Spelling suggestions: "subject:"[een] COMPUTATIONAL IMAGING"" "subject:"[enn] COMPUTATIONAL IMAGING""
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<b>WEARABLE BIG DATA HARNESSING WITH DEEP LEARNING, EDGE COMPUTING AND EFFICIENCY OPTIMIZATION</b>Jiadao Zou (16920153) 03 January 2024 (has links)
<p dir="ltr">In this dissertation, efforts and innovations are made to advance subtle pattern mining, edge computing, and system efficiency optimization for biomedical applications, thereby advancing precision medicine big data.</p><p dir="ltr">Brain visual dynamics encode rich functional and biological patterns of the neural system, promising for applications like intention decoding, cognitive load quantization and neural disorder measurement. We here focus on the understanding of the brain visual dynamics for the Amyotrophic lateral sclerosis (ALS) population. We leverage a deep learning framework for automatic feature learning and classification, which can translate the eye Electrooculography (EOG) signal to meaningful words. We then build an edge computing platform on the smart phone, for learning, visualization, and decoded word demonstration, all in real-time. In a further study, we have leveraged deep transfer learning to boost EOG decoding effectiveness. More specifically, the model trained on basic eye movements is leveraged and treated as an additional feature extractor when classifying the signal to the meaningful word, resulting in higher accuracy.</p><p dir="ltr">Efforts are further made to decoding functional Near-Infrared Spectroscopy (fNIRS) signal, which encodes rich brain dynamics like the cognitive load. We have proposed a novel Multi-view Multi-channel Graph Neural Network (mmGNN). More specifically, we propose to mine the multi-channel fNIRS dynamics with a multi-stage GNN that can effectively extract the channel- specific patterns, propagate patterns among channels, and fuse patterns for high-level abstraction. Further, we boost the learning capability with multi-view learning to mine pertinent patterns in temporal, spectral, time-frequency, and statistical domains.</p><p dir="ltr">Massive-device systems, like wearable massive-sensor computers and Internet of Things (IoTs), are promising in the era of big data. The crucial challenge is about how to maximize the efficiency under coupling constraints like energy budget, computing, and communication. We propose a deep reinforcement learning framework, with a pattern booster and a learning adaptor. This framework has demonstrated optimally maximizes the energy utilization and computing efficiency on the local massive devices under a one-center fifteen-device circumstance.</p><p dir="ltr">Our research and findings are expected to greatly advance the intelligent, real-time, and efficient big data harnessing, leveraging deep learning, edge computing, and efficiency optimization.</p>
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MAJORIZED MULTI-AGENT CONSENSUS EQUILIBRIUM FOR 3D COHERENT LIDAR IMAGINGTony Allen (18502518) 06 May 2024 (has links)
<pre>Coherent lidar uses a chirped laser pulse for 3D imaging of distant targets.However, existing coherent lidar image reconstruction methods do not account for the system's aperture, resulting in sub-optimal resolution.Moreover, these methods use majorization-minimization for computational efficiency, but do so without a theoretical treatment of convergence.<br> <br>In this work, we present Coherent Lidar Aperture Modeled Plug-and-Play (CLAMP) for multi-look coherent lidar image reconstruction.CLAMP uses multi-agent consensus equilibrium (a form of PnP) to combine a neural network denoiser with an accurate physics-based forward model.CLAMP introduces an FFT-based method to account for the effects of the aperture and uses majorization of the forward model for computational efficiency.We also formalize the use of majorization-minimization in consensus optimization problems and prove convergence to the exact consensus equilibrium solution.Finally, we apply CLAMP to synthetic and measured data to demonstrate its effectiveness in producing high-resolution, speckle-free, 3D imagery.</pre><p></p>
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OVERCOMING THE RAYLEIGH LIMIT FOR HIGH-RESOLUTION OPTICAL IMAGING: QUANTUM ANDCLASSICAL METHODSHyunsoo Choi (18989168) 12 July 2024 (has links)
<p><br></p><p dir="ltr">Achieving high optical resolution imaging is one of the most important goals in the history of optics. However, due to finite aperture sizes, a diffraction limit is imposed on optical imaging. Therefore, the Rayleigh limit, which describes the minimum separation at which two point sources are resolvable, has served as a critical limit in optical resolution. Many methods have been studied to break the limit and succeed in resolving nearby sources below the Rayleigh criterion but only beyond a certain distance. Furthermore, it has been demonstrated that quantum-inspired optics techniques maintain consistent variance in estimating the separation of point sources even at low separations, but only with prior information like a known number of sources and equal brightness. Therefore, achieving the ultimate optical resolution remains an open question. This thesis will conclusively address this challenge considering real-world scenarios, i.e., no prior information or controlled lab environment as well as low signal-to-noise ratio (SNR), turbulence, and other practical challenges.</p><p><br></p><p dir="ltr">In information theory, the estimation variance of a random parameter can be quantified using the inverse of Fisher information. By maximizing the Fisher information, one can minimize the variance in estimation. In my thesis, we have shown that the measurement can be accelerated without sacrificing optical resolution using the adaptive mode so that quantum Fisher information per detected photon is maximized. The notable attribute that sets it apart from other quantum-inspired methods is that it does not require any prior information, making it more feasible for practical application. We have further shown that the space domain awareness (SDA) challenge can be effectively handled with the aforementioned approach with a very limited photon budget and even in the presence of turbulence. Toward solving the challenges, we designed a photon statistics-based direct imaging method that can also serve as a baseline method for quantum optics. In my thesis, atmospheric turbulence is also deeply explored and the effect is mitigated using reinforcement learning.</p><p><br></p>
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RECONSTRUCTION OF HIGH-SPEED EVENT-BASED VIDEO USING PLUG AND PLAYTrevor D. Moore (5930756) 16 January 2019 (has links)
<div>Event-Based cameras, also known as neuromophic cameras or dynamic vision sensors, are an imaging modality that attempt to mimic human eyes by asynchronously measuring contrast over time. If the contrast changes sufficiently then a 1-bit event is output, indicating whether the contrast has gone up or down. This stream of events is sparse, and its asynchronous nature allows the pixels to have a high dynamic range and high temporal resolution. However, these events do not encode the intensity of the scene, resulting in an inverse problem to estimate intensity images from the event stream. Hybrid event-based cameras, such as the DAVIS camera, provide a reference intensity image that can be leveraged when estimating the intensity at each pixel during an event. Normally, inverse problems are solved by formulating a forward and prior model and minimizing the associated cost, however, for this problem, the Plug and Play (P&P) algorithm is used to solve the inverse problem. In this case, P&P replaces the prior model subproblem with a denoiser, making the algorithm modular, easier to implement. We propose an idealized forward model that assumes the contrast steps measured by the DAVIS camera are uniform in size to simplify the problem. We show that the algorithm can swiftly reconstruct the scene intensity at a user-specified frame rate, depending on the chosen denoiser’s computational complexity and the selected frame rate.</div>
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ADVANCED PRIOR MODELS FOR ULTRA SPARSE VIEW TOMOGRAPHYMaliha Hossain (17014278) 26 September 2023 (has links)
<p dir="ltr">There is a growing need to reconstruct high quality tomographic images from sparse view measurements to accommodate time and space constraints as well as patient well-being in medical CT. Analytical methods perform poorly with sub-Nyquist acquisition rates. In extreme cases with 4 or fewer views, effective reconstruction approaches must be able to incorporate side information to constrain the solution space of an otherwise under-determined problem. This thesis presents two sparse view tomography problems that are solved using techniques that exploit. knowledge of the structural and physical properties of the scanned objects.</p><p dir="ltr"><br></p><p dir="ltr">First, we reconstruct four view CT datasets obtained from an in-situ imaging system used to observe Kolsky bar impact experiments. Test subjects are typically 3D-printed out ofhomogeneous materials into shapes with circular cross sections. Two advanced prior modelsare formulated to incorporate these assumptions in a modular fashion into the iterativeradiographic inversion framework. The first is a Multi-Slice Fusion and the latter is TotalVariation regularization that operates in cylindrical coordinates.</p><p dir="ltr"><br></p><p dir="ltr">In the second problem, artificial neural networks (NN) are used to directly invert a temporal sequence of four radiographic images of discontinuities propagating through an imploding steel shell. The NN is fed the radiographic features that are robust to scatter and is trained using density simulations synthesized as solutions to hydrodynamic equations of state. The proposed reconstruction pipeline learns and enforces physics-based assumptions of hydrodynamics and shock physics to constrain the final reconstruction to a space ofphysically admissible solutions.</p>
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