BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Department of Electrical & Computer Engineering - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://ece.northeastern.edu
X-WR-CALDESC:Events for Department of Electrical & Computer Engineering
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20190310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20191103T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20200308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20201101T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20210314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20211107T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201202T100000
DTEND;TZID=America/New_York:20201202T110000
DTSTAMP:20260506T192728
CREATED:20201117T013509Z
LAST-MODIFIED:20201117T013509Z
UID:4575-1606903200-1606906800@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Leili Hayati
DESCRIPTION:PhD Dissertation Defense: Ceramic Magnetic Wires at Wireless Communication Frequencies \nLeili Hayati \nLocation: Online \nAbstract: Ferrite magnetic devices play an important role in modern wireless telecommunication systems. They generally require permanent magnets in order to magnetically polarize the ferrite material component used in these devices. The permanent magnets are bulky and take up most of the size and weight of a magnetic circuit. The aim of this research is to do away with permanent magnet bias circuits as utilized in circulators and ferrite planar devices\, especially in wireless communication systems operating below 2 GHz. Recently\, ferromagnetic nanowires (NWs) have been embedded into porous templates\, are used to design various microwave magnetic and electronics devices. The main advantage of magnetic NWs is that in zero magnetic field\, the microwave absorption frequency can be easily tuned over a large range of frequencies. Clearly\, the metallic nature of the magnetic NWs contributed to the high loss. It is expected that insulating magnetic NWs will improve the insertion loss sufficiently to produce viable ferrite devices at wireless communication frequencies below 2GHz and at higher frequencies. There are no pure insulating magnetic materials. However\, there are ferrites that are nearly insulating and are ferrimagnetic. Their saturation magnetization is much lower than the metallic ferromagnetic counterpart. This is a desirable property for magnetic device operating below 2 GHz. Of all the ferrite materials yttrium iron garnet (YIG) exhibits the lowest FMR linewidth ever measured and low saturation magnetization. In this work\, an array of high-purity YIG NWs embedded in a porous silicon membrane\, were synthesized using sol-gel method and the magnetic properties of the pure YIG Nanoparticles and the composite substrate were characterized by utilizing vibrating sample magnetometer (VSM) technique. From the ferromagnetic resonance (FMR) spectra\, it has been found that the measurements are characterized by a uniaxial magnetic anisotropy energy due to the high aspect ratio of the NWs. Based on the magnetic parameters of the composite substrate and characterizing YIG NWs\, a coplanar waveguide was designed by HFSS software. By applying a small external magnetic field and changing the internal magnetic H field by ±8%\, the phase of S21 parameter shifts up to 30̊ degrees near 1.7GHz.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-leili-hayati/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201202T100000
DTEND;TZID=America/New_York:20201202T110000
DTSTAMP:20260506T192728
CREATED:20201119T022728Z
LAST-MODIFIED:20201119T022728Z
UID:4580-1606903200-1606906800@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Bilgehan Donmez
DESCRIPTION:PhD Dissertation Defense: Topology Error Detection in Power System State Estimation \nBilgehan Donmez \nLocation: Teams Link \nAbstract: Growth of renewable energy\, changes in weather patterns\, and increases in cyber- and physical-attacks are examples of recent challenges in power system operation. To keep up with these rapid transformations\, it is imperative to improve the tools used in modern-day control centers.\nAs the centerpiece of system operations\, improvements in state estimation (SE) accuracy would result in better situational awareness for system operators. The state estimate can often be compromised when there are errors in the assumed topology of the network. Therefore\, topology error detection plays a key role in SE. In the first part of this dissertation\, topology errors in the external systems\, which are the neighboring control areas\, are investigated. When a subset of measurements coming from an external area is lost\, some parts of the system can become unobservable. Since SE cannot be carried out for the unobservable portion of the system\, the topology of the external system cannot be tracked in its usual way. This dissertation offers a computationally efficient external line outage detection algorithm that uses only the internal bus phase angles\, any available phasor measurement units (PMUs)\, and the pre-contingency system topology of the system. Coupled with a post-verification step\, this method is shown to be effective in detecting external line outages.\nThe second part of the dissertation focuses on topology errors in the internal system. The conventional SE implementations use the simplified bus-branch (BB) electrical network provided by the topology processor (TP). When the status of circuit breakers are not reported correctly to the TP\, the electrical equivalent it creates will be inaccurate. Therefore\, topology errors usually result in SE convergence problems or yield significantly biased estimates. To properly detect these types of errors\, rather than using the typical BB representation\, the network model is expanded to include circuit breakers and other switching devices in substations. SE is then reformulated to work with this detailed node-breaker (NB) model.\nAlthough the expansion of the model introduces operational and computational challenges\, several strategies are employed to counter these issues. The proposed innovations include the formulations of two separate equality-constrained SE algorithms\, the development of optimal meter placement algorithms\, and utilization of parallel processing. As demonstrated through the simulations conducted\, the methods developed in this dissertation are practical enough for adaptation to real-world systems.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-bilgehan-donmez/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201202T140000
DTEND;TZID=America/New_York:20201202T150000
DTSTAMP:20260506T192728
CREATED:20201130T201126Z
LAST-MODIFIED:20201130T201212Z
UID:4601-1606917600-1606921200@ece.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Kathan Vyas
DESCRIPTION:MS Thesis Defense: Data-Efficient analysis of Human Behavior by Spatio-Temporal Pose Generation and Inference \nKathan Vyas \nLocation: Zoom Link  \nPasscode: 474462 \nAbstract: Identifying human pose over time provides critical information towards understanding human behavior and their physical interaction with the environment surrounding them. In the past few decades\, the human pose estimation topic has witnessed groundbreaking research in the computer vision field thanks to the powerful deep learning models. These models are trained using several thousands of labeled sample images if not more. Such extensive data requirement posed a fundamental problem for domains (i.e. Small Data domains)\, in which data collection or labeling is expensive or limited due to privacy or security concerns such as healthcare. In this thesis\, we present a data-efficient learning pipeline to address small data problem in a healthcare-related human pose estimation application. In particular\, we infer spatio-temporal human poses to analyze typical vs. atypical behaviors in children with Autism spectrum disorder (ASD). To mitigate data limitation\, we propose two thrusts in our learning pipeline. The first thrust is a data-efficient machine learning approach\, in which a pre-trained (on adult pose images) pose estimation model with deep structure is fine-tuned on a small set of children pose videos\, provided to us by our collaborators. We implement a non-linear particle filter interpolation to deal with any missing body keypoints in the estimated poses and employ a novel PoTion (pose motion) based temporal aggregation technique to evaluate poses over time. The second thrust is a synthetic data augmentation approach\, in which we build a framework to create synthetic 3D humans with articulated bodies in order to render more pose images/videos in our application contexts. We use a novel 3D registration approach based on RANSAC and implement iterative closest point (ICP) to obtain 3D meshes from the scanned point clouds from both adult and kid mannequins\, which is then rigged and articulated in the Blender to generate our human avatars. We then infuse these avatars in various synthetic environments to create contexts similar to the target application\, which is a kid with both typical and atypical behaviors in a home-like environment.
URL:https://ece.northeastern.edu/event/ece-ms-thesis-defense-kathan-vyas/
END:VEVENT
END:VCALENDAR