No / The ability of a cell to contract plays an important role in determining the ability of the cell to migrate, proliferate and associating with other cells. The transduction of the force in soft substrate such as the liquid crystal surface is a method proposed to study the traction forces of single cells. In this work, finite element method was used to study the compressive forces induced by the keratinocyte to the liquid crystal surface via the anchorage of focal contacts. The constitutive finite element model of the liquid crystal-focal contacts was established. The stress and displacement were analyzed using linear static stress analysis for a quiescent cell. The data for lateral displacements obtained from the experiment were provided as inputs to develop the model and verified through the output acquired for both simulation and experiment. The simulation results indicated that the cell compressive stresses were in the range of 14.93 ± 1.9 nN/μm2 per focal contact. Based on the result obtained, it was suggested to model focal contact-liquid crystal interface with a compressive model that can better approximate the mechanism observed

My thesis is to understand the concept of compressive sensing algorithms. Compressive sensing will be a future alternate technique for the Nyquist rate, specific to some applications where sparsity property plays a major role. Software implementation of compressive sensing (CS) takes more time to reconstruct a signal from CS measurements, so we use the orthogonal matching pursuit and basis pursuit algorithms. We have used an image size of 256x256 is used for reconstruction and also implemented a field-programmable gate array (FPGA) of the orthogonal matching pursuit using an image.

Thesis advisor: Willie J. Padilla / This thesis will describe the implementation of novel imaging applications with electromagnetic metamaterials. Metamaterials have proven to be host to a multitude of interesting physical phenomena and give rich insight electromagnetic theory. This thesis will explore not only the physical theory that give them their interesting electromagnetic properties, but also the many applications of metamaterials. There is a strong need for efficient, low cost imaging solutions, specifically in the longer wavelength regime. While this technology has often been at a standstill due to the lack of natural materials that can effectively operate at these wavelengths, metamaterials have revolutionized the creation of devices to fit these needs. Their scalability has allowed them to access regimes of the electromagnetic spectrum previously unobtainable with natural materials. Along with metamaterials, mathematical techniques can be utilized to make these imaging systems streamlined and effective. Chapter 1 gives a background not only to metamaterials, but also details several parts of general electromagnetic theory that are important for the understanding of metamaterial theory. Chapter 2 discusses one of the most ubiquitous types of metamaterials, the metamaterial absorber, examining not only its physical mechanism, but also its role in metamaterial devices. Chapter 3 gives a theoretical background of imaging at longer wavelengths, specifically single pixel imaging. Chapter 3 also discusses the theory of Compressive Sensing, a mathematical construct that has allowed sampling rates that can exceed the Nyquist Limit. Chapter 4 discusses work that utilizes photoexcitation of a semiconductor to modulate THz radiation. These physical methods were used to create a dynamic THz spatial light modulator and implemented in a single pixel imaging system in the THz regime. Chapter 5 examines active metamaterial modulation through depletion of carriers in a doped semiconductor via application of a bias voltage and its implementation into a similar single pixel imaging system. Additionally, novel techniques are used to access masks generally unobtainable by traditional single pixel imagers. Chapter 6 discusses a completely novel way to encode spatial masks in frequency, rather than time, to create a completely passive millimeter wave imager. Chapter 7 details the use of telecommunication techniques in a novel way to reduce image acquisition time and further streamline the THz single pixel imager. Finally, Chapter 8 will discuss some future outlooks and draw some conclusions from the work that has been done. / Thesis (PhD) — Boston College, 2015. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Physics.

Computational sensing has demonstrated the ability to ameliorate or eliminate many trade-offs in traditional sensors. Rather than attempting to form a perfect image, then sampling at the Nyquist rate, and reconstructing the signal of interest prior to post-processing, the computational sensor attempts to utilize a priori knowledge, active or passive coding of the signal-of-interest combined with a variety of algorithms to overcome the trade-offs or to improve various task-specific metrics. While it is a powerful approach to radically new sensor architectures, published research tends to focus on architecture concepts and positive results. Little attention is given towards the practical issues when faced with implementing computational sensing prototypes. I will discuss the various practical challenges that I encountered while developing three separate applications of computational sensors. The first is a compressive sensing based object tracking camera, the SCOUT, which exploits the sparsity of motion between consecutive frames while using no moving parts to create a psuedo-random shift variant point-spread function. The second is a spectral imaging camera, the AFSSI-C, which uses a modified version of Principal Component Analysis with a Bayesian strategy to adaptively design spectral filters for direct spectral classification using a digital micro-mirror device (DMD) based architecture. The third demonstrates two separate architectures to perform spectral unmixing by using an adaptive algorithm or a hybrid techniques of using Maximum Noise Fraction and random filter selection from a liquid crystal on silicon based computational spectral imager, the LCSI. All of these applications demonstrate a variety of challenges that have been addressed or continue to challenge the computational sensing community. One issue is calibration, since many computational sensors require an inversion step and in the case of compressive sensing, lack of redundancy in the measurement data. Another issue is over multiplexing, as more light is collected per sample, the finite amount of dynamic range and quantization resolution can begin to degrade the recovery of the relevant information. A priori knowledge of the sparsity and or other statistics of the signal or noise is often used by computational sensors to outperform their isomorphic counterparts. This is demonstrated in all three of the sensors I have developed. These challenges and others will be discussed using a case-study approach through these three applications.

O objetivo deste estudo foi comparar, in vitro, a resistência à compressão de 30 raízes bovinas, tratadas endodonticamente, divididas em 3 grupos com 10 raízes cada e restauradas com pino metálico fundido (Grupo 1), pino pré-fabricado de fibra de vidro (Grupo 2) e pino anatômico (Grupo 3). Todas as raízes, com comprimentos de 15mm, foram preparadas internamente para que a espessura dentinária cervical ficasse com 1,5mm, com o objetivo de aumentar a largura do canal radicular. O espaço para o pino foi preparado com 10mm de comprimento. O diâmetro do pino de fibra de vidro escolhido foi o número 2, do sistema utilizado, para que houvesse um espaço entre ele e a estrutura radicular. O Grupo 1 recebeu pino e núcleo metálico fundido cimentado com cimento de fosfato de zinco. O Grupo 2 recebeu pino préfabricado de fibra de vidro cimentado com cimento resinoso Panavia F e núcleo de resina composta Z100. O Grupo 3 recebeu um pino de fibra de vidro modelado no interior do canal com resina composta Z100, cimentado com cimento resinoso Panavia F e núcleo de resina composta Z100. Em todas as raízes dos grupos, um coping metálico foi cimentado com cimento de fosfato de zinco. As raízes foram incluídas em resina acrílica quimicamente ativada, previamente inserida no interior de tubos de PVC e submetidas a testes de compressão em máquina de ensaio Kratos, modelo k2000MP. A carga de compressão foi aplicada a um ângulo de 135 graus ao longo eixo do dente, na face palatina, até que ocorresse uma trinca ou and analysed. The results revealed a statistical difference among groups. Group 1 presented a stronger resistance to fracture than the other groups, which did not present statistical difference. The roots restored with post and cast metallic core were the only ones that presented radicular fracture in the apical root third. The roots that were restored with fibre glass posts presented similar fractures, from the cervical root third to the middle root third. / The aim of this study was to compare, in vitro, the resistance to compression strength of 30bovine roots among canines and incisors endodontically treated, divided into 3 groups with 10 roots each and restored with cast metallic post (Group 1), prefabricated fibre glass post (Group 2), and anatomical post (Group 3). All the roots, with a length of 15mm, were internally prepared so as to their cervical dentin wall thickness would be of 1,5mm, in order to enhance the width of the radicular canal. The space for the post was prepared with 10mm. The diameter of the glass fibre post chosen was the number 2, of the adopted system, so as to create a space between the glass fibre post and the radicular structure. Group 1 received post and cast metallic core cemented with zinc phosphate cement. Group 2 received prefabricated fibre glass post cemented with resin cement Panavia F and composite resin core Z100. Group 3 received a fibre glass post modeled inside the canal with composite resin Z100, cemented with resin cement Panavia F and composite resin core Z100. In all groups, all the roots were cemented with a metallic coping with zinc phosphate cement. The roots were mounted inside pvc pipes with chemically activated acrylic resin, and submitted to compression tests in a universal testing machine Kratos, model k2000MP. The compressive load was done in an angle of 135o in relation to the long axes of the roots, until the occurrence of failure or fracture. The amount of load correspondent to the occurrence of failure or fracture was statistically registered and analysed. The results revealed a statistical difference among groups. Group 1 presented a stronger resistance to fracture than the other groups, which did not present statistical difference. The roots restored with post and cast metallic core were the only ones that presented radicular fracture in the apical root third. The roots that were restored with fibre glass posts presented similar fractures, from the cervical root third to the middle root third.

A compressão de dados é uma área de muito interesse em termos computacionais devido à necessidade de armazená-los e transmiti-los. Em particular, a compressão de malhas possui grande interesse em função do crescimento de sua utilização em jogos tridimensionais e modelagens diversas. Nos últimos anos, uma nova teoria de aquisição e reconstrução de sinais foi desenvolvida, baseada no conceito de esparsidade na minimização da norma L1 e na incoerência do sinal, chamada Compressive Sensing (CS). Essa teoria possui algumas características marcantes, como a aleatoriedade de amostragem e a reconstrução via minimização, de modo que a própria aquisição do sinal é feita considerando somente os coeficientes significativos. Qualquer objeto que possa ser interpretado como um sinal esparso permite sua utilização. Assim, ao se representar esparsamente um objeto (sons, imagens) é possível aplicar a técnica de CS. Este trabalho verifica a viabilidade da aplicação da teoria de CS na compressão de malhas, de modo que seja possível um sensoreamento e representação compressivos na geometria de uma malha. Nos experimentos realizados, foram utilizadas variações dos parâmetros de entrada e técnicas de minimização da Norma L1. Os resultados obtidos mostram que a técnica de CS pode ser utilizada como estratégia de compressão da geometria das malhas. / Data compression is an area of a major interest in computational terms due to the issues on storage and transmission. Particularly, mesh compression has wide usage due to the increase of its application in games and three-dimensional modeling. In recent years, a new theory of acquisition and reconstruction of signals was developed, based on the concept of sparsity and in the minimization of the L1 norm and the incoherency of the signal, called Compressive Sensing (CS). This theory has some remarkable features, such as random sampling and reconstruction by minimization, in a way that the signal acquisition is done by considering only its significant coefficients. Any object that can be interpreted as a sparse sign allows its use. Thus, representing an object sparsely (sounds, images), you can apply the technique of CS. This work explores the viability of CS theory on mesh compression, so that it is possible a representative and compressive sensing on the mesh geometry. In the performed experiments, different parameters and L1 Norm minimization strategies were used. The results show that CS can be used as a mesh geometry compression strategy.

A compressão de dados é uma área de muito interesse em termos computacionais devido à necessidade de armazená-los e transmiti-los. Em particular, a compressão de malhas possui grande interesse em função do crescimento de sua utilização em jogos tridimensionais e modelagens diversas. Nos últimos anos, uma nova teoria de aquisição e reconstrução de sinais foi desenvolvida, baseada no conceito de esparsidade na minimização da norma L1 e na incoerência do sinal, chamada Compressive Sensing (CS). Essa teoria possui algumas características marcantes, como a aleatoriedade de amostragem e a reconstrução via minimização, de modo que a própria aquisição do sinal é feita considerando somente os coeficientes significativos. Qualquer objeto que possa ser interpretado como um sinal esparso permite sua utilização. Assim, ao se representar esparsamente um objeto (sons, imagens) é possível aplicar a técnica de CS. Este trabalho verifica a viabilidade da aplicação da teoria de CS na compressão de malhas, de modo que seja possível um sensoreamento e representação compressivos na geometria de uma malha. Nos experimentos realizados, foram utilizadas variações dos parâmetros de entrada e técnicas de minimização da Norma L1. Os resultados obtidos mostram que a técnica de CS pode ser utilizada como estratégia de compressão da geometria das malhas. / Data compression is an area of a major interest in computational terms due to the issues on storage and transmission. Particularly, mesh compression has wide usage due to the increase of its application in games and three-dimensional modeling. In recent years, a new theory of acquisition and reconstruction of signals was developed, based on the concept of sparsity and in the minimization of the L1 norm and the incoherency of the signal, called Compressive Sensing (CS). This theory has some remarkable features, such as random sampling and reconstruction by minimization, in a way that the signal acquisition is done by considering only its significant coefficients. Any object that can be interpreted as a sparse sign allows its use. Thus, representing an object sparsely (sounds, images), you can apply the technique of CS. This work explores the viability of CS theory on mesh compression, so that it is possible a representative and compressive sensing on the mesh geometry. In the performed experiments, different parameters and L1 Norm minimization strategies were used. The results show that CS can be used as a mesh geometry compression strategy.

La thèse s'inscrit dans la perspective de l'explosion du trafic de données générée par l'augmentation du nombre d'utilisateurs ainsi que la croissance du débit qui doivent être prises en compte dans la définition des futures générations de communications radiocellulaires. Une solution est la technologie «large-scale MIMO » (systèmes MIMO de grande dimension) qui pose plusieurs défis. La conception des nouveaux algorithmes de détection de faible complexité est indispensable vu que les algorithmes classiques ne sont plus adaptés à cette configuration à cause de leurs mauvaises performances de détection ou de leur complexité trop élevée fonction du nombre d'antennes. Une première contribution de la thèse est un algorithme basé sur la technique de l'acquisition comprimée en exploitant les propriétés des signaux à alphabet fini. Appliqué à des systèmes MIMO de grande dimension, déterminés et sous-déterminés, cet algorithme réalise des performances (qualité de détection, complexité) prometteuses et supérieures comparé aux algorithmes de l'état de l'art. Une étude théorique approfondie a été menée pour déterminer les conditions optimales de fonctionnement et la distribution statistique des sorties. Une seconde contribution est l'intégration de l'algorithme original dans un récepteur itératif en différenciant les cas codé (code correcteur d'erreurs présent) et non codé. Un autre défi pour tenir les promesses des systèmes large scale MIMO (efficacité spectrale élevée) est l'estimation de canal. Une troisième contribution de la thèse est la proposition d'algorithmes d'estimation semi-aveugles qui fonctionnent avec une taille minimale des séquences d'apprentissage (égale au nombre d'utilisateurs) et atteignent des performances très proches de la borne théorique. / The thesis is part of the prospect of the explosion of data traffic generated by the increase of the number of users as well as the growth of the bit rate which must be taken into account in the definition of future generations of radio-cellular communications. A solution is the large-scale MIMO technology (MIMO systems oflarge size) which poses several challenges. The design of the new low complexity detection algorithms is indispensable since the conventional algorithms are no longer adapted to this configuration because of their poor detection performance or their too high complexity depending on the number of antennas. A first contribution of the thesis is an algorithm based on the technique of compressed sensing by exploiting the propertiesof the signals with finite alphabet. Applied to large-scale, determined and under-determined MIMO systems, this algorithm achieves promising and superior performance (quality ofdetection, complexity) compared to state-ofthe-art algorithms. A thorough theoretical study was conducted to determine the optimal operating conditions and the statistical distribution of outputs. A second contribution is the integration of the original algorithm into an iterative receiver by differentiating the coded and uncoded cases. Another challenge to keeping the promise of large- scale MIMO systems (high spectral efficiency) is channel estimation. A third contribution of the thesis is the proposal of semi-blind channel estimation algorithms that work with a minimum size of pilot sequences (equal to the number of users) and reach performances very close to the theoretical bound.

Compressive sensing (CS) is a novel imaging technology based on the inherent redundancy of natural scenes. The minimum number of required measurements which defines the maximum image compression rate is lower-bounded by the sparsity of the image but is dependent on the type of acquisition patterns employed. Increased measurements by the Rice single pixel camera (SPC) slows down the acquisition process, which may cause the image recovery to be more susceptible to background noise and thus limit CS's application in imaging, detection, or classifying moving targets. In this study, two methods (hybrid-subspace sparse sampling (HSS) for imaging and secant projection on a manifold for classification are applied to solving this problem. For the HSS method, new image pattern are designed via robust principle component analysis (rPCA) on prior knowledge from a library of images to sense a common structure. After measuring coarse scale commonalities, the residual image becomes sparser, and then fewer measurements are needed. For the secant projection case, patterns that can preserve the pairwise distance between data points based on manifold learning are designed via semi-definite programming. These secant patterns turn out to be better in object classification over those learned from PCA. Both methods considerably decrease the number of required measurements for each task when compared with the purely random patterns of a more universal CS imaging system.

La thèse porte sur le déploiement d'un réseau de capteurs pour le diagnostic énergétique d'un bâtiment universitaire. Elle s'inscrit dans la thématique Bâtiment intelligent et durable de l'Université de Limoges. Il s'agit au cours de cette thèse, dans un premier temps, d'optimiser l'architecture d'un réseau de capteurs Zigbee ainsi que les méthodes d'interrogation de ces capteurs, pour minimiser la consommation énergétique des nœuds du réseau. On s'appuiera notamment sur des concepts de "compressive sensing" pour augmenter la durée de vie des nœuds autonomes qui pourra être éventuellement renforcée par des organes de récupération d'énergie. / This thesis deals with the roll out of Wireless Sensor Network for the energetic monitoring of an existing building of the University. This work wil be incorporated in the framework of the smart building program of the University of Limoges. The work aims to optimize the architecture of a Zigbee network as well as data collection methods to minimize the energy consumption of the network's nodes. Methods based on the compressive sensing concepts will be investigated to reduce the number of nodes and to extend the lifetime of the nodes. Those methods will eventually be complemented with energy harvesting techniques.