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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210603T110000
DTEND;TZID=America/New_York:20210603T120000
DTSTAMP:20260423T105353
CREATED:20210526T201737Z
LAST-MODIFIED:20210526T201737Z
UID:4960-1622718000-1622721600@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Mehdi Nasrollahpourmotlaghzanjani
DESCRIPTION:PhD Proposal Review: RFICs for Biomedical Magnetic and Magnetoelectric Microsystems \nMehdi Nasrollahpourmotlaghzanjani \nLocation: Zoom Links \nAbstract: Design and analysis of the advanced biomedical circuit and systems in wide variety of applications has emerged a significant interest. Not only in different engineering disciplines\, but also in a variety of applications such as neuroscience\, COVID-19\, etc. In this study\, we are proposing an implantable device\, handheld device for detecting different diseases and the RFIC design for the ME antenna and sensor evaluations.\nFirst\, we show and miniaturized implantable device for deep brain implantation that provides wireless power transfer efficiency (PTE) of 1 to 2 orders of magnitude higher than the reported micro-coils for brain stimulation. The proposed device will simultaneously measure the as magnetic field activity when neurons are firing. In the second part we will go over the RFIC design for the bio-implant devices\, evaluation of the ME antennas for communication purposes and the circuit interface to measure the ME and GMI sensors. For final part\, we will discuss the handheld device design for early diagnosis of different diseases such as\, lung cancer\, Alzheimer\, Covid-19\, etc through exhaled breath on the molecularly imprinted polymer (MIP) gas sensors.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-mehdi-nasrollahpourmotlaghzanjani/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210526T130000
DTEND;TZID=America/New_York:20210526T140000
DTSTAMP:20260423T105353
CREATED:20210524T222653Z
LAST-MODIFIED:20210524T222653Z
UID:4958-1622034000-1622037600@ece.northeastern.edu
SUMMARY:PhD Dissertation Defense: Kunpeng Li
DESCRIPTION:PhD Dissertation Defense: Visual Learning with Limited Supervision \nKunpeng Li \nLocation: Zoom Link \nAbstract: Deep learning models have achieved remarkable success in many computer vision tasks. However\, they typically rely on large amounts of carefully labeled training data whose annotating process is usually expensive\, time-consuming and even infeasible when considering the task complexity and scarcity of expert knowledge.\nIn this dissertation talk\, I will discuss several explorations along the direction of visual learning with limited supervision. They are mainly about learning from data with weak forms of annotations and learning from multi-modal data pairs. Specifically\, I will first present a guided attention learning framework to conduct semantic segmentation by mainly using image-level labels\, as such weak form of annotation can be collected much more efficiently than pixel-level labels. Under mild assumptions\, our framework can also be used as a plug-in to existing convolutional neural networks to improve their generalization performance. This is achieved by guiding the network to focus on correct things when learning concepts from a limited set of training samples. Besides\, I will also introduce models that can effectively learn from multi-modal data pairs without relying on dense annotations of visual semantic concepts. Our models incorporate relational reasoning ability into the visual representation learning process so that it can be better aligned with the supervision from corresponding text descriptions.
URL:https://ece.northeastern.edu/event/phd-dissertation-defense-kunpeng-li/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210525T100000
DTEND;TZID=America/New_York:20210525T110000
DTSTAMP:20260423T105353
CREATED:20210517T174657Z
LAST-MODIFIED:20210517T174657Z
UID:4945-1621936800-1621940400@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Mohammad Hossein Hajkazemi
DESCRIPTION:PhD Dissertation Defense: High-performance Translation Layers for Cloud Immutable Storage \nMohammad Hossein Hajkazemi \nLocation: Zoom Link \nAbstract: Most storage interfaces support in-place updates: blocks can be rewritten\, files can be modified at byte granularity\, fields may be updated in database table rows. Yet internally these layers often rely on out-of-place (immutable) writes. In some cases\, this may be necessary to use media\, such as flash\, SMR (shingled magnetic recording) and IMR (interlaced magnetic recording) disk\, which do not allow overwrites. In others\, it is used to simplify the implementation of transactions and/or crash consistency\, in the form of journaling\, write-ahead logging\, shadow paging\, etc. \nIn a storage system\, translation layers perform out-of-place writes\, and they are implemented in different layers of storage stack from the file system to the storage device firmware depending on the application. In this dissertation I focus on translation layers for cloud immutable storage technologies to improve the cloud I/O performance. As a part of my thesis\, I focus on translation layers for state-of-the-art immutable storage media such as SMR and IMR used in cloud environments\, proposing several novel algorithms to improve their efficiency. I also introduce FSTL\, a framework to design and implement SMR translation layer. Finally\, I describe Collage\, a virtual disk I developed over S3 (could be implemented over a similar object storage) using a translation layer which performs large\, sequential\, out-of-place writes for high performance. It optionally uses fast local storage for write logging and as a write-back cache\, guaranteeing prefix consistency under all failure conditions and recovery of all acknowledged writes if the local cache is not lost. Collage supports snapshots and cloned volumes\, performs well over erasure-coded storage\, and allows consistent asynchronous volume replication over geographic distances. I show that Collage can achieve massive performance improvements (e.g.\, over 100x for microbenchmarks and 10x for macro-benchmarks) over CEPH RBD\, a popular open-source scale-out virtual disk implementation.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-mohammad-hossein-hajkazemi/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210521T110000
DTEND;TZID=America/New_York:20210521T120000
DTSTAMP:20260423T105353
CREATED:20210503T175740Z
LAST-MODIFIED:20210503T175740Z
UID:4881-1621594800-1621598400@ece.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Daniel Uvaydov
DESCRIPTION:MS Thesis Defense titled DeepSense: Fast Wideband Spectrum Sensing Through Real-Time In-the-Loop Deep Learning \nDaniel Uvaydov \nLocation: Microsoft Teams \nAbstract: Spectrum sharing will be a key technology to tackle spectrum scarcity in the sub-6 GHz bands. To fairly access the shared bandwidth\, wireless users will necessarily need to quickly sense large portions of spectrum and opportunistically access unutilized bands. The key unaddressed challenges of spectrum sensing are that (i) it has to be performed with extremely low latency over large bandwidths to detect tiny spectrum holes and to guarantee strict real-time digital signal processing (DSP) constraints; (ii) its underlying algorithms need to be extremely accurate\, and flexible enough to work with different wireless bands and protocols to find application in real-world settings. To the best of our knowledge\, the literature lacks spectrum sensing techniques able to accomplish both requirements. In this paper\, we propose DeepSense\, a software/hardware framework for real-time wideband spectrum sensing that relies on real-time deep learning tightly integrated into the transceiver’s baseband processing logic to detect and exploit unutilized spectrum bands. DeepSense uses a convolutional neural network (CNN) implemented in the wireless platform’s hardware fabric to analyze a small portion of the unprocessed baseband waveform to automatically extract the maximum amount of information with the least amount of I/Q samples. We extensively validate the accuracy\, latency and generality performance of DeepSense with (i) a 400 GB dataset containing hundreds of thousands of WiFi transmissions collected “in the wild” with different Signal-to-Noise-Ratio (SNR) conditions and over different days; (ii) a dataset of transmissions collected using our own software-defined radio testbed; and (iii) a synthetic dataset of LTE transmissions under controlled SNR conditions. We also measure the real-time latency of the CNNs trained on the three datasets with an FPGA implementation\, and compare our approach with a fixed energy threshold mechanism. Results show that our learning-based approach can deliver a precision and recall of 98% and 97% respectively and a latency as low as 0.61ms.
URL:https://ece.northeastern.edu/event/ece-ms-thesis-defense-daniel-uvaydov/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210513T140000
DTEND;TZID=America/New_York:20210513T150000
DTSTAMP:20260423T105353
CREATED:20210503T175624Z
LAST-MODIFIED:20210510T175607Z
UID:4880-1620914400-1620918000@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Siyue Wang
DESCRIPTION:PhD Proposal Review: Towards Robust and Secure Deep Learning Models and Beyond \nSiyue Wang \nLocation: Zoom Link \nAbstract: Modern science and technology witness the breakthroughs made by deep learning during the past decades. Fueled by rapid improvements of computational resources\, learning algorithms\, and massive amount of data\, deep neural networks (DNNs) have played a dominant role in more and more real-world applications. Nonetheless\, there is a spring of bitterness mingling with this remarkable success – recent studies reveals that there are two main security threats of DNNs which limit its widespread usage: 1) the robustness of DNN models under adversarial attacks\, and 2) the protection and verification of intellectual properties of well-trained DNN models. \nIn this dissertation\, we fist focus on the security problems of how to build robust DNNs under adversarial attacks\, where deliberately crafted small perturbations added to the clean input can lead to wrong prediction results with high confidence. We approach the solution by incorporating stochasticity into DNN models. We propose multiple schemes to harden the DNN models when facing adversarial threats\, including Defensive Dropout (DD)\, Hierarchical Random Switching (HRS)\, and Adversarially Trained Model Switching (AdvMS). \nThe second part of this dissertation focuses on how to effectively protect the intellectual property for DNNs and reliably identify their ownership. We propose Characteristic Examples (C-examples) for effectively fingerprinting DNN models\, featuring high-robustness to the well-trained DNN and its derived versions (e.g. pruned models) as well as low-transferability to unassociated models. The generation process of our fingerprints does not intervene with the training phase and no additional data are required from the training/testing set.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-siyue-wang/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210507T110000
DTEND;TZID=America/New_York:20210507T120000
DTSTAMP:20260423T105353
CREATED:20210506T233919Z
LAST-MODIFIED:20210506T233919Z
UID:4928-1620385200-1620388800@ece.northeastern.edu
SUMMARY:ECE Faculty Seminar: Sumientra Rampersad
DESCRIPTION:Faculty Seminar: Is temporal interference the key to noninvasive deep brain stimulation? Answers from simulation studies in mice and humans. \nSumientra Rampersad \nLocation: Zoom Link \nAbstract: Transcranial current stimulation (tCS) has been used for two decades to noninvasively investigate and influence brain function in both healthy volunteers and clinical populations. While many positive effects have been found\, the goals of high focality\, accurate targeting and deep stimulation are yet to be achieved. Transcranial temporal interference stimulation (tTIS) is a new form of tCS that might improve the method on all three fronts. tTIS uses two alternating currents to create an amplitude-modulated electric field that can peak deep in the brain. A recent murine study showed promising effects of tTIS and concluded that the technique may be used as a noninvasive form of deep brain stimulation in humans\, but results from human experiments have not yet been published. In this talk I will present results of finite element simulations with realistic head models to investigate the electric fields induced by tTIS in the brain\, comparing results in murine and human head models for tTIS and conventional tCS. Due to the nonlinear nature of tTIS\, conventional methods to optimize tCS fields for a specific brain target cannot be used. I will present two nonconvex optimization methods for tTIS and compare their efficiency and results. Finally\, I will discuss the implications of the results of these simulation and optimization studies for potential applications of tTIS in humans. \nBio: Sumientra Rampersad is an Assistant Research Professor in the Department of Electrical and Computer Engineering at Northeastern University in Boston\, where she leads the Brain Stimulation & Simulation Lab. Dr. Rampersad’s research aims to improve understanding of the working mechanisms behind neuromodulation and improve its application using computational methods and experiments with human subjects. She investigates invasive (ECoG\, sEEG) and noninvasive (tCS\, TMS) brain stimulation\, as well as peripheral stimulation\, and is especially interested in bridging the gap between modeling and experiments through model-based experimentation. Her research in collaboration with various academic and clinical partners has been awarded funding by NIA\, NINDS and NIMH. Dr. Rampersad was previously a research scientist in Northeastern’s Cognitive Systems Lab and obtained her PhD at the Radboud University Donders Institute in Nijmegen\, the Netherlands.
URL:https://ece.northeastern.edu/event/ece-faculty-seminar-sumientra-rampersad/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210429T120000
DTEND;TZID=America/New_York:20210429T133000
DTSTAMP:20260423T105353
CREATED:20210426T175332Z
LAST-MODIFIED:20210426T175332Z
UID:4870-1619697600-1619703000@ece.northeastern.edu
SUMMARY:Distinguished Speaker Series in Robotics
DESCRIPTION:We cordially invite you to join the\nDISTINGUISHED SPEAKER SERIES IN ROBOTICS\nThursday\, April 29\, 12:00 – 1:30pm \n\nVirtual Meeting – Zoom Link | Meeting ID: 928 6786 9946 | Passcode: 103234 \nhttps://northeastern.zoom.us/j/92867869946?pwd=VTA5R1EwRmZKUjdSeHRpYXpVM09Kdz09 \n\nManual Skills and Dexterity in Robots and Humans \nAude Billard \nProfessor of Robotics\, Swiss Federal Institute of Technology (EPFL)\, Switzerland \n\nPart 1: Robots have moved from imitating humans to exceeding humans’ capabilities – sometimes: The design of robots’ manipulation capabilities is driven by our admiration for humans’ exquisite dexterity and motor agility. Yet\, robots are far from reproducing the complexity of human cognition\, for some skills robots do better than humans. Thanks to their powerful motors and the speed of computation of their computer-driven circuits\, robots can beat humans in precision and reactivity. This talk will give an overview of several approaches developed at LASA to endow robots with the ability to react extremely rapidly in the face of unexpected changes in the environment\, combining control with dynamical systems and machine learning. We use human demonstrations to guide the design of the controller’s parameters to modulate the compliance and to determine the range of feasible paths. A review of these algorithms will be accompanied with illustrations of their implementation for controlling uni-manual and bi-manual manipulation. I will conclude by showing some examples of super-human capabilities for catching objects with a dexterity that exceeds that of human beings. \nPart 2: Understanding bimanual skill – a case study in watchmaking: Human dexterity still eludes largely robotics. In an effort to better understand and model this dexterity\, we took on an adventure and decided to follow a cohort of apprentices at watchmaking\, a craft unique in its requirement for precise control of finger movements. Precise control of force is also of essence to prevent breakage of the tiny\, and often highly valuable\, pieces. In a two-year long training\, apprentice acquire the ability to go beyond their natural perception of touch\, so as to sense when the piece clicks and the screw in. Most impressive is the ability with which they acquire unusual but efficient hand postures. Our study unveils how the two hands work in coordination to distribute control variables and achieve better precision than when using a single hand. \nBio: Aude Billard is professor in robotics at the School of Engineering at the Swiss Federal Institute of Technology in Lausanne (EPFL). Trained in physics and robotics\, she has been a pioneer in the application of machine learning for robotic control and human-robot interactions. Billard’s research focuses on manual control and dexterity\, inspired by human skill. Her work on robotics and human-robot interactions has been recognized numerous times by the Institute of Electrical and Electronics Engineers (IEEE) and she currently holds a leadership position on the executive committee of the IEEE Robotics and Automation Society (RAS) as the vice president of publication activities. \n\nPresented by the Institute for Experiential Robotics and Action Club
URL:https://ece.northeastern.edu/event/distinguished-speaker-series-in-robotics/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210427T150000
DTEND;TZID=America/New_York:20210427T160000
DTSTAMP:20260423T105353
CREATED:20210421T193929Z
LAST-MODIFIED:20210421T193929Z
UID:4865-1619535600-1619539200@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Yue Zheng
DESCRIPTION:PhD Dissertation Defense: Modular Plug-and-Play Photovoltaic Subpanel System \nYue Zheng \nLocation: Zoom Link \nAbstract: 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.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-yue-zheng/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210427T110000
DTEND;TZID=America/New_York:20210427T120000
DTSTAMP:20260423T105353
CREATED:20210415T211755Z
LAST-MODIFIED:20210415T211755Z
UID:4847-1619521200-1619524800@ece.northeastern.edu
SUMMARY:Rare Earth Element-Based Magnets: Science\, Supply and Sustainability in 2021 and Beyond
DESCRIPTION: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. \nRegistration is required in advance of the lecture: Register here \nRare 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. \nIn 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.
URL:https://ece.northeastern.edu/event/rare-earth-element-based-magnets-science-supply-and-sustainability-in-2021-and-beyond/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210426T100000
DTEND;TZID=America/New_York:20210426T110000
DTSTAMP:20260423T105353
CREATED:20210420T181019Z
LAST-MODIFIED:20210420T181019Z
UID:4858-1619431200-1619434800@ece.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Anran Wei
DESCRIPTION:MS Thesis Defense: A soft-switching non-inverting buck-boost converter \nAnran Wei \nLocation: Zoom Link \nAbstract: 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.
URL:https://ece.northeastern.edu/event/ece-ms-thesis-defense-anran-wei/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210423T130000
DTEND;TZID=America/New_York:20210423T140000
DTSTAMP:20260423T105353
CREATED:20210421T194056Z
LAST-MODIFIED:20210421T194056Z
UID:4866-1619182800-1619186400@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Lichen Wang
DESCRIPTION:PhD Dissertation Defense: Correlation Discovery for Multi-view and Multi-label Learning \nLichen Wang \nLocation: Zoom Link \nAbstract: 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.\nIn 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.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-lichen-wang/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210422T123000
DTEND;TZID=America/New_York:20210422T133000
DTSTAMP:20260423T105353
CREATED:20210414T213642Z
LAST-MODIFIED:20210414T213642Z
UID:4845-1619094600-1619098200@ece.northeastern.edu
SUMMARY:MS Thesis Defense: Duschia Bodet
DESCRIPTION:MS Thesis Defense: Modulations to Exploit the THz Band \nDuschia Bodet \nLocation: Zoom Link \nAbstract: 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.
URL:https://ece.northeastern.edu/event/ms-thesis-defense-duschia-bodet/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210422T103000
DTEND;TZID=America/New_York:20210422T113000
DTSTAMP:20260423T105353
CREATED:20210405T174659Z
LAST-MODIFIED:20210405T174659Z
UID:4823-1619087400-1619091000@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Linbin Chen
DESCRIPTION:PhD Dissertation Defense: Low Power Designs using Approximate Computing and Emerging Memories at Nanoscales \nLinbin Chen \nLocation: Zoom Link \nAbstract: 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.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-linbin-chen/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210422T100000
DTEND;TZID=America/New_York:20210422T110000
DTSTAMP:20260423T105353
CREATED:20210420T180653Z
LAST-MODIFIED:20210420T180709Z
UID:4855-1619085600-1619089200@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Seyedmehdi Sadeghzadeh
DESCRIPTION:PhD Dissertation Defense: Physical Layer Security in Multi-User Wireless Networks: Impact of Interference and Artificial Noise on Large-Scale Analysis \nSeyedmehdi Sadeghzadeh \nLocation: Zoom Link \nAbstract: 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.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-seyedmehdi-sadeghzadeh/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210422T100000
DTEND;TZID=America/New_York:20210422T110000
DTSTAMP:20260423T105353
CREATED:20210414T213301Z
LAST-MODIFIED:20210414T213301Z
UID:4843-1619085600-1619089200@ece.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Tianhong Xu
DESCRIPTION:MS Thesis Defense: A novel simple power analysis (SPA) attack on Elliptic Curve Cryptography (ECC) \nTianhong Xu \nLocation: Zoom Link \nAbstract: 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.
URL:https://ece.northeastern.edu/event/ece-ms-thesis-defense-tianhong-xu/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210422T090000
DTEND;TZID=America/New_York:20210422T100000
DTSTAMP:20260423T105353
CREATED:20210420T215252Z
LAST-MODIFIED:20210420T215252Z
UID:4860-1619082000-1619085600@ece.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Peter Kelly
DESCRIPTION:MS Thesis Defense: Design of a Thruster-assisted Bipedal Robot \nPeter Kelly \nLocation: Zoom Link \nAbstract: During the past few years\, legged robot technology has been rapidly advancing.\nHowever\, 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.
URL:https://ece.northeastern.edu/event/ece-ms-thesis-defense-peter-kelly/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210421T173000
DTEND;TZID=America/New_York:20210421T173000
DTSTAMP:20260423T105353
CREATED:20210421T193821Z
LAST-MODIFIED:20210421T193821Z
UID:4864-1619026200-1619026200@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Muhamed Yildiz
DESCRIPTION:PhD Dissertation Defense: Interpretable Machine Learning for Retinopathy of Prematurity \nMuhamed Yildiz \nLocation: Zoom Link \nAbstract: 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.\nFirst\, 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.\nThen\, 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. \nFurthermore\, 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.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-muhamed-yildiz/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210421T100000
DTEND;TZID=America/New_York:20210421T170000
DTSTAMP:20260423T105353
CREATED:20210406T210701Z
LAST-MODIFIED:20210406T210701Z
UID:4831-1618999200-1619024400@ece.northeastern.edu
SUMMARY:MS Thesis Defense: Yuezhou Liu
DESCRIPTION:MS Thesis Defense: Optimizations of Caching Networks: Fairness and Application to Mobile Networks \nYuezhou Liu \nLocation: Zoom Link \nAbstract: 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.
URL:https://ece.northeastern.edu/event/ms-thesis-defense-yuezhou-liu/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210421T100000
DTEND;TZID=America/New_York:20210421T110000
DTSTAMP:20260423T105353
CREATED:20210420T180528Z
LAST-MODIFIED:20210420T180528Z
UID:4854-1618999200-1619002800@ece.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Matin Raayai Ardakani
DESCRIPTION:MS Thesis Defense: A Framework for Denoising Two and Three-dimensional Monte CarloPhoton Transport Simulations Using Convolutional Neural Networks \nMatin Raayai Ardakani \nLocation: Zoom Link \nAbstract: 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).\nTo 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.\nIn 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.\nFinally\, 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.
URL:https://ece.northeastern.edu/event/ece-ms-thesis-defense-matin-raayai-ardakani/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210421T090000
DTEND;TZID=America/New_York:20210421T100000
DTSTAMP:20260423T105353
CREATED:20210420T175730Z
LAST-MODIFIED:20210420T175730Z
UID:4851-1618995600-1618999200@ece.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Rubens Lacouture
DESCRIPTION:MS Thesis Defense: GPUBLQMR: GPU-Accelerated Sparse Block Quasi-Minimum Residual Linear Solver \nRubens Lacouture \nLocation: Zoom Link \nAbstract: 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.\nPreconditioned 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.\nIn 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.
URL:https://ece.northeastern.edu/event/ece-ms-thesis-defense-rubens-lacouture/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210420T160000
DTEND;TZID=America/New_York:20210420T170000
DTSTAMP:20260423T105353
CREATED:20210414T213500Z
LAST-MODIFIED:20210414T213500Z
UID:4844-1618934400-1618938000@ece.northeastern.edu
SUMMARY:MS Thesis Defense: Hao Chen
DESCRIPTION:MS Thesis Defense: Reconstruction of Sulcal Geometry in Brain Stimulation Models using Spherical Harmonics \nHao Chen \nLocation: Zoom Link \nAbstract: 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.\nIn 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.
URL:https://ece.northeastern.edu/event/ms-thesis-defense-hao-chen/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210420T140000
DTEND;TZID=America/New_York:20210420T150000
DTSTAMP:20260423T105353
CREATED:20210420T175556Z
LAST-MODIFIED:20210420T175556Z
UID:4850-1618927200-1618930800@ece.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Griffin Knipe
DESCRIPTION:MS Thesis Defense: Unifying Performance and Security Evaluation for Microarchitecture Design Exploration \nGriffin Knipe \nLocation: Zoom Link \nAbstract: 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.\nDespite 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.\nThis 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.
URL:https://ece.northeastern.edu/event/ece-ms-thesis-defense-griffin-knipe/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210420T133000
DTEND;TZID=America/New_York:20210420T143000
DTSTAMP:20260423T105353
CREATED:20210420T175838Z
LAST-MODIFIED:20210420T175838Z
UID:4852-1618925400-1618929000@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Peng Chang
DESCRIPTION:PhD Dissertation Defense: Model-Based Manipulation of Linear Flexible Objects \nPeng Chang \nLocation: Teams Meetings \nAbstract: 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.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-peng-chang/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210420T120000
DTEND;TZID=America/New_York:20210420T130000
DTSTAMP:20260423T105353
CREATED:20210414T213115Z
LAST-MODIFIED:20210414T213115Z
UID:4842-1618920000-1618923600@ece.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Ashutosh Singh
DESCRIPTION:MS Thesis Defense: Variation is the Norm: Brain State Dynamics Evoked By Emotional Video Clips \nAshutosh Singh \nLocation: Zoom Link \nAbstract: 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.
URL:https://ece.northeastern.edu/event/ece-ms-thesis-defense-ashutosh-singh/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210420T110000
DTEND;TZID=America/New_York:20210420T120000
DTSTAMP:20260423T105353
CREATED:20210412T184906Z
LAST-MODIFIED:20210412T184906Z
UID:4834-1618916400-1618920000@ece.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Yize Li
DESCRIPTION:MS Thesis Defense: Supervised Classification on Deep Neural Network Attack Toolchains \nYize Li \nLocation: Zoom Link \nAbstract: 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.
URL:https://ece.northeastern.edu/event/ece-ms-thesis-defense-yize-li/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210420T100000
DTEND;TZID=America/New_York:20210420T110000
DTSTAMP:20260423T105353
CREATED:20210420T180838Z
LAST-MODIFIED:20210420T180838Z
UID:4857-1618912800-1618916400@ece.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Danton Zhao
DESCRIPTION:MS Thesis Defense: LiDAR with a Silicon Photomultiplier for Applications in Adverse Weather \nDanton Zhao \nLocation: Zoom Link \nAbstract: 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.
URL:https://ece.northeastern.edu/event/ece-ms-thesis-defense-danton-zhao/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210419T173000
DTEND;TZID=America/New_York:20210419T183000
DTSTAMP:20260423T105353
CREATED:20210303T194634Z
LAST-MODIFIED:20210303T194634Z
UID:4773-1618853400-1618857000@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Ilkay Yildiz
DESCRIPTION:PhD Dissertation Defense: Spectral Ranking Regression \nIlkay Yildiz \nLocation: Zoom Link \nAbstract: 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. \nWe 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. \nFurthermore\, 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. \nFinally\, 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.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-ilkay-yildiz/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210419T150000
DTEND;TZID=America/New_York:20210419T160000
DTSTAMP:20260423T105353
CREATED:20210420T180007Z
LAST-MODIFIED:20210420T180007Z
UID:4853-1618844400-1618848000@ece.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Kaier Liang
DESCRIPTION:MS Thesis Defense: Rough-Terrain Locomotion and Unilateral Contact Force Regulations With a Multi-Modal Legged Robot \nKaier Liang \nLocation: Zoom Link \nAbstract: 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.\nThis 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.
URL:https://ece.northeastern.edu/event/ece-ms-thesis-defense-kaier-liang/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210419T090000
DTEND;TZID=America/New_York:20210419T100000
DTSTAMP:20260423T105353
CREATED:20210412T185223Z
LAST-MODIFIED:20210412T185223Z
UID:4836-1618822800-1618826400@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Ahmet Oner
DESCRIPTION:PhD Dissertation Defense: Improving the Resilience of the Power Grid \nAhmet Oner \nLocation: Teams Meeting \nAbstract: 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. \nFirst\, 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. \nTo 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. \nLastly\, 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.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-ahmet-oner/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210416T153000
DTEND;TZID=America/New_York:20210416T163000
DTSTAMP:20260423T105353
CREATED:20210412T185358Z
LAST-MODIFIED:20210412T185358Z
UID:4837-1618587000-1618590600@ece.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Mehrshad Zandigohar
DESCRIPTION:MS Thesis Defense: Real-Time Grasp Type Estimation for a Robotic Prosthetic Hand \nMehrshad Zandigohar \nLocation: Zoom Link \nAbstract: 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.\nInstead of solely relying on EMG signals\, our work enables augmenting EMG intent inference with physical state probability through machine learning and computer vision method.\nTo 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.\nMoreover\, 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.\nThe 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.
URL:https://ece.northeastern.edu/event/ece-ms-thesis-defense-mehrshad-zandigohar/
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