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X-WR-CALDESC:Events for Northeastern University College of Engineering
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DTSTART;TZID=America/New_York:20220223T120000
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DTSTAMP:20260411T200428
CREATED:20220131T211623Z
LAST-MODIFIED:20220131T211623Z
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SUMMARY:Library Webinar: Getting Started with Zotero
DESCRIPTION:Learn how to use Zotero to increase your efficiency. Zotero is a free\, open-source tool that lets you quickly create bibliographies and in-text citations in your chosen reference style. This online session will cover: how to install Zotero\, how to export references from a database to Zotero\, how to create groups to organize your research information\, how to create an online account\, and how to use Zotero with Microsoft Word. \nNOTE: EndNote\, RefWorks\, Zotero\, and Mendeley are similar\, so you only need to choose one. \nThis webinar will be recorded. To receive a copy of the recording\, please register using your Northeastern email address below. \nRegister here: bit.ly/citationmgmtworkshops \nAll the times of the webinars are in EST.
URL:https://coe.northeastern.edu/event/library-webinar-getting-started-with-zotero/
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DTSTART;TZID=America/New_York:20220223T120000
DTEND;TZID=America/New_York:20220223T130000
DTSTAMP:20260411T200428
CREATED:20220218T181713Z
LAST-MODIFIED:20220218T181713Z
UID:30361-1645617600-1645621200@coe.northeastern.edu
SUMMARY:Accelerating Research Along the Path to Commercialization
DESCRIPTION:There are a variety of steps required to transition technologies from the research lab to the marketplace. Each step comes with its own set of questions and challenges. How do you protect your innovation and when is the right time? What is the right path to market? What are the obstacles to get there? What resources are available for researchers and entrepreneurs? \nRepresentatives from Northeastern’s Center for Research Innovation (CRI) will help to answer these questions. The CRI is focused on accelerating the advancement of Northeastern research from lab to market\, maximizing its impact\, for the benefit of society. \nTheir talk will be followed by a Q&A session\, providing ample opportunity for researchers to raise any questions and discuss issues related to intellectual property\, technology commercialization\, and entrepreneurship. \nSpeakers:  \nMark Saulich \nAs Associate Director of Commercialization\, Mark and his team are focused on the commercialization of Northeastern research. Industry engagement is at the core of their efforts\, identifying opportunities to solve real world challenges by leveraging Northeastern innovations. Prior to joining the CRI team\, Mark spent several years working at yet2\, a global open innovation consulting company\, leading technology scouting projects for several Fortune 1000 companies. \nKatie Hemphill \nAs Director of Technology Ventures and Talent Network\, Katie leads the development of a pipeline that encourages the discovery\, formation\, launch and growth of new ventures. In addition to managing the various venture programs at CRI\, she continues to cultivate a team of executive talent who mentor and support spinouts as they launch and scale. Prior to joining CRI\, Katie served as Associate Director of the McCarthy(s) Venture Mentoring Network (VMN) at Northeastern’s Center for Entrepreneurship Education at D’Amore-McKim School of Business. The VMN is a global network of volunteer mentors who give time and talent to early-stage startups based on timely business challenges.
URL:https://coe.northeastern.edu/event/accelerating-research-along-the-path-to-commercialization/
LOCATION:024 East Village\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
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DTSTART;TZID=America/New_York:20220223T130000
DTEND;TZID=America/New_York:20220223T140000
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SUMMARY:ECE PhD Proposal Review: Md Navid Akbar
DESCRIPTION:PhD Proposal Review: Variational and Siamese Models in Functional and Structural Medical Image Analysis \nMd Navid Akbar \nLocation: Zoom Link \nAbstract: Machine learning (ML) models have recently shown great promise in medical image analysis. Instead of a one-size-fits-all\, a customized model is generally needed to map a target outcome from an imaging modality. To this end\, this proposal presents three such supervised models developed for three different imaging modalities.\nIn the first\, a deep convolutional neural network (CNN) maps 3D cortical motor representation\, obtained by transcranial magnetic stimulation (TMS)\, to the corresponding motor evoked potentials captured by surface electromyography (EMG). This modeling is bi-directional: with trivial changes\, it can operate in both the forward and inverse directions. TMS as a functional imaging technique is still in its infancy\, but its potential application in presurgical planning necessitates a reliable data-driven model. Our variational autoencoder inspired CNN is a pioneering step in that direction: with a normalized root mean square error up to below 14%\, and an R-squared similarity up to above 87%\, for cortical representation reconstruction in the inverse path. As the next steps\, we plan to investigate other training strategies and collect additional data to assess robustness.\nIn the second\, a Siamese CNN (with a pretrained DenseNet121 backbone) is developed to predict the continuous spectrum of pulmonary edema severity\, from frontal chest X-rays. While existing deep learning frameworks have been promising in detecting the presence or absence of such edema\, or even its discrete grades of severity\, prediction of the continuous-valued severity remains a challenge. Using lower resolution images and only 1/51-th the size of training data compared to the state-of-the-art\, our work beats it by achieving a mean area under the receiver operating characteristic curve (AUC) score of 91% (improvement by 4%)\, when tested on the open-source MIMIC-CXR database.\nFinally\, a complete preprocessing and ML classification pipeline is developed for identifying which traumatic brain injury (TBI) patients will go on to develop late seizures\, from diffusion-weighted MRI (dMRI). Physical deformations following moderate-severe TBI present problems for standard processing of dMRI\, complicating the extraction of neuroimaging features. Following the novel application of a normalization technique to dMRI\, in conjunction with univariate feature selection and a linear discriminant analysis classifier\, our model improves the performance over the standard pipeline by 8% in mean accuracy and 7% in mean AUC. In future work\, we would like to explore classification using a fusion of dMRI with electroencephalogram (EEG) and functional MRI (fMRI) modalities.
URL:https://coe.northeastern.edu/event/ece-phd-proposal-review-md-navid-akbar/
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DTSTART;TZID=America/New_York:20220223T173000
DTEND;TZID=America/New_York:20220223T183000
DTSTAMP:20260411T200428
CREATED:20220223T151859Z
LAST-MODIFIED:20220223T151859Z
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SUMMARY:ECE PhD Proposal Review: Miead Tehrani-Moayyed
DESCRIPTION:PhD Proposal Review: RF Channel Models for Static and Mobile Scenarios: From Simulations to Models for Large-scale Emulations \nMiead Tehrani-Moayyed \nLocation: ISEC 432 \nAbstract: The extremely high data rates provided by communications at higher frequency bands\, e.g.\, millimeter waves (mmWave)\, can help address the unprecedented demands of next-generation wireless networks. However\, as several impairments limit wireless coverage at higher frequencies\, accurate models of wireless scenarios and testing at scale are needed to show actual potential and to realize the promises that the new wireless technologies can bring forth. Large-scale accurate simulations and wireless networks emulators are now a time and cost-effective solution to perform these tests in a lab before deployment in the field. This dissertation work focuses on modeling\, calibration\, and validation of realistic RF scenarios for wireless network emulation at scale.\nThe contributions of our work include (i) investigating the characteristic of the wireless channel at higher frequencies (mmWave) and the performance evaluation of mmWave communications on top of the recently released NR standard for 5G cellular networks\, and (ii) a framework to create RF scenarios for emulators like \emph{Colosseum} starting from rich forms of input\, like those obtained by ray-tracers or via real-field measurements.\n(i) We derive channel propagation models via ray-tracing simulations for mmWave transmissions with applications to vehicle-to-everything (V2X) communications. We analyze aspects related to blockage modeling\, the effects of antenna beamwidth\, beam alignment\, and multipath fading in urban scenarios and emphasize the importance of capturing diffuse scattered rays for improved large-scale and small-scale radio channel propagation models. Furthermore\, we compare the performance of mmWave 5G NR with the 4G long-term evolution (LTE) standard on a realistic environment and show the impact of MIMO technology to improve the performance of 5G NR cellular networks. As transmitted radio signals are received as clusters of multipath rays\, identifying these clusters provides better spatial and temporal characteristics of the channel. We deal with the clustering process and its validation across a wide range of frequencies in the mmWave spectrum below 100 GHz. We analyze how the clustering solution changes with narrower-beam antennas\, and we provide a comparison of the cluster characteristics for different types of antennas.\n(ii) Our framework to model wireless scenarios for large-scale emulators optimally scales down the large set of RF data in input to the fewer parameters allowed by the emulator by using efficient clustering techniques and channel impulse response re-sampling. We demonstrate the effectiveness of the proposed framework through modeling realistic scenarios for Colosseum starting from the rich input from a commercial-grade ray-tracing software: Wireless Insite by Remcom. We propose to finish our investigation (a)~by introducing ways of dealing with mobility in emulated scenarios\, and to perform adequate channel sounding to validate them\, and (b)~by indicating ways to provide input to the emulator through actual wireless measurements in the field. Particularly\, as campaigns in the field provide measurements for a sparse set of locations\, we plan to use deep learning techniques to “interpolate” channel parameters for a larger set of locations\, determining the trade-offs for achieving desired accuracy and reasonable computational requirements.
URL:https://coe.northeastern.edu/event/ece-phd-proposal-review-miead-tehrani-moayyed/
LOCATION:432 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
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