäähtymisessä syntyviin mikrorakenteisiin. Jatkuvan jäähtymisen S-käyrältä voidaan ennustaa hitsausliitokseen syntyvät mikrorakenteet. S-käyrät voidaan laatia hitsausolosuhteiden mukaisesti, jolloin faasimuutoskäyttäytyminen sularajalla saadaan selvitettyä. Tämän diplomityön tavoitteena oli kehittää hitsausvirtalähteen ohjaustapaa lämmöntuontiin ja jatkuvan jäähtymisen S-käyriin perustuen. Jatkuvan jäähtymisen S-käyrillä ja lämmöntuontiin perustuvalla hitsausparametrien säädöllä on yhteys. Työssä tutkittiin, miten haluttuun jäähtymisnopeuteen johtava lämmöntuonti voidaan määrittää S-käyrälle luotettavasti. Työssä perehdyttiin jatkuvan jäähtymisen S-käyriin ja eri jäähtymisnopeuksilla hitsausliitokseen syntyviin mikrorakenteisiin sekä hitsaus-inverttereiden ohjaus- ja säätötekniikkaan. Teoriaosuuden jälkeen tarkasteltiin eri vaihtoehtoja, miten hitsattavan materiaalin koostumusvaihtelut sekä lämmöntuontiin vaikuttavat tekijät voidaan ottaa huomioon virtalähteen ohjauksessa lämmöntuonnin perusteella. S-käyrältä määritettyjen lämmöntuonnin arvojen perusteella tehtiin kahdet koehitsaukset, joissa käytettiin kolmea eri aineenpaksuutta. Tulosten perusteella arvioitiin lämmöntuonnin arvojen toimivuutta käytännössä ja tutkittiin liitokseen syntyviä mikrorakenteita. Tutkimuksen pohjalta esitettiin jatkokehitystoimenpiteitä, joiden mukaan voidaan edetä lämmöntuontiin perustuvan säätöjärjestelmän kehitysprojektissa.

AMB system for high-speed motors using automatic commissioning

The general trend towards increasing e ciency and energy density drives the industry to high-speed technologies. Active Magnetic Bearings (AMBs) are one of the technologies that allow contactless support of a rotating body. Theoretically, there are no limitations on the rotational speed. The absence of friction, low maintenance cost, micrometer precision, and programmable sti ness have made AMBs a viable choice for highdemanding applications. Along with the advances in power electronics, such as signi cantly improved reliability and cost, AMB systems have gained a wide adoption in the industry. The AMB system is a complex, open-loop unstable system with multiple inputs and outputs. For normal operation, such a system requires a feedback control. To meet the high demands for performance and robustness, model-based control techniques should be applied. These techniques require an accurate plant model description and uncertainty estimations. The advanced control methods require more e ort at the commissioning stage. In this work, a methodology is developed for an automatic commissioning of a subcritical, rigid gas blower machine. The commissioning process includes open-loop tuning of separate parts such as sensors and actuators. The next step is to apply a system identi cation procedure to obtain a model for the controller synthesis. Finally, a robust model-based controller is synthesized and experimentally evaluated in the full operating range of the system. The commissioning procedure is developed by applying only the system components available and a priori knowledge without any additional hardware. Thus, the work provides an intelligent system with a self-diagnostics feature and an automatic commissioning.

Modeling and Parameter Estimation of Double-Star Permanent Magnet Synchronous Machines

The power rating of wind turbines is constantly increasing; however, keeping the voltage rating at the low-voltage level results in high kilo-ampere currents. An alternative for increasing the power levels without raising the voltage level is provided by multiphase machines. Multiphase machines are used for instance in ship propulsion systems, aerospace applications, electric vehicles, and in other high-power applications including wind energy conversion systems. A machine model in an appropriate reference frame is required in order to design an efficient control for the electric drive. Modeling of multiphase machines poses a challenge because of the mutual couplings between the phases. Mutual couplings degrade the drive performance unless they are properly considered. In certain multiphase machines there is also a problem of high current harmonics, which are easily generated because of the small current path impedance of the harmonic components. However, multiphase machines provide special characteristics compared with the three-phase counterparts: Multiphase machines have a better fault tolerance, and are thus more robust. In addition, the controlled power can be divided among more inverter legs by increasing the number of phases. Moreover, the torque pulsation can be decreased and the harmonic frequency of the torque ripple increased by an appropriate multiphase configuration. By increasing the number of phases it is also possible to obtain more torque per RMS ampere for the same volume, and thus, increase the power density. In this doctoral thesis, a decoupled d–q model of double-star permanent-magnet (PM) synchronous machines is derived based on the inductance matrix diagonalization. The double-star machine is a special type of multiphase machines. Its armature consists of two three-phase winding sets, which are commonly displaced by 30 electrical degrees. In this study, the displacement angle between the sets is considered a parameter. The diagonalization of the inductance matrix results in a simplified model structure, in which the mutual couplings between the reference frames are eliminated. Moreover, the current harmonics are mapped into a reference frame, in which they can be easily controlled. The work also presents methods to determine the machine inductances by a finite-element analysis and by voltage-source inverters on-site. The derived model is validated by experimental results obtained with an example double-star interior PM (IPM) synchronous machine having the sets displaced by 30 electrical degrees. The derived transformation, and consequently, the decoupled d–q machine model, are shown to model the behavior of an actual machine with an acceptable accuracy. Thus, the proposed model is suitable to be used for the model-based control design of electric drives consisting of double-star IPM synchronous machines.

Problem solving and computer-assisted instruction in science education : analysis of research findings and the research process /

The quantitative component of this study examined the effect of computerassisted instruction (CAI) on science problem-solving performance, as well as the significance of logical reasoning ability to this relationship. I had the dual role of researcher and teacher, as I conducted the study with 84 grade seven students to whom I simultaneously taught science on a rotary-basis. A two-treatment research design using this sample of convenience allowed for a comparison between the problem-solving performance of a CAI treatment group (n = 46) versus a laboratory-based control group (n = 38). Science problem-solving performance was measured by a pretest and posttest that I developed for this study. The validity of these tests was addressed through critical discussions with faculty members, colleagues, as well as through feedback gained in a pilot study. High reliability was revealed between the pretest and the posttest; in this way, students who tended to score high on the pretest also tended to score high on the posttest. Interrater reliability was found to be high for 30 randomly-selected test responses which were scored independently by two raters (i.e., myself and my faculty advisor). Results indicated that the form of computer-assisted instruction (CAI) used in this study did not significantly improve students' problem-solving performance. Logical reasoning ability was measured by an abbreviated version of the Group Assessment of Lx)gical Thinking (GALT). Logical reasoning ability was found to be correlated to problem-solving performance in that, students with high logical reasoning ability tended to do better on the problem-solving tests and vice versa. However, no significant difference was observed in problem-solving improvement, in the laboratory-based instruction group versus the CAI group, for students varying in level of logical reasoning ability.Insignificant trends were noted in results obtained from students of high logical reasoning ability, but require further study. It was acknowledged that conclusions drawn from the quantitative component of this study were limited, as further modifications of the tests were recommended, as well as the use of a larger sample size. The purpose of the qualitative component of the study was to provide a detailed description ofmy thesis research process as a Brock University Master of Education student. My research journal notes served as the data base for open coding analysis. This analysis revealed six main themes which best described my research experience: research interests, practical considerations, research design, research analysis, development of the problem-solving tests, and scoring scheme development. These important areas ofmy thesis research experience were recounted in the form of a personal narrative. It was noted that the research process was a form of problem solving in itself, as I made use of several problem-solving strategies to achieve desired thesis outcomes.

Points quantiques : caractérisation et applications en sciences pharmaceutiques

L’imagerie médicale a longtemps été limitée à cause des performances médiocres des fluorophores organiques. Récemment la recherche sur les nanocristaux semi-conducteurs a grandement contribué à l’élargissement de la gamme d’applications de la luminescence dans les domaines de l’imagerie et du diagnostic. Les points quantiques (QDs) sont des nanocristaux de taille similaire aux protéines (2-10 nm) dont la longueur d’onde d’émission dépend de leur taille et de leur composition. Le fait que leur surface peut être fonctionnalisée facilement avec des biomolécules rend leur application particulièrement attrayante dans le milieu biologique. Des QDs de structure « coeur-coquille » ont été synthétisés selon nos besoins en longueur d’onde d’émission. Dans un premier article nous avons modifié la surface des QDs avec des petites molécules bi-fonctionnelles portant des groupes amines, carboxyles ou zwitterions. L’effet de la charge a été analysé sur le mode d’entrée des QDs dans deux types cellulaires. À l’aide d’inhibiteurs pharmacologiques spécifiques à certains modes d’internalisation, nous avons déterminé le mode d’internalisation prédominant. L’endocytose par les radeaux lipidiques représente le mode d’entrée le plus employé pour ces QDs de tailles similaires. D’autres modes participent également, mais à des degrés moindres. Des disparités dans les modes d’entrée ont été observées selon le ligand de surface. Nous avons ensuite analysé l’effet de l’agglomération de différents QDs sur leur internalisation dans des cellules microgliales. La caractérisation des agglomérats dans le milieu de culture cellulaire a été faite par la technique de fractionnement par couplage flux-force (AF4) associé à un détecteur de diffusion de la lumière. En fonction du ligand de surface et de la présence ou non de protéines du sérum, chacun des types de QDs se sont agglomérés de façon différente. À l'aide d’inhibiteur des modes d’internalisation, nous avons corrélé les données de tailles d’agglomérats avec leur mode d’entrée cellulaire. Les cellules microgliales sont les cellules immunitaires du système nerveux central (CNS). Elles répondent aux blessures ou à la présence d’inflammagènes en relâchant des cytokines pro-inflammatoires. Une inflammation non contrôlée du CNS peut conduire à la neurodégénérescence neuronale et est souvent observée dans les cas de maladies chroniques. Nous nous sommes intéressés au développement d’un nanosenseur pour mesurer des biomarqueurs du début de l’inflammation. Les méthodes classiques pour étudier l’inflammation consistent à mesurer le niveau de protéines ou molécules relâchées par les cellules stressées (par exemple monoxyde d’azote, IL-1β). Bien que précises, ces méthodes ne mesurent qu’indirectement l’activité de la caspase-1, responsable de la libération du l’IL-1β. De plus ces méthode ne peuvent pas être utilisées avec des cellules vivantes. Nous avons construit un nanosenseur basé sur le FRET entre un QD et un fluorophore organique reliés entre eux par un peptide qui est spécifiquement clivé par la caspase-1. Pour induire l’inflammation, nous avons utilisé des molécules de lipopolysaccharides (LPS). La molécule de LPS est amphiphile. Dans l’eau le LPS forme des nanoparticules, avec des régions hydrophobes à l’intérieure. Nous avons incorporé des QDs dans ces régions ce qui nous a permis de suivre le cheminement du LPS dans les cellules microgliales. Les LPS-QDs sont internalisés spécifiquement par les récepteurs TLR-4 à la surface des microglies. Le nanosenseur s’est montré fonctionnel dans la détermination de l’activité de la caspase-1 dans cellules microgliales activées par le LPS. Éventuellement, le senseur permettrait d’observer en temps réel l’effet de thérapies ciblant l’inflammation, sur l’activité de la caspase-1.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Digitales stochastisches Magnetfeld-Sensorarray

Digitales stochastisches Magnetfeld-Sensorarray Stefan Rohrer Im Rahmen eines mehrjährigen Forschungsprojektes, gefördert von der Deutschen Forschungsgesellschaft (DFG), wurden am Institut für Mikroelektronik (IPM) der Universität Kassel digitale Magnetfeldsensoren mit einer Breite bis zu 1 µm entwickelt. Die vorliegende Dissertation stellt ein aus diesem Forschungsprojekt entstandenes Magnetfeld-Sensorarray vor, das speziell dazu entworfen wurde, um digitale Magnetfelder schnell und auf minimaler Fläche mit einer guten räumlichen und zeitlichen Auflösung zu detektieren. Der noch in einem 1,0µm-CMOS-Prozess gefertigte Test-Chip arbeitet bis zu einer Taktfrequenz von 27 MHz bei einem Sensorabstand von 6,75 µm. Damit ist er das derzeit kleinste und schnellste digitale Magnetfeld-Sensorarray in einem Standard-CMOS-Prozess. Konvertiert auf eine 0,09µm-Technologie können Frequenzen bis 1 GHz erreicht werden bei einem Sensorabstand von unter 1 µm. In der Dissertation werden die wichtigsten Ergebnisse des Projekts detailliert beschrieben. Basis des Sensors ist eine rückgekoppelte Inverter-Anordnung. Als magnetfeldsensitives Element dient ein auf dem Hall-Effekt basierender Doppel-Drain-MAGFET, der das Verhalten der Kippschaltung beeinflusst. Aus den digitalen Ausgangsdaten kann die Stärke und die Polarität des Magnetfelds bestimmt werden. Die Gesamtanordnung bildet einen stochastischen Magnetfeld-Sensor. In der Arbeit wird ein Modell für das Kippverhalten der rückgekoppelten Inverter präsentiert. Die Rauscheinflüsse des Sensors werden analysiert und in einem stochastischen Differentialgleichungssystem modelliert. Die Lösung der stochastischen Differentialgleichung zeigt die Entwicklung der Wahrscheinlichkeitsverteilung des Ausgangssignals über die Zeit und welche Einflussfaktoren die Fehlerwahrscheinlichkeit des Sensors beeinflussen. Sie gibt Hinweise darauf, welche Parameter für das Design und Layout eines stochastischen Sensors zu einem optimalen Ergebnis führen. Die auf den theoretischen Berechnungen basierenden Schaltungen und Layout-Komponenten eines digitalen stochastischen Sensors werden in der Arbeit vorgestellt. Aufgrund der technologisch bedingten Prozesstoleranzen ist für jeden Detektor eine eigene kompensierende Kalibrierung erforderlich. Unterschiedliche Realisierungen dafür werden präsentiert und bewertet. Zur genaueren Modellierung wird ein SPICE-Modell aufgestellt und damit für das Kippverhalten des Sensors eine stochastische Differentialgleichung mit SPICE-bestimmten Koeffizienten hergeleitet. Gegenüber den Standard-Magnetfeldsensoren bietet die stochastische digitale Auswertung den Vorteil einer flexiblen Messung. Man kann wählen zwischen schnellen Messungen bei reduzierter Genauigkeit und einer hohen lokalen Auflösung oder einer hohen Genauigkeit bei der Auswertung langsam veränderlicher Magnetfelder im Bereich von unter 1 mT. Die Arbeit präsentiert die Messergebnisse des Testchips. Die gemessene Empfindlichkeit und die Fehlerwahrscheinlichkeit sowie die optimalen Arbeitspunkte und die Kennliniencharakteristik werden dargestellt. Die relative Empfindlichkeit der MAGFETs beträgt 0,0075/T. Die damit erzielbaren Fehlerwahrscheinlichkeiten werden in der Arbeit aufgelistet. Verglichen mit dem theoretischen Modell zeigt das gemessene Kippverhalten der stochastischen Sensoren eine gute Übereinstimmung. Verschiedene Messungen von analogen und digitalen Magnetfeldern bestätigen die Anwendbarkeit des Sensors für schnelle Magnetfeldmessungen bis 27 MHz auch bei kleinen Magnetfeldern unter 1 mT. Die Messungen der Sensorcharakteristik in Abhängigkeit von der Temperatur zeigen, dass die Empfindlichkeit bei sehr tiefen Temperaturen deutlich steigt aufgrund der Abnahme des Rauschens. Eine Zusammenfassung und ein ausführliches Literaturverzeichnis geben einen Überblick über den Stand der Technik.

Theoretical and experimental study of intracavity laser absorption spectroscopy

In this thesis, a dual mode tunable gas sensor based on intracavity laser absorption spectroscopy (ICLAS) principle is investigated, both, numerically and experimentally. In order to minimize the cost and size of the gas sensor, relative intensity noise (RIN) is implemented as a detection parameter. Investigation is performed to determine the effect of injection current, operating temperature, mode spacing, and cavity length on RIN. It has been found that it is best to operate the gas sensor at smaller mode spacing and near the threshold current or at larger mode spacing and far above the threshold current for the use of RIN as the readout parameter.

Optical Antennas for Biosensing Applications

The aim of the thesis is to theoretically investigate optical/plasmonic antennas for biosensing applications. The full 3-D numerical electromagnetic simulations have been performed by using finite integration technique (FIT). The electromagnetic properties of surface plasmon polaritons (SPPs) and the localized surface plasmons (LSPs) based devices are studied for sensing purpose. The surface plasmon resonance (SPR) biosensors offer high refractive index sensitivity at a fixed wavelength but are not enough for the detection of low concentrations of molecules. It has been demonstrated that the sensitivity of SPR sensors can be increased by employing the transverse magneto-optic Kerr effect (TMOKE) in combination with SPPs. The sensor based on the phenomena of TMOKE and SPPs are known as magneto-optic SPR (MOSPR) sensors. The optimized MOSPR sensor is analyzed which provides 8 times higher sensitivity than the SPR sensor, which will be able to detect lower concentration of molecules. But, the range of the refractive index detection is limited, due to the rapid decay of the amplitude of the MOSPR-signal with the increase of the refractive indices. Whereas, LSPs based sensors can detect lower concentrations of molecules, but their sensitivity is small at a fixed wavelength. Therefore, another device configuration known as perfect plasmonic absorber (PPA) is investigated which is based on the phenomena of metal-insulator-metal (MIM) waveguide. The PPA consists of a periodic array of gold nanoparticles and a thick gold film separated by a dielectric spacer. The electromagnetic modes of the PPA system are analyzed for sensing purpose. The second order mode of the PPA at a fixed wavelength has been proposed for the first time for biosensing applications. The PPA based sensor combines the properties of the LSPR sensor and the SPR sensor, for example, it illustrates increment in sensitivity of the LSPR sensor comparable to the SPR and can detect lower concentration of molecules due to the presence of nanoparticles.

Interaction and Intelligent Behavior

We introduce basic behaviors as primitives for control and learning in situated, embodied agents interacting in complex domains. We propose methods for selecting, formally specifying, algorithmically implementing, empirically evaluating, and combining behaviors from a basic set. We also introduce a general methodology for automatically constructing higher--level behaviors by learning to select from this set. Based on a formulation of reinforcement learning using conditions, behaviors, and shaped reinforcement, out approach makes behavior selection learnable in noisy, uncertain environments with stochastic dynamics. All described ideas are validated with groups of up to 20 mobile robots performing safe--wandering, following, aggregation, dispersion, homing, flocking, foraging, and learning to forage.

DAI-DEPUR: an integrated and distributed architecture for wastewater treatment plants supervision

The activated sludge process - the main biological technology usually applied to wastewater treatment plants (WWTP) - directly depends on live beings (microorganisms), and therefore on unforeseen changes produced by them. It could be possible to get a good plant operation if the supervisory control system is able to react to the changes and deviations in the system and can take the necessary actions to restore the system’s performance. These decisions are often based both on physical, chemical, microbiological principles (suitable to be modelled by conventional control algorithms) and on some knowledge (suitable to be modelled by knowledge-based systems). But one of the key problems in knowledge-based control systems design is the development of an architecture able to manage efficiently the different elements of the process (integrated architecture), to learn from previous cases (spec@c experimental knowledge) and to acquire the domain knowledge (general expert knowledge). These problems increase when the process belongs to an ill-structured domain and is composed of several complex operational units. Therefore, an integrated and distributed AI architecture seems to be a good choice. This paper proposes an integrated and distributed supervisory multi-level architecture for the supervision of WWTP, that overcomes some of the main troubles of classical control techniques and those of knowledge-based systems applied to real world systems

Can wavelets improve the representation of forecast error covariances in variational data assimilation?

Two wavelet-based control variable transform schemes are described and are used to model some important features of forecast error statistics for use in variational data assimilation. The first is a conventional wavelet scheme and the other is an approximation of it. Their ability to capture the position and scale-dependent aspects of covariance structures is tested in a two-dimensional latitude-height context. This is done by comparing the covariance structures implied by the wavelet schemes with those found from the explicit forecast error covariance matrix, and with a non-wavelet- based covariance scheme used currently in an operational assimilation scheme. Qualitatively, the wavelet-based schemes show potential at modeling forecast error statistics well without giving preference to either position or scale-dependent aspects. The degree of spectral representation can be controlled by changing the number of spectral bands in the schemes, and the least number of bands that achieves adequate results is found for the model domain used. Evidence is found of a trade-off between the localization of features in positional and spectral spaces when the number of bands is changed. By examining implied covariance diagnostics, the wavelet-based schemes are found, on the whole, to give results that are closer to diagnostics found from the explicit matrix than from the nonwavelet scheme. Even though the nature of the covariances has the right qualities in spectral space, variances are found to be too low at some wavenumbers and vertical correlation length scales are found to be too long at most scales. The wavelet schemes are found to be good at resolving variations in position and scale-dependent horizontal length scales, although the length scales reproduced are usually too short. The second of the wavelet-based schemes is often found to be better than the first in some important respects, but, unlike the first, it has no exact inverse transform.