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Description
Microfluidics is the study of fluid flow at very small scales (micro -- one millionth of a meter) and is prevalent in many areas of science and engineering. Typical applications include lab-on-a-chip devices, microfluidic fuel cells, and DNA separation technologies. Many of these microfluidic devices rely on micron-resolution velocimetry measurements to improve microchannel design and characterize existing devices. Methods such as micro particle imaging velocimetry (microPIV) and micro particle tracking velocimetry (microPTV) are mature and established methods for characterization of steady 2D flow fields. Increasingly complex microdevices require techniques that measure unsteady and/or three dimensional velocity fields. This dissertation presents a method for three-dimensional velocimetry of unsteady microflows based on spinning disk confocal microscopy and depth scanning of a microvolume. High-speed 2D unsteady velocity fields are resolved by acquiring images of particle motion using a high-speed CMOS camera and confocal microscope. The confocal microscope spatially filters out of focus light using a rotating disk of pinholes placed in the imaging path, improving the ability of the system to resolve unsteady microPIV measurements by improving the image and correlation signal to noise ratio. For 3D3C measurements, a piezo-actuated objective positioner quickly scans the depth of the microvolume and collects 2D image slices, which are stacked into 3D images. Super resolution microPIV interrogates these 3D images using microPIV as a predictor field for tracking individual particles with microPTV. The 3D3C diagnostic is demonstrated by measuring a pressure driven flow in a three-dimensional expanding microchannel. The experimental velocimetry data acquired at 30 Hz with instantaneous spatial resolution of 4.5 by 4.5 by 4.5 microns agrees well with a computational model of the flow field. The technique allows for isosurface visualization of time resolved 3D3C particle motion and high spatial resolution velocity measurements without requiring a calibration step or reconstruction algorithms. Several applications are investigated, including 3D quantitative fluorescence imaging of isotachophoresis plugs advecting through a microchannel and the dynamics of reaction induced colloidal crystal deposition.
ContributorsKlein, Steven Adam (Author) / Posner, Jonathan D (Thesis advisor) / Adrian, Ronald (Committee member) / Chen, Kangping (Committee member) / Devasenathipathy, Shankar (Committee member) / Frakes, David (Committee member) / Arizona State University (Publisher)
Created2011
Description
In this work, we present approximate adders and multipliers to reduce data-path complexity of specialized hardware for various image processing systems. These approximate circuits have a lower area, latency and power consumption compared to their accurate counterparts and produce fairly accurate results. We build upon the work on approximate adders and multipliers presented in [23] and [24]. First, we show how choice of algorithm and parallel adder design can be used to implement 2D Discrete Cosine Transform (DCT) algorithm with good performance but low area. Our implementation of the 2D DCT has comparable PSNR performance with respect to the algorithm presented in [23] with ~35-50% reduction in area. Next, we use the approximate 2x2 multiplier presented in [24] to implement parallel approximate multipliers. We demonstrate that if some of the 2x2 multipliers in the design of the parallel multiplier are accurate, the accuracy of the multiplier improves significantly, especially when two large numbers are multiplied. We choose Gaussian FIR Filter and Fast Fourier Transform (FFT) algorithms to illustrate the efficacy of our proposed approximate multiplier. We show that application of the proposed approximate multiplier improves the PSNR performance of 32x32 FFT implementation by 4.7 dB compared to the implementation using the approximate multiplier described in [24]. We also implement a state-of-the-art image enlargement algorithm, namely Segment Adaptive Gradient Angle (SAGA) [29], in hardware. The algorithm is mapped to pipelined hardware blocks and we synthesized the design using 90 nm technology. We show that a 64x64 image can be processed in 496.48 µs when clocked at 100 MHz. The average PSNR performance of our implementation using accurate parallel adders and multipliers is 31.33 dB and that using approximate parallel adders and multipliers is 30.86 dB, when evaluated against the original image. The PSNR performance of both designs is comparable to the performance of the double precision floating point MATLAB implementation of the algorithm.
ContributorsVasudevan, Madhu (Author) / Chakrabarti, Chaitali (Thesis advisor) / Frakes, David (Committee member) / Gupta, Sandeep (Committee member) / Arizona State University (Publisher)
Created2013
Description
Controlled release formulations for local, in vivo drug delivery are of growing interest to device manufacturers, research scientists, and clinicians; however, most research characterizing controlled release formulations occurs in vitro because the spatial and temporal distribution of drug delivery is difficult to measure in vivo. In this work, in vivo magnetic resonance imaging (MRI) of local drug delivery is performed to visualize and quantify the time resolved distribution of MRI contrast agents. I find it is possible to visualize contrast agent distributions in near real time from local delivery vehicles using MRI. Three dimensional T1 maps are processed to produce in vivo concentration maps of contrast agent for individual animal models. The method for obtaining concentration maps is analyzed to estimate errors introduced at various steps in the process. The method is used to evaluate different controlled release vehicles, vehicle placement, and type of surgical wound in rabbits as a model for antimicrobial delivery to orthopaedic infection sites. I are able to see differences between all these factors; however, all images show that contrast agent remains fairly local to the wound site and do not distribute to tissues far from the implant in therapeutic concentrations. I also produce a mathematical model that investigates important mechanisms in the transport of antimicrobials in a wound environment. It is determined from both the images and the mathematical model that antimicrobial distribution in an orthopaedic wounds is dependent on both diffusive and convective mechanisms. Furthermore, I began development of MRI visible therapeutic agents to examine active drug distributions. I hypothesize that this work can be developed into a non-invasive, patient specific, clinical tool to evaluate the success of interventional procedures using local drug delivery vehicles.
ContributorsGiers, Morgan (Author) / Caplan, Michael R (Thesis advisor) / Massia, Stephen P (Committee member) / Frakes, David (Committee member) / McLaren, Alex C. (Committee member) / Vernon, Brent L (Committee member) / Arizona State University (Publisher)
Created2013
Description
Image understanding has been playing an increasingly crucial role in vision applications. Sparse models form an important component in image understanding, since the statistics of natural images reveal the presence of sparse structure. Sparse methods lead to parsimonious models, in addition to being efficient for large scale learning. In sparse modeling, data is represented as a sparse linear combination of atoms from a "dictionary" matrix. This dissertation focuses on understanding different aspects of sparse learning, thereby enhancing the use of sparse methods by incorporating tools from machine learning. With the growing need to adapt models for large scale data, it is important to design dictionaries that can model the entire data space and not just the samples considered. By exploiting the relation of dictionary learning to 1-D subspace clustering, a multilevel dictionary learning algorithm is developed, and it is shown to outperform conventional sparse models in compressed recovery, and image denoising. Theoretical aspects of learning such as algorithmic stability and generalization are considered, and ensemble learning is incorporated for effective large scale learning. In addition to building strategies for efficiently implementing 1-D subspace clustering, a discriminative clustering approach is designed to estimate the unknown mixing process in blind source separation. By exploiting the non-linear relation between the image descriptors, and allowing the use of multiple features, sparse methods can be made more effective in recognition problems. The idea of multiple kernel sparse representations is developed, and algorithms for learning dictionaries in the feature space are presented. Using object recognition experiments on standard datasets it is shown that the proposed approaches outperform other sparse coding-based recognition frameworks. Furthermore, a segmentation technique based on multiple kernel sparse representations is developed, and successfully applied for automated brain tumor identification. Using sparse codes to define the relation between data samples can lead to a more robust graph embedding for unsupervised clustering. By performing discriminative embedding using sparse coding-based graphs, an algorithm for measuring the glomerular number in kidney MRI images is developed. Finally, approaches to build dictionaries for local sparse coding of image descriptors are presented, and applied to object recognition and image retrieval.
ContributorsJayaraman Thiagarajan, Jayaraman (Author) / Spanias, Andreas (Thesis advisor) / Frakes, David (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Locomotion of microorganisms is commonly observed in nature and some aspects of their motion can be replicated by synthetic motors. Synthetic motors rely on a variety of propulsion mechanisms including auto-diffusiophoresis, auto-electrophoresis, and bubble generation. Regardless of the source of the locomotion, the motion of any motor can be characterized by the translational and rotational velocity and effective diffusivity. In a uniform environment the long-time motion of a motor can be fully characterized by the effective diffusivity. In this work it is shown that when motors possess both translational and rotational velocity the motor transitions from a short-time diffusivity to a long-time diffusivity at a time of pi/w. The short-time diffusivities are two to three orders of magnitude larger than the diffusivity of a Brownian sphere of the same size, increase linearly with concentration, and scale as v^2/2w. The measured long-time diffusivities are five times lower than the short-time diffusivities, scale as v^2/{2Dr [1 + (w/Dr )^2]}, and exhibit a maximum as a function of concentration. The variation of a colloid's velocity and effective diffusivity to its local environment (e.g. fuel concentration) suggests that the motors can accumulate in a bounded system, analogous to biological chemokinesis. Chemokinesis of organisms is the non-uniform equilibrium concentration that arises from a bounded random walk of swimming organisms in a chemical concentration gradient. In non-swimming organisms we term this response diffusiokinesis. We show that particles that migrate only by Brownian thermal motion are capable of achieving non-uniform pseudo equilibrium distribution in a diffusivity gradient. The concentration is a result of a bounded random-walk process where at any given time a larger percentage of particles can be found in the regions of low diffusivity than in regions of high diffusivity. Individual particles are not trapped in any given region but at equilibrium the net flux between regions is zero. For Brownian particles the gradient in diffusivity is achieved by creating a viscosity gradient in a microfluidic device. The distribution of the particles is described by the Fokker-Planck equation for variable diffusivity. The strength of the probe concentration gradient is proportional to the strength of the diffusivity gradient and inversely proportional to the mean probe diffusivity in the channel in accordance with the no flux condition at steady state. This suggests that Brownian colloids, natural or synthetic, will concentrate in a bounded system in response to a gradient in diffusivity and that the magnitude of the response is proportional to the magnitude of the gradient in diffusivity divided by the mean diffusivity in the channel.
ContributorsMarine, Nathan Arasmus (Author) / Posner, Jonathan D (Thesis advisor) / Adrian, Ronald J (Committee member) / Frakes, David (Committee member) / Phelan, Patrick E (Committee member) / Santos, Veronica J (Committee member) / Arizona State University (Publisher)
Created2013
Description
There is a growing interest for improved high-accuracy camera calibration methods due to the increasing demand for 3D visual media in commercial markets. Camera calibration is used widely in the fields of computer vision, robotics and 3D reconstruction. Camera calibration is the first step for extracting 3D data from a 2D image. It plays a crucial role in computer vision and 3D reconstruction due to the fact that the accuracy of the reconstruction and 3D coordinate determination relies on the accuracy of the camera calibration to a great extent. This thesis presents a novel camera calibration method using a circular calibration pattern. The disadvantages and issues with existing state-of-the-art methods are discussed and are overcome in this work. The implemented system consists of techniques of local adaptive segmentation, ellipse fitting, projection and optimization. Simulation results are presented to illustrate the performance of the proposed scheme. These results show that the proposed method reduces the error as compared to the state-of-the-art for high-resolution images, and that the proposed scheme is more robust to blur in the imaged calibration pattern.
ContributorsPrakash, Charan Dudda (Author) / Karam, Lina J (Thesis advisor) / Frakes, David (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2012
Description
Diabetic retinopathy (DR) is a common cause of blindness occurring due to prolonged presence of diabetes. The risk of developing DR or having the disease progress is increasing over time. Despite advances in diabetes care over the years, DR remains a vision-threatening complication and one of the leading causes of blindness among American adults. Recent studies have shown that diagnosis based on digital retinal imaging has potential benefits over traditional face-to-face evaluation. Yet there is a dearth of computer-based systems that can match the level of performance achieved by ophthalmologists. This thesis takes a fresh perspective in developing a computer-based system aimed at improving diagnosis of DR images. These images are categorized into three classes according to their severity level. The proposed approach explores effective methods to classify new images and retrieve clinically-relevant images from a database with prior diagnosis information associated with them. Retrieval provides a novel way to utilize the vast knowledge in the archives of previously-diagnosed DR images and thereby improve a clinician's performance while classification can safely reduce the burden on DR screening programs and possibly achieve higher detection accuracy than human experts. To solve the three-class retrieval and classification problem, the approach uses a multi-class multiple-instance medical image retrieval framework that makes use of spectrally tuned color correlogram and steerable Gaussian filter response features. The results show better retrieval and classification performances than prior-art methods and are also observed to be of clinical and visual relevance.
ContributorsChandakkar, Parag Shridhar (Author) / Li, Baoxin (Thesis advisor) / Turaga, Pavan (Committee member) / Frakes, David (Committee member) / Arizona State University (Publisher)
Created2012
Description
Recent advances in camera architectures and associated mathematical representations now enable compressive acquisition of images and videos at low data-rates. While most computer vision applications of today are composed of conventional cameras, which collect a large amount redundant data and power hungry embedded systems, which compress the collected data for further processing, compressive cameras offer the advantage of direct acquisition of data in compressed domain and hence readily promise to find applicability in computer vision, particularly in environments hampered by limited communication bandwidths. However, despite the significant progress in theory and methods of compressive sensing, little headway has been made in developing systems for such applications by exploiting the merits of compressive sensing. In such a setting, we consider the problem of activity recognition, which is an important inference problem in many security and surveillance applications. Since all successful activity recognition systems involve detection of human, followed by recognition, a potential fully functioning system motivated by compressive camera would involve the tracking of human, which requires the reconstruction of atleast the initial few frames to detect the human. Once the human is tracked, the recognition part of the system requires only the features to be extracted from the tracked sequences, which can be the reconstructed images or the compressed measurements of such sequences. However, it is desirable in resource constrained environments that these features be extracted from the compressive measurements without reconstruction. Motivated by this, in this thesis, we propose a framework for understanding activities as a non-linear dynamical system, and propose a robust, generalizable feature that can be extracted directly from the compressed measurements without reconstructing the original video frames. The proposed feature is termed recurrence texture and is motivated from recurrence analysis of non-linear dynamical systems. We show that it is possible to obtain discriminative features directly from the compressed stream and show its utility in recognition of activities at very low data rates.
ContributorsKulkarni, Kuldeep Sharad (Author) / Turaga, Pavan (Thesis advisor) / Spanias, Andreas (Committee member) / Frakes, David (Committee member) / Arizona State University (Publisher)
Created2012
Description
Owing to the surge in development of endovascular devices such as coils and flow diverter stents, doctors are inclined to approach surgical cases non-invasively more often than before. Treating brain aneurysms as a bulging of a weakened area of a blood vessel is no exception. Therefore, promoting techniques that can help surgeons have a better idea of treatment outcomes are of invaluable importance.
In order to investigate the effects of these devices on intra-aneurysmal hemodynamics, the conventional computational fluid dynamics (CFD) approach uses the explicit geometry of the device within an aneurysm and discretizes the fluid domain to solve the Navier-Stokes equations. However, since the devices are made of small struts, the number of mesh elements in the boundary layer region would be considerable. This cumbersome task led to the implementation of the porous medium assumption. In this approach, the explicit geometry of the device is eliminated, and relevant porous medium assumptions are applied. Unfortunately, as it will be shown in this research, some of the porous medium approaches used in the literature are over-simplified. For example, considering the porous domain to be homogeneous is one major drawback which leads to significant errors in capturing the intra-aneurysmal flow features. Specifically, since the devices must comply with the complex geometry of an aneurysm, the homogeneity assumption is not valid.
In this research, a novel heterogeneous porous medium approach is introduced. This results in a substantial reduction in the total number of mesh elements required to discretize the flow domain while not sacrificing the accuracy of the method by over-simplifying the utilized assumptions.
In order to investigate the effects of these devices on intra-aneurysmal hemodynamics, the conventional computational fluid dynamics (CFD) approach uses the explicit geometry of the device within an aneurysm and discretizes the fluid domain to solve the Navier-Stokes equations. However, since the devices are made of small struts, the number of mesh elements in the boundary layer region would be considerable. This cumbersome task led to the implementation of the porous medium assumption. In this approach, the explicit geometry of the device is eliminated, and relevant porous medium assumptions are applied. Unfortunately, as it will be shown in this research, some of the porous medium approaches used in the literature are over-simplified. For example, considering the porous domain to be homogeneous is one major drawback which leads to significant errors in capturing the intra-aneurysmal flow features. Specifically, since the devices must comply with the complex geometry of an aneurysm, the homogeneity assumption is not valid.
In this research, a novel heterogeneous porous medium approach is introduced. This results in a substantial reduction in the total number of mesh elements required to discretize the flow domain while not sacrificing the accuracy of the method by over-simplifying the utilized assumptions.
ContributorsYadollahi Farsani, Hooman (Author) / Herrmann, Marcus (Thesis advisor) / Frakes, David (Thesis advisor) / Chong, Brian (Committee member) / Peet, Yulia (Committee member) / Oswald, Jay (Committee member) / Arizona State University (Publisher)
Created2018
Description
The application of novel visualization and modeling methods to the study of cardiovascular disease is vital to the development of innovative diagnostic techniques, including those that may aid in the early detection and prevention of cardiovascular disorders. This dissertation focuses on the application of particle image velocimetry (PIV) to the study of intracardiac hemodynamics. This is accomplished primarily though the use of ultrasound based PIV, which allows for in vivo visualization of intracardiac flow without the requirement for optical access, as is required with traditional camera-based PIV methods.
The fundamentals of ultrasound PIV are introduced, including experimental methods for its implementation as well as a discussion on estimating and mitigating measurement error. Ultrasound PIV is then compared to optical PIV; this is a highly developed technique with proven accuracy; through rigorous examination it has become the “gold standard” of two-dimensional flow visualization. Results show good agreement between the two methods.
Using a mechanical left heart model, a multi-plane ultrasound PIV technique is introduced and applied to quantify a complex, three-dimensional flow that is analogous to the left intraventricular flow. Changes in ventricular flow dynamics due to the rotational orientation of mechanical heart valves are studied; the results demonstrate the importance of multi-plane imaging techniques when trying to assess the strongly three-dimensional intraventricular flow.
The potential use of ultrasound PIV as an early diagnosis technique is demonstrated through the development of a novel elasticity estimation technique. A finite element analysis routine is couple with an ensemble Kalman filter to allow for the estimation of material elasticity using forcing and displacement data derived from PIV. Results demonstrate that it is possible to estimate elasticity using forcing data derived from a PIV vector field, provided vector density is sufficient.
The fundamentals of ultrasound PIV are introduced, including experimental methods for its implementation as well as a discussion on estimating and mitigating measurement error. Ultrasound PIV is then compared to optical PIV; this is a highly developed technique with proven accuracy; through rigorous examination it has become the “gold standard” of two-dimensional flow visualization. Results show good agreement between the two methods.
Using a mechanical left heart model, a multi-plane ultrasound PIV technique is introduced and applied to quantify a complex, three-dimensional flow that is analogous to the left intraventricular flow. Changes in ventricular flow dynamics due to the rotational orientation of mechanical heart valves are studied; the results demonstrate the importance of multi-plane imaging techniques when trying to assess the strongly three-dimensional intraventricular flow.
The potential use of ultrasound PIV as an early diagnosis technique is demonstrated through the development of a novel elasticity estimation technique. A finite element analysis routine is couple with an ensemble Kalman filter to allow for the estimation of material elasticity using forcing and displacement data derived from PIV. Results demonstrate that it is possible to estimate elasticity using forcing data derived from a PIV vector field, provided vector density is sufficient.
ContributorsWesterdale, John Curtis (Author) / Adrian, Ronald (Thesis advisor) / Belohlavek, Marek (Committee member) / Squires, Kyle (Committee member) / Trimble, Steve (Committee member) / Frakes, David (Committee member) / Arizona State University (Publisher)
Created2015