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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240328T110000
DTEND;TZID=America/New_York:20240328T120000
DTSTAMP:20260425T134752
CREATED:20240306T200314Z
LAST-MODIFIED:20240321T230531Z
UID:6816-1711623600-1711627200@ece.northeastern.edu
SUMMARY:Huan Wang PhD Dissertation Defense
DESCRIPTION:Announcing:\nPhD Dissertation Defense \nName:\nHuan Wang \nTitle:\nTowards Efficient Deep Learning in Computer Vision via Network Sparsity and Distillation \nDate:\n3/28/2024 \nTime:\n11:00:00 AM \nZoom \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. The dissertation focuses on two overarching approaches\, network pruning and knowledge distillation\, to enhance the efficiency of deep learning models in the context of computer vision. Network pruning 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. \nIn this defense presentation\, I will start with the background and major challenges of leveraging these techniques to improve the efficiency of deep neural networks. Then\, I shall present the proposed solutions for various vision tasks\, including image classification\, single-image super-resolution\, novel view synthesis / neural rendering / NeRF / NeLF\, text-to-image generation / diffusion models\, and photorealistic head avatars. Extensive results and analyses will justify the efficacy of the proposed approaches\, demonstrating that pruning and distillation make a generic and complete framework for efficient deep learning in various domains. Finally\, a comprehensive summary (with takeaways) and outlook of the future work will conclude the presentation.
URL:https://ece.northeastern.edu/event/human-wang-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240321T120000
DTEND;TZID=America/New_York:20240321T133000
DTSTAMP:20260425T134752
CREATED:20240319T181109Z
LAST-MODIFIED:20240319T181109Z
UID:6837-1711022400-1711027800@ece.northeastern.edu
SUMMARY:Julian Gutierrez PhD Dissertation Defense
DESCRIPTION:Announcing:\nPhD Dissertation Defense \nName:\nJulian Gutierrez \nTitle:\nTowards Real-Time Safe Flight Paths for Urban Air Mobility \nDate:\n3/21/2024 \nTime:\n12:00:00 PM \nLocation: Zoom \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 to provide situational awareness when processing the raw output of a GNSS sensor. This effort focuses on multipath mitigation\, which reduces the error in the estimated position solution. Third\, 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://ece.northeastern.edu/event/julian-gutierrez-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231220T140000
DTEND;TZID=America/New_York:20231220T150000
DTSTAMP:20260425T134752
CREATED:20231215T231627Z
LAST-MODIFIED:20231215T231627Z
UID:6662-1703080800-1703084400@ece.northeastern.edu
SUMMARY:Xiang Zhang PhD Proposal Review
DESCRIPTION:Title:Confidentiality and Privacy Preserving:  Intertwining Deep Learning and  Side-channel Analysis \nMeeting ID: 976 4324 8925 Passcode: 779251 \nCommittee Members:\nProf. Yunsi Fei (Advisor)\nProf. Adam Ding\nProf. Lili Su \nAbstract:\nIn the past decade\, deep learning-empowered technologies have significantly permeated our daily lives\, revolutionizing diverse application domains with superb performance.  In hardware security\, deep learning has been employed for power or electromagnetic side-channel analysis (SCA) and protection\, and the security of deep learning implementations starts gaining traction. \nThis dissertation delves into the intertwining deep learning techniques and side-channel analysis.  It addresses two critical questions: how to extend deep learning to other types of SCAs; what confidentiality and privacy vulnerabilities deep learning models have. \nOur research work first explores deep learning-assisted cache side-channel attacks and introduces innovative countermeasures grounded in the principles of adversarial samples against deep learning. We first design a novel high-frequency cache monitor\,  which runs concurrent to the victim execution and collects run-time timing traces\, while previous cache monitors are only able to collect timing samples. Such timing traces facilitate follow-on non-profiled Differential Deep Learning Analysis (DDLA) for secret retrieval. We also propose a novel countermeasure against the new DDLA\, leveraging the concept of adversarial examples\, which deliberately introduces obfuscation operations in the victim program so as to generate ‘adversarial’ timing traces and therefore circumvent the follow-on DDLA. \nThe second part of the dissertation addresses the vulnerability of deep neural network (DNN) implementations and presents novel methodologies for enhancing user privacy. It introduces a technique for extracting deep learning models through software-based power side channels. By manipulating model inputs and leveraging the on-chip Intel Running Average Power Limit (RAPL) sensors reporting\, the entire model parameters can be extracted when the model inference is executed on modern processors. To protect both the model confidentiality and the input privacy\, this dissertation proposes to obfuscate the model inputs while preserving the end-to-end functionality. It introduces an encoder to transform the inputs before feeding the DNN model\, and appends a decoder after the model outputs to recover the intended results. The approach\, compared to traditional encryption or masking techniques\, is more efficient and can effectively protect both user privacy and model confidentiality. \nThe overall goal of the dissertation is to further investigate the power of deep learning in SCA and countermeasure and safeguard secure DNN implementations.
URL:https://ece.northeastern.edu/event/xiang-zhang-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231212T110000
DTEND;TZID=America/New_York:20231212T170000
DTSTAMP:20260425T134752
CREATED:20231208T195540Z
LAST-MODIFIED:20231208T195540Z
UID:6658-1702378800-1702400400@ece.northeastern.edu
SUMMARY:Deepak Prabhala MS Thesis Defense
DESCRIPTION:Title: “Smart Microwave Devices with Programable Printed Circuit Board (PPCB): Design with Liquid Crystal Elastomer Polymers in Transmission Lines and Circulators” \nCommittee Members:\n1) Professor Nian X. Sun (Advisor)\n2) Professor Marvin Onabajo\n3) Professor Yongmin Liu \nAbstract:\nThis study explores the innovative application of liquid crystal elastomer (LCE) polymers in the design and implementation of microwave transmission lines and circulators. Liquid crystal elastomers\, known for their unique combination of liquid crystalline and elastomeric properties\, offer a unique approach to developing flexible and tunable microwave devices. The research focuses on a thorough study of the electro-mechanical properties of LCEs to achieve novel functionalities in the design of transmission lines and circulators for microwave communication systems in HFSS simulations. The first part of the study delves into the characterization of the dielectric and mechanical properties of the chosen LCE polymer. Subsequently\, the design and fabrication of a flexible and tunable transmission line using LCE are discussed. The LCE-based transmission line aims to measure the insertion loss and return loss with different widths\, lengths\, and thicknesses of the LCE polymer. The study investigates the impact of temperature on the transmission line’s performance\, offering insights into potential applications in reconfigurable microwave systems. The second phase of the research explores the utilization of LCE in the development of a microwave circulator\, a vital component in microwave communication networks. The circulator design incorporates the unique properties of LCE by using a stepped dielectric variation approach for broadband isolation. This innovation holds promise for enhancing the efficiency and adaptability of microwave systems in communication and radar applications. The findings of this research contribute to offering a pathway for integrating liquid crystal elastomers into flexible and reconfigurable microwave devices. This thesis aims to advance the understanding of smart microwave devices and inspire further exploration into the application of liquid crystal elastomer polymers in cutting-edge technologies.
URL:https://ece.northeastern.edu/event/deepak-prabhala-ms-thesis-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231212T090000
DTEND;TZID=America/New_York:20231212T100000
DTSTAMP:20260425T134752
CREATED:20231208T195140Z
LAST-MODIFIED:20231208T195212Z
UID:6654-1702371600-1702375200@ece.northeastern.edu
SUMMARY:Durga Suresh PhD Proposal Review
DESCRIPTION:Title: Network Security Management and Threat Mitigation in the Open Cloud\n \nCommittee Members:\nProf. Miriam Leeser (Advisor)\nProf. Michael Zink\nProf. Xiaolin Xu \nAbstract:\nCloud computing and advanced cyberinfrastructures are increasingly vital to the functioning of Internet systems. Every day\, more devices are added to the cloud\, to provide greater resource utilization\, availability\, and scalability. Due to the expanding reliance on cloud computing\, securing the cloud is paramount. Tackling the issue of securing the cloud is crucial not only for preserving the functionality and reliability of cloud-based systems but also for protecting the critical data and services that depend on these platforms. \nCloud computing models include public clouds\, private clouds\, community clouds\, and hybrid clouds. Private\, community\, and hybrid clouds provide security\, but with an important trade-off; namely\, user access restriction in the cloud. The proposed research uses the Open Cloud Testbed (OCT) which is part of the National Science Foundation’s (NSF) Computer and Information Systems Engineering(CISE) Community Research Infrastructure(CRI) program. OCT is an example of a public cloud that allows users two things: 1) an isolated set of nodes to perform experiments with bare metal access\, which can potentially lead to security issues\, and 2) the ability to test out the solutions for both using the cloud and adding security to it. The proposed research aims to target a system like the OCT\, specifically targeting a public cloud environment. \nThis system will be designed to allow access to the switch\, enabling control and management of traffic within the cloud network. This research aims to mitigate network security threats in the public cloud network. The aim of this research is multifold. First\, we identify and classify the behavior of users in the cloud. We then provide an approach to creating a network security management policy that will deal with 1)detecting network intruders that scan the cloud network and remove their access to the network\, and  2) managing heavy hitters that can cause Denial of Service (DOS) and Distributed Denial of Service (DDOS) attacks in the cloud network by using the heavy hitter detection system and prevent them from putting more traffic on the network. Both network intruder detection and heavy hitter management systems use Access Control Lists (ACL)as a means to prevent the user from putting traffic on the cloud network. Lastly\, we perform experiments to handle these threats and measure the success of the experimental setup concerning network attacks. The proposed approach will ensure network security by creating a framework for network security management policy to minimize threats in the cloud network and other resources directly attached to the network. The proposed research aims to enhance cybersecurity by employing network intruder detection techniques to identify potential threats\, implementing heavy hitter management to mitigate threats effectively\, and developing and enforcing a network security management policy to prevent future threats.
URL:https://ece.northeastern.edu/event/durga-suresh-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231208T120000
DTEND;TZID=America/New_York:20231208T123000
DTSTAMP:20260425T134752
CREATED:20231208T195358Z
LAST-MODIFIED:20231208T195358Z
UID:6656-1702036800-1702038600@ece.northeastern.edu
SUMMARY:Ate Darabi PhD Proposal Review
DESCRIPTION:Title:\nComplex Delayed Networks and Their Application in Epidemic Analysis: Modeling\, Analysis\, and Strategic Management \nCommittee Members:\nProf. Milad Siami (Advisor)\nProf. Bahram Shafai\nProf. Rozhin Hajian \nAbstract:\nIn the face of crowd-related disasters like pandemics and mass attacks\, the complex dynamics of human interactions demand comprehensive modeling approaches. This proposal adopts a network-based perspective\, leveraging the delayed Susceptible-Infected-Susceptible (SIS) model for epidemics and the Predator-Swarm-Guide (PSG) model for crowd movement\, to gain insights into the dynamics of these critical situations. \nIn epidemic networks\, time delays and uncertainties can significantly change the epidemic behavior and result in successive echoing waves of the spread between various population clusters. We examine these effects on linear SIS dynamics\, evaluating network stability and performance loss. We prove that network performance loss is correlated with the structure of the underlying graph\, intrinsic time delays\, epidemic characteristics\, and external shocks. This performance measure is then used to develop an optimal traffic restriction algorithm for network performance enhancement\, resulting in reduced infection in the metapopulation.   An epidemic-based centrality index is also proposed to evaluate the impact of every subpopulation on network performance\, and its asymptotic behavior is investigated. This index converges to local or eigenvector centralities under specific parameters. Moreover\, given that epidemic-based centrality depends on the epidemic properties of the disease\, it may yield distinct node rankings as the disease characteristics slowly change over time or as different types of infections spread. This unique characteristic of epidemic-based centrality enables it to adjust to various epidemic features. The derived centrality index is then adopted to improve the network robustness against external shocks on the epidemic network. \nThe PSG model addresses mass attack scenarios\, considering individuals’ efforts to evade adversaries and seek guidance. Environmental factors like impermeable walls and psychological elements are incorporated into this model. The preliminary results highlight the role of coordinated cooperation in minimizing casualties. The objective is to reduce casualties through a hybrid motion optimization approach for individuals and the guiding agent.
URL:https://ece.northeastern.edu/event/ate-darabi-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231207T140000
DTEND;TZID=America/New_York:20231207T150000
DTSTAMP:20260425T134752
CREATED:20231205T000346Z
LAST-MODIFIED:20231205T000346Z
UID:6643-1701957600-1701961200@ece.northeastern.edu
SUMMARY:Yisi Liu MS Thesis Defense
DESCRIPTION:Title: Experimental research on the Nonlinear Magnetoelectric Effect of the VLF ME Antennas \nCommittee Members:\nProf. Nian Sun (Advisor)\nProf. Yongmin Liu\nProf. Xufeng Zhang \nAbstract:\nMagnetoelectric (ME) coupling effects in ferromagnetic and piezoelectric composites involve the control of electric polarization (P) by applying a magnetic field (H) (direct ME effect)\, or the manipulation of magnetization (M) through an electric field (E) (converse ME effect) . These effects are facilitated by the mechanical deformation in the ferroic phases resulting from the combination of magnetostriction and piezoelectricity. In single-phase materials\, the breakthrough in achieving large ME coefficients has further advanced the development of ME materials and devices. Consequently\, numerous multifunctional ME devices\, such as mechanical antennas\, magnetic sensors\, tunable inductors\, and filters\, have been developed. This thesis has provides a summary and categorization of these devices based on their physical mechanism and type of ME effects. The inclusion of mechanical ME antennas based on piezoelectric/magnetostrictive heterostructures with acoustic actuation reflects the significant interest in this topic. Notably\, a maximum communication distance of 120 m for a very low frequency (VLF) communication system has been achieved using a pair of mechanical ME antennas. Subsequently\, we will focus on introducing and reviewing the materials and devices related to the ME effect\, as well as the application of ME mechanical antennas in very low frequency (VLF) communication systems. \nIn addition to that\, we developed a transmitter with a Metglas/PZT/Metglas structure antenna. Our study focuses on investigating the transmission effects of this antenna when employing direct antenna modulation techniques to enhance data transmission. Through our research\, we have introduced a novel modulation method by modulating the antenna. We observed that this modulation method produces a more stable and stronger signal. \n 
URL:https://ece.northeastern.edu/event/yisi-liu-ms-thesis-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231207T100000
DTEND;TZID=America/New_York:20231207T110000
DTSTAMP:20260425T134752
CREATED:20231204T235514Z
LAST-MODIFIED:20231204T235514Z
UID:6639-1701943200-1701946800@ece.northeastern.edu
SUMMARY:Mauro Belgiovine PhD Proposal Review
DESCRIPTION:Title: Wireless Intelligence: A Comprehensive Exploration of AI-Driven Solutions in Channel Estimation\, Beam Refinement\, and Protocol Classification for Next Generation Networks \nCommittee Members:\nProf. Kaushik Chowdhury (advisor)\nProf. Stratis Ioannidis\nDr. Chris Dick \nAbstract:\nhis thesis explores the transformative impact of artificial intelligence (AI) on wireless systems through model-driven simulations and real-world datasets\, with a focus on enhancing both local and cellular wireless networks through the deployment of highly customized deep learning solutions that target specific bottlenecks affecting traditional signal processing based communication. \nThe research delves into three key areas that address critical challenges in the current wireless landscape. The first focal point of the investigation involves channel estimation using deep learning techniques to denoise pilots and expedite the accurate estimation of Channel State Information (CSI). By leveraging deep learning methodologies\, the proposed solution aims to enhance the reliability and computation for MIMO and massive MIMO channel estimation\, thereby contributing to improved communication efficiency and reduced errors. The second major topic encompasses the application of reinforcement learning for 5G New Radio (NR) millimeter-wave (mmWave) beam refinement. The study aims to develop a Deep Reinforcement Learning algorithm capable of adjusting beamsteering angles\, starting from a coarse beam scanning procedure and further refining them for higher transmission efficiency. This innovation is expected to substantially decrease traffic overhead while simultaneously enhancing beam steering precision\, thus optimizing the performance of mmWave communication. The third and final area of focus introduces a transformer-based WiFi multi-protocol classifier\, strategically deployed on a DeepWave Air-T edge device\, which is equipped with Module on Chip (MoC) low power CPU-GPU and programmable Software Defined Radio (SDR). This classifier outperforms existing modulation classification models and legacy methods under lower SNR conditions\, leveraging TensorRT’s model compression capabilities to efficiently process extended sequences of raw IQ samples\, ensuring high performance at a low computational cost. The proposed solution addresses the growing demand for efficient and adaptable wireless communication systems\, paving the way for advancements in edge-based processing and intelligent protocol classification. \nThis work seeks to contribute significantly to the ongoing AI revolution in wireless systems by addressing crucial issues in channel estimation\, beam refinement\, and protocol classification. The outcomes of this research hold the potential to redefine the landscape of wireless communication\, offering enhanced performance\, reduced overhead\, and increased adaptability in both local and cellular networks.
URL:https://ece.northeastern.edu/event/mauro-belgiovine-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231206T150000
DTEND;TZID=America/New_York:20231206T170000
DTSTAMP:20260425T134752
CREATED:20231204T235947Z
LAST-MODIFIED:20231204T235947Z
UID:6641-1701874800-1701882000@ece.northeastern.edu
SUMMARY:Suyash Pradhan MS Thesis Defense
DESCRIPTION:Title: COPILOT: Cooperative Perception using Lidar for Handoffs between Road Side Units \nCommittee Members:\nProf. Kaushik Chowdhury (Advisor)\nProf. Stratis Ioannidis\nProf. Jennifer Dy \nAbstract:\nThis thesis presents COPILOT\, a ML-based approach that allows vehicles requiring ubiquitous high bandwidth connectivity to identify the most suitable road side units (RSUs) through proactive handoffs. By cooperatively exchanging the data obtained from local 3D Lidar point clouds within adjacent vehicles and with coarse knowledge of their relative positions\, COPILOT identifies transient blockages to all candidate RSUs along the path under study. Such cooperative perception is critical for choosing RSUs with highly directional links required for mmWave bands\, which majorly degrade in the absence of LOS. COPILOT proposes three modules that operate in an inter-connected manner: (i) As an alternative to sending raw Lidar point clouds\, it extracts and transmits low-dimensional intermediate features to lower the overhead of inter-vehicle messaging; (ii) It utilizes an attention-mechanism to place greater emphasis on data collected from specific vehicles\, as opposed to nearest neighbor and distance-based selection schemes\, and (iii) it experimentally validates the outcomes using an outdoor testbed composed of an autonomous car and Talon AD7200 60GHz routers emulating the RSUs\, accompanied by the public release of the datasets. Results reveal COPILOT yields upto 69.8% and 20.42% improvement in latency and throughput compared to traditional reactive handoffs for mmWave networks\, respectively
URL:https://ece.northeastern.edu/event/suyash-pradhan-ms-thesis-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231204T103000
DTEND;TZID=America/New_York:20231204T113000
DTSTAMP:20260425T134752
CREATED:20231127T213905Z
LAST-MODIFIED:20231127T213905Z
UID:6620-1701685800-1701689400@ece.northeastern.edu
SUMMARY:Cheng Gongye PhD Dissertation Defense
DESCRIPTION:Title:\nHardware Security Vulnerabilities in Deep Neural Networks and Mitigations \nDate:\n12/4/2023 \nTime:\n10:30:00 AM \nCommittee Members:\nProf. Yunsi Fei (Advisor)\nProf. Aidong Ding\nProf. Xue Lin\nProf. Xiaolin Xu \nAbstract:\nIn the past decade\, Deep Neural Networks (DNNs) have become pivotal in numerous fields\, including security-sensitive autonomous driving and privacy-critical medical diagnosis. This Ph.D. dissertation delves into the hardware security of DNNs\, discovering their vulnerabilities to fault and side-channel attacks and exploring novel countermeasures essential for their safe deployment in critical applications. \nFault attacks disrupt computation or inject faults into parameters\, compromising the integrity of targeted applications. This dissertation demonstrates a power-glitching fault injection attack on FPGA-based DNN accelerators\, common in cloud environments\, which exploits vulnerabilities in the shared power distribution network and results in model misclassification. In response to these threats\, we introduce a novel\, lightweight defense mechanism to protect DNN parameters from adversarial bit-flip attacks. The proposed framework incorporates a dynamic channel-shuffling obfuscation scheme coupled with a logits-based model integrity monitor. The approach effectively safeguards various DNN models against bit-flip attacks\, without necessitating retraining or structural changes to the models. Furthermore\, our research expands the scope of fault analysis beyond just the parameters of DNN models. We thoroughly examine the entire implementation of commercial products\, defying the prevailing assumption that quantized DNNs are inherently resistant to bit-flips. \nSide-channel attacks exploit information leakage of system implementations\, such as power consumption and electromagnetic emanations\, to reveal system secrets and therefore compromise confidentiality. This dissertation makes significant contributions to side-channel assisted model extraction of DNNs. We present a floating-point timing side-channel attack on x86 CPUs that reverse-engineers DNN model parameters in software implementations. For hardware accelerators\, we target the state-of-the-art AMD-Xilinx deep-learning processor unit (DPU)\, a reconfigurable engine dedicated to convolutional neural networks (CNNs) and representing the most complex commercial FPGA accelerator with encrypted IPs. Our work demonstrates that electromagnetic analysis can be leveraged to recover the data flow and scheduling of the DNN accelerators\, facilitating follow-on architecture and parameter extraction attacks. To mitigate EM side-channel model extraction attacks\, we introduce a novel defense mechanism that devises a random importance-aware activation mask on input pixels to disrupt the operation alignment on EM traces\, with minimal performance and efficiency impacts. \nOverall\, this dissertation significantly deepens the understanding of hardware security of DNN models. It makes important contributions in discovering novel and critical vulnerabilities of DNN inference pertaining to system implementations\, and proposing effective and practical solutions for securing DNNs in mission-critical environments. The research work marks a substantial step forward in the development of resilient and secure AI systems.
URL:https://ece.northeastern.edu/event/cheng-gongye-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231129T163000
DTEND;TZID=America/New_York:20231129T170000
DTSTAMP:20260425T134752
CREATED:20231127T214136Z
LAST-MODIFIED:20231127T214136Z
UID:6622-1701275400-1701277200@ece.northeastern.edu
SUMMARY:Aria Masoomi PhD Proposal Review
DESCRIPTION:Title:\nMaking Deep Neural Network Transparent \nDate:\n11/29/2023 \nTime:\n4:30:00 PM \nCommittee Members:-\nProf. Jennifer Dy\nProf. Eduardo Sontag\nProf. Mario Sznaier\nProf. Peter Castaldi \nAbstract:\nAs machine learning algorithms are deployed ubiquitously to a variety of domains\, it is imperative to make these often black-box models transparent. The ability to interpret and comprehend the reasoning behind machine learning models plays a pivotal role in increasing user trust. It not only offers insights into how a model functions but also opens avenues for model enhancements. \nThis research delves into the realm of interpretability\, focusing on the dichotomy between ‘intrinsic’ and ‘post hoc’ interpretability. Intrinsic interpretability involves constraining the complexity of the machine learning model itself\, resulting in models inherently interpretable due to their simplicity\, such as decision trees or sparse linear regression. On the other hand\, post hoc interpretability employs techniques that assess the model’s behavior after training\, offering insights into the model’s outcomes. Examples of post hoc techniques include permutation feature importance and the Shapley value method for feature importance. \nThe core contribution of this Thesis proposal lies in the development of novel methods to enhance both intrinsic and post hoc interpretability. These methods aim to advance the field by offering new perspectives on understanding machine learning models\, thereby contributing to the ongoing discourse on model transparency and user trust.
URL:https://ece.northeastern.edu/event/aria-masoomi-phd-proposal-review-2/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231129T150000
DTEND;TZID=America/New_York:20231129T160000
DTSTAMP:20260425T134752
CREATED:20231127T213756Z
LAST-MODIFIED:20231127T213756Z
UID:6618-1701270000-1701273600@ece.northeastern.edu
SUMMARY:Aria Masoomi PhD Proposal Review
DESCRIPTION:Title:\nMaking Deep Neural Network Transparent \nDate:\n11/29/2023 \nTime:\n3:00:00 pm \nCommittee Members:\nProf. Jennifer Dy (Advisor)\nProf. Mario Sznaier\nProf. Eduardo Sontag\nProf. Peter Castaldi \nAbstract:\nAs machine learning algorithms are deployed ubiquitously to a variety of domains\, it is imperative to make these often black-box models transparent.\nThe ability to interpret and comprehend the reasoning behind machine learning models plays a pivotal role in increasing  user trust. It not only offers insights into how a model functions but also opens avenues for model enhancements. \nThis research delves into the realm of interpretability\, focusing on the dichotomy between ‘intrinsic’ and ‘post hoc’ interpretability. Intrinsic interpretability involves constraining the complexity of the machine learning model itself\, resulting in models inherently interpretable due to their simplicity\, such as decision trees or sparse linear regression. On the other hand\, post hoc interpretability employs techniques that assess the model’s behavior after training\, offering insights into the model’s outcomes. Examples of post hoc techniques include permutation feature importance and the Shapley value method for feature importance. \nThe core contribution of this Thesis proposal lies in the development of novel methods to enhance both intrinsic and post hoc interpretability. These methods aim to advance the field by offering new perspectives on understanding machine learning models\, thereby contributing to the ongoing discourse on model transparency and user trust.
URL:https://ece.northeastern.edu/event/aria-masoomi-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231127T080000
DTEND;TZID=America/New_York:20231127T170000
DTSTAMP:20260425T134752
CREATED:20231127T213640Z
LAST-MODIFIED:20231127T213640Z
UID:6616-1701072000-1701104400@ece.northeastern.edu
SUMMARY:Bruno Souto Maior Muniz Morais PhD Dissertation Defense
DESCRIPTION:Title:\nEnabling Domain Platform Design for Streaming Applications: A Holistic Approach \nCommittee Members:\nGunar Schirner (Advisor)\nProf. David Kaeli\nProf. Hamed Tabkhi (UNCC) \nTime:\n10:00:00 AM \nLocation: ISEC 601 \nAbstract:\nIn recent years\, more demanding streaming applications make striking a balance between high compute performance and efficiency paramount in platforms designs for edge computing. In addition\, designing a platform that is optimized for a single application is costly due to non-recurring engineering (NRE) costs. In contrast\, multiple applications can be grouped in domains\, e.g. computer vision\, software-defined radio. Leveraging shared characteristics of similar applications within a domain\, e.g. structural composition/computation patterns\, a single domain platform that caters to these similarities and accelerates applications can be generated\, thus benefiting multiple applications at once and dramatically improving NRE and time-to-market (TTM). \nThis dissertation introduces methodologies atvarious abstraction levels to enable streamlined domain platform design for streaming applications. Thrust 1 introduces high level DSE methods based on integer linear programming (ILP)\, Tile-based Synchronization Aware ILP (TSAR-ILP). Initially\, single-application platform allocations are considered using TSAR-ILP. While TSAR-ILP only focuses on applications in isolation\, its formulation lays the foundations for DmTSAR-ILP\, a method that performs domain DSE with multiple applications\, obtaining an optimal unified platform allocation that and achieving an increase of 22.5% in throughput\, while being 70x faster when compared to previous methods (MG-DmDSE). However\, DmTSAR-ILP aims to aggregate all applications fairly. This presents a challenge when the designer wishes to focus on a subset of applications. To enable ultimate flexibility in a product-oriented setting\, modeled after a market analysis process\, this dissertation introduces ProdDSE. ProdDSE enables application prioritization while also introducing concurrent application modeling and a multi-objective optimization (area\, performance) approach. This enables up to a 3.4x boost in performance depending on use case\, while also providing gains in DSE runtime (4.3x faster). \nThrust 2 introduces Sedona\, a domain-specific language (DSL) and exploration enviroment that captures parametric dataflow application descriptions with language features dedicated to streaming applications. A design identified by Thrust 1 can be further refined using the tools in Thrust 2\, by capturing the connectivity of a design using Sedona. Then\, automatic wiring is performed for target outputs such as timing-aware simulations or RTL-level code\, enabling structural manipulation at a high-level description without the burden of low-level manual integration. \nFinally\, to better guide the high-level decisions performed in Thrust 1 and further exploration/integration in Thrust 2\, Thrust 3 considers the implications of HWACC topology choices in an HWACC-rich SoC. The ACTAR flow is introduced to explore different topologies in a RISC-V based SoC and the side-effects of topology and memory sizing choices on the system-wide performance and synchronization burdens due computation offloading to HWACCs. This produces valuable and actionable insights for designers to make informed choices on system-level compositions depending on application communication and computation demands.
URL:https://ece.northeastern.edu/event/bruno-souto-maior-muniz-morais-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230803T100000
DTEND;TZID=America/New_York:20230803T110000
DTSTAMP:20260425T134752
CREATED:20230508T193859Z
LAST-MODIFIED:20230508T193859Z
UID:6321-1691056800-1691060400@ece.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://ece.northeastern.edu/event/yu-yins-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230721T140000
DTEND;TZID=America/New_York:20230721T153000
DTSTAMP:20260425T134752
CREATED:20230718T175222Z
LAST-MODIFIED:20230718T175222Z
UID:6433-1689948000-1689953400@ece.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://ece.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:20260425T134752
CREATED:20230711T180015Z
LAST-MODIFIED:20230711T180015Z
UID:6425-1689858000-1689861600@ece.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://ece.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:20260425T134752
CREATED:20230626T213009Z
LAST-MODIFIED:20230626T213036Z
UID:6407-1688058000-1688059800@ece.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://ece.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:20260425T134752
CREATED:20230624T220848Z
LAST-MODIFIED:20230624T220848Z
UID:6402-1687768200-1687771800@ece.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://ece.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:20260425T134752
CREATED:20230606T193237Z
LAST-MODIFIED:20230606T193237Z
UID:6395-1687514400-1687518000@ece.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://ece.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:20260425T134752
CREATED:20230522T212041Z
LAST-MODIFIED:20230522T212041Z
UID:6354-1687266000-1687269600@ece.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://ece.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:20260425T134752
CREATED:20230624T221028Z
LAST-MODIFIED:20230624T221028Z
UID:6404-1687248000-1687280400@ece.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://ece.northeastern.edu/event/alfred-p-navatos-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230605T110000
DTEND;TZID=America/New_York:20230605T123000
DTSTAMP:20260425T134752
CREATED:20230522T211405Z
LAST-MODIFIED:20230522T211405Z
UID:6345-1685962800-1685968200@ece.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://ece.northeastern.edu/event/can-qins-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230602T110000
DTEND;TZID=America/New_York:20230602T120000
DTSTAMP:20260425T134752
CREATED:20230508T193647Z
LAST-MODIFIED:20230508T193647Z
UID:6319-1685703600-1685707200@ece.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://ece.northeastern.edu/event/cheng-gongyes-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230526T123000
DTEND;TZID=America/New_York:20230526T133000
DTSTAMP:20260425T134752
CREATED:20230522T211528Z
LAST-MODIFIED:20230522T211528Z
UID:6347-1685104200-1685107800@ece.northeastern.edu
SUMMARY:Guillem Reus Muns' PhD Dissertation Defense
DESCRIPTION:“AI for communication and sensing in RF environments” \nCommittee Members:\nProf. Kaushik Chowdhury (Advisor)\nProf. Stratis Ioannidis\nProf. Hanumant Singh \nAbstract:\nThe recent growth of Internet of Things (IoT)\, as well as other new\nrevolutionary applications utilizing wireless spectrum are leading the way towards the realization of next-generation wireless systems that jointly utilize communications and sensing. However\, such systems offer many degrees of freedom\, and optimizing them for a specific task is difficult to accomplish with deterministic and classical approaches. For this reason\, data-driven and AI-based methods have been pursued actively by the research community\, as they are able to find solutions that often come close to or exceed the performance of the deterministic counterparts with fractional design complexity. This thesis presents\, through real systems and with experimental validation\, our progressive efforts in four broad areas\, where AI enables the operation of aerial and terrestrial systems that combine sensing and communications. The following key use cases with distinct contributions are investigated: \ni) Sensing-aided communications for air and ground systems. First\, we present a UAV communication method that defines constellation points in space that map to transmitter frequency bands and are detected at the Base Station using millimeter wave sensors. Second\, we explore alternative vehicle-to-infrastructure mmWave beamforming methods\, leveraging a) vehicle position and velocity estimation using in-band standard compliant 802.11ad radar and b) camera images and GPS location information. \nii) Signal classification using communication signals\, where we propose a) a UAV classification method using uniquely UAV-transmitted signals and b) an RF fingerprinting technique that improves class separation by combining triplet loss with regular classification techniques. \niii) ‘SenseORAN’\, a revolutionary architectural design that aims to reuse the cellular infrastructure for sensing purposes in order to address spectrum access challenges in the CBRS band. This is enabled by Open Radio Access Network (O-RAN)\, a cellular architecture concept that promotes virtualized RANs where disaggregated components are connected via open interfaces and supports intelligent controllers running custom logic. iv) ‘AirFC’\, an over-the-air computation method that implements fully connected neural networks inference leveraging multi-antenna wireless systems.
URL:https://ece.northeastern.edu/event/guillem-reus-muns-phd-dissertation-defense/
LOCATION:Admissions Visitor Center (West Village F)
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230526T090000
DTEND;TZID=America/New_York:20230526T100000
DTSTAMP:20260425T134752
CREATED:20230522T211659Z
LAST-MODIFIED:20230522T211659Z
UID:6350-1685091600-1685095200@ece.northeastern.edu
SUMMARY:Yuezhou Liu's PhD Proposal Review
DESCRIPTION:Committee Members:\nProf. Edmund Yeh (Advisor)\nProf. Stratis Ioannidis\nProf. Lili Su\nProf. Carlee Joe-Wong \nAbstract:\nSignificant advances in edge and mobile computing capabilities enable machine learning to occur at geographically diverse locations in networks\, e.g.\, cloud\, edge\, and mobile devices. The training data needed in those learning tasks may not be fully generated locally. Moreover\, some promising distributed learning paradigms enable devices to collaboratively train a model\, which requires communication among the devices for exchanging necessary information. Thus\, optimizing network strategies for the transmission/exchange of ML/AI ingredients (e.g.\, input data\, model parameters\, gradients) is important for facilitating efficient in-network distributed ML. While there exist many works that use ML to optimize network operation strategies\, few works study optimized networks that boost ML performance. This dissertation tries to fill the gap by studying several network optimization problems for distributed ML. Different from classic network optimization problems for data delivery or edge computing that optimize energy consumption\, delay\, throughput\, etc.\, we also pay attention to ML-related metrics such as model accuracy and training convergence time. \nWe first propose an experimental design network paradigm\, wherein learner nodes train possibly different ML models via consuming data streams generated by data source nodes over a network. We formulate this problem as a social welfare optimization problem in which the global objective is defined as the sum of experimental design objectives of individual learners\, and the decision variables are the data transmission strategies subject to network constraints. We show that\, assuming Bayesian linear regression models and Poisson data streams in steady state\, the global objective is continuous DR-submodular\, which enables the design of efficient approximate algorithms with approximation guarantees. We will further extend our framework to incorporate more practical ML applications\, such as ML with arbitrary nonlinear models. \nThe second half of this dissertation studies network optimization for Federated learning (FL)\, a distributed paradigm for collaboratively learning models without having clients disclose their private data. We propose to use caching for improving FL efficiency concerning the total model training time for convergence. Instead of having all clients download the latest global model from a parameter server\, we select a subset of clients to access\, with smaller delays\, a somewhat stale global model stored in caches. We propose CacheFL — a cache-enabled variant of FedAvg\, and provide theoretical convergence guarantees in the general setting where the local data is imbalanced and heterogeneous. With this result\, we determine the caching strategies that minimize total wall-clock training time at a given convergence threshold for both stochastic and deterministic communication/computation delays.
URL:https://ece.northeastern.edu/event/yuezhou-lius-phd-proposal-review/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230522T103000
DTEND;TZID=America/New_York:20230522T113000
DTSTAMP:20260425T134752
CREATED:20230522T211916Z
LAST-MODIFIED:20230522T211916Z
UID:6352-1684751400-1684755000@ece.northeastern.edu
SUMMARY:Mengting Yan's PhD Dissertation Defense
DESCRIPTION:“Integrated Circuit Design Methods for Temperature-based Hardware Trojan Detection and Parametric Frequency Division in Next-Generation Systems-on-a-Chip” \nCommittee Members:\nProf. Marvin Onabajo (Advisor)\nProf. Yong-bin Kim\nProf. Yunsi Fei \nAbstract:\nNew needs for next-generation systems-on-a-chip (SoC) are emerging as the trend of globalization in the semiconductor industry becomes increasingly ubiquitous and the demand for low-power Internet-of-Things (IoT) devices continues to soar. Among various research directions\, this dissertation focuses on enhancing hardware security and on providing low-noise frequency sources for next-generation SoCs. Within this scope\, the described research addresses the challenge to improve on-chip anomaly detection capabilities\, and separately lays a foundation for the design of circuits to reduce the phase noise of on-chip oscillators. \nIn the first part of this dissertation\, an on-chip temperature-based Hardware Trojan (HT) detection system is introduced. The approach to detect inserted HTs relies on thermal profiling of the circuit under test (CUT) and side-channel analysis of the obtained temperature data. On-chip electrothermal coupling is modeled as part of a simulation technique that associates local thermal activities with circuit-level power consumption using a standard electrical simulator. To monitor the thermal profiles on chips with high sensitivity to local temperature changes as well as to enhance the resilience to flicker noise\, a fully-differential temperature sensor equipped with a chopping mechanism has been designed in 130-nm complementary metal-oxide-semiconductor (CMOS) technology\, which has a sensitivity of 840 V/◦C. The simulated temperature sensor output in the presence of noise and process variations is quantized by an analog-to-digital converter (ADC) model and processed using principal component analysis (PCA)\, which allows to determine the minimum detectable Trojan power and the design requirements for the on-chip ADC. With a modeled 8-bit ADC\, simulations of the HT detection system reveal a detection rate of 100% with a Trojan power down to 2.4 μW within the thermal profile of a CUT consuming 508 μW. A prototype 8-bit 1 MS/s successive approximation register (SAR) ADC for such a system was designed in 130-nm CMOS technology\, fabricated\, and tested. The measured effective number of bits (ENOB) is 7.27 bits up to the Nyquist frequency\, with a power consumption of 103.2 μW from a 1.2 V supply. Furthermore\, a 3-step analog calibration loop has been designed to compensate for the voltage offsets within the sensor circuits in the presence of device mismatches and process-temperature variations. The calibration loop settles within 300 μs to complete the offset calibration\, such that the input-referred offset has a standard deviation of 5.86 μV based on Monte Carlo simulations. \nIn the second part of this dissertation\, the on-chip realization of a parametric frequency divider (PFD) is explained. The low-power 2:1 frequency division at sub-6 GHz plays a critical role in on-chip phase noise reduction systems that exhibit nonlinear operations\, indicating promise for future integration into radio frequency (RF) SoCs. In particular\, the first current-driven PFD with an output frequency of 2.4 GHz is introduced\, which consists of three major blocks: (1) a custom PFD driver stage with a buffer to ease input driving\, (2) a purely passive PFD core with inductor-capacitor (LC) resonators\, and (3) an output driving stage with embedded band-pass filtering that suppresses undesirable output harmonics. A prototype PFD chip was fabricated in standard 65-nm CMOS technology\, and the corresponding measurement results are presented to characterize the performance of the new PFD. The minimum required supply voltage for the PFD driver is 1.4 V with an input frequency of 4.8 GHz\, whereas the PFD has an operating frequency range from 4.5 GHz to 5.1 GHz with a supply voltage of 1.5 V. To the best of the author’s knowledge\, the proposed PFD is the first on-chip CMOS implementation for sub-6 GHz applications\, which balances the trade-offs among frequency range\, power consumption\, and chip area constraints.
URL:https://ece.northeastern.edu/event/mengting-yans-phd-dissertation-defense/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230428T130000
DTEND;TZID=America/New_York:20230428T150000
DTSTAMP:20260425T134752
CREATED:20230426T174141Z
LAST-MODIFIED:20230426T174141Z
UID:6293-1682686800-1682694000@ece.northeastern.edu
SUMMARY:Balaji Sundareshan's MS Thesis Defense
DESCRIPTION:“Cross-View Action Recognition using Transformers” \nCommittee Members:\n1. Prof. Octavia Camps (Advisor)\n2. Prof. Mario Sznaier\n3. Prof. Huaizu Jiang \nAbstract:\nCross-view action recognition (CVAR) seeks to recognize a human action when observed from a previously unseen viewpoint. This is a challenging problem since the appearance of action changes significantly with the viewpoint. Applications of CVAR include surveillance and monitoring of assisted living facilities where is not practical or feasible to collect large amounts of training data when adding a new camera. In this thesis\, we propose a method to perform cross-view action recognition from 2D skeleton data using Transformers. First\, we understand the interpretability of the basline network and its submodules by visualizing the saliency map. Next\, we integrate Transformers at different parts of the network for both single-clip and multi-clip and understand the impact on the performance. In the end\, we also discuss the necessity of pretraining sub-modules in the network and their impact on the performance.
URL:https://ece.northeastern.edu/event/balaji-sundareshans-ms-thesis-defense/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230426T160000
DTEND;TZID=America/New_York:20230426T170000
DTSTAMP:20260425T134752
CREATED:20230426T174309Z
LAST-MODIFIED:20230426T174309Z
UID:6295-1682524800-1682528400@ece.northeastern.edu
SUMMARY:Rui Huang's MS Thesis Defense
DESCRIPTION:“Sputter Deposition and Characterization of Highly Textured BixTe1-x Thin Films” \nCommittee Members:\nProf. Nian-Xiang Sun (Advisor)\nProf. Marvin Onabajo\nProf. Yongmin Liu \nAbstract:\nThe discovery of topological insulators (TIs) provides a direction for scientists to understand the Quantum Spin Hall Effect (QSHE) and Spin-Orbit Coupling (SOP)  in condensed matter physics. After a decade\, people found that after the introduction of magnetism into TI\, the Time Reversal Symmetry (TSR) is broken\, producing Magnetic Topological Insulators (MTI). Meanwhile\, with the deposition of TI on the Magnetic Insulator (MI)\, the Spin-Orbit Torque was found in TI/MI structures. Introducing dopants into TI is another method to produce MTI. Mn-doped\,  Cr-doped\, and Ni-doped TI thin films have been explored recently. Thus\, the 3D TI\, Bi2Te3\, and MTI\, Ni: Bi2Te3\, thin film-based materials have been applied to some energy-efficient spintronic devices. However\, according to the Bi-Te phase diagram\, Bi2Te3 is one of the Bi-Te family. The narrow range of the Bi2Te3 phase is a challenge for people to deposit the correct phase on the InP (111) wafer due to the potential effect of defects.  In this Master thesis\, the textured BixTe1-x and Ni-doped BixTe1-x thin films are deposited on the InP (111) substrate through the RF Magnon Sputtering Tool with a Te capping layer under different deposition powers\, temperatures\, and post-annealing time. After the X-ray diffraction measurement on three samples with various conditions\, the textured Bi8Te7\, Bi8Te9\, and Ni: Bi8Te7 thin films are concluded based on the comparison between the theoretical XRD results with the experimental ones.
URL:https://ece.northeastern.edu/event/rui-huangs-ms-thesis-defense/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230425T130000
DTEND;TZID=America/New_York:20230425T143000
DTSTAMP:20260425T134752
CREATED:20230420T223701Z
LAST-MODIFIED:20230420T223701Z
UID:6273-1682427600-1682433000@ece.northeastern.edu
SUMMARY:Rashida Nayeem's PhD Dissertation Defense
DESCRIPTION:“Human control of objects with nonlinear internal dynamics: Predictability as primary objective” \nLocation:\nEgan Research Ctr 206 \nCommittee Members:\nProf. Dagmar Sternad (Advisor)\nProf. Eduardo Sontag\nProf. Mario Sznaier\nDr. David Lin (Massachusetts General Hospital & Harvard Medical School) \nAbstract:\nHumans physically interact with complex objects in numerous daily activities. An example is picking up a cup of coffee where interaction forces arise between the hand and the sloshing liquid. For successful actions\, error corrections based on real-time sensed information are insufficient\, hence humans need to predict and preempt the evolving dynamics. Our previous work on the transport of a “cup of coffee” showed that humans seek to make the interaction dynamics simple\, i.e.\, predictable. Extending from previous work\, this thesis used a virtual paradigm where the “cup of coffee” was simplified to a cup with a ball sliding inside\, retaining the challenges of “a cup filled with coffee”: underactuation and nonlinearity. A series of experiments examined human strategies in different contexts to demonstrate that predictability is a control priority. The first experimental and modeling study examined how subjects explored and prepared the 2D cup-and-ball system prior to continuous interaction. Results showed that subjects converged to a small set of initial conditions that shortened initial transients\, enabling subjects to reach a more predictable steady state faster. Two follow-up studies examined the role of visual and haptic information and revealed that despite suboptimal exploration of the solution space\, subjects increased predictability of hand object interactions. System identification showed that visual information enabled subjects to simplify input-output behavior via appropriate object preparation. When deprived of haptic information subjects still achieved increased predictability but sacrificed orbital stability. A final study extended this basic paradigm to a clinical application to investigate if these insights could help in assessment of motor impairment after stroke in this functionally relevant ‘self-feeding’ task. To facilitate testing in a clinic\, a real-life 3D device was custom-developed where individuals after stroke moved a cup with a rolling ball inside on a table. Our theory-based predictability metric proved highly sensitive to quantify the degree of motor impairment after stroke. Taken together\, this thesis elucidated principles of human motor control in a complex interactive task. The insights have significant applications in clinical testing and may also inform robot manipulation of this understudied movement challenge.
URL:https://ece.northeastern.edu/event/rashida-nayeems-phd-dissertation-defense/
LOCATION:206 Egan\, 360 Huntington Ave\, 206 Egan\, Boston\, MA\, 02115\, United States
GEO:42.3376753;-71.0888734
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=206 Egan 360 Huntington Ave 206 Egan Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave\, 206 Egan:geo:-71.0888734,42.3376753
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230424T110000
DTEND;TZID=America/New_York:20230424T123000
DTSTAMP:20260425T134752
CREATED:20230420T223436Z
LAST-MODIFIED:20230420T223740Z
UID:6277-1682334000-1682339400@ece.northeastern.edu
SUMMARY:Cunzheng Dong's PhD Proposal Review
DESCRIPTION:“Acoustically Actuated Magnetoelectric Antennas for VLF Communication and Magnetic Sensing” \nCommittee Members:\nProf. Nian Sun (Advisor)\nProf. Yongmin Liu\nProf. Hossein Mosallaei \nAbstract:\nSince the discovery of strong magnetoelectric (ME) coupling in two-phase ME laminate composites\, strain-mediated ME heterostructures have attracted a great deal of attention from academic and industrial research groups for their potential usage in magnetic sensors\, voltage tunable inductors\, magnetic tunable filters\, and miniaturized mechanical antennas\, etc. Acoustically actuated ME antennas have recently been demonstrated as a promising solution for very low frequency (VLF) communications and magnetic fields detection\, for their 2-3 orders of reduced dimensions\, outstanding sensitivity at resonance\, and robust immunity to electrical interferences than conventional electric antennas. Their performance and noise analysis are deeply investigated and discussed in this proposal review. \nFirstly\, A portable VLF communication system using one pair of ME antennas operating at their electromechanical resonance (EMR) is presented. The measured near-field radiation pattern reveals ME antennas are equivalent to magnetic dipole antennas. The magnetic field radiated by the ME transmitter has been predicted along with distance from near-field to far-field. The measured magnetic field distribution coincided well with the prediction\, and the maximum communication distance of 120 m has been achieved by single antenna unit. Antenna arrays are widely used as an effective approach to enhance radiation field intensity. By tunning all the driving signal for each antenna unit at the same frequency and in phase\, the total radiation field strength has been linearly enhanced by one order with 12 antenna arrays. Furthermore\, nonlinear antenna modulation (NAM) has also been successfully demonstrated on the ME antennas. \nSecondly\, a Metglas/Quartz based ME resonator as magnetic sensor for reception of VLF magnetic signals is presented. Metglas is a highly permeable magnetostrictive material which can effectively concentrate the magnetic fields. Moreover\, the high magnetostriction and low coercivity of Metglas can generate a distinct strain change in response to subtle magnetic fields. Piezoelectric single crystal Quartz is often used as electronic oscillators due to their extremely high Q factor with low noise and high stability. The combined properties of these two materials provide ME sensors an extremely high sensitivity and low magnetic noise of less than 10 fT at the EMR frequency. The VLF signal reception capability of the proposed ME sensor was also compared with a conventional VLF loop antenna and the PZT-5A based ME sensor. \nLastly\, a compact and sensitive system was developed to characterize the magnetomechanical properties\, such as the saturation magnetostriction\, piezomagnetic coefficient\, delta-E effect and magnetomechanical coupling factor of magnetic thin films. These magnetomechanical properties are critical in determining the performance of ME antennas. For saturation magnetostriction and piezomagnetic coefficient measurement\, a high precision optical probe was harnessed to measure the deflection of the magnetic thin film/Si cantilever due to strain change induced by domain rotation. The same cantilever samples were used for delta-E effect and magnetomechanical coupling factor characterization\, the DC bias magnetic field induced cantilever resonance frequency shift was used for calculating the change of elastic modulus.
URL:https://ece.northeastern.edu/event/cunzheng-dongs-phd-proposal-review/
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