ベイズ法によるマイクロフォンアレイ処理

京都大学0048新制・課程博士博士(情報学)甲第18412号情博第527号新制||情||93(附属図書館)31270京都大学大学院情報学研究科知能情報学専攻(主査)教授 奥乃 博, 教授 河原 達也, 准教授 CUTURI CAMETO Marco, 講師 吉井 和佳学位規則第4条第1項該当Doctor of InformaticsKyoto UniversityDFA

Ultra-high-speed imaging of bubbles interacting with cells and tissue

Ultrasound contrast microbubbles are exploited in molecular imaging, where bubbles are directed to target cells and where their high-scattering cross section to ultrasound allows for the detection of pathologies at a molecular level. In therapeutic applications vibrating bubbles close to cells may alter the permeability of cell membranes, and these systems are therefore highly interesting for drug and gene delivery applications using ultrasound. In a more extreme regime bubbles are driven through shock waves to sonoporate or kill cells through intense stresses or jets following inertial bubble collapse. Here, we elucidate some of the underlying mechanisms using the 25-Mfps camera Brandaris128, resolving the bubble dynamics and its interactions with cells. We quantify acoustic microstreaming around oscillating bubbles close to rigid walls and evaluate the shear stresses on nonadherent cells. In a study on the fluid dynamical interaction of cavitation bubbles with adherent cells, we find that the nonspherical collapse of bubbles is responsible for cell detachment. We also visualized the dynamics of vibrating microbubbles in contact with endothelial cells followed by fluorescent imaging of the transport of propidium iodide, used as a membrane integrity probe, into these cells showing a direct correlation between cell deformation and cell membrane permeability

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing

Information theoretic perspectives on en- and decoding in audition and vision

In cognitive neuroscience, encoding and decoding models mathematically relate stimuli in the outside world to neuronal or behavioural responses. While both stimuli and responses can be multidimensional variables, these models are on their own limited to bivariate descriptions of correspondences. In order to assess the cognitive or neuroscientific significance of such correspondences, a key challenge is to set them in relation to other variables. This thesis uses information theory to contextualise encoding and decoding models in example cases of audition and vision. In the first example, encoding models based on a certain operationalisation of the stimulus are relativised by models based on other operationalisations of the same stimulus material that are conceptually simpler and shown to predict the same neuronal response variance. This highlights the ambiguity inherent in an individual model. In the second example, a methodological contribution is made to the problem of relating the bivariate dependency of stimuli and responses to the history of response components with high degrees of predictability. This perspective demonstrates that only a subset of all stimulus-correlated response variance can be expected to be genuinely caused by the stimulus, while another subset is the consequence of the response’s own dynamics. In the third and final example, complex models are used to predict behavioural responses. Their predictions are grounded in experimentally controlled stimulus variance, such that interpretations of what the models predicted responses with are facilitated. Together, these three perspectives underscore the need to go beyond bivariate descriptions of correspondences in order to understand the process of perception