In this thesis is dealed the technical term diagnostic with Fluke Ti55 thermo imager. In the introduction of description is analyzed noncontact temperature measurement including principle, uncertainties measurement and measurement system. In other parts of the work is focused on thermal imaging, termogram, description Fluke Ti55 termo imager and measurement of mechanical stress. The concluding part of the description and processing of measurements, including their evaluation.

The High Efficiency HyperSpectral Imager (HEHSI) is a Fourier Transform hyperspectral imager based on a Sagnac interferometer. This thesis research concentrates on the design upgrade and calibration of HEHSI from a proof of concept instrument to a prototype field instrument. Stability is enhanced by removing degrees of freedom and alignment is enhanced by providing for in-situ adjustments. The use of off the shelf components allows for reduced development time and cost constraints. HEHSI is capable of multiple configurations to accommodate sensors and optics with specialized capabilities for multiple wavelength ranges and viewing conditions. With a spectral response of 400 to 1000 nanometers in the visible and very near IR as well as 900 to 1700nm in the Near IR. Creation and use of a real time feedback alignment utility allow quantifiable signal comparison and image alignment. Advances allow for HEHSI to remain aligned during data collection sessions and confirmation of alignment through quantitative measures.

A relatively new picosatellite standard, CubeSats have traditionally been used for simple educational missions. As CubeSats become more complex and utilize more complex sensors such as imagers, they gain enhanced credibility as satellite platforms. Imaging systems on CubeSats have the potential to be used for a variety of uses, such as earth and weather monitoring, attitude determination, and remote sensing. However the size and power limitations of CubeSats pose an interesting challenge to the design of a capable, robust imaging system. This thesis outlines the objectives and requirements of CP-3’s imaging system, and describes the development process and methods. Test results from the imaging system are included, as well as lessons learned gleaned from CP-3’s on-orbit operations. This document can serve as a guideline for other teams wishing to develop imaging systems. While other developers may have different requirements or constraints, this roadmap illustrates each of the many considerations that must be taken into account when designing an imaging system.

The performance of the auroral large imagining system (ALIS_4D) and an automatic track detection algorithm was evaluated for space debris surveillance and tracking. The evaluation of the ALIS_4D was done through a numerical simulation and data annotations, while the track detection results were manually evaluated. The effect of auroral conditions, filters, and the detection mode were evaluated for the performance of both.  It was found that  ALIS_4D can detect resident space objects. The peak detection rate per hour was dependent on the time of the year, day, and the limiting magnitude set by the filters and the sensor among others. The peak was simulated to be approximately 120 in January and 70 in April and September. A space object observation campaign was performed in April 2020 for 90 minutes. During that period across the used four stations 61 unique objects were detected and 37 unique objects were detected at the Abisko station, that was used for the simulation. During the observation time there was auroral activity which can block the line-of-sight to resident space objects.  The track detection algorithm was evaluated for data gathered in a dedicated space situational awareness (SSA) mode and other modes. In SSA mode, the algorithm found 60% of the subsections of the image with visible traces. The false detection rate was 17% when no auroras were present and 56% when there were. In other modes the evaluation was simplified due to large number of false positives. When assumed best case scenario 99.2% of the detections were false. The auroral activity and the used mode had the most significant effect on the track detection algorithm performance. It was found that in SSA mode the used filter did not effect on the track detection performance.

Unmanned Aerial Systems (UAS) are a growing asset. Currently UAS are on the cutting edge with resources being spent developing the capabilities mostly for military use. This project is intended to create a system for non-defense customers. Specifically, the Operationally Responsive Aircraft (ORA) will appeal to academic institutions, individual consumers, future customers new to the UAS industry, as well as anybody trying to get airtime for custom sensors. The system developed in this project utilizes dual aluminum external payload bays attached to a ParkZone Radian aircraft. Each external payload bay can contain approximately 500 $\text{cm}^3$, with a height and width limit of 4.1 cm and 11.0 cm respectively. The custom sensors must weigh less than or equal to 3.2 lbs combined. The external payload bays were designed to hold an imaging payload which produces a composite map of the land surveyed. The system incorporates an Arduino Uno, SD Shield, as well as a CMOS camera and board. The processor saves individual images to an SD card. Once the aircraft has landed, the operator combines the images with Microsoft Research Image Composite Editor to create the composite map. This imaging payload has a NIIRS value of 4.0 +/- 0.4, which is equivalent to identifying a basketball court within a residential environment.

The Special Sensor Microwave/Imager (SSM/I) radiometer is a useful tool for monitoring snow conditions and estimating snow water equivalent and wetness because it is sensitive to the changes in the physical and dielectric properties of snow. Development and improvement of SSM/I snow-related algorithms is hampered generally by the lack of quantitative snow wetness data and the restriction of a fixed uniform footprint. Currently, there is a need for snow classification algorithms for terrain where forests overlie snow cover. A field experiment was conducted to examine the relationship between snow wetness and meteorological variables. Based on the relationship, snow wetness was estimated concurrently with SSM/I local crossing time at selected footprints to develop an SSM/I snow wetness algorithm. For the improvement of existing algorithms, SSM/I observations were linked with concurrent ground-based snow data over a study area containing both sparse- and medium-vegetated regions. Unsupervised cluster analysis was applied to separate SSM/I brightness temperature (Tb) data into groups. Six typical SSM/I Tb signatures, based on cluster means of desired snow classes, were identified. An artificial neural network (ANN) classifier was designed to learn the typical Tb patterns Ill for land-surface snow cover classification. An ANN approximator was trained with the relations between inputs of SSM/I Tb observations and outputs of ground-based snow water equivalent and wetness. Results indicated that snow wetness estimated from concurrent air temperature could provide the ground-based data needed for the development of SSM/I algorithms. The use of cluster means might be sufficient in ANN supervised learning for snow classification, and the ANN has the potential to be trained for retrieving different snow parameters simultaneously from SSM/I data. It is concluded that the ANN approach may overcome the drawbacks and limitations of the existing SSM/I algorithms for land-surface snow classification and parameter estimation over varied terrain. This study demonstrated a nonlinear retrieval method towards making the inferences of snow conditions and parameters from SSM/I data over varied terrain operational.

Le test en production des imageurs CMOS est réalisé avec des testeurs qui utilisent des sources de lumière précises, aussi bien au niveau du test de plaquettes qu'au niveau du test de boîtiers. Ce besoin rends le test de ces produits plus compliqué et coûteux . En outre, ces types de tests ne peuvent pas être réalisés directement sur l'imageur afin d'incorporer des fonctions d'auto test. Celles-ci sont intéressantes pour la réduction des coûts du test de production et pour le diagnostic de l'imageur. Le diagnostic est très important lors de la production des imageurs et aussi lors de leur utilisation dans certaines applications, en particulier quand ils sont soumis à des sources de stress importantes. En général, les utilisateurs des imageurs possèdent rarement l'équipement nécessaire pour vérifier leur fonctionnalité. Dans cette thèse, nous étudions et évaluons une technique d'auto test (BIST) pour les capteurs de vision CMOS. Cette technique réalise un test structurel de l'imageur. Le test structurel est basé sur des stimuli électriques appliqués dans l'anode de la photodiode et dans les transistors du pixel. La qualité de l'auto test est évaluée en fonction de métriques de test qui tiennent en compte des déviations du process et la présence de fautes catastrophiques et paramétriques. La technique d'auto test est validée pour deux imageurs, l'un utilisant des pixels à intégration et l'autre des pixels logarithmiques. Une validation expérimentale est réalisé pour le cas de l'imageur logarithmique.

This thesis presents two hybrid luminescence-based biochemical photosensory microsystems: a CMOS/microfluidic chemiluminescence contact imager, and a CMOS/thin-film fluorescence contact imager. A compact, low-power analog-to-digital converter (ADC) architecture for use in such sensory microsystems is also proposed. Both microsystems are prototyped in a standard 0.35um CMOS technology. The CMOS/microfluidic microsystem integrates a 64x128-pixel CMOS imager and a soft polymer microfluidic network. Circuit techniques are employed to reduce the dark current and circuit noise for low-level light sensitivity. Experimental validation is performed by detecting luminol chemiluminescence and electrochemiluminescence. The CMOS/thin-film microsystem integrates an existing 128x128-pixel CMOS imager and a prefabricated, high-performance optical filter. Experimental validation is performed by detecting human DNA labeled with Cyanine-3 fluorescent dye. The proposed ADC architecture employs a novel digital-to-analog converter with a flexible trade-off between the integration area and the conversion speed. The area savings and good linearity of the DAC are verified by simulations.

This thesis presents two hybrid luminescence-based biochemical photosensory microsystems: a CMOS/microfluidic chemiluminescence contact imager, and a CMOS/thin-film fluorescence contact imager. A compact, low-power analog-to-digital converter (ADC) architecture for use in such sensory microsystems is also proposed. Both microsystems are prototyped in a standard 0.35um CMOS technology. The CMOS/microfluidic microsystem integrates a 64x128-pixel CMOS imager and a soft polymer microfluidic network. Circuit techniques are employed to reduce the dark current and circuit noise for low-level light sensitivity. Experimental validation is performed by detecting luminol chemiluminescence and electrochemiluminescence. The CMOS/thin-film microsystem integrates an existing 128x128-pixel CMOS imager and a prefabricated, high-performance optical filter. Experimental validation is performed by detecting human DNA labeled with Cyanine-3 fluorescent dye. The proposed ADC architecture employs a novel digital-to-analog converter with a flexible trade-off between the integration area and the conversion speed. The area savings and good linearity of the DAC are verified by simulations.

Camera-on-a-chip systems have tried to include carefully chosen signal processing units for better functionality, performance and also to broaden the applications they can be used for. Image processing sensors have been possible due advances in CMOS active pixel sensors (APS) and neuromorphic focal plane imagers. Some of the advantages of these systems are compact size, high speed and parallelism, low power dissipation, and dense system integration. One can envision using these chips for portable and inexpensive video cameras on hand-held devices like personal digital assistants (PDA) or cell-phones In neuromorphic modeling of the retina it would be very nice to have processing capabilities at the focal plane while retaining the density of typical APS imager designs. Unfortunately, these two goals have been mostly incompatible. We introduce our MAtrix Transform Imager Architecture (MATIA) that uses analog floating--gate devices to make it possible to have computational imagers with high pixel densities. The core imager performs computations at the pixel plane, but still has a fill-factor of 46 percent - comparable to the high fill-factors of APS imagers. The processing is performed continuously on the image via programmable matrix operations that can operate on the entire image or blocks within the image. The resulting data-flow architecture can directly perform all kinds of block matrix image transforms. Since the imager operates in the subthreshold region and thus has low power consumption, this architecture can be used as a low-power front end for any system that utilizes these computations. Various compression algorithms (e.g. JPEG), that use block matrix transforms, can be implemented using this architecture. Since MATIA can be used for gradient computations, cheap image tracking devices can be implemented using this architecture. Other applications of this architecture can range from stand-alone universal transform imager systems to systems that can compute stereoscopic depth.