Department of Electrical & Computer Engineering

ECE PhD Proposal Review: Hamed Mohebbi Kalkhoran
2022-01-20 15:30 - 16:30 (America/New_York)
PhD Proposal Review: Machine Learning Approaches for Classification of Myriad Underwater Acoustic Events Over Continental-shelf Scale Regions with Passive Ocean Acoustic Waveguide Remote Sensing
Hamed Mohebbi Kalkhoran
Location: Zoom Link
Abstract: Underwater acoustic data contain a myriad of sound sources that include bioacoustics related to marine life such as marine mammals and fishes; man-made such as ships, sonar, and airguns; as well as natural geophysical processes such as earthquake, hurricane, and volcanic eruption. Among underwater acoustic events, marine mammal vocalization classification is one of the most challenging problems due to their transient broadband calls, high variation in the calls of a specie (intra-class variation), and high similarity between the calls of some species. In this thesis, we investigate machine learning approaches for classifying marine mammal vocalizations for real-time applications. We utilize acoustic data from a 160-element coherent hydrophone array and employ the passive ocean acoustic waveguide remote sensing technique to enable sensing and detections over instantaneous wide areas more than 100 km in diameter from the array. A variety of computational accelerating approaches, combining hardware and software, that make the methods desirable for real-time applications are also developed.
Humpback whale behavior, population distribution and structure can be inferred from long term underwater passive acoustic monitoring of their vocalizations. Here we employ machine learning approaches to classify humpback whale vocalizations into song and non-song calls. We use wavelet signal denoising and coherent array processing to enhance the signal-to-noise ratio. To build features vector for every time sequence of the beamformed signals, we employ Bag of Words approach to time-frequency features. Finally, we apply Support Vector Machine (SVM), Neural Networks, and Naive Bayes to classify the acoustic data and compare their performances. Best results are obtained using Mel Frequency Cepstrum Coefficient (MFCC) features and SVM which leads to 94% accuracy and 72.73% F1-score for humpback whale song versus non-song vocalization classification.
To classify a large variety of whale species by their calls, we extracted time-frequency features from Power Spectrogram Density (PSD) of the beamformed signals. Then we used these features to train three classifiers, which are SVM, Neural Networks, and Random forest to classify six whale species: Fin, Sei, Blue, Minke, Humpback, and general Odontocetes. Best results were obtained with Random forest classifier, which achieved 95% accuracy, and 85% F1 score. To detect transient sound sources, first we applied Per-Channel Energy Normalization (PCEN) on the PSD of the beamformed signals. We applied thresholding on the PCEN data followed by morphological image opening to find potential sound sources and reduce noisy detections. Then we applied connected component analysis to obtain the final detected sounds for each bearing. To estimate the Direction of Arrival (DoA) of detected sounds, we applied non-maximum suppression (NMS), which is widely used in object detection applications in computer vision, on the detected sounds. We used mean power of each detected sound as the scores for NMS. To speed up the data processing, we investigated a variety of accelerating approaches, such as analyzing the effect of floating point precision, applying parallel processing, and implementing fast algorithms to run on GPU.

ECE PhD Proposal Review: Vedant Sumaria
2022-01-20 11:00 - 12:00
PhD Proposal Review: Exploring Micro-Machined Glass Shell Resonators For Sensor Application
Vedant Sumaria
Location: Zoom
Abstract: Optical resonators have been playing an important role in modern optics. They are fundamental in any laser device, etalon for optical filtering, accurate measurement for non-linear optics. Bulk optical resonators that use two or more mirrors are usually used in all branches of modern linear and non-linear optics. There are many limitations in using such systems because they cannot provide high performance (high quality (Q) factor) and their size, weight, and alignment, creates stability problems. To solve these problems, there was an emerging class of miniaturized dielectric cavity based optical resonators that exploited the light confinement phenomenon through internal reflection. These resonators have a circular symmetry, and they sustain modes known as the Whispering Gallery Modes (WGM) that is nothing but electromagnetic waves that circulate and are confined within the structure. Fabrication of these dielectric optical resonators is simpler and comparatively inexpensive. They demonstrate higher mode stability and higher performance.
In this proposal review, I will discuss the working principles of a WGM resonator and study the various loss mechanisms to improve the quality factor. Further I will discuss the fabrication of on chip glass-blown microspherical shell resonators. These on-chip spherical glass shells are micrometers to millimeters in diameter with ultra-smooth surfaces and micrometer wall thicknesses which can sustain optical resonance modes with high Q-factors up to 50 million. Further we discuss various methods used to etch the backside silicon to create a liquid core optical resonator. This etching leads to increase in the surface roughness leading to loss of resonance. We optimized etching methods and parameters to keep the resonance as high as 18 million. By etching the silicon resonator’s temperature sensitivity is improved from -1.15 GHz/K to 2.23 GHz/K. This optical WGM sensor is then novel biosensor consisting of a chip-scale whispering gallery mode resonators with High-Q factor and a micro-caloric system. The silicon released shell resonator is elastically coupled to a kapton tubing system. Temperature change in the system induces thermal expansion and thermorefractive changes which can be sensitively monitored through changes in the optical resonance characteristics. We demonstrate a measurement resolution less than 10mK and a method of measuring temperature change to eliminate background noise that shows a great potential for detection of various biomolecules such as urea. We also discuss the possibility to use the sensor as an extremely sensitive IR sensor. Finally, we talk about the future work in immobilization of urease and glucose oxidase to test for analytes like urea and glucose with concentrations in micro-mole.

ECE PhD Dissertation Defense: Andac Demir
2022-01-14 14:00 - 15:00
PhD Dissertation Defense: Automated Bayesian Network Exploration for Nuisance-Robust Inference
Andac Demir
Location: Zoom Link
Abstract: A fundamental challenge in the analysis of physiological signals is learning useful features that are robust to nuisance factors e.g., inter-subject & inter-session variability, and achieve the highest nuisance-invariant classification performance. Towards resolving this problem, we introduce 3 frameworks: AutoBayes, which is an AutoML approach to conduct neural architecture search for research prototyping, and GNN based frameworks: EEG-GNN and EEG-GAT.
The ultimate goal of the AutoBayes framework is to identify the conditional relationship between a physiological dataset, associated task labels, nuisance variations and potential latent variables in order to robustly infer the task labels invariant of nuisance factors. Nuisance factors in the case of physiological datasets could be variations in subjects or sessions, but we only focus on subject variations in the experiments. AutoBayes enumerates all plausible Bayesian networks between data, labels, nuisance variations and potential latent variables, detects and prunes the unnecessary edges according to Bayes-Ball Algorithm, and then trains the resulting DNN architectures for different hyperparameter configurations in an adversarial / non-adversarial or a variational / non-variational setting to achieve the highest validation performance. Instead of hyperparameter tuning for model optimization, AutoBayes concentrates on the architecture search of plausible Bayesian networks, and achieves state-of-the-art performance across several physiological datasets. Furthermore, we ensemble several Bayesian networks by stacking their posterior probability vectors in a higher level learning space, train a shallow MLP as a meta learner, and measure the task and nuisance classification performance on a hold-out dataset. We observe that exploration of different inference Bayesian networks has a significant benefit in improving the robustness of the machine learning pipeline, and the parallel activity of vast assemblies of different Bayesian network models significantly reduces variation across subjects in the cross-validation setting.
In the second part of the dissertation, we benchmark the performance of EEG-GNN and EEG-GAT against the AutoBayes framework. CNN’s have been frequently used to extract subject-invariant features from EEG for classification tasks, but this approach holds the underlying assumption that electrodes are equidistant analogous to pixels of an image and hence fails to explore/exploit the complex functional neural connectivity between different electrode sites. We overcome this limitation by tailoring the concepts of convolution and pooling applied to 2D grid-like inputs for the functional network of electrode sites. Furthermore, we develop various GNN models that project electrodes onto the nodes of a graph, where the node features are represented as EEG channel samples collected over a trial, and nodes can be connected by weighted/unweighted edges according to a flexible policy formulated by a neuroscientist. The empirical evaluations show that our proposed GNN-based framework, EEG-GNN, outperforms standard CNN classifiers across ErrP and RSVP datasets, as well as allowing neuroscientific interpretability and explainability to deep learning methods tailored to EEG related classification problems. Besides that, EEG-GAT employs multi-head attention mechanism in conjunction with the GNN architecture to learn the graph topology of observations instead of utilizing a graph shift operator that is heuristically constructed by a domain expert. This implicitly allows the exploration of the functional neural connectivity peculiar to a cognitive task between pairs of EEG electrode sites as well as EEG channel selection, which is critical for reducing computational cost, and designing portable EEG headsets.

ECE PhD Dissertation Defense: Sungho Kang
2022-01-11 11:00 - 17:00
PhD Dissertation Defense: Plasmonically Enhanced Infrared Sensing Microsystems
Sungho Kang
Location: Zoom Link
Abstract: Infrared (IR) spectroscopic sensing has become a key technique in multidisciplinary environments such as military applications, industrial safety control, and smart homes, by providing an accurate and non-disruptive analysis of the target objects. Recently the demand for high performance and compact IR spectroscopy systems has been steadily growing due to the advent of Internet of Things and the burgeoning development of miniaturized sensors. The key challenge lies in realizing high performance IR detectors that have low noise, high IR throughput, and spectral sensitivity in a miniaturized form factor. This challenge has been tackled in the study of micro-electromechanical sensing systems and metamaterial absorbers, in which the ultra-high resolution sensing capability and the near-perfect IR absorption properties can be simultaneously exploited in a minimized footprint. The metal-insulator-metal (MIM) IR absorbers, in particular, are characterized by the near-unity absorptance with lithographically tunable peak absorption wavelength and spectral selectivity in an ultra-thin form factor, suitable for the implementation of miniaturized spectroscopic IR microsystems. The exceptional IR absorption characteristics realized by the MIM IR absorbers and their sub-wavelength form factor allow for seamless integration with the existing IR sensing microsystem and the unprecedented IR sensing performance for the next generation IoT sensing solutions. In this defense, novel development of miniaturized IR spectroscopic sensor and maintenance-free wireless human sensors based on the two key technologies are presented: (1) multispectral resonant IR detector array and (2) plasmonically-enhanced long-wavelength infrared micromechanical photoswitch. This study shows that the demonstrated technologies can replace the traditional IR sensors with the new generation IR sensing microsystems that are characterized by their high performance, compact form factor, power efficiency and low cost.

ECE PhD Proposal Review: Anahita Moradmand
2021-12-20 11:00 - 12:00
PhD Proposal Review: Robust Observer Structures and Control Design for Linear and Nonlinear Dynamical Systems with Applications
Anahita Moradmand
Location: Zoom Link
Abstract: This thesis focuses on special class of observers and controllers for different and seperable nonlinear systems. In the first step, a robust fault detection approach using observers for linear systems is proposed where we combine the unknown inout observer UIO and an extended proportional integral observer PIO, which has a fading term for robust fault detection. The integrated observer is called proportional integral fading unknown input observer (PIFUIO).
Furthermore, we extend our result to nonlinear systems with special structures as the design of nonlinear observer had limitation for general types of nonlinear systems. In the second step, analysis and design of positive systems is considered whereby positive stabilization and the design of positive unknown input observer (PUIO) are introduced. Also, the robust stability analysis of this class of systems is studied in which the robust stability is formulated in terms of LMI. the class of separable positive nonlinear systems is also analyzed and the design of observer and controller are provided. Finally, we extend our desgin from Lipschitz type nonlinearity to state-dependent type by focusing on interconnected systems where we propose a distributed control architecture to take advantage of the global performance similar to centralized control and leverage the benefits of decentralized control.

ECE PhD Dissertation Defense: Jinghan Zhang
2021-12-16 15:00 - 16:00
PhD Dissertation Defense: Domain Design Space Exploration: Designing a Unified Platform for a Domain of Streaming Applications
Jinghan Zhang
Location: ISEC 362 or Zoom Link
Abstract: Many demanding streaming applications share functional and structural similarities with other applications in their respective domain, e.g., video analytics, software-defined radio, and radar. This opens the opportunity for specialization to achieve the needed efficiency and/or performance.
Platforms integrating many accelerators (ACCs) is a primary approach for efficient, high-performance stream computing.
However, designing one platform for each application is not economical due to the high costs of nonrecurring engineering (NRE) and time-to-market (TTM).
To this end, the concept of domain platforms is proposed, which takes advantage of similarities across applications and designs one unified platform to accelerate a domain of applications instead of focusing on a single reference application.
This dissertation approaches designing domain platforms from a function-level (kernel-level) acceleration through a heterogeneous ACC-rich platform, where each ACC is specialized to accelerate a particular function.
There is a great challenge to select ACCs allocated in the domain platform, considering the large design space and performance balance across many applications.
However, current Design Space Exploration (DSE) tools only focus on an individual application in isolation (e.g., one particular vision flow) for allocating a platform, but not a set of similar applications.
This dissertation introduces Greedy Guided Mutation (GGM) to speed up the mutation in the GIDE algorithm, which calculates an ACC score according to current allocation to guide mutation.
Alternatively, Rapid Domain Platform Performance Prediction (RDP^3) methods are introduced to replace a large number of the slow platform assessment in domain DSE, which avoids the complex application binding exploration.
In the experiments, GGM reduces 84.8% of exploration time with a 0.23% loss of the final OpenVX domain platform’s performance.
RDP^3 using a machine learning method yields an even more significant speedup, saving 90.8% of exploration time with only 0.0003% performance loss.
DmDSE is a milestone to broaden DSE scope from individual applications to the domain level. It tremendously pushes the domain platform design from manually and engineering experience guided into a general automatic process.

Distinguished Speaker Series in Robotics: Gregory S. Chirikjian
2021-12-16 14:00 - 15:00 | ISEC Auditorium, 805 Columbus Ave, Boston, MA, 02115, United States
Distinguished Speaker Series in Robotics
Gregory S. Chirikjian: Robot Imagination: Affordance-Based Reasoning about Unknown Objects
Location: ISEC Auditorium or Zoom Link
Abstract: Today’s robots are very brittle in their intelligence. This follows from a legacy of industrial robotics where robots pick and place known parts repetitively. For humanoid robots to function as servants in the home and in hospitals they will need to demonstrate higher intelligence, and must be able to function in ways that go beyond the stiff prescribed programming of their industrial counterparts. A new approach to service robotics is discussed here. The affordances of common objects such as chairs, cups, etc., are defined in advance. When a new object is encountered, it is scanned and a virtual version is put into a simulation wherein the robot “imagines’’ how the object can be used. In this way, robots can reason about objects that they have not encountered before, and for which they have no training using. Videos of physical demonstrations will illustrate this paradigm, which the presenter has developed with his students Hongtao Wu, Meng Xin, Sipu Ruan, and others.
Bio: Gregory S. Chirikjian received undergraduate degrees from Johns Hopkins University in 1988, and a Ph.D. degree from the California Institute of Technology, Pasadena, in 1992. From 1992 until 2021, he served on the faculty of the Department of Mechanical Engineering at Johns Hopkins University, attaining the rank of full professor in 2001. Additionally, from 2004-2007, he served as department chair. Starting in January 2019, he moved the National University of Singapore, where he is serving as Head of the Mechanical Engineering Department, where he has hired 14 new professors so far. Chirikjian’s research interests include robotics, applications of group theory in a variety of engineering disciplines, applied mathematics, and the mechanics of biological macromolecules. He is a 1993 National Science Foundation Young Investigator, a 1994 Presidential Faculty Fellow, and a 1996 recipient of the ASME Pi Tau Sigma Gold Medal. In 2008, Chirikjian became a fellow of the ASME, and in 2010, he became a fellow of the IEEE. From 2014-15, he served as a program director for the US National Robotics Initiative, which included responsibilities in the Robust Intelligence cluster in the Information and Intelligent Systems Division of CISE at NSF. Chirikjian is the author of more than 250 journal and conference papers and the primary author of three books, including Engineering Applications of Noncommutative Harmonic Analysis (2001) and Stochastic Models, Information Theory, and Lie Groups, Vols. 1+2. (2009, 2011). In 2016, an expanded edition of his 2001 book was published as a Dover book under a new title, Harmonic Analysis for Engineers and Applied Scientists.

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ECE PhD Proposal Review: Jaehyeon Ryu
2021-12-16 09:30 - 10:30
PhD Proposal Review: Engineering Functional Nanomesh for Advanced Neuroelectronics
Jaehyeon Ryu
Location: Zoom Link
Abstract: Transparent electronics have emerged as promising platforms for neural interfacing by enabling simultaneous electrophysiological recording and optical measurements. Also, there are high demands for stretchable devices due to their low modulus and compatible interface with irregular and soft neural tissue. However, current transparent, stretchable approaches are usually limited by their scalability for neuroelectronic applications. Here, I present multi-functional nanomesh as an approach to achieve stretchable, transparent microelectrode arrays (MEAs) with excellent scalability. By stacking mechanical supporting polymer, gold, and conductive polymer in a nanomesh structure on elastomer substrate, multilayer nanomesh-based MEAs show excellent stretchability, transparency, and electrochemical properties with single neuron scale dimensions. The performance of these multi-functional nanomesh-based MEAs has been characterized through bench testing, and I plan to perform in vivo validation in the remaining period of my thesis. These highly stretchable and transparent multilayer nanomesh MEAs are promising for applications ranging from neuroscience to biomedical devices.

ECE PhD Proposal Review: Leonardo Bonati
2021-12-10 13:45 - 14:45
PhD Proposal Review: Softwarized Approaches for the Open RAN of NextG Cellular Networks
Leonardo Bonati
Location: 532 ISEC
Abstract: The 5th (5G) and 6th generations (6G) of cellular networks, also known as NextG, will bring unprecedented flexibility to the wireless cellular ecosystem. Because of its typically closed and rigid market, the telco industry has incurred high costs and non-trivial obstacles in delivering those new services and functionalities to satisfy the requirements and the demand of NextG networks. To break this trend the industry is now moving towards open architectures based on softwarized approaches, which will afford network operators flexible control and unprecedented adaptability to heterogeneous conditions, including traffic and application requirements. Now, by simply expressing a high-level intent, operators will be able to instantiate bespoke services on-demand on a generic hardware infrastructure, and to adapt such services to the current network conditions. Through disaggregation, network elements will split their functionalities across multiple components—possibly provided by different vendors—interconnected through well-defined open interfaces. The separation of control functions from the hardware fabric, and the introduction of standardized control interfaces, will ultimately enable definition and use of softwarized control loops, which will bring embedded intelligence and real-time analytics to effectively realizing the vision of autonomous and self-optimizing networks.
This dissertation work focuses on the design, prototyping and experimental evaluation of softwarized approaches for the new open Radio Access Network (RAN) of NextG cellular networks. We analyze the architectural enablers, challenges and requirements for a programmatic zero-touch control of the very many network elements and propose practical solutions for its realization. We prototype solutions by leveraging open-source software implementations of cellular protocol stacks and frameworks, and heterogeneous virtualization technologies, including the srsRAN and OpenAirInterface cellular implementations, and the O-RAN framework. The contributions of this work include (i) the first demonstration of O-RAN data-driven control loops in a large-scale experimental testbed using open-source, programmable RAN and RAN Intelligent Controller (RIC) components through xApps of our design, and (ii) CellOS, a zero-touch cellular operating system that automatically generates and executes distributed control programs for simultaneous optimization of heterogeneous control objectives on multiple network slices starting from a high-level intent expressed by the operators. The effectiveness of our solutions in achieving superior control and performance of the RAN is demonstrated on state-of-the-art experimental facilities, including software-defined radio-based laboratory setups and open access experimental wireless platforms, such as Colosseum, Arena, and the POWDER-RENEW platform from the U.S. PAWR program.

ECE PhD Proposal Review: Michele Pirro
2021-12-10 11:00 - 12:00
PhD Proposal Review: Scandium-doped Aluminum Nitride for new MEMS technologies
Michele Pirro
Location: 432 ISEC
Zoom Link Meeting ID: 938 0086 0379 https://northeastern.zoom.us/u/abZS2SmtT1
Abstract: The increasing demand for data is pushing the MEMS industry to more performant and area-efficient systems to be used in IOT nodes as sensors and RF-components. In this market, AlN plays a pivotal role thanks to the piezoelectric properties accompanied with good stability over power and temperature in miniaturized devices. In fact, AlN is already present in different commercial MEM systems, such as duplexers, ultrasound generators, energy harvesters and so on, proving a mature mass-production process flow. The required more stringent specifications in terms of bandwidth, losses and efficiency are pushing towards piezoelectric materials with higher coupling coefficient, but still in a compatible post-CMOS process flow. Luckily, recent works showed how it is possible to enhance the piezoelectric effect by doping AlN with Scandium, allowing up to 400% increase in the d33 piezoelectric coefficient. The enhanced acoustic transduction along with the recent demonstration of ferroelectric switching and the post-IC compatibility, is making Sc-doped AlN a new material with the potential not only to replace AlN, but also to integrate different functionalities within the same component. Academy and industry all over the world are actively researching the actual potential of the material but there is still a lack of information on high-Sc concentration, which would allow lower-voltage switching along with higher d33. This work has the main objective to show Sc-concentration > 28% and their piezo/ferroelectric response for a new class of microelectromechanical devices.
The proposal will discuss the advance in the process flow of high Sc- concentrations, showing the impact of the deposition parameters on the material properties. Thin films with good crystallinity on IC-substrate and enhanced d33 are reported, along with first attempts to resonator-devices. An in-depth ferroelectric characterization will show how coercive field and leakage current are the main limiting factors the material is facing to integrate its memory effect. For this purpose, the work will present how tuning of Sc-concentration, substrate-rf and bulk stress can ease these limiting factor, opening to new acoustic devices with memory functionalities. The last part will focus on the co-integration of acoustic properties with ferroelectric switching for tunable filters and ultrasonic generators in post-IC compatible substrates.

ECE PhD Dissertation Defense: Arjun Singh
2021-12-09 15:00 - 16:00
PhD Dissertation Defense: Design, Modeling, and Operation of Plasmonic Devices for Smart Communication Systems in the Terahertz Band
Arjun Singh
Location: 332 ISEC or Teams Link
Abstract: The terahertz (THz) band is an attractive spectral resource for realizing future communication systems, with the potential of supporting very high-speed data rates and increasingly dense networks. However, the lack of a well-developed technology that operates at these frequencies has remained a challenge for the scientific community. The very high propagation losses at THz frequencies and the decimating impact of everyday objects on THz wave propagation necessitate an up-haul of the conventional communication link, with smart control over the radiation, propagation, and detection of THz signals. Additionally, device physics at THz frequencies, among them the very high gain requirement and large electrical size, may render the often-held assumptions of the propagation model invalid. An interdisciplinary approach spanning device design and operation, and wireless propagation and signal processing is required.
To this end, the proposed research herein addresses the facilitation of an end-to-end communication link with graphene plasmonics as the cornerstone of the fundamental device physics. The devices designed can be utilized to effectively overcome the limited communication distance –The grand challenge of the THz band. Different from other undertakings, every attempt is made to ac-knowledge and accommodate the complex trade-offs in the design process. First, a novel graphene based plasmonic array architecture is proposed, explained, and modeled. The fundamental radiating element of the array architecture, called the plasmonic front-end, consists of a self-sufficient plasmonic source, a plasmonic modulator that acts as a phase controller, and a plasmonic nano-antenna for effective radiation. The designed array is compact and provides complete beamsteering support, with a new tailored algorithm developed for beamforming weight selection. Numerical evaluations and full-wave finite difference frequency domain (FDFD) simulations with COMSOL Multi-physics are utilized to verify array operation. Exploiting these properties, a multi-beam array design is presented next, where orthogonal spatial filters are utilized to provide support for spatial multiplexing towards the realization of ultra-massive MIMO (UM-MIMO). Taking this further, the design considerations of an interleaved plasmonic array are presented, in which the beamsteering capability is utilized to simultaneously achieve radio frequency interference (RFI) mitigation with channel capacity maximization for multi-user scenarios. Additionally, to realize the vision of a smart communication system with a programmable wireless environment, a hybrid reflectarray is presented. The fundamental element is modeled as a jointly designed and integrated metal-graphene patch. Numerical and simulation results are utilized to demonstrate the attractive properties of the reflectarray as compared to other proposed counterparts, including an independence from the incoming angle of the impinging wave, dynamic phase control capability, and strong reflection efficiency. The requirements of a THz communication link and their impact on the common communication protocols are considered next. It is shown that certain scenarios may render regular array operation invalid, motivating codebook designs that function in the massive near-field Fresnel zone of electrically large THz devices. Numerical simulations and theoretical analysis are presented to highlight their potential in improving system performance and capacity while reducing the system complexity. Finally, the significant milestones in the fabrication process of these devices are also presented.

ECE PhD Dissertation Defense: Sara Banian
2021-12-07 14:00 - 15:00
PhD Dissertation Defense: Content-Aware AI-Driven Design Assistance Frameworks for Graphic Design Layouts
Sara Banian
Location: Zoom Link
Abstract: Designing user interfaces (UIs) for mobile interaction is widespread but still challenging. It is important for the overall user satisfaction and application success. During the design process, designers express their requirements through images describing the UI’s layout, structure, and content. Designers, however, encounter key challenges throughout the design process. For example, searching for inspiring design examples is challenging because current search systems rely on only text-based queries and do not consider the UI structure and content. Furthermore, these systems often focus on overall page-level layout over individual UI components. Also, creating wireframe templates is difficult for many designers as it necessitates an understanding of different design guidelines. Therefore, it is critical to support designers by developing effective design tools to help them be more productive and creative.
In this dissertation, I aim to explore how to develop design assistance methodologies to augment the process of UI layout design, with a particular focus on visual search and layout generation. Specifically, for this exploration, I seek to investigate the use of advanced deep learning models in the context of mobile UI layout design. Processing layouts differs from processing pixel-level images in that it necessitates processing both the semantic (e.g., labels) and spatial (e.g., coordinates) content of the layout to model the data properly. To achieve this, I explore the design problems from both the data and the model side. First, I present a large-scale UI dataset that accurately specifies the interface’s view hierarchy (i.e., UI components and their location). Second, I contribute the VINS framework, which is composed of three systems LayVis, CompVis, and TransVis that addresses layout-based visual search, component-based visual search, and layout generation, respectively.
First, I introduce LayVis, an object-detection layout-based retrieval model. It takes as input a UI image and retrieves visually similar design examples. Next, I introduce CompVis, a component-based visual search system to easily retrieve individual UI components via convolutional neural networks (CNNs). Specifically, for a given query, the system allows to retrieve (1) text label synonyms, (2) similar UI components, and (3) design examples containing such components. Finally, I present TransVis, a transformer-based generative framework that investigate how to generate UI layouts according to user specifications and following design practices. It specifically models UI layouts as an ordered sequence of elements based on spatial and semantic relationships for (1) generating complete UI layouts, (2) auto-completing existing UI layouts seamlessly, and (3) supporting many design elements per layout.
Overall, the work presented in this dissertation contributes to augmenting the UI layout design. Through quantitative and qualitative evaluation of VINS, we conclude the following: (1) Advanced deep learning models can aid in the development of design assistance methodologies for layout design; and (2) Designers perceive the use of VINS inspiring and useful. Such insights, combined with the open-sourced large-scale dataset, can help the research community develop more effective AI-based data-driven design tools. This work presents future opportunities to investigate different deep learning models within the context of layout design and how designers interact with these AI-based models.

ECE PhD Dissertation Defense: Murphy Wonsick
2021-12-03 10:00 - 11:00
PhD Dissertation Defense: Supervisory Control for Humanoid Robots Through Virtual Reality Interfaces
Murphy Wonsick
Location: ISEC 655
Abstract: Recent advancements in robotics have allowed robots to become capable enough to be used in a wide variety of domains that are dangerous for humans to operate in, such as disaster relief operations, exploration of extraterrestrial planets, bomb disposal, or nuclear decommissioning efforts. However, current supervisory control interfaces that allow humans to explore and interact in these environments through remote presence and teleoperation are complex and often require expert operators. Virtual reality provides a medium to create immersive and easy-to-use teleoperation interfaces. Virtual reality allows operators to visualize and interact with 3D data in a 3D environment that is not possible with traditional interfaces that make use of 2D devices, such as monitors, keyboards, mice, tablets, and/or game controllers. Yet, development of supervisory control virtual reality interfaces for robot operation is still very limited. Most present work in virtual reality interfaces focuses on direct teleoperation and not on high-level control that supervisory control interfaces can provide. In this dissertation, we focus on developing virtual reality supervisory control interfaces for remote robot operation. We specifically focus on high degree-of-freedom robots, such as humanoid robots or mobile manipulator robots, as they are the most suited types of robots for remote operation. To accomplish this, we first look to better understand and define humanoid robot capabilities using NASA’s humanoid robot, Valkyrie. Following, we synthesize the current state-of-the-art supervisory control interfaces for humanoid robots to create our own supervisory control interface using traditional devices. We then use this information to create a virtual reality supervisory control interface for Valkyrie. Finally, we look to improve virtual reality interfaces for robot operation through a user-centered design approach to inform future development on virtual reality interfaces.

ECE PhD Proposal Review: Tong Jian
2021-12-02 15:00 - 16:00
PhD Proposal Review: Robust Sparsified Deep Learning
Tong Jian
Location: Zoom Link
(ID: 75807284369, Passcode: [REDACTED]
Abstract: In this thesis, we investigate and address robustness concerns about DNN-based real-life applications on resource constrained systems, environment adaptation, and adversarial learning, respectively. We propose a means of compressing a Radio Frequency (RF) deep neural network architecture through weight pruning, and provide a systems-level analysis of the implementation of such a pruned architecture at resource-constrained edge devices. In particular, we jointly train and sparsify neural networks tailored to edge hardware implementations. Under only negligible accuracy loss (less than 1%), we can achieve at most 27.2x pruning rate for 50-device classification. We demonstrate the efficacy of our approach over multiple edge hardware platforms and our method yields significant inference speedups, 11.5x on the FPGA and 3x on the smartphone, as well as high efficiency.
Furthermore, we propose a new learn-prune-share (LPS) algorithm for achieving robustness to environment adaptation in the field of lifelong learning. Our method maintains a parsimonious neural network model and achieves exact no forgetting by splitting the network into task-specific partitions via an ADMM-based weight pruning strategy. Moreover, a novel selective knowledge sharing scheme is integrated seamlessly into the ADMM optimization framework to address knowledge reuse. We show that our approach achieves significant improvement over the state-of-the-art methods on multiple real-life datasets.
Finally, we investigate the HSIC bottleneck as regularizer (HBaR) as a means to enhance adversarial robustness. We show that the HSIC bottleneck enhances robustness to adversarial attacks both theoretically and experimentally. In particular, we prove that the HSIC bottleneck regularizer reduces the sensitivity of the classifier to adversarial examples. Our experiments on multiple benchmark datasets and architectures demonstrate that incorporating an HSIC bottleneck regularizer attains competitive natural accuracy and improves adversarial robustness, both with and without adversarial examples during training.

ECE PhD Proposal Review: Zulqarnain Qayyum Khan
2021-12-01 15:30 - 16:30
PhD Proposal Review: Interpretable Machine Learning for Affective Neuroscience and Psychophysiology
Zulqarnain Qayyum Khan
Location: Zoom Link
Abstract: In this thesis, we leverage Machine Learning to investigate questions of interest in affective psychophysiology and neuroscience . We argue for and apply appropriate existing methods where possible and analyze the results they provide. Where existing methods fail to provide an answer we propose and build new models. We demonstrate the use of Hierarchical Clustering to investigate autonomic nervous system reactivity during an active coping stressor task, revealing physiological indices of challenge and threat. Similarly, we leverage Dirichlet Process Gaussian Mixture Modelling (DP-GMM) to reveal the variation in affective experience during a context-aware experience sampling study and to investigate the relationship between emotional granularity and cardiorespiratory physiological activity using resting state data for participants in the same study. We propose and develop Neural Topographic Factor Analysis (NTFA), a novel factor analysis model for fMRI data with a deep generative prior that teases apart participant and stimulus driven variation and commonalities and learns a latent space that can shed light on important neuroscientific phenomenon such as individual variation and degeneracy.
Based on the work we have already done, we propose three further lines of research that we intend to include in this thesis. First, NTFA can essentially be viewed as a family of models, where appropriate modifications can be made depending on what questions are needed to be answered. Leveraging this, we propose explicitly adapting NTFA to tackle the question of degeneracy in neural responses. This involves introducing another latent space which can be used to capture and visualize the interaction of each participant with each stimulus in a given fMRI study. The arrangement of inferred embeddings in this latent space can then suggest presence or absence of different types of degeneracy in neural responses among participants in response to the presented stimuli. Second, during the course of this interdisciplinary research we realized that there is a need for a comprehensive work that sheds light on the assumptions and limitations of some of the most popular machine learning methods used commonly in the sciences (specially psychology), and provide recommendations on how researchers can be more mindful of the underlying assumptions machine learning methods make. This can then equip users of ML methods to draw more appropriate conclusions from the results they get. We intend to include this in our thesis. Third, continuing along the same lines, there is also a need for better explanation models for the increasingly complicated ML models in use today. This is especially true in health sciences where the knowledge of why an ML model made a particular decision is almost as important as that decision being accurate. To this end we propose a theoretical work that ties the reliability of explanation models to the robustness of the models they are trying to explain.

ECE PhD Dissertation Defense: Flavius Pop
2021-12-01 12:00 - 13:00
PhD Dissertation Defense: Intrabody Communication for Real-Time Monitoring of Implanted Medical Devices based on Piezoelectric Micromachined Ultrasonic Transducers
Flavius Pop
Location: Zoom Link
Abstract: Nowadays when we think about medical devices and patient monitoring, we can easily imagine ourselves laying down in a hospital bad, wires coming out of everywhere, and being looked after by nurses and physicians. Scary and not that comfortable! For this reason, medical wearable devices are becoming more popular for at-home monitoring and transmitting data back to the hospital. Sometimes wearables are not enough, this is why Implanted Medical Devices (IMDs) are still required to monitor many vital signs (blood flow, insulin level, neurons reading etc.) and act upon these readings (nerve stimulation, heart defibrillation, insulin pumping etc.). In order to be minimally invasive, reduce the risk of infection and rejection from the body, and last a long time (avoiding any further surgery) the IMDs require robust wireless communication technology to communicate with the external world. In this presentation I am going to show how we can implement an ultrasonic wireless communication link based on Piezoelectric Micromachined Ultrasonic Transducers (pMUTs) arrays. PMUT arrays can be integrated with existing IMDs, used for wireless power charging, and can enable communication links for receiving and transmitting data. During the first part of the presentation I will show the modeling and design of the pMUT arrays, followed by the fabrication process and the device’s characterization for system level validation. At this point, the communication link is implemented with arrays implanted in a tissue phantom and the channel is characterized at several distances. During the second part of the presentation I will show novel techniques to improve the ultrasonic communication link such as duplexing matching networks for bandwidth definition and direct modulation for implantation depth increase and direct bitstream feeding. In the future I envision that the number of IMDs are going to increase, and therefore I developed a scanning protocol that will allow medical doctors to find all implanted devices. This is the equivalent of an “ultrasonic stethoscope”. Given the small form-factor of the IMDs these will have little to no space for a battery, limiting the operation lifetime. For this reason, I developed an Ultrasonic Wakeup Receiver (UWuRx) based and on the direct modulation system and on a Micromachined Electro-Mechanical System (MEMS) switch which allows for near zero-power consumption in the idle state. This UWuRx enabled on-demand device usability and limited the idle power consumption, which leads to battery life extension.

ECE PhD Dissertation Defense: Kai Li
2021-12-01 09:00 - 10:00
PhD Dissertation Defense: Reconfigurable and Intelligent Wireless Charging Surfaces
Kai Li
Location: 232 ISEC
Abstract: Reconfigurable intelligent surfaces (RISs) have received significant attention for theirpotential to transform the environments by intelligently reconfiguring the surfaces, infrastructures,and engineering the electrical and magnetic fields. On the other hand, while wireless power transfer has advanced, there has been limited progress on increasing the charging coverage, such as charging over large surfaces, multi-device charging, and automation. This dissertation aims to address these challenges and design and develop first-of-its-kindtheory and practice to transform ordinary surfaces into contactless, intelligent, and multi-devicewireless chargers. First, the combination between magnetic resonance and the so-called concept of‘energy hopping’ across wireless inter-connected coils turns a large surface into a programmablewireless charging surface. The magnetic fields are carefully shaped on the fly over the surface,enabling them to distribute energy efficiently at multiple locations on demand and charge differenttypes of devices. Two frameworks are developed: SoftCharge can deliver 23 W up-to 20cm over a larger surface, and iSurface enables the creation of arbitrary and configurable power spots and power flow paths over 2D and 3D resonator surfaces. Inspired by the strong coupled magnetic resonance wireless power transfer, two intelligentsurface sensing frameworks, SoftSense, and iSense, are introduced that create collaborative surface-based object sensing and tracking using networked coils. SoftSense allows detection of the type of object and where it is placed on a large surface. iSense enables robot tracking over large surfaces.We validate our design on real sensing prototypes, and experimental results show that each sensing coil only consumes few milliwatts and has 90% accuracy for velocity estimation.Combined with meta-surface, we extended the intelligent charging surfaces to enhances safety, end-to-end power transfer efficiency, and customized power pattern over the surface.Toward this, we design and develop a new system call meta-resonance wireless power transfer system that consists of power distribution layer and meta-resonance layer, along with a new theory and prototype for fine-tuned and controllable power amplifying, power blocking and normal power passing over the surface. We aim to create customized pattern and different application from portable devices(phone, tablet) to medical devices, and industrial devices with high safety and high power transfer efficiency.

Learn about the Co-op Program (Disciplinary) Webinar
2021-12-01 08:00 - 09:00
Please join our Assistant Dean of Co-op at a webinar discussing the Co-op experiential learning opportunities available for graduate students in the departments of Bioengineering, Chemical Engineering, Civil & Environmental Engineering, Electrical & Computer Engineering, and Mechanical & Industrial Engineering.
Register

ECE PhD Proposal Review: Sara Garcia Sanchez
2021-11-30 12:00 - 13:00
PhD Proposal Review: Learning and Shaping the Wireless Environment: An Integrated View of Sensing, Computing and Communication
Sara Garcia Sanchez
Location: Microsoft Teams
Abstract: The explosive growth in Internet of Things (IoT) deployments and anticipated data volumes that will be generated within future autonomous vehicles require collecting and processing large amounts of data, generally transmitted over the wireless channel. In this context, conventional permanent deployments limited to leverage the existing wireless environment, often fall short of meeting the required capacity demand. To address this limitation, this dissertation takes a hands-on approach to equip communication systems with technology to perceive and collaborate with and within the environment. Specifically, we build (i) accurate physics-oriented predictive models and multimode sensing techniques to gain awareness of the existing channel, as well as (ii) novel multidisciplinary approaches to intelligently modify the wireless channel towards the communication link benefit.
In this dissertation, we first prove that combining wireless channel modelling, multimode sensing and robotics provides significant link performance gains. To this extent, we adopt a systems approach to study how millimeter wave (mmWave) radio transmitters on Unmanned Aerial Vehicles (UAVs) provide high throughput links under typical hovering conditions. Based on sensing and modelling efforts, we propose techniques to exploit the information contained in the spatial and angular domains of empirically collected data from GPS, cameras and RF signals. We demonstrate hovering impact mitigation by (i) selecting near-to-optimum transmission parameters as compared to the mmWave standard IEEE 802.11ad and (ii) proposing corrective coordinated actions at the UAVs from the robotic controls. These methods achieve mmWave beam-tracking and robust link deployment under event(s) impacting link performance, such as hovering or blockage in the light of sight between transmitter and receiver.
Then, this dissertation experimentally demonstrates how the wireless environment can be interactively programmed through the use of Reconfigurable Intelligent Surfaces (RIS) to partially offload computation into the wireless domain. In particular, we propose AirNN, a system capable of realizing analog over-the-air convolutions, accurately enough to substitute their digital equivalent in a Convolutional Neural Network (CNN).
As proposed future work, this dissertation will explore innovative uses of the RIS technology in MIMO systems for 6G and beyond. Specifically, we will investigate (i) how the use of RIS helps overcome environmental limitations of a highly spatially correlated MIMO system, and (ii) whether the use of RIS can enable the use of MIMO techniques with a single antenna at the receiver.

ECE PhD Proposal Review: Sara Garcia Sanchez
2021-11-30 12:00 - 13:00
PhD Proposal Review: Learning and Shaping the Wireless Environment: An Integrated View of Sensing, Computing and Communication
Sara Garcia Sanchez
Location: TBA
Abstract: The explosive growth in Internet of Things (IoT) deployments and anticipated data volumes that will be generated within future autonomous vehicles require collecting and processing large amounts of data, generally transmitted over the wireless channel. In this context, conventional permanent deployments limited to leverage the existing wireless environment, often fall short of meeting the required capacity demand. To address this limitation, this dissertation takes a hands-on approach to equip communication systems with technology to perceive and collaborate with and within the environment. Specifically, we build (i) accurate physics-oriented predictive models and multimode sensing techniques to gain awareness of the existing channel, as well as (ii) novel multidisciplinary approaches to intelligently modify the wireless channel towards the communication link benefit.
In this dissertation, we first prove that combining wireless channel modelling, multimode sensing and robotics provides significant link performance gains. To this extent, we adopt a systems approach to study how millimeter wave (mmWave) radio transmitters on Unmanned Aerial Vehicles (UAVs) provide high throughput links under typical hovering conditions. Based on sensing and modelling efforts, we propose techniques to exploit the information contained in the spatial and angular domains of empirically collected data from GPS, cameras and RF signals. We demonstrate hovering impact mitigation by (i) selecting near-to-optimum transmission parameters as compared to the mmWave standard IEEE 802.11ad and (ii) proposing corrective coordinated actions at the UAVs from the robotic controls. These methods achieve mmWave beam-tracking and robust link deployment under event(s) impacting link performance, such as hovering or blockage in the light of sight between transmitter and receiver.
Then, this dissertation experimentally demonstrates how the wireless environment can be interactively programmed through the use of Reconfigurable Intelligent Surfaces (RIS) to partially offload computation into the wireless domain. In particular, we propose AirNN, a system capable of realizing analog over-the-air convolutions, accurately enough to substitute their digital equivalent in a Convolutional Neural Network (CNN).
As proposed future work, this dissertation will explore innovative uses of the RIS technology in Multiple Input Multiple Output (MIMO) systems for 6G and beyond. Specifically, we will investigate (i) how the use of RIS helps overcome environmental limitations of a highly spatially correlated MIMO channels, and (ii) whether the use of RIS can enable the use of MIMO techniques with a single antenna at the receiver.

ECE PhD Proposal Review: Ziqiang Cai
2021-11-19 09:00 - 10:00
PhD Proposal Review: Near-infrared Optical Modulation by Hybrid Graphene Metasurfaces
Ziqiang Cai
Location: Zoom Link
Abstract: The field of metasurfaces has emerged as one of the most promising frontiers in optical research due to the compact footprint and flexibility in light manipulation. To further advance the practical applications of metasurfaces, tunable or reconfigurable metasurfaces are highly desirable. One approach is to utilize graphene by taking advantage of its tunable optical properties upon electrical bias. Graphene metasurfaces have been extensively studied in many applications, including polarization tuning, phase tuning, photodetectors, chemical sensing, tunable lens, etc. However, the working wavelengths of the reported graphene metasurfaces are limited in mid-infrared and terahertz spectra.
In this proposal review, I will discuss a graphene metasurface that can push the working wavelength into the near-infrared region (≤ 3.0 µm). The device combines graphene with plasmonic structures made of gold to enhance the interband transition of graphene, resulting in decent tunability at near-infrared wavelengths. The tuning process of our graphene metasurface shows distinct differences in comparison with the graphene metasurfaces operating in the mid-infrared or terahertz spectra, which can be accurately predicted by both theory and simulation. The measured results show a reflection modulation ΔR of about 10% and a modulation depth ΔR/Rmax of 17% at 2.42 µm.
Finally, by using an anisotropic plasmonic structure, our hybrid graphene metasurface can simultaneously operate in the near-infrared and mid-infrared spectra. The measured modulation depth is 18.2% at 2.30 µm and 24.7% at 5.67 µm. Our research substantially broadens the working wavelength of graphene metasurfaces, and manifest potential applications in near-infrared electro-optic modulators, reconfigurable lenses, and polarization modulators.

MS Robotics Webinar
2021-11-05 08:00 - 09:00
Please join faculty and graduate admissions staff at a webinar discussing MS Robotics departmental course offerings and experiential learning opportunities in the Graduate School of Engineering.

ECE PhD Proposal Review: Cooper Loughlin
2021-11-04 10:00 - 11:00
PhD Proposal Review: Unsupervised Machine Learning Approaches to Sequential Data Analysis
Cooper Loughlin
Location: Remote
Abstract: Analysis of sequential data is central to many fields of science and engineering. Often, sequences are collections of observations made over time and space with little or no contextual information. The goal of analysis may be to evaluate relationships, identify unusual observations, or forecast future behavior based on historical data. Unsupervised modeling of sequences (e.g., time series) can illuminate the underlying structure of the data and enable analysis.
In this proposal, we discuss a statistical model for multivariate time series and an associated inference algorithm. We develop a preliminary model for a particularly challenging class of multivariate time series where the observations are counts (non-negative integers) that are nonuniformly sampled in time. We develop a state space model and inference algorithm based on Monte Carlo integration and Expectation-Maximization. This preliminary work highlights some key challenges still to be addressed. In particular, continuously variable sampling intervals, computational complexity of sampling, and long-term dependencies among observations are properties of real data that are not handled well by the preliminary model. Recent developments in unsupervised sequence modeling using deep learning techniques are introduced including variational auto-encoders, recurrent neural networks, and ordinary differential equation recurrent neural networks. We propose utilizing these deep learning techniques to improve the state of the art in sequential data analysis.

Electrical & Computer Engineering
2021-11-03 10:30 - 11:00
Please join faculty and graduate admissions staff at a webinar discussing the Electrical and Computer Engineering departmental program offerings and experiential learning opportunities in the Graduate School of Engineering.

ECE PhD Proposal Review: Jared Miller
2021-11-03 10:00 - 11:00
PhD Proposal Review: Nonlinear and Time-Delay Systems Analysis using Occupation Measures
Jared Miller
Location: Zoom Link
Abstract: Techniques to analyze nonlinear systems include peak and reachable set estimation. The reachable set of a system is the set of states accessible by trajectories of a dynamical system at specified times given initial conditions. The peak estimation problem finds extreme values of a state function along trajectories. Examples of peak estimation include finding the maximum height of a wave, voltage on a power line, speed of a vehicle, and infection rate of an epidemic. These problems may be posed as infinite dimensional linear programs (LP) in occupation measures, where occupation measures are Borel measures that contain all information about trajectories. Under mild assumptions, a sequence of Linear Matrix Inequalities (LMI) in increasing degree will converge from outside to the LP optimum, which is in turn equal to the true optimum of the program in trajectories.
The first part of this thesis expands upon the occupation measure formulation for peak estimation. The safety of trajectories with respect to an unsafe set may be quantified by measuring the constraint violation (safety margins), which is a maximum peak estimation problem. The distance of closest approach between trajectories and an unsafe set may be bounded through a modification of the peak estimation problem. Peak estimation may be applied to dynamics possessing a broad class of uncertainties, which includes the data-driven setting of black-box polynomial dynamics. A modular MATLAB toolbox is developed to solve and interpret these variations on peak estimation problems.
The second part of this thesis introduces an occupation measure framework for analysis and control of time-delay systems. The evolution of time delay systems depends on present and past values of the state. Some instances of time delay systems with their associated delays include epidemic models (incubation period), population dynamics (gestation time), and fluid modeling (transport time of fluid moving in a pipe). An occupation measure framework is developed to define weak solutions over a finite time interval of nonlinear time-delay systems with a finite number of bounded discrete delays. Applications of this time-delay weak solution include optimal control (including dead-time), peak estimation, and reachable set estimation of time delay systems.

ECE PhD Proposal Review: Ramtin Khalili
2021-10-29 11:00 - 12:00
PhD Proposal Review: Efficient State and Parameter Estimation in Three-Phase Power Systems
Ramtin Khalili
Location: Microsoft Link
Abstract: As the number of renewable energy sources, bulk energy storage systems, and non-conventional loads increase and connect to the power grid not only at transmission but also sub-transmission and distribution levels, three-phase unsymmetrical network monitoring becomes necessary for reliable operation and control of the overall power grid. The use of modal decomposition of measurement equations has already been shown to simplify the formulation and resulting computational complexity of three-phase state estimation of systems where all the transmission lines are three-phase and fully transposed. When there are untransposed and/or mixed-phase lines, modal decomposition can no longer fully decouple the three-phase measurement equations. This shortcoming is eliminated by a simple yet practical solution based on the commonly used numerical compensation techniques. Thus, it enables the application of the powerful decoupling approach to any type of three-phase networks which may contain untransposed or mixed-phase lines and are fully observable by PMUs. This implicit restriction is then removed by using a transformation that enables the use of SCADA measurements which are more commonly available in power grids. Furthermore, It has been shown that network parameter errors can bias the state estimation solution. Network parameter errors are common due to aging, changes in the ambient temperature, human data entry error, etc. So, an efficient approach is proposed to detect and correct the network parameter errors in three-phase untransposed transmission lines. Preliminary results to illustrate the performance of the proposed methods and associated algorithms will be presented using different test systems.

ECE PhD Proposal Review: Cesar Antonio Galvez Nunez
2021-10-29 10:00 - 11:00
PhD Proposal Review: Fault Location in Radial and Meshed Networks Containing Distributed Energy Resources (DERs)
Cesar Antonio Galvez Nunez
Location: Microsoft Teams Link
Abstract: Rapidly increasing numbers of Distributed Energy Resources (DERs) connected to transmission and distribution networks via Inverter Based Power Sources (IBPSs) introduce new challenges in detecting and locating faults. Distribution networks are historically designed to operate as radial systems with unidirectional power flows, which may no longer hold true due to the presence of large numbers of IBPSs. The commonly used impedance-based fault location methods are no longer reliable due to the limitations imposed by unknown fault resistance, asymmetry of lines, and presence of IBPSs, which need to comply with the new grid codes for Fault Ride Through (FRT) requirements. In this proposal, a new fault location method that can be used for radial and meshed networks containing DERs and addresses the limitations of conventional methods mentioned above will be introduced. The approach requires a limited number of digital fault recorders (DFR) to be placed in the network and uses the Discrete Wavelet Transform (DWT) to compute the first arrival times of fault-generated traveling waves. The proposal first presents a new two-terminal fault location technique used strictly for radial distribution networks, and then extends this to the general case of combined transmission and distribution networks with radial or meshed configurations. The method is further extended to be applied to hybrid AC/DC complex transmission grids containing DERs and High Voltage Direct Current (HVDC) lines. Preliminary results will be presented illustrating these methods on typical power grids and fault scenarios.

ECE PhD Dissertation Defense: Hongjia Li
2021-10-28 10:00 - 11:00
PhD Dissertation Defense: Automation Design and DNN Acceleration Frameworks: from software implementation to hardware physical design
Hongjia Li
Location: Northeastern Zoom Link
Abstract: With the breakthrough of Deep Neural Networks (DNNs) in the past decade, neural network-based computer vision has made huge progress, achieving exceptional performance. Tasks such as object detection, activity detection, and medical diagnosis are deployed in a wide range of applications, including autonomous driving, robot vision and training, human-computer interaction, and augmented reality. To increase the demand of application accuracy, DNN models are tuned to large scales by adding more parameters and layers. Meanwhile, mobile devices are rapidly becoming the central computer and carrier for deep learning tasks. However, real-time execution has been limited due to the computation/storage resource constraints on mobile devices.
The first part of this dissertation, I will present our unified real-time mobile acceleration of DNNs framework, seamlessly integrating hardware-friendly, structured model compression with mobile-targeted compiler optimization. The goal of our framework is to provide an unprecedented, real-time performance of such large-scale neural network inference using simply off-the-shelf mobile devices. Our proposed fine-grained block-based pruning scheme can be universally applicable to all types of DNN layers, such as CONV layers with different kernel sizes and fully connected layers. Different weight pruning schemes, such as unstructured pruning, filter/column pruning, and our block-based pruning, are analyzed and compared given the specific deep learning problems. To validate our framework, various applications are implemented and demonstrated, object detection, medical diagnosis. All applications can achieve real-time inference on mobile devices, outperforming the current mobile acceleration framework by up to 6.7X in speed.
For the second part of this dissertation, I will dive into an efficient automate framework for Adiabatic Quantum-Flux-Parametron (AQFP) technology, meeting the unique features and constraints. Superconductive electronics (SCE) based on the Josephson junction (JJ) single flux quantum (SFQ) logic cells have evolved into a within-reach “beyond-CMOS” technology. Placement is the primary step in physical design of the electronic systems as it directly affects the maximum frequency and routability of circuits. Algorithms for global placement, the core step in the placement process, typically minimize the total wirelength of a design as the main objective as it indirectly affects the routability and timing of circuits. Although minimizing the total wirelength improves the timing of the circuit in general, it does not directly target optimizing the delay of timing critical paths. Timing and routability driving placement methods are therefore needed. The currently mature design automation tools for CMOS cannot be directly applied to the design of superconducting electronics. In this dissertation, I will present our proposed timing-aware AQFP-specific placement and routing framework, given a path balanced AQFP netlist with clock phases. The proposed framework will reduce the solution complexity by making effective use of the row-wise placement/routing opportunity as each AQFP cell is assigned to a specific row (clock phase).

LEADERs Event: AI Challenges in the Deployment of Advanced Driver-Assistance Systems
2021-10-27 14:00 - 15:00
This presentation will help attendees learn about integrating AI in auto industry. While AI has been able to achieve remarkable success over the last 10 years, can it really be trusted? What does trustworthy AI look like in the context of connected vehicles and advanced driver-assistance systems (ADAS)? To create an ADAS for all, it’s paramount that the AI systems in the vehicle be trusted and work for everyone. This is especially important given the nature of ADAS as a safety-critical, cyber-physical, and people-centric system of systems. In this talk, the presenter will outline a number of challenges in building AI systems which could potentially be deployed in future ADAS.
Speaker Dr. Jacob Bond leads trustworthy AI research at General Motors R&D. In addition to his work on ensuring AI systems in the vehicle can be trusted, his research looks at how to keep AI systems private and how to ensure cloud and vehicle systems can establish secure communications. After receiving a Ph.D. in computational mathematics and cryptography from Purdue University, he joined General Motors’ Product Cybersecurity organization, focusing on applications of public-key cryptography. Jacob then began investigating the security of AI systems, moving to GM R&D and expanding his work to encompass the trustworthiness of AI systems.

ECE PhD Proposal Review: Chengju Yu
2021-10-22 12:30 - 13:30
PhD Proposal Review: Development of Interface-Engineered Thin Films and Magnetodielectric Bulk Composites for MMIC Applications
Chengju Yu
Location: Zoom Link
Abstract: Magnetodielectric materials are ubiquitous in electronic, energy, automotive, communication, and medical systems over radio frequency bands from high frequency to quasi-optical frequencies. With recent developments in modern power and communication technologies, improvements in magnetic materials and related components have attracted a great deal of attention from academic and industrial research groups.
In this proposal review, we demonstrate multiple paths to the development of next generation magnetodielectric thin films and bulk composites that offer disruptive advances to performance and size reduction, including:
(i) Consistent and reliable processing protocols are established using interface-engineered barium magnetoplumbite films deposited on Si-polar SiC substrates with AlN capping layers and MgO nucleation layers for microwave and millimeter-wave monolithic integrated circuits (MMICs);
(ii) Both thin and thick yttrium iron garnet films are achieved using PLD and LPE with outstanding crystalline and magnetic properties to meet the needs of magnonics and spintronics technologies; (iii) Inductor cores are developed for power generation, conversion, conditioning functions for use in power electronic systems and high-power pulse generators operating at 100s kHz and 100s MHz frequencies, respectively. Power loss and thermal management models of non-linear magnetic inductors are established and implemented with viable paths demonstrated using interface-engineered composites as a means of achieving high magnetization, high permeability, low core losses.
The common theme of all three projects is the engineering of the chemistry, structure, magnetic and electric properties of the interface between the principal layers, films, and grains that constitute the product in order to optimize performance.
