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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230918T083000
DTEND;TZID=America/New_York:20230918T150000
DTSTAMP:20260518T001530
CREATED:20230915T172337Z
LAST-MODIFIED:20230915T172337Z
UID:38782-1695025800-1695049200@coe.northeastern.edu
SUMMARY:Technical Exchange Program between NU and DFG Delegation
DESCRIPTION:8:30-8:50 am: Lab tour of the NU Cleanroom\, labs\, etc. (Meet at the entrance of Egan Building\, 120 Forsyth Street\, Boston\, MA 02115\, Led by Nian Sun)\n8:50-9am Move to McLeod Suites\, Curry Student Center\, Northeastern University\n9:00-9:15: Opening remarks from NU and DFG (VPRD Kim Holloway\, Senator Professor Marion Merklein)\n9:15-10:30: Presentations from NU (9 speakers with 8 minutes each)\n\nRavinder Dahiya\, Electrical and Computer Engineering\nMatteo Rinaldi\, Electrical and Computer Engineering\nYongmin Liu\, Electrical and Computer Engineering\, Mechanical and Industrial Engineering\nXufeng Zhang\, Electrical and Computer Engineering\nBen Davaji\, Electrical and Computer Engineering\nSwastik Kar\, Physics Department\nPaul Stevenson\, Physics Department\nRuobing Bai\, Mechanical and Industrial Engineering\nHongli Zhu\, Mechanical and Industrial Engineering\n\n\n10:30-10:45: Coffee break\n10:45-11:45: Presentations from NU and DFG Delegation (7 speakers with 8 minutes each)\n\nSrinivas Tadigadapa\, Electrical and Computer Engineering\nSrirupa Chakraborty\, Chemical Engineering\nNian Sun\, Electrical and Computer Engineering\nArne Berger\, University of Applied Sciences Anhalt\nRobert Böhm\, Leipzig University of Applied Sciences\nAlexander Prange\, University of Applied Sciences Niederrhein\nJessica Friess\, University of Applied Sciences Niederrhein\n\n\n12-1:30pm: Lunch break (working lunch at McLeod Suites)\n1:30-2:30pm: Presentations from DFG Delegation (7 speakers with 8 minutes each)\n\nMargit Geißler Bonn-Rhein-Seig University of Applied Sciences\nJens Helbig\, Nürnberg Tech\nBenjamin Neding\, University of Applied Sciences Lübeck\nJörn V. Wochnowski University of Applied Sciences Lübeck\nRomana Piat\, University of Applied Sciences Darmstadt\nHolger Saage\, Univ. of Applied Sciences Landshut\, Competence Center for Lightweight Design\nJulian Tornow\, Hochschule Ruhr-West University of Applied Sciences\n\n\n2:30-3pm: DFG / NU discussions on potential collaborations\, programs\, etc.
URL:https://coe.northeastern.edu/event/technical-exchange-program-between-nu-and-dfg-delegation/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230901T130000
DTEND;TZID=America/New_York:20230901T140000
DTSTAMP:20260518T001530
CREATED:20230816T150411Z
LAST-MODIFIED:20230816T150432Z
UID:37858-1693573200-1693576800@coe.northeastern.edu
SUMMARY:Mostafa Abedi PhD Proposal Review
DESCRIPTION:Title: Power-Efficient and Security-Enhancing Techniques for Ultra-low Power IoT  Devices \nCommittee Members: \nProf. Aatmesh Shrivastava (Advisor)\nProf. Marvin Onabajo\nProf. Nian X. Sun \nAbstract:\nInternet-of-things (IoT) devices often rely on ambient energy sources such as photovoltaic (PV) cells and thermoelectric generators (TEGs) for their operation. Minimizing power loss through ambient energy harvesting optimization can significantly extend the battery life or support battery-free sensor nodes in IoT devices. A maximum power point tracking (MPPT) circuit is often used for impedance matching to maximize energy transfer efficiency. This research proposes an ultra-low power\, high-tracking efficiency MPPT circuit based on Hill-Climbing (HC) algorithm suitable for micro-power DC harvesters. The proposed system employs a modified version of the hill-climbing algorithm. In case of input power changes and consequent deviation of the harvester from the MPP\, an integrated Power Change Detector (PCD) is proposed to reactivate the MPPT circuit. The PCD detects changes in input power and activates the MPPT circuit\, enabling automatic activation and resulting in substantial power savings. Furthermore\, due to the proposed power estimation technique\, the MPPT is not dependent on the internal structure of the energy source\, and its tracking efficiency is unrelated to the conversion ratio of the converter. This approach enables us to achieve a peak tracking efficiency of over 99.9\%. To adjust the input power of the harvester to track the maximum power point\, we propose a new\, efficient Pulse Width Modulation (PWM) circuit. This circuit exhibits a wide duty cycle range\, low power consumption\, linearity\, and robustness against variations. \nThis research also focuses on increasing the security of IoT devices. In the past\, chip fabrication was mostly done internally by semiconductor firms. Now\, it is more collaborative\, pulling in designs from various sources and having a few factories produce them. This new way of working means that companies that only handle design might face more challenges like the threat of hardware Trojans (HT) being added either during the design phase or production. With that in mind\, we introduce a different circuit design approach. We aim to find these Trojans\, particularly the newer analog Trojans. The idea is to boost the security of IoT devices by detecting these issues early. In addition\, to improve the security of IoT systems\, we propose an ultra-low power energy monitoring system (EMS) to detect and mitigate denial-of-sleep (DoSL) attacks. In this project\, we explore a new method of defense against DoSL attacks by monitoring energy consumption. We will implement a low-power system to monitor the lifetime of the IoT node by continuously evaluating the harvested\, stored\, and consumed energy in the node.
URL:https://coe.northeastern.edu/event/mostafa-abedi-phd-proposal-review/
LOCATION:532 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230825T100000
DTEND;TZID=America/New_York:20230825T110000
DTSTAMP:20260518T001530
CREATED:20230816T145957Z
LAST-MODIFIED:20230816T145957Z
UID:37862-1692957600-1692961200@coe.northeastern.edu
SUMMARY:Julian Gutierrez PhD Proposal Review
DESCRIPTION:Title: Towards Real-Time Safe Flight Paths for Urban Air Mobility \nCommittee Members:\nProf. David Kaeli\, (Advisor)\nProf. Pau Closas\nDr. Evan Dill (NASA)\nDr. Natasha Neogi (NASA) \nAbstract:\nThe emergence and development of advanced technologies and vehicle types have created a growing demand for introducing new forms of flight operations. These new and increasingly complex operational paradigms\, such as Advanced and Urban Air Mobility (AAM/UAM)\, present regulatory authorities and the aviation community with the challenge of finding methods to integrate these emerging operations without significant additional risk to pedestrians and infrastructure. Predictive and autonomous risk mitigation capabilities become critical to meet this challenge. However\, urban environments experience effects that are computationally expensive to model\, limiting conventional aviation concepts\, policy\, and risk prediction tools from being effectively translated into this space. With the emergence of High-Performance Computing (HPC) ecosystems in the last two decades\, we can use these software and hardware capabilities to help bridge the gap between real-time predictive responses and modeling accuracy. \nIn this dissertation we first present a simulation framework to estimate the quality of Global Navigation Satellite System (GNSS) performance for autonomous aircraft in urban environments. We propose a new algorithm designed for HPC to accelerate modeling the characteristic effects of dense urban canyons on GNSS\, allowing the extension of established GNSS integrity techniques into urban navigation. Additionally\, we provide a thorough validation of the simulator\, which proves high-accuracy modeling when compared to sensors in the real world. Second\, we use this simulation framework as the input into a new 4D path-planning algorithm based on an adaptation of the Bellman-Ford algorithm. HPC techniques are employed to accelerate the algorithm to produce flight paths that minimize exposure to GNSS risks. We evaluate the computational cost of satellite availability fluctuations by prioritizing events when satellite availability changes as triggers for these updates.
URL:https://coe.northeastern.edu/event/julian-gutierrez-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230818T143000
DTEND;TZID=America/New_York:20230818T153000
DTSTAMP:20260518T001530
CREATED:20230817T142938Z
LAST-MODIFIED:20230817T142938Z
UID:37895-1692369000-1692372600@coe.northeastern.edu
SUMMARY:Xu Yizhe MS Thesis Defense
DESCRIPTION:Title:\nIntegration of Polyimide Flexible PCB Wings in Northeastern’s Aerobat \nLocation:\nRoom: ISEC 532\, Teams link \nCommittee Members:\nProf. Alireza Ramezani(Advisor)\nProf. Rifat Sipahi \nAbstract:\nThe principal aim of this Master’s thesis is to propel the optimization of the membrane wing structure of the Northeastern Aerobat through origami techniques and enhancing its capacity for secure hovering within confined spaces. Bio-inspired drones offer distinctive capabilities that pave the way for innovative applications\, encompassing wildlife monitoring\, precision agriculture\, search and rescue operations\, as well as the augmentation of residential safety. The evolved noise-reduction mechanisms of birds and insects prove advantageous for drones utilized in tasks like surveillance and wildlife observation\, ensuring operation devoid of disturbances. Traditional flying drones equipped with rotary or fixed wings encounter notable constraints when navigating narrow pathways. While rotary and fixed-wing systems are conventionally harnessed for surveillance and reconnaissance\, the integration of onboard sensor suites within micro aerial vehicles (MAVs) has garnered interest in vigilantly monitoring hazardous scenarios in residential settings. Notwithstanding the agility and commendable fault tolerance exhibited by systems such as quadrotors in demanding conditions\, their inflexible body structures impede collision tolerance\, necessitating operational spaces free of collisions. Recent years have witnessed an upsurge in integrating soft and pliable materials into the design of such systems; however\, the pursuit of aerodynamic efficiency curtails the utilization of excessively flexible materials for rotor blades or propellers. This thesis introduces a guard design incorporating feedback-driven stabilizers\, enabling stable hovering flights within Northeastern’s Robotics-Inspired Study and Experimentation (RISE) cage.
URL:https://coe.northeastern.edu/event/xu-yizhe-ms-thesis-defense/
LOCATION:532 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230818T140000
DTEND;TZID=America/New_York:20230818T150000
DTSTAMP:20260518T001530
CREATED:20230817T143207Z
LAST-MODIFIED:20230817T143207Z
UID:37889-1692367200-1692370800@coe.northeastern.edu
SUMMARY:Haoling Li MS Thesis Defense
DESCRIPTION:Title: Ultracompact and Conformal Magnetodielectric Antennas \nCommittee Members:\nProf. Nian-Xiang Sun (Advisor)\nProf. Xufeng Zhang\nProf. Marvin Onabajo \nAbstract:\nNovel approaches are needed for improving antenna performance\, enhancing efficiency\, and reducing the size\, profile\, number\, and signature of antennas. Efficient conformal antennas are increasingly replacing traditional antennas across platforms such as ships\, aircraft\, and human interfaces. Magnetodielectric antennas made with high-hesistivity magnetic materials are getting more and more attention. Defined as the maximum magnetic conductivity\, hesitivity is directly related to the radiation efficiency of magnetodielectric antennas\, with a higher hesitivity corresponding to higher attainable efficiency. In this study\, new ultra-compact conformal magnetodielectric antennas are demonstrated\, employing commercially available ferrite ceramic substrates. Through rigorous simulation and fabrication\, a comprehensive comparison of our magnetodielectric antennas with reference monopole antennas demonstrated superior efficiency\, enhanced gain\, bandwidth\, and a substantial reduction in antenna size compared to monopole antennas. State-of-the-art hesitivity as high as 6×10^6 Ω/m has been reported in CoZrNb alloy films\, with an expectation of further 10× improvement in thin carrier substrates. This study forecasts the potential development of new magnetic materials with higher hesitivity\, leading to further advancements in magnetodielectric antennas with enhanced radiation efficiency and ground plane immunity.
URL:https://coe.northeastern.edu/event/haoling-li-ms-thesis-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230818T100000
DTEND;TZID=America/New_York:20230818T110000
DTSTAMP:20260518T001530
CREATED:20230817T143431Z
LAST-MODIFIED:20230817T143431Z
UID:37897-1692352800-1692356400@coe.northeastern.edu
SUMMARY:Jaehyeon Ryu PhD Dissertation Defense
DESCRIPTION:Title: Materials Strategies for Scaling Soft Neuroelectrode Arrays \nLocation: Snell 012/Teams \nCommittee Members:\nProf. Hui Fang (Advisor)\nProf. Yongmin Liu\nProf. Ryan Koppes \nAbstract:\nThe evolution of electronics to seamlessly interface with biological tissue hinges on addressing multifaceted material constraints spanning electrochemical\, electrical\, and mechanical domains. Conventional bioelectronic interfaces\, while endowed with established electrochemical functionality\, remain hampered by rigidity that contradicts the pliability of surrounding tissue. While conductive materials exhibiting tissue-like softness and stretchability have been realized\, their potential for electrochemical probing of tissue is impeded by strain-induced performance degradation and an ill-suited integration with the irregular tissue interface. Nevertheless\, a significant challenge in ultrasoft bioelectronics pertains to scalability for achieving cellular resolution\, primarily due to mechanical disparities between conventional microelectronic materials and soft elastomer substrates. In this thesis\, by using a novel approach involving a multifunctional nanomesh\, composed of distinct purposefully designed layers including polymer for mechanical buffering\, metal for electrical conduction\, and low impedance coating for electrochemical interfacing in the same nanomeshed structure\, the resultant microelectrodes\, scalable down to 20μm at cellular resolution\, exhibit comparable performance to rigid devices alongside a stretchablity of approximately 50%\, with potential for future enhancement through in-plane structural optimizations. In addition\, we introduce a high-density neuroelectronic array featuring 256 filamentary neuroelectrodes on a flexible substrate. These electrodes are integrated with a single-transistor multiplexing acquisition circuit\, effectively reducing noise and footprint while potentially extending device lifetime. Remarkably\, the array’s rollable contact pad design allows for minimally invasive delivery through a syringe. Experimental validation demonstrates the array’s capability to record neural signals with high tone decoding accuracy. Utilizing high-density arrays of these microelectrode arrays\, this unique frame works holds significant promise for advancing the field of neural interfacing\, enabling a wide range of application from fundamental neuroscience studies to various biomedical applications.
URL:https://coe.northeastern.edu/event/jaehyeon-ryu-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230817T130000
DTEND;TZID=America/New_York:20230817T140000
DTSTAMP:20260518T001530
CREATED:20230816T150620Z
LAST-MODIFIED:20230816T150620Z
UID:37856-1692277200-1692280800@coe.northeastern.edu
SUMMARY:Zhiyong Zhang PhD Proposal
DESCRIPTION:Title: Towards Indoor Mapping and Navigation with Perceptual Aliasing using Visual Semantic SLAM \nCommittee Members:\nProf. Hanumant Singh (Advisor)\nProf. Huaizu Jiang\nProf. David Rosen \nAbstract:\nModern SLAM (Simultaneous Localization And Mapping) techniques allow us to create accurate 3D maps of the environment primarily using visual sensors in GPS-denied regions. In this context\, numerous deep learning-based approaches have emerged\, enabling the extraction of rich semantic information from images\, including shapes\, objects\, and text. \nLeveraging these technologies\, our aim is to construct comprehensive 3D maps of indoor environments\, which could be utilized by robots for path planning and navigation. Additionally\, the solution can be integrated with a large language model\, enabling the robot to interact intuitively with people. \nThis research comprises four main components: Semantic Feature Extraction and Tracking with SLAM: Given that the same semantic features can appear in multiple frames\, some of which may not be conducive to feature detection and recognition (such as blurry images or distant views)\, we are developing a pipeline to ensure the optimal detection and recognition of semantic features within the most suitable frame. The pipeline also involves tracking the same feature across frames while maintaining its 3D location in the global map. \nResolving Perceptual Aliasing: Many indoor places can exhibit high visual similarity\, which confuses the robot when powered up with a prior map in its memory. Semantic features can be used to localize the robot in the map\, determining its specific floor or room. This capability can also aid SLAM in performing loop closure with high-level information. \nCross-Floor Constraints for SLAM Optimization: Most buildings contain a symmetric layout across floors\, which can be exploited to establish constraints between them. For instance\, vertically aligned rooms like 425 and 525\, as well as elevators\, offer opportunities for vertical constraint. Such constraints can enhance SLAM optimization\, resulting in improved map accuracy. \nIndoor Path Planning and Navigation: Once we have a comprehensive 3D map of the indoor environment\, path planning becomes an intuitive way to utilize this map. With semantic features integrated into the map\, the robot can associate 3D point clouds with high-level information\, such as door numbers or office names. Large language models are available to provide a more human-like way to interact with the robot. For example\, a command like “Navigate to Professor Hanumant’s office and locate the book ‘The Hitchhiker’s Guide to the Galaxy'” can be executed by the robot.
URL:https://coe.northeastern.edu/event/zhiyong-zhang-phd-proposal/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230817T090000
DTEND;TZID=America/New_York:20230817T110000
DTSTAMP:20260518T001530
CREATED:20230802T192116Z
LAST-MODIFIED:20230802T192116Z
UID:37698-1692262800-1692270000@coe.northeastern.edu
SUMMARY:Jagatpreet Nir PhD Proposal
DESCRIPTION:Title: Low Contrast Visual Sensing and Inertial Navigation in GPS Denied Environments \nCommittee Members:\nProf. Hanumant Singh\nProf. Martin Ludvigsen\nProf. Pau Closas\nProf. Michael Everrett \nAbstract:\nVisual inertial navigation has shown remarkable performance in publicly available datasets\, assuming certain ideal conditions such as textured scenes\, uniform illumination\, and static environments. However\, real-world scenarios often violate these assumptions\, resulting in significant visual degradation. Consequently\, the classical visual navigation pipelines fail and produce erroneous results\, rendering these systems ineffective for demanding field robotic missions. \nThis research aims to enhance the robustness of visual-inertial systems in visually degraded situations\, taking a comprehensive approach from both systems and algorithm perspectives. The work encompasses two primary objectives. Firstly\, it focuses on refining the characterization of MEMS-based inertial sensors and their error propagation in position\, while proposing improved dead-reckoning algorithms. Secondly\, it explores the performance limits of visual navigation under moderate to extreme visual degradation and investigates novel algorithms that leverage deep learning methods to bolster the visual navigation engine. To validate the efficacy of these advancements\, new datasets comprising drone and underwater robot scenarios are utilized\, demonstrating the applicability of this work in field robotic applications. \nBy addressing the limitations of existing visual-inertial navigation systems and developing robust algorithms\, this research aims to significantly enhance the reliability and performance of such systems in visually degraded environments\, thus expanding their potential for real-world applications in demanding field robotic missions.
URL:https://coe.northeastern.edu/event/jagatpreet-nir-phd-proposal/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230815T153000
DTEND;TZID=America/New_York:20230815T163000
DTSTAMP:20260518T001530
CREATED:20230816T150241Z
LAST-MODIFIED:20230816T150241Z
UID:37860-1692113400-1692117000@coe.northeastern.edu
SUMMARY:Sumegha Singhania MS Thesis Defense
DESCRIPTION:Title: Exploring Log of RGB Space as a Better Input for Computer Vision Tasks \nCommittee Members:\nProf. Bruce Maxwell (Advisor)\nProf. Hanumant Singh\nProf. David Rosen\nProf. Mahdi Imani \nAbstract:\nThere are specific\, physics-based rules that govern the interaction of light and matter. Though studied extensively in the greater computer vision community\, these rules are largely broken by common image processing techniques like JPEG compression and sRGB conversion. While the reliability and usability of color and intensity found in RAW images might better train networks to successfully complete vision-based tasks\, these smaller\, more heavily-processed formats have become the standard input for training sets. As a result\, many of the images used to train neural networks do not retain the inherent structure that would enable neural networks to learn more general rules that exist in the natural world. \nWe hypothesize that using linear RGB or log RGB images\, which preserve the physics of reflection\, can simplify the learning process for certain vision tasks\, enhance overall robustness and performance\, and provide invariance to visual variations that exist in real-world vision applications. Our research demonstrates that employing linear and log RGB images to train deep networks for the task of object detection improves their performance when using the same network architecture and the same set of training images. Additionally\, we also show that the networks trained on linear and log RGB show greater resilience to variations in intensity and color balance. Specifically\, the network trained on linear and log RGB inputs shows invariance to intensity and color balance variations that were not encountered during training\, while the network trained on the same images in sRGB JPEG format experiences significant performance degradation. To understand the reasons behind this disparity\, we analyze and visualize low-level features in log RGB\, linear RGB\, and JPEG data. Our findings reveal that the log space preserves certain relevant features across variations in intensity and color balance.
URL:https://coe.northeastern.edu/event/sumegha-singhania-ms-thesis-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230811T140000
DTEND;TZID=America/New_York:20230811T150000
DTSTAMP:20260518T001530
CREATED:20230731T152837Z
LAST-MODIFIED:20230731T152837Z
UID:37660-1691762400-1691766000@coe.northeastern.edu
SUMMARY:Jinkun Zhang PhD Proposal Review
DESCRIPTION:Location: ISEC305 \nTitle: Low-latency forwarding\, caching and computation placement in data-centric networks \nCommittee Members:\nProf. Edmund Yeh (Advisor)\nProf. Stratis Ioannidis\nProf. Kaushik Chowdhury \nAbstract:\nWith the exponential growth of data- and computation-intensive network applications\, such as real-time augmented reality/virtual reality rendering and large-scale language model training\, traditional cloud computing frameworks exhibit inherent limitations. These limitations include significant round-trip delays caused by backhaul network capacity bottlenecks and exorbitant costs associated with centralized computing power\, e.g.\, training GPT-4 requires over 16\,000 A100 GPUs.\nTo address these challenges\, dispersed computing has emerged as a promising next-generation networking paradigm. By enabling geographically distributed nodes with heterogeneous computation capabilities to collaborate\, dispersed computing overcomes the bottlenecks of traditional cloud computing and facilitates in-network computation tasks\, including the training of large models. \nFurthermore\, in data-centric networking\, communication and computation are resolved around data names instead of host addresses.\nThe deployment of network caches\, by enabling data reuse\, offers substantial benefits for data-centric networks.\nFor instance\, consider a scenario where multiple machine learning applications seek to train different models simultaneously. These application could (partially) share data samples and intermediate results\, and carefully designed data-reusing mechanisms become necessary. Optimal caching of data or intermediate results can significantly reduce the overall training cost\, compared to each application independently gathering and transmitting data. \nTo efficiently manage computation and storage resources in heterogeneous data-centric networks\, several frameworks have been proposed with different design objectives\, such as optimizing throughput or incorporating multicast flows. However\, previous approaches have failed to minimize average user delay despite the latency sensitivity of numerous real-world applications. \nThis proposal aims to address this gap by introducing a low-latency framework that jointly optimizes packet forwarding\, storage deployment\, and computation placement. The proposed framework effectively supports data-intensive and latency-sensitive computation applications in data-centric networks with heterogeneous communication\, storage\, and computation capabilities. \nSpecifically\, to minimize user latency in congestible networks\, we model delays caused by link transmissions and CPU computations using\ntraffic-dependent nonlinear functions. We formulate the joint forwarding\, caching\, and computation problem as an NP-hard mixed-integer non-submodular optimization\, for which no constant-factor approximation algorithms are currently known. To make progress\, we approach the joint problem by dividing it into two subproblems: the joint forwarding/computation problem and the joint forwarding/caching problem. Despite the non-convexity of the former subproblem\, we provide a set of sufficient optimality conditions that lead to a distributed algorithm with polynomial-time convergence to the global optimum. For the latter subproblem\, we demonstrate its NP-hardness and non-submodularity\, even after continuous relaxation. We show that the objective function is a sum of a convex function and a geodesic convex function\, and propose a set of conditions that provide a finite bound from the optimum. To the best of our knowledge\, our method represents the first analytical progress in addressing the joint caching and forwarding problem with arbitrary topology and non-linear costs. Furthermore\, our theoretical bound leads to a constant-factor approximation under additional assumptions. \nAs future work\, we propose to develop a novel in-network large model training framework\, building upon the aforementioned method.\nDue to the substantial model size and extensive data samples required for training\, centralized model storing and training are nearly infeasible for small and intermediate service providers.\nConsequently\, we will adopt horizontal model partitioning and distribute different model layers across the network nodes through caching.\nData samples or batches are input into the network and undergo the forward-backward procedure for training. Our objective is to jointly optimize data forwarding and model/computation placement\, thereby minimizing the total cost of transmission\, computation\, and storage. \nFurthermore\, we introduce several network resource allocation optimization problems related to data-centric networking\, thereby expanding the scope of our proposal.
URL:https://coe.northeastern.edu/event/jinkun-zhang-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230809T150000
DTEND;TZID=America/New_York:20230809T163000
DTSTAMP:20260518T001530
CREATED:20230731T152624Z
LAST-MODIFIED:20230807T134836Z
UID:37662-1691593200-1691598600@coe.northeastern.edu
SUMMARY:Yuanyuan Li PhD Dissertation Defense
DESCRIPTION:Title: Sub-modularity in Cache Networks \nCommittee Members:\nProf. Stratis Ioannidis\nProf. Lili Su\nProf. Edmund Yeh \nAbstract:\nAs information-based demand surges\, distributed network services\, e.g.\, cache networks\, play an important role to mitigate network traffic. Cache networks are a natural abstraction for many applications\, including information-centric networks\, content delivery networks\, cloud computing\, and edge/wireless IoT. How to allocate resources (routing\, placing items in caches\, flow control\, etc.) in cache networks is a crucial problem\, as resources (storage space\, and bandwidths) are usually limited. Resource allocation in networks has been traditionally approached through classic convex optimization. However\, simple problems becomes combinotorial in cache networks\, which leads to NP-hardness. Enlightened by several works studying cache networks\, we identify a useful property\, submodularity\, which is the key to approximation algorithms solving those NP hard resource allocation problem in cache networks. \nLeveraging submodularity\, we study a cache network\, in which intermediate nodes equipped with caches can serve content requests\, from different angles. \nFirst\, we model this network as a universally stable queuing system\, in which packets carrying identical responses are consolidated before being forwarded downstream. We refer to resulting queues as $\info$ or counting queues\, as consolidated packets carry a counter indicating the packet’s multiplicity. Cache networks comprising such queues are hard to analyze; we propose two approximations: one via $\mminf$ queues\, and one based on $\info$ queues under the assumption of Poisson arrivals. We show that\, in both cases\, the problem of jointly determining (a) content placements and (b) service rates admits a poly-time\, $1-1/e$ approximation algorithm. We also show that our analysis\, with respect to both algorithms and associated guarantees\, extends to (a) counting queues over items\, rather than responses\, as well as to (b) queuing at nodes and edges\, as opposed to just edges. \nSecond\, we refer to the cost reduction enabled by caching as the caching gain\, and the product of the caching gain of a content request and its request rate as \emph{caching gain rate}. We aim to study \emph{fair} content allocation strategies through a utility-driven framework\, where each request achieves a utility of its caching gain rate\, and consider a family of $\alpha$-fair utility functions to capture different degrees of fairness. The resulting problem is an NP-hard problem with a non-decreasing submodular objective function. Submodularity allows us to devise a deterministic allocation strategy with an optimality guarantee factor arbitrarily close to $1-1/e$.  When $0 < \alpha \leq 1$\, we further propose a randomized strategy that attains an improved optimality guarantee\,  $(1-1/e)^{1-\alpha}$\, in expectation. \nThird\, we study a cache network\, and model the problem of jointly optimizing caching and routing decisions with link capacity constraints over an arbitrary network topology. This problem can be formulated as a continuous diminishing-returns(DR) submodular maximization problem under multiple continuous DR-supermodular constraints\, and is NP-hard. We propose a poly-time alternating primal-dual  heuristic algorithm\, in which primal steps produce solutions within $1-\frac{1}{e}$ approximation factor from the optimal. Through extensive experiments\, we demonstrate that our proposed algorithm significantly outperforms competitors. \nForth\, we study a cache network under arbitrary adversarial request arrivals. We propose a distributed online policy based on the online tabular greedy algorithm. Our distributed policy achieves sublinear $(1-\frac{1}{e})$-regret\, also in the case when update costs cannot be neglected. \nFinally\, we propose an {\em experimental design network} paradigm\, wherein learner nodes train possibly different Bayesian linear regression 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 first show that\, assuming Poisson data streams\, the global objective is a continuous DR-submodular function. We then propose a Frank-Wolfe type algorithm that outputs a solution within a $1-1/e$ factor from the optimal. Our algorithm contains a novel gradient estimation component which is carefully designed based on Poisson tail bounds and sampling.
URL:https://coe.northeastern.edu/event/yuanyuan-li-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230809T110000
DTEND;TZID=America/New_York:20230809T120000
DTSTAMP:20260518T001530
CREATED:20230802T192340Z
LAST-MODIFIED:20230802T192340Z
UID:37696-1691578800-1691582400@coe.northeastern.edu
SUMMARY:Nasim Soltani PhD Proposal
DESCRIPTION:Title: Deep Learning for the Physical Layer: From Signal Classification to Decoding \nLocation: ISEC 532 \nCommittee Members:\nProf. Kaushik Chowdhury (Advisor)\nProf. Stratis Ioannidis\nProf. Robert Nowak \nAbstract:\nThe growth in wireless spectrum usage has created new physical layer applications and intensified the importance of the existing ones. Physical layer applications ranging from device authentication to signal decoding and interpretation are traditionally handled by deterministic signal processing algorithms. Such algorithms\, while effective\, often require long sequences of data for decision making\, or need approximations of the environmental conditions\, such as noise models\, which may not be always correct in practical conditions. For these reasons\, traditional algorithms are not suitable for making quick decisions on the high rate wireless data with higher noise and interference that is a result of crowded spectrum. To this end\, deep learning-based methods have been explored extensively by the researchers to substitute for the traditional signal processing algorithms for the physical layer. This thesis explores novel methods in this area in the following parts: \nPart I – Signal classification: In this part\, we look at two distinct problems of waveform classification and Radio Frequency (RF) fingerprinting. In the first problem\, we study two use cases of modulation classification on edge devices\, followed by waveform classification and spectrum localization in the Citizen Broadband Radio Service (CBRS) band. In the second problem\, we look at RF fingerprinting that is classifying received signals in terms of subtle impairments that each transmitter leaves in its emitted waveform\, due to its hardware manufacturing imperfections. We propose methods to overcome the wireless channel effect for RF fingerprinting in both stationary transmitters on a large scale dataset (i.e.\, 5k WiFi devices)\, and identical hovering Unmanned Aerial Vehicles (UAVs) that transmit proprietary signals. \nPart II – Signal decoding: In this part\, we introduce our design of a modular machine learning (ML)-aided Orthogonal Frequency Division Multiplexing (OFDM) receiver that improves the bit error rate (BER) of the traditional receiver. We show how a neural network-based demapper block can be used for secure data transmission. Furthermore\, we show how an ML-aided receiver can provide the possibility of reducing communication overhead by obviating the need for the first field of preamble in WiFi signals. We show that reducing the preamble length contributes to higher throughput in WiFi networks\, without BER degradation. \nPart III – As the proposed work\, we will explore the use of active learning for smart sampling of training sets in wireless communications tasks. Active learning reduces the labeling overhead that is often performed using the compute-intensive traditional signal processing algorithms\, by intelligently selecting the most informative training samples to be labeled instead of labeling the whole set. We will also design an ML-life cycle control scheme to monitor and update the performance of an ML-aided 5G receiver\, when deployed in the field with varying environmental conditions.
URL:https://coe.northeastern.edu/event/nasim-soltani-phd-proposal/
LOCATION:532 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230808T103000
DTEND;TZID=America/New_York:20230808T113000
DTSTAMP:20260518T001530
CREATED:20230802T192515Z
LAST-MODIFIED:20230802T192515Z
UID:37694-1691490600-1691494200@coe.northeastern.edu
SUMMARY:Dinesh Murugan MS Thesis Defense
DESCRIPTION:Title: Advances in Modelling\, Control\, and Perception for Soft Robotics and Autonomous Vehicle Systems \nLocation on Campus: Snell Room \nCommittee Members:\nAdvisor: Prof. Milad Siami\nProf. Bahram Shafai\nProf. Rozhin Hajian – University of Massachusetts\, Lowell \nAbstract:\nIn this research project\, we investigate the distributed consensus and vehicle platoons control problem. We first investigate the performance deterioration of commensurate fractional-order consensus networks under exogenous stochastic disturbances. We formulate fractional-order differential equations for the network dynamics using Caputo derivatives and the Laplace transform\, and employ the H_2 norm of the dynamical system as a performance measure. By developing a graph-theoretic methodology\, we relate the structural specifications of the underlying graphs to the performance measure and explicitly quantify fundamental limits on the best achievable levels of performance in fractional-order consensus networks. We also establish new connections between the sparsity of the network and the performance measure\, characterizing fundamental tradeoffs that reveal the interplay between the two. Finally\, we provide numerical illustrations to verify our theoretical results\, which could help in the design of robust fractional-order control systems in the presence of disturbances. \nAdditionally\, the study examines the real-time application of the theoretical advancements on Quanser’s Qcars\, a scaled model vehicle used for academic purposes. The findings are highly relevant to the design and implementation of large-scale consensus networks and autonomous vehicle platoons\, as they emphasize the importance of balancing network density and update cycle speed for optimal performance. \nTo extend the research’s findings to viscoelastic based networks\, the interaction between agents is modeled as a fractional-order system.
URL:https://coe.northeastern.edu/event/dinesh-murugan-ms-thesis-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230808T100000
DTEND;TZID=America/New_York:20230808T110000
DTSTAMP:20260518T001530
CREATED:20230721T142808Z
LAST-MODIFIED:20230721T142808Z
UID:37565-1691488800-1691492400@coe.northeastern.edu
SUMMARY:Yukui Luo's PhD Dissertation Defense
DESCRIPTION:Title:\nSecuring FPGA as a Shared Cloud-Computing Resource: Threats and Mitigations \nCommittee Members:\nProf. Xiaolin Xu (Advisor)\nProf. Yunsi Fei\nProf. Xue Lin \nAbstract:\nWith the widespread adoption of cloud computing\, the demand for programmable hardware acceleration devices\, such as field-programmable gate arrays (FPGA)\, has increased. These devices benefit the growth of efficient hardware accelerators\, making cloud computing possible for a wide range of research and commercial projects\, including genetic engineering\, intensive online secure trading\, the Artificial Intelligence (AI) interface\, etc. 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 FPGA instances\, significantly improving the hardware utilization and energy efficiency of the cloud FPGA. However\, due to the hardware reconfigurability of FPGA\, current virtualization technologies used for multi-tenant CPU and GPU instances are incompatible with multi-tenant FPGA. \nWe aim to enhance the security of multi-tenant FPGA by defining the threat model and evaluating security concerns from the perspectives of confidentiality\, data integrity\, and availability. As part of this goal\, we constructed multi-tenant FPGA prototypes and demonstrated potential attacks. These attacks serve as preliminary steps toward developing a secure multi-tenant FPGA virtualization system. This system involves hardware and software co-design\, which extends the multi-tenant isolation from software to hardware\, ultimately resulting in a secure FPGA shared cloud computing service.
URL:https://coe.northeastern.edu/event/yukui-luos-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230803T100000
DTEND;TZID=America/New_York:20230803T110000
DTSTAMP:20260518T001530
CREATED:20230508T153859Z
LAST-MODIFIED:20230508T153859Z
UID:36933-1691056800-1691060400@coe.northeastern.edu
SUMMARY:Yu Yin's PhD Dissertation Defense
DESCRIPTION:“Synthetic Data Generator: Understanding Human Face & Body via Image Synthesis” \nCommittee Members:\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://coe.northeastern.edu/event/yu-yins-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230801T100000
DTEND;TZID=America/New_York:20230801T113000
DTSTAMP:20260518T001530
CREATED:20230721T143101Z
LAST-MODIFIED:20230721T143101Z
UID:37563-1690884000-1690889400@coe.northeastern.edu
SUMMARY:Huan Wang's PhD Proposal Review
DESCRIPTION:Title: \nTowards Efficient Deep Learning in Computer Vision via Sparsity and Distillation \nCommittee Members: \nProf. Yun Fu (Advisor) \nProf. Octavia Camps \nProf. Zhiqiang Tao \nAbstract: \nAI\, empowered by deep learning\, has been profoundly transforming the world. However\, the excessive size of these models remains a central obstacle that limits their broader utility. Modern neural networks commonly consist of millions of parameters\, with foundation models extending to billions. The rapid expansion in model size introduces many challenges including training cost\, sluggish inference speed\, excessive energy consumption\, and negative environmental implications such as increased CO2 emissions. \nAddressing these challenges necessitates the adoption of efficient deep learning. This thesis focuses on two overarching approaches\, network sparsity and knowledge distillation\, to enhance the efficiency of deep learning models in the context of computer vision. Network sparsity focuses on eliminating redundant parameters in a model while preserving the performance. Knowledge distillation aims to enhance the performance of the target model\, referred to as the “student\,” by leveraging guidance from a stronger model\, known as the “teacher”. This approach leads to performance improvements in the target model without reducing its size. In the proposal\, I will start with the background and major challenges of leveraging these techniques towards efficient deep learning. Then\, I shall present the potential solutions in various tasks (e.g.\, image classification\, image super-resolution\, neural rendering\, and text-to-image generation)\, with preliminary results to justify the efficacy of the proposed approaches. Finally\, a comprehensive outlook of the future work will conclude this proposal.
URL:https://coe.northeastern.edu/event/huan-wangs-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230725T130000
DTEND;TZID=America/New_York:20230725T140000
DTSTAMP:20260518T001530
CREATED:20230721T142252Z
LAST-MODIFIED:20230721T142322Z
UID:37567-1690290000-1690293600@coe.northeastern.edu
SUMMARY:Batool Salehihikouei Phd Proposal Review
DESCRIPTION:Title:\nLeveraging Deep Learning on Multimodal Sensor Data for Wireless Communication: From mmWave Beamforming to Digital Twins \nCommittee Members:\nProf. Kaushik Chowdhury (Advisor)\nProf. Hanumant Singh\nProf. Josep Jornet\nDr. Mark Eisen \nAbstract:\nWith the widespread Internet of Things (IoT) devices\, a wide variety of sensors are now present in different environments. For example\, self-driving vehicles and automated warehouses depend on sensor information for navigation and management of the robots\, respectively. In this dissertation\, we present a paradigm\, where these sensors are re-purposed to assist network management in wireless communication\, especially when classic approaches fall short to provide the required quality of service (QoS). This thesis presents data-driven and AI-based methods\, where the multimodal sensor information is used for beamforming at the mmWave band\, and envisions a systematic framework for joint optimization of the navigation and network management in factory floor environments. In particular\, the contributions in this dissertation are as follows. First\, we present deep learning fusion algorithms\, where the inputs from a multitude of sensor modalities such as GPS (Global Positioning System)\, camera\, and LiDAR (Light Detection and Ranging) are combined towards predicting the optimum beam at the mmWave band. We prove that fusing the multimodal sensor data improves the prediction accuracy compared to using single modalities. Second\, we study the trade-off between the accuracy and cost of different learning strategies for multimodal beamforming. In this regard\, we make a case for using federated learning for beamforming at the mmWave band and demonstrate that it is the most successful learning strategy\, with respect to the communication overhead. Finally\, we take measures to further optimize the computation and communication overhead\, by incorporating a pruning strategy tailored to the disturbed nature of the federated learning systems. In the proposed research work\, we suggest using digital twins to overcome the challenges of scarcity of data and close-world assumption in deep learning algorithms. A digital twin is a replica of a real world entity\, which is typically used for studying the impact of any configuration settings in a safe\, digital environment. In this dissertation\, we propose using digital twins for generating training data for multimodal beamforming\, in unseen scenarios. Moreover\, we study a robotic industrial setting\, where the path planning policy is continuously updated by monitoring the dynamics of the real world\, constructing the digital twin\, and updating the policy.
URL:https://coe.northeastern.edu/event/batool-salehihikouei-phd-proposal-review/
LOCATION:532 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230721T140000
DTEND;TZID=America/New_York:20230721T153000
DTSTAMP:20260518T001530
CREATED:20230718T135222Z
LAST-MODIFIED:20230718T135222Z
UID:37521-1689948000-1689953400@coe.northeastern.edu
SUMMARY:Daniel Uvaydov's PhD Dissertation Defense
DESCRIPTION:Title: Real-Time Spectrum Sensing for Inference and Control \nCommittee Members: \nProf. Tommaso Melodia (Advisor) \nProf. Kaushik Choudhury \nProf. Francesco Restuccia \nAbstract: \nThrough growing cellular innovations\, the usage and congestion of the wireless spectrum is increasing at incredible speeds. High demand and limited supply pose a resource issue known as the “spectrum crunch”. With the high diversity of users sharing a large portion of the spectrum to request and receive diverse services\, spectrum coordination becomes very difficult. Large scale device synchronization for spectrum coordination requires high overhead and more wireless transmissions further reducing spectrum resources. However\, by monitoring the spectrum\, otherwise known as spectrum sensing\, we can develop mechanisms where users can opportunistically take action based on the current state of the spectrum\, without need for direct coordination between devices. Spectrum 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 requires very extensive and diverse datasets; (ii) it has to be performed with extremely low latency over varying bandwidths and must guarantee strict real-time processing constraints; (iii) 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. This dissertation focuses on addressing these challenges in multiple wireless applications by utilizing Deep Learning (DL) techniques as the main vehicle of spectrum sensing for both inference and control. Algorithmic spectrum sensing has generally been model-based which limits its performance in diverse settings and environments\, for this reason we explore data-driven spectrum sensing algorithms. Mainly\, this work takes a holistic approach to address spectrum sensing problems from multiple directions with the overarching goal of developing the core building blocks for the next generation of intelligent\, AI-driven\, efficient spectrum sharing systems. By leveraging mechanisms such as data augmentation\, channel attention\, voting\, and segmentation we are able to push beyond the capabilities of existing DL 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. The contributions of this work includes (i) multiple datasets and implementations for DL enabled spectrum sensing with applications in radio frequency and underwater; (ii) a method for tackling the core issue of dataset generation in supervised learning algorithms for spectrum sensing via a novel data augmentation technique; (iii) a study into one of the first ever semi-unsupervised approaches for wideband multi-class spectrum sensing.
URL:https://coe.northeastern.edu/event/daniel-uvaydovs-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:20230720T130000
DTEND;TZID=America/New_York:20230720T140000
DTSTAMP:20260518T001530
CREATED:20230711T140015Z
LAST-MODIFIED:20230711T140015Z
UID:37433-1689858000-1689861600@coe.northeastern.edu
SUMMARY:Qing Jin's PhD Dissertation Defense
DESCRIPTION:Title:Decoupling Efficiency-Performance Optimization for Modern Neural Networks \nDate: \n7/20/2023 \nCommittee Members: \nYanzhi Wang (Advisor); Prof. David Kaeli; Prof. Sunil Mittal; Prof. Jennifer Dy \nAbstract: \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://coe.northeastern.edu/event/qing-jins-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230629T170000
DTEND;TZID=America/New_York:20230629T173000
DTSTAMP:20260518T001530
CREATED:20230626T173009Z
LAST-MODIFIED:20230626T173036Z
UID:37275-1688058000-1688059800@coe.northeastern.edu
SUMMARY:Zifeng Wang's PhD Dissertation Defense
DESCRIPTION:Title: Effective and Efficient Continual Learning \nCommittee Members:\nProf. Jennifer Dy (Advisor)\nProf. Stratis Ioannidis\nProf. Yanzhi Wang \nAbstract:\nContinual Learning (CL) aims to develop models that mimic the human ability to learn continually without forgetting knowledge acquired earlier. While traditional machine learning methods focus on learning with a certain dataset (task)\, CL methods adapt a single model to learn a sequence of tasks continually. \nIn this thesis\, we target developing effective and efficient CL methods under different challenging and resource-limited settings. Specifically\, we (1) leverage the idea of sparsity to achieve cost-effective CL\, (2) propose a novel prompting-based paradigm for parameter-efficient CL\, and (3) utilize task-invariant and task-specific knowledge to enhance existing CL methods in a general way. \nWe first introduce our sparsity-based CL methods. The first method\, Learn-Prune-Share (LPS)\, splits the network into task-specific partitions\, leading to no forgetting\, while maintaining memory efficiency. Moreover\, LPS integrates a novel selective knowledge sharing scheme\, enabling adaptive knowledge sharing in an end-to-end fashion. Taking a step further\, we present Sparse Continual Learning (SparCL)\, a novel framework that leverages sparsity to enable cost-effective continual learning on edge devices. SparCL achieves both training acceleration and accuracy preservation through the synergy of three aspects: weight sparsity\, data efficiency\, and gradient sparsity. \nSecondly\, we present a new paradigm\, prompting-based CL\, that aims to train a more succinct memory system that is both data and memory efficient. We first propose a method that learns to dynamically prompt (L2P) a pre-trained model to learn tasks sequentially under different task transitions\, where prompts are small learnable parameters maintained in a memory space. We then improve L2P by proposing DualPrompt\, which decouples prompts into complementary “General” and “Expert” prompts to learn task-invariant and task-specific instructions\, respectively. \nFinally\, we propose DualHSIC\, a simple and effective CL method that generalizes the idea of leveraging task-invariant and task-specific knowledge. DualHSIC consists of two complementary components that stem from the so-called Hilbert Schmidt independence criterion (HSIC): HSIC-Bottleneck for Rehearsal (HBR) lessens the inter-task interference and HSIC Alignment (HA) promotes task-invariant knowledge sharing. \nComprehensive experimental results demonstrate the effectiveness and efficiency of our methods over the state-of-the-art methods on multiple CL benchmarks.
URL:https://coe.northeastern.edu/event/zifeng-wangs-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230626T083000
DTEND;TZID=America/New_York:20230626T093000
DTSTAMP:20260518T001530
CREATED:20230624T180848Z
LAST-MODIFIED:20230624T180848Z
UID:37265-1687768200-1687771800@coe.northeastern.edu
SUMMARY:Deniz Unal's PhD Proposal Review
DESCRIPTION:Title:\nSoftware-Defined Underwater Acoustic Networks \nCommittee Members:\nProf. Tommaso Melodia (Advisor)\nProf. Stefano Basagni\nProf. Kaushik Chowdhury\nDr. Emrecan Demirors \nAbstract:\nThe exploration\, monitoring\, and understanding of oceans play a crucial role in addressing climate change\, overseeing underwater pipelines\, and preventing maritime warfare attacks. To achieve these significant objectives\, it is vital to utilize networks of cost-effective and flexible underwater devices capable of efficiently collecting and transmitting information to the shore. However\, the progress of underwater networks heavily relies on underwater acoustic modems\, which currently face limitations such as low data rates and inflexible hardware designs\, limiting their usability to specific scenarios. To overcome these limitations\, we propose a modular software-defined acoustic networking platform built on the Zynq system-on-chip architecture that can be easily deployed in a compact form factor. Our platform distinguishes itself from existing solutions in several ways. Firstly\, it possesses the capability to adapt to varying conditions by adjusting protocol parameters at all layers of the networking stack. Secondly\, it achieves high data rate connections\, particularly over short distances. Additionally\, it seamlessly integrates with other sub-sea platforms\, including underwater drones. We demonstrate the capabilities and the performance of our platform with tasks\, such as channel estimation and characterization\, establishing high data rate Orthogonal Frequency-Division Multiplexing (OFDM) links\, and running third-party software to implement JANUS standard. In addition\, we introduce the enabling technologies for the development and implementation of underwater networks. These technologies facilitate the establishment of connectivity between underwater networks and the shore\, as well as the integration of modems with underwater vehicles. Lastly\, we provide a demonstration of the algorithmic development conducted on our platform. We mainly consider high-rate\, wideband\, adaptive links and perform experimental evaluations at sea. In particular\, we demonstrate multicarrier communications with mobile platforms with the presence of Doppler and compare the performance of forward error correction methods\, and demonstrate dataset recording for artificial intelligence research.
URL:https://coe.northeastern.edu/event/deniz-unals-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230623T100000
DTEND;TZID=America/New_York:20230623T110000
DTSTAMP:20260518T001530
CREATED:20230606T153237Z
LAST-MODIFIED:20230606T153237Z
UID:37194-1687514400-1687518000@coe.northeastern.edu
SUMMARY:Cooper Loughlin's PhD Dissertation Defense
DESCRIPTION:“Deep Generative Models for High Dimensional Spatial and Temporal Data Analysis” \nCommittee Members:\nProf. Vinay Ingle (Advisor)\nDr. Dimitris Manolakis\nProf. Purnima Ratilal-Makris \nAbstract:\nData analysis and exploitation in practical applications is challenging when observations are the result of many interacting natural and man-made phenomena. We address two important problems for which traditional methods of analysis are insufficient. One problem of practical interest is the identification of particular materials from remotely sensed hyperspectral imagery. This is traditionally accomplished by comparing image pixel spectra to those from a known material library. Such techniques are limited by spectral variability\, background interference\, and imperfect compensation of atmospheric components. Established methods address these limitations with statistical techniques. Simple probability models result in tractable methods; however\, analyses are limited by errors due\, in particular\, due to false alarms. \nAnalysis of complex time series is another challenging problem\, particularly when data are high dimensional. This arises in air quality monitoring\, where atmospheric concentration measurements of multiple pollutants are taken over time. Two analysis goals in this context are forecasting and anomaly detection. Both tasks are enabled by an accurate model for the temporal dynamics and interaction between pollutants. Air quality data are complex due to long term temporal dependencies\, non-linear dependence between pollutants\, and missing observations. Traditional multivariate time series analysis approaches\, such as the vector autoregression and linear dynamical system models\, fail to capture those characteristics necessary for a sufficient probabilistic model. \nWe use deep generative models to develop practical solutions that address these problems. This is made possible through the application of deep latent variable models. The modeling approach follows the philosophy that complex data can typically be explained by simpler underlying factors of variation. Variational autoencoders (VAEs) are deep latent variable models that emulate data generation by transforming simple\, low dimensional\, latent random vectors through a deep neural network. VAEs are trained to produce samples that resemble the training data\, thus capturing a manifold on which complex data are distributed. This philosophy is extended to time series data\, where we consider sequences of latent vectors. \nWe utilize VAEs develop a flexible generative model for hyperspectral imagery. Based on that model\, we develop a novel material identification framework which localizes target material spectra along the manifold. Through experiments on real data\, we show that the \ac{VAE} approach is better able to reject false alarms from materials with similar spectra when compared to established methods alone. We additionally develop a novel dynamical \ac{VAE} model for time series of air quality data. Using that model\, we develop practical methods for computing forecast distributions using Monte Carlo integration. We evaluate forecast distributions against real air quality data and demonstrate the ability to predict temporal dynamics and forecast uncertainty. The primary contribution of this work is to develop practical solutions to challenging data analysis problems through the use of deep generative models.
URL:https://coe.northeastern.edu/event/cooper-loughlins-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230620T130000
DTEND;TZID=America/New_York:20230620T140000
DTSTAMP:20260518T001530
CREATED:20230522T172041Z
LAST-MODIFIED:20230522T172041Z
UID:37070-1687266000-1687269600@coe.northeastern.edu
SUMMARY:Chang Liu's PhD Dissertation Defense
DESCRIPTION:“Unleashing the Potential of Transfer Learning for Visual Applications” \nCommittee Members:\nProf. Raymond Fu (Advisor)\nProf. Sarah Ostadabbas\nProf. Zhiqiang Tao \nAbstract:\nThe recent flourish of deep learning in various tasks is largely accredited to the rich and accessible labeled data. Nonetheless\, massive supervision remains a luxury for many real-world applications. Further\, the domain shift problem has also seriously impeded large-scale deployments of deep-learning models. As a remedy\, Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way\, the dependence on a large number of target domain data can be reduced for constructing target learners. \nIn this dissertation research\, I investigate two major problems in transfer learning\, domain adaptation (DG) and domain adaptation (DA)\, on various visual applications. (1) The challenge of DG lies in an over-simplified assumption\, that is\, the source and target data are independent and identically distributed (i.i.d.) while ignoring out-of-distribution (OOD) scenarios commonly encountered in practice. This issue is common in visual applications such as object recognition\, hyperparameter optimization\, and face recognition. We propose algorithms that are specifically designed for each task\, such as metric learning\, adversarial regularization\, feature disentanglement\, and meta-learning. (2) DA can be considered a special case of DG with unlabeled target data available. The major challenge is how to align the labeled source and unlabeled target data. We delve into the applications of image recognition and video recognition and propose algorithms to ensure domain-wise discriminativeness and class-wise closeness across domains. Experiments show that the proposed algorithms outperform the state-of-the-art methods on the commonly-used benchmark datasets.
URL:https://coe.northeastern.edu/event/chang-lius-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230620T080000
DTEND;TZID=America/New_York:20230620T170000
DTSTAMP:20260518T001530
CREATED:20230624T181028Z
LAST-MODIFIED:20230624T181028Z
UID:37261-1687248000-1687280400@coe.northeastern.edu
SUMMARY:Alfred P. Navato's PhD Dissertation Defense
DESCRIPTION:Title:\nEnabling Anomaly Detection in Complex Chemical Mixtures Through Multimodal Data Fusion \nDate:\n6/26/2023 \nTime:\n10:00:00 AM \nLocation:\nSH 210\, \nCommittee Members:\nProf. Mueller (Advisor)\nProf. Erdogmus\nProf. Ioannidis\nProf. Onnis-Hayden \nAbstract:\nRecently innovations in machine learning and data processing are increasingly tied to ensuring useability and interpretability when these methods are applied within end-user domains.  One societally important example of such a domain is management and operations of water infrastructure in cities\, where data collection is currently costly and limited\, enabling analytics have the potential to generate real impact for urban communities\, and correctness of results is critical to protect human and environmental health.  This dissertation holistically considers issues of generalizability\, transferability\, and applicability of a range of data fusion and machine learning approaches across end-user domains within the context of solution building for improved real-time management of wastewater infrastructure.  The first chapter provides an overview of the challenges associated with anomaly detection within the wastewater field and reviews the performance of various anomaly detection techniques implemented in other disciplines.  The second chapter discusses the barriers and opportunities in cross-disciplinary pollination of data fusion techniques.  The third chapter presents development of an unsupervised approach facilitating quantitative characterization of the complex background which is wastewater\, necessary to be able to implement any automated operational interventions.  The fourth chapter develops an approach for cost-minimization/information-maximization design of a sensor to facilitate specifically detection of chemical anomalies (defined as inflow events that might compromise wastewater treatment facilities) by using machine learning and feature selection techniques to minimize the number of input signals needed to achieve reasonable accuracies.  Together the third and fourth chapters provide a clear\, explainable\, actionable pathway forward in envisioning next generation wastewater infrastructure\, demonstrating novel and impactful use of data fusion and machine learning techniques in a real-world context.
URL:https://coe.northeastern.edu/event/alfred-p-navatos-phd-dissertation-defense/
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DTSTART;TZID=America/New_York:20230605T110000
DTEND;TZID=America/New_York:20230605T123000
DTSTAMP:20260518T001530
CREATED:20230522T171405Z
LAST-MODIFIED:20230522T171405Z
UID:37079-1685962800-1685968200@coe.northeastern.edu
SUMMARY:Can Qin's PhD Dissertation Defense
DESCRIPTION:“Unveiling the Power of Transfer Learning in Data-Driven AI” \nCommittee Members:\nProf. Raymond Fu (Advisor)\nProf. Octavia Camps\nProf. Huaizu Jiang \nAbstract:\nThe big data stands as a cornerstone of deep learning\, which has significantly improved a wide range of machine learning and computer vision tasks. Despite such a great success\, data collection is time-consuming and costly\, considering manual efforts and privacy restrictions. Transfer learning is a promising direction toward data-efficient AI by leveraging acquired data and pre-trained models as guidance. This dissertation focus on the feature and model transfer across different domains and tasks\, which can be roughly summarized into three sections. \n(1) Section One focuses on Unsupervised Domain Adaptation (UDA) without any labels in the target domain. The technical challenge of UDA is the distribution mismatch across domains. I have presented a hierarchical alignment model as the solution. \n(2) Section Two extends UDA into semi-supervised domain adaptation (SSDA) with minimal target-domain labels\, which is useful and effortless to acquire. To avoid overfitting toward labeled data\, I have proposed structural regularization verified on different classification benchmarks. \n(3) Section Three mainly explores the model transfer\, including teacher-student knowledge distillation and heterogeneous models infusion with a high potential for model compression and enhancement.
URL:https://coe.northeastern.edu/event/can-qins-phd-dissertation-defense/
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DTSTART;TZID=America/New_York:20230602T110000
DTEND;TZID=America/New_York:20230602T120000
DTSTAMP:20260518T001530
CREATED:20230508T153647Z
LAST-MODIFIED:20230508T153647Z
UID:36931-1685703600-1685707200@coe.northeastern.edu
SUMMARY:Cheng Gongye's PhD Proposal Review
DESCRIPTION:“Hardware Security Vulnerabilities in Deep Neural Networks and Mitigations” \nCommittee Members:\nProf. Yunsi Fei (Advisor)\nProf. Xue Lin\nProf. Xiaolin Xu \nAbstract:\nOver the past decade\, Deep Neural Networks (DNNs) have revolutionized numerous fields. With the increasing deployment of DNN models in security-sensitive and mission-critical applications\, such as autonomous driving\, ensuring the security and privacy of DNN inference is of paramount importance. \nThis Ph.D. dissertation investigates two primary hardware security attack vectors: fault attacks and side-channel attacks. Fault attacks compromise the integrity of a targeted application by intentionally disrupting the computation or injecting faults on parameters. Side-channel attacks exploit information leakage from the application execution through physical parameters such as power consumption\, electromagnetic emanations\, and timing to retrieve secrets\, thereby breaching confidentiality. \nFor fault attacks\, we demonstrate a power-glitching fault injection attack on FPGA-based DNN accelerators in cloud environments. The attack exploits vulnerabilities in the shared power distribution network and leverages time-to-digital converter (TDC) sensors for precise fault injection timing\, and results in model misclassification\, an integrity compromise on the targeted application. We propose a lightweight defense framework for detecting and mitigating adversarial bit-flip attacks induced by RowHammer on DNNs. This framework employs a dynamic channel-shuffling obfuscation scheme and a logits-based model integrity monitor\, offering negligible performance loss. This framework effectively protects various DNN models from RowHammer attacks without any retraining or model structure modifications. \nFor side-channel attacks\, we present a floating-point timing side channels attack to reverse-engineer multi-layer perceptron (MLP) model parameters in software implementations. This attack successfully recovers DNN parameters\, weights and biases. \nRegarding ongoing research\, we observe that previous studies often focus on academic prototypes\, resulting in limited applicability. To bridge these gaps\, we select the AMD-Xilinx DPU\, one of the most advanced DNN accelerators to date\, to conduct the analysis. We propose a side-channel attack that utilizes electromagnetic emissions to extract parameters. Furthermore\, we propose a comprehensive fault analysis of quantized DNN models by simulations and discuss potential mitigation strategies.
URL:https://coe.northeastern.edu/event/cheng-gongyes-phd-proposal-review/
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DTSTART;TZID=America/New_York:20230526T123000
DTEND;TZID=America/New_York:20230526T133000
DTSTAMP:20260518T001530
CREATED:20230522T171528Z
LAST-MODIFIED:20230522T171528Z
UID:37077-1685104200-1685107800@coe.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://coe.northeastern.edu/event/guillem-reus-muns-phd-dissertation-defense/
LOCATION:Admissions Visitor Center (West Village F)
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DTSTART;TZID=America/New_York:20230526T090000
DTEND;TZID=America/New_York:20230526T100000
DTSTAMP:20260518T001530
CREATED:20230522T171659Z
LAST-MODIFIED:20230522T171659Z
UID:37075-1685091600-1685095200@coe.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://coe.northeastern.edu/event/yuezhou-lius-phd-proposal-review/
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DTSTART;TZID=America/New_York:20230522T103000
DTEND;TZID=America/New_York:20230522T113000
DTSTAMP:20260518T001530
CREATED:20230522T171916Z
LAST-MODIFIED:20230522T171916Z
UID:37073-1684751400-1684755000@coe.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://coe.northeastern.edu/event/mengting-yans-phd-dissertation-defense/
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DTSTART;TZID=America/New_York:20230428T130000
DTEND;TZID=America/New_York:20230428T150000
DTSTAMP:20260518T001530
CREATED:20230426T134141Z
LAST-MODIFIED:20230426T134141Z
UID:36838-1682686800-1682694000@coe.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://coe.northeastern.edu/event/balaji-sundareshans-ms-thesis-defense/
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