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X-WR-CALNAME:Northeastern University College of Engineering
X-ORIGINAL-URL:https://coe.northeastern.edu
X-WR-CALDESC:Events for Northeastern University College of Engineering
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TZID:America/New_York
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231201T090000
DTEND;TZID=America/New_York:20231201T120000
DTSTAMP:20260512T124123
CREATED:20231116T213530Z
LAST-MODIFIED:20231116T213530Z
UID:40442-1701421200-1701432000@coe.northeastern.edu
SUMMARY:First-Year Engineering Fall Expos
DESCRIPTION:Join us for First-Year Engineering’s Fall Expos on Friday\, December 1\, from 9:00 AM – 12:00 PM and Monday\, December 4\, from 11:00 AM – 3:00 PM in the Curry Student Center Pit and Quad. Cornerstone of Engineering students will be showcasing their Fall projects. Themes include sumo robots\, sustainability\, carnival games\, animals and the natural world\, and interactive games. \n  \n 
URL:https://coe.northeastern.edu/event/first-year-engineering-fall-expos/2023-12-01/
LOCATION:Curry Student Center\, 360 Huntington Ave.\, Boston\, MA\, 02115\, United States
GEO:42.3394629;-71.0885286
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Curry Student Center 360 Huntington Ave. Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave.:geo:-71.0885286,42.3394629
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231204T103000
DTEND;TZID=America/New_York:20231204T113000
DTSTAMP:20260512T124123
CREATED:20231127T163905Z
LAST-MODIFIED:20231127T163905Z
UID:40523-1701685800-1701689400@coe.northeastern.edu
SUMMARY:Cheng Gongye PhD Dissertation Defense
DESCRIPTION:Title:\nHardware Security Vulnerabilities in Deep Neural Networks and Mitigations \nDate:\n12/4/2023 \nTime:\n10:30:00 AM \nCommittee Members:\nProf. Yunsi Fei (Advisor)\nProf. Aidong Ding\nProf. Xue Lin\nProf. Xiaolin Xu \nAbstract:\nIn the past decade\, Deep Neural Networks (DNNs) have become pivotal in numerous fields\, including security-sensitive autonomous driving and privacy-critical medical diagnosis. This Ph.D. dissertation delves into the hardware security of DNNs\, discovering their vulnerabilities to fault and side-channel attacks and exploring novel countermeasures essential for their safe deployment in critical applications. \nFault attacks disrupt computation or inject faults into parameters\, compromising the integrity of targeted applications. This dissertation demonstrates a power-glitching fault injection attack on FPGA-based DNN accelerators\, common in cloud environments\, which exploits vulnerabilities in the shared power distribution network and results in model misclassification. In response to these threats\, we introduce a novel\, lightweight defense mechanism to protect DNN parameters from adversarial bit-flip attacks. The proposed framework incorporates a dynamic channel-shuffling obfuscation scheme coupled with a logits-based model integrity monitor. The approach effectively safeguards various DNN models against bit-flip attacks\, without necessitating retraining or structural changes to the models. Furthermore\, our research expands the scope of fault analysis beyond just the parameters of DNN models. We thoroughly examine the entire implementation of commercial products\, defying the prevailing assumption that quantized DNNs are inherently resistant to bit-flips. \nSide-channel attacks exploit information leakage of system implementations\, such as power consumption and electromagnetic emanations\, to reveal system secrets and therefore compromise confidentiality. This dissertation makes significant contributions to side-channel assisted model extraction of DNNs. We present a floating-point timing side-channel attack on x86 CPUs that reverse-engineers DNN model parameters in software implementations. For hardware accelerators\, we target the state-of-the-art AMD-Xilinx deep-learning processor unit (DPU)\, a reconfigurable engine dedicated to convolutional neural networks (CNNs) and representing the most complex commercial FPGA accelerator with encrypted IPs. Our work demonstrates that electromagnetic analysis can be leveraged to recover the data flow and scheduling of the DNN accelerators\, facilitating follow-on architecture and parameter extraction attacks. To mitigate EM side-channel model extraction attacks\, we introduce a novel defense mechanism that devises a random importance-aware activation mask on input pixels to disrupt the operation alignment on EM traces\, with minimal performance and efficiency impacts. \nOverall\, this dissertation significantly deepens the understanding of hardware security of DNN models. It makes important contributions in discovering novel and critical vulnerabilities of DNN inference pertaining to system implementations\, and proposing effective and practical solutions for securing DNNs in mission-critical environments. The research work marks a substantial step forward in the development of resilient and secure AI systems.
URL:https://coe.northeastern.edu/event/cheng-gongye-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231206T120000
DTEND;TZID=America/New_York:20231206T130000
DTSTAMP:20260512T124123
CREATED:20231019T134500Z
LAST-MODIFIED:20240102T154342Z
UID:39802-1701864000-1701867600@coe.northeastern.edu
SUMMARY:Chemical Engineering Fall Seminar Series: Professor Cameron Abrams
DESCRIPTION:Molecular Dynamics Investigations of Thermosetting Polymers \nThermosetting polymers comprise a wide variety of monomer constituents and polymerization chemistries that in principle provide the degrees of freedom necessary to tailor these materials to a broad range of applications\, from structural composites\, coatings and barrier materials\, ballistic shielding\, and even solid rocket fuels. In this talk\, I will trace my group’s history in using molecular dynamics simulations to investigate conceptual links among molecular architectures\, intermolecular interactions\, and network structures and how they determine thermomechanical properties of polymerized materials that these applications demand. Highlights in this history include the discovery of the links between crosslink arrangements and protovoid-based toughening; toughening using partially reacted substructures; long-timescale material response through time-temperature superposition; and rationalizing improvements over petrochemically derived monomers using novel bio-based subunits. A consistent theme will be demonstration of how close collaboration with experimental groups allows for simulation predictions to be tested. I will conclude with a presentation of our group’s software package\, HTPolyNet\, that represents the first opensource\, end-to-end generator of all-atom models of network-polymerized monomer mixtures based only on monomer structures\, which should accelerate the community’s use of MD simulation to investigate thermosetting polymers. \n\nCameron F. Abrams is the Bartlett ’81 – Barry ’81 Professor of Chemical and Biological Engineering at Drexel University\, where he has served on the faculty since 2002 and as Department Head since 2017. Abrams’ research expertise lies in advancing modern molecular simulation methods with applications in protein science\, drug discovery\, complex fluids\, and high-performance materials. He is the recipient of an ONR Young Investigator Award\, an NSF CAREER Award\, and the AIChE Computational and Molecular Sciences Forum Impact Award. He received a BS in Chemical Engineering from North Carolina State University in 1995 and a PhD from the University of California\, Berkeley\, in 2000. He trained as a postdoc for two years in the Theory Group at the Max-Planck-Institute for Polymer Research in Mainz\, Germany\, before joining Drexel.
URL:https://coe.northeastern.edu/event/chemical-engineering-fall-seminar-series-professor-cameron-abrams/
LOCATION:010 Behrakis\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
GEO:42.3396156;-71.0886534
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=010 Behrakis 360 Huntington Ave Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave:geo:-71.0886534,42.3396156
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231206T130000
DTEND;TZID=America/New_York:20231206T150000
DTSTAMP:20260512T124123
CREATED:20231121T182340Z
LAST-MODIFIED:20231121T182402Z
UID:40494-1701867600-1701874800@coe.northeastern.edu
SUMMARY:Enabling Engineering Fall Showcase
DESCRIPTION:Students will present their final design projects at the Enabling Engineering Fall Showcase. The projects that will be presented are listed below. \n\nAccessible Golfing\nCaution Radar\nAccessible Plant Watering System\nSwitch Activated Cornhole\nVR App\nSwitch Activated Toys\nAdaptive Drum Set\nProsthetic Finger\n\nEnabling Engineering is a Northeastern University student group that designs and builds devices to empower individuals with physical and cognitive disabilities. Our students collaborate with clients on projects that provide greater independence\, reduce medical burdens\, and increase social connectedness. We help family members\, clinicians\, and teachers care for people with disabilities. By giving students the opportunity to participate in Enabling Engineering projects\, we are training the next generation of engineers to be knowledgeable about\, and aware of\, the needs of individuals with disabilities. \nIf you are unable to join in person\, you can join via Zoom.
URL:https://coe.northeastern.edu/event/enabling-engineering-fall-showcase-2/
LOCATION:002 Ell Hall\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
ORGANIZER;CN="Enabling Engineering":MAILTO:enable@coe.neu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231206T150000
DTEND;TZID=America/New_York:20231206T170000
DTSTAMP:20260512T124123
CREATED:20231204T185947Z
LAST-MODIFIED:20231204T185947Z
UID:40690-1701874800-1701882000@coe.northeastern.edu
SUMMARY:Suyash Pradhan MS Thesis Defense
DESCRIPTION:Title: COPILOT: Cooperative Perception using Lidar for Handoffs between Road Side Units \nCommittee Members:\nProf. Kaushik Chowdhury (Advisor)\nProf. Stratis Ioannidis\nProf. Jennifer Dy \nAbstract:\nThis thesis presents COPILOT\, a ML-based approach that allows vehicles requiring ubiquitous high bandwidth connectivity to identify the most suitable road side units (RSUs) through proactive handoffs. By cooperatively exchanging the data obtained from local 3D Lidar point clouds within adjacent vehicles and with coarse knowledge of their relative positions\, COPILOT identifies transient blockages to all candidate RSUs along the path under study. Such cooperative perception is critical for choosing RSUs with highly directional links required for mmWave bands\, which majorly degrade in the absence of LOS. COPILOT proposes three modules that operate in an inter-connected manner: (i) As an alternative to sending raw Lidar point clouds\, it extracts and transmits low-dimensional intermediate features to lower the overhead of inter-vehicle messaging; (ii) It utilizes an attention-mechanism to place greater emphasis on data collected from specific vehicles\, as opposed to nearest neighbor and distance-based selection schemes\, and (iii) it experimentally validates the outcomes using an outdoor testbed composed of an autonomous car and Talon AD7200 60GHz routers emulating the RSUs\, accompanied by the public release of the datasets. Results reveal COPILOT yields upto 69.8% and 20.42% improvement in latency and throughput compared to traditional reactive handoffs for mmWave networks\, respectively
URL:https://coe.northeastern.edu/event/suyash-pradhan-ms-thesis-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231206T193000
DTEND;TZID=America/New_York:20231206T210000
DTSTAMP:20260512T124123
CREATED:20231122T152008Z
LAST-MODIFIED:20231122T152008Z
UID:40507-1701891000-1701896400@coe.northeastern.edu
SUMMARY:The Graduate School Collaborative Webinar: Why consider Graduate School?
DESCRIPTION:Think graduate school is just for future faculty? Think again! \nJoin us as we demystify what it takes to be and become a graduate student (MS and PhD)\, as well as the many career avenues that you can take with a graduate school degree. Come learn about the following topics from some of the top Engineering Graduate Programs in the country! \n\nWhat is graduate school\nWhat to expect\nWhy consider graduate school\nHow to prepare\nThinking ahead to the application process\n\n  \nWhat: Seminar on “Why consider Graduate School?”\nWhen: Wednesday December 6th\, 2023; 7:30 – 8:30 p.m. Eastern Time\nFor Who: Undergraduate Freshman\, Sophomore\, and Juniors\nSponsored by: UC Berkeley\, Georgia Tech\, John’s Hopkins university\, University of Michigan\, Northeastern\, NYU\, Ohio State\, University of Oklahoma\, Purdue University\, Rice University\, University of Southern California\nWhere: Zoom – Register using the Link below \nQuestions about accessing the webinar? Contact Nina Parshall at parshall.8@osu.edu \n \nNote: There will be breakout sessions with each school for 30mins following the presentation (8:30pm – 9:00pm Eastern). Register to join for some exclusive networking!
URL:https://coe.northeastern.edu/event/the-graduate-school-collaborative-webinar-why-consider-graduate-school/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231207T100000
DTEND;TZID=America/New_York:20231207T110000
DTSTAMP:20260512T124123
CREATED:20231204T185514Z
LAST-MODIFIED:20231204T185514Z
UID:40692-1701943200-1701946800@coe.northeastern.edu
SUMMARY:Mauro Belgiovine PhD Proposal Review
DESCRIPTION:Title: Wireless Intelligence: A Comprehensive Exploration of AI-Driven Solutions in Channel Estimation\, Beam Refinement\, and Protocol Classification for Next Generation Networks \nCommittee Members:\nProf. Kaushik Chowdhury (advisor)\nProf. Stratis Ioannidis\nDr. Chris Dick \nAbstract:\nhis thesis explores the transformative impact of artificial intelligence (AI) on wireless systems through model-driven simulations and real-world datasets\, with a focus on enhancing both local and cellular wireless networks through the deployment of highly customized deep learning solutions that target specific bottlenecks affecting traditional signal processing based communication. \nThe research delves into three key areas that address critical challenges in the current wireless landscape. The first focal point of the investigation involves channel estimation using deep learning techniques to denoise pilots and expedite the accurate estimation of Channel State Information (CSI). By leveraging deep learning methodologies\, the proposed solution aims to enhance the reliability and computation for MIMO and massive MIMO channel estimation\, thereby contributing to improved communication efficiency and reduced errors. The second major topic encompasses the application of reinforcement learning for 5G New Radio (NR) millimeter-wave (mmWave) beam refinement. The study aims to develop a Deep Reinforcement Learning algorithm capable of adjusting beamsteering angles\, starting from a coarse beam scanning procedure and further refining them for higher transmission efficiency. This innovation is expected to substantially decrease traffic overhead while simultaneously enhancing beam steering precision\, thus optimizing the performance of mmWave communication. The third and final area of focus introduces a transformer-based WiFi multi-protocol classifier\, strategically deployed on a DeepWave Air-T edge device\, which is equipped with Module on Chip (MoC) low power CPU-GPU and programmable Software Defined Radio (SDR). This classifier outperforms existing modulation classification models and legacy methods under lower SNR conditions\, leveraging TensorRT’s model compression capabilities to efficiently process extended sequences of raw IQ samples\, ensuring high performance at a low computational cost. The proposed solution addresses the growing demand for efficient and adaptable wireless communication systems\, paving the way for advancements in edge-based processing and intelligent protocol classification. \nThis work seeks to contribute significantly to the ongoing AI revolution in wireless systems by addressing crucial issues in channel estimation\, beam refinement\, and protocol classification. The outcomes of this research hold the potential to redefine the landscape of wireless communication\, offering enhanced performance\, reduced overhead\, and increased adaptability in both local and cellular networks.
URL:https://coe.northeastern.edu/event/mauro-belgiovine-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231207T140000
DTEND;TZID=America/New_York:20231207T150000
DTSTAMP:20260512T124123
CREATED:20231204T190346Z
LAST-MODIFIED:20231204T190346Z
UID:40688-1701957600-1701961200@coe.northeastern.edu
SUMMARY:Yisi Liu MS Thesis Defense
DESCRIPTION:Title: Experimental research on the Nonlinear Magnetoelectric Effect of the VLF ME Antennas \nCommittee Members:\nProf. Nian Sun (Advisor)\nProf. Yongmin Liu\nProf. Xufeng Zhang \nAbstract:\nMagnetoelectric (ME) coupling effects in ferromagnetic and piezoelectric composites involve the control of electric polarization (P) by applying a magnetic field (H) (direct ME effect)\, or the manipulation of magnetization (M) through an electric field (E) (converse ME effect) . These effects are facilitated by the mechanical deformation in the ferroic phases resulting from the combination of magnetostriction and piezoelectricity. In single-phase materials\, the breakthrough in achieving large ME coefficients has further advanced the development of ME materials and devices. Consequently\, numerous multifunctional ME devices\, such as mechanical antennas\, magnetic sensors\, tunable inductors\, and filters\, have been developed. This thesis has provides a summary and categorization of these devices based on their physical mechanism and type of ME effects. The inclusion of mechanical ME antennas based on piezoelectric/magnetostrictive heterostructures with acoustic actuation reflects the significant interest in this topic. Notably\, a maximum communication distance of 120 m for a very low frequency (VLF) communication system has been achieved using a pair of mechanical ME antennas. Subsequently\, we will focus on introducing and reviewing the materials and devices related to the ME effect\, as well as the application of ME mechanical antennas in very low frequency (VLF) communication systems. \nIn addition to that\, we developed a transmitter with a Metglas/PZT/Metglas structure antenna. Our study focuses on investigating the transmission effects of this antenna when employing direct antenna modulation techniques to enhance data transmission. Through our research\, we have introduced a novel modulation method by modulating the antenna. We observed that this modulation method produces a more stable and stronger signal. \n 
URL:https://coe.northeastern.edu/event/yisi-liu-ms-thesis-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231208T120000
DTEND;TZID=America/New_York:20231208T123000
DTSTAMP:20260512T124123
CREATED:20231208T145358Z
LAST-MODIFIED:20231208T145358Z
UID:40788-1702036800-1702038600@coe.northeastern.edu
SUMMARY:Ate Darabi PhD Proposal Review
DESCRIPTION:Title:\nComplex Delayed Networks and Their Application in Epidemic Analysis: Modeling\, Analysis\, and Strategic Management \nCommittee Members:\nProf. Milad Siami (Advisor)\nProf. Bahram Shafai\nProf. Rozhin Hajian \nAbstract:\nIn the face of crowd-related disasters like pandemics and mass attacks\, the complex dynamics of human interactions demand comprehensive modeling approaches. This proposal adopts a network-based perspective\, leveraging the delayed Susceptible-Infected-Susceptible (SIS) model for epidemics and the Predator-Swarm-Guide (PSG) model for crowd movement\, to gain insights into the dynamics of these critical situations. \nIn epidemic networks\, time delays and uncertainties can significantly change the epidemic behavior and result in successive echoing waves of the spread between various population clusters. We examine these effects on linear SIS dynamics\, evaluating network stability and performance loss. We prove that network performance loss is correlated with the structure of the underlying graph\, intrinsic time delays\, epidemic characteristics\, and external shocks. This performance measure is then used to develop an optimal traffic restriction algorithm for network performance enhancement\, resulting in reduced infection in the metapopulation.   An epidemic-based centrality index is also proposed to evaluate the impact of every subpopulation on network performance\, and its asymptotic behavior is investigated. This index converges to local or eigenvector centralities under specific parameters. Moreover\, given that epidemic-based centrality depends on the epidemic properties of the disease\, it may yield distinct node rankings as the disease characteristics slowly change over time or as different types of infections spread. This unique characteristic of epidemic-based centrality enables it to adjust to various epidemic features. The derived centrality index is then adopted to improve the network robustness against external shocks on the epidemic network. \nThe PSG model addresses mass attack scenarios\, considering individuals’ efforts to evade adversaries and seek guidance. Environmental factors like impermeable walls and psychological elements are incorporated into this model. The preliminary results highlight the role of coordinated cooperation in minimizing casualties. The objective is to reduce casualties through a hybrid motion optimization approach for individuals and the guiding agent.
URL:https://coe.northeastern.edu/event/ate-darabi-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231211T130000
DTEND;TZID=America/New_York:20231211T143000
DTSTAMP:20260512T124123
CREATED:20231201T144400Z
LAST-MODIFIED:20231201T144400Z
UID:40639-1702299600-1702305000@coe.northeastern.edu
SUMMARY:Cookies with the Dean: Holiday Edition
DESCRIPTION:Join Dean Gregory Abowd for a holiday-themed Cookies with the Dean on Monday\, December 11th from 1:00-2:30pm in the Robinson Quad Tent near Curry Student Center. \nWe will have holiday cookies\, hot cocoa\, and free Swag available! Swing by for food\, fun\, and a chance to talk to the Dean. \nWe hope to see you there! \nThis is a 100% compostable event. Please place all waste into the marked composting bins for their disposal.
URL:https://coe.northeastern.edu/event/cookies-with-the-dean-holiday-edition/
LOCATION:The Tents at Robinson Quad\, 336 Huntington Ave\, Boston\, MA\, 02115\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231212T090000
DTEND;TZID=America/New_York:20231212T100000
DTSTAMP:20260512T124123
CREATED:20231208T145140Z
LAST-MODIFIED:20231208T145212Z
UID:40790-1702371600-1702375200@coe.northeastern.edu
SUMMARY:Durga Suresh PhD Proposal Review
DESCRIPTION:Title: Network Security Management and Threat Mitigation in the Open Cloud\n \nCommittee Members:\nProf. Miriam Leeser (Advisor)\nProf. Michael Zink\nProf. Xiaolin Xu \nAbstract:\nCloud computing and advanced cyberinfrastructures are increasingly vital to the functioning of Internet systems. Every day\, more devices are added to the cloud\, to provide greater resource utilization\, availability\, and scalability. Due to the expanding reliance on cloud computing\, securing the cloud is paramount. Tackling the issue of securing the cloud is crucial not only for preserving the functionality and reliability of cloud-based systems but also for protecting the critical data and services that depend on these platforms. \nCloud computing models include public clouds\, private clouds\, community clouds\, and hybrid clouds. Private\, community\, and hybrid clouds provide security\, but with an important trade-off; namely\, user access restriction in the cloud. The proposed research uses the Open Cloud Testbed (OCT) which is part of the National Science Foundation’s (NSF) Computer and Information Systems Engineering(CISE) Community Research Infrastructure(CRI) program. OCT is an example of a public cloud that allows users two things: 1) an isolated set of nodes to perform experiments with bare metal access\, which can potentially lead to security issues\, and 2) the ability to test out the solutions for both using the cloud and adding security to it. The proposed research aims to target a system like the OCT\, specifically targeting a public cloud environment. \nThis system will be designed to allow access to the switch\, enabling control and management of traffic within the cloud network. This research aims to mitigate network security threats in the public cloud network. The aim of this research is multifold. First\, we identify and classify the behavior of users in the cloud. We then provide an approach to creating a network security management policy that will deal with 1)detecting network intruders that scan the cloud network and remove their access to the network\, and  2) managing heavy hitters that can cause Denial of Service (DOS) and Distributed Denial of Service (DDOS) attacks in the cloud network by using the heavy hitter detection system and prevent them from putting more traffic on the network. Both network intruder detection and heavy hitter management systems use Access Control Lists (ACL)as a means to prevent the user from putting traffic on the cloud network. Lastly\, we perform experiments to handle these threats and measure the success of the experimental setup concerning network attacks. The proposed approach will ensure network security by creating a framework for network security management policy to minimize threats in the cloud network and other resources directly attached to the network. The proposed research aims to enhance cybersecurity by employing network intruder detection techniques to identify potential threats\, implementing heavy hitter management to mitigate threats effectively\, and developing and enforcing a network security management policy to prevent future threats.
URL:https://coe.northeastern.edu/event/durga-suresh-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231212T110000
DTEND;TZID=America/New_York:20231212T170000
DTSTAMP:20260512T124123
CREATED:20231208T145540Z
LAST-MODIFIED:20231208T145540Z
UID:40786-1702378800-1702400400@coe.northeastern.edu
SUMMARY:Deepak Prabhala MS Thesis Defense
DESCRIPTION:Title: “Smart Microwave Devices with Programable Printed Circuit Board (PPCB): Design with Liquid Crystal Elastomer Polymers in Transmission Lines and Circulators” \nCommittee Members:\n1) Professor Nian X. Sun (Advisor)\n2) Professor Marvin Onabajo\n3) Professor Yongmin Liu \nAbstract:\nThis study explores the innovative application of liquid crystal elastomer (LCE) polymers in the design and implementation of microwave transmission lines and circulators. Liquid crystal elastomers\, known for their unique combination of liquid crystalline and elastomeric properties\, offer a unique approach to developing flexible and tunable microwave devices. The research focuses on a thorough study of the electro-mechanical properties of LCEs to achieve novel functionalities in the design of transmission lines and circulators for microwave communication systems in HFSS simulations. The first part of the study delves into the characterization of the dielectric and mechanical properties of the chosen LCE polymer. Subsequently\, the design and fabrication of a flexible and tunable transmission line using LCE are discussed. The LCE-based transmission line aims to measure the insertion loss and return loss with different widths\, lengths\, and thicknesses of the LCE polymer. The study investigates the impact of temperature on the transmission line’s performance\, offering insights into potential applications in reconfigurable microwave systems. The second phase of the research explores the utilization of LCE in the development of a microwave circulator\, a vital component in microwave communication networks. The circulator design incorporates the unique properties of LCE by using a stepped dielectric variation approach for broadband isolation. This innovation holds promise for enhancing the efficiency and adaptability of microwave systems in communication and radar applications. The findings of this research contribute to offering a pathway for integrating liquid crystal elastomers into flexible and reconfigurable microwave devices. This thesis aims to advance the understanding of smart microwave devices and inspire further exploration into the application of liquid crystal elastomer polymers in cutting-edge technologies.
URL:https://coe.northeastern.edu/event/deepak-prabhala-ms-thesis-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231215T120000
DTEND;TZID=America/New_York:20231215T130000
DTSTAMP:20260512T124123
CREATED:20231116T162621Z
LAST-MODIFIED:20231116T162704Z
UID:40434-1702641600-1702645200@coe.northeastern.edu
SUMMARY:Night of yalda
DESCRIPTION:Honor the longest night of the year in this Persian cultural tradition to offer hope and light. \nThere will be watermelon\, pomegranate\, Persian food\, and of course Persian music. \nNot necessary\, but we will be happy if you show up in red. \nLocation: 111 Snell Library
URL:https://coe.northeastern.edu/event/night-of-yalda/
ORGANIZER;CN="Bioengineering":MAILTO:bioe@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231220T140000
DTEND;TZID=America/New_York:20231220T150000
DTSTAMP:20260512T124123
CREATED:20231215T181627Z
LAST-MODIFIED:20231215T181627Z
UID:40911-1703080800-1703084400@coe.northeastern.edu
SUMMARY:Xiang Zhang PhD Proposal Review
DESCRIPTION:Title:Confidentiality and Privacy Preserving:  Intertwining Deep Learning and  Side-channel Analysis \nMeeting ID: 976 4324 8925 Passcode: 779251 \nCommittee Members:\nProf. Yunsi Fei (Advisor)\nProf. Adam Ding\nProf. Lili Su \nAbstract:\nIn the past decade\, deep learning-empowered technologies have significantly permeated our daily lives\, revolutionizing diverse application domains with superb performance.  In hardware security\, deep learning has been employed for power or electromagnetic side-channel analysis (SCA) and protection\, and the security of deep learning implementations starts gaining traction. \nThis dissertation delves into the intertwining deep learning techniques and side-channel analysis.  It addresses two critical questions: how to extend deep learning to other types of SCAs; what confidentiality and privacy vulnerabilities deep learning models have. \nOur research work first explores deep learning-assisted cache side-channel attacks and introduces innovative countermeasures grounded in the principles of adversarial samples against deep learning. We first design a novel high-frequency cache monitor\,  which runs concurrent to the victim execution and collects run-time timing traces\, while previous cache monitors are only able to collect timing samples. Such timing traces facilitate follow-on non-profiled Differential Deep Learning Analysis (DDLA) for secret retrieval. We also propose a novel countermeasure against the new DDLA\, leveraging the concept of adversarial examples\, which deliberately introduces obfuscation operations in the victim program so as to generate ‘adversarial’ timing traces and therefore circumvent the follow-on DDLA. \nThe second part of the dissertation addresses the vulnerability of deep neural network (DNN) implementations and presents novel methodologies for enhancing user privacy. It introduces a technique for extracting deep learning models through software-based power side channels. By manipulating model inputs and leveraging the on-chip Intel Running Average Power Limit (RAPL) sensors reporting\, the entire model parameters can be extracted when the model inference is executed on modern processors. To protect both the model confidentiality and the input privacy\, this dissertation proposes to obfuscate the model inputs while preserving the end-to-end functionality. It introduces an encoder to transform the inputs before feeding the DNN model\, and appends a decoder after the model outputs to recover the intended results. The approach\, compared to traditional encryption or masking techniques\, is more efficient and can effectively protect both user privacy and model confidentiality. \nThe overall goal of the dissertation is to further investigate the power of deep learning in SCA and countermeasure and safeguard secure DNN implementations.
URL:https://coe.northeastern.edu/event/xiang-zhang-phd-proposal-review/
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