Calendar

Jun
5
Fri
Thesis Proposal: Uejima Takeshi
Jun 5 @ 10:00 am
Thesis Proposal: Uejima Takeshi

This presentation will be taking place remotely. Follow this link to enter the Zoom meeting where it will be hosted. Do not enter the meeting before 9:45 AM EDT.

Title: A Unified Visual Saliency Model for Neuromorphic Implementation

Abstract: Human eyes capture and send large amounts of data from the environment to the brain. However, the visual cortex cannot process all the information in detail at once. To deal with the overwhelming quantity of the input, the early stages of visual processing select a small subset of the input for detailed processing. Because only the fovea has high resolution imaging, the observer needs to move the eyeballs for thorough scene inspection. Therefore, eye movements can be thought as one of the observable outputs of the early visual process in the brain, which represents what is interesting and important for the observer. Modeling how the brain selects important information, and where humans fixate, is an intriguing research topic in neuroscience and computer vision and is generally referred to as visual saliency modeling. Beyond its grave scientific ramifications, a better understanding of this process will improve the effectiveness of graphic arts, advertisements, traffic signs, camouflage and many other applications.

To date, there has been some studies on developing bioinspired saliency models. Russell et al. proposed a biologically plausible visual saliency model called proto-object based saliency model. It has shown successful result to predict human fixation; however, it exclusively works on low-level features; intensity, color, and orientation. Russell et al. model has been extended by addition of a motion channel as well as a disparity (depth) channel. Texture feature, however, has neither been well studied in the visual saliency field, nor been incorporated into a proto-object based model. And no attempt has been made to combine all of these features in one model. Here, we propose an augmented version of the model that incorporates texture, motion, and disparity features.

In addition to designing the unified proto-object based model, we investigate rationality of the visual process in biological system from the viewpoint of efficiency to represent natural stimuli. This study will advance visual saliency modeling and improve the accuracy of human fixation prediction. In addition, it will deepen our knowledge on how the visual cortex deals with complex environment.

Committee Members:

Ralph Etienne-Cummings, Department of Electrical and Computer Engineering

Andreas Andreou, Department of Electrical and Computer Engineering

Philippe Pouliquen, Department of Electrical and Computer Engineering

Jun
18
Thu
Dissertation Defense: Yansong Zhu
Jun 18 @ 1:00 pm
Dissertation Defense: Yansong Zhu

This presentation will be taking place remotely. Follow this link to enter the Zoom meeting where it will be hosted. Do not enter the meeting before 12:45 PM EDT. 

Title: Improved Modeling and Image Generation for Fluorescence Molecular Tomography (FMT) and Positron Emission Tomography (PET)

Abstract: In this thesis, we aim to improve quantitative medical imaging with advanced image generation algorithms. We focus on two specific imaging modalities: fluorescence molecular tomography (FMT) and positron emission tomography (PET).

In the case of FMT, we present a novel photon propagation model for its forward model, and in addition, we propose and investigate a reconstruction algorithm for its inverse problem. In the first part, we develop a novel Neumann-series-based radiative transfer equation (RTE) that incorporates reflection boundary conditions in the model. In addition, we propose a novel reconstruction technique for diffuse optical imaging that incorporates this Neumann-series-based RTE as forward model. The proposed model is assessed using a simulated 3D diffuse optical imaging setup, and the results demonstrate the importance of considering photon reflection at boundaries when performing photon propagation modeling. In the second part, we propose a statistical reconstruction algorithm for FMT. The algorithm is based on sparsity-initialized maximum-likelihood expectation maximization (MLEM), taking into account the Poisson nature of data in FMT and the sparse nature of images. The proposed method is compared with a pure sparse reconstruction method as well as a uniform-initialized MLEM reconstruction method. Results indicate the proposed method is more robust to noise and shows improved qualitative and quantitative performance.

For PET, we present an MRI-guided partial volume correction algorithm for brain imaging, aiming to recover qualitative and quantitative loss due to the limited resolution of PET system, while keeping image noise at a low level. The proposed method is based on an iterative deconvolution model with regularization using parallel level sets. A non-smooth optimization algorithm is developed so that the proposed method can be feasibly applied for 3D images and avoid additional blurring caused by conventional smooth optimization process. We evaluate the proposed method using both simulation data and in vivo human data collected from the Baltimore Longitudinal Study of Aging (BLSA). Our proposed method is shown to generate images with reduced noise and improved structure details, as well as increased number of statistically significant voxels in study of aging. Results demonstrate our method has promise to provide superior performance in clinical imaging scenarios.

Thesis Committee

  • Arman Rahmim, Department of Electrical and Computer Engineering, Department of Radiology and Radiological Sciences (advisor, primary reader)
  • Yong Du, Department of Radiology and Radiological Sciences (secondary reader)
  • Jin Kang, Department of Electrical and Computer Engineering
  • Trac Tran, Department of Electrical and Computer Engineering
Thesis Proposal: Soohyun Lee
Jun 18 @ 3:00 pm
Thesis Proposal: Soohyun Lee

This presentation will be taking place remotely. Follow this link to enter the Zoom meeting where it will be hosted. Do not enter the meeting before 2:45 PM EDT. 

Title: Optical coherence tomography (OCT) – guided ophthalmic therapy

Abstract: Optical coherence tomography (OCT), which provides cross-sectional images noninvasively with a micro-scale in real-time, has been widely applied for the diagnosis and treatment guidance for ocular diseases.

Selective retina therapy (SRT) is an effective laser treatment method for retinal diseases associated with a degradation of the retinal pigment epithelium (RPE). The SRT selectively targets the RPE, so it reduces negative side effects and facilitates healing of the induced retinal lesions. However, the selection of proper laser energy is challenging because of ophthalmoscopically invisible lesions in the RPE and variance in melanin concentration between patients and even between regions within an eye. In the first part of this work, we propose and demonstrate SRT monitoring and temperature estimation based on speckle variance OCT (svOCT) for dosimetry control. SvOCT quantifies speckle pattern variation caused by moving particles or structural changes in biological tissues. We find that the svOCT peak values have a reliable correlation with the degree of retinal lesion formation. The temperature at the neural retina and RPE is estimated from the svOCT peak values using numerically calculated temperature, which is consistent with the observed lesion creation.

In the second part, we propose to develop a hand-held subretinal-injector actively guided by a common-path OCT (CP-OCT) distal sensor. Subretinal injection delivers drug or stem cells in the space between RPE and photoreceptor layers, so it can directly affect resident cell and tissues in the subretinal space. The technique requires high stability and dexterity of surgeon due to fine anatomy of the retina, and it is challenging because of physiological motions of surgeons like hand tremor. We mainly focus on two aspects of the CP-OCT guided subretinal-injector: (i) A high-performance fiber probe based on high index epoxy lensed-fiber to enhance the CP-OCT retinal image quality in a wet environment; (ii) Automated layer identification and tracking: Each retinal layer boundary, as well as retinal surface, is tracked using convolutional neural network (CNN)-based segmentation for accurate localization of a needle. The CNN performing retinal layer segmentation is integrated into the CP-OCT system for targeted layer distance sensing, and the CP-OCT distal sensor guided system is tested on ex vivo bovine retina.

Jul
28
Tue
Dissertation Defense: Ben Skerritt-Davis
Jul 28 @ 10:00 am
Dissertation Defense: Ben Skerritt-Davis

This presentation will be taking place remotely. Follow this link to enter the Zoom meeting where it will be hosted. Do not enter the meeting before 9:45 AM EDT.

Title: Statistical Inference in Auditory Perception

Abstract: The human auditory system effortlessly parses complex sensory inputs despite the ever-present randomness and uncertainty in real-world scenes. To achieve this, the brain tracks sounds as they evolve in time, collecting contextual information to construct an internal model of the external world for predicting future events. Previous work has shown the brain is sensitive to many predictable (and often complex) patterns in sequential sounds. However, real-world environments exhibit a broader spectrum of predictability, and moreover, the level of predictability is constantly in flux. How does the brain build robust internal representations of such stochastic and dynamic acoustic environments?

This question is addressed through the lens of a computational model based in statistical inference. Embodying theories from Bayesian perception and predictive coding, the model posits the brain collects statistical estimates from sounds and maintains multiple hypotheses for the degree of context to include in predictive processes. As a potential computational solution for perception of complex and dynamic sounds, this model is used to connect sensory inputs with listeners’ responses in a series of human behavioral and electroencephalography (EEG) experiments incorporating uncertainty. Experimental results point toward the underlying sufficient statistics collected by the brain, and the extension of these statistical representations to multiple dimensions is examined along spectral and spatial dimensions. The computational model guides interpretation of behavioral and neural responses, revealing multiplexed responses in the brain corresponding to different levels of predictive processing. In addition, the model is used to explain individual differences across listeners highlighted by uncertainty.

The proposed computational model was developed based on first principles, and its usefulness is not limited to the experiments presented here. The model was used to replicate a range of previous findings in the literature, unifying them under a single framework. Moving forward, this general and flexible model can be used as a broad-ranging tool for studying the statistical inference processes behind auditory perception, overcoming the need to minimize uncertainty in perceptual experiments and pushing what was previously considered feasible for study in the laboratory towards what is typically encountered in the “messy” environments of everyday listening.

Committee Members

Mounya Elhilali, Department of Electrical and Computer Engineering

Jason Fischer, Department of Psychological & Brain Sciences

Hynek Hermansky, Department of Electrical and Computer Engineering

James West, Department of Electrical and Computer Engineering

Jul
31
Fri
2020 CSMR REU Final Presentations @ https://wse.zoom.us/j/96982283136
Jul 31 @ 9:00 am – 11:45 am
Aug
21
Fri
Dissertation Defense: Gary Li
Aug 21 @ 11:00 am
Dissertation Defense: Gary Li

This presentation will be taking place remotely. Follow this link to enter the Zoom meeting where it will be hosted. Do not enter the meeting before 10:45 AM EDT.

Title: Task-based Optimization of Administered Activity for Pediatric Renal SPECT Imaging

Abstract: Like any real-world problem, the design of an imaging system always requires tradeoffs. For medical imaging modalities using ionization radiation, a major tradeoff is between diagnostic image quality (IQ) and risk to the patient from absorbed dose (AD). In nuclear medicine, reducing the AD requires reducing the administered activity (AA). Lower AA to the patient can reduce risk and adverse effects, but can also result in reduced diagnostic image quality. Thus, ultimately, it is desirable to use the lowest AA that gives sufficient image quality for accurate clinical diagnosis.

In this dissertation, we proposed and developed tools for a general framework for optimizing RD with task-based assessment of IQ. Here, IQ is defined as an objective measure of the user performing the diagnostic task that the images were acquired to answer. To investigate IQ as a function of renal defect detectability, we have developed a projection image database modeling imaging of 99mTc-DMSA, a renal function agent. The database uses a highly-realistic population of pediatric phantoms with anatomical and body morphological variations. Using the developed projection image database, we have explored patient factors that affect IQ and are currently in the process of determining relationships between IQ and AA in terms of these found factors. Our data have shown that factors that are more local to the target organ may be more robust than weight for estimating the AA needed to provide a constant IQ across a population of patients. In the case of renal imaging, we have discovered that girth is more robust than weight (currently used in clinical practice) in predicting AA needed to provide a desired IQ. In addition to exploring the patient factors, we also did some work on improving the task simulating capability for anthropomorphic model observer. We proposed a deep learning-based anthropomorphic model observer to fully and efficiently (in terms of both training data and computational cost) model the clinical 3D detection task using multi-slice, multi-orientation images sets. The proposed model observer is important and could be readily adapted to model human observer performance on detection tasks for other imaging modalities such as PET, CT or MRI.

Committee Members

Eric Frey – Department of Radiology and Radiological Science. Faculty adviser.

Yong Du – Department of Radiology and Radiological Science. Second reader.

Vishal Patel – Department of Electrical and Computer Engineering.

George Sgouros – Department of Radiology and Radiological Science.

Archana Venkataraman – Department of Electrical and Computer Engineering.

Dissertation Defense: Nathan Henry
Aug 21 @ 11:00 am
Dissertation Defense: Nathan Henry

This presentation will be taking place remotely. Follow this link to enter the Zoom meeting where it will be hosted. Do not enter the meeting before 10:45 AM EDT.

Title: Mid-Infrared and Terahertz Frequency Combs from Quantum Cascade Lasers

Abstract: Optical frequency combs (FC) allow for extremely high resolution and broadband spectroscopic measurements that are captured contemporaneously rather than through some scanning action. Spectroscopic access to the infrared and THz is highly coveted as many molecular resonances lie in this region. However, due to a lack of available materials, emission of FC in the IR has been difficult, with many attempts resulting in low power and efficiency. In 2014 [1] the first mid-IR FC was characterized from a free-running QCL, requiring no extra elements. However, due to the inherently short upperstate lifetime of the laser, the FC is atypical in that it is not characterized by pulses but rather frequency modulation (FM). While the QCL FC has advanced significantly, it is not fully understood. As a result, spectroscopic measurements can become unreliable, sensitive to environmental changes, and recovery of absolute frequency can be difficult.

To better understand the FC QCL, a set of rate equations adapted from the optical Bloch equations is developed and found to be fully adequate for describing the origins and dynamics of FM FC. This work addresses two modes of operation (pseudo-random and chirped FM) calculating the dynamics of a QCL modeled after real-world measurements. Using specifications of real world QCLs (THz and IR), the gain is modeled under various operational scenarios and the most efficient state is identified. The period of the FM is postulated to be determined by the relative strengths of the various hole burning mechanisms and stability is shown for multiple regimes.

Further work is presented addressing the stability of QCL FCs. We begin by deriving the linewidth of the FC generating QCL and show that indeed it can be just as narrow as more conventional FCs. Subsequent to this work we use a two-dimensional model to achieve an engineered power-law dispersion, which can mitigate offset frequency drift offering the potential to significantly lower the phase noise. It is the hope of the author that this research will be used to develop a deeper understanding of FC producing QCLs that contribute to many fields of human endeavor such as medical diagnostics, remote sensing, time standardization, etc.

Committee Members

Jacob Khurgin – Department of Electrical and Computer Engineering. Adviser.

Susanna Thon – Department of Electrical and Computer Engineering.

Amy Foster – Department of Electrical and Computer Engineering.

Sep
3
Thu
Thesis Proposal: Jonathan Jones
Sep 3 @ 3:00 pm
Thesis Proposal: Jonathan Jones

Note: This is a virtual presentation. Here is the link for where the presentation will be taking place.

Title: Fine-grained activity recognition for assembly videos

Abstract: When a collaborative robot is working with a human partner to build a piece of furniture or an industrial part, the robot must be able to perceive which parts are connected and where, and it must be able to reason about how these connections can change as the result of its partner’s actions. This need can also arise in industrial process monitoring and manufacturing applications, where an automated system verifies a product as it progresses through the assembly line. These assembly processes require systems that can reason geometrically and temporally, relating the structure of an assembly to the manipulation actions that created it.

Grounded in a behavioral study of spatial cognition, this proposal combines methods for physical and temporal reasoning to enable the analysis and automated perception of assembly actions. We develop a temporal model that relates manipulation actions to the structures they produce and describe its use in enabling fine-grained behavioral analyses. Then, we apply our sequence model to recognize assembly actions in a variety of assembly scenarios. Finally, we describe a method for part-based reasoning that makes our approach robust to occluded and previously unseen assemblies.

Committee Members

Sanjeev Khudanpur, Department of Electrical and Computer Engineering

Greg Hager, Department of Computer Science

Vishal Patel, Department of Electrical and Computer Engineering

Sep
4
Fri
Dissertation Defense: Yida Lin
Sep 4 @ 1:30 pm
Dissertation Defense: Yida Lin

This presentation will be taking place remotely. Follow this link to enter the Zoom meeting where it will be hosted. Do not enter the meeting before 1:15 PM EDT.

Title: Extending the Potential of Thin-film Optoelectronics via Optical Engineering

Abstract: Optoelectronics based on nanomaterials have become a research focus in recent years, and this technology bridges the fields of solid-state physics, electrical engineering and materials science. The rapid development in optoelectronic devices in the last century has both benefited from and spurred advancements in the science and engineering of pho- ton detection and manipulation, image sensing, high-efficiency and high-power-density light emission, displays, communications and renewable energy harvesting. A particularly promising material class for optoelectronics is colloidal nanomaterials, due to their functionality, cost -efficiency and even new physics, thanks to their exotic properties in the areas of light-matter interaction, low-dimensionality, and solution-processability which dramatically reduces the time and cost required to fabricate thin film devices, and at the same time provides wide compatibility with existing materials interfaces and device structures. This thesis focuses on exploring and assessing the capabilities of lead sulfide quantum dot-based solar cells and photodetectors. The discussion involves advances in techniques such as implementing novel photonic structures, designing and building novel characterization systems and methods, and coupling to external optical structures and components.

This thesis comprises three sections. The first section focuses on the design and adaption of photonic structures to tailor the function and response of photovoltaics and other absorption-based optoelectronics for specific applications. in the first part, we introduce consideration of complete multi-layer thin film interference effects into the design of solar cells. By numerical calculation and optimization of the film thicknesses as well as the precise fabrication control, devices with specific target colors or optical transparency levels were achieved. In the second part, we investigate the presence of 2D photonic crystal bands in absorbing materials that can be readily incorporated into nanomaterial thin films through nanostructuring of the material. We carried out simulations and theoretical analyses and proposed a method to realize simultaneous selectivity in the device reflection, transmission and absorption spectra that are critical for optoelectronic applications.

The next section focuses on designing and building a multi-modal microscopy system for thin-film optoelectronic devices, accompanied with analyses and explanation of complex experimental data. The goal of the system was to provide simultaneous 2D spatial measurements of, including but not limited to, photoluminescence spectra, time- resolved photocurrent and photovoltage responses, and a rich variety of all the possible combinations of these measurements and their associated derived quantities, collected with micrometer resolution. The multi-dimensional data helped us understand the intercorrelation between local defective regions in films and the entire device behavior, as well as a more comprehensive profile of mutual relationships between solar cell figures of merit.

In the last section, we discuss a new implementation of miniature solar concentrator arrays for lead sulfide quantum dot solar cells. First, we design and analyze the effects of a medium concentration ratio lens-type concentrator made from polydimethylsiloxane, a flexible organosilicon polymer. The concentrators were designed and optimized with the aid of ray-tracing simulation tools to achieve the best compatibility with colloidal nanomaterial-based solar cells. Experimentally, we produced an integrated concentrator system delivering 20-fold current and power enhancements close to the theoretical pre- dictions, and also used our concentrator measurements to explain the rarely explored carrier dynamics critical to high-power operation of thin film solar cells. Next, we design a wide-acceptance-angle dielectric solar concentrator that can be adapted to many types of high- efficiency small-area solar cells. The design was generated using rigorous optical models that define the behaviors of light rays and was verified with ray-tracing optical simulations to yield results for the full annual 2D time-resolved collectible power for the resulting system. Finally, we discuss strategies for further extending the possibilities of nanomaterial-based optoelectronics for future challenges in energy production and related applications.

Committee Members

Susanna Thon – Department of Electrical and Computer Engineering

Jacob Khurgin – Department of Electrical and Computer Engineering

Mark Foster – Department of Electrical and Computer Engineering

Sep
10
Thu
Thesis Proposal: Vishwanath Sindagi
Sep 10 @ 3:00 pm
Thesis Proposal: Vishwanath Sindagi

Note: This is a virtual presentation. Here is the link for where the presentation will be taking place.

Title: Single Image-based Crowd Counting Using Deep Learning Techniques

Abstract: With ubiquitous usage of surveillance cameras and advances in computer vision, crowd scene analysis has gained a lot of interest in the recent years. In this work, we focus on the task of estimating crowd count and high-quality density maps which has wide applications in video surveillance, traffic monitoring, public safety, urban planning, scene understanding and flow monitoring. Also, the methods developed for crowd counting can be extended to counting tasks in other fields such as cell microscopy, vehicle counting, environmental survey, etc. The task of crowd counting and density estimation has seen significant progress in the recent years. However, due to the presence of various complexities such as occlusions, high clutter, non-uniform distribution of people, non-uniform illumination, intra-scene and inter-scene variations in appearance, scale and perspective, the resulting accuracies are far from optimal. Furthermore, existing methods tend to perform poorly on datasets that are different from the dataset used for training the models.

In this work, we specifically address two of the major issues plaguing the crowd counting community: (i) scale variations and (ii) poor cross-dataset performance. In order to address the problem of scale variations, we analyze existing scale-aware counting models and identify that their poor performance is due to the lack of contextual information and the poor quality of predicted density maps. We propose to overcome these issues by incorporating multiple context cues into the learning process, and additionally improving the quality of the predicted density maps using adversarial training. Finally, we explore the use of contextual information as weak image-level labels to improve cross-dataset performance.

Committee Members

Rama Chellappa, Department of Electrical and Computer Engineering

Carlos Castillo, Department of Electrical and Computer Engineering

Vishal Patel, Department of Electrical and Computer Engineering

 

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