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X-WR-CALDESC:Events for Department of Electrical & Computer Engineering
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DTSTART;TZID=America/New_York:20220922T130000
DTEND;TZID=America/New_York:20220922T140000
DTSTAMP:20260429T235318
CREATED:20221103T184721Z
LAST-MODIFIED:20221103T184721Z
UID:5924-1663851600-1663855200@ece.northeastern.edu
SUMMARY:Justin Crabb's PhD Proposal Review
DESCRIPTION:“Multiphysics Simulation of Graphene Transistors for On-Chip Plasmonic THz Signal Generation and Modulation” \nAbstract: \nTerahertz communication is envisioned as a key technology not only for the next generation of macro-scale networks (e.g.\, 6G+)\, but also for transformative networking applications at the nanoscale (e.g.\, wireless nanosensor networks and wireless networks on chip). This proposal focuses on the development of a multiphysics simulation platform for a plasmonic THz nanogenerator with on-chip modulation. The in-house developed finite-element-method platform\, which self-consistently solves the hydrodynamic and Maxwell’s equations\, is utilized to provide extensive numerical results demonstrating the device’s functionality along with ultra-wide bandwidth and high modulation index capabilities. \nFirst\, a comprehensive theory of the Dyakonov-Shur (DS) plasma instability in current-biased graphene transistors is presented. Using the hydrodynamic approach\, equations describing the DS instability in the two-dimensional electron fluid in graphene at arbitrary values of electron drift velocity are derived. These non-linear equations together with Maxwell’s equations are used for numerical analysis of the spatial and temporal evolution of the graphene electron system after the DS instability is triggered by random current fluctuations. Conditions necessary for the onset of the DS instability and the properties of the final stationary state of the graphene electron system are analyzed. \nNext\, a detailed numerical analysis of the DS plasma instability in the DC current-biased graphene transistor with the gate shifted with respect to the middle of the transistor conducting channel is presented. The geometric asymmetry is shown to be sufficient to trigger the DS instability in the two-dimensional electron gas in the transistor channel. Sustained plasma oscillations in the instability endpoint are demonstrated and the properties of these oscillations are analyzed for different positions of the gate and at different values of other physical and geometric FET parameters. The obtained results show the possibility of designing a tunable on-chip source of THz electromagnetic radiation based on the graphene transistor with a shifted gate. \nFollowing\, the on-chip THz nano-generator with amplitude and frequency modulation capabilities is presented. The proposed device uses and leverages the tunability of the Dyakonov-Shur instability for the growth and modulation of plasmonic oscillations in the two-dimensional electron gas channel of the graphene transistor. \n  \nCommittee: \nProf. Josep Jornet (Advisor) \nProf. Tommaso Melodia \nProf. Matteo Rinaldi \nProf. Hossein Mosallaei
URL:https://ece.northeastern.edu/event/justin-crabbs-phd-proposal-review/
LOCATION:432 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
GEO:42.3396156;-71.0886534
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220825T140000
DTEND;TZID=America/New_York:20220825T150000
DTSTAMP:20260429T235318
CREATED:20221103T181955Z
LAST-MODIFIED:20221103T181955Z
UID:5884-1661436000-1661439600@ece.northeastern.edu
SUMMARY:Tarik Kelestemur's PhD Dissertation Defense
DESCRIPTION:Location: ISEC 532 \n“Combining Classical and Learning-based Methods for Visual and Tactile Manipulation” \nAbstract: \nRobots that operate in dynamic and ever-changing environments need to make sense of their surroundings and act in them safely and efficiently. This requires the integration of multiple sensory modalities such as visual and tactile. Humans can naturally fuse different feedbacks from the environment or substitute them with one another to perform everyday tasks. For example\, to use a computer mouse\, we first locate the mouse using vision and then use touch feedback from our fingers to precisely localize the buttons. Ideally\, we would like robots to have human-level perception and control of the environment to achieve various tasks. This dissertation address two significant problems toward this overarching goal. \nThe first problem we consider in this dissertation is figuring out how to use tactile information in conjunction with visual feedback. Robotic manipulators that interact with objects and environments are often equipped with visual sensors such as RGB and depth cameras. They estimate the state of their environment using these sensors and act upon the estimated state. While a large body of previous work has shown that we can achieve impressive results only with visual sensors\, more precise and delicate tasks require touch information which gives direct feedback from the environment. To this end\, we propose methods for efficiently combining the tactile and visual information to leverage the advantages of these modalities.\nThe second problem we investigate is how to build visual and tactile manipulation methods that can generalize over the different novel environments and objects. The rise of deep learning has enabled robots to solve challenging perception and control problems using visual and tactile observations while generalizing to novel objects and environments. However\, a common issue among deep learning-based methods is that these methods usually work only within the distribution of the training data and do not perform well when they are presented with unseen examples. Furthermore\, they cannot distinguish whether they are dealing with in or out-of-distribution data. We propose to address this issue by combining well-established and principled algorithmic priors with the generalization capabilities of deep learning. \nIn the first part of this dissertation\, we investigate the problem of pose estimation of the robotic grippers with respect to the environment and objects. The proposed framework introduces a learnable Bayes filter that can estimate the position of a gripper in a single image of the environment. We learn the observation and motion models of the Bayes filter using modern neural network architectures and use recursive belief updates for tracking the position of the gripper over time. Later\, the belief estimation is used as an input to policies where the aim is to solve manipulation tasks using tactile feedback. In the second part\, we look at the problem of estimating shapes from partial observations. We propose a method called DeepGPIS that combines a powerful deep learning-based implicit shape representation with a non-parametric inference approach model for implicit surfaces (GPIS) which allows us to generate complete shapes of novel objects and estimate their predictive uncertainties. \nCommittee: \nProf. Taskin Padir (Advisor) \nProf. Robert Platt (Advisor) \nProf. David Rosen (Advisor)
URL:https://ece.northeastern.edu/event/tarik-kelestemurs-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220822T150000
DTEND;TZID=America/New_York:20220822T160000
DTSTAMP:20260429T235318
CREATED:20221103T182044Z
LAST-MODIFIED:20221103T182044Z
UID:5886-1661180400-1661184000@ece.northeastern.edu
SUMMARY:Hamed Mohebbi Kalkhoran's PhD Dissertation Defense
DESCRIPTION:“Machine learning approaches for classification of myriad underwater acoustic events over continental-shelf scale regions with passive ocean acoustic waveguide remote sensing” \nAbstract: \nUnderwater acoustic data contain a myriad of sound sources. Among underwater acoustic events\, marine mammal vocalization classification is one of the most challenging problems due to their transient broadband calls\, high variation in the calls of a specie\, and high similarity between the calls of some species. Here\, we developed machine learning approaches for classifying marine mammal vocalizations for real-time applications. We utilize acoustic data from a 160-element coherent hydrophone array and employ the passive ocean acoustic waveguide remote sensing technique to enable sensing and detections over instantaneous wide areas more than 100 km in diameter from the array. A variety of computational accelerating approaches\, combining hardware and software\, that make the methods desirable for real-time applications are also developed. \nThe humpback whale vocalizations can be divided into two classes\, song and non-song calls. Here we use wavelet signal denoising and coherent array processing to enhance the signal-to-noise ratio. To build features vector for every time sequence of the beamformed signals\, we employ Bag of Words approach to time-frequency features. Finally\, we apply Support Vector Machine (SVM)\, Neural Networks\, and Naive Bayes to classify the acoustic data and compare their performances. Best results are obtained using Mel Frequency Cepstrum Coefficient (MFCC) features and SVM which leads to 94% accuracy and 72.73% F1-score for humpback whale song versus non-song vocalization classification. \nTo classify a large variety of whale species vocalizations\, we extracted time-frequency features from Power Spectrogram Density (PSD) of the beamformed signals. Then we used these features to train three classifiers\, which are SVM\, Neural Networks\, and Random forest to classify six whale species: Fin\, Sei\, Blue\, Minke\, Humpback\, and general Odontocetes. We also trained a set of Convolutional Neural Networks (CNN) to detect and classify each of these six whale vocalization categories directly using Per-Channel Energy Normalization (PCEN) spectrograms. Best results were obtained with Random forest classifier\, which achieved 95% accuracy\, and 85% F1 score. To detect transient sound sources\, first we applied PCEN on the PSD of the beamformed signals. We applied thresholding on the PCEN data followed by morphological image opening to find potential sound sources and reduce noisy detections. Then we applied connected component analysis to obtain the final detected sounds for each bearing. To estimate the Direction of Arrival (DoA) of detected sounds\, we applied non-maximum suppression (NMS)\, which is widely used in object detection applications in computer vision\, on the detected sounds. We used mean power of each detected sound as the scores for NMS. To speed up the data processing\, we investigated a variety of accelerating approaches\, such as analyzing the effect of floating point precision\, applying parallel processing\, and implementing fast algorithms to run on GPU. During an experiment in the U.S. Northeast coast on board the US research vessel RV Endeavor in September 2021\, we utilized the software and hardware advances developed here to record underwater acoustic data using Northeastern University in-house fabricated large aperture 160- element coherent hydrophone array with sampling frequency of 100 kHz per element. \nCommittee: \nProf. Purnima Ratilal (Advisor) \nProf. Themistoklis Sapsis \nProf. Devesh Tiwari
URL:https://ece.northeastern.edu/event/hamed-mohebbi-kalkhorans-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220817T120000
DTEND;TZID=America/New_York:20220817T130000
DTSTAMP:20260429T235318
CREATED:20221103T182520Z
LAST-MODIFIED:20221103T182520Z
UID:5896-1660737600-1660741200@ece.northeastern.edu
SUMMARY:Mengshu Sun's PhD Dissertation Defense
DESCRIPTION:“Deep Learning Acceleration on Edge Devices with Algorithm/Hardware Co-Design” \nAbstract: \nAs deep learning has succeeded in a broad range of applications in recent years\, there is an increasing trend towards deploying deep neural networks (DNNs) on edge devices such as FPGAs and mobile phones. However\, there exists a significant gap between the extraordinary accuracy of state-of-the-art DNNs and efficient implementations on edge devices\, due to their limited resources for DNNs with high computation and memory intensity. With the target of simultaneously accelerating the inference and maintaining the accuracy of DNNs\, efficient implementations are investigated of deep learning on low-power and resource-constrained devices\, by presenting algorithm/hardware co-design frameworks that incorporate hardware-friendly DNN compression algorithms with hardware design optimizations.\nFirst\, the DNN compression algorithms are explored\, leveraging quantization and weight pruning techniques. As for quantization\, intra-layer mixed precision/scheme weight quantization is proposed to boost utilization of heterogeneous FPGA resources and therefore improving the FPGA throughput\, by assigning multiple precisions and/or multiple schemes at the filter level within each layer and maintaining the same ratio of filters across all the layers for each type of quantization assignment. As for weight pruning\, novel structured and fined-grained sparsity schemes are proposed and obtained with the reweighted regularization pruning algorithm\, and then incorporated into acceleration frameworks on FPGAs to make the acceleration rate of sparse models approach the pruning rate of the number of operations.\nSecond\, the hardware implementations are studied\, proposing an automatic DNN acceleration framework to generate DNN accelerators to satisfy a target frame rate (FPS). Unlike previous approaches that start from model compression and then optimizing the FPS for hardware implementations\, this automatic framework will provide an estimation of the FPS with the FPGA resource utilization analysis and performance analysis modules\, and the bit-width is reduced until the target FPS is met and the mixing ratio for quantization precisions/schemes is automatically determined to guide the quantization process and the accelerator implementation on hardware. A resource utilization model is developed to overcome the difficulty in estimating the LUT consumption\, and a novel computing engine for DNNs is designed with various optimization techniques in support of DNN compression to improve the computation parallelism and resource utilization efficiency. \nCommittee: \nProf. Xue Lin (Advisor)\nProf. Miriam Leeser\nProf. Xiaolin Xu
URL:https://ece.northeastern.edu/event/mengshu-suns-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220817T110000
DTEND;TZID=America/New_York:20220817T120000
DTSTAMP:20260429T235318
CREATED:20221103T182252Z
LAST-MODIFIED:20221103T182252Z
UID:5890-1660734000-1660737600@ece.northeastern.edu
SUMMARY:Mithun Diddi's PhD Dissertation Defense
DESCRIPTION:“Multiple UAVs for Synchronous – Shared Tasks and Long-term Autonomy” \nAbstract: \nThis thesis focuses on the use of multiple unmanned aerial vehicles(UAVs) in a distributed framework from a systems perspective to synchronously perform shared tasks such as aerial beamforming and coordinated mapping and to enhance the reliability of performing periodic (mapping) tasks at remote locations for long-term autonomous(LTA) missions. We present an autonomy stack for multiple\, heterogeneous UAVs with a simulation framework. We implemented the end-to-end pipelines for perception and communication applications involving multiple UAVs. \nRepeated deployments in harsh-weather\, real-world locations are challenging and are limited by the need for human operators. These infrastructure-poor\, remote locations pose unique challenges to long-term autonomous missions. In these locations\, harvesting power onboard using solar panels may be a viable alternative for recharging batteries.\nIn the first part of the thesis\, we focus on hardware architecture for UAVs to enable reliable LTA missions with minimal human intervention. We developed a Size\, Weight\, and Power(SWaP) constrained Smart charging stack to minimize hotel loads seen during the recharging process and enable efficient charging of batteries. This leads to the design of a standalone\, solar rechargeable quadcopter.\nReal-world applications such as reconstructing a dynamic scene from multiple viewpoints and distributed aerial beamforming require multiple robots(agents) to coordinate and synchronously act to accomplish shared tasks. These tasks require spatially distant\, multiple UAVs to have time\, phase\, and frequency synchronization. We demonstrate a Synchronous UAV(S-UAV) architecture for wireless synchronization based on GPS disciplined oscillators and the associated software framework needed for temporal registration of data across multiple UAVs.\nWe have built four S-UAVs and demonstrate the ability to 3D reconstruct a dynamic scene from overlapping viewpoints. Dynamic baseline camera arrays formed using multiple S-UAVs are used to synchronously capture a dynamic environment with people moving around. A single-time instance of synchronously captured images of the scene is used to 3D reconstruct the dynamic environment while preserving static scene assumptions of Structure from Motion(SFM). \nIn the second part of the thesis\, we focus on multi UAV autonomy framework for real-world applications of UAVs in perception\, wireless communications\, and reliable LTA missions. We present ‘Simplenav\,’ a navigation stack for heterogeneous\, multiple UAVs\, and ‘OctoRosSim\,’ a computationally lightweight multi-UAV simulation framework for validating the multi-UAV planning and autonomy pipeline. We demonstrate this framework with novel applications of end-to-end autonomy pipelines developed for a coordinated swarm of UAVs. \nCommittee: \nProf. Hanumant Singh (Advisor) \nProf. Kaushik Chowdhury \nProf. Taskin Padir
URL:https://ece.northeastern.edu/event/mithun-diddis-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:20220815T140000
DTEND;TZID=America/New_York:20220815T150000
DTSTAMP:20260429T235318
CREATED:20221103T182152Z
LAST-MODIFIED:20221103T182152Z
UID:5888-1660572000-1660575600@ece.northeastern.edu
SUMMARY:Nikita Mirchandani's PhD Dissertation Defense
DESCRIPTION:“Ultra-Low Power and Robust Analog Computing Circuits and System Design Framework for Machine Learning Applications” \nAbstract: \nAs the scaling of CMOS transistors has almost halted\, performance gains of digital systems have also started to stagnate. There is a renewed interest in alternate computing techniques such as in-memory computing\, hybrid computing\, approximate computing\, and analog computing. In particular\, analog computing has reemerged as a promising alternative to save power and improve performance specifically for machine-learning (ML) applications. Power and chip area efficiency make analog computing highly appealing for implementing deep learning algorithms on-chip\, computing circuits for the internet-of-things (IoT) devices\, and implantable and wearable biomedical devices. However\, compared to digital computing\, analog computing methods have not nearly been utilized to their fullest potential due to longstanding challenges related to reliability\, programmability\, power consumption\, and high susceptibility to variations. \nThe subject of this dissertation research is to develop robust ultra-low power analog hardware suitable for machine learning applications. First\, a robust analog design methodology is presented to address issues of variability in analog circuits. A constant transconductance design technique using switched capacitor circuits is presented. The design approach is then applied to build circuits for ML applications. An analog vector matrix multiplier (VMM) is designed to be used in the convolutional layer in an ML analog computing vision hardware platform. Computing circuits are tested as part of an image classification DNN algorithm on the MNIST dataset and can achieve a classification accuracy of 96.1%.\nThe design approach is also used to design an analog computing system architecture for detection of seizures using EEG signals. A conventional EEG monitoring system includes an analog front-end (AFE)\, ADC\, digital filtering stage\, EEG feature extraction engine\, and SVM classification. Such systems suffer from high power and chip area requirements. The corresponding analog architecture is composed of AFE amplifiers to provide gain for the incoming signal. The AFE is followed by an analog filtering stage\, where spectral power from each of the bands is used as a feature for seizure classification. The output of each filter is applied to a corresponding feature extraction circuit to continuously monitor the onset of a seizure in an ultra-lower power mode with sub-threshold analog processing. The system level architecture is first modeled to obtain classification accuracy of seizures. Simulation times for the design of such complex analog systems can be prohibitively long\, particularly when the impacts of nonidealities such as noise\, nonlinearity\, and device mismatches have to be considered at the system level. The simulation time is reduced by building accurate models of the analog blocks for faster simulations. The analog models help to define the required specifications for each block in order to achieve a specified system-level classification accuracy.\nInfrastructure circuits like oscillators and voltage regulators for the proposed SoC are presented. A 254 nW 21 kHz on-chip RC oscillator with 21.5 ppm/oC temperature stability is presented to provide stable clock source for the proposed SoC. Finally\, novel lightweight hardware security primitives are described to equip individual IoT device with side-channel resistant crypto-implementations\, and unique ID or key \ngeneration. \nCommittee: \nProf. Aatmesh Shrivastava (Advisor) \nProf. Marvin Onabajo \nProf. Yong-Bin Kim
URL:https://ece.northeastern.edu/event/nikita-mirchandanis-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:20220805T093000
DTEND;TZID=America/New_York:20220805T103000
DTSTAMP:20260429T235318
CREATED:20221103T182421Z
LAST-MODIFIED:20221103T182421Z
UID:5894-1659691800-1659695400@ece.northeastern.edu
SUMMARY:Mahdiar Sadeghi's PhD Dissertation Defense
DESCRIPTION:“Model-based decision making in life sciences” \nAbstract: \nMathematical models are key tools in rational decision-making processes. A “good” model is expected to reproduce experimental observations\, which enables predictions outside the previous experimental settings. The accuracy of predictions depends on the assumptions used to model the system. The objective of this study is to explore possible approaches to deploy models in order to generate new hypotheses in life sciences. A few biological systems relevant to protein translation\, chemotherapy\, immunotherapy\, and epidemics are considered. Models are analyzed numerically/analytically to optimize a new decision/control. In protein translation processes\, it is discovered that no switching policy is better than constant rates to maximize ribosome flow. In a particular experimental setting of chemotherapy\, a new dosing plan for chemotherapy is identified and predicted to result in maximum shrinkage of the tumor volume. In immunotherapy\, key features of binding kinetics of T-cell engagers in pre-clinical experiments are discussed. Moreover\, epidemic models under social distancing guidelines are studied. Considering a single-interval social distancing based on the start time and the duration of the social distancing shows a surprising linear relationship. Some of the results presented in this dissertation are shown to be valid in multiple applications. \n  \nCommittee: \nProf. Eduardo Sontag (Advisor)Dr. Irina KarevaProf. Carey RappaportProf. Bahram ShafaiProf. Mark Niedre
URL:https://ece.northeastern.edu/event/mahdiar-sadeghis-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220804T140000
DTEND;TZID=America/New_York:20220804T150000
DTSTAMP:20260429T235318
CREATED:20221103T182334Z
LAST-MODIFIED:20221103T182334Z
UID:5892-1659621600-1659625200@ece.northeastern.edu
SUMMARY:Tong Jian's PhD Dissertation Defense
DESCRIPTION:“Robust Sparsified Deep Learning” \nAbstract: \nThis dissertation studies robustness issues around DNN deployments on resource constrained systems\, under both environmental and adversarial input adaptation. We propose a means of compressing a Radio Frequency deep neural network architecture through weight pruning\, and provide a systems-level analysis of the implementation of such a pruned architecture at resource-constrained edge devices. In particular\, we jointly train and sparsify neural networks tailored to edge hardware implementations. \nNext\, we propose a new learn-prune-share (LPS) algorithm for achieving robustness to environment adaptation in the field of lifelong learning. Our method maintains a parsimonious neural network model and achieves exact no forgetting by splitting the network into task-specific partitions via a weight pruning strategy optimized by the Alternating Direction Methods of Multipliers (ADMM). Moreover\, a novel selective knowledge sharing scheme is integrated seamlessly into the ADMM optimization framework to address knowledge reuse.\nFurthermore\, we investigate the Hilbert-Schmidt Information Bottleneck as regularizer (HBaR) as a means to enhance adversarial robustness. We show that the Hilbert-Schmidt Information bottleneck enhances robustness to adversarial attacks both theoretically and experimentally. In particular\, we prove that the HSIC bottleneck regularizer reduces the sensitivity of the classifier to adversarial examples. \nFinally\, we propose a novel framework Pruning-without-Adversarial-training (PwoA) for the purpose of achieving adversarial robustness on resource-constrained systems. PwoA can efficiently prune a previously trained robust neural network while maintaining adversarial robustness\, without further generating adversarial examples. We leverage concurrent self-distillation and pruning to preserve knowledge in the original model as well as regularizing the pruned model via the HBaR. \nCommittee: \nProf. Stratis Ioannidis (Advisor) \nProf. Jennifer Dy\nProf. Kaushik Chowdhury \nProf. Yanzhi Wang
URL:https://ece.northeastern.edu/event/tong-jians-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220727T143000
DTEND;TZID=America/New_York:20220727T153000
DTSTAMP:20260429T235318
CREATED:20221103T182600Z
LAST-MODIFIED:20221103T182600Z
UID:5898-1658932200-1658935800@ece.northeastern.edu
SUMMARY:Kimia Shayestehfard's PhD Proposal Review
DESCRIPTION:“Permutation Invariant Graph Learning” \nAbstract:\nGraphs are widely used in many areas such as biology\, engineering\, and social sciences to model sets of objects and their interactions and relationships. Tasks addressed by applying machine learning to graphs\, known as graph learning\, include node and graph classification\, edge prediction\, transfer learning\, and generative modeling/distribution sampling\, to name a few.\nDue to high prevalence and multitude of applications of graphs across different fields\, graph neural networks have been developed in the past few years. Graph neural networks have shown tremendous success at producing node embeddings that capture structural and relational information of a graph and are discriminative for downstream tasks. However\, graph learning algorithms still deal with a major challenge\, namely\, the lack of permutation invariance: In a dataset of sampled graphs\, nodes may be ordered arbitrarily\, and aligning them is combinatorial and computationally expensive. Moreover\, many graph distance algorithms do not satisfy metric properties\, which can significantly hamper the fidelity of the downstream tasks. In this work we address the challenges posed by permutation invariance via combining fast and tractable metric graph alignment methods with graph neural networks. We propose a tractable\, non-combinatorial method for solving the graph transfer learning problem by combining classification and embedding losses with a continuous\, convex penalty motivated by tractable graph distances. We demonstrate that our method successfully predicts labels across graphs with almost perfect accuracy; in the same scenarios\, training embeddings through standard methods leads to predictions that are no better than random. Furthermore\, we propose a framework that combines fast and tractable graph alignment methods with a family of deep generative models and are thus invariant to node permutations. These models can be learned by solving convex optimization problems. Our experiments demonstrate that our models successfully learn graph distributions\, outperforming competitors by at least 66% in two relevant performance scores and improve the computation time up to 20 times over existing metric graph alignment methods. \nCommittee: \nProf. Stratis Ioannidis (Advisor) \nProf. Dana Brooks (Advisor) \nProf. Tina Eliassi-Rad
URL:https://ece.northeastern.edu/event/kimia-shayestehfards-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220721T143000
DTEND;TZID=America/New_York:20220721T153000
DTSTAMP:20260429T235318
CREATED:20221103T182855Z
LAST-MODIFIED:20221103T182855Z
UID:5902-1658413800-1658417400@ece.northeastern.edu
SUMMARY:Siyue Wang's PhD Dissertation Defense
DESCRIPTION:“Towards Robust and Secure Deep Learning Models and Beyond” \nAbstract: \nModern science and technology witness the breakthroughs of deep learning during the past decades. Fueled by the rapid improvements of computational resources\, learning algorithms\, and massive amounts of data\, deep neural networks (DNNs) have played a dominant role in many real world applications. Nonetheless\, there is a spring of bitterness mingling with this remarkable success – recent studies have revealed the limitations of DNNs which raise safety and reliability concerns of its widespread usage: 1) the robustness of DNN models under adversarial attacks and facing instability problems of edge devices\, and 2) the protection and verification of intellectual properties of well-trained DNN models.In this dissertation\, we first investigate how to build robust DNNs under adversarial attacks\, where deliberately crafted small perturbations added to the clean inputs can lead to wrong prediction results with high confidence. We approach the solution by incorporating stochasticity into DNN models. We propose multiple schemes to harden the DNN models when facing adversarial threats\, including Defensive Dropout (DD)\, Hierarchical Random Switching (HRS)\, and Adversarially Trained Model Switching (AdvMS). Besides\, we also propose a stochastic fault-tolerant training scheme that can generally improve the robustness of DNNs when facing the instability problem on DNN accelerators without focusing on optimizations for individual devices.The second part of this dissertation focuses on how to effectively protect the intellectual property for DNNs and reliably identify their ownership. We propose Characteristic Examples (C-examples) for effectively fingerprinting DNN models\, featuring high-robustness to the well-trained DNN and its derived versions (e.g. pruned models) as well as low-transferability to unassociated models. To better perform functionality verification of DNNs implemented on edge devices for on-device inference applications\, we also propose Intrinsic Examples. Intrinsic Examples as fingerprinting of DNN can detect adversarial third-party attacks that embed misbehaviors through re-training. The generation process of our fingerprints does not intervene with the training phase and no additional data are required from the training/testing set. \nCommittee: \nProf. Xue Lin (Advisor)Prof. Yunsi FeiProf. Yanzhi Wang
URL:https://ece.northeastern.edu/event/siyue-wangs-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220721T090000
DTEND;TZID=America/New_York:20220721T100000
DTSTAMP:20260429T235318
CREATED:20221103T182650Z
LAST-MODIFIED:20221103T182650Z
UID:5900-1658394000-1658397600@ece.northeastern.edu
SUMMARY:Abhimanyu Sheshashayee's PhD Dissertation Defense
DESCRIPTION:Location: 532 ISEC \n“Wake-up Radio-enabled Wireless Networking: Measurements and Evaluation of Data Collection Techniques in Static and Mobile Scenarios” \nAbstract: \nMulti-hop wireless networks such as Wireless Sensor Networks and in general\, networks without the support of a fixed infrastructure\, which enable most applications of the Internet of Things\, are comprised of wirelessly communicating nodes that are often powered by batteries. In many relevant scenarios—ranging from precision agriculture to oceanographic surveillance—it is inconvenient or impossible to replenish or replace the energy systems of these nodes\, which limits the operational lifespan of the network. One of the most significant sources of power consumption comes from idle listening on the node’s wireless transceiver (main radio). This consumption can be reduced by endowing the nodes with Wake-up Radio (WuR) technology: Nodes keep their main radio off while listening for a signal via an ultra-low-power auxiliary radio used only for wake-up purposes. When the appropriate signal is received\, the node turns its main radio on\, conducts the necessary exchange of packets\, and then turns off its main radio. This strategy allows for a considerable reduction in power consumption.This dissertation investigates data collection approaches that leverage WuR technology to maximize the lifespan of multi-hop networks for data gathering\, via routing and via a Mobile Data Collector (MDC). We analyze contemporary WuR technology\, isolating the main criticalities of the state-of-the-art\, including range and data rates. We use WuR prototypes with highly desirable characteristics to conduct experiments to measure effective communication ranges\, in both static and mobile scenarios. We then examine the application of WuR technology to data collection based on multi-hop routing. We devise new techniques and evaluate the effects of different WuR characteristics on the performance of routing\, considering for the first time what the network performance could be if we could overcome the limitation of current WuRs.The culmination of this dissertation focuses on mobile data collection protocols and approaches. We conduct a comprehensive survey of mobile data collection studies and protocols. We develop a robust taxonomy to set the framework for our analyses of various methodologies and elements of mobile data collection. Guided by our review of the literature\, we define two collection strategies: a simple naïve strategy\, and a novel AI-driven adaptive strategy. Both strategies leverage WuR technology to minimize the amount of time SNs remain awake. Considering both duty cycle-based and WuR based scenarios\, we conduct extensive experiments with a quad-rotor UAV-MDC and a network of WuR-enabled wireless sensor motes. We replicate these experiments in our simulator\, informed by the parameters and characteristics observed in our real-world experiments. Having validated our simulations\, we proceed to execute exhaustive simulation-based experiments. We evaluate the effects of scale (namely\, network size and deployment region size) on the performance of the naïve and adaptive strategies\, and we contrast the energy efficiency. The WuR-based scenarios experience considerably lower time spent awake\, which gives rise to longer network lifespan. The adaptive strategy minimizes the time taken for each collection cycle\, thereby reducing the amount of time spent awake in the duty cycle-based scenarios. The adaptive strategy also results in a noticeable reduction in both the awake duration and latency for the WuR-based scenarios. \nCommittee: \nProfessor Stefano Basagni (Advisor)Professor Kaushik ChowdhuryProfessor Tommaso Melodia
URL:https://ece.northeastern.edu/event/abhimanyu-sheshashayees-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220713T110000
DTEND;TZID=America/New_York:20220713T120000
DTSTAMP:20260429T235318
CREATED:20221103T183115Z
LAST-MODIFIED:20221103T183115Z
UID:5906-1657710000-1657713600@ece.northeastern.edu
SUMMARY:Leonardo Bonati's PhD Dissertation
DESCRIPTION:“Softwarized Approaches for the Open RAN of NextG Cellular Networks” \nAbstract: \nThe 5th and 6th generations of cellular networks (5G and 6G)\, also known as NextG\, will bring unprecedented flexibility to the wireless cellular ecosystem. Because of a typically closed and rigid market\, the telco industry has incurred high costs and non-trivial obstacles for delivering new services and functionalities that satisfy the requirements and the demands of NextG networks. To break this trend the industry is now moving toward open architectures based on softwarized approaches\, which afford network operators flexible control and unprecedented adaptability to heterogeneous conditions\, including traffic and application requirements. Now\, by simply expressing a high-level intent\, operators will be able to instantiate bespoke services on-demand on a generic hardware infrastructure\, and to adapt such services to the current network conditions. Through disaggregation\, network elements will split their functionalities across multiple components—possibly provided by different vendors—interconnected through well-defined open interfaces. The separation of control functions from the hardware fabric\, and the introduction of standardized control interfaces\, will ultimately enable the definition and use of softwarized control loops\, which will bring embedded intelligence and real-time analytics to effectively realizing the vision of autonomous and self-optimizing networks.\nThis dissertation work focuses on the design\, prototyping and experimental evaluation of softwarized approaches for the Open Radio Access Network (RAN) of NextG cellular networks. We analyze the architectural enablers\, challenges\, and requirements for a programmatic zero-touch control of the very many network elements and propose practical solutions for its realization. We prototype solutions by leveraging open-source software implementations of cellular protocol stacks and frameworks\, and heterogeneous virtualization technologies\, including the srsRAN and OpenAirInterface cellular implementations\, and the O-RAN framework. The contributions of this work include (i) the first demonstration of O-RAN data-driven control loops in a large-scale experimental testbed using open-source\, programmable RAN and RAN Intelligent Controller (RIC) components through xApps of our design; (ii) CellOS\, a zero-touch cellular operating system that automatically generates and executes distributed control programs for simultaneous optimization of heterogeneous control objectives on multiple network slices starting from a high-level intent expressed by the operators; (iii) OpenRAN Gym\, the first publicly-available research platform for the design\, prototyping\, and experimentation at scale of data-driven O-RAN solutions\, and (iv) OrchestRAN\, a network intelligence orchestration framework for Open RAN that automates the deployment of data-driven inference and control solutions. The effectiveness of our solutions in achieving superior control and performance of the RAN is demonstrated at scale on state-of-the-art experimental facilities\, including software-defined radio-based laboratory setups and open access experimental wireless platforms\, such as Colosseum\, Arena\, and the POWDER and COSMOS platforms from the U.S. PAWR program.
URL:https://ece.northeastern.edu/event/leonardo-bonatis-phd-dissertation/
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:20220712T153000
DTEND;TZID=America/New_York:20220712T163000
DTSTAMP:20260429T235318
CREATED:20221103T183319Z
LAST-MODIFIED:20221103T183319Z
UID:5910-1657639800-1657643400@ece.northeastern.edu
SUMMARY:Zulqarnain Qayyum Khan's PhD Dissertation Defense
DESCRIPTION:“Interpretable Machine Learning for Affective Psychophysiology and Neuroscience” \nAbstract: \nIn this thesis\, we leverage existing Machine Learning (ML) models where appropriate and develop novel models to advance the understanding of affective psychophysiology and neuroscience. Additionally\, considering the increased use of ML as a toolbox\, we highlight underlying assumptions and limitations of basic ML methods to help better contextualize the conclusions drawn from application of ML in this domain. Similarly\, given the increasingly opaque ML models\, the resulting rise of methods to explain these models\, and the importance of explainability to interdisciplinary research\, we investigate theoretical properties of these explainers.\nAffective pyschophysiology research typically uses supervised analyses which leave little room for exploration. Studies of motivated performance tasks often focus on two states of threat and challenge\, exhibiting somewhat inconsistent physiological properties. Using unsupervised analysis of physiology data\, we find evidence for the presence of a third state for the first time\, that may help explain these inconsistencies. Similarly\, prototypical view of emotion often searches for consistency and specificity\, as opposed to constructionist account of emotion which proposes emotion categories as populations of situation-specific variable instances. In results supportive of this constructionist view\, we find large variability in both the number and nature of clusters in unsupervised analyses of ambulatory physiological data. Similarly\, in functional neuroimaging a largely unsolved challenge is to develop models that appropriately account for the commonalities and variations among participants and stimuli\, scale to large amounts of data\, and reason about uncertainty in an unsupervised manner. Such models are needed to investigate important neuroscientific phenomena such as individual variation and degeneracy. We develop Neural Topographic Factor Analysis (NTFA)\, a novel ML model for fMRI data with a deep generative prior that teases apart participant and stimulus driven variation and commonalities\, and demonstrate its potential in investigating individual variation and degeneracy.\nWe further utilize this interdisciplinary research experience to shed light on assumptions and limitations of some of the basic ML methods commonly used in the sciences (especially psychological science). These methods are often used as software packages. We argue that researchers need to be more mindful of their underlying assumptions when drawing conclusions. Along the same lines\, ML methods themselves are becoming increasingly blackbox\, making it harder to reason about underlying assumptions. This has led to an increased focus on explainers\, which provide interpretability to ML methods that is critical for interdisciplinary research. The theoretical properties of these explainers\, however\, remain understudied. We further the research in this direction by defining explainer astuteness as a measure of robustness and theoretically demonstrate that smooth classifiers lend themselves to more astute explanations. \nCommittee: \nProf. Jennifer Dy (Advisor)\nProf. Lisa Feldman Barrett\nProf. Dana Brooks\nProf. Karen Quigley\nProf. Octavia Camps
URL:https://ece.northeastern.edu/event/zulqarnain-qayyum-khans-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220711T100000
DTEND;TZID=America/New_York:20220711T230000
DTSTAMP:20260429T235318
CREATED:20221103T183012Z
LAST-MODIFIED:20221103T183012Z
UID:5904-1657533600-1657580400@ece.northeastern.edu
SUMMARY:Bengisu Ozbay's PhD Proposal Review
DESCRIPTION:“Fast Identification via Subspace Clustering and Applications to Dynamic and Geometric Scene Understanding” \nAbstract: \nMore and more data is needed in order to build new machine learning and computer vision techniques. Using human operators to identify these vast datasets would be too expensive\, hence the use of unsupervised learning has grown more common. Piecewise linear or affine models can be used in a broad range of applications connected to system identification and computer vision.\nIn this proposal\, we suggest an efficient method that only requires singular value decomposition of matrices whose size is unaffected by the total number of points. This method only has to be performed (number of clusters) times. We discovered that it is feasible to find the polynomials that represent the hyperplanes by doing a singular value decomposition (SVD) on the empirical moments matrix containing the data. In this approach\, the notion of using polynomials and Christoffel functions to conduct SVDs in order to partition data into sets\, each of which originates from a different cluster\, is central. Data may be segmented and then the parameters of each group can be extracted using application-specific techniques. In particular\, the problems that are taken into consideration in this proposal include identification of Auto-regressive with Extra Input (SARX) models\, affine linear subspace clustering\, two-view motion segmentation\, and identification of a group of nonlinear systems known as Wiener systems.\nThis proposal is structured as follows: to begin with\, we offer a semi-algebraic clustering framework for locating reliable subsets from the data\, which belongs in a union of varieties and segments the data sequentially using Christoffel polynomials. We employ this strategy for switched system identification and affine subspace clustering challenges. In both instances\, the data resides in linear affine varieties. To expand the given approach beyond linear affine arrangements\, we reformulate it for quadratic surfaces and further apply it to the two-view motion segmentation task. Finally\, using this suggested semi-algebraic formulation\, we are able to detect a class of nonlinearities\, namely Wiener systems with an even nonlinearity\, which is indeed an NP-hard issue.
URL:https://ece.northeastern.edu/event/bengisu-ozbays-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220707T130000
DTEND;TZID=America/New_York:20220707T140000
DTSTAMP:20260429T235318
CREATED:20221103T183515Z
LAST-MODIFIED:20221103T183515Z
UID:5912-1657198800-1657202400@ece.northeastern.edu
SUMMARY:Alexandria Will-Cole's PhD Proposal Review
DESCRIPTION:“Morphology\, Magnetism\, and Transport in Nanomaterials and Nanocomposites” \nAbstract: \nMagnetic thin film materials and bilayer composites enable unprecedented new applications\, ranging from magnetic-based microelectromechanical systems (magnetoelectric sensors\, ultracompact magnetoelectric antennas\, etc.)\, terahertz emitters\, to spin-orbit-torque driven magnetic memories. Here we focus on two subdisciplines within magnetics – magnetoelectrics and spintronics heterostructures. \nThe first aspect of the talk is focused on magnetoelectrics. Strain-mediated magnetoelectric coupling (i.e.\, voltage/electric field control of magnetism\, or magnetic field control of electrical polarization) in bilayer composites has received heightened attention in the research community for applications in memory\, motors\, sensors\, communication etc. The composite ME effect is dependent on the magnetostrictive effect (magnetic-mechanical coupling) and the piezoelectric effect (electrical-mechanical coupling)\, and therefore to improve the composites each constituent phase needs to be optimal. Here we demonstrate the feasibility of machine learning\, specifically Bayesian Optimization methods\, to optimize ferromagnetic materials\, specifically (Fe100−y Gay)1−xBx (x=0–21 & y=9–17) and (Fe100−y Gay)1−xCx (x=1–26 and y=2–18) to demonstrate optimization of structure-property relationships\, specifically the compositional effect on magnetostriction and ferromagnetic resonance linewidth. Following the materials optimization study\, we present voltage control of ultrafast demagnetization in ME heterostructure of (Fe81Ga19)88B12/ Pb(Mg1/3Nb2/3)O3–PbTiO3. Previous studies implement multiple strategies to tune ultrafast demagnetization namely via the laser pump wavelength\, fluence\, polarization\, and pulse duration as these control the total absorbed energy into the film. Here we present an alternate strategy to tune ultrafast demagnetization with application of an electric field in the ME heterostructure to induce magnetic axis rotation. Additionally\, we studied magnetic anisotropy changes and E-field tuning behavior following ultrafast demagnetization. \nThe second aspect of this talk is focused on spintronics heterostructures\, namely ferromagnetic (FM)/topological insulator (TI) or ferrimagnetic insulator (FI)/topological insulator (TI) bilayer composites\, and TI sputter growth and characterization. Bilayer FM/TI and FI/TI heterostructures are promising for spintronic memory applications due to their low switching energy and therefore power efficiency. TIs have been grown with molecular beam epitaxy (oriented\, epitaxial films) and RF magnetron sputtering (amorphous to crystalline oriented films) and have demonstrated large spin-to-charge conversion efficiencies. However\, the reactivity of TIs with FM films is often overlooked in the spin-orbit-torque literature\, even though there are reports that it is energetically favorable for topological insulators to react with metals and form interfacial layers. Here we present the interfacial reaction and antiferromagnetic phase formation between MBE-grown Sb2Te3 and sputtered Ni80Fe20 films. Since FM/TI interfaces are highly reactive and form novel interfacial phases\, which can encourage spin memory loss\, it is critical to explore heterostructures with cleaner interfaces. Recently\, we synthesized chemically stable Y3Fe5O12/Bi2Te3 films\, which should have a chemically sharp interface. We present preliminary structural and magnetic characterization\, followed by proposed experiments to study proximity induced magnetization in these bilayer composites. Concurrent to our investigation spintronic heterostructures\, we seek to optimize sputter deposition of TIs. However\, sputtering TIs requires enhanced control over defects/stoichiometry as these influence bulk transport. We present preliminary results and propose experiments to elucidate structure-transport relationships\, such that we can provide strategies to controllably suppress bulk conduction to access topologically protected surface states. \nCommittee:\nProf. Nian X. Sun (advisor)\nProf. Don Heiman (co-advisor)\nProf. Yongmin Liu\nDr. A. Gilad Kusne\nDr. Todd Monson
URL:https://ece.northeastern.edu/event/alexandria-will-coles-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220707T130000
DTEND;TZID=America/New_York:20220707T140000
DTSTAMP:20260429T235318
CREATED:20221103T183228Z
LAST-MODIFIED:20221103T183228Z
UID:5908-1657198800-1657202400@ece.northeastern.edu
SUMMARY:Sara Garcia Sanchez's PhD Dissertation Defense
DESCRIPTION:“Learning and Shaping the Wireless Environment: An Integrated View of Sensing\, Computing and Communication” \nAbstract: \nThe explosive growth in Internet of Things (IoT) deployments and anticipated data volumes that will be generated within future autonomous devices require collecting and processing large amounts of data\, generally transmitted over the wireless channel. Rigid infrastructure deployment that does not adapt to the changing wireless environment is not well suited to handle these new demands. To address this limitation\, this dissertation takes a hands-on approach to equip communication systems with technology to learn from\, interact with and actuate within the environment. Specifically\, we build (i) accurate physics-based predictive models and multimodal sensing techniques to gain awareness of the existing channel\, as well as (ii) novel multidisciplinary approaches to intelligently shape the wireless channel towards enhancing the communication link. \nWe first prove that combining wireless channel modeling\, multimodal sensing and robotics provides significant link performance gains. To this extent\, we adopt a systems approach to study how millimeter wave (mmWave) radio transmitters on Unmanned Aerial Vehicles (UAVs) provide high throughput links under typical hovering conditions. Based on sensing and modeling efforts\, we propose techniques to exploit the information contained in the spatial and angular domains of empirically collected data from GPS\, cameras and RF signals. We demonstrate how to mitigate the impact of hovering by (i) selecting near-to-optimum transmission parameters as compared to the mmWave standard IEEE 802.11ad\, and (ii) proposing corrective coordinated actions at the UAVs from the robotic controls. These methods achieve mmWave beam-tracking and robust link deployment under event(s) impacting link performance\, such as hovering or blockage in the light of sight between transmitter and receiver.\nFinally\, we experimentally demonstrate how the wireless environment can be interactively shaped through the use of Reconfigurable Intelligent Surfaces (RIS). First\, we propose AirNN\, a system capable of partially offloading computation into the wireless domain by realizing analog convolutions with over-the-air computation. We demonstrate that such computation is accurate enough to substitute its digital equivalent in a Convolutional Neural Network (CNN). Second\, we propose a RIS-based spatio-temporal signal modification approach for channel hardening (i.e.\, ensure low power fluctuations in the received signal) in a Single-Input Single-Output link and under rich multipath\, which is common for IoT 5G+ deployments. We prove that our approach achieves channel hardening similar to a classical Single-Input Multiple-Output (SIMO) system while only using a single antenna element at the receiver end. \nAll the above theoretical advances are validated with rigorous analysis and experimentation. \nCommittee: \nProf. Kaushik Chowdhury (Advisor) \nProf. Stefano Basagni \nProf. Josep Jornet
URL:https://ece.northeastern.edu/event/sara-garcia-sanchezs-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220627T140000
DTEND;TZID=America/New_York:20220627T150000
DTSTAMP:20260429T235318
CREATED:20221103T183838Z
LAST-MODIFIED:20221103T183838Z
UID:5916-1656338400-1656342000@ece.northeastern.edu
SUMMARY:Xiaolong Ma's PhD Dissertation Defense
DESCRIPTION:“Towards Efficient Deep Neural Network Execution with Model Compression and Platform-specific Optimization” \nAbstract: \nDeep learning or deep neural network (DNN)\, as one of the most powerful machine learning techniques\, has become the fundamental element and core enabler of the artificial intelligence. Many incredible\, bleeding-edge applications\, such as community/shared virtual reality experiences and self-driving cars\, will crucially rely on the ubiquitous availability and real-time executability of the high-quality deep learning models. Among the variety of the AI-associated platforms\, mobile and embedded computing devices have become key carriers of deep learning to facilitate the widespread of machine intelligence. In this talk\, I will first focus on a compression-compilation co-design method that deploy a unique sparse model on an off-the-shelf mobile device with real-time execution speed. This method advances the state-of-the-art by introducing a new dimension\, fine-grained pruning patterns inside the coarse-grained structures\, revealing a previously unknown point in the design space. The designed patterns are interpretable\, and can be obtained by a fully automatic pattern-aware pruning framework that achieves pattern library extraction\, pattern assignment (pruning) and weight training simultaneously. With the higher accuracy enabled by fine-grained pruning patterns\, the unique insight is to use the compiler to re-gain and guarantee high hardware efficiency. We take a step forward by considering a more practical scenario\, that the deployment-execution mode for AI tasks no longer satisfy the user preference\, and enabling edge training becomes inevitable since it promotes much better personalized intelligent services while strengthen users’ privacy by avoiding data egress from their devices. To this end\, I will demonstrate my approaches that use sparsity to achieve fast and efficient training on the edge devices. I will evaluate the static lottery ticket sparse training\, and then demonstrate a high-accuracy and low-cost dynamic sparse training framework that makes the edge training possible. It successfully incorporates the pattern-based sparsity into sparse training\, and also exploit the data-level sparsity to further improve the acceleration. I will conclude by using our sparse training method on a distributed training scenario\, which demonstrates the state-of-the-art accuracy and great flexibility for modern AI model training. \nCommittee: \nProf. Yanzhi Wang (Advisor) \nProf. Xue Lin \nProf. David Kaeli
URL:https://ece.northeastern.edu/event/xiaolong-mas-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220616T120000
DTEND;TZID=America/New_York:20220616T130000
DTSTAMP:20260429T235318
CREATED:20221103T183733Z
LAST-MODIFIED:20221103T183733Z
UID:5914-1655380800-1655384400@ece.northeastern.edu
SUMMARY:Hussein Hussein's PhD Proposal Review
DESCRIPTION:“Parametric Circuits for Enhanced Sensing and RF Signal Processing” \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. \nCommittee: \nProf. Cristian Cassella (Advisor)\nProf. Marvin Onabajo\nProf. Matteo Rinaldi\nProf. Andrea Alù
URL:https://ece.northeastern.edu/event/hussein-husseins-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220608T093000
DTEND;TZID=America/New_York:20220608T103000
DTSTAMP:20260429T235318
CREATED:20221103T183939Z
LAST-MODIFIED:20221103T183939Z
UID:5918-1654680600-1654684200@ece.northeastern.edu
SUMMARY:Ziyue Xu's PhD Proposal Review
DESCRIPTION:“High Efficiency RF Energy Harvesting and Power Management Circuits Techniques for IoT Application” \nAbstract: \nAs the number of Internet of Things (IoT) devices is continuing to grow\, there is a need that a significant percentage these devices operate at ultra-low power (ULP) levels\, either using harvested energy or using a small battery with a long lifetime. Energy harvesting techniques can help to achieve long lifetimes\, but the system should be able to operate efficiently with a small amount of harvested energy and often from low voltages. Energy harvesting from solar\, thermal\, vibration\, and radio-frequency (RF) are increasingly being used to realize batteryless operation for IoT and biomedical applications. A typical multi-input energy harvesting system including multiple energy transducers\, maximum power point tracking (MPPT)\, matching network (MN)\, and DC-DC converter. Solar cells and thermoelectric generators have a few mV to hundreds of mV open-circuit voltage that require maximum power tracking to make sure the optimal power extraction is achieved. The piezoelectric transducer is modeled as AC source with internal resistance from 10s Ω to kΩ that requires AC-DC conversion\, known as rectification to better use the energy. And the following DC-DC regulation stage is to regulate the output voltage to deal with the sudden change of the load or the input voltage drop. Among these techniques\, RF energy harvesting system is particularly promising for biomedical and IoT devices where other sources are not readily available. Several of these applications are utilizing widely used WiFi and Bluetooth low-energy (BLE) communication standards. These applications along with the wirelessly-powered neural implantable medical devices (n-IMD) for neural stimulation and recording are also benefiting from ultra-low power (ULP) circuits and systems design advancements. Since the available RF power decreases rapidly with distance\, it is desirable to design rectifiers that are able to operate with low incident power. This Ph.D. proposal presents a simplified design approach and analysis of RF energy harvesting rectifiers for different design objectives. The proposal also includes the design of a new self-biased gate (SBG) rectifier with a non-linear gate biasing technique. At lower power levels\, the SBG rectifier drops the entirety of output voltage to create a higher gate bias. However\, to address the issue of leakage at higher input power levels\, the gate-biasing technique drops only a fraction of the output voltage. This approach helps to realize high efficiency across input power range. The fully integrated\, high-efficiency SBG-based RF energy harvesting circuit can also provide a high output voltage of 9.3 V with a 30% end-to-end efficiency (PHE). Further\, to enhance the available RF energy to a remotely located RF energy receiver\, the proposal presents a highly efficient distributed RF beamforming technique. To improve the power delivery in the downstream power management circuits\, a boost converter architecture that can reduce switching noise injection by changing its switching frequency is also presented. The associated power management system includes a boost converter operating in DCM\, FVC and a digital control loop. The system is capable of providing a stable 1V supply for RF receiver front-ends with very low performance impact. \n  \nCommittee Members: \nProf. Aatmesh Shrivastava (Advisor) \nProf. Marvin Onabajo \nProf. Nian X. Sun
URL:https://ece.northeastern.edu/event/ziyue-xus-phd-proposal-review/
LOCATION:432 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
GEO:42.3396156;-71.0886534
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=432 ISEC 360 Huntington Ave Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave:geo:-71.0886534,42.3396156
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220429T150000
DTEND;TZID=America/New_York:20220429T161500
DTSTAMP:20260429T235318
CREATED:20220422T181251Z
LAST-MODIFIED:20220422T181251Z
UID:5559-1651244400-1651248900@ece.northeastern.edu
SUMMARY:Data-Centric Networking: Theory\, Algorithms and Applications
DESCRIPTION:Professor Edmund Yeh will give an invited seminar titled “Data-Centric Networking: Theory\, Algorithms and Applications” in the Electrical Engineering Seminar Series at Harvard University John A. Paulson School of Engineering and Applied Sciences. \nFriday\, Apr 29\, 2022\n3:00 pm to 4:15 pm |  Harvard John A. Paulson\, School of Engineering and Applied Science\, SEC LL2.221 or Virtually  \nIn the era of big data\, domain experts in various engineering and science fields are facing unprecedented challenges in data access\, distribution\, processing and analysis\, and in the coordinated use of limited computing\, storage and network resources.  To meet this challenge\, data-centric network design approaches such as Named Data Networking (NDN) have been proposed\, which focus on enabling end users to obtain the data they want\, rather than simply on communication between specific nodes. \nIn this talk\, we present new frameworks for the optimization of key functionalities supported by data-centric networking\, which are broadly applicable to content delivery networks\, peer-to-peer networks\, wireless heterogeneous networks\, and distributed computing networks.  The frameworks enable joint (in-network) caching\, request routing\, and congestion control\, for optimizing metrics including routing costs\, data retrieval delay\, and content-based fairness.  We meet the challenge of the underlying NP-hard problems by exploiting submodularity\, matroid structure\, DR-submodularity\, and by leveraging tools including concave relaxation\, stochastic gradient ascent\, continuous greedy and Lagrangian barrier algorithms.  We develop polynomial-time approximation algorithms with proven optimality guarantees\, with particular emphasis on adaptive and distributed implementations.  We further discuss the extension of these frameworks for jointly optimal wireless user association\, interference management and content caching in wireless heterogeneous networks\, and for jointly optimal computation scheduling\, caching and request forwarding in distributed computing networks. \nFinally\, we discuss an ongoing project which applies the optimization frameworks and algorithms to facilitate data distribution and computation in the Large Hadron Collider (LHC) high-energy physics network\, one of the largest data applications in the world.
URL:https://ece.northeastern.edu/event/data-centric-networking-theory-algorithms-and-applications/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220422T180000
DTEND;TZID=America/New_York:20220422T200000
DTSTAMP:20260429T235318
CREATED:20220419T174430Z
LAST-MODIFIED:20220419T174645Z
UID:5548-1650650400-1650657600@ece.northeastern.edu
SUMMARY:ECELEBRATION
DESCRIPTION:Honoring Diversity in ECE – a celebration of Electrical and Computer Engineering students\, faculty\, and staff with marginalized identities! Register as an honoree to be awarded a certificate\, or as an observer. Let’s reset and re-connect to build a stronger community rooted in mutual respect and empathy for one another. Monday\, April 18th is the last day to register for Friday’s banquet.
URL:https://ece.northeastern.edu/event/ecelebration/
LOCATION:Curry Student Center\, 360 Huntington Ave.\, Boston\, MA\, 02115\, United States
ORGANIZER;CN="Diversity%2C Equity%2C and Inclusion":MAILTO:diversity@northeastern.edu
GEO:42.3394629;-71.0885286
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Curry Student Center 360 Huntington Ave. Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave.:geo:-71.0885286,42.3394629
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220304T110000
DTEND;TZID=America/New_York:20220304T120000
DTSTAMP:20260429T235318
CREATED:20220216T200737Z
LAST-MODIFIED:20220222T235806Z
UID:5468-1646391600-1646395200@ece.northeastern.edu
SUMMARY:MathWorks Week: AI for Humans
DESCRIPTION:Join MathWorks engineers and Northeastern faculty for these insightful seminars in Climate Change\, Quantum Computing\, and AI. \n\nAI for Humans\nFriday\, March 4\, 11 am – 12 pm\nRegister: MathWorksNUSymposiumAI.eventbrite.com \nFundamentals of AI\nNeha Sardesai\, MathWorks \nHow to apply machine learning and deep learning to images and signals. You’ll see how MATLAB® provides an environment to apply advanced techniques without requiring coding or experience in machine learning and deep learning. \nInvariant Representation Learning for Human Pose Estimation withSmall Data\nSarah Ostadabbas\, Professor\, Dept. of Electrical and Computer Engineering \nDescriptions of the state-of-the-art representation learning algorithms for visual perception tasks in the contexts of human pose estimation\, especially when we are facing problems where data collection or labeling is expensive (i.e. Small Data domains). \nMachine learning for retina image analysis for Retinopathy ofPrematurity (ROP) severity assessment.\nDeniz Erdogmus\, Professor\, Dept. of Electrical and Computer Engineering \nDiscussion of the use of active learning\, deep learning\, and Siamese neural networks to develop deep neural network models for automated retina image analysis to diagnose and assess the severity of retinopathy of prematurity in babies born prematurely.
URL:https://ece.northeastern.edu/event/mathworks-week-at-northeastern-university-2022-03-04/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220303T150000
DTEND;TZID=America/New_York:20220303T160000
DTSTAMP:20260429T235319
CREATED:20220216T200737Z
LAST-MODIFIED:20220222T235653Z
UID:5466-1646319600-1646323200@ece.northeastern.edu
SUMMARY:MathWorks Week: Chemistry\, Deep Learning and Quantum Computing
DESCRIPTION:Join MathWorks engineers and Northeastern faculty for these insightful seminars in Climate Change\, Quantum Computing\, and AI. \n\nChemistry\, Deep Learning and Quantum Computing\nThursday\, March 3\, 3 pm – 4 pm\nRegister: MathWorksNUSymposiumQuantumComputing.eventbrite.com \nGraph Neural Networks for Chemistry Using MATLAB\nHossein Jooya\, MathWorks \nMATLAB’s new features in handling chemical structures\, from small organic molecules to proteins will be demonstrated. Graph-convolutional (GC) and graph-attention (GA) networks are explained with various examples from toxicity prediction to molecular optimization. Attendees will have access to the shared code modules and can adapt them for their own research with hand-in-hand support from MathWorks technical team. \nPhotonic Quantum Technologies\nSunil Mittal\, Professor\, Dept. of Electrical and Computer Engineering \nThis talk will discuss the generation\, manipulation\, and measurements of quantum states of light\, such as entangled photons\, for applications in photonic quantum computation\, quantum communications\, and sensing. \nDo You Trust Your Quantum Computers with Correct Answers?\nDevesh Tiwari\, Professor\, Dept. of Electrical and Computer Engineering \nNoisy Intermediate-Scale Quantum (NISQ) machines are increasingly being used to develop quantum algorithms and establish use cases for quantum computing. These devices\, however\, are highly error-prone and produce output which can be far from the correct output of the quantum algorithm. This talk will discuss some promising approaches towards estimating the correct program output on erroneous quantum devices.
URL:https://ece.northeastern.edu/event/mathworks-week-at-northeastern-university-2022-03-03/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220303T140000
DTEND;TZID=America/New_York:20220303T163000
DTSTAMP:20260429T235319
CREATED:20220118T232703Z
LAST-MODIFIED:20220118T232703Z
UID:5380-1646316000-1646325000@ece.northeastern.edu
SUMMARY:COE PhD Expo
DESCRIPTION:The College of Engineering is excited to announce the fourth annual COE PhD Research Expo\, and we invite all COE PhD students to submit a poster abstract. This is a wonderful opportunity to highlight your research and meet alumni\, academic\, and industry leaders. \nThe expo will take place during COE’s Graduate Candidate Day\, where PhD candidates could learn first-hand about the exciting research our PhD students are conducting. \n***We are closely monitoring COVID-related updates and abiding by the University policies to ensure safe attendance of the event. Should the expo be conducted virtually\, we will send out follow-up communications about the updated logistics of the event. \nSubmit an Abstract. Deadline: January 24\, 2022\nIn consultation with your research advisor\, submit a poster abstract. We welcome posters that have been presented elsewhere. \nAbstract word limit is 200 words. Authors accepted to participate will be notified by Feb. 2nd of their selection. \nPrepare with a Workshop In addition\, the COE Communications Lab will host workshops on the following dates: \nAbstract – Thursday\, January 20 at 5pm \n\nJoin with Zoom Link\n\nData Visualization – Thursday\, January 27 at 5pm \n\nJoin with Zoom Link\n\nPoster preparation – Thursday\, February 3 at 5pm \n\nJoin with Zoom Link\n\nElevator Pitch/ Presentation – Thursday\, February 24 at 5pm \n\nJoin with Zoom Link\n\nWe are excited to offer this opportunity to showcase the research of our PhD students and to provide a platform for you to gain valuable experience and network with academic and industry leaders.
URL:https://ece.northeastern.edu/event/coe-phd-expo/
LOCATION:Raytheon Amphitheater (240 Egan)\, 360 Huntington Ave\, 240 Egan\, Boston\, MA\, 02115\, United States
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220301T103000
DTEND;TZID=America/New_York:20220301T113000
DTSTAMP:20260429T235319
CREATED:20220216T200737Z
LAST-MODIFIED:20220222T235523Z
UID:5464-1646130600-1646134200@ece.northeastern.edu
SUMMARY:MathWorks Week: Climate\, Energy and the Built Environment
DESCRIPTION:Join MathWorks engineers and Northeastern faculty for these insightful seminars in Climate Change\, Quantum Computing\, and AI. \n\nClimate\, Energy and the Built Environment\nTuesday\, March 1\, 10:30 am – 11:30 am\nRegister: MathWorksNUSymposiumClimate.eventbrite.com \nInsights of climate changes from the Common Era: an Artificial Intelligence view\nJianghao Wang\, MathWorks \nThe rapid global warming seen in observations over the past 150 years shows nearly global coherence\, the spatiotemporal coherence of climate epochs earlier in the Common Era (the past 2\,000 years)\, however\, has yet to be robustly tested. Understanding how the climate system works and how historical temperature changes shed light on the study of anthropogenic climate change. \nModeling the Stochastic Dynamics of Rotating Wind Turbine Blades\nLuca Caracoglia\, Professor\, Dept. of Civil and Environmental Engineering \nThis presentation describes the results of recent research activities\, examining the dynamic modeling of wind turbine blades under the influence of various sources of input error and noise. The presentation will focus on the flutter phenomenon. Flutter is a flow-induced dynamic instability that results from the coupling between flap-wise bending mode and torsional mode of the rotating blade. \nLocating Damage in Structural Systems\nDennis Bernal\, Professor\, Dept. of Civil and Environmental Engineering \nThis presentation outlines the basic ideas behind some techniques used to localize damage applicable in cases where the structure is large\, and the number of sensors is small. Visual inspection has been the traditional procedure used to check the condition of structural systems but there is significant interest in devising ways to replace or enhance this approach by incorporating information from sensors.
URL:https://ece.northeastern.edu/event/mathworks-week-at-northeastern-university/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220223T173000
DTEND;TZID=America/New_York:20220223T183000
DTSTAMP:20260429T235319
CREATED:20220223T201859Z
LAST-MODIFIED:20220223T201859Z
UID:5500-1645637400-1645641000@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Miead Tehrani-Moayyed
DESCRIPTION:PhD Proposal Review: RF Channel Models for Static and Mobile Scenarios: From Simulations to Models for Large-scale Emulations \nMiead Tehrani-Moayyed \nLocation: ISEC 432 \nAbstract: The extremely high data rates provided by communications at higher frequency bands\, e.g.\, millimeter waves (mmWave)\, can help address the unprecedented demands of next-generation wireless networks. However\, as several impairments limit wireless coverage at higher frequencies\, accurate models of wireless scenarios and testing at scale are needed to show actual potential and to realize the promises that the new wireless technologies can bring forth. Large-scale accurate simulations and wireless networks emulators are now a time and cost-effective solution to perform these tests in a lab before deployment in the field. This dissertation work focuses on modeling\, calibration\, and validation of realistic RF scenarios for wireless network emulation at scale.\nThe contributions of our work include (i) investigating the characteristic of the wireless channel at higher frequencies (mmWave) and the performance evaluation of mmWave communications on top of the recently released NR standard for 5G cellular networks\, and (ii) a framework to create RF scenarios for emulators like \emph{Colosseum} starting from rich forms of input\, like those obtained by ray-tracers or via real-field measurements.\n(i) We derive channel propagation models via ray-tracing simulations for mmWave transmissions with applications to vehicle-to-everything (V2X) communications. We analyze aspects related to blockage modeling\, the effects of antenna beamwidth\, beam alignment\, and multipath fading in urban scenarios and emphasize the importance of capturing diffuse scattered rays for improved large-scale and small-scale radio channel propagation models. Furthermore\, we compare the performance of mmWave 5G NR with the 4G long-term evolution (LTE) standard on a realistic environment and show the impact of MIMO technology to improve the performance of 5G NR cellular networks. As transmitted radio signals are received as clusters of multipath rays\, identifying these clusters provides better spatial and temporal characteristics of the channel. We deal with the clustering process and its validation across a wide range of frequencies in the mmWave spectrum below 100 GHz. We analyze how the clustering solution changes with narrower-beam antennas\, and we provide a comparison of the cluster characteristics for different types of antennas.\n(ii) Our framework to model wireless scenarios for large-scale emulators optimally scales down the large set of RF data in input to the fewer parameters allowed by the emulator by using efficient clustering techniques and channel impulse response re-sampling. We demonstrate the effectiveness of the proposed framework through modeling realistic scenarios for Colosseum starting from the rich input from a commercial-grade ray-tracing software: Wireless Insite by Remcom. We propose to finish our investigation (a)~by introducing ways of dealing with mobility in emulated scenarios\, and to perform adequate channel sounding to validate them\, and (b)~by indicating ways to provide input to the emulator through actual wireless measurements in the field. Particularly\, as campaigns in the field provide measurements for a sparse set of locations\, we plan to use deep learning techniques to “interpolate” channel parameters for a larger set of locations\, determining the trade-offs for achieving desired accuracy and reasonable computational requirements.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-miead-tehrani-moayyed/
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
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220223T130000
DTEND;TZID=America/New_York:20220223T140000
DTSTAMP:20260429T235319
CREATED:20220223T201800Z
LAST-MODIFIED:20220223T201800Z
UID:5498-1645621200-1645624800@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Md Navid Akbar
DESCRIPTION:PhD Proposal Review: Variational and Siamese Models in Functional and Structural Medical Image Analysis \nMd Navid Akbar \nLocation: Zoom Link \nAbstract: Machine learning (ML) models have recently shown great promise in medical image analysis. Instead of a one-size-fits-all\, a customized model is generally needed to map a target outcome from an imaging modality. To this end\, this proposal presents three such supervised models developed for three different imaging modalities.\nIn the first\, a deep convolutional neural network (CNN) maps 3D cortical motor representation\, obtained by transcranial magnetic stimulation (TMS)\, to the corresponding motor evoked potentials captured by surface electromyography (EMG). This modeling is bi-directional: with trivial changes\, it can operate in both the forward and inverse directions. TMS as a functional imaging technique is still in its infancy\, but its potential application in presurgical planning necessitates a reliable data-driven model. Our variational autoencoder inspired CNN is a pioneering step in that direction: with a normalized root mean square error up to below 14%\, and an R-squared similarity up to above 87%\, for cortical representation reconstruction in the inverse path. As the next steps\, we plan to investigate other training strategies and collect additional data to assess robustness.\nIn the second\, a Siamese CNN (with a pretrained DenseNet121 backbone) is developed to predict the continuous spectrum of pulmonary edema severity\, from frontal chest X-rays. While existing deep learning frameworks have been promising in detecting the presence or absence of such edema\, or even its discrete grades of severity\, prediction of the continuous-valued severity remains a challenge. Using lower resolution images and only 1/51-th the size of training data compared to the state-of-the-art\, our work beats it by achieving a mean area under the receiver operating characteristic curve (AUC) score of 91% (improvement by 4%)\, when tested on the open-source MIMIC-CXR database.\nFinally\, a complete preprocessing and ML classification pipeline is developed for identifying which traumatic brain injury (TBI) patients will go on to develop late seizures\, from diffusion-weighted MRI (dMRI). Physical deformations following moderate-severe TBI present problems for standard processing of dMRI\, complicating the extraction of neuroimaging features. Following the novel application of a normalization technique to dMRI\, in conjunction with univariate feature selection and a linear discriminant analysis classifier\, our model improves the performance over the standard pipeline by 8% in mean accuracy and 7% in mean AUC. In future work\, we would like to explore classification using a fusion of dMRI with electroencephalogram (EEG) and functional MRI (fMRI) modalities.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-md-navid-akbar/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220216T133000
DTEND;TZID=America/New_York:20220216T143000
DTSTAMP:20260429T235319
CREATED:20220214T210524Z
LAST-MODIFIED:20220214T210524Z
UID:5459-1645018200-1645021800@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Yuanyuan Li
DESCRIPTION:PhD Proposal Review: Submodularity in Cache Networks \nYuanyuan Li \nLocation: Zoom Link \nAbstract: As 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 M/M/1c 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 M/M/∞ queues\, and one based on M/M/1c 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 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 α-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 < α ≤ 1\, we further propose a randomized strategy that attains an improved optimality guarantee\, (1-1/e)^(1-α)\, in expectation.\nThird\, 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 (1-1/e)-regret\, also in the case when update costs cannot be neglected.\nFinally\, we propose an 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://ece.northeastern.edu/event/ece-phd-proposal-review-yuanyuan-li/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220215T140000
DTEND;TZID=America/New_York:20220215T150000
DTSTAMP:20260429T235319
CREATED:20220209T213529Z
LAST-MODIFIED:20220209T213529Z
UID:5439-1644933600-1644937200@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Stella Banou
DESCRIPTION:PhD Proposal Review: Coupling Methods for Wireless Intra-body Communication and Sensing \nStella Banou \nLocation: 432 ISEC \nAbstract: Advances in miniaturized bio-compatible Internet of Things (IoT) device design and wireless connectivity have resulted in rapid strides towards realizing the vision of connected health and ubiquitous monitoring of physiological conditions. Core enablers of this capability are wearable and implanted IoT devices\, albeit with limitations arising from their low energy storage and computational power. This thesis goes beyond the RF-only communication standards by exploring alternate communication modalities that are more amenable for inter- and intra-body communication. In summary\, this thesis explores the conductive and radiating nature of the human body as a channel for three non-RF coupling communication methods – Galvanic\, Magnetic and Capacitive coupling.\nIn part I\, an implementation of Galvanic Coupling-based beamforming is presented for implant to wearable communication. The key idea here is to exploit the conductivity of human tissue and transmit weak electrical signals by coupling them via electrodes to muscle tissue in a way that concentrates energy at the receiver location. In part II\, we focus on realizing a relay network of IoT devices for both implant-implant and implant to on-skin sensor communication using Magnetic Resonance Coupling. The advantage of this method over Galvanic Coupling is that the former reduces attenuation when signals pass through human tissue. In part III\, we enhance the scope of the connected health paradigm to now include sensing for proximity and for automated encouraging of healthy habits that mitigate the spread of communicable diseases using Capacitive Coupling.\nAs part of proposed work\, we will design a novel human antenna field to sense and communicate with other IoT devices in the near field – within 2.5 meters\, also using Capacitive Coupling. This will complete the full cycle of data flow\, from implanted to wearable devices and finally connect the body network to the computational cloud for the next generation of IoT-enabled healthcare.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-stella-banou/
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:20220215T090000
DTEND;TZID=America/New_York:20220215T100000
DTSTAMP:20260429T235319
CREATED:20220214T210441Z
LAST-MODIFIED:20220214T210441Z
UID:5457-1644915600-1644919200@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Abhimanyu Venkatraman Sheshashayee
DESCRIPTION:PhD Proposal Review: Wake-up Radio-enabled Wireless Networking: Measurements and Evaluation of Data Collection Techniques in Static and Mobile Scenarios \nAbhimanyu Venkatraman Sheshashayee \nLocation: 432 ISEC \nAbstract: Multi-hop Wireless Networks such as Wireless Sensor Networks and similar networks that enable most applications of the Internet of Things\, are comprised of wirelessly communicating nodes that are powered by batteries. In many relevant scenarios\, it is inconvenient or impossible to replenish or replace the batteries of these nodes\, which limits the operational lifespan of the network. One of the most significant sources of power consumption comes from idle listening on the node’s main radio. This can be ameliorated by Wake-up Radio (WuR) technology: Nodes keep their main radio off while listening for a signal via an ultra-low-power auxiliary radio used only for wake-up purposes. When the appropriate signal is received\, the node turns its main radio on\, conducts the necessary exchange of packets\, and then turns off its main radio. This strategy allows for a considerable reduction in power consumption.\nThis dissertation studies data collection approaches that leverage WuR technology to maximize the lifespan of multi-hop networks for data gathering via routing and via a Mobile Data Collector (MDC). We analyze contemporary WuR technology\, isolating the main criticalities of the state-of-the-art\, including range and data rates. We use a prototype with highly desirable characteristics to conduct experiments to measure its effective communication range\, in both static and mobile scenarios. We then examine the application of WuR technology to data collection scenarios based on multi-hop routing. We devise new techniques and evaluate the effects of different WuR characteristics on the performance of routing\, considering for the first time what the network performance could be if we could overcome the limitation of current WuRs.\nThe remainder of the dissertation will focus on mobile data collection protocols and approaches. We are conducting a comprehensive survey of mobile data collection protocols. We plan to execute exhaustive simulation-based experiments with selected protocols applied to various scenarios. We will evaluate the performance of those protocols and determine how their features influence their performance. We will use the information gleaned from our investigations to develop a novel mobile data collection protocol that effectively utilizes WuR technology to maximize network lifespan. The effectiveness of our protocol will be evaluated using both simulations and physical experiments\, sporting an ad hoc testbed of WuR-enabled nodes and a quad-rotor drone for the MDC.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-abhimanyu-venkatraman-sheshashayee/
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END:VCALENDAR