A masters thesis focussing on achieving improvements in transmission reliability and energy efficiency for a battery-powered wireless sensor node on the edge of an industrial heterogeneous wireless network that consists predominantly of mains-powered nodes. A router-switching technique is proposed to allow the sensor node to make gains in transmission reliability and energy efficiency by taking advantage of the scenario where multiple wireless routers are in range and switching between them, instead of only being able to transmit to one router. The research involves simulation of a number of network scenarios where the router-switching technique is enabled and disabled, to measure the advantage gained for the sensor in terms of its functional lifetime. The simulation is based on an abstract model that focusses on the edge of the mains-powered area of the network, where the battery-powered sensor is located. The simulation results show that for many cases, router-switching provides a higher level of transmission reliability and lower levels of energy consumption than the scenario where router-switching is disabled, as well as improvements in data loss rates.

The research contained in the following dissertation spans a diverse range of scientific scholarship, including; chemometrics for integrated sensing and processing (ISP), near infrared and acoustic resonance spectrometry for analyte quantification and classification, and an ISP acoustic sensor as an alternative to conventional acoustic spectrometry. These topics may at first seem disjointed; however, closer inspection reveals that chemometrics, spectrometry, and sensors taken together form the umbrella under which applied spectrometry and analytical chemistry fall. The inclusion of each of these three serves to paint the complete portrait of the role of applied spectrometry for the advancement of process analytical technology. To illustrate the totality of this portrait, this research seeks to introduce and substantiate three key claims. (1) When applicable, optical spectrometry and acoustic spectrometry are preferred alternatives to slower and more invasive methods of analysis. (2) Chemometrics can be implemented directly into the physical design of spectrometers, thus sparing the need for computationally demanding post-collection multivariate analyses. (3) Using this principle, ISP sensors can be developed specifically for use in highly applied situations, making possible automatic analyte quantification or classification without the computational burden and extensive data analysis typically associated with conventional spectrometry. More concisely, these three claims can be stated as follows: spectrometry has a broad range of uses, chemometrics for ISP makes spectrometry more efficient, and for all analytical problems with a spectrometric solution, an ISP sensor, specifically tailored to the needs of the experiment, can more effectively solve the same analytical problem.

Gas sensor control is potentially one of the most important techniques of tomorrow for the environment. All over the world cars are preferred for transportation, and accordingly the number of cars increases, unfortunately, together with pollutants. Boilers and powerplants are other sources of pollutants to the environment. Metal-Insulator-Silicon Carbide (MISiC) Field-effect sensors in car applications and boilers have the potential to reduce the amount of pollutants. These devices are sensitive to several gases in exhaust and flues gases, such as hydrogen, hydrocarbons, and ammonia (for the selective catalytic reduction (SCR) application). These applications require specific and long term stable sensors. The car industry for instance wants sensors that will stand at least 240 000 km. This thesis presents studies of the active layers in MIS Fieldeffect gas sensors. Fundamental studies of the sensor mechanism has been performed in ultra high vacuum, UHV, to understand the gas response mechanism in more detail, and to find out how the sensing mechanism is affected by the catalytic active gate material. The influence of four different insulating layers was studied at atmospheric pressure. The catalytic layer has also been altered to metal oxide nanoparticles with or without impregnation of catalytic metals. Nanoparticles are potential candidates to be used as the gate material for high temperature, long-term stable FET sensor devices. The combination of catalytic metals and metal oxides may prevent reconstruction of the metal. The use of nanoparticles will increase the number of triple points (catalytic material and insulator in contact with gas), which are crucial e.g. for the ammonia sensitivity. Another challenging aspect of nanoparticles is the possibility to get selectivity to different gases based on the particle size. The goal is to find new sensitive, selective and more long term stable materials, which meet the requirements above. From the UHV studies we learned that the two catalytic active metals Pt and Pd, do behave in a similar way, although there are some quantitative differences. Values for the heat of adsorption on both the Pd and Pt surfaces are estimated as well as the dipole moments for the adsorbates on the insulator surface. The insulators play an important role in the sensing mechanism, since the adsorption of hydrogen atoms (or protons) that are detected by the sensor occur on the insulator surface. By changing the insulator material the saturation response of the sensors is affected. It was shown that Al2O3 gave a higher saturated response to hydrogen in Pt-MIS capacitors at 140°C as compared to Ta2O5, SiO2, and Si3N4. We have tested wet synthesized ruthenium dioxide and ruthenium nanoparticles, which are electrically conducting and catalytically active sensing material. RuO2 is especially interesting as a high temperature material since it is already oxidized. Both materials show a sensitivity pattern comparable to porous platinum. The temperature dependence of the gas response indicates a higher catalytic activity of the RuO2 as compared to Ru nanoparticles. Nanoparticles synthesized by aerosol technology provide several advantages like a good adhesion of the particles to the substrate, many possible material combinations and efficient methods for particle separation according to size. The methods to use this technology for sensing materials in MISiC sensors are now under development and some preliminary results are obtained. / On the day of the public defence of the doctoral thesis, the status of articles I and II was: accepted for publication.

Atmospheric turbulence limits the resolving power of astronomical telescopes by distorting the paths of light between distant objects of interest and the imaging camera at the telescope. After many light-years of travel, passing through the turbulence in that last 100km of a photon’s journey results in a blurred image in the telescope, no less than 1” (arc-second) in width. To achieve higher resolutions, corresponding to smaller image widths, various methods have been proposed with varying degrees of effectiveness and practicality. Space telescopes avoid atmospheric turbulence completely and are limited in resolution solely by the size of their mirror apertures. However, the design and maintenance cost of space telescopes, which increases prohibitively with size, has limited the number of space telescopes deployed for astronomical imaging purposes. Ground based telescopes can be built larger and more cheaply, so atmospheric compensation schemes using adaptive optical cancellation mirrors can be a cheaper substitute for space telescopes. Adaptive optics is referred to here as the use of electronic control of optical component to modify the phase of an incident ray within an optical system like an imaging telescope. Fast adaptive optics systems operating in real-time can be used to correct the optical aberrations introduced by atmospheric turbulence. To compensate those aberrations, they must first be measured using a wavefront sensor. The wavefront estimate from the wavefront sensor can then be applied, in a closed-loop system, to a deformable mirror to compensate the incoming wavefront. Many wavefront sensors have been proposed and are in used today in adaptive optics and atmospheric turbulence measurement systems. Experimental results comparing the performance of wavefront sensors have also been published. However, little detailed analyses of the fundamental similarities and differences between the wavefront sensors have been performed. This study concentrates on fourmain types of wavefront sensors, namely the Shack-Hartmann, pyramid, geometric, and the curvature wavefront sensors, and attempts to unify their description within a common framework. The quad-cell is a wavefront slope detector and is first examined as it lays the groundwork for analysing the Shack-Hartmann and pyramid wavefront sensors. The quad-cell slope detector is examined, and a new measure of performance based on the Strehl ratio of the focal plane image is adopted. The quad-cell performance based on the Strehl ratio is compared using simulations against the Cramer-Rao bound, an information theoretic or statistical limit, and a polynomial approximation. The effects of quad-cell modulation, its relationship to extended objects, and the effect on performance are also examined briefly. In the Shack-Hartmann and pyramid wavefront sensor, a strong duality in the imaging and aperture planes exists, allowing for comparison of the performance of the two wavefront sensors. Both sensors subdivide the input wavefront into smaller regions, and measure the local slope. They are equivalent in every way except for the order in which the subdivision and slope measurements were carried out. We show that this crucial difference leads to a theoretically higher performance from the pyramid wavefront sensor. We also presented simulations showing the trade-off between sensor precision and resolution. The geometric wavefront sensor can be considered to be an improved curvature wavefront sensor as it uses a more accurate algorithm based on geometric optics to estimate the wavefront. The algorithm is relatively new and has not found application in operating adaptive optics systems. Further analysis of the noise propagation in the algorithm, sensor resolution, and precision is presented. We also made some observations on the implementation of the geometric wavefront sensor based on image recovery through projections.

The landscape is under constant threat from human kind and cannot evolve fast enough to protect itself adequately. By augmenting an ecosystem’s natural resilience with cybernetic technology, it will be better equipped to ensure its survival in an urban setting. This practicum will investigate the creation of a cybernetically-enhanced ecosystem, the cyborg landscape, and how this organism(s) will know and understand the world around it. This practicum has been inspired by the idea of the cyborg, research on plant intelligence, installations, artistic interventions and ideas of chance and performance introduced by composer John Cage. The cyborg plant is a strategy used to expand on the limitations of a plant allowing adaptation to situations and environments. To become a cyborg, is to have an intimate bond between technology and organism, both functioning as one to overcome limitations limiting survival in the environment.

X-ray sensors are essential to many applications which are not limited to diagnostics and imaging technologies. Such sensors are extensively used in industry, medicine, research and space technology for applications such as safety, security, quality control, imaging and treatment. Depending on the effect of the radiation on the matter employed in the sensor, different types of X-ray sensors are fabricated. However, available techniques of X-ray detection have been under development due to specific shortcomings such as finite life time, low sensitivity, and post-processing requirements. This thesis is focused on design, fabrication and characterization of novel radiation sensors based on bio/nanomaterials. Bacteriorhodopsin (BR), a proton pump protein in the cell membrane of Halobacterium Salinarum, has been used to fabricate a sensor to measure dose and dose rate of X-ray beam in the kilovoltage and megavoltage energy range. The mass attenuation coefficients, effective atomic numbers and electron densities of BR and its comprising amino acids have been calculated for 1 keV-100 GeV photons to better understand the interaction of BR with X-ray photons. A theoretical formulation for calculating the change in the conductivity of nanoparticles under radiation is also provided. In particular, the dependence of radiation induced conductivity to irradiated particle size is given. In addition to that, an X-ray sensor based on thin film of bismuth sulfide has been fabricated using laser micromachining and chemical deposition techniques. This sensor has been characterized under a diagnostic X-ray machine with kilovoltage energy beam.

As a specialist in power distribution for the aerospace industry, Leach International-Europe intends to master both design and production of the current sensor – a strategic component – while offering a novel technology, free from important disadvantages of the previous generation. This report puts forward some research directions to replace the present generation, based on the Hall Effect and equipping most of commercial and military aircrafts. Relying on a feasibility study, and after reviewing the state of the art, this work heads quickly on exploiting Faraday’s Law. The latter allows getting a voltage as an output directly from the magnetic field generated by the current flowing inside the conductor. The different solutions introduced here are based on analytical calculations (when it is possible), simulations and numerical calculations and are grounded on designing a sensor with an acceptable sensitivity.

This dissertation presents contact force sensors that are based on an emerging fibre-optic sensing technology, the in-fibre Bragg grating (FBG), for contact force measurements between cartilage surfaces in the human hip. There are two main motivations for force measurement in hips (and other joints). First, there is clinical evidence that suggests excessive force magnitude and duration can cause painful degeneration of joints. Second, insights from ex vivo force measurements during simulated physiologic loading are the basis of the rationale for corrective surgeries meant to halt degeneration and restore proper joint function by restoring natural joint mechanics. The current standard tools for force measurements in joints are force/stress sensitive films. There are problems associated with inserting these films into joints that affect the force/stress measurements. To insert the films, the joint must be dissected of surrounding soft tissues and, ultimately, the joint must be taken apart (disarticulated). Following disarticulation, films are fixed to cartilage surfaces, and the joint is re-assembled so that physiologic loads can be applied. The negative consequence of dissection and disarticulation is that the natural mechanics of the intact joint are permanently lost and, therefore, film measurements do not indicate the actual joint mechanics. Moreover, covering cartilage surfaces with rigid films alters the natural contact mechanics of the joint. The force sensors presented in this dissertation are designed for local force measurement over the region of the optical fibre containing the FBG and address limitations of force/stress sensitive films. The FBG force sensors are extremely small (major diameters ranging from 0.165 mm to 0.24 mm) and can be inserted into joint spaces without dissection of soft tissues and disarticulation thereby allowing the joint to remain intact. Theoretical and experimental results indicate that FBG sensor measurements are less affected by the mechanical properties of cartilage than are film sensors. The sensors presented in this dissertation also address limitations with previous FBG based force sensors and are the first application of FBGs in intact human hips. The sensors are smaller, and therefore less invasive, and insensitive to orientation, axial strain and temperature, unlike other FBG sensors presented in the literature. / Graduate

Due to rapid advancements in sensing and computation technology, multiple types of sensors have been embedded in various applications, on-line automatically collecting massive production information. Although this data-rich environment provides great opportunity for more effective process control, it also raises new research challenges on data analysis and decision making due to the complex data structures, such as heterogeneous data dependency, and large-volume and high-dimensional characteristics. This thesis contributes to the area of System Informatics and Control (SIAC) to develop systematic data fusion methodologies for effective quality control and performance improvement in complex systems. These advanced methodologies enable (1) a better handling of the rich data environment communicated by complex engineering systems, (2) a closer monitoring of the system status, and (3) a more accurate forecasting of future trends and behaviors. The research bridges the gaps in methodologies among advanced statistics, engineering domain knowledge and operation research. It also forms close linkage to various application areas such as manufacturing, health care, energy and service systems. This thesis started from investigating the optimal sensor system design and conducting multiple sensor data fusion analysis for process monitoring and diagnosis in different applications. In Chapter 2, we first studied the couplings or interactions between the optimal design of a sensor system in a Bayesian Network and quality management of a manufacturing system, which can improve cost-effectiveness and production yield by considering sensor cost, process change detection speed, and fault diagnosis accuracy in an integrated manner. An algorithm named “Best Allocation Subsets by Intelligent Search” (BASIS) with optimality proof is developed to obtain the optimal sensor allocation design at minimum cost under different user specified detection requirements. Chapter 3 extended this line of research by proposing a novel adaptive sensor allocation framework, which can greatly improve the monitoring and diagnosis capabilities of the previous method. A max-min criterion is developed to manage sensor reallocation and process change detection in an integrated manner. The methodology was tested and validated based on a hot forming process and a cap alignment process. Next in Chapter 4, we proposed a Scalable-Robust-Efficient Adaptive (SERA) sensor allocation strategy for online high-dimensional process monitoring in a general network. A monitoring scheme of using the sum of top-r local detection statistics is developed, which is scalable, effective and robust in detecting a wide range of possible shifts in all directions. This research provides a generic guideline for practitioners on determining (1) the appropriate sensor layout; (2) the “ON” and “OFF” states of different sensors; and (3) which part of the acquired data should be transmitted to and analyzed at the fusion center, when only limited resources are available. To improve the accuracy of remaining lifetime prediction, Chapter 5 proposed a data-level fusion methodology for degradation modeling and prognostics. When multiple sensors are available to measure the degradation mechanism of a same system, it becomes a high dimensional and challenging problem to determine which sensors to use and how to combine them together for better data analysis. To address this issue, we first defined two essential properties if present in a degradation signal, can enhance the effectiveness for prognostics. Then, we proposed a generic data-level fusion algorithm to construct a composite health index to achieve those two identified properties. The methodology was tested using the degradation signals of aircraft gas turbine engine, which demonstrated a much better prognostic result compared to relying solely on the data from an individual sensor. In summary, this thesis is the research drawing attention to the area of data fusion for effective employment of the underlying data gathering capabilities for system modeling, performance assessment and improvement. The fundamental data fusion methodologies are developed and further applied to various applications, which can facilitate resources planning, real-time monitoring, diagnosis and prognostics.

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