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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221003T130000
DTEND;TZID=America/New_York:20221003T140000
DTSTAMP:20260617T172220
CREATED:20221103T185029Z
LAST-MODIFIED:20221103T185029Z
UID:5928-1664802000-1664805600@ece.northeastern.edu
SUMMARY:Vedant Sumaria's PhD Dissertation Defense
DESCRIPTION:“Exploring Micro-Machined Glass Shell Resonators For Sensor Application” \nAbstract: \nThis work presents the exploration of the chip-scale glass blowing technique for novel sensing methods. On-chip microspherical glass shells (MSG) of hundreds of micrometers in diameter with ultra-smooth surfaces and sub-micrometer wall thicknesses have been fabricated and have been shown to sustain optical whispering gallery mode resonance with high Q-factors of greater than 25 million. These resonators exhibit a temperature sensitivity of 1.17 GHz K−1 and can be configured as ultra-high-sensitivity thermal sensors for a wide range of applications. \nWe demonstrate a thermal infrared (IR) detector based on a high-quality factor (Q) whispering gallery mode (WGM) borosilicate glass microspherical shell resonator and investigate its performance in detecting IR radiation in the wavelength range of 1 –20 μ m. The resonator exhibits a temperature sensitivity of 1.17 GHz/K with a Q-factor of 3 million and can be configured as a high-sensitivity infrared sensor. The microspherical shell IR sensor achieved a noise-equivalent power (NEP) of 944.89 pW/√Hz experimentally. A laser Doppler vibrometer (LDV) is used to measure the physical expansion of the microspherical glass resonator when IR radiation is absorbed. A dimensional change of ≈100 fm is shown to be resolved. \nA comparison of two calorimetric biosensing systems with relatively high-throughput sample analysis is also reported. The calorimetric biosensor system compared are a thin (20 μm) micro-machined Y-cut quartz crystal resonator (QCR) and a MSG (6 μm thick) Whispering Gallery Mode (WGM) resonator as a temperature sensor placed close to a chemical reaction chamber with the immobilized enzyme. The enzymes (urease and glucose oxidase) were immobilized on superparamagnetic nanoparticles using covalent bonding. This configuration enables a sensing system where the reaction chamber is physically separated from the analyte solution of interest and thereby free from fouling effects typically associated with biochemical reactions occurring on the sensor surface. The performance of this biosensing system is compared by the detection of 0-250 mM urea and glucose in phosphate buffer. \nFurther\, we present MSG WGM resonator-based thermal sensor array which is configured with a 3D printed reaction chamber that utilizes the backside silicon of the resonator for sensitive calorimetric biosensing applications. The coupling of heat from the reaction chamber to the WGM resonator is achieved via conduction from the analyte medium. The sensor was aligned to the opening of the 3D-printed reaction chamber\, and the device was mounted using a thermo-elastic epoxy. This sensor configuration allows for a very robust sensing platform with no fouling of the sensor surface or degradation in its performance metrics. Resonance frequency tracking using the Pound-Drever- Hall locking method was used for enzymatic activity measurements. Results of the catalytic reaction of glucose with glucose oxidase and the hydrolysis reaction of urea by urease are reported. In addition\, body fluids such as blood plasma\, serum\, and blister fluid are tested\, which match very well with the experimental results. From the analysis of the signal-to-noise ratio of the glucose sensor\, a resolution of 100 nM could be obtained\, improving the detection limit by a factor of 10\,000 compared to QCR sensors. \nCommittee: \nProf. Srinivas Tadigadapa (Advisor) \nProf. Matteo Rinaldi \nProf. Yongmin Liu \nProf. Rosemary Smith
URL:https://ece.northeastern.edu/event/vedant-sumarias-phd-dissertation-defense/
LOCATION:442 Dana\, 360 Huntington Ave\, 442 DA\, Boston\, MA\, 02115\, United States
GEO:42.3387508;-71.0923044
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=442 Dana 360 Huntington Ave 442 DA Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave\, 442 DA:geo:-71.0923044,42.3387508
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221006T183000
DTEND;TZID=America/New_York:20221006T200000
DTSTAMP:20260617T172220
CREATED:20220913T195118Z
LAST-MODIFIED:20220913T195237Z
UID:5805-1665081000-1665086400@ece.northeastern.edu
SUMMARY:COE Selecting a Major Panel
DESCRIPTION:Not sure what to major in?\nConsidering switching majors? \nHear upperclassmen across all engineering disciplines share about their experiences! \nJoin via Microsoft Teams using your NU email \nEmail Liza Russell at russell.li@northeastern.edu for more information or to receive the link by email
URL:https://ece.northeastern.edu/event/coe-selecting-a-major-panel/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221014T150000
DTEND;TZID=America/New_York:20221014T160000
DTSTAMP:20260617T172220
CREATED:20221103T191427Z
LAST-MODIFIED:20221103T191427Z
UID:5932-1665759600-1665763200@ece.northeastern.edu
SUMMARY:Meruyert Assylbekova's PhD Dissertation Defense
DESCRIPTION:“Aluminum Nitride and Scandium-doped Aluminum Nitride materials and devices for beyond 6 GHz communication” \nAbstract: \nWith 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 life¬changing 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 Front¬End (RFFE) architectures. 5G smartphones are expected to carry over the legacy sub-¬6 GHz bands\, which translates into an increased number of filters. In 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. While 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. \nCommittee: \nProf. Matteo Rinaldi (advisor) \nProf. Nicol McGruer \nProf. Cristian Cassella
URL:https://ece.northeastern.edu/event/meruyert-assylbekovas-phd-dissertation-defense/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221017T130000
DTEND;TZID=America/New_York:20221017T140000
DTSTAMP:20260617T172220
CREATED:20221103T191510Z
LAST-MODIFIED:20221103T191510Z
UID:5934-1666011600-1666015200@ece.northeastern.edu
SUMMARY:Sila Deniz Calisgan's PhD Dissertation Defense
DESCRIPTION:“ADVANCEMENTS ON ZERO STANDBY POWER MEMS SENSORS” \nAbstract: \nDue to the fast development of the internet of things\, and unattended wireless sensor networks\, the number of connected devices worldwide is expected to increase exponentially in the future. In order to maintain such large networks of physical and virtual objects\, there is a need for sensors\, actuators and devices with dimensions and power consumption that are orders of magnitude smaller than the state-of-the-art. Currently no existing technology could enable the implementation of large-scale wireless sensor networks in remote locations due to the prohibitive cost associated with installation and maintenance. The fundamental technical challenge lies in the continuous power consumption of state-of-the-art sensor technologies: Commercially available sensors are not smart enough to identify targets of interest without consuming any power and rely on active electronics to detect and discriminate signal of interest. Therefore\, they consume power continuously to monitor the environment even when there is no relevant data to be detected\, which results in a short battery lifetime limited to very few months. This dissertation presents improvements on a new class of zero-power microsystems that fundamentally break the paradigm\, with zero-power consumption\, until awakened by a specific physical signature. This approach is applied to multiple sensing modalities. In particular\, I have experimentally demonstrated zero-power wireless sensors triggered by different physical and chemical quantities such as: infrared radiation; radio frequency signals; acoustic signals and volatile organic chemicals. The capabilities of the zero-power sensors result in a nearly unlimited duration of operation\, with a groundbreaking impact on the proliferation of the internet of things. \n  \nCommittee: \nProf. Matteo Rinaldi (Advisor)Prof. Marilyn MinusProf. Srinivas TadigadapaProf. Zhenyun Qian
URL:https://ece.northeastern.edu/event/sila-deniz-calisgans-phd-dissertation-defense/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221018T100000
DTEND;TZID=America/New_York:20221018T110000
DTSTAMP:20260617T172220
CREATED:20220906T174215Z
LAST-MODIFIED:20220906T174215Z
UID:5790-1666087200-1666090800@ece.northeastern.edu
SUMMARY:GSE Wonder Week: Learn about Electrical + Computer Engineering
DESCRIPTION:Join this webinar to learn more about Electrical & Computer Engineering Graduate Programs
URL:https://ece.northeastern.edu/event/gse-wonder-week-learn-about-electrical-computer-engineering/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221020T120000
DTEND;TZID=America/New_York:20221020T130000
DTSTAMP:20260617T172220
CREATED:20221103T191700Z
LAST-MODIFIED:20221103T191700Z
UID:5936-1666267200-1666270800@ece.northeastern.edu
SUMMARY:Neset Unver Akmandor's PhD Proposal Review
DESCRIPTION:“Improving Computational Efficiency of Motion Planning Algorithms for Mobile and Time-Dependent Robotic Tasks in Dynamic Environments” \nAbstract: \nRobots will become a part of our lives at home as personal assistants. Although their current functionality is highly restricted to specific tasks and environments\, their practicality encourages robotics engineers for further advancement. Especially\, mobile robots with manipulation capabilities have a huge potential to support humans in physically demanding workplaces\, such as warehouses and hospitals. Considering the complexity of the human level tasks and the dynamic settings\, the state-of-the-art robot motion planning methods need to be improved in terms of their computational efficiency. To contribute on closing the gap\, this proposal presents three novelties whose applications focus on mobile robots in dynamic environments. First\, we introduce a reactive navigation framework in 3D workspaces. The proposed approach does not rely on the global map information and achieves fast navigation by employing motion primitives and their heuristic evaluations on the-fly. Second\, we present a Deep Reinforcement Learning based navigation approach in which we define the occupancy observations as heuristic evaluations of motion primitives\, rather than using raw sensor data. It utilizes occupancy observations in different data structures to analyze their effects on both training process and navigation performance. We train and test our methodology on two different robots within challenging physics-based simulation environments including static and dynamic obstacles. Finally\, we propose a computationally efficient framework for trajectory planning for robots with high degrees-of freedom while adapting its system model\, constraints and time-dependent target state using the latest information from the dynamic environment. \n  \nCommittee: \nDr. Taskin Padir (Advisor)Dr. Pau ClosasDr. Michael EverettDr. Erdal Kayacan
URL:https://ece.northeastern.edu/event/neset-unver-akmandors-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221020T180000
DTEND;TZID=America/New_York:20221020T190000
DTSTAMP:20260617T172220
CREATED:20220831T190823Z
LAST-MODIFIED:20220831T190823Z
UID:5787-1666288800-1666292400@ece.northeastern.edu
SUMMARY:PlusOne Information Session
DESCRIPTION:Learn about the PlusOne Accelerated Master’s Degree Program \nA master’s degree can provide you with an additional level of expertise in an area aligned with your career goals. As a currently enrolled Bachelor of Science (BS) student in the College of Engineering at Northeastern\, you have the opportunity to earn a Master of Science degree (MS) in an accelerated time period with the PlusOne program. Once accepted into the program in an approved PlusOne pathway\, which is a BS and MS PlusOne combination\, you can earn an MS degree with\, in most cases\, just one extra year of study beyond your undergraduate degree program. \nIn this virtual information session\, College of Engineering undergraduate and graduate academic advisors will provide an overview of the PlusOne program to give you the knowledge and next steps to take advantage of the program if you choose. \nWHAT YOU WILL LEARN:\n• What is PlusOne\n• Benefits of the program\n• Eligibility\n• Co-op considerations\n• Financial considerations\n• Selecting your pathway\n• Academic advising resources\n• Timeline to apply\n• The application process\n• Course registration\n• Transitioning to graduate school \nLearn more and apply: coe.northeastern.edu/plusone
URL:https://ece.northeastern.edu/event/plusone-information-session-3/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221024T110000
DTEND;TZID=America/New_York:20221024T120000
DTSTAMP:20260617T172220
CREATED:20221103T213221Z
LAST-MODIFIED:20221103T213221Z
UID:5940-1666609200-1666612800@ece.northeastern.edu
SUMMARY:Yixuan He's PhD Proposal Review
DESCRIPTION:Committee: \nProf. Yong-Bin Kim. Advisor \nProf. Marvin Onabajo \nProf. Lombardi Fabrizio \n  \nAbstract: \nIn order to match the needs of powerful neural networks and meet the hard constraints from hardware\, binary neural networks are treated as hardware-friendly deep learning algorithms due to the fact that it can achieve similar inference accuracy with fewer computing resources comparing to traditional convolutional neural networks. As for its VLSI implementations\, the computing-in-memory (CIM) technology has been proved to solve the memory-wall bottleneck problem shown in traditional von Neumann machine and can be a perfect choice to implement neural networks with binary data. Therefore\, this work proposes a novel time-domain computing-in-memory core that implements XNOR-and-accumulate of binary neural networks with all-digital elements. This new technique uses 8T-SRAM cells to perform XNOR operations inside memory array and accumulates the related XNOR output values in time-domain with specialized racing structures and delay lines. The circuit is built and simulated in Cadence using Samsung 65nm CMOS technology with 1V power supply. The results show correct functionality\, 2730 GOPS throughput and 431 TOPS/W power efficiency. With further exploration\, the time-domain computation can be a new candidate in the field of in-memory-computing for deep learning applications since it has its own superiorities in terms of throughput\, power efficiency in comparison to other mixed-signal or traditional digital methods.
URL:https://ece.northeastern.edu/event/yixuan-hes-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221028T130000
DTEND;TZID=America/New_York:20221028T140000
DTSTAMP:20260617T172220
CREATED:20221103T213401Z
LAST-MODIFIED:20221103T213401Z
UID:5944-1666962000-1666965600@ece.northeastern.edu
SUMMARY:Guillem Reus Muns' PhD Proposal Review
DESCRIPTION:Location: ISEC 332 \n“AI for communications and sensing in RF environments” \nAbstract: \nThe recent growth of Internet of Things (IoT)\, as well as other new revolutionary applications utilizing wireless spectrum are leading the way towards realization of next generation wireless systems that jointly utilize communications and sensing. However\, such systems offer many degrees of freedom\, and optimizing them for a specific task is difficult to accomplish with deterministic and classical approaches. For this reason\, data-driven and AI-based methods have been pursued actively by the research community\, as they are able to find solutions that often come close to or exceed the performance of the deterministic counterparts with a fractional execution complexity. This thesis presents\, through real systems and with experimental validation\, our progressive efforts in three broad areas\, where AI enables the operation of aerial and terrestrial systems that combine sensing and communications. This dissertation explores the following key use cases with distinct contributions made in each: \ni) Sensing-aided communications for air and ground systems. First\, we present a UAV communication method that defines constellation points in space that map to transmitter frequency bands and are detected at the Base Station using millimeter wave sensors. Second\, we explore alternative vehicle-to-infrastructure mmWave beamforming methods\, leveraging a) vehicle position and velocity estimation using in-band standard compliant 802.11ad radar and b) camera images and GPS location information.\nii) Signal classification using communication signals\, where we propose a) a UAV classification method using uniquely UAV-transmitted signals and b) an RF fingerprinting technique that improves class separation by combining triplet loss with regular classification techniques.\niii) ‘AirFC’\, an over-the-air computation method that implements fully connected neural networks inference leveraging multi-antenna systems. \nFinally\, the proposed work will address challenges in the CBRS band\, where a tiered structure is implemented to access the spectrum. Hence\, continuous sensing is needed to make sure that radar (tier 1) is not interfered by cellular systems (tier 2). Here\, we propose reusing the already existing cellular infrastructure to act as a radar detector\, which enhances their functionality to go beyond that of regular wireless communications. \nCommittee: \nProf. Kaushik Chowdhury (Advisor) \nProf. Hanumant Singh \nProf. Stratis Ioannidis
URL:https://ece.northeastern.edu/event/guillem-reus-muns-phd-proposal-review/
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