Adaptive optics (AO) systems have become an essential part of ground-based telescopes and enable diffraction-limited imaging at near-IR and mid-IR wavelengths. For several key science applications the required wavefront quality is higher than what current systems can deliver. For instance obtaining high quality diffraction-limited images at visible wavelengths requires residual wavefront errors to be well below 100 nm RMS. High contrast imaging of exoplanets and disks around nearby stars requires high accuracy control of low-order modes that dominate atmospheric turbulence and scatter light at small angles where exoplanets are likely to be found. Imaging planets using a high contrast corona graphic camera, as is the case for the Spectro-Polarimetric High-contrast Exoplanet Research (SPHERE) on the Very Large Telescope (VLT), and the Gemini Planet Imager (GPI), requires even greater wavefront control accuracy. My dissertation develops a highly sensitive non-linear Curvature Wavefront Sensor (nlCWFS) that can deliver diffraction-limited (λ/D) images, in the visible, by approaching the theoretical sensitivity limit imposed by fundamental physics. The nlCWFS is derived from the successful curvature wavefront sensing concept but uses a non-linear reconstructor in order to maintain sensitivity to low spatial frequencies. The nlCWFS sensitivity makes it optimal for extreme AO and visible AO systems because it utilizes the full spatial coherence of the pupil plane as opposed to conventional sensors such as the Shack-Hartmann Wavefront Sensor (SHWFS) which operate at the atmospheric seeing limit (λ/r₀). The difference is equivalent to a gain of (D/r₀)² in sensitivity, for the lowest order mode, which translates to the nlCWFS requiring that many fewer photons. When background limited the nlCWFS sensitivity scales as D⁴, a combination of D² gain due to the diffraction limit and D² gain due to telescope's collecting power. Whereas conventional wavefront sensors only benefit from the D² gain due to the telescope's collecting power. For a 6.5 m telescope, at 0.5 μm, and seeing of 0.5", the nlCWFS can deliver for low order modes the same wavefront measurement accuracy as the SHWFS with 1000 times fewer photons. This is especially significant for upcoming extremely large telescopes such as the Giant Magellan Telescope (GMT) which has a 25.4 m aperture, the Thirty Meter Telescope (TMT) and the European Extremely Large Telescope (E-ELT) which has a 39 m aperture.

With the purpose of classifying and modelling stress, different sensors, signal features, machine learning methods, and stress experiments have been compared. Two databases have been studied: the MIT driver stress database and a new experimental database, where three stress tasks have been performed for 9 subjects: the Trier Social Stress Test, the Socially Evaluated Cold Pressor Test and the d2 test, of which the latter is not classically used for generating stress. Support vector machine, naive Bayes, k-nearest neighbor and probabilistic neural network classification techniques were compared, with support vector machines achieving the highest performance in general (99.5 ±0.6 %$on the driver database and 91.4 ± 2.4 % on the experimental database). For both databases, relevant features include the mean of the heart rate and the mean of the galvanic skin response, together with the mean of the absolute derivative of the galvanic skin response signal. A new feature is also introduced with great performance in stress classification for the driver database. Continuous models for estimating stress levels have also been developed, based upon the perceived stress levels given by the subjects during the experiments, where support vector regression is more accurate than linear and variational Bayesian regression. / I syfte att klassificera och modellera stress har olika sensorer, signalegenskaper, maskininlärningsmetoder och stressexperiment jämförts. Två databaser har studerats: MIT:s förarstressdatabas och en ny databas baserad på egna experiment, där stressuppgifter har genomförts av nio försökspersoner: Trier Social Stress Test,  Socially Evaluated Cold Pressor Test och d2-testet, av vilka det sistnämnda inte normalt används för att generera stress. Support vector machine-, naive Bayes-, k-nearest neighbour- och probabilistic neural network-algoritmer har jämförts, av vilka support vector machine har uppnått den högsta prestandan i allmänhet (99.5 ± 0.6 % på förardatabasen, 91.4 ± 2.4 %  på experimenten). För båda databaserna har signalegenskaper såsom medelvärdet av hjärtrytmen och hudens ledningsförmåga, tillsammans med medelvärdet av beloppet av hudens ledningsförmågas derivata identifierats som relevanta. En ny signalegenskap har också introducerats, med hög prestanda i stressklassificering på förarstressdatabasen. En kontinuerlig modell har också utvecklats, baserad på den upplevda stressnivån angiven av försökspersonerna under experimenten, där support vector regression har uppnått bättre resultat än linjär regression och variational Bayesian regression.

One factor controlling a vehicles total cost is the time it is available to generate revenue for the customer, time spent in a repair shop decreases that amount. Scania vehicles are equipped with onboard diagnostics which produces fault-codes if the internal signals deviate from their intended values. These fault codes are not always enough to isolate the problem which means that the workshop staff are required to use external measuring systems to diagnose the vehicle. Combining data from internal sensors with an external measuring system can be problematic and the results inconclusive. This thesis presents a solution where signals from external and internal sensors can be logged and analyzed together. A study on various hardware typologies are presented, key problems are identified and discussed. A prototype was created based on the research done during this project. This prototype was successfully used to troubleshoot and find a problem on a test rig where an error had been purposely introduced, reducing the whole troubleshooting process to a fraction of the time it normally takes. The hardware and software necessary to create this prototype is described in this thesis. / Konceptutveckling och prototypframtagning hur man ska kunna implementera externa sensorer till ett fordon f¨or att kunna fels¨oka snabbare och enklare. Olika plattformar unders¨oktes med f¨or- och nackdelar. Efterforskning resulterade i en f¨orsta prototyp best˚aende av mikrokontroller och f¨orslag till PCB designen.

The recommended procedures for implementing COTMAN; a cotton management expert system; suggest frequent crop scouting at numerous locations for each field. Machine based height sensors coupled with the ability to spatially record height values make it possible to locate regions of a field that are height representative of the entire field. A machine based height measurement system called HMAP was used to assess plant height in various fields in the 2003 growing season while the same fields were monitored with COTMAN. The plant height data was used to determine an optimal COTMAN sampling scheme for each field consisting of significantly fewer sampling locations than recommended by COTMAN. It was possible to ascertain equivalent information from COTMAN using two sites selected from height data in place of six sites selected per COTMAN recommendations. The HMAP system was extended to monitor rate of growth in real time in addition to plant height by comparing historical plant height data recorded on previous field passes to current height values. The rate of growth capable HMAP system will make it possible to track cotton growth and development with an automated system.

Increasing oil prices and concerns about global warming have reinforced the interest in biofuels for domestic and district heating, most commonly through combustion of solid biomass like wood logs, hog fuel and pellets. Combustion at non-optimal conditions can, however, lead to substantial emissions of noxious compounds like unburned hydrocarbons, carbon monoxide, and nitrogen oxides as well as the generation of soot. Depending on the rate of combustion more or less air is needed per unit time to completely oxidize the fuel; deficiency of air leading to emissions of unburned matter and too much of excess air to slow combustion kinetics and emissions of mainly carbon monoxide. The rate of combustion is influenced by parameters like fuel quality – moisture and ash content etc. – and in what phase the combustion takes place (in the gas phase through combustion of evaporated substances or on the surface of char coal particles), none of which is constant over time. The key to boiler operation, both from an environmental as well as a power to fuel economy point of view, is thus the careful adjustment of the air supply throughout the combustion process. So far, no control schemes have been applied to small-scale combustors, though, mainly due to the lack of cheap and simple means to measure basic flue gas parameters like oxygen, total hydrocarbon, and carbon monoxide concentrations. This thesis reports about investigations on and characterization of silicon carbide (SiC) based Metal Insulator Semiconductor (MIS) field effect gas sensors regarding their utility in emissions monitoring and combustion control applications as well as the final development of a sensor based control system for wood fired domestic heating systems. From the main sensitivity profiles of such sensor devices, with platinum (Pt) and iridium (Ir) as the catalytic metal contacts (providing the gas sensing ability), towards some typical flue gas constituents as well as ammonia (NH3), a system comprising four individual sensors operated at different temperatures was developed, which through the application of Partial Least Squares (PLS) regression, showed good performance regarding simultaneous monitoring of propene (a model hydrocarbon) and ammonia concentrations in synthetic flue gases of varying content. The sensitivity to CO was, however, negligible. The sensor system also performed well regarding ammonia slip monitoring when tested in real flue gases in a 5.6 MW boiler running SNCR (Selective Non-Catalytic reduction of nitrogen oxides with ammonia). When applied to a 200 kW wood pellet fuelled boiler a similar sensor system was, however, not able to follow the flue gas hydrocarbon concentration in all encountered situations. A PCA (Principal Components Analysis) based scheme for the manipulation of sensor and flue gas temperature data, enabling monitoring of the state of combustion (deficiency or too much of excess air), was however possible to develop. The discrepancy between laboratory and field test results was suspected and later on shown to depend on the larger variation in CO and oxygen concentrations in the flue gases as compared to the laboratory tests. Detailed studies of the CO response characteristics for Pt gate MISiC sensors revealed a highly non-linear sensitivity towards CO, a large response only encountered at high CO/O2 ratios or low temperatures. The response exhibits a sharp switch between a small and a large value when crossing a certain CO/O2 ratio at constant operating temperature, correlated to the transition from an oxygen dominated to an almost fully CO covered Pt surface, originating from the difference in adsorption kinetics between CO and O2. Indications were also given pointing towards an increased sensitivity to background hydrogen as being the mediator of at least part of the CO response. Some general characteristics regarding the response mechanism of field effect sensors with differently structured metal contacts were also indicated. The CO response mechanism of Pt metal MISiC sensors could also be utilized in developing a combustion control system based on two sensors and a thermocouple, which when tested in a 40 kW wood fired boiler exhibited a good performance for fuels with extremely low to normal moisture content, substantially decreasing emissions of unburned matter.

Independent mobility is critical to quality of life for people of all ages, and impaired mobility leaves one with both physical and mental disadvantages. Unfortunately, there are some individuals unable to operate an electric wheelchair due to physical, perceptual, or cognitive deficits. The prime objective of this research was to develop a prototype system which can provide mobility assistant to individuals who would otherwise find it difficult or impossible to operate a power wheelchair. To accomplish this goal, a prototype system consisting of several components including an embedded microcontroller and multiple sensors has been designed which can be added to a standard power wheelchair and make it smart. The control system algorithm designed for this prototype model is based on the fuzzy logic control theory and its main purpose is to augment the user ability to navigate the wheelchair and will provide a safe and comfortable journey to the user.

No description available.

Condition monitoring of machines and structures is commonly utilized in order to prevent failures before they can occur. For these reasons, data such as temperature, vibrations or displacements are collected and analysed. Sensors collect this information, which is sent to a base station to be examined. Wired sensors have been used since the appearance of condition monitoring maintenance; however, wireless sensors are becoming more popular in this area. The use of wired sensors can be very expensive, due to the cost related to the installation and maintenance of the wiring between the sensors and the base station. In wind turbines, wired sensor networks are starting to be substituted by wireless sensor networks. However, for tidal turbines, such as those developed by Delta Stream, this is still a challenge. The use of batteries to supply energy to sensors is not an optimal solution for turbines that are located in remote areas. Batteries have a limited life and their replacement is costly and complicated. Thus, alternative sources of energy have to be found. The environment found in a tidal turbine provides several sources of profitable energy, such as vibration and temperature differences which can be used to supply energy by means of energy harvesters. The aim of this project is to demonstrate the operation of self-powered short-range wireless sensor nodes for a potential use in a Delta Stream nacelle of tidal turbine. This project focuses on the wireless communication inside the nacelle (where most of the sensors are located) using a land protocol (Zigbee), and the energy harvesting using waste heat by means of thermoelectric devices. In order to prove the operation of the whole system (thermoelectric generator and sensor node), a power management circuit was also constructed and tested.

No description available.

Chronic stress is endemic to modern society. If we could monitor our mental state, we may be able to develop insights about how we respond to stress. However, it is unfeasible to continuously annotate stress levels all the time. In the studies conducted for this dissertation, a minimally-invasive wearable sensor platform and physiological data processing methods were developed to analyze a number of physiological correlates of mental stress. We present a minimally obtrusive wearable sensor system that incorporates embedded and wireless communication technologies. The system is designed such that it provides a balance between data collection and user comfort. The system records the following stress related physiological and contextual variables: heart rate variability (HRV), respiratory activity, electrodermal activity (EDA), electromyography (EMG), body acceleration, and geographical location. We assume that if the respiratory influences on HRV can be removed, the residual HRV will be more salient to stress in comparison with raw HRV. We develop three signal processing methods to separate HRV into a respiration influenced and residual HRV. The first method consists of estimating respiration-induced portion of HRV using a linear system identification method (autoregressive moving average model with exogenous inputs). The second method consists of decomposing HRV into respiration-induced principal dynamic mode and residual using nonlinear dynamics decomposition method (principal dynamic mode analysis). The third method consists of splitting HRV into respiration-induced power spectrum and residual in frequency domain using spectral weighting method. These methods were validated on a binary discrimination problem of two psychophysiological conditions: mental stress and relaxation. The linear system identification method, nonlinear dynamics decomposition method, and spectral weighting method classified stress and relaxation conditions at 85.2 %, 89.2 %, and 81.5 % respectively. When tonic and phasic EDA features were combined with the linear system identification method, the nonlinear dynamics decomposition method, and the spectral weighting method, the average classification rates were increased to 90.4 %, 93.2 %, and 88.1 % respectively. To evaluate the developed wearable sensors and signal processing methods on multiple subjects, we performed case studies. In the first study, we performed experiments in a laboratory setting. We used the wearable sensors and signal processing methods to discriminate between stress and relaxation conditions. We achieved 81 % average classification rate in the first case study. In the second study, we performed experiments to detect stress in ambulatory settings. We collected data from the subjects who wore the sensors during regular daily activities. Relaxation and stress conditions were allocated during daily activities. We achieved a 72 % average classification rate in ambulatory settings. Together, the results show achievements in recognizing stress from wearable sensors in constrained and ambulatory conditions. The best results for stress detection were achieved by removing respiratory influence from HRV and combining features from EDA.