Calendar

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

 

Oct
15
Thu
Thesis Proposal: Niharika Shimona D’Souza
Oct 15 @ 3:00 pm
Thesis Proposal: Niharika Shimona D'Souza

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

Title: Mapping Brain Connectivity to Behavior: from Network Optimization Frameworks to Deep-Generative Hybrid Models

Abstract: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder characterized by multiple impairments and levels of disability that vary widely across the ASD spectrum. Currently, the most common methods of quantifying symptom severity are almost solely based on a trained clinician’s evaluation. Recently, neuroimaging techniques such as resting state functional MRI (rs-fMRI) and Diffusion Tensor Imaging (DTI) have been gaining popularity for studying aberrant brain connectivity in ASD. My thesis aims at linking the symptomatic characterization of ASD with the functional and structural organization of a typical patient’s brain as given by rs-fMRI and DTI respectively. My talk is organised into two main parts, as follows:

Network Optimization Models for rs-fMRI connectomics and clinical severity:
Analysis of a multi-subject rs-fMRI imaging study often begins at the group level, for example, estimating group-averaged functional connectivity across all subjects. The failure of data-driven machine learning techniques such as PCA, k-PCA, SVMs etc. are largely attributed to their failure at capturing both the group structure and the individual patient variability, due to which they fail to generalize to unseen patients. To overcome these limitations, we developed a matrix factorization technique to represent the rs-fMRI correlation matrices by decomposing them into a sparse set of representative subnetworks modeled by rank one outer products. The subnetworks are combined using patient-specific non-negative coefficients. The network representations are fixed across the entire group, however, the strength of the subnetworks can vary across individuals. We significantly extend prior work in the area by using these very network coefficients to simultaneously predict behavioral measures via techniques ranging from simple linear regression models to parametric kernel methods, to Artificial Neural Networks (ANNs). The main novelty of the algorithms lies in jointly optimizing for the regression/ANN weights in conjunction with the rs-fMRI matrix factors. By leveraging techniques from convex and non-convex optimization, these frameworks significantly outperform several state-of-the art machine learning, graph theoretic and deep learning baselines at generalization to unseen patients.

Deep-Generative Hybrid Frameworks for Integrating Multimodal and Dynamic Connectivity with Behavior:
There is now growing evidence that functional connectivity between regions is a dynamically process evolving over a static anatomical connectivity profile, and that modeling this evolution is crucial to understanding ASD. Thus, we propose an integrated deep-generative framework, that jointly models complementary information from resting-state functional MRI (rs-fMRI) connectivity and diffusion tensor imaging (DTI) tractography to extract predictive biomarkers of a disease. The generative part of our framework is a structurally-regularized Dynamic Dictionary Learning (sr-DDL) model that decomposes the dynamic rs-fMRI correlation matrices into a collection of shared basis networks and time varying patient-specific loadings. This matrix factorization is guided by the DTI tractography matrices to learn anatomically informed connectivity profiles. The deep part of our framework is an LSTM-ANN block, which models the temporal evolution of the patient sr-DDL loadings to predict multidimensional clinical severity. Once again, our coupled optimization procedure collectively estimates the basis networks, the patient-specific dynamic loadings, and the neural network weights. Our hybrid model outperforms state-of-the-art baselines in a cross validated setting and extracts interpretable multimodal neural signatures of brain dysfunction in ASD.

In recent years, graph neural networks have shown great promise in brain connectivity research due to their ability to underscore subtle interactions between communicating brain regions while exploiting the underlying hierarchy of brain organization. To conclude, I will present some ongoing explorations based on end-to-end graph convolutional networks that directly model the evolution of the rs-fMRI signals/connectivity patterns over the underlying anatomical DTI graphs.

Committee Members

Archana Venkataraman, Department of Electrical and Computer Engineering

Rene Vidal, Department of Biomedical Engineering

Carey E. Priebe, Department of Applied Mathematics & Statistics

Stewart Mostofsky, Director of Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute

Kilian Pohl, Program Director, Image Analysis, Center for Health Sciences,and Biomedical Computing, SRI International; Associate Professor of Psychiatry and Behavioral Sciences, Stanford University

 

Oct
16
Fri
Dissertation Defense: Golnoosh Kamali
Oct 16 @ 12:00 pm
Dissertation Defense: Golnoosh Kamali

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

Title: Transfer function models of cortico-cortical evoked potentials for the localization of seizures in medically refractory epilepsy patients

Abstract: Surgical resection of the seizure onset zone (SOZ) could potentially lead to seizure-freedom in medically refractory epilepsy (MRE) patients. However, localizing the SOZ is a time consuming, subjective process involving visual inspection of intracranial electroencephalographic (iEEG) recordings captured during invasive passive patient monitoring. Cortical stimulation is currently performed on patients undergoing invasive EEG monitoring for the main purpose of mapping functional brain networks such as language and motor networks. We hypothesized that the evoked responses from single pulse electrical stimulation (SPES) can be used to localize the SOZ as they may express the natural frequencies and connectivity of the iEEG network. We constructed patient specific transfer function models from evoked responses recorded from 22 MRE patients that underwent SPES evaluation and iEEG monitoring. We then computed the frequency and connectivity dependent “peak gain” of the system, as measured by the H_∞ norm from systems theory, and the corresponding “floor gain,” which is the gain at which the H_∞ dipped 3dB below the DC gain. In cases for which clinicians had high confidence in localizing the SOZ, the highest peak gain transfer functions with the smallest “floor gains” corresponded to when the clinically annotated SOZ and early spread regions were stimulated. In more complex cases, there was a large spread of the peak gains when the clinically annotated SOZ was stimulated. Interestingly for patients who had successful surgeries, our ratio of peak-to-floor (PF) gains, agreed with clinical localization, no matter the complexity of the case. For patients with failed surgeries, the PF ratio did not match clinical annotations. Our findings suggest that transfer function gains and their corresponding frequency responses computed from SPES evoked responses may improve SOZ localization and thus surgical outcomes.

Committee Members

Sridevi V. Sarma, Department of Biomedical Engineering

Joon Y. Kang, Department of Neurology

Archana Venkataraman, Department of Electrical and Computer Engineering

Nathan E. Crone, Department of Neurology

Oct
23
Fri
Dissertation Defense: Gaspar Tognetti
Oct 23 @ 2:00 pm
Dissertation Defense: Gaspar Tognetti

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

Title: Circuits and Architecture for Bio-Inspired AI Accelerators

Abstract: Technological advances in microelectronics envisioned through Moore’s law have led to more powerful processors that can handle complex and computationally intensive tasks. Nonetheless, these advancements through technology scaling have come at an unfavorable cost of significantly larger power consumption, which has posed challenges for data processing centers and computers at the scale. Moreover, with the emergence of mobile computing platforms constrained by power and bandwidth for distributed computing, the necessity for more energy-efficient scalable local processing has become more significant.

Unconventional Compute-in-Memory (CiM) architectures such as the analog winner-takes-all associative-memory, the Charge-Injection Device (CID) processor, and analog-array processing have been proposed as alternatives. Unconventional charge-based computation has been employed for neural network accelerators in the past, where impressive energy efficiency per operation has been attained in 1-bit vector-vector multiplications (VMMs), and in recent work, multi-bit vector-vector multiplications. A similar approach was used in earlier work, where a charge-injection device array was utilized to store binary coded vectors, and computations were done using binary or multi-bit inputs in the charge domain; computation is carried out by counting quanta of charge at the thermal noise limit, using packets of about 1000 electrons. These systems are neither analog nor digital in the traditional sense but employ mixed-signal circuits to count the packets of charge and hence we call them Quasi-Digital. By amortizing the energy costs of the mixed-signal encoding/decoding over compute-vectors with a large number of elements, high energy efficiencies can be achieved.

In this dissertation, I present a design framework for AI accelerators using scalable compute-in-memory architectures. On the device level, two primitive elements are designed and characterized as target storage technologies: (i) a multilevel non-volatile computational cell and (ii) a pseudo Dynamic Random-Access Memory (pseudo-DRAM) computational bit-cell. Experimental results in deep-submicron CMOS processes demonstrate successful operation; subsequently, behavioral models were developed and employed in large-scale system simulations and emulations. Thereafter, at the level of circuit description, compute-in-memory crossbars and mixed-signal circuits were designed, allowing seamless connectivity to digital controllers. At the level of data representation, both binary and stochastic-unary coding are used to compute Vector-Vector Multiplications (VMMs) at the array level, demonstrating successful experimental results and providing insight into the integration requirements that larger systems may demand. Finally, on the architectural level, two AI accelerator architectures for data center processing and edge computing are discussed. Both designs are scalable multi-core Systems-on-Chip (SoCs), where vector-processor arrays are tiled on a 2-layer Network-on-Chip (NoC), enabling neighbor communication and flexible compute vs. memory trade-off. General purpose Arm/RISCV co-processors provide adequate bootstrapping and system-housekeeping and a high-speed interface fabric facilitates Input/Output to main memory.

Committee Members

Andreas Andreou, Department of Electrical and Computer Engineering

Ralph Etienne-Cummings, Department of Electrical and Computer Engineering

Philippe Pouliquen, Department of Electrical and Computer Engineering

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