Northeastern University College of Engineering

BioE Seminar Series Presents: Christoph Juchem
2020-11-18 12:00 - 13:00 (America/New_York)
Christoph Juchem, Ph.D.
Associate Professor in the Departments of Biomedical Engineering and Radiology, Columbia University, New York New York
“Magnetic Resonance Imaging and B0 Shimming with the Dynamic Multi-Coil Technique (DYNAMITE)”
ABSTRACT:
In my talk, I will present a technique for B0 magnetic field control that is based on the combination of fields generated by a matrix of small, individually driven generic coils. This multi-coil approach enables the accurate generation of simple and complex magnetic field shapes in a flexible fashion. B0 shimming with the dynamic multi-coil technique (DYNAMITE) outperforms conventional methods based on spherical harmonic functions and provides unrivaled magnetic field homogeneity in mouse, rat and human brain. Along with the efficiency gains of DYNAMITE shimming compared to spherical harmonic approaches, the multi-coil concept has the potential to replace conventional shim systems that are based on sets of dedicated SH coils and allow optimal object-specific shim solutions. The technology furthermore lends itself to spatial encoding. I will present MRI results, including concomitant imaging and B0 shimming, in which all fields are purely DYNAMITE-based and conclude with the first realization of DYNAMITE MRI of the in vivo human brain. The obtained image fidelity is comparable to MRI with conventional gradient coils, paving the way for full-fledged human DYNAMITE MRI systems.
BIOGRAPHY:
Dr. Juchem is an Associate Professor in the Departments of Biomedical Engineering and Radiology at Columbia University. In his research, he develops technology and methods to realize the full clinical potential of magnetic resonance applications. Dr. Juchem has 18 years of experience in developing and conducting in vivo MR experiments at 3.0-11.7 Tesla field in humans and animal models. He served as Co-Director of Yale University’s 7T Brain MR Spectroscopy Core, Chair of the ISMRM Engineering Study group, and he serves on the editorial board of NMR in Biomedicine.
If interested in attending, please email Elizabeth Chesley at e.chesley@northeastern.edu for the Zoom link.

ChE Seminar Series Presents: Matthew J. Eckelman
2020-11-18 12:00 - 13:00
Title: TBA
Matthew J. Eckelman, Ph.D.
Associate Professor, Civil and Environmental Engineering
Affiliated Faculty, Chemical Engineering
Affiliated Faculty, Marine and Environmental Sciences
Affiliated Faculty, School of Public Policy and Urban Affairs

IEM-sponsored virtual event: OGS + Visa Compliance
2020-11-19 08:00 - 09:00
November 19: IEM-sponsored virtual event: OGS + Visa Compliance
8:00 AM EST
Join link: This event will be run via Unibuddy. Connect with our ambassadors + learn the platform here.
Audience: All admits for Spring, 2021 including deferrals from a previous term.

3 Minute Thesis Competition - Video Submission
2020-11-24
The annual GWiSE 3 Minute Thesis Competition 2020 is finally here! The 3MT is an academic competition that challenges Ph.D. students to describe their research within three minutes. This is a great opportunity to practice pitching your research to a non-specialist audience and to improve your science communication. Northeastern GWiSE and the Northeastern University Library have partnered to make 3 Minute Thesis possible with some pretty cool prizes:

First place: 100$ Grubhub card, an interview on the Dean’s podcast, 100$ credit for 3D printing at the library
Second place: 50$ Grubhub card, an interview on the Dean’s podcast, 50$ credit for 3D printing at the library
Third place: 25$ Grubhub card, an interview on the Dean’s podcast

RSVP to participate here.
More details for submission will be sent to those who RSVP. The deadline for video submission is Tuesday, November 24th via email to gwise.neu@gmail.com. Video requirements, 3-minute recording over :

1st slide: title and author’s name
2nd slide: thesis content

The live event will take place on Wednesday, December 2nd from 2 PM to 4 PM ET on Zoom! All grad students are welcome to attend and/or present. The event will work in this way:

GWiSE will host the event on Zoom and play prerecorded videos of participants’ explaining their thesis in under 3 minutes
After each video is shown, the judges will have time to discuss the presentations and assign scores
GWiSE will proclaim the winners and offer the prizes!

Reminder, please RSVP to participate here. The deadline for video submission is on the 24th of November. To submit your video, send a video file to gwise.neu@gmail.com. The actual event is on Wednesday, December 2nd.

ECE PhD Dissertation Defense: Joseph Robinson
2020-11-24 14:00 - 15:00
PhD Dissertation Defense: Automatic Face Understanding: Recognizing Families in Photos
Joseph Robinson
Location: Zoom Link
Abstract: Visual kinship recognition has an abundance of practical uses. For this, we built the largest database for kinship recognition, FIW. Built entirely in-house with no cost using a semi-automatic labeling scheme. Specifically, we first aligned faces detected in family photos with names in the corresponding text metadata to mine the label proposals with high confidence. The remaining data were labeled using a novel clustering algorithm that used label proposals as side information to guide more accurate clusters. Great savings in time and human input was had. Statistically, FIW shows enormous gains over its predecessors. We have several benchmarks in kinship verification, family classification, tri-subject verification, and large-scale search & retrieval. We also trained CNNs on FIW and deployed the model on the renowned KinWild I and II to gain state-of-the-art (SOTA). Most recently, we further augmented FIW with multimedia (MM) for 200 of its 1,000 families- a labeled collection we dubbed FIW-MM. Now, video dynamics, audio, and text captions can be used in the decision making of kinship recognition systems.
FIW continues to pave the way for this research track: (1) advanced SOTA (e.g., marginalized denoising auto-encoder based on metric learning that preserves intrinsic structures of kin-data and encapsulates discriminating information in learned features); (2) introduced generative models to predict a child’s appearance from a parent pair (i.e., proposed an adversarial autoencoder conditioned on age and gender to map between facial appearance and these higher-level features for control of age and gender); (3) designed evaluations with benchmarks to support challenges, workshops, and tutorials at top tier conferences (e.g., CVPR, MM, FG, ICME), and a premiere Kaggle Competition. We expect FIW will significantly impact research and reality.
Additionally, we tackled the classic problem of facial landmark localization in images. This is a task that has been in focus for decades, and many solutions have been proposed. However, there are revamped interests in pushing facial landmark detection technologies to handle more challenging data with deep networks now prevailing throughout machine learning. A majority of these networks have objectives based on L1 or L2 norms, which inherit several disadvantages. First of all, the locations of landmarks are determined from generated heatmaps (i.e., confidence maps) from which predicted landmark locations (i.e., the means) get penalized without accounting for the spread: a high scatter corresponds to low confidence and vice-versa. To address this, we introduced a LaplaceKL objective that penalizes for low confidence. Another issue is a dependency on labeled data, which is expensive to collect and susceptible to error. We addressed both issues by proposing an adversarial training framework that leverages unlabeled data to improve model performance. Our method claims SOTA on renowned benchmarks. Furthermore, our model is robust with a reduced size: 1/8 the number of channels (i.e., 0.0398 MB) is comparable to state-of-that-art in real-time on a CPU. Thus, our method is of high practical value to real-life applications.
Finally, we built the Balanced Faces in the Wild (BFW) dataset to serve as a proxy to measure bias across ethnicity and gender subgroups, allowing us to characterize FR performances per subgroup. We show performances are non-optimal when a single score threshold is used to determine whether sample pairs are genuine or imposter. Furthermore, actual performance ratings vary greatly from the reported across subgroups. Thus, claims of specific error rates only hold for populations matching that of the validation data. We mitigate the imbalanced performances using a novel domain adaptation learning scheme on the facial encodings extracted using SOTA deep nets. Not only does this technique balance performance, but it also boosts the overall performance. A benefit of the proposed is to preserve identity information in facial features while removing demographic knowledge in the lower dimensional features. The removal of demographic knowledge prevents future potential biases from being injected into decision making. Additionally, privacy concerns are satisfied by this removal. We explore why this works qualitatively with hard samples. We also show quantitatively that subgroup classifiers can no longer learn from the encodings mapped by the proposed.

ECE PhD Dissertation Defense: Aykut Onol
2020-11-25 12:00 - 13:00
PhD Dissertation Defense: Planning of Contact-Interaction Trajectories Using Numerical Optimization
Aykut Onol
Location: Zoom Link
Abstract: Dynamic multi-contact behaviors, such as locomotion and item manipulation, remain to be a challenge for today’s robotic systems. This is primarily due to the discontinuous and non-smooth dynamics introduced by contacts. For mobile manipulators (e.g., humanoids) to become useful for dangerous, dirty, and dull tasks, such as those in disaster response, they need to be capable of interacting with their cluttered, constrained, and changing environments. It is therefore essential to develop methods that would enable robots to plan and execute contact-rich motions in dynamic surroundings.
In this dissertation research, we investigate the planning of contact-interaction trajectories and utilize numerical optimal control techniques to solve this problem in a generalizable and computationally-tractable way. We develop a contact-implicit trajectory optimization framework for the automatic discovery of dynamic contact-rich behaviors given only a high-level goal, i.e., the desired configuration of the environment. A variable smooth contact model is introduced to improve the convergence of gradient-based optimization without compromising the physical fidelity of resulting motions. This is achieved by employing smooth virtual forces that act as a decoupled relaxation of the rigid-body contact model. Second, we develop a sequential convex optimization procedure that provides reliable convergence characteristics while solving this non-convex problem. Third, a penalty loop approach is proposed to generalize this method to a wide range of robotic applications.
In addition to these, we develop a novel Coulomb friction model and an on-the-fly contact constraint activation method using state-triggered constraints, STCs. STCs are a more modular alternative to complementarity constraints which are widely used to model discrete aspects in contact-related problems. Our extensive simulation experiments demonstrate that STCs hold immense promise to efficiently model a broad range of discrete elements in the planning and control of contact-interaction trajectories. As a result, this dissertation presents methods that enable the planning of dynamic contact-rich behaviors for different robots and tasks without requiring any parameter tuning or tailored initial guess.

ECE MS Thesis Defense: Sila Deniz Calisgan
2020-11-30 09:30 - 10:30
MS Thesis Defense: MEMS Infrared Resonant Detectors With Near-Zero Power Readout For Miniaturized Low Power Systems
Sila Deniz Calisgan
Location: Online
Abstract: The demand for low-cost and low-power microsystems for spectrally-selective IR sensing has been rising with the proliferation of Internet of Things (IoT) for applications such as security surveillance and natural disaster monitoring. As a result, there is a need for low-power, high sensitivity IR sensors with minimum deployment and maintenance cost that can detect trace levels of chemicals. This thesis reports on the first experimental demonstrations of passive integrated microsystems based on transmission spectroscopy using narrowband uncooled microelectromechanical resonant infrared (IR) detectors. Moreover, the MEMS-CMOS integrated microsystem can turn itself ON to quantify the intensity of infrared radiation when an above-threshold IR signature is present, but otherwise remain dormant with near-zero standby power consumption. The proposed sensor system combines the unique advantage of two recently developed technologies, namely, the zero-power nature of micromechanical photoswitches (MPs) and the high resolution of aluminum nitride (AlN) MEMS resonant infrared detectors, to achieve an unprecedented IR sensing capability. Thanks to the spectral selectivity enabled by the plasmonically enhanced thermo-mechanical transduction in MEMS structures, the proposed sensor system is capable of discriminating the spectral content of incoming IR radiation for the identification of events of interest. The prototype presented here is automatically powered up by the MP when the incoming IR radiation exceeds 440 nW showing a high IR detection resolution in active state and a near-zero power consumption (~3 nW) in standby. The ultrathin plasmonic absorber with narrow bandwidth (FWHM<17% ) and near-perfect IR absorption (η>92%) coupled with the high IR detection capability ( NEP~ 463 pW/√Hz) of the AlN resonator was exploited for a filter-free spectroscopic chemical sensor based on uncooled AlN resonant IR detectors with a minimum concentration detection limit of <0.01% (Benzonitrile in Hexane).

ECE PhD Proposal Review: Berkan Kadioglu
2020-11-30 13:00 - 14:00
PhD Proposal Review: Sample Complexity of Pairwise Ranking Regression
Berkan Kadioglu
Location: Zoom
Abstract: We consider a rank regression setting, in which a dataset of $N$ samples with features in $mathbb{R}^d$ is ranked by an oracle via $M$ pairwise comparisons.
Specifically, there exists a latent total ordering of the samples; when presented with a pair of samples, a noisy oracle identifies the one ranked higher w.r.t. the underlying total ordering. A learner observes a dataset of such comparisons, and wishes to regress sample ranks from their features.
We show that to learn the model parameters with $epsilon > 0$ accuracy, it suffices to conduct $M in Omega(dNlog^3 N/epsilon^2)$ comparisons uniformly at random when $N$ is $Omega(d/epsilon^2)$.

Graduate Women in Science and Engineering (GWiSE) Game Night
2020-11-30 19:00 - 20:00
Come play jackbox games with GWiSE 11/30 @7PM on Teams! We will vote on which game to play!
Join here!

IEM-sponsored virtual event: Talk to a Regional Campus Advisor: Seattle + Silicon Valley Admitted Students
2020-12-01 21:00
December 1: IEM-sponsored virtual event: Talk to a Regional Campus Advisor: Seattle + Silicon Valley Admitted Students
9:00 PM EST
Join link: This event will be run via Unibuddy. Connect with our ambassadors + learn the platform here.
Audience: All admits for Spring, 2021 including deferrals from a previous term.

IEM-sponsored virtual event: Discussion: Spring Semester, What to Expect?
2020-12-02 08:00 - 09:00
December 2: IEM-sponsored virtual event: Discussion: Spring Semester, What to Expect?
8:00 AM EST
Join link: This event will be run via Unibuddy. Connect with our ambassadors + learn the platform here.
Audience: All admits for Spring, 2021 including deferrals from a previous term.

ECE PhD Dissertation Defense: Leili Hayati
2020-12-02 10:00 - 11:00
PhD Dissertation Defense: Ceramic Magnetic Wires at Wireless Communication Frequencies
Leili Hayati
Location: Online
Abstract: Ferrite magnetic devices play an important role in modern wireless telecommunication systems. They generally require permanent magnets in order to magnetically polarize the ferrite material component used in these devices. The permanent magnets are bulky and take up most of the size and weight of a magnetic circuit. The aim of this research is to do away with permanent magnet bias circuits as utilized in circulators and ferrite planar devices, especially in wireless communication systems operating below 2 GHz. Recently, ferromagnetic nanowires (NWs) have been embedded into porous templates, are used to design various microwave magnetic and electronics devices. The main advantage of magnetic NWs is that in zero magnetic field, the microwave absorption frequency can be easily tuned over a large range of frequencies. Clearly, the metallic nature of the magnetic NWs contributed to the high loss. It is expected that insulating magnetic NWs will improve the insertion loss sufficiently to produce viable ferrite devices at wireless communication frequencies below 2GHz and at higher frequencies. There are no pure insulating magnetic materials. However, there are ferrites that are nearly insulating and are ferrimagnetic. Their saturation magnetization is much lower than the metallic ferromagnetic counterpart. This is a desirable property for magnetic device operating below 2 GHz. Of all the ferrite materials yttrium iron garnet (YIG) exhibits the lowest FMR linewidth ever measured and low saturation magnetization. In this work, an array of high-purity YIG NWs embedded in a porous silicon membrane, were synthesized using sol-gel method and the magnetic properties of the pure YIG Nanoparticles and the composite substrate were characterized by utilizing vibrating sample magnetometer (VSM) technique. From the ferromagnetic resonance (FMR) spectra, it has been found that the measurements are characterized by a uniaxial magnetic anisotropy energy due to the high aspect ratio of the NWs. Based on the magnetic parameters of the composite substrate and characterizing YIG NWs, a coplanar waveguide was designed by HFSS software. By applying a small external magnetic field and changing the internal magnetic H field by ±8%, the phase of S21 parameter shifts up to 30̊ degrees near 1.7GHz.

ECE PhD Dissertation Defense: Bilgehan Donmez
2020-12-02 10:00 - 11:00
PhD Dissertation Defense: Topology Error Detection in Power System State Estimation
Bilgehan Donmez
Location: Teams Link
Abstract: Growth of renewable energy, changes in weather patterns, and increases in cyber- and physical-attacks are examples of recent challenges in power system operation. To keep up with these rapid transformations, it is imperative to improve the tools used in modern-day control centers.
As the centerpiece of system operations, improvements in state estimation (SE) accuracy would result in better situational awareness for system operators. The state estimate can often be compromised when there are errors in the assumed topology of the network. Therefore, topology error detection plays a key role in SE. In the first part of this dissertation, topology errors in the external systems, which are the neighboring control areas, are investigated. When a subset of measurements coming from an external area is lost, some parts of the system can become unobservable. Since SE cannot be carried out for the unobservable portion of the system, the topology of the external system cannot be tracked in its usual way. This dissertation offers a computationally efficient external line outage detection algorithm that uses only the internal bus phase angles, any available phasor measurement units (PMUs), and the pre-contingency system topology of the system. Coupled with a post-verification step, this method is shown to be effective in detecting external line outages.
The second part of the dissertation focuses on topology errors in the internal system. The conventional SE implementations use the simplified bus-branch (BB) electrical network provided by the topology processor (TP). When the status of circuit breakers are not reported correctly to the TP, the electrical equivalent it creates will be inaccurate. Therefore, topology errors usually result in SE convergence problems or yield significantly biased estimates. To properly detect these types of errors, rather than using the typical BB representation, the network model is expanded to include circuit breakers and other switching devices in substations. SE is then reformulated to work with this detailed node-breaker (NB) model.
Although the expansion of the model introduces operational and computational challenges, several strategies are employed to counter these issues. The proposed innovations include the formulations of two separate equality-constrained SE algorithms, the development of optimal meter placement algorithms, and utilization of parallel processing. As demonstrated through the simulations conducted, the methods developed in this dissertation are practical enough for adaptation to real-world systems.

BioE Seminar Series Presents: Jessica Wagenseil
2020-12-02 12:00 - 13:00
Jessica Wagenseil, Ph.D.
Associate Professor, Department of Mechanical Engineering and Materials Science, Washington University, St. Louis MO
“Elastin mechanobiology in aortic development”
ABSTRACT:
The extracellular matrix protein, elastin, provides reversible extensibility to the aorta that is critical for proper function of the cardiovascular system. Elastin is deposited during late embryonic and early postnatal growth, at the same time that blood pressure and flow are increasing. This relationship suggests that mechanobiological signals for elastin deposition are linked to hemodynamic forces. I will discuss how reduced or absent elastin affects aortic mechanics, cardiovascular hemodynamics, and aortic wall development in genetically modified mouse models. I will also discuss how altered hemodynamics, specifically reduced blood flow, affects elastin amounts, aortic mechanics, and wall development in developing chick embryos. I will introduce mathematical models that we use to better understand the cause and effect relationships between elastin amounts and cardiovascular hemodynamics. The combination of experimental work in diverse animal models and mathematical modeling will advance our understanding of how the aortic wall is constructed to provide appropriate extensibility for normal cardiovascular function. The knowledge will aid in designing tissue-engineered arteries and in the treatment of cardiovascular diseases associated with elastin defects
BIOGRAPHY:
Dr. Wagenseil joined the Mechanical Engineering and Materials Science Department at Washington University in 2013. She was previously in the Biomedical Engineering Department at Saint Louis University. She got her B.S. at UC San Diego and did her doctoral and postdoctoral training at Washington University. Dr. Wagenseil studies vascular mechanics focusing on the extracellular matrix in development and disease. Dr. Wagenseil received the 2020 Renato Iozzo Award from the American Society for Matrix Biology
If interested in attending, please email Elizabeth Chesley at e.chesley@northeastern.edu for the Zoom link.

ECE MS Thesis Defense: Kathan Vyas
2020-12-02 14:00 - 15:00
MS Thesis Defense: Data-Efficient analysis of Human Behavior by Spatio-Temporal Pose Generation and Inference
Kathan Vyas
Location: Zoom Link
Passcode: [REDACTED]
Abstract: Identifying human pose over time provides critical information towards understanding human behavior and their physical interaction with the environment surrounding them. In the past few decades, the human pose estimation topic has witnessed groundbreaking research in the computer vision field thanks to the powerful deep learning models. These models are trained using several thousands of labeled sample images if not more. Such extensive data requirement posed a fundamental problem for domains (i.e. Small Data domains), in which data collection or labeling is expensive or limited due to privacy or security concerns such as healthcare. In this thesis, we present a data-efficient learning pipeline to address small data problem in a healthcare-related human pose estimation application. In particular, we infer spatio-temporal human poses to analyze typical vs. atypical behaviors in children with Autism spectrum disorder (ASD). To mitigate data limitation, we propose two thrusts in our learning pipeline. The first thrust is a data-efficient machine learning approach, in which a pre-trained (on adult pose images) pose estimation model with deep structure is fine-tuned on a small set of children pose videos, provided to us by our collaborators. We implement a non-linear particle filter interpolation to deal with any missing body keypoints in the estimated poses and employ a novel PoTion (pose motion) based temporal aggregation technique to evaluate poses over time. The second thrust is a synthetic data augmentation approach, in which we build a framework to create synthetic 3D humans with articulated bodies in order to render more pose images/videos in our application contexts. We use a novel 3D registration approach based on RANSAC and implement iterative closest point (ICP) to obtain 3D meshes from the scanned point clouds from both adult and kid mannequins, which is then rigged and articulated in the Blender to generate our human avatars. We then infuse these avatars in various synthetic environments to create contexts similar to the target application, which is a kid with both typical and atypical behaviors in a home-like environment.

3 Minute Thesis Competition
2020-12-02 14:00 - 16:00

IEM-sponsored virtual event: OGS + Visa Compliance
2020-12-03 08:00 - 09:00
December 3: IEM-sponsored virtual event: OGS + Visa Compliance
8:00 AM EST
Join link: This event will be run via Unibuddy. Connect with our ambassadors + learn the platform here.
Audience: All admits for Spring, 2021 including deferrals from a previous term.

ADSE Trivia Night
2020-12-03 18:00 - 20:00
Join ADSE for a graduate student virtual trivia night on December 3rd from 6:00-8:00 pm and help us support a local Black-owned business as well as raise money for the Read in Color program from Little Free Library! Form a team of up to 4 members and answer a total of 30 questions on variable topics.
1st place: $25 to each team member
2nd place: $20 to each team member
3rd place: $15 to each team member
*$5 gift cards from Delectable Desires will be sent to your email address after you attend the event (first 25 people).
RSVP here: https://docs.google.com/forms/d/e/1FAIpQLSfXwFoSYPkKZCf8wXfmq2qnVrhRIU2GXL5jERp3bIVYpdM9Eg/viewform

ECE MS Thesis Defense: Yuxuan Cai
2020-12-04 15:00 - 16:00
MS Thesis Defense: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design
Yuxuan Cai
Location: Zoom Link
Abstract: The rapid development and wide utilization of object detection techniques have aroused attention on both accuracy and speed of object detectors. However, the current state-of-the- art object detection works are either accuracy-oriented using a large model but leading to high latency or speed-oriented using a lightweight model but sacrificing accuracy. In this work, we propose YOLObile framework, a real-time object detection on mobile devices via compression compilation co-design. A novel block-punched pruning scheme is proposed for any kernel size. To improve computational efficiency on mobile devices, a GPU-CPU collaborative scheme is adopted along with advanced compiler-assisted optimizations. Experimental results indicate that our pruning scheme achieves 14× compression rate of YOLOv4 with 49.0 mAP. Under our YOLObile framework, we achieve 17 FPS inference speed using GPU on Samsung Galaxy S20. By incorporating our proposed GPU-CPU collaborative scheme, the inference speed is increased to 19.1 FPS, and outperforms the original YOLOv4 by 5× speedup.

IEM-sponsored virtual event: About Hybrid NU-Flex
2020-12-07 08:00 - 09:00
December 7: IEM-sponsored virtual event: About Hybrid NU-Flex
8:00 AM EST
Join link: This event will be run via Unibuddy. Connect with our ambassadors + learn the platform here.
Audience: All admits for Spring, 2021 including deferrals from a previous term.

ECE PhD Proposal Review: Trinayan Baruah
2020-12-07 16:00 - 17:00
PhD Proposal Review: Improving the Virtual Memory Efficiency of GPUs
Trinayan Baruah
Location: Zoom Link
Abstract: GPUs have been adopted widely based their ability to exploit data-level parallelism found in modern-day applications, ranging from high performance computing to machine learning. This widespread adoption has, in part, been accelerated by the development of more intuitive high-level programming languages, efficient runtimes and drivers, and easier mechanisms to manage data movement. Modern day GPUs and multi-GPU systems utilize virtual memory systems, enabling programmers to access large address spaces that are beyond the physical memory limits a GPU. There mechanisms have built in mechanisms for memory translation, sparing the programmer from having to reason about complex data-movement operations. Virtual memory support on a GPU includes both hardware and software support. At the hardware level, Translation Lookaside Buffers (TLBs) are used to cache translations close to the compute units. At the software level, the programming model supports a unified memory model which automates the movement of pages across multiple devices in a a system. Despite the improvements in programmability, due to the inefficiency in existing TLB mechanisms for TLB management and page migration, the performance of current virtual memory support on GPUs is sub-optimal.
In this dissertation, we first identify the key challenges in virtual memory support for GPUs today. We then propose mechanisms to reduce the bottlenecks arising from virtual memory management at both a hardware level and at the runtime level. This allows GPUs to fully enjoy the benefits of virtual memory, while ensuring high performance. We also develop simulation tools that enable researchers to explore new and novel virtual memory features in future single GPU and multi-GPU systems.
To enhance hardware support for virtual memory on a GPU, we explore a mechanism that enables prefetching of page-table entries into the GPUs TLBs, thereby reducing the number of TLB misses and improving performance. We also leverage the fact that many page-table entries can be shared across different GPU cores. We design a low-cost interconnect that enables sharing of page-table entries across the GPU cores. To improve the performance of unified memory on multi-GPU systems, we propose a hardware/software mechanism that monitors accesses to each page, and uses this information when making page-migration decisions. We also propose mechanisms to reduce the cost of TLB shootdowns on the GPU during page-migration in NUMA multi-GPU systems.

ECE PhD Dissertation Defense: Raffaele Guida
2020-12-08 14:00 - 15:00
PhD Dissertation Defense: Remotely Rechargeable Embedded Platforms for Next Generation IoT Systems in Critical Environments
Raffaele Guida
Location: Teams Meeting
Abstract: In the near future, a new generation of miniaturized, multi-function and smart wireless devices for Internet of Things (IoT) systems, designed for real-time monitoring and with real-time reconfiguration will be deployed in critical and challenging environments, e.g., underwater and inside the human body. These futuristic IoT platforms can now be realized thanks to advances in low-power electronics and wireless communications. However, the need for long-term and reliable power supply, together with the need to support innovative functions, impose new powering requirements that cannot be satisfied by traditional batteries. Batteries have in fact a major impact on the size and lifetime of the device, and often need to be replaced through complex, expensive and non-scalable procedures. For example, powering of Internet of Underwater Things (IoUT) devices in deep water remains one of the main challenges, since these systems are typically powered by batteries that need to be recharged through difficult and expensive operations.
Furthermore, existing medical implants do not provide at once the miniaturized end-to-end sensing-computation-communication-recharging capabilities to implement Implantable Internet of Medical Things (IIoMT) applications.
This dissertation fills the existing research gaps by presenting innovative designs of battery-less devices remotely rechargeable through ultrasonic wireless power transfer. Specifically, two major systems are presented, U-Verse – the first FDA-compliant IIoMT platform packing sensing, computation, communication, and recharging circuits into a penny-scale platform – and the first IoUT battery-less sensor node that can be wirelessly recharged through ultrasonic waves.
U-Verse uses a single miniaturized transducer for data exchange and for wireless charging. To predict U-Verse’s performance, a mathematical model of its charging efficiency is derived and experimentally validated. A matching circuit to maximize the amount of power transferred from the outside is proposed, and the design of a full-fledged cm-scale printed circuit board (PCB) is presented. Extensive experimental evaluation indicates that U-Verse (i) is able to recharge a 330mF and 15F energy storage unit – several orders of magnitude higher than existing work – respectively under 20 and 60 minutes at a depth of 5cm; (ii) achieves stored charge duration of up to 610 and 40 hours in case of battery and supercapacitor energy storage, respectively. Finally, U-Verse is demonstrated through (i) a closed-loop application where a periodic sensing/actuation task sends data via ultrasounds through real porcine meat; and (ii) a real-time reconfigurable pacemaker. As for the underwater sensor node, the architecture of an underwater platform capable of extracting electrical energy from ultrasonic waves is first introduced. Then, the interfacing of the system with an underwater communication unit is illustrated. The design of a prototype where the storage unit is realized with a batch of supercapacitors is also discussed. Experimental results show that the harvested energy is sufficient to provide the sensor node with the power necessary to perform a sensing operation and power a modem for ultrasonic communications. Given the reduced attenuation of ultrasonic waves in water, the proposed approach proves to cover longer distances with less transmission power than alternative solutions. Last, the overall operating efficiency of the system is evaluated.

ECE PhD Dissertation Defense: Sheng Lin
2020-12-08 15:00 - 16:00
PhD Dissertation Defense: Platform-specific Model Compression for Deep Neural Networks with Joint Methods
Sheng Lin
Location: Zoom Link
Abstract: Deep learning has delivered its powerfulness in many application domains, especially in computer vision, natural language processing and speech recognition. As the backbone of deep learning, deep neural networks (DNNs) consist of multiple layers of various types with hundreds to thousands of neurons. Embedded platforms are now becoming essential for deep learning deployment due to their portability, versatility, and energy efficiency. The large model size of DNNs, while providing excellent accuracy, also burdens the hardware platforms with intensive computation and storage. To consider the requirements of specific tasks, many researchers have investigated reducing DNN model size for efficient implementation in hardware devices with reasonable accuracy prediction. However, it lacks a systemic investigation on platform-specific DNN acceleration frameworks.
In this dissertation, we present several software-hardware co-design techniques to speed up the DNN algorithm on specific platforms. At the software level, we present joint model compression techniques for DNN model training and inference with reasonable accuracy performance. At the hardware level, these algorithms and methods are targeting storage reduction, low power consumption, efficient inference, and data security. By using joint methods to optimize different types of networks, the targeted hardware platforms can reduce asymptotic complexity of both computation and storage, making our approach distinguished from existing approaches. First, we present a Fast Fourier Transform-based DNN model for inference phase on embedded platforms. Second, we build a framework for two most commonly used model compression techniques, low-bit linear weight quantization and its combination with different weight pruning methods. Third, we apply quantization techniques for the always-on keyword spotting system and eliminate the energy-consuming ADC with an energy-efficient analog processing circuit. Finally, we propose a federated learning framework to protect user’s data privacy while reducing overall communication cost during the training process.

ChE Seminar: Near-term and Long-term Perspectives of Battery Technologies
2020-12-09 12:00 - 13:00
ChE Seminar Series Presents:
Arumugam Manthiram | Professor
Walker Department of Mechanical Engineering
McKetta Department of Chemical Engineering
Materials Science and Engineering Program & Texas Materials Institute
Near-term and Long-term Perspectives of Battery Technologies
Abstract: A widespread adoption of battery technologies for electric vehicles and grid electricity storage of renewable energies requires optimization of cost, cycle life, safety, energy density, power density, and environmental impact, all of which are directly linked to severe materials challenges. After providing a brief account of the current status, this presentation will focus on the development of advanced materials and new battery chemistries for near-term and long-term battery technologies. Particularly, lithium-based batteries based on cobalt-free layered oxide and sulfur cathodes will be presented. The challenges of bulk and surface instability and chemical crossover during charge-discharge cycling, dynamics and stabilization of lithium plating and striping, advanced characterization methodologies to develop an in-depth understanding, and approaches to overcome the challenges will be presented.
Bio: Arumugam Manthiram is currently the Cockrell Family Regents Chair in Engineering and Director of the Texas Materials Institute and the Materials Science and Engineering Program at the University of Texas at Austin (UT-Austin). He received his Ph.D. degree in chemistry from the Indian Institute of Technology Madras in 1981. After working as a postdoctoral researcher at the University of Oxford and at UT-Austin with 2019 Chemistry Nobel Laureate Professor John Goodenough, he became a faculty member in the Department of Mechanical Engineering at UT-Austin in 1991. Dr. Manthiram’s research is focused on rechargeable batteries and fuel cells. He has authored more than 820 journal articles with 70,000 citations and an h-index of 132.
Dr. Manthiram is a Fellow of six professional societies: Materials Research Society, Electrochemical Society, American Ceramic Society, Royal Society of Chemistry, American Association for the Advancement of Science, and World Academy of Materials and Manufacturing Engineering. He is an elected member of the World Academy of Ceramics. He received the university-wide (one per year) Outstanding Graduate Teaching Award in 2012, Battery Division Research Award from the Electrochemical Society in 2014, Distinguished Alumnus Award of the Indian Institute of Technology Madras in 2015, Billy and Claude R. Hocott Distinguished Centennial Engineering Research Award in 2016, Honorary Mechanical Engineer of the ME Academy of Distinguished Alumni Award in 2019, Henry B. Linford Award for Distinguished Teaching from the Electrochemical Society in 2020, and the International Battery Association Research Award in 2020. He is a Web of Science Highly Cited Researcher each year during 2017 – 2020. He delivered the 2019 Chemistry Nobel Prize Lecture in Stockholm on behalf of Professor John Goodenough.

IEM-sponsored virtual event: 10 Questions to Ask Your College, including College of Engineering staff
2020-12-10 09:00 - 10:00
December 10: IEM-sponsored virtual event: 10 Questions to Ask Your College, including College of Engineering staff
8:00 AM EST
Join link: This event will be run via Unibuddy. Connect with our ambassadors + learn the platform here.
Audience: All admits for Spring, 2021 including deferrals from a previous term.

ECE PhD Dissertation Defense: Chenyang Zhu
2020-12-10 09:00 - 10:00
PhD Dissertation Defense: Remote Monitoring of Multiple Ships over Instantaneous Continental-shelf Scale Region with a Large-aperture Coherent Hydrophone Array
Chenyang Zhu
Location: Zoom Link
Abstract: Multiple mechanized ocean vessels, including both surface ships and submerged vehicles, can be simultaneously monitored over instantaneous continental-shelf scale regions >10,000 km 2 via passive ocean acoustic waveguide remote sensing. A large-aperture densely-sampled coherent hydrophone array system is employed in the Norwegian Sea in Spring 2014 to provide directional sensing in 360 degree horizontal azimuth and to significantly enhance the signal-to-noise ratio (SNR) of ship-radiated underwater sound, which improves ship detection ranges by roughly two orders of magnitude over that of a single hydrophone. Here, 30 mechanized ocean vessels spanning ranges from nearby to over 150 km from the coherent hydrophone array, are detected, localized and classified. The vessels are comprised of 20 identified commercial ships and 10 unidentified vehicles present in 8 h/day of POAWRS observation for two days. The underwater sounds from each of these ocean vessels received by the coherent hydrophone array are dominated by narrowband signals that are either constant frequency tonals or have frequencies that waver or oscillate slightly in time. The estimated bearing-time trajectory of a sequence of detections obtained from coherent beamforming are employed to determine the horizontal location of each vessel using the Moving Array Triangulation (MAT) technique. For commercial ships present in the region, the estimated horizontal positions obtained from passive acoustic sensing are verified by Global Positioning System (GPS) measurements of the ship locations found in historical AIS database. We provide time-frequency characterizations of the underwater sounds radiated from the commercial ships and the unidentified vessels. The time-frequency features along with the bearing-time trajectory of the detected signals are applied to simultaneously track and distinguish these vessels.
Next, three approaches for simultaneous ship long-range automatic detection, acoustic signature characterization, and bearing-time trajectory estimation have been developed and applied, each focusing on a different aspect of a ship’s radiated underwater sound received on a large-aperture densely-sampled coherent hydrophone array. (i) Ships narrowband machinery tonal sound is analyzed via temporal coherence using Mean Magnitude-Squared Coherence (MMSC) calculations. (ii) Ships broadband cavitation noise amplitude modulated by propeller rotation is examined using Cyclic Spectral Coherence (CSC) analysis that provides estimates for propeller blade pass rotation frequency, shaft rotation frequency, and hence the number of propeller blades. (iii) Mean power spectral densities averaged across specific broad bandwidths are calculated to detect and compare output sound pressure levels from acoustically energetic ships. Each of these techniques are applied after coherent beamforming of the received acoustic signals on a coherent hydrophone array, leading to significantly enhanced signal-to-noise ratios for simultaneous detection and characterization of multiple ships over continental-shelf scale regions. The approaches are illustrated by application to
roughly two hours of acoustic recordings of a 160-element coherent hydrophone array deployed in the Norwegian Sea during an experiment in February 2014. Six ocean vessels are simultaneously detected and their acoustic signatures characterized, located at a variety of bearings and ranges out to 200 km from the coherent hydrophone array, with speeds ranging from 0.5 knots to 13 knots, verified by Global Positioning System (GPS) information from Automatic Identification System (AIS) database. Hybrid usage of the three methods provide a robust approach for ship characterization in terms of machinery tonal sound signature, propeller rotation signature, and ship broadband energetics that can be employed for efficient ship classification. The CSC approach is demonstrated to be also useful for automatic detection and bearing-time estimation of repetitive marine mammal vocalizations present in coherent hydrophone array recordings, providing estimates of inter-pulse-train and inter-pulse intervals from CSC spectra cyclic fundamental and first recurring peak frequencies respectively.

ECE MS Thesis Defense: Xinan Huang
2020-12-11 14:00 - 15:00
MS Thesis Defense: Exploring Effectiveness of Naive Spatio-Temporal Exploits for Depth Completion
Xinan Huang
Location: Zoom Link
Abstract: With an increasing need for usable depth for autonomous navigation systems such as self-driving cars, depth completion is becoming an increasingly studied subject. RGB data provide much-needed aid in providing good recreation of dense depth maps from sparse LiDAR output. Yet, these data are also provided in sequential form. And thus for this thesis, we aim to explore how effective using network layers that exploit Spatio-temporal features would be in achieving higher depth completion accuracy. We propose adding 3D convolutional layers and ConvGRU layers to a preexisting depth completion network and perform ablation studies on the effectiveness of these methods. We were able to verify that naive approaches are able to garner improvements quantitatively and qualitatively, but training results show that additional geometric constraints would perhaps boost such exploits even further for better depth completion results.

ECE PhD Dissertation Defense: Qifan Li
2020-12-11 15:00 - 16:00
PhD Dissertation Defense: Development of Magnetodielectric Materials with Low Loss and High Snoek’s Product for Microwave Applications
Qifan Li
Location: Teams Link
Abstract: Exhibiting both relative magnetic permeability and electric permittivity greater than unity, magnetodielectric materials have been attracting great attention in both academia and industry for next-generation communication, sensing, and radar applications. It is always of great interest for researchers to tailor the magnetic properties of magnetodielectric materials for high permeability, low magnetic loss and large Snoek’s product towards higher-frequency applications.
Hexagonal ferrites form an important group of magnetodielectric materials. Besides the six best known hexagonal structures, i.e., M-, W-, X-, Y-, Z- and U-type hexaferrites, some unique hexagonal structures, named 18H hexaferrites, were discovered in 1970s. For the first time, the dynamic magnetic properties and their temperature dependence of polycrystalline Mg-Zn 18H hexaferrites at microwave frequencies are investigated. Owing to a remarkably low damping coefficient, the frequency dispersion of complex permeability reveals a narrow and strong resonance. The Mg-Zn 18H hexaferrites show excellent loss tangent of 0.07 at 3 and 4 GHz. Accordingly, narrow FMR linewidths in the range of 486-660 Oe are measured. The temperature dependence of the damping coefficient is 0.0004 /°C, indicating a small variation of the intrinsic loss with temperature. These results are the best performance among the polycrystalline microwave ferrites reported so far for the S- and C-band applications.
Magnetodielectric composites, prepared by dispersing magnetic particles homogenously in an electrically insulating matrix, are another type of magnetodielectric materials. It is crucial to predict the effective magnetic properties of the multi-phase mixture. A modified effective medium theory is proposed by extending the traditional formulas with the effects of particle-size distribution and clustering of inclusions. Its accuracy is verified by two kinds of magnetodielectric composites over wide ranges of both particle concentration and frequency.
The magnetic properties of microwave ferrites are strongly affected by their polycrystalline microstructure, which is mainly controlled by the sintering process. The two-step sintering technique is systematically studied for the preparation of hexaferrites. With optimal combinations of sintering temperatures in each step, significant reduction in magnetic loss and enhancement in Snoek’s product are achieved with uniform and fine-grained structures.
Precise measurement of broadband permeability and permittivity is crucial to develop advanced magnetodielectric materials. A straightforward, explicit and noniterative method is proposed by eliminating the error from the direct measurement of sample position in the standard Nicolson-Ross-Weir method. Based on the results from two kinds of magneto-dielectric materials measured in two sets of test fixtures of different geometries, this method is theoretically and experimentally proven to have high and position-independent accuracy over a wide frequency range.
Finally, a patch antenna on Mg-18H magnetodielectric substrate is designed to operate at 3.6 GHz for 5G wireless communication. Benefiting from the large refractive index of the magnetodielectric material, the size of the patch antenna is significantly reduced. Moreover, compared to the dielectric substrate providing the same miniaturization factor, magnetodielectric antennas exhibit significant advantages for larger bandwidth and gain.

ECE PhD Dissertation Defense: Ning Liu
2020-12-12 10:30 - 11:30
PhD Dissertation Defense: Real-World Applicable Deep Learning Techniques: From Efficient Modeling to Automated Model Optimization
Ning Liu
Location: Zoom Link
Abstract: Recently, deep neural networks (DNNs) have been widely studied and achieved tremendous success in a variety of real-world applications, such as computer vision, medical diagnosis and machine translation. Deep reinforcement learning (DRL), as an emerging powerful deep learning technique, combines DNNs with reinforcement learning into an interactive system. DRL opens up many new applications in domains such as healthcare, robotics and smart grids. With the rapid evolution of IT infrastructures, cloud computing has been witnessed as the prevailing computing paradigm. The underlying infrastructure of cloud computing relies on a large amount of data centers. The energy efficiency issue from “cloud” becomes more crucial and calls for more attentions.
In this dissertation, to solve the real-world energy efficiency problems, we take advantage of the deep learning and deep reinforcement learning techniques for efficient modeling of “cloud” applications. We present a DNN-based power management framework for regulation service and a novel DRL-based hierarchical framework for solving the overall resource allocation and power management problem. On the other hand, the powerful DNNs themselves are massive, consuming tremendous energy. Therefore, we explore the efficiency on deep neural networks. We propose an automatic model pruning framework to reduce the storage and computation requirements and accelerate inference. Our framework outperforms the prior work on automatic model compression by up to 33× in pruning rate (120× reduction in the actual parameter count) under the same accuracy. Significant inference speedup has been observed from the proposed framework on actual measurements on smartphone.

IEM-sponsored virtual event: About Hybrid NU-Flex
2020-12-14 08:00 - 09:00
December 14: IEM-sponsored virtual event: About Hybrid NU-Flex
8:00 AM EST
Join link: This event will be run via Unibuddy. Connect with our ambassadors + learn the platform here.
Audience: All admits for Spring, 2021 including deferrals from a previous term.
