Northeastern University College of Engineering

ECE PhD Proposal Review: Kaidi Xu
2020-12-17 14:00 - 15:00 (America/New_York)
PhD Proposal Review: Towards Empirical Implementation and Theoretical Analysis in Adversarial Machine Learning
Kaidi Xu
Location: Zoom Link
Abstract: Deep learning or deep neural networks (DNNs) have achieved extraordinary performance in many application domains such as image classification, object detection and recognition, natural language processing and medical image analysis. It has been well accepted that DNNs are vulnerable to adversarial attacks, which raises concerns of DNNs in security-critical applications and may result in disastrous consequences. Adversarial attacks are usually implemented by generating adversarial examples, i.e., adding sophisticated perturbations
onto benign examples, such that adversarial examples are classified by the DNN as target (wrong) labels instead of the correct labels of the benign examples. The adversarial machine learning aims to study this phenomenon and leverage it to build robust machine learning systems and explain DNNs.
In this dissertation, we present the mechanism of adversarial machine learning in both empirical and theoretical ways. Specifically, we first introduce a uniform adversarial attack generation framework, structured attack (StrAttack), which explores group sparsity in adversarial perturbations by sliding a mask through images aiming for extracting key spatial structures. Second, we discuss the feasibility of adversarial attacks in the physical world and introduce a powerful framework, Expectation over Transformation (EoT). Utilize EoT with Thin Plate Spline (TPS) transformation, we can generate Adversarial T-shirts, a robust physical adversarial example for evading person detectors even if it could undergo non-rigid deformation due to a moving person’s pose changes.
Third, we stand on the defense side and propose the first adversarial training method based on Graph Neural Network.
Finally, we introduce Linear relaxation based perturbation analysis (LiRPA) for neural networks, which computes provable linear bounds of output neurons given a certain amount of input perturbation.
LiRPA studies the adversarial example in a theoretical way and can guarantee the test accuracy of a model by given perturbation constraints.
In the future, we plan to study a novel patch transformer network to truthfully model real-world physical transformations empirically. In addition, at the formal robustness direction, we plan to explore the complete verification, that given sufficient time, the verifier should give a definite “yes/no” answer for a property under verification. Our LiRPA framework combining with GPUs may accelerate this procedure.

IEM-sponsored virtual event: Is It Safe to Return to Campus?
2020-12-17 08:00 - 09:00
December 17: IEM-sponsored virtual event: Is It Safe to Return to Campus?
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: Amirreza Farnoosh
2020-12-16 14:00 - 15:00
PhD Proposal Review: Unsupervised Learning of Low-Dimensional Dynamical Representations from Spatiotemporal Data
Amirreza Farnoosh
Location: Zoom Link
Abstract: Ever-improving sensing technologies offer a fast and accurate collection of large-scale spatiotemporal data, recorded from multimodal sensors of heterogeneous natures, in various application domains, ranging from medicine and biology to robotics and traffic control. In this proposal, we are learning the underlying representation of these data in an unsupervised manner, tailored towards several emerging applications, namely indoor navigation and mapping, neuroscience hypothesis testing, and time series segmentation and forecasting.
As such, (1) we present an unsupervised framework for real-time depth and view-angle estimation from an inertially augmented video recorded from an indoor scene by employing geometric-based machine learning and deep learning models. (2) We introduce a hierarchical deep generative factor analysis framework for temporal modeling of neuroimaging datasets. Our model approximates high dimensional data by a product between time-dependent weights and spatially dependent factors which are in turn represented in terms of lower dimensional latents. This framework can be extended to perform clustering in the low dimensional temporal latent or perform factor analysis in the presence of a control signal. (3) We present a deep switching dynamical system for dynamical modeling of multidimensional time-series data. Specifically, we employ a deep vector auto-regressive latent model switched by a chain of discrete latents to capture higher-order multimodal latent dependencies. This results in a flexible model that (i) provides a collection of potentially interpretable states abstracted from the process dynamics, and (ii) performs short- and long-term vector time series prediction in a complex multi-relational setting.

ECE PhD Proposal Review: Pu Zhao
2020-12-16 14:00 - 15:00
PhD Proposal Review: Towards Robust Image Classification with Deep learning and Real-Time DNN Inference on Mobile
Pu Zhao
Location: Zoom Link
Abstract: As the rapidly increasing popularity of deep learning, deep neural networks (DNN) have become the fundamental and essential building blocks in various applications such as image classification and object detection. However, there are two main issues which potentially limit the wide application of DNNs: 1) the robustness of DNN models raises security concerns, and 2) the large computation and storage requirements of DNN models lead to difficulties for its wide deployment on popular yet resource-constrained devices such as mobile phones.
To investigate the DNN robustness, we explore the DNN attack, robustness evaluation and defense. More specifically, for DNN attack, we achieve various attack goals (e.g. adversarial examples and fault sneaking attacks) with different algorithms (e.g. alternating direction method of multipliers (ADMM) and natural gradient descent (NGD) attacks) under various conditions (white-box and black-box attacks). For robustness evaluation, we propose a fast evaluation method to obtain the model perturbation bound such that any model perturbation within the bound does not alter the model classification outputs or incur model mis-behaviors. For the DNN defense, we investigate the defense performance with model connection techniques and successfully mitigate the fault sneaking and backdoor attacks.
With a deeper understanding of the DNN robustness, we further explore the deployment problem of DNN models on edge devices with limited resources. To satisfy the storage and computation limitation on edge devices, we adopt model pruning to remove the redundancy in models, thus reducing the storage and computation during inference. Besides, as some applications have real-time requirements with high inference speed sensitivities such as object detection on autonomous cars, we further try to implement real-time DNN inference for various DNN applications on mobile devices with pruning and compiler optimization. To summary, we mainly investigate the DNN robustness and implement real-time DNN inference on the mobile.

ECE PhD Proposal Review: Hongjia Li
2020-12-16 10:00 - 11:00
PhD Proposal Review: Automation Design and DNN Acceleration Algorithms: From Software Implementation to Hardware Physical Design
Hongjia Li
Location: Zoom Link
Abstract: Deep learning has been growing at a fast pace in recent years and has been expanded into many application fields, with a wide range from image recognition, object detection to medical applications. Meanwhile, edging devices such as 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 these devices.
In this proposal review, I will dive into some aspects of DNN acceleration methods, including model compression techniques and software implementation optimizations. The goal is to achieve an unprecedented, real-time performance of large-scale neural network inference on edging devices. Additionally, an efficient physical design automation design is introduced for Adiabatic Quantum-Flux-Parametron (AQFP) circuits, meeting the unique features and constraints.

ECE PhD Proposal Review: Majid Sabbagh
2020-12-16 10:00 - 11:00
PhD Proposal Review: The perils of shared computing: A hardware security perspective
Majid Sabbagh
Location: Teams Link
Abstract: The enormous computation power of modern processors and accelerators has rendered them shared computing resources for multiple users and applications, both in the cloud and on the edge. Despite software techniques for security such as virtualization and containers, recently a new attack surface is emerging that pertains to the hardware vulnerabilities of shared computing resources, posing serious threats to shared computing.
Fault attacks (FAs) and Side-Channel Attacks (SCAs) are two hardware-oriented attacks that target the system implementations. FAs aim to tamper the integrity of application execution through different fault injection methods, to compromise the data or disrupt computation at run-time. SCAs exploit the information leakage of sensitive applications in physical parameters, such as power consumption, electromagnetic emanations, and timing, to breach the confidentiality of the application.
In this dissertation, we introduce a new class of FAs against Graphics Processing Units (GPUs), called overdrive fault attacks. We discover the security vulnerability of GPU’s voltage-frequency scaling (VFS) mechanism, a common feature to balance power consumption and performance. An out-of-specification configuration of GPU voltage and frequency can be set by an adversary on the host CPU, through the software interfaces to GPU’s power management units. This setting will cause timing violations for the computation and result in silent data corruptions (SDCs). We apply the overdrive fault attacks on two common victim applications. One is cryptographic applications accelerated by GPU. We launch a differential fault analysis (DFA) attack on an AES kernel running on an AMD RX 580 GPU and successfully recover the secret key. The other victim is deep neural network (DNN) inference. In modern GPUs that support multiple kernels, the adversary is able to track the execution of the victim DNN through shared resources and control the timing of fault injections precisely. We launch a successful attack on a convolutional neural network kernel running on an NVIDIA RTX 2080 SUPER GPU with misclassifications. We further study the characteristics of fault injections and the fault propagation through the network.
We evaluate a timing side-channel attack called Prime+Probe attack on Central Processing Units (CPUs) and propose a Side-Channel Attack DEtection Tool (SCADET). SCADET is a methodology and a tool that analyzes an x86 program’s memory accesses. It records and analyzes the memory accesses using dynamic binary instrumentation by running the program in a controlled environment to accurately identify the malicious access patterns corresponding to the Prime+Probe attack.
Finally, I propose an FPGA-based RISC-V processor prototype as an evaluation platform for various cache timing attacks and transient attacks, and implement a taint tracking-based countermeasure against transient attacks. For the first phase, we have ported spectre v1 and v2 and return-stack-buffer attack to the SonicBOOM RISC-V processor.

ECE MS Thesis Defense: Jinghan Zhang
2020-12-16 09:30 - 10:30
MS Thesis Defense: Allocating One Common Accelerator-Rich Platform for Many Streaming Applications
Jinghan Zhang
Location: 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 (e.g. heterogeneous computing) to achieve the needed efficiency and/or performance. However, current Design Space Exploration (DSE) focuses on an individual application in isolation (e.g. one particular vision flow), but not a set of similar applications. Hence, optimizations that occur due to considering multiple applications simultaneously are missed. New DSE methodologies and tools are needed with a broader scope of application sets instead of individual applications.
This thesis introduces a novel Domain DSE approach focusing on streaming applications. Key contributions are: (1) a formalized method to extract the functional and structural similarities of domain applications, (2) domain application generation to provide enough synthetic domains as study cases, (3) a rapid platform performance estimation and comparison at two abstraction levels: Domain Score (DS) and Analytic Performance Estimation (APE) model, (4) a methodology to evaluate a platform’s benefit for a set of applications, and (5) two novel algorithms, Dynamic Score Selection (DSS) and GenetIc Domain Exploration (GIDE), for hardware/software partitioning of a domain-specific platform to maximize the throughput across domain applications (under certain constraints).
This thesis demonstrates DSS’s and GIDE’s benefits using OpenVX applications and synthetic domains. The DSS and GIDE generated domain-specific platforms improve performance over application-specific platforms by 58%, and 75% for OpenVX, as well as by 23% and 48% for synthetic applications. GIDE’s platforms reach 99.8% (OpenVX) and 97.6% (synthetic) throughput of the domain optimal platform obtained through exhaustive search.

Cooperative Education Webinar
2020-12-16 08:00 - 09:00
You’re invited to learning more about the Graduate Cooperative Education program in the College of Engineering. The webinar will feature a talk by Assistant Dean of Co-op, Lorraine Mountain.
Cooperative education has been a historical strength of Northeastern University’s experiential learning brand. Each year students at every level and on every campus location participate in this signature model of experiential learning.
Our global presence spans all seven continents allowing our co-op employer partners and students alike to gain early access in tapping the increasingly competitive job market on a global stage. Northeastern’s vast network yields a multitude of global cooperative education opportunities across all industries and sectors.

WEBINAR DETAILS:
Topic: Co-op at Northeastern University
Date: Wednesday, December 16
Time: 8:00 – 9:00 AM EST

WEBINAR REGISTRATION INSTRUCTIONS:
https://us02web.zoom.us/webinar/register/WN_gfvqKoxIQfOHhLYnattN5g

Paper Snowflakes with GWiSE!
2020-12-15 19:00 - 20:00
Join Graduate Women in Science and Engineering for some DIY Paper Snowflakes on 12/15 at 7 PM! All you need is paper and scissors!
Here is the link to the MS Teams meeting.

ECE PhD Proposal Review: Arjun Singh
2020-12-15 11:00 - 12:00
PhD Proposal Review: Design, Modeling and Operation of Plasmonic Devices for Smart Communication Systems in the Terahertz Band
Arjun Singh
Location: Teams Link
Abstract: The terahertz (THz) band is an attractive spectral resource for future communication systems, for 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. To overcome these obstacles, novel plasmonic devices that exploit the attractive properties of graphene have been proposed. However, there are several challenges, such as low output power and high reflection losses, that are not yet addressed. The objective of the proposed research herein is to facilitate an end-to-end communication link with graphene plasmonics as the cornerstone of the fundamental device physics. The devices designed can be utilized at both the communication endpoints, as well as across the channel, to effectively overcome the limited communication distance – The grand challenge of the THz band.
To this end, a graphene-based plasmonic array architecture is first 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. The array designed through an integration of these front-ends 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. The array is also demonstrated to provide a strong effective isotropic radiated power (EIRP), that increases exponentially with array size. To mitigate the negative effects of the channel environment, such as unwanted blockages and high path losses for simpler devices, 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 proposed reflectarray as compared to other proposed counterparts, including independence from the incoming angle of the impinging wave, dynamic phase control capability, and a strong reflection efficiency. The unique design properties of the plasmonic array, as well as the hybrid reflectarray, open the option of incorporating techniques such as multi-beam beamforming design and interleaved, independent arrays, to boost the channel capacity.
As a part of the proposed work, the impact of the design properties of these devices on the communications link will be investigated by developing the fundamental problem and considering all trade-offs. The undertaking will be significantly more robust and conclusive than those that have been performed previously, both due to the consideration of a complete end to end link, as well as the incorporation of the characteristics of the device design model. Finally, preliminary fabrication results in the realization of these devices are presented, and the roadmap ahead is outlined.

Research and Funding Opportunities in Civil and Environmental Engineering
2020-12-14 10:00 - 11:00
As you consider pursuing graduate school, the Department of Civil and Environmental Engineering at Northeastern University would like to invite you to the first in our new Graduate Programs in Civil and Environmental Engineering Webinar Series.

This first webinar will provide you an overview of research and funding opportunities with our department, as well as how our interdisciplinary programs are preparing students for both traditional and emerging fields in civil and environmental engineering.

Hosted by our Associate Chair for Graduate Studies Associate Professor Andrew Myers and the Faculty Advisor for our Data and Systems program, Assistant Professor Amy Mueller, attendees will learn about fellowships, current research, past graduates’ successes, and have an opportunity to ask questions from our faculty presenters.

Located in Boston, Massachusetts, New England’s largest city, Northeastern University is a wonderful place to study and live. Our city is home to world-class entertainment, restaurants, and sporting venues, a diverse and dynamic economy, and thriving community of academic institutions.

While the deadline for PhD applicants is December 15, attendees for this webinar will receive both a deadline extension and an application fee waiver code. MS applicant deadlines remain the same.

Graduate Programs in Civil and Environmental Engineering Webinar 1: Research and Funding Opportunities
Monday, December 14, 2020
10:00 – 11:00 AM EST
Register Here

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.

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.

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 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: 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.

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.

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.

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.

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 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.

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 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.

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

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.

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

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 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.

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.
