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This thesis considers two types of applications of quantum feedback control; feedback creation of nonclassical states of light, and controlling nonclassical properties of an ensemble of atoms. An electro-optical feedback loop will create an in-loop field with nonclassical photon statistics similar to squeezed light, resulting in fluorescence line-narrowing of a two-level atom coupled to such light. We extend this theory to study a three-level atom coupled to broadband squashed light, and confirm the two-level atom line-narrowing using a more realistic non-Markovian description of the feedback loop. The second type of application utilizes continuous QND measurement of atomic ensembles. If we measure the collective spin, then the system experiences conditional spin squeezing dependent on the measurement results. We show that feedback based on these results can continuously drive the system into the same conditioned state, resulting in deterministically reproducible spin squeezing. If we measure the atom number fluctuations of a BEC, then, due to the nonlinearity of atomic self interactions, this is also information about phase fluctuations. We show that feedback based on this information can greatly reduce the collisional broadening of the linewidth of an atom laser out-coupled from the condensate.

A real-time, mobile Scanning Electrical Mobility Spectrometer (SEMS) was designed using a Condensation Particle Counter (CPC) and Differential Mobility Analyzer (DMA) to measure the size distribution of nanoparticles. The SEMS was calibrated using monodisperse Polystyrene Latex (PSL) particles, and was then applied to study the size distribution of TiO2 nanoparticle aerosols generated by spray drying water suspensions of the nanoparticles. The nanoparticle aerosol size distribution, the effect of surfactant, and the effect of residual solvent droplets were determined. The SEMS system was designed by integrating the Electrical System, the Fluid Flow System, and the SEMS Software. It was calibrated using aerosolized Polystyrene Latex (PSL) spheres with nominal diameters of 99 nm and 204 nm. TiO2 nanoparticle aerosols were generated by atomizing water suspensions of TiO2 nanoparticles using a Collison nebulizer. Size distribution of the TiO2 aerosol was measured by the SEMS, as well as by TEM. Furthermore, the effect of surfactant, Tween 20 at four different concentrations between 0.01mM and 0.80mM, and stability of aerosol concentration with time were studied. It was hypothesized that residual particles in DI water observed during the calibration process were a mixture of impurities in water and unevaporated droplets. Solid impurities were captured on TEM grids using a point-to-plane Electrostatic Precipitator (ESP) and analyzed by Energy Dispersive Spectroscopy (EDS) while the contribution of unevaporated liquid droplets to residual particles was confirmed by size distribution measurements of aerosolized DI water in different humidity conditions. The calibration indicated that the mode diameter was found to be at 92.5nm by TEM and 95.8nm by the SEMS for 99nm nominal diameter particles, a difference of 3.6%. Similarly, the mode diameter for 204nm nominal diameter particles was found to be 194.9nm by TEM and 191nm by SEMS, a difference of 2.0%. Measurements by SEMS for TiO2 aerosol generated by Collison nebulizer indicated the mode diameters of 3mM, 6mM, and 9mM concentrations of TiO2 suspension to be 197.5nm, 200.0nm and 195.2nm respectively. On the other hand, the mode diameter was found to be approximately 95nm from TEM analysis of TiO2 powder. Additionally, concentration of particles generated decreased with time. Dynamic Light Scattering (DLS) measurements indicated agglomeration of particles in the suspension. Furthermore, the emulation of single particle distribution was not possible even after using Tween 20 in concentrations between 0.01mM and 0.80mM. From the study of residual particles in DI water, it was found that residual particles observed during the aerosolization of suspensions of DI water were composed of impurities present in DI water and unevaporated droplets of DI water. Although it was possible to observe solid residual particles on the TEM grid, EDS was not able to determine the chemical composition of these particles.

Purpose – The purpose of the study is to expand the knowledge of how wheel motor currents can be combined with machine learning to be used in a collision detection system for autonomous robots, in order to decrease the number of external sensors and open new design opportunities and lowering production costs. Method – The study is conducted with design science research where two artefacts are developed in a cooperation with Globe Tools Group. The artefacts are evaluated in how they categorize data given by an autonomous robot in the two categories collision and non-collision. The artefacts are then tested by generated data to analyse their ability to categorize. Findings – Both artefacts showed a 100 % accuracy in detecting the collisions in the given data by the autonomous robot. In the second part of the experiment the artefacts show that they have different decision boundaries in how they categorize the data, which will make them useful in different applications. Implications – The study contributes to an expanding knowledge in how machine learning and wheel motor currents can be used in a collision detection system. The results can lead to lowering production costs and opening new design opportunities. Limitations – The data used in the study is gathered by an autonomous robot which only did frontal collisions on an artificial lawn. Keywords – Machine learning, K-Nearest Neighbour, Multilayer Perceptron, collision detection, autonomous robots, Collison detection based on current. / Syfte – Studiens syfte är att utöka kunskapen om hur hjulmotorstömmar kan kombineras med maskininlärning för att användas vid kollisionsdetektion hos autonoma robotar, detta för att kunna minska antalet krävda externa sensorer hos dessa robotar och på så sätt öppna upp design möjligheter samt minska produktionskostnader Metod – Studien genomfördes med design science research där två artefakter utvecklades i samarbete med Globe Tools Group. Artefakterna utvärderades sedan i hur de kategoriserade kollisioner utifrån en given datamängd som genererades från en autonom gräsklippare. Studiens experiment introducerade sedan in data som inte ingick i samma datamängd för att se hur metoderna kategoriserade detta. Resultat – Artefakterna klarade med 100% noggrannhet att detektera kollisioner i den giva datamängden som genererades. Dock har de två olika artefakterna olika beslutsregioner i hur de kategoriserar datamängderna till kollision samt icke-kollisioner, vilket kan ge dom olika användningsområden Implikationer – Examensarbetet bidrar till en ökad kunskap om hur maskininlärning och hjulmotorströmmar kan användas i ett kollisionsdetekteringssystem. Studiens resultat kan bidra till minskade kostnader i produktion samt nya design möjligheter Begränsningar – Datamängden som användes i studien samlades endast in av en autonom gräsklippare som gjorde frontalkrockar med underlaget konstgräs. Nyckelord – Maskininlärning, K-nearest neighbor, Multi-layer perceptron, kollisionsdetektion, autonoma robotar