Northeastern University College of Engineering

ECE PhD Dissertation Defense: Kai Li
2021-04-16 13:00 - 14:00 (America/New_York)
PhD Dissertation Defense: Robust Visual Learning with Limited Labels
Kai Li
Location: Zoom Link
Abstract: The recent flourish of deep learning in various tasks is largely credited to the rich and accessible labeled data. Nonetheless, massive supervision remains a luxury for many real-world applications: It is costly and time-consuming to collect and annotate a large amount of training data. Sometimes it is even infeasible to get large training datasets because for certain tasks only a few or even no examples are available, or annotating requires expert knowledge.
In this dissertation research, I investigate techniques systematically addressing the problem of learning with limited labels from the following three aspects. The first aspect is learning to generalize from limited label supervision. I develop few-shot learning algorithms that perform data augmentation in the feature space and that generate task-specific networks based on the limited supervision provided. The second aspect is learning to reuse label supervision from a relevant but different task. I propose domain adaptation algorithms that adapt label supervision from a richly-labeled source domain to a scarcely-labeled target domain with consistency learning, data augmentation and adversarial learning. The last aspect is learning representations without label supervision. I develop algorithms that learn semantic-rich representations that allow to reliably establish relations among high-dimensional data. This is achieved by explicitly modeling the intrinsic relationship among data points during the representation learning process.

Friday Fun Lab
2021-04-16 13:30 - 17:00 | Curry Student Center, 360 Huntington Ave., Boston, MA, 02115, United States
First-year engineering students showcase their success at Northeastern’s Friday Fun Lab! Play interactive electronic games that were designed and built by the Cornerstone of Engineering students. All games are designed with COVID safety protocol in mind, so stop by and check out these amazing projects today!

ECE MS Thesis Defense: Mehrshad Zandigohar
2021-04-16 15:30 - 16:30
MS Thesis Defense: Real-Time Grasp Type Estimation for a Robotic Prosthetic Hand
Mehrshad Zandigohar
Location: Zoom Link
Abstract: For lower arm amputees, prosthetic hands promise to restore most of physical interaction capabilities. This requires to accurately predict hand gestures capable of grabbing varying objects and execute them timely as intended by the user. Current approaches often rely on physiological signal inputs such as Electromyography (EMG) signal from residual limb muscles to infer the intended motion. However, limited signal quality, user diversity and high variability adversely affect the system robustness.
Instead of solely relying on EMG signals, our work enables augmenting EMG intent inference with physical state probability through machine learning and computer vision method.
To this end, we: (i) study state-of-the-art deep neural network architectures to select a performant sources of knowledge transfer for the prosthetic hand; (ii) use a dataset containing object images and probability distribution of grasp types as a new form of labeling where instead of using absolute values of zero and one as the conventional classification labels, our labels are a set of probabilities whose sum is 1. The proposed method generates probabilistic predictions which could be fused with EMG prediction of probabilities over grasps by using the visual information from the palm camera of a prosthetic hand.
Moreover, As robotic prosthetic hands are targeted for amputees with the goal of assisting them for their daily life activities, it is crucial to have a portable and reliable system. Although embedded devices employed in such systems, provide portability and comfort for the end user, their limited computational resources comparing to a desktop or server computer impose longer latencies when executing such applications, making them unreliable and generally impractical to use. Therefore, it is critical to optimize the aforementioned applications especially DNNs to meet the specified deadline, resulting in a real-time system. Therefore, for real-time execution of grasp estimation we propose: (iii) the concept of layer removal as a means of constructing TRimmed Networks (TRNs) that are based on removing problem-specific features of a pretrained network used in transfer learning, and (iv) NetCut, a methodology based on an empirical or an analytical latency estimator, which only proposes and retrains TRNs that can meet the application’s deadline, hence reducing the exploration time significantly. We demonstrate that TRNs can expand the Pareto frontier that trades off latency and accuracy to provide networks that can meet arbitrary deadlines with potential accuracy improvement over off-the-shelf networks. Our experimental results show that such utilization of TRNs, while transferring to a simpler dataset, in combination with NetCut, can lead to the proposal of networks that can achieve relative accuracy improvement of up to 10.43% among existing off-the-shelf neural architectures while meeting a specific deadline, and 27x speedup in exploration time.
The proposed methods in this work enable robust and realistic prediction of the grasp type as well as real-time execution of the detection pipeline, resulting in the improved overall satisfaction of the targeted population.

ECE PhD Dissertation Defense: Ahmet Oner
2021-04-19 09:00 - 10:00
PhD Dissertation Defense: Improving the Resilience of the Power Grid
Ahmet Oner
Location: Teams Meeting
Abstract: The power grid constitutes one of the most critical infrastructures that have significant interdependencies with various others such as communication, transportation, emergency, and health-care delivery systems. A disruption in the operation of the power grid may affect the operation of all others in an undesirable manner. Therefore, improving the resiliency of power grids can also help increase the resiliency of other critical infrastructures. This dissertation presents methods to improve the resiliency of power grids against extreme events and/or system changes.
First, generation dispatch, adaptable load shedding strategy, and pro-active line switching are combined in order to maximize the resiliency of the overall power grid against extreme events. The moving event is monitored, and the control actions are adjusted accordingly to improve the resilience under changing conditions affected by the natural disaster during its active period. Then, that study is further extended and made it robust against voltage instability. The details of the methodology and its implementation are presented.
To reduce the probability of voltage problems and line flow limit violations, and to improve power quality, distributed generators (DG) are placed strategically ahead of the event using outage forecasts based on historical outage data. Therefore, a possible set of outage scenarios is considered, and a minimum number of required DG placements are determined to maintain system feasibility for all considered scenarios.
Lastly, reactive power sources are placed to solve the voltage instability problems, which are caused by the lack of reactive power in the system. The computational burden of optimal placement problem presents a practical limitation for applying it to very large scale systems considering multi-contingency cases. This part presents a practical and easily implementable solution that will address this limitation.

ECE MS Thesis Defense: Kaier Liang
2021-04-19 15:00 - 16:00
MS Thesis Defense: Rough-Terrain Locomotion and Unilateral Contact Force Regulations With a Multi-Modal Legged Robot
Kaier Liang
Location: Zoom Link
Abstract: The study for legged locomotion has made lots of achievements. However, the stability of the state-of-the-art bipedal robots are still vulnerable to external perturbation, cannot negotiate extreme rough terrains, and cannot directly regulate unilateral contact force.
This thesis will introduce a thruster-assisted bipedal walking robot called Harpy. The objective is to integrate the merits of legged and aerial robots in a single platform. The robot’s dynamics is simulated with simplifying assumptions. Furthermore, this research will show that the employment of thruster allows to stabilize the robot’s frontal dynamics and apply model predictive control (MPC) to jump over obstacles to achieve multi-modal locomotion. In addition, we will capitalize the thruster actions to demonstrate an optimization-free approach by regulating contact forces using an Explicit Reference Governor (ERG). Then, we will focus on ERG-based fine-tuning of the joint’s desired trajectories to satisfy unilateral contact force constraints.

ECE PhD Dissertation Defense: Ilkay Yildiz
2021-04-19 17:30 - 18:30
PhD Dissertation Defense: Spectral Ranking Regression
Ilkay Yildiz
Location: Zoom Link
Abstract: We consider learning from ranking labels generated as follows: given a query set of samples in a dataset, a labeler ranks the samples w.r.t.~her preference. Such ranking labels scale exponentially with query set size; most importantly, in practice, they often exhibit lower variance compared to class labels.
We propose a new neural network architecture based on siamese networks to incorporate both class and comparison labels, i.e., rankings of sample pairs, in the same training pipeline using Bradley-Terry and Thurstone loss functions. Our architecture leads to a significant improvement in predicting both class and comparison labels, increasing classification AUC by as much as 35% and comparison AUC by as much as 6% on several real-life datasets. We further show that, by incorporating comparisons, training from few samples becomes possible: a deep neural network of 5.9 million parameters trained on 80 images attains a 0.92 AUC when incorporating comparisons.
Furthermore, we tackle the problem of accelerating learning over the exponential number of rankings. We consider a ranking regression problem in which we learn Plackett-Luce scores as functions of sample features. We solve the maximum likelihood estimation problem by using the Alternating Directions Method of Multipliers (ADMM), effectively separating the learning of scores and model parameters. This separation allows us to express scores as the stationary distribution of a continuous-time Markov Chain. Using this equivalence, we propose two spectral algorithms for ranking regression that learn shallow regression model parameters up to 579 times faster than the Newton’s method.
Finally, we bridge the gap between deep neural networks (DNNs) and efficient spectral algorithms that regress rankings under the Plackett-Luce model. We again solve the ranking regression problem using ADMM, and thus, express scores as the stationary distribution of a Markov chain. Moreover, we replace the standard l_2-norm proximal penalty of ADMM with Kullback-Leibler (KL) divergence. This is a more suitable distance metric for Plackett-Luce scores, which form a probability distribution, and significantly improves prediction performance. Our resulting spectral algorithm is up to 175 times faster than siamese networks over four real-life datasets comprising ranking observations. At the same time, it consistently attains equivalent or better prediction performance than siamese networks, by up to 26% higher Top-1 Accuracy and 6% higher Kendall-Tau correlation.

ECE MS Thesis Defense: Danton Zhao
2021-04-20 10:00 - 11:00
MS Thesis Defense: LiDAR with a Silicon Photomultiplier for Applications in Adverse Weather
Danton Zhao
Location: Zoom Link
Abstract: As Light Detection and Ranging (LiDAR) integration becomes more widespread in the field of remote sensing for autonomous navigation, the impact of degraded visual environments will quickly need to be addressed. The particles responsible for the degradation not only reduce the reflected signal from targets of interest but can also trigger false returns given sufficient density. Of particular interest for solutions to this problem are Geiger-mode avalanche photodiodes, as these detectors provide high photon sensitivity and high time accuracy with a caveat. In this thesis, I will be discussing the work that I have done in modeling and addressing artifacts that were generated in the data as a result of using Geiger-mode detectors.

ECE MS Thesis Defense: Yize Li
2021-04-20 11:00 - 12:00
MS Thesis Defense: Supervised Classification on Deep Neural Network Attack Toolchains
Yize Li
Location: Zoom Link
Abstract: Deep learning, while an important machine learning technique, is susceptible to adversarial example attacks. Adversarial examples generated by adding perturbations on clean images/video frames can lead to mis-predictions of deep neural networks. Moreover, deep learning/machine learning can be used to deceive humans by generating adversarial falsified media e.g., deepfake attacks. The thesis work will study the above two attack scenarios, i.e., machine-centric adversary and human-centric adversary, with targets to fool ML decisions and human decisions, respectively. We aim to build a generalizable and scalable supervised learning system for classifying attack attributes behind the machine-centric attacks as well as the human-centric attacks. We start from building an integrated Attack Toolchain Library (ATL) with a broad coverage of both machine-centric and human-centric adversaries, as well as through an integrated user interface for great flexibility and extensibility to serve our downstream tasks. Based on the developed ATL, we further design a meta-classifier pipeline architecture for predicting attack attributes. The proposed overall meta-classifier shows effectiveness in dealing with false alarms and data distribution shift, and generalization to both machine-centric and human-centric attacks.

ECE MS Thesis Defense: Ashutosh Singh
2021-04-20 12:00 - 13:00
MS Thesis Defense: Variation is the Norm: Brain State Dynamics Evoked By Emotional Video Clips
Ashutosh Singh
Location: Zoom Link
Abstract: Past affective neuroscience studies have attempted to identify a “biomarker” or consistent pattern of brain activity (as measured externally using, for instance, fMRI) to indicate the presence of a single pre-defined category of emotion (e.g., fear) that remains consistent throughout all instances of that category for an individual across contexts and even across individuals. In this thesis, we investigated variation rather than consistency during emotional experiences. Using fMRI data acquired while individuals watched affect-invoking video clips that have been normed for their evoked emotion categories in prior population studies. Towards this end, we developed a probabilistic model of the temporal dynamics associated with the hypothetical affect-related brain states, fitted to the measured brain activity of the participants. We characterized brain states traversed while individuals watched these clips as distinct state occupancy periods between state transitions, inferred by blood oxygen level-dependent (BOLD) signal patterns captured in fMRI measurements. We found substantial variability in the state occupancy probability distributions across individuals watching the same video, hence supporting the hypothesis that when it comes to the brain correlates of emotional experience, variation may indeed be the norm. Studying the mean activation pattern associated with each state, as well as covariance (in the Gaussian conditional measurement model we assumed), we further improve our understanding of the variability between instances of these brain states. Additionally, we analyzed the presence of potential clusters of brain state trajectories among participants who showed less divergence in their response to each of these videos and checked for their consistency throughout all the video clips.

ECE PhD Dissertation Defense: Peng Chang
2021-04-20 13:30 - 14:30
PhD Dissertation Defense: Model-Based Manipulation of Linear Flexible Objects
Peng Chang
Location: Teams Meetings
Abstract: Manipulation of deformable objects plays an important role in various scenarios such as manufacturing, service, healthcare, and security. Linear flexible objects such as cables, wires, and ropes are common in these scenarios. However, the high dimensionality of the linear flexible objects brings challenges to the modeling and planning in manipulation tasks, and automatic manipulation of these objects is computationally expensive due to their infinite degrees of freedom in the free spaces. In this dissertation, we investigate model-based manipulation of linear flexible objects such as cables. We contribute to different models including geometrical and physical models to represent the linear flexible objects. With these models, we then develop manipulation plans and strategies to achieve the automation of the linear flexible object manipulation tasks in both simulation and real-world. Besides, we also investigate human-robot collaboration to complete a sample assembly task involving linear flexible object manipulation.

ECE MS Thesis Defense: Griffin Knipe
2021-04-20 14:00 - 15:00
MS Thesis Defense: Unifying Performance and Security Evaluation for Microarchitecture Design Exploration
Griffin Knipe
Location: Zoom Link
Abstract: Computer architects develop microarchitectural features that boost instruction-level parallelism to improve CPU performance. While performance may be improved, adding new features increases the CPU’s design complexity. This further compounds the effort required to complete design verification. Trustworthy design verification is paramount to microarchitecture design, as silicon chips cannot easily be patched in the field.
Despite the best efforts for security verification, researchers have created transient execution side-channel attacks which can exploit microarchitecture performance features to leak data across ISA-prescribed security boundaries. This motivates the unification of performance evaluation and security verification techniques to ensure that new microarchitectural features are understood from multiple design perspectives.
This thesis presents Yori, a RISC-V microarchitecture simulator that aims to enable computer architects to evaluate microarchitecture performance and security using a single framework. As Yori is a work-in-progress, this thesis presents the work-to-date, focusing on a detailed model of the reference microarchitecture and evaluation of the current model accuracy. We describe a viable methodology to interface between the Yori simulator and an existing security verification tool. We conclude the thesis, laying out a plan to complete this marriage of performance and security.

MS Thesis Defense: Hao Chen
2021-04-20 16:00 - 17:00
MS Thesis Defense: Reconstruction of Sulcal Geometry in Brain Stimulation Models using Spherical Harmonics
Hao Chen
Location: Zoom Link
Abstract: Over the past few years, there has been increasing interest in transcranial electrical stimulation (tCS) and thus it has been the subject of a growing number of simulation studies. Indeed, some federal agencies in the US now require model-based simulations to be included as part of tCS grant proposal. In order to obtain more accurate simulation results and guide the relevant research, it is of important to assess the impact of the accuracy of the anatomical 3D brain model that these studies depend on. However, due to the partial volume problem, many 3D reconstruction results based on MR images are inaccurate with respect to the details of the geometry of the sulci. Specifically, when the sulci are on the scale of, or even smaller than, the voxel resolution of the MRI, these models generally really in a binary approximation, either making the sulcus wider in the model than in reality or eliminating it altogether. In this thesis, we describe a method for modeling the 3D reconstruction of the brain that may facilitate controlled study of the effect of these approximations. The general approach is to model the brain surface using a spherical harmonic expansion, then modify the expansion coefficients in an attempt to selectively and smoothly control sulcal width. In the first part of the thesis, we describe and evaluate an approach in which we experimentally selected two groups of spherical harmonic coefficients within a specified range that could simultaneously affect a chosen sample of the gyri. For the coefficients in the first group, the widths of all gyri in the sample were increased by enlarging the corresponding coefficients for each spherical harmonic. Conversely, for each coefficient in the second group, this adjustment caused the widths of the sampled gyri to decrease simultaneously. We evaluated the method by alternately increasing / decreasing the coefficients in the first group, and decreasing / increasing those in the second, by a chosen range of factors, and observing the effects on the model cortical surface. Experimental results showed that the widths of most of the sulci and gyri were simultaneously adjusted according to the desired effect.
In the second part of the thesis, we tried to build a volume mesh starting from the modified spherical harmonic surfaces. It turned out that this problem was particularly challenging because most of the surface models in our study had self-intersection points. We used a well-known software package for mesh processing, iso2mesh, to successfully remove the self-intersection points on all surfaces were removed finally, but this process seemed to create small holes in the surfaces of the models. Despite these holes, with a few exceptions, the widths of most sulci (gyri) were still simultaneously increased (decreased) with the coefficient adjustments. This result provides a direction for further study towards controlled study of the influence of the partial volume problem on modeling of tCS.

Making a Graduate School Plan
2021-04-20 17:30 - 18:30
In this intensive workshop, we’ll walk through the steps of identifying a right fit graduate program and help you plan out the who, what, when, where, and how of applying to graduate schools. Bring a laptop if you have one, a notebook, and come prepared with some thoughts about what’s next! If you can’t make it, contact URF@Northeastern.edu to access our Canvas workshop site (graduating seniors only or alumni only please).
Registration

ECE MS Thesis Defense: Rubens Lacouture
2021-04-21 09:00 - 10:00
MS Thesis Defense: GPUBLQMR: GPU-Accelerated Sparse Block Quasi-Minimum Residual Linear Solver
Rubens Lacouture
Location: Zoom Link
Abstract: Solutions of linear systems of equations is the central point of many scientific and engineering research problems across a variety of domains. In many cases, the solution of linear systems can even take most of the simulation time which presents a huge computational bottleneck issue. This can hinder the scalability of various scientific software hindering for larger problems. For large-scale simulations, this can result in having to find the solutions of millions of unknowns, making this an ideal problem to exploit parallelism to improve performance.
Preconditioned Krylov subspace methods have proven effective and robust in various applications. The block Quasi-Minimum Residual (BLQMR) method as developed by Boyse et al. has been shown to be efficient for solving systems of equations with multiple righthand sides. This method is based on the conventional Quasi-Minimum Residual (QMR) method which is generalized using the block Lanczos algorithm to solve multiple solutions simultaneously. In particular, it is shown that this method accelerates the convergence behavior based on the set number of righthand sides, grouped to be solved simultaneously. Block iterative solver methods are often characterized by a high degree of parallelism.
In this thesis, we show how BLQMR can be successfully implemented on a distributed memory computer taking advantage of Graphics Processing Units (GPU) accelerators. We leveraged the processing power of GPUs to show how the proposed GPU-accelerated BLQMR approach can out-perform state-of-the-art linear solvers and results in an ideal behavior for solving challenging linear algebra problems through data from various numerical experiments. The library code developed in this work is written using the CUDA framework. The performance of the parallel algorithm is optimized using several CUDA optimization strategies and the speedup of the parallel GPU implementation over the existing sequential CPU implementations is reported.

ECE MS Thesis Defense: Matin Raayai Ardakani
2021-04-21 10:00 - 11:00
MS Thesis Defense: A Framework for Denoising Two and Three-dimensional Monte CarloPhoton Transport Simulations Using Convolutional Neural Networks
Matin Raayai Ardakani
Location: Zoom Link
Abstract: The Monte Carlo (MC) method is considered to be the gold standard for modeling light propagation inside turbid media, proving superior to other Radiative Transfer Equation (RTE) solvers relying on variational principles. However, like most MC-based algorithm, a large number of independently launched photons is needed for converging to the correct result and combating its inherent stochastic noise, yielding longer computation times, even when accelerated on GraphicProcessing Units (GPUs).
To remove this noise from the output without increasing the number of photons used for simulation, modified versions of commonly used filters for image and volumetric data based on non-local self similarity has been used in the past. Current state-of-the-art denoising approaches rely on Convolutional Neural Networks (CNN) to remove spatially variant noise, but the high dynamic range of MC simulations has hindered their adaptation to remove MC noise.
In this thesis, we address this problem by presenting a supervised framework for using CNNs to denoise MC simulations. First, a dataset is created with each entry comprising of a unique configuration simulated with different numbers of photons. The simulation configurations are generated using a simple generative model that introduces objects with both smooth and sharp edges into the volume. By selecting the group of fluence maps simulated with the maximum number of photons in the dataset as labels, we train a range of CNN-based models to learn the underlying mapping between noisy and clean images. The CNN input is converted to log scale and normalized to reduce the high dynamic range, and converted back after inference. The trained CNNs are then shown to have better performance compared to using an Adaptive Non-local Means filter, in terms of mean square error (MSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR) in the image domain.
Finally, we purpose our own architecture that combines DnCNN and UNet, a strategy that can learn both local and global residual noise maps, achieving state-of-the-art performance compared to existing CNN methods. Future avenues of research and challenges for denoising 3D simulations are also discussed.

MS Thesis Defense: Yuezhou Liu
2021-04-21 10:00 - 17:00
MS Thesis Defense: Optimizations of Caching Networks: Fairness and Application to Mobile Networks
Yuezhou Liu
Location: Zoom Link
Abstract: In-network caching is playing a more and more important role in today’s network architectures, because of the explosive growth of data traffic due to the proliferation of mobile devices and demands for high-volume media content, as well as the development of low-latency applications, such as VR/AR and cloud gaming. The replication of popular contents in the caches that located closer to end users than central servers, can significantly reduce backbone traffic, benefit request latency, and balance the load of central servers. In this thesis, we study two problems in the field of network caching. In the first part, we consider fair caching policies in caching networks with arbitrary topology. We introduce a utility maximization framework to find a caching decision that reduces aggregate expected request routing cost in the network while taking fairness issues into consideration. The utility maximization problem is NP-hard, and we propose two efficient approximation algorithms to solve it. In the second part, we study how caching may affect user association in mobile networks. We jointly optimize the user association decision and caching at both base stations (BSs) and gateways (GWs). The resulting problem is also NP-hard. We propose a polynomial-time algorithm based on concave approximation and pipage rounding that produces a solution within a constant factor of 1-1/e from the optimal. Simulation results show that the proposed algorithm outperforms schemes that combine cache-independent user association methods with traditional caching strategies (e.g., LRU) in terms of minimizing the aggregate expected routing cost and backhaul traffic while achieving a high data sum rate in the access network.

ChE Seminar Series: Biomaterials to unlock the regenerative capacity of tissues
2021-04-21 12:00 - 13:00
ChE Seminar Series Presets: Dr. Tatiana Segura
Tatiana Segura, PhD
Professor of Biomedical Engineering, Duke University
Biomaterials to unlock the regenerative capacity of tissues
Abstract: Injectable materials that can conform to the shape of a desired space are used in a variety of fields including medicine. The ability to fill a tissue defect with an injectable material can be used for example to deliver drugs, augment tissue volume, or promote repair of an injury. This talk will explore the development of injectable materials that are based on assembled particle building blocks, for tissue repair. We find that using microparticle building blocks to build the scaffold generates a porous network by the space left behind between adjacent building blocks. Due to the injectability of this microporous material we have explored its wide applicability to tissue repair applications ranging from skin to brain wounds. In this talk, I will describe how MAP scaffolds can modulate the wound healing immune response and lead to regenerative wound healing.
Biography: Professor Tatiana Segura received her BS degree in Bioengineering from the University of California Berkeley and her doctorate in Chemical Engineering from Northwestern University. Her graduate work in designing and understanding non-viral gene delivery from hydrogel scaffolds was supervised by Prof. Lonnie Shea. She pursued post-doctoral training at the Swiss Federal Institute of Technology, Lausanne under the guidance of Prof. Jeffrey Hubbell, where her focus was self-assembled polymer systems for gene and drug delivery. Professor Segura’s Laboratory studies the use of materials for minimally invasive in situ tissue repair. On this topic, she has published 113 peered reviewed publications to date. She has been recognized with the Outstanding Young Investigator Award from the American Society of Gene and Cell Therapy, the American Heart Association National Scientist Development Grant, and the CAREER award from National Science Foundation. She was Elected to the College of Fellows at the American Institute for Medical and Biological Engineers (AIMBE) in 2017. She spent the first 11 years of her career at UCLA department of Chemical and Biomolecular Engineering and has recently relocated to Duke University, where she holds appointments in Biomedical Engineering, Neurology and Dermatology.

ECE PhD Dissertation Defense: Muhamed Yildiz
2021-04-21 17:30
PhD Dissertation Defense: Interpretable Machine Learning for Retinopathy of Prematurity
Muhamed Yildiz
Location: Zoom Link
Abstract: Retinopathy of Prematurity (ROP), a leading cause of childhood blindness, is diagnosed by clinical ophthalmoscopic examinations or reading retinal images. Plus disease, defined as abnormal tortuosity and dilation of the posterior retinal blood vessels, is the most important feature to determine treatment-requiring ROP. State-of-the-art ROP detection systems employ convolutional neural networks (CNNs) %cite{brown2018automated} and achieve up to $0.947$ and $0.982$ area under the ROC curve (AUC) in the discrimination of textit{normal} and textit{plus} levels of ROP. However, due to their black-box nature, clinicians are reluctant to trust diagnostic predictions of CNNs.
First, we aim to create an interpretable, feature extraction-based pipeline, namely, I-ROP ASSIST, that achieves CNN like performance when diagnosing plus disease from retinal images. Our method segments retinal vessels, detects the vessel centerlines. Then, our method extracts features relevant to ROP, including tortuosity and dilation measures, and uses these features for classification via logistic regression, support vector machines and neural networks to assess a severity score for the input. For predicting textit{normal} and textit{plus} levels of ROP on a dataset containing 5512 posterior retinal images, we achieve $0.88$ and $0.94$ AUC, respectively. Our system combining automatic retinal vessel segmentation, tracing, feature extraction and classification is able to diagnose plus disease in ROP with CNN like performance.
Then, we introduce a novel method for extracting tortuosity features. Current feature extraction pipelines of retinal image analysis systems extract tortuosity features based on the derivatives of vessel centerlines or a segment of a vessel. Our method eliminates the need for finding vessel centerlines by introducing a method for calculating curvature at each pixel in the fundus image. When calculating curvature, we use the geometric interpretation of eigenvectors of the Hessian of an interpolation function. By selecting an appropriate interpolation function, our method can be applied in many domains, including corner detection, noise removal and image registration. We present the results of our method on artificial images that contains curved structures such as circle, sine waves as well as real images from MNIST and our retinal fundus image dataset. Experimental results shows that our model accurately captures the high curvature parts of the blood vessels.
Furthermore, we aim to address the interpretability problem of CNN-based ROP detection system. Incorporating visual attention capabilities in CNNs enhances interpretability by highlighting regions in the images that CNNs utilize for prediction. Generic visual attention methods do not leverage structural domain information such as tortuosity and dilation of retinal blood vessels in ROP diagnosis. We propose the Structural Visual Guidance Attention Networks (SVGA-Net) method, that leverages structural domain information to guide visual attention in CNNs. SVGA-Net achieves $0.979$ and $0.987$ AUC to predict textit{normal} and textit{plus} levels of ROP. Moreover, SVGA-Net consistently results in higher AUC compared to visual attention CNNs without guidance, baseline CNNs, and CNNs with structured masks.

ECE MS Thesis Defense: Peter Kelly
2021-04-22 09:00 - 10:00
MS Thesis Defense: Design of a Thruster-assisted Bipedal Robot
Peter Kelly
Location: Zoom Link
Abstract: During the past few years, legged robot technology has been rapidly advancing.
However, even the most advanced bipedal legged robots are susceptible to strong disturbances and slippery or impassible terrain. By introducing thrusters to enable hybrid legged-aerial locomotion, these problems can be circumvented by increasing a robot’s stability and allowing it to jump over obstacles. Harpy is a bipedal robot with eight actuators and two thrusters that serves as a hardware platform for developing control algorithms to advance research in thruster assisted bipedal legged locomotion. This thesis explores the conception, simulation, and electromechanical design process of the robot, which prioritizes thrust-to-weight ratio, impact resistance, power density, and modularity. The fabrication process of actuators and the leg which enable the robot to be both light and strong and testing of the leg design and thrusters is also discussed.

ECE MS Thesis Defense: Tianhong Xu
2021-04-22 10:00 - 11:00
MS Thesis Defense: A novel simple power analysis (SPA) attack on Elliptic Curve Cryptography (ECC)
Tianhong Xu
Location: Zoom Link
Abstract: Elliptic Curve Cryptography (ECC), as a widely used public-key cryptography, is vulnerable to simple power analysis(SPA) attacks. There are many countermeasures against simple power analysis(SPA) attacks on ECC implementation, the Always-add algorithm is one of the most popular countermeasures. This research proposes a new SPA attack which is effective to the ECC encrypting implemented with Always-add algorithm, it uses deep-learning tools and statistical method to retrieve a secret key from only one EM trace collected from a ASIC circuit running ECC encryption.

ECE PhD Dissertation Defense: Seyedmehdi Sadeghzadeh
2021-04-22 10:00 - 11:00
PhD Dissertation Defense: Physical Layer Security in Multi-User Wireless Networks: Impact of Interference and Artificial Noise on Large-Scale Analysis
Seyedmehdi Sadeghzadeh
Location: Zoom Link
Abstract: In this thesis, we study the physical layer security in downlink multi-user wireless networks. Traditionally, security has been addressed by cryptography at the higher layers of the communication stack. Security at the physical layer has been a major research topic in recent years. We study two different precoder designs alongside artificial noise (AN) to mitigate multi-user interference and deteriorate reception at the eavesdropper (Eve). We study the large scale analysis to calculate the secrecy sum-rate for these two cases and analyze the effect of AN on the system. First, we consider the worst case scenario, when eavesdropper’s (Eve’s) rate is not deteriorated by the interference caused by the legitimate users. Later, we investigate how interference from legitimate users would affect the large scale security sum rate. At the end, we assume more practical situation where the channel state information at the transmitter is not perfect due to feedback limitation and estimation error.

ECE PhD Dissertation Defense: Linbin Chen
2021-04-22 10:30 - 11:30
PhD Dissertation Defense: Low Power Designs using Approximate Computing and Emerging Memories at Nanoscales
Linbin Chen
Location: Zoom Link
Abstract: A power efficient integrated circuit design is essential for mobile and embedded computer systems. This dissertation proposes several novel low power designs using approximate computing and emerging memories for computers with arithmetic circuits and large on-chip caches. Initially, low power approximate designs are proposed both for fixed point radix-2 and high-radix division at circuit-level. Then, an approximate parallel CORDIC algorithm and its hardware implementation are developed. Trade-offs between circuit metrics and error characteristics are pursued by simulation and analysis. The proposed approximate arithmetic designs have excellent performance for image processing applications while significantly reducing power consumption. Then, hybrid cache designs integrating SRAM with emerging memories are also investigated. An intra-cell, as well as inter-subarray and inter-bank hybrid caches with SRAM, eDRAM and NVM (such as PCM or STT-MRAM) are proposed. Architectural level approaches such as special migration structures and policies are designed to address the eDRAM refresh requirements and the NVM large write latency issue. An analytical circuit-level model based on NVsim focusing on hybrid granularity and an architecture level model based on gem5 focusing on a migration policy are developed. To explore the hybrid cache’s benefits to main memory, a combined-cache design for addressing endurance issues of multi-level non-volatile memory in embedded system is proposed. It is shown that these hybrid cache designs exhibit smaller area and lower leakage than conventional designs so with great potential to be used for large-capacity on-chip caches in mobile and embedded systems.

The Weird World of Quantum Matter
2021-04-22 10:30 - 11:30
JOINT SPECIAL COLLOQUIUM BY COLLEGE OF SCIENCE AND COLLEGE OF ENGINEERING
The Weird World of Quantum Matter
Professor Prineha Narang, Harvard University
Quantum materials host many spectacular functionalities enabled by their unusual excited-state and nonequilibrium quantum effects. Understanding these phenomena that involve a variety of time and length scales has remained elusive. My research focuses on addressing this grand challenge by developing next-generation, predictive theoretical and computational approaches at the frontiers of quantum science and engineering [1-3] and paves the way for technologies ranging from scalable quantum information processing and networks, to ultra-high efficiency optoelectronic and energy conversion systems. I will discuss how this research is helping unravel the microscopic dynamics, decoherence and optically excited collective phenomena in quantum matter. I will also present selected examples of our ab initio design and control of active defects in quantum materials and our predictions of linear and nonlinear dynamics and transport in topological semimetals. Finally, I will comment on driving quantum matter far out-of-equilibrium to control complex coupled degrees-of-freedom.
Website: narang.seas.harvard.edu
Dr. Narang received her MS and PhD in Applied Physics from Caltech. She has received many awards including an NSF CAREER award. She has been named a Moore Inventor Fellow by the Gordon and Betty Moore Foundation, CIFAR Azrieli Global Scholar by the Canadian Institute for Advanced Research, a Top Innovator by MIT Tech Review (MIT TR35), and a Young Scientist by the World Economic Forum.
Zoom meeting link:
https://northeastern.zoom.us/j/97384220271

MS Thesis Defense: Duschia Bodet
2021-04-22 12:30 - 13:30
MS Thesis Defense: Modulations to Exploit the THz Band
Duschia Bodet
Location: Zoom Link
Abstract: Terahertz (THz)-band (0.1-10 THz) communication has been envisioned as a key technology to enable wireless Terabit-per-second (Tbps) links. At THz frequencies, the path-loss is governed by the spreading loss and the molecular absorption loss. The latter also determines the available transmission bandwidth, which drastically shrinks with distance. As a result, traditional modulation schemes cannot fully take advantage the THz channel, and new modulation schemes are needed if THz channel communications are going to reach their full potential. Several solutions have been presented including Hierarchical Bandwidth Modulations (HBM), which is the only presented work that not only compensates for molecular absorption losses but leverages those losses to improve the capabilities of the system. The focus of this thesis is two-fold. First the design of HBM is formalized, exploring the trade-offs and its achievable performance as a function of different system parameters. Secondly, these trade-offs and performance metrics are verified using a one-of-a-kind experimental testbed for ultrabroadband communication networks. The results show that with proper design HBM successfully achieves its goal of exploiting the distance-dependent characteristics of the THz channel.

Order of the Engineer Ring Ceremony
2021-04-22 14:00 - 15:00
This year the College of Engineering is pleased to invite the Class of 2020 to join the Class of 2021 in a combined virtual Order of the Engineer Ring Ceremony. Eligible students will be inducted into the Order, receive a ring, and a certificate.
The Order of the Engineer is an organization composed of engineers within the United States who have publicly accepted the obligation of an Engineer, a formal statement of an engineer’s responsibilities to both the public and the profession. As part of this ceremony, you will receive a stainless-steel ring, which symbolizes and reminds engineers of their obligation to serve the public and demonstrates the common bond engineers share.
Zoom link: https://northeastern.zoom.us/j/94311514917

Plant Shift Initiative | Speaker Series: Engineering & Climate Correction
2021-04-22 15:00 - 16:30
There is no debate that in order to combat climate change we need to think of creative solutions. Join us on Earth Day, April 22nd, to hear more about the Plant Shift Initiative which is dedicated to spark new “plant-based” ideas in all forms of productions and activities.
For decades, entrepreneurs and leaders have invented new systems to minimize the carbon footprint in every part of our lives. To kick off this series, we will hear from disrupters Sebastiano Cossia Castiglioni, PNT’23, Co-Founder and Director of Natural Order Acquisition Corp., Dale Vince, Founder of Ecotricity, and Paul Watson, Founder and Chief Executive Officer of Sea Shepherd. They will share with you how they engineered new designs and systems within their industries to be more plant-based.
Register

ECE PhD Dissertation Defense: Lichen Wang
2021-04-23 13:00 - 14:00
PhD Dissertation Defense: Correlation Discovery for Multi-view and Multi-label Learning
Lichen Wang
Location: Zoom Link
Abstract: Correlation indicates the interactions or connections across different instances. It exists in a wide range of real-world applications such as social network, scene understanding, and time-series data analysis. Correlation provides the unique and informative knowledge to reveal the connections across instances, and it plays an essential and important role in machine learning field. However, recovering and utilizing correlation is challenging. First, it is hard to explicitly define and understand the correlations. Second, there are not sufficient datasets which contain the well-labeled task-specific correlations. Third, how to efficiently utilize the learned correlations for other down-stream tasks have not been well-explored.
In this dissertation research, we investigate the techniques to effectively discover various kinds of correlations in machine learning tasks including multi-view learning, multi-label learning, image/scene understanding, time-series data analysis, human action recognition, and graph representation learning. Specifically, we propose algorithms from the following perspectives: (1) designing an advanced correlation discovery network to automatically explore the label correlations in multi-label scenarios, (2) proposing a multi-view fusion strategy which effectively dig the latent correlations across different views, (3) exploring the correlations and structural knowledge from graph structured objects in an inductive and unsupervised scenario. To demonstrate the effectiveness of the proposed algorithms, various experiments on commonly used datasets have been implemented and the results shows the superiority of our algorithms over the other state-of-the-art methods.

ECE MS Thesis Defense: Anran Wei
2021-04-26 10:00 - 11:00
MS Thesis Defense: A soft-switching non-inverting buck-boost converter
Anran Wei
Location: Zoom Link
Abstract: There are numerous applications in which DC-DC converters with wide range of voltage gain are required. Non-inverting buck-boost converter is a classical topology that can provide wide range of voltage conversion and bidirectional power transfer; thus, it is frequently used in industrial applications. However, the conventional hard-switching configuration, which transfers power through a link inductor, can only reach a high voltage conversion ratio at the expense of low efficiency due to switching loss. This thesis proposes a soft switching non-inverting buck-boost converter. This converter uses a small film capacitor in parallel with the link inductor to provide zero voltage switching (ZVS) by allowing the link capacitor and link inductor resonate between power transfer states. Principles of the operation of this converter are presented in this thesis and its performance is evaluated through simulations and experiments.

Rare Earth Element-Based Magnets: Science, Supply and Sustainability in 2021 and Beyond
2021-04-27 11:00 - 12:00
University Distinguished Professor Vincent Harris is presenting “Rare Earth Element-Based Magnets: Science, Supply and Sustainability in 2021 and Beyond” as part of the Jefferson Science Fellowship Program of the National Academies of Sciences and Engineering.
Registration is required in advance of the lecture: Register here
Rare earth elements (REEs) and their supply chain have become topics of great interest to the U.S. diplomatic and national security communities. Presently, China dominates REE markets in all facets of processing from earth extraction to metals as well as value and commercialization verticals. Beijing has shown no hesitancy in using its position of market dominance to advance its broader political goals and agendas.
In this presentation, we focus on REE-based magnets and associated challenges faced in 2021. We explore REE science and applications, supply and policy, and sustainability and environmental impact. We examine what the future holds in terms of alternative sources, recycling, and the practice of designing components around the need to employ REEs. Finally, we report on steps taken by the global community to offset China’s monopoly on rare earths.

ECE PhD Dissertation Defense: Yue Zheng
2021-04-27 15:00 - 16:00
PhD Dissertation Defense: Modular Plug-and-Play Photovoltaic Subpanel System
Yue Zheng
Location: Zoom Link
Abstract: This thesis designs, builds and tests plug-and-play photovoltaic (PV) panels. A prototype modular PV system is built consisting of a dozen small PV units that can slide in and out of a mechanical frame without impacting other units. Each unit contains one PV subpanel and a DC-DC converter with a distributed maximum power point tracking (dMPPT) control board. Each PV unit works at its maximum power, while every output of the converter is connected in parallel to a DC bus. A new combined control strategy is proposed in which the decision to use centralized or distributed control depends on the system efficiency at the varying load operating points. A disadvantage of this dMPPT structure is that in each PV unit, the DC-DC converter must convert the entire power from its PV subpanel. Therefore, this research also explores the use of Differential Power Processing (DPP) system, which harvests maximum power while only processing a small amount of power due to the mismatches between PV panels. Thus, DPP structure reduces power loss compared to traditional dMPPT structure. Since it processes only a small amount of power, differential power processing structure has the potential to further be integrated on a chip and become installed in the junction box during the assembling process. Finally, the research proposes to implement the plug-and-play features of the solar PV system using wireless power transfer (WPT) instead of hard wire connectors. A series-to-series topology of WPT system (L-R-C series circuit) for one PV unit is proposed. In this system, the DC-DC converter on the PV side is used to perform MPPT, while the DC-AC inverter simultaneously perturbs its switching frequency to match possible variations in resonance frequencies. Wireless communication is used between transmitter and receiver. Thus, the maximum efficiency point on the constant output voltage trajectory can be tracked dynamically under wide and varying operating conditions.
