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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230809T150000
DTEND;TZID=America/New_York:20230809T163000
DTSTAMP:20260519T001628
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:20260519T001628
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:20260519T001628
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:20260519T001628
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:20260519T001628
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:20260519T001628
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:20260519T001628
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:20260519T001628
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230720T130000
DTEND;TZID=America/New_York:20230720T140000
DTSTAMP:20260519T001628
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:20260519T001628
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:20260519T001628
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:20260519T001628
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:20260519T001628
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:20260519T001628
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/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230605T110000
DTEND;TZID=America/New_York:20230605T123000
DTSTAMP:20260519T001628
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/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230602T110000
DTEND;TZID=America/New_York:20230602T120000
DTSTAMP:20260519T001628
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/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230526T123000
DTEND;TZID=America/New_York:20230526T133000
DTSTAMP:20260519T001628
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)
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230526T090000
DTEND;TZID=America/New_York:20230526T100000
DTSTAMP:20260519T001628
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/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230522T103000
DTEND;TZID=America/New_York:20230522T113000
DTSTAMP:20260519T001628
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/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230428T130000
DTEND;TZID=America/New_York:20230428T150000
DTSTAMP:20260519T001628
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/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230426T160000
DTEND;TZID=America/New_York:20230426T170000
DTSTAMP:20260519T001628
CREATED:20230426T134309Z
LAST-MODIFIED:20230426T134309Z
UID:36836-1682524800-1682528400@coe.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://coe.northeastern.edu/event/rui-huangs-ms-thesis-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230425T130000
DTEND;TZID=America/New_York:20230425T143000
DTSTAMP:20260519T001628
CREATED:20230420T183701Z
LAST-MODIFIED:20230420T183701Z
UID:36787-1682427600-1682433000@coe.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://coe.northeastern.edu/event/rashida-nayeems-phd-dissertation-defense/
LOCATION:206 Egan\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
GEO:42.3376753;-71.0888734
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=206 Egan 360 Huntington Ave Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave:geo:-71.0888734,42.3376753
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230424T110000
DTEND;TZID=America/New_York:20230424T123000
DTSTAMP:20260519T001628
CREATED:20230420T183436Z
LAST-MODIFIED:20230420T183740Z
UID:36785-1682334000-1682339400@coe.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://coe.northeastern.edu/event/cunzheng-dongs-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230420T080000
DTEND;TZID=America/New_York:20230420T170000
DTSTAMP:20260519T001628
CREATED:20230420T183901Z
LAST-MODIFIED:20230420T183901Z
UID:36789-1681977600-1682010000@coe.northeastern.edu
SUMMARY:Chenghao Wang's MS Thesis Defense
DESCRIPTION:“Legged Walking on Inclined Surfaces” \nCommittee Members:\nProf. Alireza Ramezani(Advisor)\nProf. Miriam Leeser\nProf. Bahram Shafai \nAbstract:\nThe main contributions of this MS Thesis are centered around taking steps towards successful multi-modal demonstrations using Northeastern’s legged-aerial robot\, Husky Carbon. This work discusses the challenges involved in achieving multi-modal locomotion such as trotting-hovering and thruster-assisted incline walking and reports progress made towards overcoming these challenges. Animals like birds use a combination of legged and aerial mobility\, as seen in Chukars’s wing-assisted incline running (WAIR)\, to achieve multi-modal locomotion. Chukars use forces generated by their flapping wings to manipulate ground contact forces and traverse steep slopes and overhangs. Husky’s design takes inspiration from birds such as Chukars. This MS thesis presentation outlines the mechanical and electrical details of Husky’s legged and aerial units. The thesis presents simulated incline walking using a high-fidelity model of the Husky Carbon over steep slopes of up to 45 degrees.
URL:https://coe.northeastern.edu/event/chenghao-wangs-ms-thesis-defense/
LOCATION:532 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
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DTSTART;TZID=America/New_York:20230413T160000
DTEND;TZID=America/New_York:20230413T170000
DTSTAMP:20260519T001628
CREATED:20230405T134232Z
LAST-MODIFIED:20230405T134232Z
UID:36500-1681401600-1681405200@coe.northeastern.edu
SUMMARY:Hussein Hussein’s PhD Dissertation Defense
DESCRIPTION:“Parametric Circuits for Enhanced Sensing and RF Signal Processing” \nCommittee Members: \nProf. Cristian Cassella (Advisor) \nProf. Marvin Onabajo \nProf. Matteo Rinaldi \nProf. Andrea Alù \nAbstract: \nMassive deployments of wireless sensor nodes (WSNs) that continuously detect physical\, biological or chemical parameters are needed to truly benefit from the unprecedented possibilities opened by the Internet‑of‑Things (IoT). Just recently\, new sensors with higher sensitivities have been demonstrated by leveraging advanced on‑chip designs and microfabrication processes. Yet\, WSNs using such sensors require energy to transmit the sensed information. Consequently\, they either contain batteries that need to be periodically replaced or energy harvesting circuits whose low efficiencies prevent a frequent and continuous sensing\, even impacting the maximum range of communication. Here\, we discuss a new battery-less and harvester-free remote sensing tag\, namely the subharmonic tag (SubHT)\, leveraging unique nonlinear characteristics to fundamentally break any previous paradigms for passive WSNs. SubHT can sense and transmit information without requiring supplied or harvested DC power. Also\, it transmits the sensed information at a difference frequency from the one of its interrogation signal\, rendering its reader immune from multi-path\, from clutter and from its own self‑interference. Also\, even though SubHT may not require any advanced and expensive manufacturing\, its unique nonlinear response enables extraordinary high sensitivities and dynamic ranges that can even surpass those achieved by the most advanced on-chip sensors. More interestingly\, SubHT can be even configured to operate in a “threshold sensing” mode\, making it able to respond to any interrogation signal only when the sensed parameter has exceeded a remotely reprogrammable threshold\, as well as to memorize any violation in a sensed parameter without requiring any memory components. In this talk\, the first SubHT prototypes for temperature sensing will be showcased. Even more\, we will show how including high quality factor (Q) resonators in a SubHT’s network allows to implement even more functionalities\, such as the long-range identification or tracking of any items or localization and navigation in a GPS denied environment. Yet\, the dynamics exploited by SubHT can also be leveraged to address various needs along radio-frequency (RF) chains. In this regard\, we show how the SubHT’s nonlinear dynamics can be leveraged to build components\, such as parametric filters\, frequency selective limiters and signal to noise enhancers\, that improve the stability of RF frequency synthesizers and instinctually suppress co-site or self-interferes\, paving an unprecedented path towards integrated radios with improved performance and longer battery-life time.
URL:https://coe.northeastern.edu/event/hussein-husseins-phd-dissertation-defense/
LOCATION:432 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
GEO:42.3396156;-71.0886534
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DTSTART;TZID=America/New_York:20230411T120000
DTEND;TZID=America/New_York:20230411T130000
DTSTAMP:20260519T001628
CREATED:20230405T135216Z
LAST-MODIFIED:20230405T135216Z
UID:36462-1681214400-1681218000@coe.northeastern.edu
SUMMARY:Tirthak Patel's Dissertation Defense
DESCRIPTION:“Robust System Software for Quantum Computing” \nCommittee Members: \nProf. Devesh Tiwari (Advisor) \nProf. David Kaeli \nProf. Ningfang Mi \nProf. Gene Cooperman \nProf. Kenneth Brown \nAbstract: \nDespite rapid progress in quantum computing in the last decade\, the limited usability of quantum computers remains a major roadblock toward its wider adoption. Current noisy intermediate-scale quantum (NISQ) computers produce highly erroneous program outputs for quantum-advantage-proven algorithms — that is\, algorithms that are infeasible or orders of magnitude slower on classical supercomputing and high-performance computing (HPC) clusters. Unfortunately\, currently\, quantum computing programmers lack robust system software tools and methods to make meaningful use of erroneous program executions on quantum computers. \nThis lack of capability is the core motivation behind the fundamental question this dissertation poses: “can we build system software tools for programmers to make the quantum program execution and output meaningful on NISQ machines?” This dissertation answers this question in the affirmative— experimentally demonstrating on real-system quantum computers that it is possible to extract near-accurate program output from noisy executions on today’s erroneous quantum computers\, ironically using classical HPC resources and knowledge. This dissertation demonstrates how to achieve this goal without requiring user intervention\, domain knowledge about quantum algorithms\, or additional quantum hardware support. \nUnfortunately\, as this dissertation uncovers\, progressing toward making quantum computers usable is a double-edged sword. In the near future\, only a few entities in the world may have access to powerful quantum computers\, and these quantum computers will be used to solve previously-unsolved large-scale optimization problems\, possibly without an explicit trust model between the service provider and the customer. Therefore\, this dissertation envisions that the solutions to such large-scale optimization problems will be considered sensitive and will need to be protected. This dissertation takes the first few steps toward preparing us for that future by developing a novel method that intelligently obfuscates near-accurate program output and quantum circuit structure to preserve a customer’s privacy under a specified computation model and resource availability. \nThe approaches introduced in this dissertation open up new research avenues for hybrid quantum-classical computing and lower the barrier to entry for quantum computing research for the experimental computer systems and HPC community by open-sourcing multiple novel datasets and software frameworks implemented for real-system quantum computers. \nCandidate Bio: \nTirthak Patel is an incoming Assistant Professor in the Department of Computer Science at Rice University; currently\, a PhD candidate at Northeastern University\, advised by Professor Devesh Tiwari. Tirthak conducts systems-level research at the intersection of quantum computing and high-performance computing (HPC). His research contributions have appeared at rigorously peer-reviewed publication venues including ASPLOS\, Supercomputing (SC)\, HPDC\, HPCA\, and USENIX FAST\, and have been recognized with multiple award distinctions. He has received the ACM-IEEE CS George Michael Memorial HPC Fellowship\, the NSERC Alexander Graham Bell Canada Graduate Scholarship\, and the Northeastern University Outstanding Graduate Student in Research award\, for his research contributions toward making noisy quantum computing systems useful and helping HPC programmers solve computationally challenging problems.
URL:https://coe.northeastern.edu/event/tirthak-patels-dissertation-defense/
LOCATION:442 Dana\, 360 Huntington Ave\, 442 DA\, Boston\, MA\, 02115\, United States
GEO:42.3387508;-71.0923044
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DTSTART;TZID=America/New_York:20230404T123000
DTEND;TZID=America/New_York:20230404T133000
DTSTAMP:20260519T001628
CREATED:20230328T134955Z
LAST-MODIFIED:20230328T134955Z
UID:36351-1680611400-1680615000@coe.northeastern.edu
SUMMARY:Cheng Gongye's MS Thesis Defense
DESCRIPTION:“Using Floating-Point Timing Side-Channels to Reverse Engineer Deep Neural Networks” \nCommittee Members:\nProf. Yunsi Fei (Advisor)\nProf. Aidong Ding\nProf. Xiaolin Xu \nAbstract: \nTrained Deep Neural Network (DNN) models have become valuable intellectual property. A new attack surface has emerged for DNNs: model reverse engineering. Several recent attempts have utilized various common side channels. However\, recovering DNN parameters\, weights and biases\, remains a challenge. In this paper\, we present a novel attack that utilizes a floating-point timing side channel to reverse-engineer parameters of multi-layer perceptron (MLP) models in software implementation\, entirely and precisely. To the best of our knowledge\, this is the first work that leverages a floating-point timing side channel for effective DNN model recovery.
URL:https://coe.northeastern.edu/event/cheng-gongyes-ms-thesis-defense/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230403T100000
DTEND;TZID=America/New_York:20230403T110000
DTSTAMP:20260519T001628
CREATED:20230320T165517Z
LAST-MODIFIED:20230320T165517Z
UID:36259-1680516000-1680519600@coe.northeastern.edu
SUMMARY:Jared Miller Ph.D Defense/Proposal Announcement
DESCRIPTION:“Safety Analysis for Nonlinear and Time-Delay Systems using Occupation Measures” \nInternational Village 022 \nCommittee Members:\nProf. Mario Sznaier (Advisor)\nProf. Octavia Camps\nProf. Bahram Shafai\nProf. Eduardo Sontag\nProf. Didier Henrion (LAAS-CNRS) \nAbstract:\nThis research extends an occupation measure framework to analyze the behavior and safety of dynamical systems. A motivating application of trajectory analysis is in peak estimation\, which finds the extreme values of a state function along trajectories. Examples of peak estimation include finding the maximum height of a wave\, voltage on a power line\, speed of a vehicle\, and infected population in an epidemic. Peak estimation can be applied towards safety quantification\, such as by measuring the safety of a trajectory by its distance of closest approach to an unsafe set. \nA finite-dimensional but nonconvex peak estimation problem can be converted into an infinite-dimensional linear program (LP) in measures\, which is in turn bounded by a convergent sequence of semidefinite programs. The LP is posed in terms of an initial\, a terminal\, and an occupational measure\, where the occupation measure contains all possible information about the dynamical systems’ trajectories. This research applies measure-based methods towards safety quantification (e.g. distance estimation\, control effort needed to crash)\, hybrid systems\, bounded-uncertain systems (including for data-driven analysis)\, stochastic systems\, and time-delay systems. The modularity of this measure-based framework allows for multiple problem variations to be applied simultaneously (e.g. distance estimation under time-delays)\, and for optimization models to be synthesized using MATLAB. Solving these optimization problems results in certifiable guarantees on system performance and behavior.
URL:https://coe.northeastern.edu/event/jared-miller-ph-d-defense-proposal-announcement/
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DTSTART;TZID=America/New_York:20230315T140000
DTEND;TZID=America/New_York:20230315T153000
DTSTAMP:20260519T001628
CREATED:20230315T141507Z
LAST-MODIFIED:20230315T141507Z
UID:36192-1678888800-1678894200@coe.northeastern.edu
SUMMARY:Sadjad Asghari Esfeden's PhD Dissertation Defense
DESCRIPTION:“Spatiotemporal Localization of Object Handover for Human Robot Collaboration” \nCommittee Members: \nProf. Deniz Erdogmus (Advisor) \nProf. Taskin Padir \nProf. Eugene Tunik \nProf. Mathew Yarossi \nAbstract: \nHuman-robot interaction in a physical world like handover of objects requires perception systems to be efficient in localizing the object of interest. We propose an approach to estimate the location of the object with a low-cost RGB camera in a real-time inference for human-robot handover. While handover can take place in a short amount of time\, it is important for a robot to keep track of the object and fill in the gaps of missing detections in the perception module\, especially when the object is partially or completely occluded. A robot needs to proactively detect and track the object since the human decides where and when to transfer the object to the robot in a human to robot object handover.  In order to develop a perception system for robot to be capable of constantly localizing the object and predict its location and time of transfer\, we integrate an object detection algorithm with a tracking framework. The evaluation of this pipeline shows promising results for the goal of localization and tracking of the handover object and can help its location prediction in future.
URL:https://coe.northeastern.edu/event/sadjad-asghari-esfedens-phd-dissertation-defense/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230303T140000
DTEND;TZID=America/New_York:20230303T153000
DTSTAMP:20260519T001628
CREATED:20230227T145344Z
LAST-MODIFIED:20230227T145344Z
UID:35988-1677852000-1677857400@coe.northeastern.edu
SUMMARY:Kerem Enhos' PhD Proposal
DESCRIPTION:“Software-Defined Inter-medium Visible Light Communication and Underwater Acoustic Networks” \nCommittee Members:\nProf. Tommaso Melodia (Advisor)\nProf. Kaushik Chowdhury\nProf. Stefano Basagni\nDr. Emrecan Demirors \nAbstract:\n“Multi-Domain Operations” paradigm has been receiving significant attention both in military and civilian worlds. To realize this novel paradigm\, it is imperative to establish robust communication links to transfer data between devices operating in multiple domains. However\, as of today\, establishing high data rate\, robust\, secure\, and bi-directional communication links between aerial and underwater assets across the air-water interface is still an open problem. We address these challenges with software-defined visible light networking to establish bi-directional wireless links through the air-water interface. After generating a simulation model for inter-medium communication channel\, we also empirically derived an optimal parameter selection for carrierless amplitude and phase (CAP) modulation. Then\, we design and prototype a software-defined visible light  communication (VLC) modem and conducted extensive experimental evaluation. Apart from inter-medium communication\, software-defined networking can also be leveraged for underwater acoustic communication (UWAC)\, where we designed and assessed coexistence of multi-dimensional chirp spread spectrum (MCSS) with other UWAC schemes. We first evaluated the performance of the proposed communication scheme in a heterogeneous network setting  where it co-exists with a ZP-OFDM communication link\, then in a homogeneous network setting where all links are using MCSS scheme. Finally\, we used  this software-defined networking system to implement a single-input  multiple-output (SIMO) system for UWAC modems that are  deployed in a  distributed manner. Then\, we conduct a thorough experimental evaluation in  ocean environment for various subcarrier bandwidths and constellations  using three distributed receivers.
URL:https://coe.northeastern.edu/event/kerem-enhos-phd-proposal/
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