No description available.

In this work, we use optical methods to study electrochemical reactions and processes occurring on the nanometer length scale. Optical methods are advantageous over traditional electrochemical methods because of their high spatial resolution and sensitivity at both the single nanoparticle and single molecule level. This dissertation describes a series of studies in which super-localization and dark-field optical imaging is used to provide insight into spatial and temporal heterogeneity in nanoscale electrochemical systems with <25 nm spatial resolution. In the first set of experiments, three-dimensional (3-D) super-resolution imaging is used to determine the tip-substrate distance in nanoscale scanning electrochemical microscopy (SECM) with precision better than 25 nm. Correlating the tip-substrate distance using both optical and electrochemical techniques showed excellent agreement. Second, single nanoparticles (NP) were delivered through a nanopipette, and their resistive-pulse signals were correlated with a fluorescence optical signal. The diffusion trajectories of individual NP delivered to the external solution and to an electrified interface were obtained by 3-D super-resolution imaging, and showed signatures of both sub-diffusive and super-diffusive behavior, depending on the balance of forces between the flow from the pipette and the applied potential at the electrified substrate. Next, we studied the influence of surface oxide layers on single silver NP electrodissolution by tracking the intensity and spatial variation of scattering from single nanoparticles over time. We discovered that silver NPs can undergo electrodissolution in either a spatially symmetric or asymmetric manner, based on the nature of the surface oxide layer. Moreover, we also reported the simultaneous electrodeposition of silver oxide at the electrode surface during the electrodissolution of silver NPs, which enabled us to study the effect of multiple simultaneous redox reactions and their effects on one another. Overall, these experiments reveal local heterogeneity in nanoscale electrochemical processes and allow for many single nanoparticles to be measured in parallel, revealing relationships that are hidden using traditional electrochemical measurements. / Chemistry

No description available.

No description available.

No description available.

La microscopie à éclairements structurés(structured illumination microscopy, SIM) permet de dépasser la limite de résolution en microscopie optique due à la diffraction, en éclairant l’objet avec un ensemble de motifs périodiques parfaitement connus. Cependant, il s’avère difficile de contrôler exactement la forme des motifs éclairants. Qui plus est, de fortes distorsions de la grille de lumière peuvent être générées par l’échantillon lui-même dans le volume d’étude, ce qui peut provoquer de forts artefacts dans les images reconstruites. Récemment, des approches dites blind-SIM ont été proposées, où les images sont acquises à partir de motifs d’éclairement inconnus, non-périodiques, de type speckle,bien plus faciles à générer en pratique. Le pouvoir de super résolution de ces méthodes a été observé, sans forcément être bien compris théoriquement. Cette thèse présente deux nouvelles méthodes de reconstruction en microscopie à éclairements structurés inconnus (blind speckle-SIM) : une approche conjointe et une approche marginale. Dans l’approche conjointe, nous estimons conjointement l’objet et les motifs d’éclairement au moyen d’un modèle de type Basis Pursuit DeNoising (BPDN) avec une régularisation en norme lp,q où p=>1 et 0<q<=1. La norme lp,q est introduite afin de prendre en compte une hypothèse de parcimonie sur l’objet. Dans l’approche marginale, nous reconstruisons uniquement l’objet et les motifs d’éclairement sont traités comme des paramètres de nuisance. Notre contribution est double. Premièrement, une analyse théorique démontre que l’exploitation des statistiques d’ordre deux des données permet d’accéder à un facteur de super résolution de deux, lorsque le support de la densité spectrale du speckle correspond au support fréquentiel de la fonction de transfert du microscope. Ensuite, nous abordons le problème du calcul numérique de la solution. Afin de réduire à la fois le coût de calcul et les ressources en mémoire, nous proposons un estimateur marginal à base de patches. L’élément clé de cette méthode à patches est de négliger l’information de corrélation entre les pixels appartenant à différents patches. Des résultats de simulations et en application à des données réelles démontrent la capacité de super résolution de nos méthodes. De plus, celles-ci peuvent être appliquées aussi bien sur des problèmes de reconstruction 2D d’échantillons fins, mais également sur des problèmes d’imagerie 3D d’objets plus épais. / Conventional structured illumination microscopy (SIM) can surpass the resolution limit inoptical microscopy caused by the diffraction effect, through illuminating the object with a set of perfectly known harmonic patterns. However, controlling the illumination patterns is a difficult task. Even worse, strongdistortions of the light grid can be induced by the sample within the investigated volume, which may give rise to strong artifacts in SIM reconstructed images. Recently, blind-SIM strategies were proposed, whereimages are acquired through unknown, non-harmonic,speckle illumination patterns, which are much easier to generate in practice. The super-resolution capacity of such approaches was observed, although it was not well understood theoretically. This thesis presents two new reconstruction methods in SIM using unknown speckle patterns (blind-speckle-SIM): one joint reconstruction approach and one marginal reconstruction approach. In the joint reconstruction approach, we estimate the object and the speckle patterns together by considering a basis pursuit denoising (BPDN) model with lp,q-norm regularization, with p=>1 and 0<q<=1. The lp,q-norm is introduced based on the sparsity assumption of the object. In the marginal approach, we only reconstruct the object, while the unknown speckle patterns are considered as nuisance parameters. Our contribution is two fold. First, a theoretical analysis demonstrates that using the second order statistics of the data, blind-speckle-SIM yields a super-resolution factor of two, provided that the support of the speckle spectral density equals the frequency support of the microscope point spread function. Then, numerical implementation is addressed. In order to reduce the computational burden and the memory requirement of the marginal approach, a patch-based marginal estimator is proposed. The key idea behind the patch-based estimator consists of neglecting the correlation information between pixels from different patches. Simulation results and experiments with real data demonstrate the super-resolution capacity of our methods. Moreover, our proposed methods can not only be applied in 2D super-resolution problems with thin samples, but are also compatible with 3D imaging problems of thick samples.

The Shannon sampling framework is widely used for discrete representation of analog bandlimited signals, starting from samples taken at the Nyquist rate. In many practical applications, signals are not bandlimited. In order to accommodate such signals within the Shannon-Nyquist framework, one typically passes the signal through an anti-aliasing filter, which essentially performs bandlimiting. In applications such as RADAR, SONAR, ultrasound imaging, optical coherence to-mography, multiband signal communication, wideband spectrum sensing, etc., the signals to be sampled have a certain structure, which could manifest in one of the following forms: (i) sparsity or parsimony in a certain bases; (ii) shift-invariant representation; (iii) multi-band spectrum; (iv) finite rate of innovation property, etc.. By using such structure as a prior, one could devise efficient sampling strategies that operate at sub-Nyquist rates. In this Ph.D. thesis, we consider the problem of sampling and reconstruction of finite-rate-of-innovation (FRI) signals, which fall in one of the two classes: (i) Sum-of-weighted and time-shifted (SWTS) pulses; and (ii) Sum-of-weighted exponential (SWE). Finite-rate-of-innovation signals are not necessarily bandlimited, but they are specified by a finite number of free parameters per unit time interval. Hence, the FRI reconstruction problem could be solved by estimating the parameters starting from measurements on the signal. Typically, parameter estimation is done using high-resolution spectral estimation (HRSE) techniques such as the annihilating filter, matrix pencil method, estimation of signal parameter via rotational invariance technique (ESPRIT), etc.. The sampling issues include design of the sampling kernel and choice of the sampling grid structure. Following a frequency-domain reconstruction approach, we propose a novel technique to design compactly supported sampling kernels. The key idea is to cancel aliasing at certain set of uniformly spaced frequencies and make sure that the rest of the frequency response is specified such that the kernel follows the Paley-Wiener criterion for compactly supported functions. To assess the robustness in the presence of noise, we consider a particular class of the proposed kernel whose impulse response has the form of sum of modulated splines (SMS). In the presence of continuous-time and digital noise cases, we show that the reconstruction accuracy is improved by 5 to 25 dB by using the SMS kernel compared with the state-of-the-art compactly supported kernels. Apart from noise robustness, the SMS kernel also has polynomial-exponential reproducing property where the exponents are harmonically related. An interesting feature of the SMS kernel, in contrast with E-splines, is that its support is independent of the number of exponentials. In a typical SWTS signal reconstruction mechanism, first, the SWTS signal is trans formed to a SWE signal followed by uniform sampling, and then discrete-domain annihilation is applied for parameter estimation. In this thesis, we develop a continuous-time annihilation approach using the shift operator for estimating the parameters of SWE signals. Instead of using uniform sampling-based HRSE techniques, operator-based annihilation allows us to estimate parameters from structured non-uniform samples (SNS), and gives more accurate parameters estimates. On the application front, we first consider the problem of curve fitting and curve completion, specifically, ellipse fitting to uniform or non-uniform samples. In general, the ellipse fitting problem is solved by minimizing distance metrics such as the algebraic distance, geometric distance, etc.. It is known that when the samples are measured from an incomplete ellipse, such fitting techniques tend to estimate biased ellipse parameters and the estimated ellipses are relatively smaller than the ground truth. By taking into account the FRI property of an ellipse, we show how accurate ellipse fitting can be performed even to data measured from a partial ellipse. Our fitting technique first estimates the underlying sampling rate using annihilating filter and then carries out least-squares regression to estimate the ellipse parameters. The estimated ellipses have lesser bias compared with the state-of-the-art methods and the mean-squared error is lesser by about 2 to 10 dB. We show applications of ellipse fitting in iris images starting from partial edge contours. We found that the proposed method is able to localize iris/pupil more accurately compared with conventional methods. In a related application, we demonstrate curve completion to partial ellipses drawn on a touch-screen tablet. We also applied the FRI principle to imaging applications such as frequency-domain optical-coherence tomography (FDOCT) and nuclear magnetic resonance (NMR) spectroscopy. In these applications, the resolution is limited by the uncertainty principle, which, in turn, is limited by the number of measurements. By establishing the FRI property of the measurements, we show that one could attain super-resolved tomograms and NMR spectra by using the same or lesser number of samples compared with the classical Fourier-based techniques. In the case of FDOCT, by assuming a piecewise-constant refractive index of the specimen, we show that the measurements have SWE form. We show how super-resolved tomograms could be achieved using SNS-based reconstruction technique. To demonstrate clinical relevance, we consider FDOCT measurements obtained from the retinal pigment epithelium (RPE) and photoreceptor inner/outer segments (IS/OS) of the retina. We show that the proposed method is able to resolve the RPE and IS/OS layers by using only 40% of the available samples. In the context of NMR spectroscopy, the measured signal or free induction decay (FID) can be modelled as a SWE signal. Due to the exponential decay, the FIDs are non-stationary. Hence, one cannot directly apply autocorrelation-based methods such as ESPRIT. We develop DEESPRIT, a counterpart of ESPRIT for decaying exponentials. We consider FID measurements taken from amino acid mixture and show that the proposed method is able to resolve two closely spaced frequencies by using only 40% of the measurements. In summary, this thesis focuses on various aspects of sub-Nyquist sampling and demonstrates concrete applications to super-resolution imaging.

Approximate Nearest-Neighbour Field (ANNF\ maps between two related images are commonly used by computer vision and graphics community for image editing, completion, retargetting and denoising. In this work we generalize ANNF computation to unrelated image pairs. For accurate ANNF map computation we propose Feature Match, in which the low-dimensional features approximate image patches along with global colour adaptation. Unlike existing approaches, the proposed algorithm does not assume any relation between image pairs and thus generalises ANNF maps to any unrelated image pairs. This generalization enables ANNF approach to handle a wider range of vision applications more efficiently. The following is a brief description of the applications developed using the proposed Feature Match framework. The first application addresses the problem of detecting the optic disk from retinal images. The combination of ANNF maps and salient properties of optic disks leads to an efficient optic disk detector that does not require tedious training or parameter tuning. The proposed approach is evaluated on many publicly available datasets and an average detection accuracy of 99% is achieved with computation time of 0.2s per image. The second application aims to super-resolve a given synthetic image using a single source image as dictionary, avoiding the expensive training involved in conventional approaches. In the third application, we make use of ANNF maps to accurately propagate labels across video for segmenting video objects. The proposed approach outperforms the state-of-the-art on the widely used benchmark SegTrack dataset. In the fourth application, ANNF maps obtained between two consecutive frames of video are enhanced for estimating sub-pixel accurate optical flow, a critical step in many vision applications. Finally a summary of the framework for various possible applications like image encryption, scene segmentation etc. is provided.

<p dir="ltr">Single-particle tracking (SPT) provides high-resolution spatial-temporal information on biomolecule dynamics. However, localization inaccuracies, limited track lengths, heterogeneous fluorescence backgrounds, and potential molecular motion blur pose significant challenges that hinder the accurate extraction of movement trajectories and their underlying motion behavior. The conventional SPT pipeline struggles to comprehensively address detection, localization, linkage, and parameter inference simultaneously, resulting in information loss during sequential processing. To overcome these challenges, we propose SPTnet, an end-to-end deep learning framework that leverages a transformer-based architecture to optimize trajectory and motion parameter estimations in parallel through a global loss. SPTnet bypasses traditional SPT processes, directly inferring molecular trajectories and motion parameters from fluorescence microscopy video frames with precision approaching the statistical information limit. Our results demonstrate that SPTnet outperforms conventional methods under commonly encountered but challenging conditions such as short trajectories, low signal-to-noise ratio (SNR), heterogeneous backgrounds, motion blur, and especially when molecules exhibit non-Brownian behaviors.</p><p dir="ltr">Besides SPT, we used single-molecule localization microscopy (SMLM) to study cytokinetic protein in fission yeast. During cytokinesis, myosin-II constricts the contractile ring that separates one cell into two daughter cells. The fission yeast cytokinetic contractile ring contains two types of myosin Ⅱ, Myo2 and Myp2. However, the precise ultrastructural arrangement of the two type Ⅱ myosins remains in question. We investigated the relative spatial arrangement of Myo2p and Myp2p within contractile ring using two-color super-resolution microscopy based on salvaged fluorescence imaging. Quantitative analysis of the nanoscale images should provide useful information for modeling contractile ring assembly and constriction.</p><p><br></p>