Modern bike share systems (BSSs) allow users to rent from a fleet of bicycles at hubs across the designated service area. With clear evidence of cycling being a health-positive form of active transport, furthering our understanding of the underlying processes that affect BSS ridership is essential to continue further adoption. Using 286,587 global positioning system (GPS) trajectories over a 12-month period between January 1st, 2018 and December 31st, 2018 from a BSS called SoBi (Social Bicycles) Hamilton, the number of trips on every traveled link in the service area are predicted. A GIS-based map-matching toolkit is used to generate cyclists’ routes along the cycling network of Hamilton, Ontario to determine the number of observed unique trips on every road segment (link) in the study area. To predict trips, several variables were created at the individual link level including accessibility measures, distances to important locations in the city, proximity to active travel infrastructure (SoBi hubs, bus stops), and bike infrastructure. Linear regression models were used to estimate trips. Eigenvector spatial filtering (ESF) was used to explicitly model spatial autocorrelation. The results suggest the largest positive predictors of cycling traffic in terms of cycling infrastructure are those that are physically separated from the automobile network (e.g., designated bike lanes). Additionally, hub-trip distance accessibility, a novel measure, was found to be the most significant variable in predicting trips. A demonstration of how the model can be used for strategic planning of road network upgrades is also presented. / Thesis / Master of Science (MSc)

This paper suggests an empirical framework for analysing income distribution dynamics and cross-region convergence in the European Union of 27 member states, 1995- 2003. The framework lies in the research tradition that allows the state income space to be continuous, puts emphasis on both shape and intra-distribution dynamics and uses stochastic kernels for studying transition dynamics and implied long-run behaviour. In this paper stochastic kernels are described by conditional density functions, estimated by a product kernel estimator of conditional density and represented by means of novel visualisation tools. The technique of spatial filtering is used to account for spatial effects, in order to avoid misguided inferences and interpretations caused by the presence of spatial autocorrelation in the income distributions. The results reveal a slow catching-up of the poorest regions and a process of polarisation, with a small group of very rich regions shifting away from the rest of the cross-section. This is well evidenced by both, the unfiltered and the filtered ergodic density view. Differences exist in detail, and these emphasise the importance to properly deal with the spatial autocorrelation problem. (authors' abstract)

Magnetic resonance imaging (MRI) is a very versatile imaging modality which can be used to acquire several different types of images. Some examples include anatomical images, images showing local brain activation and images depicting different types of pathologies. Brain activation is detected by means of functional magnetic resonance imaging (fMRI). This is useful e.g. in planning of neurosurgical procedures and in neurological research. To find the activated regions, a sequence of images of the brain is collected while a patient or subject alters between resting and performing a task. The variations in image intensity over time are then compared to a model of the variations expected to be found in active parts of the brain. Locations with high correlation between the intensity variations and the model are considered to be activated by the task. Since the images are very noisy, spatial filtering is needed before the activation can be detected. If adaptive filtering is used, i.e. if the filter at each location is adapted to the local neighborhood, very good detection performance can be obtained. This thesis presents two methods for adaptive spatial filtering of fMRI data. One of these is a modification of a previously proposed method, which at each position maximizes the similarity between the filter response and the model. A novel feature of the presented method is rotational invariance, i.e. equal sensitivity to activated regions in different orientations. The other method is based on bilateral filtering. At each position, this method averages pixels which are located in the same type of brain tissue and have similar intensity variation over time. A method for robust correlation estimation is also presented. This method automatically detects local bursts of noise in a signal and disregards the corresponding signal segments when the correlation is estimated. Hence, the correlation estimate is not affected by the noise bursts. This method is useful not only in analysis of fMRI data, but also in other applications where correlation is used to determine the similarity between signals. Finally, a method for correcting artifacts in complex MR images is presented. Complex images are used e.g. in the Dixon technique for separate imaging of water and fat. The phase of these images is often affected by artifacts and therefore need correction before the actual water and fat images can be calculated. The presented method for phase correction is based on an image integration technique known as the inverse gradient. The method is shown to provide good results even when applied to images with severe artifacts.

One of the most important parts of engineered and biological systems is the ability to acquire and interpret information from the surrounding world accurately and in time-scales relevant to the tasks critical to system performance. This classical concept of efficient signal acquisition has been a cornerstone of signal processing research, spawning traditional sampling theorems (e.g. Shannon-Nyquist sampling), efficient filter designs (e.g. the Parks-McClellan algorithm), novel VLSI chipsets for embedded systems, and optimal tracking algorithms (e.g. Kalman filtering). Traditional techniques have made minimal assumptions on the actual signals that were being measured and interpreted, essentially only assuming a limited bandwidth. While these assumptions have provided the foundational works in signal processing, recently the ability to collect and analyze large datasets have allowed researchers to see that many important signal classes have much more regularity than having finite bandwidth. One of the major advances of modern signal processing is to greatly improve on classical signal processing results by leveraging more specific signal statistics. By assuming even very broad classes of signals, signal acquisition and recovery can be greatly improved in regimes where classical techniques are extremely pessimistic. One of the most successful signal assumptions that has gained popularity in recet hears is notion of sparsity. Under the sparsity assumption, the signal is assumed to be composed of a small number of atomic signals from a potentially large dictionary. This limit in the underlying degrees of freedom (the number of atoms used) as opposed to the ambient dimension of the signal has allowed for improved signal acquisition, in particular when the number of measurements is severely limited. While techniques for leveraging sparsity have been explored extensively in many contexts, typically works in this regime concentrate on exploring static measurement systems which result in static measurements of static signals. Many systems, however, have non-trivial dynamic components, either in the measurement system's operation or in the nature of the signal being observed. Due to the promising prior work leveraging sparsity for signal acquisition and the large number of dynamical systems and signals in many important applications, it is critical to understand whether sparsity assumptions are compatible with dynamical systems. Therefore, this work seeks to understand how dynamics and sparsity can be used jointly in various aspects of signal measurement and inference. Specifically, this work looks at three different ways that dynamical systems and sparsity assumptions can interact. In terms of measurement systems, we analyze a dynamical neural network that accumulates signal information over time. We prove a series of bounds on the length of the input signal that drives the network that can be recovered from the values at the network nodes~[1--9]. We also analyze sparse signals that are generated via a dynamical system (i.e. a series of correlated, temporally ordered, sparse signals). For this class of signals, we present a series of inference algorithms that leverage both dynamics and sparsity information, improving the potential for signal recovery in a host of applications~[10--19]. As an extension of dynamical filtering, we show how these dynamic filtering ideas can be expanded to the broader class of spatially correlated signals. Specifically, explore how sparsity and spatial correlations can improve inference of material distributions and spectral super-resolution in hyperspectral imagery~[20--25]. Finally, we analyze dynamical systems that perform optimization routines for sparsity-based inference. We analyze a networked system driven by a continuous-time differential equation and show that such a system is capable of recovering a large variety of different sparse signal classes~[26--30].

Spatial interaction models of the gravity type are widely used to describe origin-destination flows. They draw attention to three types of variables to explain variation in spatial interactions across geographic space: variables that characterize the origin region of interaction, variables that characterize the destination region of interaction, and variables that measure the separation between origin and destination regions. A violation of standard minimal assumptions for least squares estimation may be associated with two problems: spatial autocorrelation within the residuals, and spatial autocorrelation within explanatory variables. This paper compares a spatial econometric solution with the spatial statistical Moran eigenvector spatial filtering solution to accounting for spatial autocorrelation within model residuals. An example using patent citation data that capture knowledge flows across 257 European regions serves to illustrate the application of the two approaches.

According to ICH Q8 process analytic technology (PAT) should be established to monitor critical quality attributes (CQAs) during manufacturing processes. Ethinylestradiol (EE) is a highly active and low dosed steroid hormone that is prone to oxidative degradation. The stability of this compound is therefore a critical quality attribute that has to be considered during drug formulation development. Beside the stability of EE, granule particle size and moisture are CQAs influencing the tableting ability of the resulting granules and the stability of EE. Both CQAs should be monitored during the production process. The investigations described in this thesis evaluate the implementation of in-line-sensors for monitoring of particle size (spatial filtering technology, SFT) and granule moisture (microwave resonance technology, MRT) during the fluid bed granulation process and the influence of process-variations on the stability of EE. The aim of these investigations was to develop an effective and mild fluid bed granulation process for a new model formulation based on microcrystalline cellulose as replacement for lactose as main filler excipient. The EE degradation products 6-alpha-hydroxy-EE, 6-beta-hydroxy-EE, 9(11)-dehydro-EE and 6-oxo-EE were quantified as an index for the stability of EE. It could be demonstrated that the surface of the filler substance influences the stability of EE due to the impact of water molecules. Hence, spraying sequence was determined to be a useful tool to improve the stability of EE. Correlations could be established for 6-oxo-EE with granule moisture and thermic parameters. The implementation of the SFT-sensor in the granulation process was successful. Measurement with the MRT-sensor for monitoring of granule moisture has to be improved.:Chapter I 1 1 Introduction Chapter II 41 2 Materials and Methods Chapter III 54 3 In-line Monitoring of Particle Size in a Fluid Bed Granulator: investigations concerning positioning and configuration of the sensor Chapter IV 72 4 Influence of in line monitored fluid bed granulation process parameters on the stability of Ethinylestradiol Chapter V 90 5 Influence of filler excipients on stability of EE Chapter VI 105 6 Discussion and Conclusion Chapter VII 131 7 Summary Chapter VIII 138 8 Zusammenfassung Reference List 146 Appendix 158

L'objectif de cette recherche est de construire un modèle réduit de petite dimension pour prévoir les réponses dynamiques dans une bande BF sur les parties rigides d'un véhicule automobile complet. Un tel modèle réduit "léger" est une aide à la phase de conception en "Avant Projet" de ces véhicules qui ont la particularité de présenter de nombreux modes élastiques locaux en BF dues à la présence de nombreuses parties flexibles et d'équipements. Pour la construction du modèle réduit, nous avons introduit une base non usuelle de l'espace admissible des déplacements globaux. La construction de cette base requiert la décomposition en sous-domaines du domaine de la structure qui peut présenter une très grande complexité géométrique et dont les modèles EF font intervenir de très nombreux types d'éléments finis. Cette décomposition en sous-domaines a été réalisée par la Fast Marching Method que nous avons due étendre pour pouvoir traiter la complexité des modèles EF des véhicules automobiles. Puis les équations matricielles du modèle EF sont projetées sur cette base. Afin de prendre en compte les incertitudes sur les paramètres du modèle, les incertitudes de modèle induites par les erreurs de modélisation et enfin les incertitudes liées à la non prise en compte des contributions locales dans le modèle réduit des déplacements globaux, un unique modèle probabiliste non paramétrique de ces trois sources d'incertitude a été implémenté sur le modèle réduit construit avec les vecteurs propres globaux. Les paramètres de dispersion de ce modèle probabiliste ont été identifiés en utilisant le principe du maximum de vraisemblance et des réponses obtenues à l'aide d'un modèle stochastique de référence qui inclut des informations expérimentales résultant de travaux précédents. Le modèle réduit stochastique, pour la prévision des déplacements globaux sur les parties rigides dans la bande BF qui a été développé, a été validé sur un modèle de structure automobile "nue" puis a été appliqué avec succès sur un modèle complet de véhicule automobile / The objective of this research is to construct a reduced-order model to predict the dynamical response, in the LF band, of the stiff parts of a complete automotive vehicle in order to facilitate the draft design. The vehicles under consideration have many elastic modes in LF due to the presence of many flexible parts and equipments. To build such a model, we introduced a non-usual basis of the admissible space of global displacements. The construction of this basis requires the decomposition of the domain of the structure. This subdomain decomposition is performed by using the Fast Marching Method that we have extended to take into account the high complexity of the mesh of an automotive vehicle. Then the matrix equations of the FE model are projected on this basis. To take into account the system parameters uncertainties, the model uncertainties induced by the modeling errors and finally, the uncertainties related to the neglecting of local contributions in the reduced-order model, a nonparametric probabilistic model of the three sources of uncertainties has been implemented on the reduced-order model constructed with the global displacements eigenvectors. The dispersion parameters of the probabilistic model are identified using the maximum likelihood method and the responses obtained from a stochastic reference model which includes experimental data resulting from previous works. This stochastic model which has been designed for the prediction of the global displacements of the rigid parts in the LF band is validated on a simple structure of an automotive model and has been successfully applied on a complete model of automotive vehicle

In this study, a neural network(NN) based algorithm is proposed for real time multiple source tracking problem based on a previously reported work. The proposed algorithm namely modified neural network based multiple source tracking algorithm (MN-MUST) performs direction of arrival(DoA) estimation in three stages which are the detection, filtering and DoA estimation stages. The main contributions of this proposed system are: reducing the input size for the uncorrelated source case (reducing the training time) of NN system without degradation of accuracy and insertion of a nonlinear spatial filter to isolate each one of the sectors where sources are present, from the others. MN-MUST algorithm finds the targets correctly no matter whether the targets are located within the same angular sector or not. In addition as the number of targets exceeds the number of antenna elements the algorithm can still perform sufficiently well. Mutual coupling in array does not influence MN-MUST algorithm performance. iv MN-MUST algorithm is further improved for a cylindrical microstrip patch antenna array by using the advantages of directive antenna pattern properties. The new algorithm is called cylindrical patch array MN-MUST(CMN-MUST). CMN-MUST algorithm consists of three stages as MN-MUST does. Detection stage is exactly the same as in MN-MUST. However spatial filtering and DoA estimation stage are reduced order by using the advantages of directive antenna pattern of cylindirical microstrip patch array. The performance of the algorithm is investigated via computer simulations, for uniform linear arrays, a six element uniform dipole array and a twelve element uniform cylindrical microstrip patch array. The simulation results are compared to the previously reported works and the literature. It is observed that the proposed algorithm improves the previously reported works. The algorithm accuracy does not degrade in the presence of the mutual coupling. A uniform cylindrical patch array is successfully implemented to the MN-MUST algorithm. The implementation does not only cover full azimuth, but also improv the accuracy and speed. It is observed that the MN-MUST algorithm provides an accurate and efficient solution to the targettracking problem in real time.

In the past two decades, Fluorescence microscopy has imparted tremendous impact in Biology and Imaging. Several super-resolution Fluorescence imaging techniques (e.g. PALM, STED, STORM, 4Pi and structured illumination) have enabled diff raction-unlimited imaging. But high resolution is limited to a depth of few tens of microns. Thus, deep tissue imaging and simultaneous volume imaging have become a highly sought after feature in Fluorescence microscopy. The research work in this thesis address these issues by using spatial filtering techniques to tailor the point spread function (PSF) which uniquely characterizes the optical sys-tem. The advantage of this approach lies in the fact that intricate details about the focal region can be computed and designed with the help of well established theory and experimentation. In particular, this technique was applied to both spherical and cylindrical lenses. The former was used to generate Bessel-like, non-diffracting beams which demonstrated the ability to penetrate deep inside tissue-like media and thereby yielded an imaging depth of nearly 650μm as compared to about 200μm for a state-of-the-art confocal microscope. The latter gave rise to light-sheet and it's extended version that is ideal for planar imaging at large penetration depths. Another development is the generation of multiple light-sheet illumination pattern that can simultaneously illuminate several planes of the specimen. The proposed multiple light-sheet illumination microscopy (MLSIM) technique may enable volume imaging in Fluorescence microscopy. The first two chapters of this thesis are introductory in nature and provides a general overview of the principles of Fluorescence microscopy and three state-of-the-art Fluorescence imaging techniques; namely confocal, multi-photon and light-sheet based microscopy. Confocal microscopes are widely considered as a standard tool for biologists and this discussion shows that even though they have made signi ficant contributions in the fields of biophysics, biophotonics and nanoscale imaging, their inability to achieve better penetration depth has prevented their use in thick, scattering samples such as biological tissue. The system PSF of a confocal microscope broadens as it goes deeper in-side a scattering sample resulting in poor-resolution thereby destroying the very concept of high resolution, noise-free imaging. Additionally, confocal microscopy suffers from in-creased photo-bleaching due to o -layer (above and below the focal plane) excitation and low temporal resolution since it requires point-by-point scanning mechanism. On the other hand, multi-photon microscopy offers several advantages over confocal microscopy such as reduced photo-bleaching and inherent optical sectioning ability, however, it still lacks in providing high temporal resolution. Light-sheet based microscopy have gained popularity in recent years and promises to deliver high spatio-temporal resolution with minimized photo-bleaching. Recently, a considerable amount of research has been dedicated to further develop this promising technique for a variety of applications. The ability to look deeper inside a biological specimen has profound implications. How-ever, at depths of hundreds of microns, several effects (such as scattering, PSF distortion and noise) deteriorates the image quality and prohibits detailed study of key biological phenomenon. Chapter 3 of this thesis describes the original research work which experimentally addresses to this issue. Here, Bessel-like beam is employed in conjugation with an orthogonal detection scheme to achieve imaging at large penetration depth. Bessel beams are penetrative, non-di ffracting and have self-reconstruction properties making them a natural choice for imaging scattering prone specimens which are otherwise inaccessible by other microscopy imaging techniques such as, Widefield, CLSM, 4PI, Structural illumination microscopy and others. In this case such a Bessel-like beam is generated by masking the back-aperture of the excitation objective with a ring-like spatial filter. The proposed excitation scheme allow continuous scanning by simply translating the detection optics. Additionally, only a pencil-like region of the specimen can be illuminated at a given instance thereby reducing premature photobleaching of neighboring regions. This illumination scheme coupled with orthogonal detection shows the ability of selective imaging from a desired plane deep inside the specimen. In such a configuration, the lateral resolution of the illumination arm determines the axial resolution of the overall imaging system. Such an imaging system is a boon for obtaining depth information from any desired specimen layer that includes nano-particle tracking in thick tissue. Experiments performed by imaging the Fluorescent polymer tagged-CaCO3 particles and yeast cell in a tissue-like gel-matrix demonstrates penetration depth that extends up to 650 m. This will advance the field of fluorescence imaging microscopy and imaging. Similar to the ability to observe deep inside a sample, simultaneous 3D monitoring of whole specimens play a vital role in understanding many developmental process in Biology. At present, light-sheet based microscopy is the prime candidate amongst the various microscopy techniques, that is capable of providing high signal-to-background-ratio as far as planar imaging is concerned. Since spatial filtering technique was found to successfully give rise to novel features (such as large penetration depth) in a fluorescence microscope setup, a logical extension would be to implement a similar approach with a light-sheet based microscope setup. These implementations are discussed in Chapter 4 of this thesis where spatial filtering is employed with cylindrical lenses. For facilitating computational and experimental studies, a vectorial formalism was derived to give an explicit computable integral solution of the electric field generated at the focal region of a cylindrical lens. This representation is based on vectorial diffraction theory and further enables the computation of the point spread function of a cylindrical lens. Commonly used assumptions are made in the derivation such as no back-scattering and negligible contribution from evanescent fields. Stationary phase approximation along with the Fresnel transmission coefficients are employed for evaluating the polarization dependent electric field components. Computational studies were carried out to determine the polarization effects and calculate the system resolution. Experimental comparison of light-sheet intensity pro les show good agreement with the theoretical calculations and hence validate the model. This formalism was derived as a first step since it gives the essential understanding of tightly focused E-fields of a high N.A. cylindrical lens systems and thereby helps in further understanding the effect of spatial filtering. As the next step, generation of extended light-sheet for fluorescence microscopy is pro-posed by introducing a specially designed double-window spatial filter at the back-aperture of a cylindrical lens. The filter allows the light to pass through the periphery and center of a cylindrical lens. When illuminated with a plane wave, the proposed filter results in an extended depth-of-focus along with side-lobes which are due to other interferences in the transverse focal plane. Computational studies show a maximum extension of light-sheet by 3:38 times for single photon excitation, and 3:68 times for multi-photon excitation as compared to state-of-art single plane illumination microscopy (SPIM) system and essentially implies a larger field of view. Finally, generation of multiple light-sheet pattern is proposed and demonstrated using a different spatial filter placed at the back aperture of a cylindrical lens. A complete imaging setup consisting of multiple light-sheets for illumination and an orthogonal detection arm, is implemented for volume imaging in fluorescence microscopy. This proposed scheme is a single shot technique that enables whole volume imaging by simultaneously exciting multiple specimen layers. Experimental results confirm the generation of multiple light-sheets of thickness 6:6 m with an inter-sheet spacing of 13:4 m. Imaging of 3 5 m sized fluorescently coated Yeast cells (encaged in Agarose gel-matrix) is per-formed and conclusively demonstrates the usefulness and potential of multiple light-sheet illumination microscopy (MLSIM) for volume imaging. As part of the future scope of the research work presented in this thesis, the Bessel-beam based improved depth microscopy technique may attract applications in particle tracking deep inside tissues and optical injection apart from fluorescence imaging applications. The vectorial formalism derived for cylindrical lens can be used to predict other, complex optical setups involving cylindrical lenses. Extended light-sheet generation proposed in this work by using appropriate spatial filtering with a cylindrical lens, complements the existing and popular selective plane illumination microscopy technique and may facilitate the study of large biological specimens (such as, full-grown Zebra sh and tissue) with high spatial resolution and reduced photobleaching. Finally, the MLSIM technique presented in this thesis may accelerate the field of developmental biology, cell biology, fluorescence imaging and 3D optical data storage.

Light allows to non-invasively study the complex and dynamic biological phenomenon undergoing within cells and tissues in their native state. The development of super-resolution microscopes in recent years has helped to overcome the fundamental limitation imposed by Abbe’s diffraction limit, thereby revolutionizing the field of molecular and cellular biology. With the advancement of various super-resolution techniques (like STED, PALM, and 4Pi) it is now possible to visualize the nanometeric cellular structures and their dynamics in real time. The limitations of existing fluorescence microscopy techniques are: poor axial resolution when compared to their lateral counterpart, and their inability to produce high resolution images of dynamic samples. This thesis covers two broadly connected areas of fluorescence imaging techniques while addressing these limitations. First, the PSF engineering and spatial filtering technique for axial super-resolution microscopy and second, the integration of light sheet illumination PSF with microfluidic cytometry for imaging cells on-the-go. The first chapter gives an explicit description on the fundamentals of fluorescence imaging. This introductory chapter includes a variety of optical microscopes, PSF engineering, the resolution limit imposed by the wave nature of light, the photochemistry of the fluorescent dyes, and their proper selection for fluorescence experiments. In addition to the state-of-art imaging techniques, namely Laser Scanning Confocal Microscopy and Light Sheet Microscopy, this chapter also gives a brief explanation on the evolution of imaging cytometry techniques. Their high speed analytic capability (i.e sorting and counting) makes this technique an important tool in health care diagnosis and other various biomedical applications. The chapter ends with a discussion on the operating principle of the flow cytometers and their limitations. The second chapter in this thesis describes the spatial filtering technique for engineering the PSF to eliminate the side-lobes in the system PSF of the 4Pi Confocal Microscopes. Employing an amplitude mask with binary light transmission windows (also called binary filters), the incident light is structured to minimize the secondary lobes. These lobes are responsible for exciting the off-focal planes in the specimen, hence provide incorrect map of the fluorophore distribution in the object. The elimination of the side-lobes is essential for the artifact-free axial super-resolution microscopy. This second chapter describes the spatial filtering technique in details (its mathematical formulation, application in fluorescence microscopy for generation of desired PSF including Bessellike beam). Specifically, spatial filtering technique is employed in 4Pi type-C Confocal Microscope. The spatial mask used results in the reduction of the side-lobes in 1PE case while they are nearly eliminated in 2PE variant of the proposed technique. The side-lobes are reduced by 46% and 76% for 1PE and 2PE when compared to the existing 4Pi type-C Confocal Microscope system. Moreover, OTF of the proposed system confirms the presence of higher frequencies in the Fourier domain indicating high resolution imaging capability. Apart from the resolution in lateral and axial dimension, achieving high resolution while imaging dynamic samples is another challenge that is limiting the field of fluorescence microscopy to flourish. The third and fourth chapters are entirely dedicated towards the work that was carried out to develop imaging techniques on a microfluidic platform for imaging dynamic samples. The fusion of microscopy and flow cytometry has given rise to the celebrated field of imaging flow cytometry. In recent years, the focus has shifted towards miniaturized cytometry devices. Apart from the reduced cost of the sample reagents and the assays, portability and easy handling make the microfluidic devices more relevant to developing countries. The commercially available cytometers are bulky and quite costly. In addition to these practical concerns, they are complex in operation and limited in performance. Most of the existing cytometers use different inlets for sheath and sample flow to achieve the hydrodynamic focusing of the sample assays in a narrow and confined region. The laser beam in the illumination arm interrogates with the flowing samples at this region and the response is captured by the detection optics. The same principle is extensively used in most of the microfluidic based flow cytometers reported till date. Apart from the hydrodynamic force other effects like electro-osmotic, acoustic, and dielectrophoresis have also been exploited to achieve flow focusing in the microfluidic channel. Despite omitting the necessity of external syringe pump as required in pressure driven based cytometers, they all rely upon point-source based excitation scheme and thereby can not interrogate the cells flowing through the entire microfluidic channel. The third chapter describes the integration of light sheet illumination PSF with microfluidic flow cytometry for simultaneous counting and imaging cells on-the-go. The chapter starts with the description on photolithography procedure for preparing SU8 master and PDMS casting procedure adopted to prepare dedicated microfluidic chips for the developed imaging system. The research work reported here demonstrates the proof-ofprinciple of light sheet based imaging flow cytometer. A light sheet fills the entire microfluidic channel and thus omits the necessity of flow focusing and point-scanning based technology. Another advantage lies in the orthogonal detection geometry that totally cuts-off the incident light, thereby substantially reducing the background in the acquired images. Compared to the existing state-of-the-art techniques, the proposed technique shows marked improvement. Using fluorescently coated Saccharomyces cerevisiae cells, cell counting with throughput as high as 2090 cells/min was recorded. Overall the proposed system is cost-effective and simple in channel geometry. Apart from achieving efficient counting in operational regime of low flow rate, high contrast images of the dynamic samples are also acquired using the proposed cytometry technique. Further, visualization of intra-cellular organelles is achieved during flow in light sheet based high-throughput cytometry system. The fourth chapter demonstrates the proof of concept of light-sheet-based microfluidic cytometer in conjugation with 2π/3 detection system for high-throughput interrogation and high resolution imaging. This system interrogates the flow channel using a sheet of light rather than the existing point-scanning based techniques. This ensures single-shot scanning of specimens flowing through the microfluidic flow channel at variable flow rates. In addition to high throughput counting at low flow rate, visualization of the intra-cellular organelle (mitochondrial network in human cancerous cells) during flow is achieved with sub-cellular resolution. Using mitochondrial network tagged HeLa cells, a maximum count of 2400 cells/min at the optimized flow rate of 700 nl/min was recorded. The 2π/3 detection system ensures efficient photon collection and minimal background caused by scattered illumination light. The other advantage of this kind of detection system which includes 8f detection optics, is the capability to produce variable magnification using the same high NA objective. This thesis opens up in vivo imaging of sub-cellular structures and simultaneous cell counting in a miniaturized flow cytometry system. The developed imaging cytometry technique may find immediate applications in the diverse field of healthcare diagnostics, lab-on-chip technology, and fluorescence microscopy. The concluding chapter summarizes the results with a brief discussion on the future aspects of this field (e.g., live-cell imaging of infectious RBC in microfluidic device and 3D optical sectioning of flowing cells). The field of imaging flow cytometry has immense applications in the overlapping areas of physics and biology. The hydrodynamic forces which are used to achieve flow focusing of the sample assays can have an adverse effect in the cell morphology, thereby altering the cellular functions. Light sheet based cytometry system lifts off the requirement of flow focusing and ensures a single shot scanning of entire samples flowing through the microfluidic channel. The similar concept can be used to study the developmental biology of an entire organism, such as C. elegans. This enables the direct observation of developmental and physiological changes in the entire body. Such an organism can be kept alive for a longer duration in microfluidic chambers, and the neural development and mating behaviors can be extensively studied.