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DTSTART;TZID=America/New_York:20220608T090000
DTEND;TZID=America/New_York:20220608T100000
DTSTAMP:20260514T005256
CREATED:20220505T214421Z
LAST-MODIFIED:20220505T214421Z
UID:31373-1654678800-1654682400@coe.northeastern.edu
SUMMARY:Gordon Institute Virtual Information Session
DESCRIPTION:Learn how you can earn a Graduate Certificate in Engineering Leadership as a stand-alone certificate or in combination with one of twenty Master of Science degrees offered through Northeastern’s College of Engineering\, College of Science\, or Khoury College of Computer Sciences.  \nThe National Academy of Engineering recognized The Gordon Institute of Engineering Leadership (GIEL)for its innovative curriculum that combines technical education\, leadership capabilities\, and the “Challenge Project”: an opportunity for students to receive master’s level credit while working in industry.  \nBy aligning technical proficiency with leadership capabilities\, GIEL accelerates the development of high-potential engineers and prepares them to lead complex projects early in their careers. Upon completion of the program\, more than 88% of the 2020 class reported increased leadership responsibility\, while more than 50% of the 2020 class reported being promoted within one year of graduation.  \nOur Director of Admissions will be directly answering your application questions for Fall 2022.  \nYou will have the opportunity to hear from Alumni on how The Gordon Institute propelled their engineering careers. Program professors will also be present to answer curriculum questions. 
URL:https://coe.northeastern.edu/event/gordon-institute-virtual-information-session-5/
ORGANIZER;CN="Gordon Engineering Leadership program":MAILTO:gordonleadership@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220608T093000
DTEND;TZID=America/New_York:20220608T103000
DTSTAMP:20260514T005256
CREATED:20221103T143939Z
LAST-MODIFIED:20221103T143939Z
UID:34080-1654680600-1654684200@coe.northeastern.edu
SUMMARY:Ziyue Xu's PhD Proposal Review
DESCRIPTION:“High Efficiency RF Energy Harvesting and Power Management Circuits Techniques for IoT Application” \nAbstract: \nAs the number of Internet of Things (IoT) devices is continuing to grow\, there is a need that a significant percentage these devices operate at ultra-low power (ULP) levels\, either using harvested energy or using a small battery with a long lifetime. Energy harvesting techniques can help to achieve long lifetimes\, but the system should be able to operate efficiently with a small amount of harvested energy and often from low voltages. Energy harvesting from solar\, thermal\, vibration\, and radio-frequency (RF) are increasingly being used to realize batteryless operation for IoT and biomedical applications. A typical multi-input energy harvesting system including multiple energy transducers\, maximum power point tracking (MPPT)\, matching network (MN)\, and DC-DC converter. Solar cells and thermoelectric generators have a few mV to hundreds of mV open-circuit voltage that require maximum power tracking to make sure the optimal power extraction is achieved. The piezoelectric transducer is modeled as AC source with internal resistance from 10s Ω to kΩ that requires AC-DC conversion\, known as rectification to better use the energy. And the following DC-DC regulation stage is to regulate the output voltage to deal with the sudden change of the load or the input voltage drop. Among these techniques\, RF energy harvesting system is particularly promising for biomedical and IoT devices where other sources are not readily available. Several of these applications are utilizing widely used WiFi and Bluetooth low-energy (BLE) communication standards. These applications along with the wirelessly-powered neural implantable medical devices (n-IMD) for neural stimulation and recording are also benefiting from ultra-low power (ULP) circuits and systems design advancements. Since the available RF power decreases rapidly with distance\, it is desirable to design rectifiers that are able to operate with low incident power. This Ph.D. proposal presents a simplified design approach and analysis of RF energy harvesting rectifiers for different design objectives. The proposal also includes the design of a new self-biased gate (SBG) rectifier with a non-linear gate biasing technique. At lower power levels\, the SBG rectifier drops the entirety of output voltage to create a higher gate bias. However\, to address the issue of leakage at higher input power levels\, the gate-biasing technique drops only a fraction of the output voltage. This approach helps to realize high efficiency across input power range. The fully integrated\, high-efficiency SBG-based RF energy harvesting circuit can also provide a high output voltage of 9.3 V with a 30% end-to-end efficiency (PHE). Further\, to enhance the available RF energy to a remotely located RF energy receiver\, the proposal presents a highly efficient distributed RF beamforming technique. To improve the power delivery in the downstream power management circuits\, a boost converter architecture that can reduce switching noise injection by changing its switching frequency is also presented. The associated power management system includes a boost converter operating in DCM\, FVC and a digital control loop. The system is capable of providing a stable 1V supply for RF receiver front-ends with very low performance impact. \n  \nCommittee Members: \nProf. Aatmesh Shrivastava (Advisor) \nProf. Marvin Onabajo \nProf. Nian X. Sun
URL:https://coe.northeastern.edu/event/ziyue-xus-phd-proposal-review/
LOCATION:432 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
GEO:42.3396156;-71.0886534
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220609T150000
DTEND;TZID=America/New_York:20220609T190000
DTSTAMP:20260514T005256
CREATED:20220308T192014Z
LAST-MODIFIED:20220308T192014Z
UID:30664-1654786800-1654801200@coe.northeastern.edu
SUMMARY:FPP Virtual Fair STEM - LATAM
DESCRIPTION:Representatives from Northeastern University Graduate School of Engineering will be participating in this event.
URL:https://coe.northeastern.edu/event/fpp-virtual-fair-stem-latam/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220616T120000
DTEND;TZID=America/New_York:20220616T130000
DTSTAMP:20260514T005256
CREATED:20221103T143733Z
LAST-MODIFIED:20221103T143733Z
UID:34086-1655380800-1655384400@coe.northeastern.edu
SUMMARY:Hussein Hussein's PhD Proposal Review
DESCRIPTION:“Parametric Circuits for Enhanced Sensing and RF Signal Processing” \nAbstract: \nMassive deployments of wireless sensor nodes (WSNs) that continuously detect physical\, biological or chemical parameters are needed to truly benefit from the unprecedented possibilities opened by the Internet‑of‑Things (IoT). Just recently\, new sensors with higher sensitivities have been demonstrated by leveraging advanced on‑chip designs and microfabrication processes. Yet\, WSNs using such sensors require energy to transmit the sensed information. Consequently\, they either contain batteries that need to be periodically replaced or energy harvesting circuits whose low efficiencies prevent a frequent and continuous sensing\, even impacting the maximum range of communication. Here\, we discuss a new battery-less and harvester-free remote sensing tag\, namely the subharmonic tag (SubHT)\, leveraging unique nonlinear characteristics to fundamentally break any previous paradigms for passive WSNs. SubHT can sense and transmit information without requiring supplied or harvested DC power. Also\, it transmits the sensed information at a difference frequency from the one of its interrogation signal\, rendering its reader immune from multi-path\, from clutter and from its own self‑interference. Also\, even though SubHT may not require any advanced and expensive manufacturing\, its unique nonlinear response enables extraordinary high sensitivities and dynamic ranges that can even surpass those achieved by the most advanced on-chip sensors. More interestingly\, SubHT can be even configured to operate in a “threshold sensing” mode\, making it able to respond to any interrogation signal only when the sensed parameter has exceeded a remotely reprogrammable threshold\, as well as to memorize any violation in a sensed parameter without requiring any memory components. In this talk\, the first SubHT prototypes for temperature sensing will be showcased. Even more\, we will show how including high quality factor (Q) resonators in a SubHT’s network allows to implement even more functionalities\, such as the long-range identification or tracking of any items or localization and navigation in a GPS denied environment. Yet\, the dynamics exploited by SubHT can also be leveraged to address various needs along radio-frequency (RF) chains. In this regard\, we show how the SubHT’s nonlinear dynamics can be leveraged to build components\, such as parametric filters\, frequency selective limiters and signal to noise enhancers\, that improve the stability of RF frequency synthesizers and instinctually suppress co-site or self-interferes\, paving an unprecedented path towards integrated radios with improved performance and longer battery-life time. \nCommittee: \nProf. Cristian Cassella (Advisor)\nProf. Marvin Onabajo\nProf. Matteo Rinaldi\nProf. Andrea Alù
URL:https://coe.northeastern.edu/event/hussein-husseins-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220622T173000
DTEND;TZID=America/New_York:20220622T183000
DTSTAMP:20260514T005256
CREATED:20220505T214355Z
LAST-MODIFIED:20220505T214355Z
UID:31375-1655919000-1655922600@coe.northeastern.edu
SUMMARY:Gordon Institute Virtual Information Session
DESCRIPTION:Learn how you can earn a Graduate Certificate in Engineering Leadership as a stand-alone certificate or in combination with one of twenty Master of Science degrees offered through Northeastern’s College of Engineering\, College of Science\, or Khoury College of Computer Sciences.  \nThe National Academy of Engineering recognized The Gordon Institute of Engineering Leadership (GIEL)for its innovative curriculum that combines technical education\, leadership capabilities\, and the “Challenge Project”: an opportunity for students to receive master’s level credit while working in industry.  \nBy aligning technical proficiency with leadership capabilities\, GIEL accelerates the development of high-potential engineers and prepares them to lead complex projects early in their careers. Upon completion of the program\, more than 88% of the 2020 class reported increased leadership responsibility\, while more than 50% of the 2020 class reported being promoted within one year of graduation.  \nOur Director of Admissions will be directly answering your application questions for Fall 2022.  \nYou will have the opportunity to hear from Alumni on how The Gordon Institute propelled their engineering careers. Program professors will also be present to answer curriculum questions. 
URL:https://coe.northeastern.edu/event/gordon-institute-virtual-information-session-4/
ORGANIZER;CN="Gordon Engineering Leadership program":MAILTO:gordonleadership@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220623T100000
DTEND;TZID=America/New_York:20220623T110000
DTSTAMP:20260514T005256
CREATED:20220526T204748Z
LAST-MODIFIED:20220526T204748Z
UID:31536-1655978400-1655982000@coe.northeastern.edu
SUMMARY:A Conversational Webinar on MS DAE in Vancouver
DESCRIPTION:A conversational webinar regarding the Masters in Data Analytics & Engineering program in Vancouver.
URL:https://coe.northeastern.edu/event/a-conversational-webinar-on-ms-dae-in-vancouver/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220627T140000
DTEND;TZID=America/New_York:20220627T150000
DTSTAMP:20260514T005256
CREATED:20221103T143838Z
LAST-MODIFIED:20221103T143838Z
UID:34083-1656338400-1656342000@coe.northeastern.edu
SUMMARY:Xiaolong Ma's PhD Dissertation Defense
DESCRIPTION:“Towards Efficient Deep Neural Network Execution with Model Compression and Platform-specific Optimization” \nAbstract: \nDeep learning or deep neural network (DNN)\, as one of the most powerful machine learning techniques\, has become the fundamental element and core enabler of the artificial intelligence. Many incredible\, bleeding-edge applications\, such as community/shared virtual reality experiences and self-driving cars\, will crucially rely on the ubiquitous availability and real-time executability of the high-quality deep learning models. Among the variety of the AI-associated platforms\, mobile and embedded computing devices have become key carriers of deep learning to facilitate the widespread of machine intelligence. In this talk\, I will first focus on a compression-compilation co-design method that deploy a unique sparse model on an off-the-shelf mobile device with real-time execution speed. This method advances the state-of-the-art by introducing a new dimension\, fine-grained pruning patterns inside the coarse-grained structures\, revealing a previously unknown point in the design space. The designed patterns are interpretable\, and can be obtained by a fully automatic pattern-aware pruning framework that achieves pattern library extraction\, pattern assignment (pruning) and weight training simultaneously. With the higher accuracy enabled by fine-grained pruning patterns\, the unique insight is to use the compiler to re-gain and guarantee high hardware efficiency. We take a step forward by considering a more practical scenario\, that the deployment-execution mode for AI tasks no longer satisfy the user preference\, and enabling edge training becomes inevitable since it promotes much better personalized intelligent services while strengthen users’ privacy by avoiding data egress from their devices. To this end\, I will demonstrate my approaches that use sparsity to achieve fast and efficient training on the edge devices. I will evaluate the static lottery ticket sparse training\, and then demonstrate a high-accuracy and low-cost dynamic sparse training framework that makes the edge training possible. It successfully incorporates the pattern-based sparsity into sparse training\, and also exploit the data-level sparsity to further improve the acceleration. I will conclude by using our sparse training method on a distributed training scenario\, which demonstrates the state-of-the-art accuracy and great flexibility for modern AI model training. \nCommittee: \nProf. Yanzhi Wang (Advisor) \nProf. Xue Lin \nProf. David Kaeli
URL:https://coe.northeastern.edu/event/xiaolong-mas-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220629T120000
DTEND;TZID=America/New_York:20220629T143000
DTSTAMP:20260514T005256
CREATED:20220621T210230Z
LAST-MODIFIED:20220621T210230Z
UID:31679-1656504000-1656513000@coe.northeastern.edu
SUMMARY:CILS Seminar & Demo: Nanosurf Drive AFM
DESCRIPTION:Come learn about Nanosurf’s DriveAFM\, a tip-scanning atomic force microscope used for all areas of applications from materials to life science. \nAn instrument demonstration will follow in the CILS Core Facility in the ISEC basement\, 090 from 1:30-2:30pm. \nThe DriveAFM overcomes drawbacks of other tip-scanning instruments and provides atomic resolution together with fast scanning\, fast force spectroscopy\, and large scan sizes up to 100 µm. \n  \nTopic: CILS Seminar & Demo: Nanosurf DriveAFM\nTime: Jun 29\, 2022 12:00 PM Eastern Time (US and Canada) \nJoin Zoom Meeting\nhttps://northeastern.zoom.us/j/91205821278 \nMeeting ID: 912 0582 1278\nOne tap mobile\n+13017158592\,\,91205821278# US (Washington DC)\n+13126266799\,\,91205821278# US (Chicago) \nJoin by Skype for Business\nhttps://northeastern.zoom.us/skype/91205821278 \n 
URL:https://coe.northeastern.edu/event/cils-seminar-demo-nanosurf-drive-afm/
LOCATION:136 ISEC\, 360 Huntington Ave\, 136 ISEC\, Boston\, MA\, 02115\, United States
GEO:42.3401758;-71.0892797
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220630T080000
DTEND;TZID=America/New_York:20220630T090000
DTSTAMP:20260514T005256
CREATED:20220614T173730Z
LAST-MODIFIED:20220614T173730Z
UID:31637-1656576000-1656579600@coe.northeastern.edu
SUMMARY:A Conversation on ECE and BioE programs in Portland
DESCRIPTION:A conversational regarding the Master’s in ECE and Bioengineering program at the Roux Institute in Portland\, Maine.
URL:https://coe.northeastern.edu/event/a-conversation-on-ece-and-bioe-programs-in-portland/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
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