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X-WR-CALDESC:Events for Department of Electrical & Computer Engineering
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DTSTART;TZID=America/New_York:20211103T100000
DTEND;TZID=America/New_York:20211103T110000
DTSTAMP:20260521T015718
CREATED:20211004T174453Z
LAST-MODIFIED:20211004T174453Z
UID:5220-1635933600-1635937200@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Jared Miller
DESCRIPTION:PhD Proposal Review: Nonlinear and Time-Delay Systems Analysis using Occupation Measures \nJared Miller \nLocation: Zoom Link \nAbstract: Techniques to analyze nonlinear systems include peak and reachable set estimation. The reachable set of a system is the set of states accessible by trajectories of a dynamical system at specified times given initial conditions. The peak estimation problem finds extreme values of a state function along trajectories. Examples of peak estimation include finding the maximum height of a wave\, voltage on a power line\, speed of a vehicle\, and infection rate of an epidemic. These problems may be posed as infinite dimensional linear programs (LP) in occupation measures\, where occupation measures are Borel measures that contain all information about trajectories. Under mild assumptions\, a sequence of Linear Matrix Inequalities (LMI) in increasing degree will converge from outside to the LP optimum\, which is in turn equal to the true optimum of the program in trajectories.\nThe first part of this thesis expands upon the occupation measure formulation for peak estimation. The safety of trajectories with respect to an unsafe set may be quantified by measuring the constraint violation (safety margins)\, which is a maximum peak estimation problem. The distance of closest approach between trajectories and an unsafe set may be bounded through a modification of the peak estimation problem. Peak estimation may be applied to dynamics possessing a broad class of uncertainties\, which includes the data-driven setting of black-box polynomial dynamics. A modular MATLAB toolbox is developed to solve and interpret these variations on peak estimation problems.\nThe second part of this thesis introduces an occupation measure framework for analysis and control of time-delay systems. The evolution of time delay systems depends on present and past values of the state. Some instances of time delay systems with their associated delays include epidemic models (incubation period)\, population dynamics (gestation time)\, and fluid modeling (transport time of fluid moving in a pipe). An occupation measure framework is developed to define weak solutions over a finite time interval of nonlinear time-delay systems with a finite number of bounded discrete delays. Applications of this time-delay weak solution include optimal control (including dead-time)\, peak estimation\, and reachable set estimation of time delay systems.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-jared-miller/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211103T103000
DTEND;TZID=America/New_York:20211103T110000
DTSTAMP:20260521T015718
CREATED:20211019T180502Z
LAST-MODIFIED:20211022T000104Z
UID:5243-1635935400-1635937200@ece.northeastern.edu
SUMMARY:Electrical & Computer Engineering
DESCRIPTION:Please join faculty and graduate admissions staff at a webinar discussing the Electrical and Computer Engineering departmental program offerings and experiential learning opportunities in the Graduate School of Engineering.
URL:https://ece.northeastern.edu/event/electrical-computer-engineering/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211104T100000
DTEND;TZID=America/New_York:20211104T110000
DTSTAMP:20260521T015718
CREATED:20211028T184322Z
LAST-MODIFIED:20211028T184322Z
UID:5275-1636020000-1636023600@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Cooper Loughlin
DESCRIPTION:PhD Proposal Review: Unsupervised Machine Learning Approaches to Sequential Data Analysis \nCooper Loughlin \nLocation: Remote \nAbstract: Analysis of sequential data is central to many fields of science and engineering. Often\, sequences are collections of observations made over time and space with little or no contextual information. The goal of analysis may be to evaluate relationships\, identify unusual observations\, or forecast future behavior based on historical data. Unsupervised modeling of sequences (e.g.\, time series) can illuminate the underlying structure of the data and enable analysis. \nIn this proposal\, we discuss a statistical model for multivariate time series and an associated inference algorithm. We develop a preliminary model for a particularly challenging class of multivariate time series where the observations are counts (non-negative integers) that are nonuniformly sampled in time. We develop a state space model and inference algorithm based on Monte Carlo integration and Expectation-Maximization. This preliminary work highlights some key challenges still to be addressed. In particular\, continuously variable sampling intervals\, computational complexity of sampling\, and long-term dependencies among observations are properties of real data that are not handled well by the preliminary model. Recent developments in unsupervised sequence modeling using deep learning techniques are introduced including variational auto-encoders\, recurrent neural networks\, and ordinary differential equation recurrent neural networks. We propose utilizing these deep learning techniques to improve the state of the art in sequential data analysis.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-cooper-loughlin/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211105T080000
DTEND;TZID=America/New_York:20211105T090000
DTSTAMP:20260521T015718
CREATED:20211019T180913Z
LAST-MODIFIED:20211022T000424Z
UID:5245-1636099200-1636102800@ece.northeastern.edu
SUMMARY:MS Robotics Webinar
DESCRIPTION:Please join faculty and graduate admissions staff at a webinar discussing MS Robotics departmental course offerings and experiential learning opportunities in the Graduate School of Engineering.
URL:https://ece.northeastern.edu/event/ms-robotics-webinar/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211119T090000
DTEND;TZID=America/New_York:20211119T100000
DTSTAMP:20260521T015718
CREATED:20211118T010643Z
LAST-MODIFIED:20211118T010643Z
UID:5291-1637312400-1637316000@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Ziqiang Cai
DESCRIPTION:PhD Proposal Review: Near-infrared Optical Modulation by Hybrid Graphene Metasurfaces \nZiqiang Cai \nLocation: Zoom Link \nAbstract: The field of metasurfaces has emerged as one of the most promising frontiers in optical research due to the compact footprint and flexibility in light manipulation. To further advance the practical applications of metasurfaces\, tunable or reconfigurable metasurfaces are highly desirable. One approach is to utilize graphene by taking advantage of its tunable optical properties upon electrical bias. Graphene metasurfaces have been extensively studied in many applications\, including polarization tuning\, phase tuning\, photodetectors\, chemical sensing\, tunable lens\, etc. However\, the working wavelengths of the reported graphene metasurfaces are limited in mid-infrared and terahertz spectra.\nIn this proposal review\, I will discuss a graphene metasurface that can push the working wavelength into the near-infrared region (≤ 3.0 µm). The device combines graphene with plasmonic structures made of gold to enhance the interband transition of graphene\, resulting in decent tunability at near-infrared wavelengths. The tuning process of our graphene metasurface shows distinct differences in comparison with the graphene metasurfaces operating in the mid-infrared or terahertz spectra\, which can be accurately predicted by both theory and simulation. The measured results show a reflection modulation ΔR of about 10% and a modulation depth ΔR/Rmax of 17% at 2.42 µm.\nFinally\, by using an anisotropic plasmonic structure\, our hybrid graphene metasurface can simultaneously operate in the near-infrared and mid-infrared spectra. The measured modulation depth is 18.2% at 2.30 µm and 24.7% at 5.67 µm. Our research substantially broadens the working wavelength of graphene metasurfaces\, and manifest potential applications in near-infrared electro-optic modulators\, reconfigurable lenses\, and polarization modulators.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-ziqiang-cai/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211130T120000
DTEND;TZID=America/New_York:20211130T130000
DTSTAMP:20260521T015718
CREATED:20211129T194635Z
LAST-MODIFIED:20211129T194635Z
UID:5318-1638273600-1638277200@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Sara Garcia Sanchez
DESCRIPTION:PhD Proposal Review: Learning and Shaping the Wireless Environment: An Integrated View of Sensing\, Computing and Communication \nSara Garcia Sanchez \nLocation: TBA \nAbstract: The explosive growth in Internet of Things (IoT) deployments and anticipated data volumes that will be generated within future autonomous vehicles require collecting and processing large amounts of data\, generally transmitted over the wireless channel. In this context\, conventional permanent deployments limited to leverage the existing wireless environment\, often fall short of meeting the required capacity demand. To address this limitation\, this dissertation takes a hands-on approach to equip communication systems with technology to perceive and collaborate with and within the environment. Specifically\, we build (i) accurate physics-oriented predictive models and multimode sensing techniques to gain awareness of the existing channel\, as well as (ii) novel multidisciplinary approaches to intelligently modify the wireless channel towards the communication link benefit.\nIn this dissertation\, we first prove that combining wireless channel modelling\, multimode sensing and robotics provides significant link performance gains. To this extent\, we adopt a systems approach to study how millimeter wave (mmWave) radio transmitters on Unmanned Aerial Vehicles (UAVs) provide high throughput links under typical hovering conditions. Based on sensing and modelling efforts\, we propose techniques to exploit the information contained in the spatial and angular domains of empirically collected data from GPS\, cameras and RF signals. We demonstrate hovering impact mitigation by (i) selecting near-to-optimum transmission parameters as compared to the mmWave standard IEEE 802.11ad and (ii) proposing corrective coordinated actions at the UAVs from the robotic controls. These methods achieve mmWave beam-tracking and robust link deployment under event(s) impacting link performance\, such as hovering or blockage in the light of sight between transmitter and receiver.\nThen\, this dissertation experimentally demonstrates how the wireless environment can be interactively programmed through the use of Reconfigurable Intelligent Surfaces (RIS) to partially offload computation into the wireless domain. In particular\, we propose AirNN\, a system capable of realizing analog over-the-air convolutions\, accurately enough to substitute their digital equivalent in a Convolutional Neural Network (CNN).\nAs proposed future work\, this dissertation will explore innovative uses of the RIS technology in Multiple Input Multiple Output (MIMO) systems for 6G and beyond. Specifically\, we will investigate (i) how the use of RIS helps overcome environmental limitations of a highly spatially correlated MIMO channels\, and (ii) whether the use of RIS can enable the use of MIMO techniques with a single antenna at the receiver.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-sara-garcia-sanchez/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211130T120000
DTEND;TZID=America/New_York:20211130T130000
DTSTAMP:20260521T015718
CREATED:20211130T004002Z
LAST-MODIFIED:20211130T004002Z
UID:5323-1638273600-1638277200@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Sara Garcia Sanchez
DESCRIPTION:PhD Proposal Review: Learning and Shaping the Wireless Environment: An Integrated View of Sensing\, Computing and Communication \nSara Garcia Sanchez \nLocation: Microsoft Teams \nAbstract: The explosive growth in Internet of Things (IoT) deployments and anticipated data volumes that will be generated within future autonomous vehicles require collecting and processing large amounts of data\, generally transmitted over the wireless channel. In this context\, conventional permanent deployments limited to leverage the existing wireless environment\, often fall short of meeting the required capacity demand. To address this limitation\, this dissertation takes a hands-on approach to equip communication systems with technology to perceive and collaborate with and within the environment. Specifically\, we build (i) accurate physics-oriented predictive models and multimode sensing techniques to gain awareness of the existing channel\, as well as (ii) novel multidisciplinary approaches to intelligently modify the wireless channel towards the communication link benefit.\nIn this dissertation\, we first prove that combining wireless channel modelling\, multimode sensing and robotics provides significant link performance gains. To this extent\, we adopt a systems approach to study how millimeter wave (mmWave) radio transmitters on Unmanned Aerial Vehicles (UAVs) provide high throughput links under typical hovering conditions. Based on sensing and modelling efforts\, we propose techniques to exploit the information contained in the spatial and angular domains of empirically collected data from GPS\, cameras and RF signals. We demonstrate hovering impact mitigation by (i) selecting near-to-optimum transmission parameters as compared to the mmWave standard IEEE 802.11ad and (ii) proposing corrective coordinated actions at the UAVs from the robotic controls. These methods achieve mmWave beam-tracking and robust link deployment under event(s) impacting link performance\, such as hovering or blockage in the light of sight between transmitter and receiver.\nThen\, this dissertation experimentally demonstrates how the wireless environment can be interactively programmed through the use of Reconfigurable Intelligent Surfaces (RIS) to partially offload computation into the wireless domain. In particular\, we propose AirNN\, a system capable of realizing analog over-the-air convolutions\, accurately enough to substitute their digital equivalent in a Convolutional Neural Network (CNN).\nAs proposed future work\, this dissertation will explore innovative uses of the RIS technology in MIMO systems for 6G and beyond. Specifically\, we will investigate (i) how the use of RIS helps overcome environmental limitations of a highly spatially correlated MIMO system\, and (ii) whether the use of RIS can enable the use of MIMO techniques with a single antenna at the receiver.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-sara-garcia-sanchez-2/
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