BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Northeastern University College of Engineering - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
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
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: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
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20220313T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20221106T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20230312T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20231105T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211203T083000
DTEND;TZID=America/New_York:20211203T170000
DTSTAMP:20260521T034019
CREATED:20211202T155836Z
LAST-MODIFIED:20211202T155836Z
UID:29617-1638520200-1638550800@coe.northeastern.edu
SUMMARY:First Year Engineering Expo
DESCRIPTION:Come by the Curry Student Center Indoor Pit to see the projects that our first-year engineering students have been working on this term.
URL:https://coe.northeastern.edu/event/first-year-engineering-expo/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211203T100000
DTEND;TZID=America/New_York:20211203T110000
DTSTAMP:20260521T034019
CREATED:20211201T210751Z
LAST-MODIFIED:20211201T210751Z
UID:29587-1638525600-1638529200@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Murphy Wonsick
DESCRIPTION:PhD Dissertation Defense: Supervisory Control for Humanoid Robots Through Virtual Reality Interfaces \nMurphy Wonsick \nLocation: ISEC 655 \nAbstract: Recent advancements in robotics have allowed robots to become capable enough to be used in a wide variety of domains that are dangerous for humans to operate in\, such as disaster relief operations\, exploration of extraterrestrial planets\, bomb disposal\, or nuclear decommissioning efforts. However\, current supervisory control interfaces that allow humans to explore and interact in these environments through remote presence and teleoperation are complex and often require expert operators. Virtual reality provides a medium to create immersive and easy-to-use teleoperation interfaces. Virtual reality allows operators to visualize and interact with 3D data in a 3D environment that is not possible with traditional interfaces that make use of 2D devices\, such as monitors\, keyboards\, mice\, tablets\, and/or game controllers. Yet\, development of supervisory control virtual reality interfaces for robot operation is still very limited. Most present work in virtual reality interfaces focuses on direct teleoperation and not on high-level control that supervisory control interfaces can provide. In this dissertation\, we focus on developing virtual reality supervisory control interfaces for remote robot operation. We specifically focus on high degree-of-freedom robots\, such as humanoid robots or mobile manipulator robots\, as they are the most suited types of robots for remote operation. To accomplish this\, we first look to better understand and define humanoid robot capabilities using NASA’s humanoid robot\, Valkyrie. Following\, we synthesize the current state-of-the-art supervisory control interfaces for humanoid robots to create our own supervisory control interface using traditional devices. We then use this information to create a virtual reality supervisory control interface for Valkyrie. Finally\, we look to improve virtual reality interfaces for robot operation through a user-centered design approach to inform future development on virtual reality interfaces.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-murphy-wonsick/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211203T120000
DTEND;TZID=America/New_York:20211203T130000
DTSTAMP:20260521T034019
CREATED:20211014T192354Z
LAST-MODIFIED:20211014T192354Z
UID:27853-1638532800-1638536400@coe.northeastern.edu
SUMMARY:Bioengineering PhD Student Seminar Series
DESCRIPTION:Join us Friday\, December 3 at 12:00 PM in Churchill Hall 101 for the Bioengineering PhD Student Seminar Series! Our first presenter will be Bioengineering PhD student Hector Millan Coto “Longitudinal effects of electronic cigarette smoking on lung mechanics on Apoe Mice”. Our second presenter will be Amber Williams “Non-Invasive\, Real-Time detection of Circulating Tumor Cell Clusters using Diffuse Light”.
URL:https://coe.northeastern.edu/event/bioengineering-phd-student-seminar-series-5/
LOCATION:101 Churchill\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
ORGANIZER;CN="Bioengineering":MAILTO:bioe@northeastern.edu
GEO:42.3396156;-71.0886534
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=101 Churchill 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:20211203T120000
DTEND;TZID=America/New_York:20211203T130000
DTSTAMP:20260521T034019
CREATED:20211124T175226Z
LAST-MODIFIED:20211129T145937Z
UID:29538-1638532800-1638536400@coe.northeastern.edu
SUMMARY:Dialogue of Civilization in Turkey (Info Session)
DESCRIPTION:We are excited to announce the DOC program in Turkey in Summer 1 2022 (link). This program includes two courses: \n\nIE 4512: Engineering Economy: Explores economic modeling\, financial analysis\, and decision-making approaches\nIE 4699: ST in Industrial Engineering: A variety of topics such as sports\, politics\, finance\, entrepreneurship\, business development\, healthcare\, manufacturing\, machine learning\, religion\, culture\, art\, history in the Turkish environment will be covered.\n\nProgram facts: \n\nThe program will take ~ 4 weeks\, in two (2) cities (Istanbul\, and Izmir)\, and be hosted by three universities.\nThis program covers a NUPath\, “Interpreting Culture”.\n\nIf you are interested\, you can attend the Info Session details planned for next week. By attending this meeting\, you can familiarize yourself with the program\, its itinerary\, and learn about host universities\, program activities\, and kickstart your application process. You can find the meeting details below: \nDOC in Turkey Info Session: \n\nDate/Time: Friday Dec 3rd\, 12:00 pm -1:00 pm\nLocation: In-person (SL 119) or via Zoom\nPlease RSVP by Wed\, Dec 1st using this link. (Required)\nPizza will be served at the meeting\, so just bring your soda\nPlease feel free to circulate this email to your eligible friends (in Engineering\, Business\, Finance\, Economy\, etc.) who might be interested\n\nAll students are highly encouraged to attend the Info Session in person. If you are not able to attend\, please use the RSVP link to provide your info and the recorded session will be sent to you afterward.
URL:https://coe.northeastern.edu/event/dialogue-of-civilization-in-turkey-info-session/
ORGANIZER;CN="Mechanical & Industrial Engineering":MAILTO:mie-web@coe.neu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211206T080000
DTEND;TZID=America/New_York:20211206T090000
DTSTAMP:20260521T034019
CREATED:20211123T213812Z
LAST-MODIFIED:20211123T213812Z
UID:29500-1638777600-1638781200@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Mehdi Nasrollahpourmotlaghzanjani
DESCRIPTION:PhD Dissertation Defense: RFICs for Biomedical Magnetic and Magnetoelectric Microsystems \nMehdi Nasrollahpourmotlaghzanjani \nLocation: Zoom Link \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 passive devices and sensor evaluations to diagnose different diseases.\nFirst\, we show a 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. The proposed rectangular ME antenna wireless power transfer efficiency is 0.304 %\, which is considerably higher than that of micro-coils. Measurements results show that the maximum achievable power transfer of a ME antenna outperforms that of an on-silicon coil by approximately 7 times for a Tx-Rx distance of 0.76 cm and 3.3 times for a Tx-Rx distance of 2.16 cm.\nIn 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. A low-noise amplifier (LNA) topology with tunable input matching and noise cancellation utilized in a Bluetooth receiver frontend is introduced and described in this study\, which was designed and optimized to interface with a magnetoelectric (ME) antenna in a 0.35 µm MEMS-compatible CMOS process. Input matching at the LNA-antenna interface is controlled with a circuit that varies the effective impedance of the gate inductor using a control voltage. Tunability of 455 MHz around 2.4 GHz is achieved for the optimum S11 frequency with a control voltage range of 0.3 V to 1.2 V. Besides\, a miniaturized CMOS oscillator using microelectromechanical system (MEMS) resonating at 159 MHz frequency is designed and simulated to drive ME sensors. The proposed oscillator provides a phase noise as low as -131.3 dBc/Hz at 10 kHz and -137.9 dBc/Hz at 100 kHz offset frequencies while consuming 2.24 mW power.\nFor 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. A novel gas sensor has been developed that might be applied to diagnose Covid-19 from the exhaled breath instantly. The handheld device is designed to read the sensor activities and send the data to the android phone to show if the patient is at risk or not. For this purpose\, a lock-in amplifier is designed to read the resistance in ac domain and transmit the digitized data through Bluetooth communication link.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-mehdi-nasrollahpourmotlaghzanjani/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211207T080000
DTEND;TZID=America/New_York:20211210T170000
DTSTAMP:20260521T034019
CREATED:20211207T152941Z
LAST-MODIFIED:20211207T153011Z
UID:29691-1638864000-1639155600@coe.northeastern.edu
SUMMARY:Experiential Entrepreneurship Intersession Opportunity
DESCRIPTION:Want to hone your entrepreneurial skills over winter break while working directly with tech startups? Registration is now open for the Experiential Entrepreneurship Intersession course running from January 3rd through January 14th. \nOffered virtually or in-person through the Roux Institute at Northeastern University\, students will learn about the venture creation process and work hand-in-hand with tech startups that have emerged from the accelerator and residency programs at the Roux Institute. \n
Past guest lecturers have included: \n\nAli Goldstein Norup\, co-founder of kpiReady and current Head of VC and Startup Ecosystem\, North Americas at Google Cloud\nBen Chesler\, co-founder of Imperfect Foods and current Associate Director of Entrepreneurship at the Roux Institute\nJesse Bardo\, co-founder of EverTrue and current Director at Silicon Valley Bank\n\nAnd\, if you register by December 8th\, you will receive an invite to the Techstars Demo Day in Portland\, Maine. The event will gather the Maine startup community for an in-person presentation from each of the 10 companies selected for the inaugural Roux Institute Techstars Accelerator class. Following you’ll be invited for a reception at the Roux Institute where guests will meet and mingle with the startups\, investors\, and community members. \nTo view the course: \n\n
Visit Banner and select the term\, Spring 2022 Semester. Even though Intersession Term courses meet between semesters\, they have been administratively assigned to Spring 2022 semester.\nClick Advanced Search on the Browse Classes page.\nIn the attribute field\, choose Intersession Term Course. All the Intersession Term offerings will appear.\n\n
Registration for intersession will close Friday\, December 10th at 11:59 (EST).
URL:https://coe.northeastern.edu/event/experiential-entrepreneurship-intersession-opportunity/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211207T140000
DTEND;TZID=America/New_York:20211207T150000
DTSTAMP:20260521T034019
CREATED:20211129T193827Z
LAST-MODIFIED:20211129T193827Z
UID:29575-1638885600-1638889200@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Sara Banian
DESCRIPTION:PhD Dissertation Defense: Content-Aware AI-Driven Design Assistance Frameworks for Graphic Design Layouts \nSara Banian \nLocation: Zoom Link \nAbstract: Designing user interfaces (UIs) for mobile interaction is widespread but still challenging. It is important for the overall user satisfaction and application success. During the design process\, designers express their requirements through images describing the UI’s layout\, structure\, and content. Designers\, however\, encounter key challenges throughout the design process. For example\, searching for inspiring design examples is challenging because current search systems rely on only text-based queries and do not consider the UI structure and content. Furthermore\, these systems often focus on overall page-level layout over individual UI components. Also\, creating wireframe templates is difficult for many designers as it necessitates an understanding of different design guidelines. Therefore\, it is critical to support designers by developing effective design tools to help them be more productive and creative.\nIn this dissertation\, I aim to explore how to develop design assistance methodologies to augment the process of UI layout design\, with a particular focus on visual search and layout generation. Specifically\, for this exploration\, I seek to investigate the use of advanced deep learning models in the context of mobile UI layout design. Processing layouts differs from processing pixel-level images in that it necessitates processing both the semantic (e.g.\, labels) and spatial (e.g.\, coordinates) content of the layout to model the data properly. To achieve this\, I explore the design problems from both the data and the model side. First\, I present a large-scale UI dataset that accurately specifies the interface’s view hierarchy (i.e.\, UI components and their location). Second\, I contribute the VINS framework\, which is composed of three systems LayVis\, CompVis\, and TransVis that addresses layout-based visual search\, component-based visual search\, and layout generation\, respectively.\nFirst\, I introduce LayVis\, an object-detection layout-based retrieval model. It takes as input a UI image and retrieves visually similar design examples. Next\, I introduce CompVis\, a component-based visual search system to easily retrieve individual UI components via convolutional neural networks (CNNs). Specifically\, for a given query\, the system allows to retrieve (1) text label synonyms\, (2) similar UI components\, and (3) design examples containing such components. Finally\, I present TransVis\, a transformer-based generative framework that investigate how to generate UI layouts according to user specifications and following design practices. It specifically models UI layouts as an ordered sequence of elements based on spatial and semantic relationships for (1) generating complete UI layouts\, (2) auto-completing existing UI layouts seamlessly\, and (3) supporting many design elements per layout.\nOverall\, the work presented in this dissertation contributes to augmenting the UI layout design. Through quantitative and qualitative evaluation of VINS\, we conclude the following: (1) Advanced deep learning models can aid in the development of design assistance methodologies for layout design; and (2) Designers perceive the use of VINS inspiring and useful. Such insights\, combined with the open-sourced large-scale dataset\, can help the research community develop more effective AI-based data-driven design tools. This work presents future opportunities to investigate different deep learning models within the context of layout design and how designers interact with these AI-based models.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-sara-banian/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211208T120000
DTEND;TZID=America/New_York:20211208T130000
DTSTAMP:20260521T034019
CREATED:20211201T211738Z
LAST-MODIFIED:20211201T211738Z
UID:29611-1638964800-1638968400@coe.northeastern.edu
SUMMARY:ChE Seminar Series: Catalytic Oxidation of Methane\, the “Other” Greenhouse Gas
DESCRIPTION:ChE Seminar Series Presents: \nDr. Michael Harold \nDepartment of Chemical & Biomolecular Engineering\, University of Houston \nAbstract: \nThe abundant domestic natural gas resources has motivated the accelerated development of natural gas powered vehicles and stationary engines.  With the primary constituent of abundant NG being methane (CH4)\, NG has a higher H:C ratio than gasoline or diesel and therefore its combustion produces less CO2.   However\, CH4 is itself a more potent greenhouse gas (GHG) than CO2 with a GHG potential about 85 times that of CO2. Uncombusted CH4 must be eliminated in order to clear the way for the growth in the NG engine market. Current state-of-the-art Platinum Group Metal (PGM) catalysts are ineffective in eliminating methane. Our research is focused on the study and development of a new class of cost effective structured catalysts with reduced PGM loadings for both stoichiometric and lean methane oxidation. For stoichiometric oxidation we show that the combination of spinel mixed metal oxide (AB2O4) addition and lean-rich feed modulation results in significant enhancement in the catalyst performance. Detailed study of feed modulation parameters (frequency\, amplitude)\, catalyst design (composition\, architecture) and spatiotemporal reactor features provide insight into and optimization of the underlying mechanism. The enhancement is attributed to the transient oxidation of methane conversion inhibitors CO and H2 by the spinel. Up to a 30% reduction in PGM loading is possible with negligible loss in performance. For lean oxidation we study and develop an in situ method to regenerate methane oxidation catalysts. Periodic reductant (H2\, CO) pulsing mitigates the detrimental water poisoning of Pd-Pt catalyst. The pulsing is able to regenerate the catalyst deactivated by water by removal of OH-groups from the catalysts surface\, but also promoted its activity after repeated application of pulsing for several hours. This state of high activity is stable for several hours under the tested lean conditions. \nBio: \nMike Harold is the Cullen Engineering Professor in the Department of Chemical and Biomolecular Engineering at the University of Houston.  With expertise in catalysis and reaction engineering\, Harold is the author of more than 180 peer-reviewed papers and book chapters and has given over 350 presentations and invited lectures.  Harold received his BS at Penn State and PhD from the University of Houston (UH).  He joined the faculty at University of Massachusetts at Amherst in 1985 where he became Associate Professor.  In 1993 Harold joined DuPont Company\, where he held technical and managerial positions.  In 2000 Harold became the Dow Chair Professor and Department Chair at UH\, a position he held for 16 years. Mike was appointed Editor-in-Chief of the AIChE Journal in 2012 and will soon end his 10 year term. His honors include the Excellence in Applied Catalysis from the Southwest Catalysis Society in 2019\, the Ester Farfel Award at UH in 2013\, and AIChE Fellow in 2014. \nPlease contact a.ramsey@northeastern.edu for the seminar link.
URL:https://coe.northeastern.edu/event/che-seminar-series-catalytic-oxidation-of-methane-the-other-greenhouse-gas/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211209T150000
DTEND;TZID=America/New_York:20211209T160000
DTSTAMP:20260521T034019
CREATED:20211207T201849Z
LAST-MODIFIED:20211207T201849Z
UID:29688-1639062000-1639065600@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Arjun Singh
DESCRIPTION:PhD Dissertation Defense: Design\, Modeling\, and Operation of Plasmonic Devices for Smart Communication Systems in the Terahertz Band \nArjun Singh \nLocation: 332 ISEC or Teams Link \nAbstract: The terahertz (THz) band is an attractive spectral resource for realizing future communication systems\, with the potential of supporting very high-speed data rates and increasingly dense networks. However\, the lack of a well-developed technology that operates at these frequencies has remained a challenge for the scientific community. The very high propagation losses at THz frequencies and the decimating impact of everyday objects on THz wave propagation necessitate an up-haul of the conventional communication link\, with smart control over the radiation\, propagation\, and detection of THz signals. Additionally\, device physics at THz frequencies\, among them the very high gain requirement and large electrical size\, may render the often-held assumptions of the propagation model invalid. An interdisciplinary approach spanning device design and operation\, and wireless propagation and signal processing is required.\nTo this end\, the proposed research herein addresses the facilitation of an end-to-end communication link with graphene plasmonics as the cornerstone of the fundamental device physics. The devices designed can be utilized to effectively overcome the limited communication distance –The grand challenge of the THz band. Different from other undertakings\, every attempt is made to ac-knowledge and accommodate the complex trade-offs in the design process. First\, a novel graphene based plasmonic array architecture is proposed\, explained\, and modeled. The fundamental radiating element of the array architecture\, called the plasmonic front-end\, consists of a self-sufficient plasmonic source\, a plasmonic modulator that acts as a phase controller\, and a plasmonic nano-antenna for effective radiation. The designed array is compact and provides complete beamsteering support\, with a new tailored algorithm developed for beamforming weight selection. Numerical evaluations and full-wave finite difference frequency domain (FDFD) simulations with COMSOL Multi-physics are utilized to verify array operation. Exploiting these properties\, a multi-beam array design is presented next\, where orthogonal spatial filters are utilized to provide support for spatial multiplexing towards the realization of ultra-massive MIMO (UM-MIMO). Taking this further\, the design considerations of an interleaved plasmonic array are presented\, in which the beamsteering capability is utilized to simultaneously achieve radio frequency interference (RFI) mitigation with channel capacity maximization for multi-user scenarios. Additionally\, to realize the vision of a smart communication system with a programmable wireless environment\, a hybrid reflectarray is presented. The fundamental element is modeled as a jointly designed and integrated metal-graphene patch. Numerical and simulation results are utilized to demonstrate the attractive properties of the reflectarray as compared to other proposed counterparts\, including an independence from the incoming angle of the impinging wave\, dynamic phase control capability\, and strong reflection efficiency. The requirements of a THz communication link and their impact on the common communication protocols are considered next. It is shown that certain scenarios may render regular array operation invalid\, motivating codebook designs that function in the massive near-field Fresnel zone of electrically large THz devices. Numerical simulations and theoretical analysis are presented to highlight their potential in improving system performance and capacity while reducing the system complexity. Finally\, the significant milestones in the fabrication process of these devices are also presented.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-arjun-singh/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211210T110000
DTEND;TZID=America/New_York:20211210T120000
DTSTAMP:20260521T034019
CREATED:20211207T153514Z
LAST-MODIFIED:20211207T153514Z
UID:29686-1639134000-1639137600@coe.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Michele Pirro
DESCRIPTION:PhD Proposal Review: Scandium-doped Aluminum Nitride for new MEMS technologies \nMichele Pirro \nLocation: 432 ISEC \nZoom Link Meeting ID: 938 0086 0379 https://northeastern.zoom.us/u/abZS2SmtT1 \nAbstract: The increasing demand for data is pushing the MEMS industry to more performant and area-efficient systems to be used in IOT nodes as sensors and RF-components. In this market\, AlN plays a pivotal role thanks to the piezoelectric properties accompanied with good stability over power and temperature in miniaturized devices. In fact\, AlN is already present in different commercial MEM systems\, such as duplexers\, ultrasound generators\, energy harvesters and so on\, proving a mature mass-production process flow. The required more stringent specifications in terms of bandwidth\, losses and efficiency are pushing towards piezoelectric materials with higher coupling coefficient\, but still in a compatible post-CMOS process flow. Luckily\, recent works showed how it is possible to enhance the piezoelectric effect by doping AlN with Scandium\, allowing up to 400% increase in the d33 piezoelectric coefficient. The enhanced acoustic transduction along with the recent demonstration of ferroelectric switching and the post-IC compatibility\, is making Sc-doped AlN a new material with the potential not only to replace AlN\, but also to integrate different functionalities within the same component. Academy and industry all over the world are actively researching the actual potential of the material but there is still a lack of information on high-Sc concentration\, which would allow lower-voltage switching along with higher d33. This work has the main objective to show Sc-concentration > 28% and their piezo/ferroelectric response for a new class of microelectromechanical devices.\nThe proposal will discuss the advance in the process flow of high Sc- concentrations\, showing the impact of the deposition parameters on the material properties. Thin films with good crystallinity on IC-substrate and enhanced d33 are reported\, along with first attempts to resonator-devices. An in-depth ferroelectric characterization will show how coercive field and leakage current are the main limiting factors the material is facing to integrate its memory effect. For this purpose\, the work will present how tuning of Sc-concentration\, substrate-rf and bulk stress can ease these limiting factor\, opening to new acoustic devices with memory functionalities. The last part will focus on the co-integration of acoustic properties with ferroelectric switching for tunable filters and ultrasonic generators in post-IC compatible substrates.
URL:https://coe.northeastern.edu/event/ece-phd-proposal-review-michele-pirro/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211210T134500
DTEND;TZID=America/New_York:20211210T144500
DTSTAMP:20260521T034019
CREATED:20211201T211501Z
LAST-MODIFIED:20211201T211536Z
UID:29600-1639143900-1639147500@coe.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Leonardo Bonati
DESCRIPTION:PhD Proposal Review: Softwarized Approaches for the Open RAN of NextG Cellular Networks \nLeonardo Bonati \nLocation: 532 ISEC \nAbstract: The 5th (5G) and 6th generations (6G) of cellular networks\, also known as NextG\, will bring unprecedented flexibility to the wireless cellular ecosystem. Because of its typically closed and rigid market\, the telco industry has incurred high costs and non-trivial obstacles in delivering those new services and functionalities to satisfy the requirements and the demand of NextG networks. To break this trend the industry is now moving towards open architectures based on softwarized approaches\, which will afford network operators flexible control and unprecedented adaptability to heterogeneous conditions\, including traffic and application requirements. Now\, by simply expressing a high-level intent\, operators will be able to instantiate bespoke services on-demand on a generic hardware infrastructure\, and to adapt such services to the current network conditions. Through disaggregation\, network elements will split their functionalities across multiple components—possibly provided by different vendors—interconnected through well-defined open interfaces. The separation of control functions from the hardware fabric\, and the introduction of standardized control interfaces\, will ultimately enable definition and use of softwarized control loops\, which will bring embedded intelligence and real-time analytics to effectively realizing the vision of autonomous and self-optimizing networks.\nThis dissertation work focuses on the design\, prototyping and experimental evaluation of softwarized approaches for the new open Radio Access Network (RAN) of NextG cellular networks. We analyze the architectural enablers\, challenges and requirements for a programmatic zero-touch control of the very many network elements and propose practical solutions for its realization. We prototype solutions by leveraging open-source software implementations of cellular protocol stacks and frameworks\, and heterogeneous virtualization technologies\, including the srsRAN and OpenAirInterface cellular implementations\, and the O-RAN framework. The contributions of this work include (i) the first demonstration of O-RAN data-driven control loops in a large-scale experimental testbed using open-source\, programmable RAN and RAN Intelligent Controller (RIC) components through xApps of our design\, and (ii) CellOS\, a zero-touch cellular operating system that automatically generates and executes distributed control programs for simultaneous optimization of heterogeneous control objectives on multiple network slices starting from a high-level intent expressed by the operators. The effectiveness of our solutions in achieving superior control and performance of the RAN is demonstrated on state-of-the-art experimental facilities\, including software-defined radio-based laboratory setups and open access experimental wireless platforms\, such as Colosseum\, Arena\, and the POWDER-RENEW platform from the U.S. PAWR program.
URL:https://coe.northeastern.edu/event/ece-phd-proposal-review-leonardo-bonati/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211214T120000
DTEND;TZID=America/New_York:20211214T130000
DTSTAMP:20260521T034019
CREATED:20211207T153629Z
LAST-MODIFIED:20211207T153629Z
UID:29679-1639483200-1639486800@coe.northeastern.edu
SUMMARY:CILS Seminar: arivis™\, Imaging Software
DESCRIPTION:Join this seminar to learn how to make microscopy image analysis more straightforward. An arivis™ representative will be presenting on topics such as image segmentation\, multiview registration\, storyboard\, colocalization\, and image processing (denoise\, decon\, etc).
URL:https://coe.northeastern.edu/event/cils-seminar-arivis-imaging-software/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211216T093000
DTEND;TZID=America/New_York:20211216T103000
DTSTAMP:20260521T034019
CREATED:20211215T205218Z
LAST-MODIFIED:20211215T205218Z
UID:29766-1639647000-1639650600@coe.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Jaehyeon Ryu
DESCRIPTION:PhD Proposal Review: Engineering Functional Nanomesh for Advanced Neuroelectronics \nJaehyeon Ryu \nLocation: Zoom Link \nAbstract: Transparent electronics have emerged as promising platforms for neural interfacing by enabling simultaneous electrophysiological recording and optical measurements. Also\, there are high demands for stretchable devices due to their low modulus and compatible interface with irregular and soft neural tissue. However\, current transparent\, stretchable approaches are usually limited by their scalability for neuroelectronic applications. Here\, I present multi-functional nanomesh as an approach to achieve stretchable\, transparent microelectrode arrays (MEAs) with excellent scalability. By stacking mechanical supporting polymer\, gold\, and conductive polymer in a nanomesh structure on elastomer substrate\, multilayer nanomesh-based MEAs show excellent stretchability\, transparency\, and electrochemical properties with single neuron scale dimensions. The performance of these multi-functional nanomesh-based MEAs has been characterized through bench testing\, and I plan to perform in vivo validation in the remaining period of my thesis. These highly stretchable and transparent multilayer nanomesh MEAs are promising for applications ranging from neuroscience to biomedical devices.
URL:https://coe.northeastern.edu/event/ece-phd-proposal-review-jaehyeon-ryu/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211216T140000
DTEND;TZID=America/New_York:20211216T150000
DTSTAMP:20260521T034019
CREATED:20211206T145333Z
LAST-MODIFIED:20211207T164422Z
UID:29663-1639663200-1639666800@coe.northeastern.edu
SUMMARY:Distinguished Speaker Series in Robotics: Gregory S. Chirikjian
DESCRIPTION:Distinguished Speaker Series in Robotics \nGregory S. Chirikjian: Robot Imagination: Affordance-Based Reasoning about Unknown Objects \nLocation: ISEC Auditorium or Zoom Link \nAbstract: Today’s robots are very brittle in their intelligence. This follows from a legacy of industrial robotics where robots pick and place known parts repetitively. For humanoid robots to function as servants in the home and in hospitals they will need to demonstrate higher intelligence\, and must be able to function in ways that go beyond the stiff prescribed programming of their industrial counterparts. A new approach to service robotics is discussed here. The affordances of common objects such as chairs\, cups\, etc.\, are defined in advance. When a new object is encountered\, it is scanned and a virtual version is put into a simulation wherein the robot “imagines’’ how the object can be used. In this way\, robots can reason about objects that they have not encountered before\, and for which they have no training using. Videos of physical demonstrations will illustrate this paradigm\, which the presenter has developed with his students Hongtao Wu\, Meng Xin\, Sipu Ruan\, and others. \nBio: Gregory S. Chirikjian received undergraduate degrees from Johns Hopkins University in 1988\, and a Ph.D. degree from the California Institute of Technology\, Pasadena\, in 1992. From 1992 until 2021\, he served on the faculty of the Department of Mechanical Engineering at Johns Hopkins University\, attaining the rank of full professor in 2001. Additionally\, from 2004-2007\, he served as department chair. Starting in January 2019\, he moved the National University of Singapore\, where he is serving as Head of the Mechanical Engineering Department\, where he has hired 14 new professors so far. Chirikjian’s research interests include robotics\, applications of group theory in a variety of engineering disciplines\, applied mathematics\, and the mechanics of biological macromolecules. He is a 1993 National Science Foundation Young Investigator\, a 1994 Presidential Faculty Fellow\, and a 1996 recipient of the ASME Pi Tau Sigma Gold Medal. In 2008\, Chirikjian became a fellow of the ASME\, and in 2010\, he became a fellow of the IEEE. From 2014-15\, he served as a program director for the US National Robotics Initiative\, which included responsibilities in the Robust Intelligence cluster in the Information and Intelligent Systems Division of CISE at NSF. Chirikjian is the author of more than 250 journal and conference papers and the primary author of three books\, including Engineering Applications of Noncommutative Harmonic Analysis (2001) and Stochastic Models\, Information Theory\, and Lie Groups\, Vols. 1+2. (2009\, 2011). In 2016\, an expanded edition of his 2001 book was published as a Dover book under a new title\, Harmonic Analysis for Engineers and Applied Scientists. \n\nReceive Further Event Notifications \nPresented by the Institute for Experiential Robotics
URL:https://coe.northeastern.edu/event/distinguished-speaker-gregory-s-chirikjian/
LOCATION:ISEC Auditorium\, 805 Columbus Ave\, Boston\, MA\, 02115\, United States
GEO:42.3377049;-71.0870109
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=ISEC Auditorium 805 Columbus Ave Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=805 Columbus Ave:geo:-71.0870109,42.3377049
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211216T150000
DTEND;TZID=America/New_York:20211216T160000
DTSTAMP:20260521T034019
CREATED:20211215T192630Z
LAST-MODIFIED:20211215T192630Z
UID:29764-1639666800-1639670400@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Jinghan Zhang
DESCRIPTION:PhD Dissertation Defense: Domain Design Space Exploration: Designing a Unified Platform for a Domain of Streaming Applications \nJinghan Zhang \nLocation: ISEC 362 or Zoom Link \nAbstract: Many demanding streaming applications share functional and structural similarities with other applications in their respective domain\, e.g.\, video analytics\, software-defined radio\, and radar. This opens the opportunity for specialization to achieve the needed efficiency and/or performance.\nPlatforms integrating many accelerators (ACCs) is a primary approach for efficient\, high-performance stream computing.\nHowever\, designing one platform for each application is not economical due to the high costs of nonrecurring engineering (NRE) and time-to-market (TTM).\nTo this end\, the concept of domain platforms is proposed\, which takes advantage of similarities across applications and designs one unified platform to accelerate a domain of applications instead of focusing on a single reference application.\nThis dissertation approaches designing domain platforms from a function-level (kernel-level) acceleration through a heterogeneous ACC-rich platform\, where each ACC is specialized to accelerate a particular function.\nThere is a great challenge to select ACCs allocated in the domain platform\, considering the large design space and performance balance across many applications.\nHowever\, current Design Space Exploration (DSE) tools only focus on an individual application in isolation (e.g.\, one particular vision flow) for allocating a platform\, but not a set of similar applications.\nThis dissertation introduces Greedy Guided Mutation (GGM) to speed up the mutation in the GIDE algorithm\, which calculates an ACC score according to current allocation to guide mutation.\nAlternatively\, Rapid Domain Platform Performance Prediction (RDP^3) methods are introduced to replace a large number of the slow platform assessment in domain DSE\, which avoids the complex application binding exploration.\nIn the experiments\, GGM reduces 84.8% of exploration time with a 0.23% loss of the final OpenVX domain platform’s performance.\nRDP^3 using a machine learning method yields an even more significant speedup\, saving 90.8% of exploration time with only 0.0003% performance loss.\nDmDSE is a milestone to broaden DSE scope from individual applications to the domain level. It tremendously pushes the domain platform design from manually and engineering experience guided into a general automatic process.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-jinghan-zhang/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211217T120000
DTEND;TZID=America/New_York:20211217T130000
DTSTAMP:20260521T034019
CREATED:20211215T144810Z
LAST-MODIFIED:20211215T144810Z
UID:29735-1639742400-1639746000@coe.northeastern.edu
SUMMARY:ADSE- End of year cookie swap and pizza party
DESCRIPTION:Good afternoon Northeastern Graduate Students\, \nPlease join the Alliance for Diversity in Science and Engineering and your fellow grad students for our end-of-year event on December 17th from 12-1 pm in Curry 440! We will have pizza and will be hosting a cookie swap\, so bring a dozen or two of your favorite homemade (or not\, we’re not going to judge) cookies! Please don’t feel pressured to bring cookies\, but certainly\, bring yourself and a friend for a final get-together this year. Please fill out the link below to RSVP so we can get an idea of how much pizza we should bring as well\, and look forward to seeing everyone there! \nhttps://tinyurl.com/ycksfmb8 \nADSE@NEU
URL:https://coe.northeastern.edu/event/adse-end-of-year-cookie-swap-and-pizza-party/
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:20211220T110000
DTEND;TZID=America/New_York:20211220T120000
DTSTAMP:20260521T034019
CREATED:20211215T192527Z
LAST-MODIFIED:20211215T192527Z
UID:29762-1639998000-1640001600@coe.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Anahita Moradmand
DESCRIPTION:PhD Proposal Review: Robust Observer Structures and Control Design for Linear and Nonlinear Dynamical Systems with Applications \nAnahita Moradmand \nLocation: Zoom Link \nAbstract: This thesis focuses on special class of observers and controllers for different and seperable nonlinear systems. In the first step\, a robust fault detection approach using observers for linear systems is proposed where we combine the unknown inout observer UIO and an extended proportional integral observer PIO\, which has a fading term for robust fault detection. The integrated observer is called proportional integral fading unknown input observer (PIFUIO).\nFurthermore\, we extend our result to nonlinear systems with special structures as the design of nonlinear observer had limitation for general types of nonlinear systems. In the second step\, analysis and design of positive systems is considered whereby positive stabilization and the design of positive unknown input observer (PUIO) are introduced. Also\, the robust stability analysis of this class of systems is studied in which the robust stability is formulated in terms of LMI. the class of separable positive nonlinear systems is also analyzed and the design of observer and controller are provided. Finally\, we extend our desgin from Lipschitz type nonlinearity to state-dependent type by focusing on interconnected systems where we propose a distributed control architecture to take advantage of the global performance similar to centralized control and leverage the benefits of decentralized control.
URL:https://coe.northeastern.edu/event/ece-phd-proposal-review-anahita-moradmand/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220111T110000
DTEND;TZID=America/New_York:20220111T170000
DTSTAMP:20260521T034019
CREATED:20220110T144722Z
LAST-MODIFIED:20220110T144722Z
UID:29837-1641898800-1641920400@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Sungho Kang
DESCRIPTION:PhD Dissertation Defense: Plasmonically Enhanced Infrared Sensing Microsystems \nSungho Kang \nLocation: Zoom Link \nAbstract: Infrared (IR) spectroscopic sensing has become a key technique in multidisciplinary environments such as military applications\, industrial safety control\, and smart homes\, by providing an accurate and non-disruptive analysis of the target objects. Recently the demand for high performance and compact IR spectroscopy systems has been steadily growing due to the advent of Internet of Things and the burgeoning development of miniaturized sensors. The key challenge lies in realizing high performance IR detectors that have low noise\, high IR throughput\, and spectral sensitivity in a miniaturized form factor. This challenge has been tackled in the study of micro-electromechanical sensing systems and metamaterial absorbers\, in which the ultra-high resolution sensing capability and the near-perfect IR absorption properties can be simultaneously exploited in a minimized footprint. The metal-insulator-metal (MIM) IR absorbers\, in particular\, are characterized by the near-unity absorptance with lithographically tunable peak absorption wavelength and spectral selectivity in an ultra-thin form factor\, suitable for the implementation of miniaturized spectroscopic IR microsystems. The exceptional IR absorption characteristics realized by the MIM IR absorbers and their sub-wavelength form factor allow for seamless integration with the existing IR sensing microsystem and the unprecedented IR sensing performance for the next generation IoT sensing solutions. In this defense\, novel development of miniaturized IR spectroscopic sensor and maintenance-free wireless human sensors based on the two key technologies are presented: (1) multispectral resonant IR detector array and (2) plasmonically-enhanced long-wavelength infrared micromechanical photoswitch. This study shows that the demonstrated technologies can replace the traditional IR sensors with the new generation IR sensing microsystems that are characterized by their high performance\, compact form factor\, power efficiency and low cost.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-sungho-kang/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220114T140000
DTEND;TZID=America/New_York:20220114T150000
DTSTAMP:20260521T034019
CREATED:20220118T143826Z
LAST-MODIFIED:20220118T143826Z
UID:29870-1642168800-1642172400@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Andac Demir
DESCRIPTION:PhD Dissertation Defense: Automated Bayesian Network Exploration for Nuisance-Robust Inference \nAndac Demir \nLocation: Zoom Link \nAbstract: A fundamental challenge in the analysis of physiological signals is learning useful features that are robust to nuisance factors e.g.\, inter-subject \& inter-session variability\, and achieve the highest nuisance-invariant classification performance. Towards resolving this problem\, we introduce 3 frameworks: AutoBayes\, which is an AutoML approach to conduct neural architecture search for research prototyping\, and GNN based frameworks: EEG-GNN and EEG-GAT.\nThe ultimate goal of the AutoBayes framework is to identify the conditional relationship between a physiological dataset\, associated task labels\, nuisance variations and potential latent variables in order to robustly infer the task labels invariant of nuisance factors. Nuisance factors in the case of physiological datasets could be variations in subjects or sessions\, but we only focus on subject variations in the experiments. AutoBayes enumerates all plausible Bayesian networks between data\, labels\, nuisance variations and potential latent variables\, detects and prunes the unnecessary edges according to Bayes-Ball Algorithm\, and then trains the resulting DNN architectures for different hyperparameter configurations in an adversarial / non-adversarial or a variational / non-variational setting to achieve the highest validation performance. Instead of hyperparameter tuning for model optimization\, AutoBayes concentrates on the architecture search of plausible Bayesian networks\, and achieves state-of-the-art performance across several physiological datasets. Furthermore\, we ensemble several Bayesian networks by stacking their posterior probability vectors in a higher level learning space\, train a shallow MLP as a meta learner\, and measure the task and nuisance classification performance on a hold-out dataset. We observe that exploration of different inference Bayesian networks has a significant benefit in improving the robustness of the machine learning pipeline\, and the parallel activity of vast assemblies of different Bayesian network models significantly reduces variation across subjects in the cross-validation setting.\nIn the second part of the dissertation\, we benchmark the performance of EEG-GNN and EEG-GAT against the AutoBayes framework. CNN’s have been frequently used to extract subject-invariant features from EEG for classification tasks\, but this approach holds the underlying assumption that electrodes are equidistant analogous to pixels of an image and hence fails to explore/exploit the complex functional neural connectivity between different electrode sites. We overcome this limitation by tailoring the concepts of convolution and pooling applied to 2D grid-like inputs for the functional network of electrode sites. Furthermore\, we develop various GNN models that project electrodes onto the nodes of a graph\, where the node features are represented as EEG channel samples collected over a trial\, and nodes can be connected by weighted/unweighted edges according to a flexible policy formulated by a neuroscientist. The empirical evaluations show that our proposed GNN-based framework\, EEG-GNN\, outperforms standard CNN classifiers across ErrP and RSVP datasets\, as well as allowing neuroscientific interpretability and explainability to deep learning methods tailored to EEG related classification problems. Besides that\, EEG-GAT employs multi-head attention mechanism in conjunction with the GNN architecture to learn the graph topology of observations instead of utilizing a graph shift operator that is heuristically constructed by a domain expert. This implicitly allows the exploration of the functional neural connectivity peculiar to a cognitive task between pairs of EEG electrode sites as well as EEG channel selection\, which is critical for reducing computational cost\, and designing portable EEG headsets.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-andac-demir/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220119T180000
DTEND;TZID=America/New_York:20220119T190000
DTSTAMP:20260521T034019
CREATED:20211215T192248Z
LAST-MODIFIED:20211215T192248Z
UID:29742-1642615200-1642618800@coe.northeastern.edu
SUMMARY:COE Global Co-op Info Session
DESCRIPTION:Join the College of Engineering Global Co-op team in learning about global co-op opportunities for Summer II/Fall 2022. \nTopics discussed will include: \n\nSearch techniques and global positions in your field\nWhat to consider when interested in a global co-op\nLogistics for moving and living abroad\nTips and resources for self-developing global positions\n\nAttendance to one of these sessions is required if you plan to do a global co-op in Summer II/Fall 2022. \nRSVP on the NUworks Events Calendar. \nPlease reach out to Sally Conant\, Global Co-op Coordinator\, s.conant@northeastern.edu or Kristina Kutsukos\, Global Co-op Coordinator\, k.kutsukos@northeastern.edu for additional information
URL:https://coe.northeastern.edu/event/coe-global-co-op-info-session-5/
LOCATION:Raytheon Amphitheater (240 Egan)\, 360 Huntington Ave\, 240 Egan\, Boston\, MA\, 02115\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220120T110000
DTEND;TZID=America/New_York:20220120T120000
DTSTAMP:20260521T034019
CREATED:20220106T144246Z
LAST-MODIFIED:20220106T144246Z
UID:29829-1642676400-1642680000@coe.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Vedant Sumaria
DESCRIPTION:PhD Proposal Review: Exploring Micro-Machined Glass Shell Resonators For Sensor Application \nVedant Sumaria \nLocation: Zoom \nAbstract: Optical resonators have been playing an important role in modern optics. They are fundamental in any laser device\, etalon for optical filtering\, accurate measurement for non-linear optics. Bulk optical resonators that use two or more mirrors are usually used in all branches of modern linear and non-linear optics. There are many limitations in using such systems because they cannot provide high performance (high quality (Q) factor) and their size\, weight\, and alignment\, creates stability problems. To solve these problems\, there was an emerging class of miniaturized dielectric cavity based optical resonators that exploited the light confinement phenomenon through internal reflection. These resonators have a circular symmetry\, and they sustain modes known as the Whispering Gallery Modes (WGM) that is nothing but electromagnetic waves that circulate and are confined within the structure. Fabrication of these dielectric optical resonators is simpler and comparatively inexpensive. They demonstrate higher mode stability and higher performance. \nIn this proposal review\, I will discuss the working principles of a WGM resonator and study the various loss mechanisms to improve the quality factor. Further I will discuss the fabrication of on chip glass-blown microspherical shell resonators. These on-chip spherical glass shells are micrometers to millimeters in diameter with ultra-smooth surfaces and micrometer wall thicknesses which can sustain optical resonance modes with high Q-factors up to 50 million. Further we discuss various methods used to etch the backside silicon to create a liquid core optical resonator. This etching leads to increase in the surface roughness leading to loss of resonance. We optimized etching methods and parameters to keep the resonance as high as 18 million. By etching the silicon resonator’s temperature sensitivity is improved from -1.15 GHz/K to 2.23 GHz/K. This optical WGM sensor is then novel biosensor consisting of a chip-scale whispering gallery mode resonators with High-Q factor and a micro-caloric system. The silicon released shell resonator is elastically coupled to a kapton tubing system. Temperature change in the system induces thermal expansion and thermorefractive changes which can be sensitively monitored through changes in the optical resonance characteristics. We demonstrate a measurement resolution less than 10mK and a method of measuring temperature change to eliminate background noise that shows a great potential for detection of various biomolecules such as urea. We also discuss the possibility to use the sensor as an extremely sensitive IR sensor. Finally\, we talk about the future work in immobilization of urease and glucose oxidase to test for analytes like urea and glucose with concentrations in micro-mole.
URL:https://coe.northeastern.edu/event/ece-phd-proposal-review-vedant-sumaria/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220120T153000
DTEND;TZID=America/New_York:20220120T163000
DTSTAMP:20260521T034019
CREATED:20220111T151529Z
LAST-MODIFIED:20220111T151529Z
UID:29839-1642692600-1642696200@coe.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Hamed Mohebbi Kalkhoran
DESCRIPTION:PhD Proposal Review: Machine Learning Approaches for Classification of Myriad Underwater Acoustic Events Over Continental-shelf Scale Regions with Passive Ocean Acoustic Waveguide Remote Sensing \nHamed Mohebbi Kalkhoran \nLocation: Zoom Link \nAbstract: Underwater acoustic data contain a myriad of sound sources that include bioacoustics related to marine life such as marine mammals and fishes; man-made such as ships\, sonar\, and airguns; as well as natural geophysical processes such as earthquake\, hurricane\, and volcanic eruption. Among underwater acoustic events\, marine mammal vocalization classification is one of the most challenging problems due to their transient broadband calls\, high variation in the calls of a specie (intra-class variation)\, and high similarity between the calls of some species. In this thesis\, we investigate machine learning approaches for classifying marine mammal vocalizations for real-time applications. We utilize acoustic data from a 160-element coherent hydrophone array and employ the passive ocean acoustic waveguide remote sensing technique to enable sensing and detections over instantaneous wide areas more than 100 km in diameter from the array. A variety of computational accelerating approaches\, combining hardware and software\, that make the methods desirable for real-time applications are also developed.\nHumpback whale behavior\, population distribution and structure can be inferred from long term underwater passive acoustic monitoring of their vocalizations. Here we employ machine learning approaches to classify humpback whale vocalizations into song and non-song calls. We use wavelet signal denoising and coherent array processing to enhance the signal-to-noise ratio. To build features vector for every time sequence of the beamformed signals\, we employ Bag of Words approach to time-frequency features. Finally\, we apply Support Vector Machine (SVM)\, Neural Networks\, and Naive Bayes to classify the acoustic data and compare their performances. Best results are obtained using Mel Frequency Cepstrum Coefficient (MFCC) features and SVM which leads to 94% accuracy and 72.73% F1-score for humpback whale song versus non-song vocalization classification.\nTo classify a large variety of whale species by their calls\, we extracted time-frequency features from Power Spectrogram Density (PSD) of the beamformed signals. Then we used these features to train three classifiers\, which are SVM\, Neural Networks\, and Random forest to classify six whale species: Fin\, Sei\, Blue\, Minke\, Humpback\, and general Odontocetes. Best results were obtained with Random forest classifier\, which achieved 95% accuracy\, and 85% F1 score. To detect transient sound sources\, first we applied Per-Channel Energy Normalization (PCEN) on the PSD of the beamformed signals. We applied thresholding on the PCEN data followed by morphological image opening to find potential sound sources and reduce noisy detections. Then we applied connected component analysis to obtain the final detected sounds for each bearing. To estimate the Direction of Arrival (DoA) of detected sounds\, we applied non-maximum suppression (NMS)\, which is widely used in object detection applications in computer vision\, on the detected sounds. We used mean power of each detected sound as the scores for NMS. To speed up the data processing\, we investigated a variety of accelerating approaches\, such as analyzing the effect of floating point precision\, applying parallel processing\, and implementing fast algorithms to run on GPU.
URL:https://coe.northeastern.edu/event/ece-phd-proposal-review-hamed-mohebbi-kalkhoran/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220121T150000
DTEND;TZID=America/New_York:20220121T160000
DTSTAMP:20260521T034019
CREATED:20211220T144658Z
LAST-MODIFIED:20211220T144658Z
UID:29778-1642777200-1642780800@coe.northeastern.edu
SUMMARY:Disability rights with Mrs. Christine Griffin
DESCRIPTION:Learn about disability rights with a Nationally recognized lawyer on Friday\, 21st January at 3 p.m. in ISEC 655\, 6th floor or virtually through zoom – https://northeastern.zoom.us/j/95320296228 \n 
URL:https://coe.northeastern.edu/event/disability-rights-with-mrs-christine-griffin/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220124T130000
DTEND;TZID=America/New_York:20220124T140000
DTSTAMP:20260521T034019
CREATED:20220118T182834Z
LAST-MODIFIED:20220118T182834Z
UID:29883-1643029200-1643032800@coe.northeastern.edu
SUMMARY:ECE Seminar: Nathan Lazarus
DESCRIPTION:ECE Seminar: Stretchable Magnetics for Soft Robotics \nNathan Lazarus \nLocation: Zoom Link \nAbstract: Recent innovations in making robots from softer biofriendly materials have opened broad new applications ranging from medicine to agriculture. Due to the reliance of much of the field on pneumatic actuation\, heavy and rigid pumps\, and control circuitry for driving pressure chambers have become a major limitation for fully soft\, untethered soft robots. In my talk\, I will discuss all aspects of creating soft electromagnets\, inductors and power circuits for electromagnetic actuation and power management in stretchable systems. Using unconventional materials like room temperature liquid metals and ferrofluids\, we demonstrate record performance for a stretchable inductor. These stretchable inductors are then used to create flexible and stretchable pumps with flow rates nearly two orders of magnitude higher than past demonstrations in the literature and integrated into a simple soft robot demonstrator. \nBio: Nathan Lazarus has worked extensively in areas ranging from mixed signal electronics to MEMS fabrication\, with his Ph.D. at Carnegie Mellon culminating in 2012 with the demonstration of the highest recorded fractional sensitivity to date for a capacitive chemical sensor topology integrated with CMOS electronics. Since joining US Army Research Laboratory in May 2012\, Dr. Lazarus’s research has focused on stretchable power electronics\, soft robotics and 3D printing. He has received numerous awards including ARL’s Honorary Award for Engineering and the Rookie of the Year Excellence in Federal Career Award (Gold) from the Baltimore Federal Executive Board. In 2019\, Dr. Lazarus was selected for the Presidential Early Career Award for Scientists and Engineers (PECASE)\, the highest honor given by the US government for researchers beginning their independent research careers.
URL:https://coe.northeastern.edu/event/ece-seminar-nathan-lazarus/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220125T120000
DTEND;TZID=America/New_York:20220125T130000
DTSTAMP:20260521T034019
CREATED:20211215T192318Z
LAST-MODIFIED:20211215T192318Z
UID:29746-1643112000-1643115600@coe.northeastern.edu
SUMMARY:COE Global Co-op Info Session
DESCRIPTION:Join the College of Engineering Global Co-op team in learning about global co-op opportunities for Summer II/Fall 2022. \nTopics discussed will include: \n\nSearch techniques and global positions in your field\nWhat to consider when interested in a global co-op\nLogistics for moving and living abroad\nTips and resources for self-developing global positions\n\nAttendance to one of these sessions is required if you plan to do a global co-op in Summer II/Fall 2022. \nRSVP on the NUworks Events Calendar. Location- Curry Student Center 333. \nPlease reach out to Sally Conant\, Global Co-op Coordinator\, s.conant@northeastern.edu or Kristina Kutsukos\, Global Co-op Coordinator\, k.kutsukos@northeastern.edu for additional information.
URL:https://coe.northeastern.edu/event/coe-global-co-op-info-session-6/
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:20220126T120000
DTEND;TZID=America/New_York:20220126T130000
DTSTAMP:20260521T034019
CREATED:20220120T190850Z
LAST-MODIFIED:20220120T202413Z
UID:29913-1643198400-1643202000@coe.northeastern.edu
SUMMARY:Materials Exhibiting Biomimetic Carbon Fixation: Kinetic Analysis\, Mechanistic Insights\, and Material Design
DESCRIPTION:ChE Seminar Series Presents: \nDorsa Parviz\, Ph.D. \nDepartment of Chemical Engineering\, Massachusetts Institute of Technology \n Abstract: \nPopulation growth and climate change necessitate a paradigm shift from current chemical and materials production methods to more sustainable approaches with a negative carbon footprint. In view of this\, I will introduce carbon fixing materials (CFM) as a new synthetic platform that\, like plants\, utilize sunlight to photocatalytically reduce ambient CO2 and add to an ever-extending carbon backbone. First\, I will describe a mathematical framework enveloping the main functions of carbon fixing materials to answer basic questions about the kinetics regimes of operation\, photocatalytic requirements\, and limits of functional materials in CFMs. I will also present mechanistic insights on the photocatalytic reduction of CO2 to C1 intermediates as desired intermediates for producing value-added products from CO2. In the second part of my talk\, I will focus on state-of-the-art 2D nanomaterials and strategies for surface engineering these materials in the colloidal state\, addressing challenges in their characterization for applications in photocatalysis. \nBio: \nDorsa Parviz is a postdoctoral researcher at the Massachusetts Institute of Technology\, working with Prof. Michael Strano in the Department of Chemical Engineering. She earned her Ph.D. in 2016 from Texas A&M University under the guidance of Prof. Micah Green\, where she pioneered techniques for high-yield production of 2D nanomaterials\, investigated their colloidal interactions and assembly\, and designed tailored nanosheet-based polymer composites and 3D networks for structural and electrode applications. During her postdoc\, she developed carbon fixing materials at MIT\, establishing a high-throughput photocatalytic reaction screening system to accomplish this vision. In addition\, she has led the research on the preparation and characterization of biocompatible engineered 2D nanomaterials with tailored structure and properties for nanotoxicity studies at NIEHS Nanosafety Center. \nIf unable to attend in person\, please contact a.ramsey@northeastern.edu for the seminar link.
URL:https://coe.northeastern.edu/event/materials-exhibiting-biomimetic-carbon-fixation-kinetic-analysis-mechanistic-insights-and-material-design/
LOCATION:024 East Village\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
GEO:42.3396156;-71.0886534
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=024 East Village 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:20220127T110000
DTEND;TZID=America/New_York:20220127T120000
DTSTAMP:20260521T034019
CREATED:20220125T181649Z
LAST-MODIFIED:20220125T181649Z
UID:29951-1643281200-1643284800@coe.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Meruyert Assylbekova
DESCRIPTION:PhD Proposal Review: Aluminum Nitride and Scandium-doped Aluminum Nitride Materials and Devices for Beyond 6 GHz Communication \nMeruyert Assylbekova \nLocation: Zoom Link \nAbstract: With almost all of the sub-­6 GHz spectrum now being allocated\, current bandwidth shortage has motivated the exploration of untapped frequencies beyond 6 GHz for future broadband wireless communication. Shift to higher frequency spectra is expected to deliver a significant performance improvement in network capacity\, data rates\, latency\, and coverage. These refinements will enable the development of new life­changing technologies such as Vehicle to Everything (V2V to V2X)\, ubiquitous Internet of Things (IoT)\, and Augmented and Virtual reality (AR and VR). Among a variety of novel 5G applications\, the implementation of 5G mobile broadband imposes especially demanding specifications on Radio Frequency Front­End (RFFE) architectures. 5G smartphones are expected to carry over the legacy sub-­6 GHz bands\, which translates into an increased number of filters.\nIn this context\, the first part of this work will introduce lithographically defined Aluminum Nitride (AlN) piezoelectric microacoustic resonators as a promising solution for the implementation of future minituarized adaptive RFFEs.\nWhile AlN has been a material of choice for acoustic filters for over two decades\, future technologies are calling for a material with superior piezoelectric strength. It has been shown that the piezoelectric activity of AlN can be enhanced by partially substituting Al with Sc to form AlScN. Thus\, the second part of this work will explore material properties of AlScN along with the challenges that need to be addressed to take full advantage of its piezoelectric and ferroelectric strength. Last\, AlScN resonators and filters will be demonstrated as promising candidates for the future beyond 6GHz technologies.
URL:https://coe.northeastern.edu/event/ece-phd-proposal-review-meruyert-assylbekova/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220127T170000
DTEND;TZID=America/New_York:20220127T180000
DTSTAMP:20260521T034019
CREATED:20211215T192337Z
LAST-MODIFIED:20211215T192337Z
UID:29749-1643302800-1643306400@coe.northeastern.edu
SUMMARY:COE Global Co-op Info Session
DESCRIPTION:Join the College of Engineering Global Co-op team in learning about global co-op opportunities for Summer II/Fall 2022. \nTopics discussed will include: \n\nSearch techniques and global positions in your field\nWhat to consider when interested in a global co-op\nLogistics for moving and living abroad\nTips and resources for self-developing global positions\n\nAttendance to one of these sessions is required if you plan to do a global co-op in Summer II/Fall 2022. \nRSVP on the NUworks Events Calendar. Location- Curry Student Center 333. \nPlease reach out to Sally Conant\, Global Co-op Coordinator\, s.conant@northeastern.edu or Kristina Kutsukos\, Global Co-op Coordinator\, k.kutsukos@northeastern.edu for additional information
URL:https://coe.northeastern.edu/event/coe-global-co-op-info-session-7/
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:20220127T170000
DTEND;TZID=America/New_York:20220127T180000
DTSTAMP:20260521T034019
CREATED:20220124T145449Z
LAST-MODIFIED:20220124T145449Z
UID:29926-1643302800-1643306400@coe.northeastern.edu
SUMMARY:How to Make Compelling Figures- a Data Visualization Workshop
DESCRIPTION:In this interactive virtual workshop\, we’ll walk you through the steps of creating and revising compelling data visualizations and graphics. \nTo get the most out of the workshop\, please bring a visualization that you would like to improve and use in your own work. The visualization can be anything from a table of numbers\, to a graph\, to an illustration or diagram. The visualization does not have to be polished and can even be an informal sketch of a visual you would like to make in the future. \nClick HERE to Register via Zoom \nThis workshop is sponsored by the CommLab and presented by Kate Kryder\, the Data Analysis and Visualization Specialist at Northeastern University Library.
URL:https://coe.northeastern.edu/event/how-to-make-compelling-figures-a-data-visualization-workshop/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220127T173000
DTEND;TZID=America/New_York:20220127T190000
DTSTAMP:20260521T034019
CREATED:20220124T145646Z
LAST-MODIFIED:20220124T145646Z
UID:29921-1643304600-1643310000@coe.northeastern.edu
SUMMARY:Galante Engineering Business Program Info Session
DESCRIPTION:Northeastern University’s Galante Engineering Business Program offers a progressive opportunity for engineering students to complement their technical engineering education with business skills by earning a graduate certificate in engineering business. Galante is founded on the values of student engagement and leadership to strengthen interpersonal and professional skills. Programmatic elements are offered to students such as workshops\, speaker series\, site visits\, seminars\, and other related personal and professional development activities as a connected cohort. \nThe Info Session Event is an opportunity for CoE students to learn about the Galante Engineering Business Program\, the opportunities it provides\, the benefits offered\, the application process\, and more. This event will be hosted on Thursday\, January 27th (01/27/22) from 5:30-7:00pm in Egan 440. Attire is business casual. Please be sure to RSVP\, and please be sure to reach out to Program Assistant Bradley Miller (b.miller@northeastern.edu) for questions and additional information\, or visit our website.
URL:https://coe.northeastern.edu/event/galante-engineering-business-program-info-session/
END:VEVENT
END:VCALENDAR