BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Department of Electrical & Computer Engineering - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Department of Electrical & Computer Engineering
X-ORIGINAL-URL:https://ece.northeastern.edu
X-WR-CALDESC:Events for Department of Electrical & Computer Engineering
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART: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
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20240310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20241103T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230526T123000
DTEND;TZID=America/New_York:20230526T133000
DTSTAMP:20260425T010216
CREATED:20230522T211528Z
LAST-MODIFIED:20230522T211528Z
UID:6347-1685104200-1685107800@ece.northeastern.edu
SUMMARY:Guillem Reus Muns' PhD Dissertation Defense
DESCRIPTION:“AI for communication and sensing in RF environments” \nCommittee Members:\nProf. Kaushik Chowdhury (Advisor)\nProf. Stratis Ioannidis\nProf. Hanumant Singh \nAbstract:\nThe recent growth of Internet of Things (IoT)\, as well as other new\nrevolutionary applications utilizing wireless spectrum are leading the way towards the 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 fractional design complexity. This thesis presents\, through real systems and with experimental validation\, our progressive efforts in four broad areas\, where AI enables the operation of aerial and terrestrial systems that combine sensing and communications. The following key use cases with distinct contributions are investigated: \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) ‘SenseORAN’\, a revolutionary architectural design that aims to reuse the cellular infrastructure for sensing purposes in order to address spectrum access challenges in the CBRS band. This is enabled by Open Radio Access Network (O-RAN)\, a cellular architecture concept that promotes virtualized RANs where disaggregated components are connected via open interfaces and supports intelligent controllers running custom logic. iv) ‘AirFC’\, an over-the-air computation method that implements fully connected neural networks inference leveraging multi-antenna wireless systems.
URL:https://ece.northeastern.edu/event/guillem-reus-muns-phd-dissertation-defense/
LOCATION:Admissions Visitor Center (West Village F)
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230526T090000
DTEND;TZID=America/New_York:20230526T100000
DTSTAMP:20260425T010216
CREATED:20230522T211659Z
LAST-MODIFIED:20230522T211659Z
UID:6350-1685091600-1685095200@ece.northeastern.edu
SUMMARY:Yuezhou Liu's PhD Proposal Review
DESCRIPTION:Committee Members:\nProf. Edmund Yeh (Advisor)\nProf. Stratis Ioannidis\nProf. Lili Su\nProf. Carlee Joe-Wong \nAbstract:\nSignificant advances in edge and mobile computing capabilities enable machine learning to occur at geographically diverse locations in networks\, e.g.\, cloud\, edge\, and mobile devices. The training data needed in those learning tasks may not be fully generated locally. Moreover\, some promising distributed learning paradigms enable devices to collaboratively train a model\, which requires communication among the devices for exchanging necessary information. Thus\, optimizing network strategies for the transmission/exchange of ML/AI ingredients (e.g.\, input data\, model parameters\, gradients) is important for facilitating efficient in-network distributed ML. While there exist many works that use ML to optimize network operation strategies\, few works study optimized networks that boost ML performance. This dissertation tries to fill the gap by studying several network optimization problems for distributed ML. Different from classic network optimization problems for data delivery or edge computing that optimize energy consumption\, delay\, throughput\, etc.\, we also pay attention to ML-related metrics such as model accuracy and training convergence time. \nWe first propose an experimental design network paradigm\, wherein learner nodes train possibly different ML models via consuming data streams generated by data source nodes over a network. We formulate this problem as a social welfare optimization problem in which the global objective is defined as the sum of experimental design objectives of individual learners\, and the decision variables are the data transmission strategies subject to network constraints. We show that\, assuming Bayesian linear regression models and Poisson data streams in steady state\, the global objective is continuous DR-submodular\, which enables the design of efficient approximate algorithms with approximation guarantees. We will further extend our framework to incorporate more practical ML applications\, such as ML with arbitrary nonlinear models. \nThe second half of this dissertation studies network optimization for Federated learning (FL)\, a distributed paradigm for collaboratively learning models without having clients disclose their private data. We propose to use caching for improving FL efficiency concerning the total model training time for convergence. Instead of having all clients download the latest global model from a parameter server\, we select a subset of clients to access\, with smaller delays\, a somewhat stale global model stored in caches. We propose CacheFL — a cache-enabled variant of FedAvg\, and provide theoretical convergence guarantees in the general setting where the local data is imbalanced and heterogeneous. With this result\, we determine the caching strategies that minimize total wall-clock training time at a given convergence threshold for both stochastic and deterministic communication/computation delays.
URL:https://ece.northeastern.edu/event/yuezhou-lius-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230522T103000
DTEND;TZID=America/New_York:20230522T113000
DTSTAMP:20260425T010216
CREATED:20230522T211916Z
LAST-MODIFIED:20230522T211916Z
UID:6352-1684751400-1684755000@ece.northeastern.edu
SUMMARY:Mengting Yan's PhD Dissertation Defense
DESCRIPTION:“Integrated Circuit Design Methods for Temperature-based Hardware Trojan Detection and Parametric Frequency Division in Next-Generation Systems-on-a-Chip” \nCommittee Members:\nProf. Marvin Onabajo (Advisor)\nProf. Yong-bin Kim\nProf. Yunsi Fei \nAbstract:\nNew needs for next-generation systems-on-a-chip (SoC) are emerging as the trend of globalization in the semiconductor industry becomes increasingly ubiquitous and the demand for low-power Internet-of-Things (IoT) devices continues to soar. Among various research directions\, this dissertation focuses on enhancing hardware security and on providing low-noise frequency sources for next-generation SoCs. Within this scope\, the described research addresses the challenge to improve on-chip anomaly detection capabilities\, and separately lays a foundation for the design of circuits to reduce the phase noise of on-chip oscillators. \nIn the first part of this dissertation\, an on-chip temperature-based Hardware Trojan (HT) detection system is introduced. The approach to detect inserted HTs relies on thermal profiling of the circuit under test (CUT) and side-channel analysis of the obtained temperature data. On-chip electrothermal coupling is modeled as part of a simulation technique that associates local thermal activities with circuit-level power consumption using a standard electrical simulator. To monitor the thermal profiles on chips with high sensitivity to local temperature changes as well as to enhance the resilience to flicker noise\, a fully-differential temperature sensor equipped with a chopping mechanism has been designed in 130-nm complementary metal-oxide-semiconductor (CMOS) technology\, which has a sensitivity of 840 V/◦C. The simulated temperature sensor output in the presence of noise and process variations is quantized by an analog-to-digital converter (ADC) model and processed using principal component analysis (PCA)\, which allows to determine the minimum detectable Trojan power and the design requirements for the on-chip ADC. With a modeled 8-bit ADC\, simulations of the HT detection system reveal a detection rate of 100% with a Trojan power down to 2.4 μW within the thermal profile of a CUT consuming 508 μW. A prototype 8-bit 1 MS/s successive approximation register (SAR) ADC for such a system was designed in 130-nm CMOS technology\, fabricated\, and tested. The measured effective number of bits (ENOB) is 7.27 bits up to the Nyquist frequency\, with a power consumption of 103.2 μW from a 1.2 V supply. Furthermore\, a 3-step analog calibration loop has been designed to compensate for the voltage offsets within the sensor circuits in the presence of device mismatches and process-temperature variations. The calibration loop settles within 300 μs to complete the offset calibration\, such that the input-referred offset has a standard deviation of 5.86 μV based on Monte Carlo simulations. \nIn the second part of this dissertation\, the on-chip realization of a parametric frequency divider (PFD) is explained. The low-power 2:1 frequency division at sub-6 GHz plays a critical role in on-chip phase noise reduction systems that exhibit nonlinear operations\, indicating promise for future integration into radio frequency (RF) SoCs. In particular\, the first current-driven PFD with an output frequency of 2.4 GHz is introduced\, which consists of three major blocks: (1) a custom PFD driver stage with a buffer to ease input driving\, (2) a purely passive PFD core with inductor-capacitor (LC) resonators\, and (3) an output driving stage with embedded band-pass filtering that suppresses undesirable output harmonics. A prototype PFD chip was fabricated in standard 65-nm CMOS technology\, and the corresponding measurement results are presented to characterize the performance of the new PFD. The minimum required supply voltage for the PFD driver is 1.4 V with an input frequency of 4.8 GHz\, whereas the PFD has an operating frequency range from 4.5 GHz to 5.1 GHz with a supply voltage of 1.5 V. To the best of the author’s knowledge\, the proposed PFD is the first on-chip CMOS implementation for sub-6 GHz applications\, which balances the trade-offs among frequency range\, power consumption\, and chip area constraints.
URL:https://ece.northeastern.edu/event/mengting-yans-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230428T130000
DTEND;TZID=America/New_York:20230428T150000
DTSTAMP:20260425T010216
CREATED:20230426T174141Z
LAST-MODIFIED:20230426T174141Z
UID:6293-1682686800-1682694000@ece.northeastern.edu
SUMMARY:Balaji Sundareshan's MS Thesis Defense
DESCRIPTION:“Cross-View Action Recognition using Transformers” \nCommittee Members:\n1. Prof. Octavia Camps (Advisor)\n2. Prof. Mario Sznaier\n3. Prof. Huaizu Jiang \nAbstract:\nCross-view action recognition (CVAR) seeks to recognize a human action when observed from a previously unseen viewpoint. This is a challenging problem since the appearance of action changes significantly with the viewpoint. Applications of CVAR include surveillance and monitoring of assisted living facilities where is not practical or feasible to collect large amounts of training data when adding a new camera. In this thesis\, we propose a method to perform cross-view action recognition from 2D skeleton data using Transformers. First\, we understand the interpretability of the basline network and its submodules by visualizing the saliency map. Next\, we integrate Transformers at different parts of the network for both single-clip and multi-clip and understand the impact on the performance. In the end\, we also discuss the necessity of pretraining sub-modules in the network and their impact on the performance.
URL:https://ece.northeastern.edu/event/balaji-sundareshans-ms-thesis-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230426T160000
DTEND;TZID=America/New_York:20230426T170000
DTSTAMP:20260425T010216
CREATED:20230426T174309Z
LAST-MODIFIED:20230426T174309Z
UID:6295-1682524800-1682528400@ece.northeastern.edu
SUMMARY:Rui Huang's MS Thesis Defense
DESCRIPTION:“Sputter Deposition and Characterization of Highly Textured BixTe1-x Thin Films” \nCommittee Members:\nProf. Nian-Xiang Sun (Advisor)\nProf. Marvin Onabajo\nProf. Yongmin Liu \nAbstract:\nThe discovery of topological insulators (TIs) provides a direction for scientists to understand the Quantum Spin Hall Effect (QSHE) and Spin-Orbit Coupling (SOP)  in condensed matter physics. After a decade\, people found that after the introduction of magnetism into TI\, the Time Reversal Symmetry (TSR) is broken\, producing Magnetic Topological Insulators (MTI). Meanwhile\, with the deposition of TI on the Magnetic Insulator (MI)\, the Spin-Orbit Torque was found in TI/MI structures. Introducing dopants into TI is another method to produce MTI. Mn-doped\,  Cr-doped\, and Ni-doped TI thin films have been explored recently. Thus\, the 3D TI\, Bi2Te3\, and MTI\, Ni: Bi2Te3\, thin film-based materials have been applied to some energy-efficient spintronic devices. However\, according to the Bi-Te phase diagram\, Bi2Te3 is one of the Bi-Te family. The narrow range of the Bi2Te3 phase is a challenge for people to deposit the correct phase on the InP (111) wafer due to the potential effect of defects.  In this Master thesis\, the textured BixTe1-x and Ni-doped BixTe1-x thin films are deposited on the InP (111) substrate through the RF Magnon Sputtering Tool with a Te capping layer under different deposition powers\, temperatures\, and post-annealing time. After the X-ray diffraction measurement on three samples with various conditions\, the textured Bi8Te7\, Bi8Te9\, and Ni: Bi8Te7 thin films are concluded based on the comparison between the theoretical XRD results with the experimental ones.
URL:https://ece.northeastern.edu/event/rui-huangs-ms-thesis-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230425T130000
DTEND;TZID=America/New_York:20230425T143000
DTSTAMP:20260425T010216
CREATED:20230420T223701Z
LAST-MODIFIED:20230420T223701Z
UID:6273-1682427600-1682433000@ece.northeastern.edu
SUMMARY:Rashida Nayeem's PhD Dissertation Defense
DESCRIPTION:“Human control of objects with nonlinear internal dynamics: Predictability as primary objective” \nLocation:\nEgan Research Ctr 206 \nCommittee Members:\nProf. Dagmar Sternad (Advisor)\nProf. Eduardo Sontag\nProf. Mario Sznaier\nDr. David Lin (Massachusetts General Hospital & Harvard Medical School) \nAbstract:\nHumans physically interact with complex objects in numerous daily activities. An example is picking up a cup of coffee where interaction forces arise between the hand and the sloshing liquid. For successful actions\, error corrections based on real-time sensed information are insufficient\, hence humans need to predict and preempt the evolving dynamics. Our previous work on the transport of a “cup of coffee” showed that humans seek to make the interaction dynamics simple\, i.e.\, predictable. Extending from previous work\, this thesis used a virtual paradigm where the “cup of coffee” was simplified to a cup with a ball sliding inside\, retaining the challenges of “a cup filled with coffee”: underactuation and nonlinearity. A series of experiments examined human strategies in different contexts to demonstrate that predictability is a control priority. The first experimental and modeling study examined how subjects explored and prepared the 2D cup-and-ball system prior to continuous interaction. Results showed that subjects converged to a small set of initial conditions that shortened initial transients\, enabling subjects to reach a more predictable steady state faster. Two follow-up studies examined the role of visual and haptic information and revealed that despite suboptimal exploration of the solution space\, subjects increased predictability of hand object interactions. System identification showed that visual information enabled subjects to simplify input-output behavior via appropriate object preparation. When deprived of haptic information subjects still achieved increased predictability but sacrificed orbital stability. A final study extended this basic paradigm to a clinical application to investigate if these insights could help in assessment of motor impairment after stroke in this functionally relevant ‘self-feeding’ task. To facilitate testing in a clinic\, a real-life 3D device was custom-developed where individuals after stroke moved a cup with a rolling ball inside on a table. Our theory-based predictability metric proved highly sensitive to quantify the degree of motor impairment after stroke. Taken together\, this thesis elucidated principles of human motor control in a complex interactive task. The insights have significant applications in clinical testing and may also inform robot manipulation of this understudied movement challenge.
URL:https://ece.northeastern.edu/event/rashida-nayeems-phd-dissertation-defense/
LOCATION:206 Egan\, 360 Huntington Ave\, 206 Egan\, Boston\, MA\, 02115\, United States
GEO:42.3376753;-71.0888734
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=206 Egan 360 Huntington Ave 206 Egan Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave\, 206 Egan:geo:-71.0888734,42.3376753
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230424T110000
DTEND;TZID=America/New_York:20230424T123000
DTSTAMP:20260425T010216
CREATED:20230420T223436Z
LAST-MODIFIED:20230420T223740Z
UID:6277-1682334000-1682339400@ece.northeastern.edu
SUMMARY:Cunzheng Dong's PhD Proposal Review
DESCRIPTION:“Acoustically Actuated Magnetoelectric Antennas for VLF Communication and Magnetic Sensing” \nCommittee Members:\nProf. Nian Sun (Advisor)\nProf. Yongmin Liu\nProf. Hossein Mosallaei \nAbstract:\nSince the discovery of strong magnetoelectric (ME) coupling in two-phase ME laminate composites\, strain-mediated ME heterostructures have attracted a great deal of attention from academic and industrial research groups for their potential usage in magnetic sensors\, voltage tunable inductors\, magnetic tunable filters\, and miniaturized mechanical antennas\, etc. Acoustically actuated ME antennas have recently been demonstrated as a promising solution for very low frequency (VLF) communications and magnetic fields detection\, for their 2-3 orders of reduced dimensions\, outstanding sensitivity at resonance\, and robust immunity to electrical interferences than conventional electric antennas. Their performance and noise analysis are deeply investigated and discussed in this proposal review. \nFirstly\, A portable VLF communication system using one pair of ME antennas operating at their electromechanical resonance (EMR) is presented. The measured near-field radiation pattern reveals ME antennas are equivalent to magnetic dipole antennas. The magnetic field radiated by the ME transmitter has been predicted along with distance from near-field to far-field. The measured magnetic field distribution coincided well with the prediction\, and the maximum communication distance of 120 m has been achieved by single antenna unit. Antenna arrays are widely used as an effective approach to enhance radiation field intensity. By tunning all the driving signal for each antenna unit at the same frequency and in phase\, the total radiation field strength has been linearly enhanced by one order with 12 antenna arrays. Furthermore\, nonlinear antenna modulation (NAM) has also been successfully demonstrated on the ME antennas. \nSecondly\, a Metglas/Quartz based ME resonator as magnetic sensor for reception of VLF magnetic signals is presented. Metglas is a highly permeable magnetostrictive material which can effectively concentrate the magnetic fields. Moreover\, the high magnetostriction and low coercivity of Metglas can generate a distinct strain change in response to subtle magnetic fields. Piezoelectric single crystal Quartz is often used as electronic oscillators due to their extremely high Q factor with low noise and high stability. The combined properties of these two materials provide ME sensors an extremely high sensitivity and low magnetic noise of less than 10 fT at the EMR frequency. The VLF signal reception capability of the proposed ME sensor was also compared with a conventional VLF loop antenna and the PZT-5A based ME sensor. \nLastly\, a compact and sensitive system was developed to characterize the magnetomechanical properties\, such as the saturation magnetostriction\, piezomagnetic coefficient\, delta-E effect and magnetomechanical coupling factor of magnetic thin films. These magnetomechanical properties are critical in determining the performance of ME antennas. For saturation magnetostriction and piezomagnetic coefficient measurement\, a high precision optical probe was harnessed to measure the deflection of the magnetic thin film/Si cantilever due to strain change induced by domain rotation. The same cantilever samples were used for delta-E effect and magnetomechanical coupling factor characterization\, the DC bias magnetic field induced cantilever resonance frequency shift was used for calculating the change of elastic modulus.
URL:https://ece.northeastern.edu/event/cunzheng-dongs-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230420T080000
DTEND;TZID=America/New_York:20230420T170000
DTSTAMP:20260425T010216
CREATED:20230420T223901Z
LAST-MODIFIED:20230420T223901Z
UID:6279-1681977600-1682010000@ece.northeastern.edu
SUMMARY:Chenghao Wang's MS Thesis Defense
DESCRIPTION:“Legged Walking on Inclined Surfaces” \nCommittee Members:\nProf. Alireza Ramezani(Advisor)\nProf. Miriam Leeser\nProf. Bahram Shafai \nAbstract:\nThe main contributions of this MS Thesis are centered around taking steps towards successful multi-modal demonstrations using Northeastern’s legged-aerial robot\, Husky Carbon. This work discusses the challenges involved in achieving multi-modal locomotion such as trotting-hovering and thruster-assisted incline walking and reports progress made towards overcoming these challenges. Animals like birds use a combination of legged and aerial mobility\, as seen in Chukars’s wing-assisted incline running (WAIR)\, to achieve multi-modal locomotion. Chukars use forces generated by their flapping wings to manipulate ground contact forces and traverse steep slopes and overhangs. Husky’s design takes inspiration from birds such as Chukars. This MS thesis presentation outlines the mechanical and electrical details of Husky’s legged and aerial units. The thesis presents simulated incline walking using a high-fidelity model of the Husky Carbon over steep slopes of up to 45 degrees.
URL:https://ece.northeastern.edu/event/chenghao-wangs-ms-thesis-defense/
LOCATION:532 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230413T160000
DTEND;TZID=America/New_York:20230413T170000
DTSTAMP:20260425T010216
CREATED:20230405T174232Z
LAST-MODIFIED:20230405T174232Z
UID:6246-1681401600-1681405200@ece.northeastern.edu
SUMMARY:Hussein Hussein’s PhD Dissertation Defense
DESCRIPTION:“Parametric Circuits for Enhanced Sensing and RF Signal Processing” \nCommittee Members: \nProf. Cristian Cassella (Advisor) \nProf. Marvin Onabajo \nProf. Matteo Rinaldi \nProf. Andrea Alù \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.
URL:https://ece.northeastern.edu/event/hussein-husseins-phd-dissertation-defense/
LOCATION:432 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
GEO:42.3396156;-71.0886534
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=432 ISEC 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:20230412T150000
DTEND;TZID=America/New_York:20230412T170000
DTSTAMP:20260425T010216
CREATED:20230405T175342Z
LAST-MODIFIED:20230405T175342Z
UID:6250-1681311600-1681318800@ece.northeastern.edu
SUMMARY:IER Open House
DESCRIPTION:Following our success with last year’s Open House\, we are again opening the lab up to visitors! This year\, demos will be held on the 5th floor of ISEC by Yingzi Lin\, Ilya Vidrin\, Alireza Ramezani\, Taskin Padir\, Kris Dorsey\, Rob Platt\, and Hanu Singh. In Richards Hall\, Dagmar Sternad\, CJ Hasson\, and Max Shepherd will be hosting demos as well.\n\n\nThe flyer for this event is attached. Please reach out to Noah (n.smith@northeastern.edu) with any questions! Make sure to register here: https://www.eventbrite.com/e/institute-for-experiential-robotics-open-house-tickets-603775106597
URL:https://ece.northeastern.edu/event/ier-open-house/
LOCATION:532 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230411T120000
DTEND;TZID=America/New_York:20230411T130000
DTSTAMP:20260425T010216
CREATED:20230405T175216Z
LAST-MODIFIED:20230405T175216Z
UID:6248-1681214400-1681218000@ece.northeastern.edu
SUMMARY:Tirthak Patel's Dissertation Defense
DESCRIPTION:“Robust System Software for Quantum Computing” \nCommittee Members: \nProf. Devesh Tiwari (Advisor) \nProf. David Kaeli \nProf. Ningfang Mi \nProf. Gene Cooperman \nProf. Kenneth Brown \nAbstract: \nDespite rapid progress in quantum computing in the last decade\, the limited usability of quantum computers remains a major roadblock toward its wider adoption. Current noisy intermediate-scale quantum (NISQ) computers produce highly erroneous program outputs for quantum-advantage-proven algorithms — that is\, algorithms that are infeasible or orders of magnitude slower on classical supercomputing and high-performance computing (HPC) clusters. Unfortunately\, currently\, quantum computing programmers lack robust system software tools and methods to make meaningful use of erroneous program executions on quantum computers. \nThis lack of capability is the core motivation behind the fundamental question this dissertation poses: “can we build system software tools for programmers to make the quantum program execution and output meaningful on NISQ machines?” This dissertation answers this question in the affirmative— experimentally demonstrating on real-system quantum computers that it is possible to extract near-accurate program output from noisy executions on today’s erroneous quantum computers\, ironically using classical HPC resources and knowledge. This dissertation demonstrates how to achieve this goal without requiring user intervention\, domain knowledge about quantum algorithms\, or additional quantum hardware support. \nUnfortunately\, as this dissertation uncovers\, progressing toward making quantum computers usable is a double-edged sword. In the near future\, only a few entities in the world may have access to powerful quantum computers\, and these quantum computers will be used to solve previously-unsolved large-scale optimization problems\, possibly without an explicit trust model between the service provider and the customer. Therefore\, this dissertation envisions that the solutions to such large-scale optimization problems will be considered sensitive and will need to be protected. This dissertation takes the first few steps toward preparing us for that future by developing a novel method that intelligently obfuscates near-accurate program output and quantum circuit structure to preserve a customer’s privacy under a specified computation model and resource availability. \nThe approaches introduced in this dissertation open up new research avenues for hybrid quantum-classical computing and lower the barrier to entry for quantum computing research for the experimental computer systems and HPC community by open-sourcing multiple novel datasets and software frameworks implemented for real-system quantum computers. \nCandidate Bio: \nTirthak Patel is an incoming Assistant Professor in the Department of Computer Science at Rice University; currently\, a PhD candidate at Northeastern University\, advised by Professor Devesh Tiwari. Tirthak conducts systems-level research at the intersection of quantum computing and high-performance computing (HPC). His research contributions have appeared at rigorously peer-reviewed publication venues including ASPLOS\, Supercomputing (SC)\, HPDC\, HPCA\, and USENIX FAST\, and have been recognized with multiple award distinctions. He has received the ACM-IEEE CS George Michael Memorial HPC Fellowship\, the NSERC Alexander Graham Bell Canada Graduate Scholarship\, and the Northeastern University Outstanding Graduate Student in Research award\, for his research contributions toward making noisy quantum computing systems useful and helping HPC programmers solve computationally challenging problems.
URL:https://ece.northeastern.edu/event/tirthak-patels-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:20230404T123000
DTEND;TZID=America/New_York:20230404T133000
DTSTAMP:20260425T010216
CREATED:20230328T174955Z
LAST-MODIFIED:20230328T174955Z
UID:6229-1680611400-1680615000@ece.northeastern.edu
SUMMARY:Cheng Gongye's MS Thesis Defense
DESCRIPTION:“Using Floating-Point Timing Side-Channels to Reverse Engineer Deep Neural Networks” \nCommittee Members:\nProf. Yunsi Fei (Advisor)\nProf. Aidong Ding\nProf. Xiaolin Xu \nAbstract: \nTrained Deep Neural Network (DNN) models have become valuable intellectual property. A new attack surface has emerged for DNNs: model reverse engineering. Several recent attempts have utilized various common side channels. However\, recovering DNN parameters\, weights and biases\, remains a challenge. In this paper\, we present a novel attack that utilizes a floating-point timing side channel to reverse-engineer parameters of multi-layer perceptron (MLP) models in software implementation\, entirely and precisely. To the best of our knowledge\, this is the first work that leverages a floating-point timing side channel for effective DNN model recovery.
URL:https://ece.northeastern.edu/event/cheng-gongyes-ms-thesis-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230403T100000
DTEND;TZID=America/New_York:20230403T110000
DTSTAMP:20260425T010216
CREATED:20230320T205517Z
LAST-MODIFIED:20230320T205517Z
UID:6217-1680516000-1680519600@ece.northeastern.edu
SUMMARY:Jared Miller Ph.D Defense/Proposal Announcement
DESCRIPTION:“Safety Analysis for Nonlinear and Time-Delay Systems using Occupation Measures” \nInternational Village 022 \nCommittee Members:\nProf. Mario Sznaier (Advisor)\nProf. Octavia Camps\nProf. Bahram Shafai\nProf. Eduardo Sontag\nProf. Didier Henrion (LAAS-CNRS) \nAbstract:\nThis research extends an occupation measure framework to analyze the behavior and safety of dynamical systems. A motivating application of trajectory analysis is in peak estimation\, which finds the extreme values of a state function along trajectories. Examples of peak estimation include finding the maximum height of a wave\, voltage on a power line\, speed of a vehicle\, and infected population in an epidemic. Peak estimation can be applied towards safety quantification\, such as by measuring the safety of a trajectory by its distance of closest approach to an unsafe set. \nA finite-dimensional but nonconvex peak estimation problem can be converted into an infinite-dimensional linear program (LP) in measures\, which is in turn bounded by a convergent sequence of semidefinite programs. The LP is posed in terms of an initial\, a terminal\, and an occupational measure\, where the occupation measure contains all possible information about the dynamical systems’ trajectories. This research applies measure-based methods towards safety quantification (e.g. distance estimation\, control effort needed to crash)\, hybrid systems\, bounded-uncertain systems (including for data-driven analysis)\, stochastic systems\, and time-delay systems. The modularity of this measure-based framework allows for multiple problem variations to be applied simultaneously (e.g. distance estimation under time-delays)\, and for optimization models to be synthesized using MATLAB. Solving these optimization problems results in certifiable guarantees on system performance and behavior.
URL:https://ece.northeastern.edu/event/jared-miller-ph-d-defense-proposal-announcement/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230322T180000
DTEND;TZID=America/New_York:20230322T190000
DTSTAMP:20260425T010216
CREATED:20230131T012350Z
LAST-MODIFIED:20230131T012350Z
UID:6072-1679508000-1679511600@ece.northeastern.edu
SUMMARY:PlusOne Information Session
DESCRIPTION:LEARN ABOUT THE PLUSONE ACCELERATED MASTER’S DEGREE PROGRAM \nA master’s degree can provide you 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\nWhat is PlusOne\nBenefits of the program\nEligibility\nCo-op considerations\nFinancial considerations\nSelecting your pathway\nAcademic advising resources\nTimeline to apply\nThe application process\nCourse registration\nTransitioning to graduate school\n\nZoom
URL:https://ece.northeastern.edu/event/plusone-information-session-4/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230315T140000
DTEND;TZID=America/New_York:20230315T153000
DTSTAMP:20260425T010216
CREATED:20230315T181507Z
LAST-MODIFIED:20230315T181507Z
UID:6210-1678888800-1678894200@ece.northeastern.edu
SUMMARY:Sadjad Asghari Esfeden's PhD Dissertation Defense
DESCRIPTION:“Spatiotemporal Localization of Object Handover for Human Robot Collaboration” \nCommittee Members: \nProf. Deniz Erdogmus (Advisor) \nProf. Taskin Padir \nProf. Eugene Tunik \nProf. Mathew Yarossi \nAbstract: \nHuman-robot interaction in a physical world like handover of objects requires perception systems to be efficient in localizing the object of interest. We propose an approach to estimate the location of the object with a low-cost RGB camera in a real-time inference for human-robot handover. While handover can take place in a short amount of time\, it is important for a robot to keep track of the object and fill in the gaps of missing detections in the perception module\, especially when the object is partially or completely occluded. A robot needs to proactively detect and track the object since the human decides where and when to transfer the object to the robot in a human to robot object handover.  In order to develop a perception system for robot to be capable of constantly localizing the object and predict its location and time of transfer\, we integrate an object detection algorithm with a tracking framework. The evaluation of this pipeline shows promising results for the goal of localization and tracking of the handover object and can help its location prediction in future.
URL:https://ece.northeastern.edu/event/sadjad-asghari-esfedens-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230303T140000
DTEND;TZID=America/New_York:20230303T153000
DTSTAMP:20260425T010216
CREATED:20230227T195344Z
LAST-MODIFIED:20230227T195344Z
UID:6157-1677852000-1677857400@ece.northeastern.edu
SUMMARY:Kerem Enhos' PhD Proposal
DESCRIPTION:“Software-Defined Inter-medium Visible Light Communication and Underwater Acoustic Networks” \nCommittee Members:\nProf. Tommaso Melodia (Advisor)\nProf. Kaushik Chowdhury\nProf. Stefano Basagni\nDr. Emrecan Demirors \nAbstract:\n“Multi-Domain Operations” paradigm has been receiving significant attention both in military and civilian worlds. To realize this novel paradigm\, it is imperative to establish robust communication links to transfer data between devices operating in multiple domains. However\, as of today\, establishing high data rate\, robust\, secure\, and bi-directional communication links between aerial and underwater assets across the air-water interface is still an open problem. We address these challenges with software-defined visible light networking to establish bi-directional wireless links through the air-water interface. After generating a simulation model for inter-medium communication channel\, we also empirically derived an optimal parameter selection for carrierless amplitude and phase (CAP) modulation. Then\, we design and prototype a software-defined visible light  communication (VLC) modem and conducted extensive experimental evaluation. Apart from inter-medium communication\, software-defined networking can also be leveraged for underwater acoustic communication (UWAC)\, where we designed and assessed coexistence of multi-dimensional chirp spread spectrum (MCSS) with other UWAC schemes. We first evaluated the performance of the proposed communication scheme in a heterogeneous network setting  where it co-exists with a ZP-OFDM communication link\, then in a homogeneous network setting where all links are using MCSS scheme. Finally\, we used  this software-defined networking system to implement a single-input  multiple-output (SIMO) system for UWAC modems that are  deployed in a  distributed manner. Then\, we conduct a thorough experimental evaluation in  ocean environment for various subcarrier bandwidths and constellations  using three distributed receivers.
URL:https://ece.northeastern.edu/event/kerem-enhos-phd-proposal/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230303T100000
DTEND;TZID=America/New_York:20230303T110000
DTSTAMP:20260425T010216
CREATED:20230223T212329Z
LAST-MODIFIED:20230223T212329Z
UID:6152-1677837600-1677841200@ece.northeastern.edu
SUMMARY:Guanying Sun's PhD Dissertation Defense
DESCRIPTION:“Optimizing Reconstruction for Mm-Wave Body Scanner Imaging” \nCommittee: \nProf. Carey Rappaport (Advisor) \nProf. Edwin Marengo \nProf. Jose Martinez-Lorenzo \nAbstract: \nIn the past decades\, due to evolving threats\, passenger screening has become an important secure measure at airport and other secure locations. Numerous passenger screening techniques have been developed by researchers in both academia and industry to detect threats from explosives and weapons. Among these developments\, the multistatic mm-wave radar Advanced Imaging Technology (AIT) system was developed at Northeastern University. A problem with this system is the sidelobes from its physical limitations\, such as the finite aperture extent and the violation of the Nyquist sampling criterion by the sparse array. Therefore\, it is important to suppress the sidelobes so that to improve the quality of the reconstruction image. In this proposal\, we investigate two categories of methods\, one is based on post-processing\, and the other is based on system configuration optimization. In the former category four methods are developed\, while in the latter two methods are proposed. \nIn the first category\, the first method is the phase coherence method which is designed to weight the coherent sum based on the phase diversity of the reconstructed solutions for different transmitters. In this method\, two ways are considered to construct the Phase Coherence Factor (PCF). The first way is to use the information of wrapped phase\, and the second way is to use the information of unwrapped phase\, which is more intuitive than the first way. The second method is the coherence factor related method. Three coherence-factor based methods are analyzed and then incorporated into the imaging procedure of our nearfield millimeter-wave radar security scanning system. The third method is the SNR-dependent coherence factor method\, which takes SNR into consideration when forming the coherence factor. This method can generate better results than the pure coherence-factor based methods by choosing a proper set of parameters. The fourth method is the block-weighting algorithm where the neighbor weight amplifies bright areas and attenuates dark areas\, while the block keeps the influence local. The effectiveness of these methods has been verified with both simulation and measurement data. \nIn the second category\, the first method is optimizing receiver positions via PSF-based multi-objective optimization. Two metrics for measuring image quality of the PSF are proposed and defined as objective functions. The solution-selection metric is introduced to select the desired solution from the numerous Pareto-optimal solutions. Simulation shows that the optimized receiver design generates images with lower sidelobe level than the uniform receiver design. The second method is the dual-frequency radar design\, where a dual frequency\, wideband antenna array is designed by combining a high frequency subarray with a low frequency subarray. The image of the dual frequency array is obtained by multiplying the images of the two subarrays. We analyzed the amplitude of the PSF theoretically and proposed a criterion for the selection of dual frequency array design. The system imaging simulation shows that the grating lobes are significantly reduced for the dual frequency array with fewer radar modules/elements than the conventional array. This design will make the new generation system superior to the conventional scanning system.
URL:https://ece.northeastern.edu/event/guanying-suns-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230302T090000
DTEND;TZID=America/New_York:20230302T100000
DTSTAMP:20260425T010216
CREATED:20230223T212222Z
LAST-MODIFIED:20230223T212222Z
UID:6150-1677747600-1677751200@ece.northeastern.edu
SUMMARY:Matthew Schinault's PhD Proposal Review
DESCRIPTION:“Development of A Large-Aperture 160-Element Coherent Hydrophone Array System for Instantaneous Wide Area Ocean Acoustic Sensing” \nAbstract: \nA large aperture coherent hydrophone towed array system comprising of 160 elements and an aperture length of 192 meters has been developed for real-time instantaneous wide-area ocean acoustic remote sensing and monitoring. The design and manufacture of these arrays requires a multidisciplinary approach to achieve acoustic performance capable for detection\, classification\, localization and tracking. Drawing from disciplines such as material science\, electrical engineering\, mechanical engineering\, hydrodynamics\, oceanography\, bioacoustics and signal processing. Due to the cost and complexity of towed array technology\, development of large aperture towed arrays has been limited at the university level. With military\, oil and gas exploration as the chief technology developers and users. The military and commercial focus is narrow and does not allow for scientific study\, resulting in significant gaps in the way we understand ocean acoustics around the globe. Here we model\, design\, fabricate and field test a broadband array for general ocean sensing that is configured to support a wide range of research to include study of marine mammals\, fish shoals\, geophysical processes\, surface or subsea man-made craft\, seismic surveying and the various challenges associated with detection\, classification and localization of underwater sound sources. \nHere\, we present the design process\, beginning with modeling and measurement of piezoelectric material properties. This allows us to perform finite element analysis\, estimating beampatterns and frequency response with a hydrophone electrical model. A pressure to voltage input model of the hydrophone is used to obtain the voltage levels produced to then configure amplification\, gain and filter stages providing a system level transfer function from analog to digital conversion. The array performance with a delay and sum beamformer is estimated for a broad range of frequencies\, with beamforming above half-lambda spacing. The components of the mechanical tow package are modeled to inform array construction estimating vibration and flow noise. A turbulent boundary layer model for flow noise estimation and environmental noise model determines the gains and cutoff frequencies necessary for performance. The comprehensive performance model is compared with a parameter estimation from test data to quantify array performance. \nTowed arrays are subject to environmental extremes\, with time at sea being costly. To increase the reliability\, the array is designed using field replaceable pressure tolerant components including hydrophones\, pre-amplifiers\, power modules\, telemetry and analog to digital conversion units. All components are verified by pressure chamber testing to ensure operation at depth. This large aperture array was able to be made without specialized facilities by utilizing modular interchangeable array interconnects allowing for conventional array populating and oil-filling methods with aperture lengths that are serviceable onboard research vessels. Array design\, fabrication and assembly was performed on-site at Northeastern University in Boston\, Massachusetts. Examples of passive acoustic data from array deployment during a sea trial in the U.S. Northeast coast are presented illustrating array capabilities. \nCommittee: \nProf. Purnima Ratilal Makris (Advisor)\nProf. Marvin Onabajo\nProf. Yongmin Liu\nDr. Alessandra Tesei
URL:https://ece.northeastern.edu/event/matthew-schinaults-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230227T130000
DTEND;TZID=America/New_York:20230227T150000
DTSTAMP:20260425T010216
CREATED:20230131T200745Z
LAST-MODIFIED:20230131T200745Z
UID:6074-1677502800-1677510000@ece.northeastern.edu
SUMMARY:COE PhD Research Expo
DESCRIPTION:The College of Engineering is excited to announce the fifth annual COE PhD Research Expo\, and we invite all COE PhD students to submit a poster abstract. The expo is an excellent opportunity for your students to highlight their research and gain presentation experience before RISE. \nEvent:   COE PhD Research Expo\nDate:     Monday\, February 27\, 2023\nTime:    1:00pm – 3:00pm\nPlace:    McLeod Suites – Curry Student Center \nThe expo will take place following National Engineer’s Week. \nStudent Abstracts: \nPlease encourage your PhD students to submit poster abstracts by February 10\, 2023. The COE Communications Lab will offer interested students a poster preparation and presentation workshop early February. We will send details of the workshop to students soon. \nFaculty Judges: \nWe are looking for around ten faculty members to serve as judges. If you are available to judge between 1:30pm and 3:00pm on Monday\, February 27th please reach out to Taryn Urbanus (t.urbanus@northeastern.edu) by Friday\, February 17th. \n 
URL:https://ece.northeastern.edu/event/coe-phd-research-expo/
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:20230227T130000
DTEND;TZID=America/New_York:20230227T140000
DTSTAMP:20260425T010216
CREATED:20230223T212432Z
LAST-MODIFIED:20230223T212432Z
UID:6154-1677502800-1677506400@ece.northeastern.edu
SUMMARY:Yu Yin's PhD Proposal Review
DESCRIPTION:Committee: \nProf. Yun Fu (Advisor) \nProf. Sarah Ostadabbas \nProf. Ming Shao \nAbstract:\nThe community has long enjoyed the benefits of synthesizing data\, as it provides a reliable and controllable source for training machine learning models while reducing the need for data collection from the real world. Human face and body synthesis are especially appealing to research communities\, where model fairness and ethical deployment are critical concerns. However\, generating digit humans that are convincing\, realistic-looking\, identity-preserving\, and high-quality are still challenging in 2D and 3D image synthesis.\nThis dissertation investigates the potential for understanding human behavior by recreating it\, and can be broadly divided into three sections. (1) In Section one\, we explore the 2D image generation models and their interaction with face applications (i.e.\, landmark localization and face recognition tasks). Specifically\, super-resolution (SR) and landmark localization of tiny faces are highly correlated tasks. To this end\, we propose joint frameworks that enable face alignment and SR to benefit from one another\, hence enhancing the performance of both tasks. Moreover\, we demonstrate that face frontalization provides an effective and efficient way for face data augmentation and further improves face recognition performance in extreme pose scenarios. (2) In Section two\, we explore the 3D parametric generation models and how they support human body pose and shape estimation. Advancing technology to monitor our bodies and behavior while sleeping and resting is essential for healthcare. However\, keen challenges arise from our tendency to rest under blankets. To mitigate the negative effects of blanket occlusion\, we use an attention-based restoration module to explicitly reduce the uncertainty of occluded parts by generating uncovered modalities\, which further update the current estimation via a cyclic fashion. (3) In Section three\, we explore the 3D Nerf-based Generative models in generating high-quality images with consistent 3D geometry. We propose a universal method to surgically fine-tune these NeRF-GAN models in order to achieve high-fidelity animation of real subjects only by a single image.
URL:https://ece.northeastern.edu/event/yu-yins-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230222T173000
DTEND;TZID=America/New_York:20230222T183000
DTSTAMP:20260425T010216
CREATED:20230210T194123Z
LAST-MODIFIED:20230214T230832Z
UID:6103-1677087000-1677090600@ece.northeastern.edu
SUMMARY:Engineers Week: Fireside Chat – Break the Mold! Think Beyond Technology to Make an Impact in Unimaginable Ways
DESCRIPTION:Featuring Award-Winning Engineer and Commentator Dr. Shini Somara and Dean Gregory Abowd \nThink about engineering in a completely different way. Engineering is all around us and involves technology and beyond to solve the complex challenges of the world. Engineering is for everyone\, and everyone is for engineering! Dr. Somara has been featured on Crash Course\, BBC World\, Discovery Channel\, and more. Get ready for an engaging\, out-of-the-box session! \nWhen: Wed.\, February 22\, 5:30 p.m. – 6:30 p.m. (doors open at 5 p.m.) \nReception to follow with refreshments – opportunity to meet and network with Dr. Somara and Dean Abowd \nWhere: 17th Floor of East Village \nWho: For engineers and non-engineers (undergraduate\, graduate\, and high school students) \nRegister at: https://neweek.sites.northeastern.edu/
URL:https://ece.northeastern.edu/event/break-the-mold-think-beyond-technology-to-make-an-impact-in-unimaginable-ways/
LOCATION:East Village\, 17th floor\, 360 Huntington Ave\, East Village 17th floor\, Boston\, MA\, 02115\, United States
GEO:42.3394629;-71.0885286
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=East Village 17th floor 360 Huntington Ave East Village 17th floor Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave\, East Village 17th floor:geo:-71.0885286,42.3394629
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230221T173000
DTEND;TZID=America/New_York:20230221T193000
DTSTAMP:20260425T010216
CREATED:20230211T010858Z
LAST-MODIFIED:20230211T010858Z
UID:6120-1677000600-1677007800@ece.northeastern.edu
SUMMARY:Engineers Week: Cookies with the Dean
DESCRIPTION:Celebrating our COE students! Opportunity to meet and talk to Dean Gregory Abowd. Enjoy snacks (popcorn\, pretzels\, various desserts\, hot chocolate)\, free swag\, and photo booths! \nWhen: Tuesday\, February 21\, 5:30-7:30 p.m. \nWhere: Robinson Quad Bamboo & Industry Tents (near Mugar Life Sciences Building – 330 Huntington Ave) \nWho: COE students
URL:https://ece.northeastern.edu/event/engineers-week-cookies-with-the-dean/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230215T113000
DTEND;TZID=America/New_York:20230215T123000
DTSTAMP:20260425T010216
CREATED:20230210T210554Z
LAST-MODIFIED:20230210T210554Z
UID:6101-1676460600-1676464200@ece.northeastern.edu
SUMMARY:Yiyue Jiang's PhD Proposal Review
DESCRIPTION:“FPGA-based Accelerator of Neural Networks for Digital Predistortion” \nCommittee: \nProf. Miriam Leeser (Advisor) \nProf. John Dooley \nProf. Stefano Basagni \nAbstract: \nPower Amplifiers (PAs) are an essential part of wireless communications. \nAs wireless standards evolve and become more demanding\,  the requirements for PAs change as well.  Specifically\, PAs need to balance linearity and energy efficiency while adhering to 5G wireless standards and beyond. PA behaviors differ based on several criteria\, including the type of PA\, power levels\, and the environment. To overcome the nonlinear behavior of a PA\, a flexible system to achieve digital predistortion (DPD) is required that can rapidly adapt to its environment. \nIn many situations\, traditional methods such as the memory polynomial model cannot adapt to all these factors. Neural networks have been used for some years in RF and microwave engineering. Early work demonstrated the suitability of neural networks to model complicated active device characteristics. Current neural network based DPD systems all do the training offline and are therefore not real-time systems. To reduce the cost to upgrade hardware and to provide more flexibility to different power amplifiers’ linearization needs\, a specific neural network based reconfigurable\, adaptive\, and real-time digital predistortion system is proposed. This system targets Zynq All Programmable System on Chip (SoC) devices which feature an ARM processor and FPGA together with RF frontend on the same chip. The system proposed in this research combines real-time DPD with on-chip training. Furthermore\, most research on FPGA based inference accelerators targets classification problems with probability output. There is no accelerator working on the signal processing problem focusing on sample-by-sample output. Our proposed system is optimized in both algorithm and implementation targeting sample-by-sample processing with high accuracy and real-time efficiency. \n 
URL:https://ece.northeastern.edu/event/yiyue-jiangs-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230203T150000
DTEND;TZID=America/New_York:20230203T170000
DTSTAMP:20260425T010216
CREATED:20230201T200236Z
LAST-MODIFIED:20230201T200236Z
UID:6078-1675436400-1675443600@ece.northeastern.edu
SUMMARY:Kubra Alemdar's PhD Proposal Review
DESCRIPTION:“Overcoming and Engineering Wireless Signals for Communication and Computation” \nAbstract: \nThe phenomenal growth of connected devices\, especially rapid expansion of IoT networks and the increasing demand for wireless services are the main driving forces for the evolution of wireless technologies. However\, the realization of such technologies requires a radical transformation of existing infrastructures to satisfy the needs of changing wireless environments. The main limitation in delivering these systems stems from a huge diversity in their demands and constraints. To address this limitation\, this dissertation shows how wireless signals and their interaction with and within wireless propagation domain can be used as communication or computational tools that enable us to achieve certain novel tasks. Specifically\, we build i) cross-functionality architectures to engineer the wireless channel to a) enable the operation of emerging technologies\, and b) demonstrate a new paradigm for computing with wireless signals\, and ii) intelligently shape the wireless channel to create reliable communication links. \nThis dissertation presents an experimentally validated software-hardware system to deliver three key contributions: We present a physical layer solution for distributed networks that provides over-the-air (OTA) clock synchronization\, called as RFCLOCK\, to overcome the hurdle of implementing fine-grained synchronization for emerging technologies. We first develop the theory for such precision synchronization and second implement it in a custom-design\, which is compatible with commercial-off-the-shelf (COTS) software-defined radios (SDRs). We compare the performance of RFClock with popular wired and GPS-based hardware solutions\, both in terms of clock performance\, as well as impact on distributed beamforming. \nNext\, we propose an RIS-based (reconfigurable reflecting surface) spatio-temporal approach to enhance the link reliability for IoTs where sensors are small-factor designs with single-antenna in rich multipath environment. We demonstrate the design of RIS and how it can effectively perturb the environment\, generating multiple wireless propagation channels and achieving performance of multi-antenna receiver in a Single-Input Single-Output (SISO) link. We compare the performance of the system with multi-antenna receiver in terms of channel hardening and outage probability. \nFinally\, we propose AirFC\, a system harnessing the capability of OTA computation to run inference on a neural network (NN) consisting of a set of fully connected layers (FC) by leveraging multi-antenna systems. We experimentally demonstrate and validate that such computation is accurate enough when compared to its digital counterpart. \nAs part of proposed research ahead\, we will address the challenges of realizing RIS-assisted communication in non-stationary conditions where the wireless channel can abruptly change due to the dynamic environment. We will first demonstrate the conditions in which conventional channel estimation methods cannot be utilized. We will then propose a learning method to create directional beams through reflections from RIS towards target locations without estimating the channel. \nLocation: 632 ISEC \nCommittee: \nProf. Kaushik Chowdhury (Advisor) \nProf. Marvin Onabajo \nProf. Josep Jornet
URL:https://ece.northeastern.edu/event/kubra-alemdars-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230202T103000
DTEND;TZID=America/New_York:20230202T123000
DTSTAMP:20260425T010216
CREATED:20230117T234535Z
LAST-MODIFIED:20230117T234535Z
UID:6055-1675333800-1675341000@ece.northeastern.edu
SUMMARY:Qing Jin's PhD Proposal Review
DESCRIPTION:“Decoupling Efficiency-Performance Optimization for Modern Neural Networks” \nCommittee:\n\nProf. Yanzhi Wang (Advisor)\nProf. David R. Kaeli\nProf. Sunil Mittal\nProf. Jennifer Dy \n\nAbstract:\n\nDeep learning has achieved remarkable success in a variety of modern applications\, but this success is often accompanied by inefficiency in terms of storage and inference speed\, which can hinder their practical use on resource-constrained hardware. Developing highly efficient neural networks that maintain high prediction accuracy is crucial and challenging. This dissertation explores the potential for simultaneously achieving high efficiency and high prediction accuracy in neural networks\, and can be broadly divided into three sections. (1) In Section One\, we explore the implementation of highly efficient generative adversarial networks (GANs) capable of generating high-quality images within a predefined computational budget. The key challenge lies in identifying the optimal architecture for the generative model while simultaneously preserving the quality of the generated images from the compressed model\, despite its reduced computational cost. To achieve this\, we propose a novel neural architecture search (NAS) algorithm and a new knowledge distillation technique. (2) In Section Two\, we explore the challenge of quantizing discriminative models without relying on high-precision multiplications. To address this issue\, we present an innovative approach to determine the optimal fixed-point formats for both weights and activations based on their statistical properties. Our results demonstrate that high accuracy in quantized neural networks can be achieved without the need for high-precision multiplications. (3) In Section Three\, we delve into the challenge of training neural networks for innovative computing platforms\, specifically processing-in-memory (PIM) systems. Through a detailed mathematical derivation of the backward propagation algorithm\, we facilitate the training of quantized models on these platforms. Additionally\, through a thorough theoretical analysis of training dynamics\, we ensure convergence and propose a systematic solution for quantizing neural networks on PIM systems.
URL:https://ece.northeastern.edu/event/qing-jins-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230202T103000
DTEND;TZID=America/New_York:20230202T113000
DTSTAMP:20260425T010216
CREATED:20230126T204948Z
LAST-MODIFIED:20230126T205022Z
UID:6070-1675333800-1675337400@ece.northeastern.edu
SUMMARY:Amani Al-shawabka's PhD Proposal Review
DESCRIPTION:“Channel-and-Adversary-Resilient Radio Fingerprinting through Data-Driven Approaches at Scale” \nCommittee: \nProf. Tommaso Melodia (Advisor)\nProf. Kaushik Chowdhury\nProf. Francesco Restuccia \nAbstract: \nRadio fingerprinting authenticates wireless devices by leveraging tiny hardware-level imperfections inevitably present in the radio circuitry. This way\, devices can be directly identified at the physical layer– thus avoiding energy-expensive upper-layer cryptography that resource-limited embedded devices may not be able to afford. Recent advances have proven that employing deep learning algorithms can achieve fingerprinting accuracy levels that were impossible to achieve by traditional low-dimensional algorithms. Still\, the wireless research community lacks an exhaustive understanding of the challenges associated with developing robust\, reliable\, and channel-resilient radio fingerprinting through deep-learning approaches for practical applications. Key challenges are the non-stationarity of the wireless channel\, and the dynamic effects introduced by the operational environment\, which significantly limit fingerprinting applications by obscuring the hardware impairments associated with the transmitted waveform.\nIn this thesis\, we (i) develop a full-fledged\, systematic investigation to quantify the impact of the wireless channel by providing a first-of-its-kind evaluation on deep-learning-based fingerprinting algorithms\, examining the worst-case scenario (employing devices with identical radio circuitry) and at scale; (ii) develop large-scale open datasets for radio fingerprinting collected in diverse\, rich\, channel conditions and environments\, and using different technologies\, including WiFi and LoRa; (iii) identify conditions where hardware impairments are still detectable; and (iv) design\, implement\, and benchmark new data-driven algorithms to counter the degradation introduced by the wireless channel. Notably\, we propose a generalized\, real-time channel- and adversary-resilient data-driven approach to authenticate wireless devices at scale in practical scenarios. To the best of our knowledge\, our work for the first time improves the fingerprinting accuracy of the worst-case scenario with up to 4x and 6.3x for WiFi and LoRa technologies\, respectively.
URL:https://ece.northeastern.edu/event/amani-al-shawabkas-phd-proposal-review-2/
LOCATION:432 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
GEO:42.3396156;-71.0886534
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=432 ISEC 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:20230130T090000
DTEND;TZID=America/New_York:20230130T100000
DTSTAMP:20260425T010216
CREATED:20230125T213940Z
LAST-MODIFIED:20230125T213940Z
UID:6065-1675069200-1675072800@ece.northeastern.edu
SUMMARY:Sai Geetha Seri's PhD Proposal Review
DESCRIPTION:“Advancing Passive Ocean Acoustic Waveguide Remote Sensing for Detection of Fish Sounds\, Seismo-Acoustic Airgun Signals\, and Marine Mammal Vocalizations including Instrumentation Enhancements” \nCommittee: \nProf Purnima Ratilal Makris (Advisor)\nProf Josep M Jornet\nDr Nils Olav Handegard \nAbstract: \nUnderwater passive acoustic monitoring is important for understanding the marine environment\, since many ocean entities produce sound that can travel long ranges especially at low frequencies. For instance\, sound plays a vital role in the communication\, navigation\, and behavior of many marine biological organisms. Human activities in the ocean\, such as shipping\, offshore piling\, and energy prospecting\, generate a wide range and levels of sound. Natural environmental processes\, such as the passage of a hurricane and offshore seismicity are sources of underwater sound. In this thesis\, the instantaneous wide-area Passive Ocean Acoustic Waveguide Remote Sensing (POAWRS) technology implemented with a coherent hydrophone array is developed further and enhanced in a number of ways. First\, the automatic detection and analysis of man-made seismo-acoustic airgun signals employed in offshore geophysical and energy exploration surveys is investigated. Next\, the POAWRS technique is applied successfully for the first time toward the analysis and identification of sounds from some oceanic fish species in the wild using an eight-element prototype hydrophone array. Probability of Detection (PoD) regions are quantified separately for both the seismo-acoustic signals and fish sounds to provide an understanding of the horizontal spatial propagation extent of the acoustic signals from these sources. Finally\, we demonstrate significant enhancements in monitoring marine mammal sounds to include real-time capability and over a wider frequency range via a new in-house developed and fabricated 160-element coherent hydrophone array system. Here\, data from three distinct receiver array systems are analyzed\, presenting a technological evolution in the sensor systems utilized to implement and advance the POAWRS approach for ocean sensing. Development and integration of data acquisition approaches for both acoustic and non-acoustic sensors contained in the in-house developed array are discussed\, including design challenges and solutions.
URL:https://ece.northeastern.edu/event/sai-geetha-seris-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230111T100000
DTEND;TZID=America/New_York:20230111T120000
DTSTAMP:20260425T010216
CREATED:20230104T212321Z
LAST-MODIFIED:20230104T212321Z
UID:6042-1673431200-1673438400@ece.northeastern.edu
SUMMARY:Yukui Luo's PhD Proposal Review
DESCRIPTION:“Securing FPGA as a Shared Cloud-Computing Resource: Threats and Mitigations” \nAbstract:\nWith the widespread adoption of cloud computing\, the demand for programmable hardware acceleration devices\, such as field-programmable gate array (FPGA)\, has increased. To further improve the performance of FPGA-enabled cloud computing\, one promising technology is to virtualize the hardware resources of an FPGA device\, which allows multiple users to share the same FPGA. This solution can provide on-demand instances at the FPGA resource and time levels\, significantly improving the utilization and energy efficiency of the FPGA devices. However\, due to the hardware reconfigurability of FPGA\, current virtualization methods for multi-tenant GPU and TPU instances are incompatible with multi-tenant FPGA virtualization.We define the threat model for multi-tenant FPGA and discuss the security issues related to Confidentiality\, Data Integrity\, and Availability. Based on an analysis of potential attacks\, we present our latest research results and propose two future research directions for mitigations: (1) a multi-tenant FPGA plug-to-play obfuscation module and (2) a hardware-software co-designed multi-tenant FPGA virtualization system\, which includes a hypervisor and a smart multi-tenant FPGA platform.\n\n\nCommittee:\n\nProf. Xiaolin Xu (Advisor) \nProf. Yunsi Fei\nProf. Xue Lin
URL:https://ece.northeastern.edu/event/yukui-luos-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221215T090000
DTEND;TZID=America/New_York:20221215T110000
DTSTAMP:20260425T010216
CREATED:20221213T011124Z
LAST-MODIFIED:20221213T011124Z
UID:6023-1671094800-1671102000@ece.northeastern.edu
SUMMARY:Daniel Uvaydov's PhD Proposal Review
DESCRIPTION:“Real-Time Spectrum Sensing for Inference and Control”\n\nAbstract:\nSpectrum sensing can enable the next generation of wireless applications ranging from opportunistic spectrum access to cognitive radio networks. The key unaddressed challenges of spectrum sensing are that (i) it has to be performed with extremely low latency over varying bandwidths and must guarantee strict real-time processing constraints; (ii) its underlying algorithms need to be extremely accurate\, and flexible enough to work with different wireless bands and protocols to find application in real-world settings. We address these challenges in multiple wireless applications by utilizing Deep Learning techniques as the main vehicle of spectrum sensing for both inference and control. By leveraging mechanisms such as data augmentation\, channel attention\, voting\, and segmentation we are able to push beyond the capabilities of existing Deep Learning techniques and create generalizable spectrum sensing algorithms. Furthermore we deploy different spectrum sensing solutions in real testbeds for over the air evaluations and applicable proof-of-concepts.\n\n\nCommittee:\n\nProf. Tommaso Melodia (Advisor) \nProf. Francesco Restuccia\nProf. Kaushik Chowdhury
URL:https://ece.northeastern.edu/event/daniel-uvaydovs-phd-proposal-review/
LOCATION:432 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
GEO:42.3396156;-71.0886534
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=432 ISEC 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:20221209T120000
DTEND;TZID=America/New_York:20221209T133000
DTSTAMP:20260425T010216
CREATED:20221201T022737Z
LAST-MODIFIED:20221201T022840Z
UID:6006-1670587200-1670592600@ece.northeastern.edu
SUMMARY:Alexey Tazin's PhD Dissertation Defense
DESCRIPTION:“Composition of UML Class Diagrams Using Category Theory and External Constraints” \nAbstract:\nIn large software development projects there is always a need for refactoring and optimization of the design. Usually software designs are represented using UML diagrams (e.g class diagrams). A software engineering team may create multiple versions of class diagrams satisfying some external constraints. In some cases\, subdiagrams of the developed diagrams can be selected and combined into one diagram. It is difficult to perform this task manually since manual process is very time consuming\, is prone to human errors\, and is not manageable for large projects. In this dissertation we present an algorithmic support for automating the generation of composed diagrams\, where the composed diagram satisfies a given collection of external constraints and is optimal with respect to a given objective function. The composition of diagrams is based on the colimit operation from category theory. The developed approach was verified experimentally by generating random external constraints (expressed in SPARQL and OWL)\, generating random class diagrams using these external constraints\, generating composed diagrams that satisfy these external constraints\, and computing class diagram metrics for each composed diagram. \nCommittee: \nProf. Mieczyslaw Kokar (Advisor) \nProf. David Kaeli \nDr. Jeff Smith
URL:https://ece.northeastern.edu/event/alexey-tazins-phd-dissertation-defense/
END:VEVENT
END:VCALENDAR