Les architectures multiprocesseur sur puce (MPSoC) basées sur les réseaux sur puce (NoC) constituent une des solutions les plus appropriées pour les applications embarquées temps réel de traitement du signal et de l’image. De part l’augmentation constante de la complexité de ces algorithmes et du type et de la taille des données manipulées, des architectures MPSoC sont nécessaires pour répondre aux contraintes de performance et de portabilité. Mais l’exploration de l’espace de conception de telles architectures devient très coûteuse en temps. En effet, il faut définir principalement le type et le nombre des coeurs de calcul, l’architecture mémoire et le réseau de communication entre tous ces composants. La validation par simulation de haut niveau manque de précision, et la simulation de bas niveau est inadaptée au vu de la taille de l’architecture. L’émulation sur FPGA devient donc inévitable. Dans le domaine de l’image, l’imagerie spectrale est de plus en plus utilisée car elle permet de multiplier les intervalles spectraux, améliorant la définition de la lumière d’une scène pour permettre un accès à des caractéristiques non visibles à l’oeil nu. De nombreux paramètres modifient les caractéristiques de l’algorithme, ce qui influence l’architecture finale. L’objectif de cette thèse est de proposer une méthode pour dimensionner au plus juste l’architecture matérielle et logicielle d’une application d’imagerie multispectrale. La première étape est le dimensionnement du NoC en fonction du trafic sur le réseau. Le développement automatique d’une plateforme d’émulation sur mono ou multi FPGA facilite cette étape et détermine le positionnement des composants de calcul. Ensuite, le dimensionnement des composants de calcul et leurs fonctionnalités sont validés à l’aide de plateformes de simulation existantes, avant la génération du modèle synthétisable sur FPGA. Le flot de conception est ouvert dans le sens qu’il accepte différents NoC à condition d’avoir le modèle source HDL de ce composant. De nombreux résultats mettent en avant les paramètres importants qui ont une influence sur les performances des architectures et du NoC en particulier. Plusieurs solutions sont décrites, commentées et critiquées. Ces travaux nous permettent de poser les premiers jalons d’une plateforme d’émulation complète MPSoC à base de NoC / The Multiprocessor-System-On-Chip (MPSoC) architectures based on the Network-On-Chip (NoC) communication are the one of the most appropriate solution for image and signal processing applications under real time constraints. Due to the ever increasing complexity of these algorithms, the types and sizes of the data manipulated, the MPSoC architectures are necessary to meet the constraints of performance and portability. However exploring the design space of such architecture is time consuming. Indeed, many parameters should be defined such as the type and the number of processing cores, the memory architecture and the communication network between all these components. Validation by high-level simulations has the lack of the precision. Low-level simulation is inadequate for such big size of the architecture. Therefore, the emulation on FPGA becomes inevitable. In image processing, spectral imaging is more and more used. This technology captures light from more frequencies than the human eye increasing the number of wavelengths. Invisible details can be extracted from a scene. The difference between all spectral imaging applications is the number of wavelengths and the precision. Many parameters affect the characteristics of the algorithm, having a huge impact on the final architecture. The objective of this thesis is to propose a method for sizing one of the most accurate hardware and software architecture for multispectral imaging application. The first step is the design of the NoC based on the network traffic. The automatic development of an emulation platform on a single FPGA or multi-FPGAs simplifies this step and determines the positioning of the computational components. Then, the design of computational components and their functions are validated using existing simulation platforms. The synthesizable model of the architecture on FPGA is then generated. The design flow is open. Several NoC structures can be inserted using the source model of this component. The set of results obtained points out the major parameters influencing the performances of architecture and the NoC itself. Several solutions are described and analyzed. These studies allow us to lay the groundwork for a complete MPSoC emulation platform based on NoC

<p>Breast conserving surgery (BCS) is a common treatment option for breast cancer patients. The goal of BCS is to remove the entire tumor from the breast while preserving as much normal tissue as possible for a better cosmetic outcome after surgery. Specifically, the excised specimen must have at least 2 mm of normal tissue surrounding the diseased mass. Unfortunately, a staggering 20-70% of patients undergoing BCS require repeated surgeries due to the incomplete removal of the tumor diagnosed post-operatively. Due to these high re-excision rates as well as limited post-operative histopathological sampling of the tumor specimen, there is an unmet clinical need for margin assessment. Quantitative diffuse reflectance spectral imaging has previously been explored as a promising, method for providing real-time visual maps of tissue composition to help surgeons determine breast tumor margins to ensure the complete removal of the disease during breast conserving surgery. We have leveraged the underlying sources of contrast in breast tissue, specifically total hemoglobin content, beta-carotene content, and tissue scattering, and developed various fiber optics based spectral imaging systems for this clinical application. Combined with a fast inverse Monte Carlo model of reflectance, previous studies have shown that this technology may be able to decrease re-excision rates for BCS. However, these systems, which all consist of a broadband source, fiber optics probes, an imaging spectrograph and a CCD, have severe limitations in system footprint, tumor area coverage, and speed for acquisition and analysis. The fiber based spectral imaging systems are not scalable to smaller designs that cover a large surveillance area at a very fast speed, which ultimately makes them impractical for use in the clinical environment. The objective of this dissertation was to design, develop, test, and show clinical feasibility of a novel wide field spectral imaging system that utilizes the same scientific principles of previously developed fiber optics based imaging systems, but improves upon the technical issues, such as size, complexity, and speed,to meet the demands of the intra-operative setting. </p><p>First, our simple re-design of the system completely eliminated the need for an imaging spectrograph and CCD by replacing them with an array of custom annular photodiodes. The geometry of the photodiodes were designed with the goal of minimizing optical crosstalk, maximizing SNR, and achieving the appropriate tissue sensing depth of up to 2 mm for tumor margin assessment. Without the imaging spectrograph and CCD, the system requires discrete wavelengths of light to launch into the tissue sample. A wavelength selection method that combines an inverse Monte Carlo model and a genetic algorithm was developed in order to optimize the wavelength choices specifically for the underlying breast tissue optical contrast. The final system design consisted of a broadband source with an 8-slot filter wheel containing the optimized set of wavelength choices, an optical light guide and quartz light delivery tube to send the 8 wavelengths of light in free space through the back apertures of each annular photodiode in the imaging array, an 8-channel integrating transimpedance amplifier circuit with a switch box and data acquisition card to collect the reflectance signal, and a laptop computer that controls all the components and analyzes the data.</p><p>This newly designed wide field spectral imaging system was tested in tissue-mimicking liquid phantoms and achieved comparable performance to previous clinically-validated fiber optics based systems in its ability to extract optical properties with high accuracy. The system was also tested in various biological samples, including a murine tumor model, porcine tissue, and human breast tissue, for the direct comparison with its fiber optics based counterparts. The photodiode based imaging system achieved comparable or better SNR, comparable extractions of optical properties extractions for all tissue types, and feasible improvements in speed and coverage for future iterations. We show proof of concept in performing fast, wide field spectral imaging with a simple, inexpensive design. With a reduction in size, cost, number of wavelengths used, and overall complexity, the system described by this dissertation allows for a more seamless scaling to higher pixel number and density in future iterations of the technology, which will help make this a clinically translatable tool for breast tumor margin assessment.</p> / Dissertation

<p>This dissertation describes two computational sensors that were used to demonstrate applications of generalized sampling of the optical field. The first sensor was an incoherent imaging system designed for compressive measurement of the power spectral density in the scene (spectral imaging). The other sensor was an interferometer used to compressively measure the mutual intensity of the optical field (coherence imaging) for imaging through turbulence. Each sensor made anisomorphic measurements of the optical signal of interest and digital post-processing of these measurements was required to recover the signal. The optical hardware and post-processing software were co-designed to permit acquisition of the signal of interest with sub-Nyquist rate sampling, given the prior information that the signal is sparse or compressible in some basis.</p> <p>Compressive spectral imaging was achieved by a coded aperture snapshot spectral imager (CASSI), which used a coded aperture and a dispersive element to modulate the optical field and capture a 2D projection of the 3D spectral image of the scene in a snapshot. Prior information of the scene, such as piecewise smoothness of objects in the scene, could be enforced by numerical estimation algorithms to recover an estimate of the spectral image from the snapshot measurement.</p> <p>Hypothesizing that turbulence between the scene and CASSI would introduce spectral diversity of the point spread function, CASSI's snapshot spectral imaging capability could be used to image objects in the scene through the turbulence. However, no turbulence-induced spectral diversity of the point spread function was observed experimentally. Thus, coherence functions, which are multi-dimensional functions that completely determine optical fields observed by intensity detectors, were considered. These functions have previously been used to image through turbulence after extensive and time-consuming sampling of such functions. Thus, compressive coherence imaging was attempted as an alternative means of imaging through turbulence.</p> <p>Compressive coherence imaging was demonstrated by using a rotational shear interferometer to measure just a 2D subset of the 4D mutual intensity, a coherence function that captures the optical field correlation between all the pairs of points in the aperture. By imposing a sparsity constraint on the possible distribution of objects in the scene, both the object distribution and the isoplanatic phase distortion induced by the turbulence could be estimated with the small number of measurements made by the interferometer.</p> / Dissertation

Spectral imaging, a combination of spectroscopy and imaging, is a powerful tool for providing in situ material classification across a spatial scene. Typically spectral imaging analyses are interested in classification, though conventionally the classification is performed only after reconstruction of the spectral datacube, which can have upwards of 10⁹ signal elements. In this dissertation, I present a computational spectral imaging system, the Adaptive Feature-Specific Spectral Imaging Classifier (AFSSI-C), which yields direct classification across the spatial scene without reconstruction of the source datacube. With a dual disperser architecture and a programmable spatial light modulator which induces spectral filtering, the AFSSI-C measures specific projections of the spectral datacube which in turn feed an adaptive Bayesian classification and feature design framework. I present my work related to the design, construction, and testing of this instrument, which ultimately demonstrated significantly improved classification accuracy compared to legacy spectral imaging systems by first showing agreement with simulation, and then comparing to expected performance of traditional systems. As a result of its open aperture and adaptive filters, the AFSSI-C achieves 250 X better accuracy than pushbroom, whiskbroom, and tunable filter systems for a four-class problem at 0 dB TSNR (task signal-to-noise ratio) - a point where measurement noise is equal to the minimum separation between the library spectra. The AFSSI-C also achieves 100 X better accuracy than random projections at 0 dB TSNR.

京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第17972号 / 理博第3916号 / 新制||理||1565(附属図書館) / 80816 / 京都大学大学院理学研究科物理学・宇宙物理学専攻 / (主査)教授 高橋 義朗, 教授 田中 耕一郎, 教授 石田 憲二 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM

京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第18798号 / 理博第4056号 / 新制||理||1583(附属図書館) / 31749 / 京都大学大学院理学研究科地球惑星科学専攻 / (主査)准教授 齊藤 昭則, 教授 田口 聡, 教授 余田 成男 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM

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Approved for public release, distribution is unlimited / The Lineate Image Near Ultraviolet Spectrometer (LINUS) is a spectral imager developed to operate in the 0.3-0.4 micron spectral region. The 2-D imager operates with a scan mirror, forming image scenes over time intervals of 10-20 minutes. Sensor calibration was conducted in the laboratory, and the system response to Sulfur Dioxide (SO2) gas was determined. The absorption profile for SO2 was measured, and curves of growth were constructed as a function of gas concentration. Test measurements were performed at the Naval Postgraduate School (NPS), from the roof of Spanagel Hall. Field observations were conducted at a coal-burning factory site at Concord, CA with the purpose of quantifying the presence of SO2. The Concord field measurement showed traces of SO2, with further analysis still required. / Civilian, DSO National Laboratories, Singapore

Dépistage du VIH, test de grossesse, mais également surveillance des eaux, détection de contaminants agro-alimentaires : la biodétection est au coeur des problématiques de santé actuelles. Dans ce contexte, les biocapteurs plasmoniques connaissent depuis quelques années un essor particulièrement important : de plus en plus de sociétés, telles que HORIBA Scientific, proposent des prototypes commerciaux, destinés tant à des utilisateurs du domaine de la recherche que de l'industrie. Basée sur le phénomène de résonance des plasmons de surface (communément appelé SPR) la biodétection plasmonique repose sur l'extrême sensibilité d’une onde évanescente se propageant à l’interface entre un film d’or, la biopuce, et le milieu diélectrique couvrant, siège des interactions biomoléculaires étudiées. De manière plus concrète, toute adsorption de matériel biologique se produisant à cette interface entraîne une modification importante des propriétés optiques d’un faisceau de lumière réfléchi par la biopuce : le principe de transduction par SPR consiste alors à mesurer directement ces variations. A l'heure actuelle, différents modes d'interrogation, offrant des performances intéressantes, mais également des limitations propres à chaque configuration. Pour répondre aux exigences de précision et de dynamique de mesure posées par de nombreuses applications, un développement théorique et instrumental, présenté dans ce document, a été initié dans le but de proposer un nouveau un nouveau mode d'interrogation des biopuces plasmoniques : l'interrogation multi-spectrale. Les résultats obtenus par cette technique ont été exploités pour concevoir et réaliser une source multi-spectrale à base de LEDs, particulièrement avantageuse vis-à-vis des configurations existant à l'heure actuelle. La caractérisation du système développé dans le cadre du diagnostic génétique (mucoviscidose) et celui du cancer, ouvre la voie à une nouvelle génération de biocapteurs performants, compacts et de coût relativement raisonnable, présentant un potentiel industriel certain. / Biodetection is at the core of the current health concerns, as shown through the variety of applications to HIV screening, food contaminant analysis or water quality monitoring. In this field, plasmonic biosensing is a well-established label-free technique on the market: commercial systems from HORIBA Scientific are currently available for both research and industrial users.Based on the surface plasmon resonance (SPR) phenomenon, plasmonic biodetection uses the high sensitivity of an evanescent wave propagating along a metallic film (forming the biochip) and the surrounding dielectric medium interface. More specifically, the adsorption of biomolecules onto the metal surface induces a strong change in the optical properties of a light beam reflected by the biochip: the main principle of plasmonic transduction consists in measuring these physical changes. Several interrogation techniques have therefore been developed to access such optical information, but they fail in meeting the most demanding user requirements for precise, real-time, high-throughput measurement.Initiated by these issues, the instrumentation work presented in this document has led to the development of a novel SPR interrogation technique, referred to as multi-spectral interrogation. Moreover, the promising results obtained have been pushed forward to propose a multi-spectral illumination system based on LEDs, providing attractive performances compared to existing configurations. The biosensing potential of the developed system, demonstrated through applications to genetic diagnosis and cancer detection, opens the door to a new generation of compact, high-performance, low-cost SPR sensors.