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X-ORIGINAL-URL:https://ece.northeastern.edu
X-WR-CALDESC:Events for Department of Electrical & Computer Engineering
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230202T103000
DTEND;TZID=America/New_York:20230202T113000
DTSTAMP:20260629T143855
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230202T103000
DTEND;TZID=America/New_York:20230202T123000
DTSTAMP:20260629T143855
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:20230203T150000
DTEND;TZID=America/New_York:20230203T170000
DTSTAMP:20260629T143855
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:20230215T113000
DTEND;TZID=America/New_York:20230215T123000
DTSTAMP:20260629T143855
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:20230221T173000
DTEND;TZID=America/New_York:20230221T193000
DTSTAMP:20260629T143855
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:20230222T173000
DTEND;TZID=America/New_York:20230222T183000
DTSTAMP:20260629T143855
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230227T130000
DTEND;TZID=America/New_York:20230227T140000
DTSTAMP:20260629T143855
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:20230227T130000
DTEND;TZID=America/New_York:20230227T150000
DTSTAMP:20260629T143855
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
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