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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211029T110000
DTEND;TZID=America/New_York:20211029T120000
DTSTAMP:20260422T164757
CREATED:20211028T183932Z
LAST-MODIFIED:20211028T184125Z
UID:5273-1635505200-1635508800@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Ramtin Khalili
DESCRIPTION:PhD Proposal Review: Efficient State and Parameter Estimation in Three-Phase Power Systems \nRamtin Khalili \nLocation: Microsoft Link \nAbstract: As the number of renewable energy sources\, bulk energy storage systems\, and non-conventional loads increase and connect to the power grid not only at transmission but also sub-transmission and distribution levels\, three-phase unsymmetrical network monitoring becomes necessary for reliable operation and control of the overall power grid. The use of modal decomposition of measurement equations has already been shown to simplify the formulation and resulting computational complexity of three-phase state estimation of systems where all the transmission lines are three-phase and fully transposed. When there are untransposed and/or mixed-phase lines\, modal decomposition can no longer fully decouple the three-phase measurement equations. This shortcoming is eliminated by a simple yet practical solution based on the commonly used numerical compensation techniques. Thus\, it enables the application of the powerful decoupling approach to any type of three-phase networks which may contain untransposed or mixed-phase lines and are fully observable by PMUs. This implicit restriction is then removed by using a transformation that enables the use of SCADA measurements which are more commonly available in power grids. Furthermore\, It has been shown that network parameter errors can bias the state estimation solution. Network parameter errors are common due to aging\, changes in the ambient temperature\, human data entry error\, etc. So\, an efficient approach is proposed to detect and correct the network parameter errors in three-phase untransposed transmission lines. Preliminary results to illustrate the performance of the proposed methods and associated algorithms will be presented using different test systems.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-ramtin-khalili/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211029T100000
DTEND;TZID=America/New_York:20211029T110000
DTSTAMP:20260422T164757
CREATED:20211020T191027Z
LAST-MODIFIED:20211020T191027Z
UID:5250-1635501600-1635505200@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Cesar Antonio Galvez Nunez
DESCRIPTION:PhD Proposal Review: Fault Location in Radial and Meshed Networks Containing Distributed Energy Resources (DERs) \nCesar Antonio Galvez Nunez \nLocation: Microsoft Teams Link \nAbstract: Rapidly increasing numbers of Distributed Energy Resources (DERs) connected to transmission and distribution networks via Inverter Based Power Sources (IBPSs) introduce new challenges in detecting and locating faults. Distribution networks are historically designed to operate as radial systems with unidirectional power flows\, which may no longer hold true due to the presence of large numbers of IBPSs. The commonly used impedance-based fault location methods are no longer reliable due to the limitations imposed by unknown fault resistance\, asymmetry of lines\, and presence of IBPSs\, which need to comply with the new grid codes for Fault Ride Through (FRT) requirements. In this proposal\, a new fault location method that can be used for radial and meshed networks containing DERs and addresses the limitations of conventional methods mentioned above will be introduced. The approach requires a limited number of digital fault recorders (DFR) to be placed in the network and uses the Discrete Wavelet Transform (DWT) to compute the first arrival times of fault-generated traveling waves. The proposal first presents a new two-terminal fault location technique used strictly for radial distribution networks\, and then extends this to the general case of combined transmission and distribution networks with radial or meshed configurations. The method is further extended to be applied to hybrid AC/DC complex transmission grids containing DERs and High Voltage Direct Current (HVDC) lines. Preliminary results will be presented illustrating these methods on typical power grids and fault scenarios.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-cesar-antonio-galvez-nunez/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211028T100000
DTEND;TZID=America/New_York:20211028T110000
DTSTAMP:20260422T164757
CREATED:20211025T211553Z
LAST-MODIFIED:20211025T211553Z
UID:5259-1635415200-1635418800@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Hongjia Li
DESCRIPTION:PhD Dissertation Defense: Automation Design and DNN Acceleration Frameworks: from software implementation to hardware physical design \nHongjia Li \nLocation: Northeastern Zoom Link \nAbstract: With the breakthrough of Deep Neural Networks (DNNs) in the past decade\, neural network-based computer vision has made huge progress\, achieving exceptional performance. Tasks such as object detection\, activity detection\, and medical diagnosis are deployed in a wide range of applications\, including autonomous driving\, robot vision and training\, human-computer interaction\, and augmented reality. To increase the demand of application accuracy\, DNN models are tuned to large scales by adding more parameters and layers. Meanwhile\, mobile devices are rapidly becoming the central computer and carrier for deep learning tasks. However\, real-time execution has been limited due to the computation/storage resource constraints on mobile devices.\nThe first part of this dissertation\, I will present our unified real-time mobile acceleration of DNNs framework\, seamlessly integrating hardware-friendly\, structured model compression with mobile-targeted compiler optimization. The goal of our framework is to provide an unprecedented\, real-time performance of such large-scale neural network inference using simply off-the-shelf mobile devices. Our proposed fine-grained block-based pruning scheme can be universally applicable to all types of DNN layers\, such as CONV layers with different kernel sizes and fully connected layers. Different weight pruning schemes\, such as unstructured pruning\, filter/column pruning\, and our block-based pruning\, are analyzed and compared given the specific deep learning problems. To validate our framework\, various applications are implemented and demonstrated\, object detection\, medical diagnosis. All applications can achieve real-time inference on mobile devices\, outperforming the current mobile acceleration framework by up to 6.7X in speed.\nFor the second part of this dissertation\, I will dive into an efficient automate framework for Adiabatic Quantum-Flux-Parametron (AQFP) technology\, meeting the unique features and constraints. Superconductive electronics (SCE) based on the Josephson junction (JJ) single flux quantum (SFQ) logic cells have evolved into a within-reach “beyond-CMOS” technology. Placement is the primary step in physical design of the electronic systems as it directly affects the maximum frequency and routability of circuits. Algorithms for global placement\, the core step in the placement process\, typically minimize the total wirelength of a design as the main objective as it indirectly affects the routability and timing of circuits. Although minimizing the total wirelength improves the timing of the circuit in general\, it does not directly target optimizing the delay of timing critical paths. Timing and routability driving placement methods are therefore needed. The currently mature design automation tools for CMOS cannot be directly applied to the design of superconducting electronics. In this dissertation\, I will present our proposed timing-aware AQFP-specific placement and routing framework\, given a path balanced AQFP netlist with clock phases. The proposed framework will reduce the solution complexity by making effective use of the row-wise placement/routing opportunity as each AQFP cell is assigned to a specific row (clock phase). \n 
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-hongjia-li/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211022T123000
DTEND;TZID=America/New_York:20211022T133000
DTSTAMP:20260422T164757
CREATED:20211020T191155Z
LAST-MODIFIED:20211020T191155Z
UID:5252-1634905800-1634909400@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Chengju Yu
DESCRIPTION:PhD Proposal Review: Development of Interface-Engineered Thin Films and Magnetodielectric Bulk Composites for MMIC Applications \nChengju Yu \nLocation: Zoom Link \nAbstract: Magnetodielectric materials are ubiquitous in electronic\, energy\, automotive\, communication\, and medical systems over radio frequency bands from high frequency to quasi-optical frequencies. With recent developments in modern power and communication technologies\, improvements in magnetic materials and related components have attracted a great deal of attention from academic and industrial research groups.\nIn this proposal review\, we demonstrate multiple paths to the development of next generation magnetodielectric thin films and bulk composites that offer disruptive advances to performance and size reduction\, including:\n(i) Consistent and reliable processing protocols are established using interface-engineered barium magnetoplumbite films deposited on Si-polar SiC substrates with AlN capping layers and MgO nucleation layers for microwave and millimeter-wave monolithic integrated circuits (MMICs);\n(ii) Both thin and thick yttrium iron garnet films are achieved using PLD and LPE with outstanding crystalline and magnetic properties to meet the needs of magnonics and spintronics technologies; (iii) Inductor cores are developed for power generation\, conversion\, conditioning functions for use in power electronic systems and high-power pulse generators operating at 100s kHz and 100s MHz frequencies\, respectively. Power loss and thermal management models of non-linear magnetic inductors are established and implemented with viable paths demonstrated using interface-engineered composites as a means of achieving high magnetization\, high permeability\, low core losses.\nThe common theme of all three projects is the engineering of the chemistry\, structure\, magnetic and electric properties of the interface between the principal layers\, films\, and grains that constitute the product in order to optimize performance.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-chengju-yu/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211014T120000
DTEND;TZID=America/New_York:20211014T130000
DTSTAMP:20260422T164757
CREATED:20211013T001304Z
LAST-MODIFIED:20211013T001304Z
UID:5239-1634212800-1634216400@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Tirthak Patel
DESCRIPTION:PhD Proposal Review: Toward System Software Stack for NISQ–era Quantum Computers \nTirthak Patel \nLocation: Zoom Link \nAbstract: Despite rapid progress in quantum computing in the last decade\, the limited usability of quantum computers remains a major roadblock toward the wider adoption of quantum computing. Prohibitively high error rates on existing Near-term Intermediate-Scale Quantum (NISQ) computers limit their usability even for quantum-advantage-proven algorithms (that is\, algorithms that are infeasible or orders of magnitude slower on classical computers). As a result\, the executions of these algorithms on existing quantum computers are highly erroneous and produce noisy program outputs. Currently\, quantum computing programmers lack system software tools and methods to estimate the correct output from these erroneous executions. \nThis dissertation demonstrates how to extract correct program output from noisy executions on today’s erroneous quantum computers. In particular\, this dissertation describes the design and implementation of a suite of cross-layer system software for extracting meaningful output from the erroneous executions using hardware-level quantum pulse control\, noise-aware quantum compilation\, and post-execution error mitigation. The real-system prototypes and experimental evaluation on IBM quantum computers demonstrate how specific quantum mechanics properties\, hardware-level pulse control\, and post-execution statistical processing can be put together to improve the usability of today’s quantum computers transparently. This dissertation achieves this without requiring user intervention\, domain knowledge about quantum algorithms\, or additional quantum hardware support. \nThis dissertation opens up new research avenues for hybrid quantum-classical computing and lowers the barrier to entry for quantum computing research via open-sourcing multiple novel datasets and system software frameworks (independently verified and results reproduced by other researchers in the community).
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-tirthak-patel/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211006T110000
DTEND;TZID=America/New_York:20211006T120000
DTSTAMP:20260422T164757
CREATED:20211004T224056Z
LAST-MODIFIED:20211004T224056Z
UID:5222-1633518000-1633521600@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Bin Sun
DESCRIPTION:PhD Proposal Review: Lightweight Neural Networks via Factorization \nBin Sun \nLocation: Zoom Link \nAbstract: Deep learning has become popular in recent years primarily due to powerful computing devices such as GPUs. However\, many applications such as face alignment\, image classification\, and gesture recognition need to be deployed to multimedia devices\, smartphones\, or embedded systems with limited resources. Thus\, there is an urgent need for high-performance but memory-efficient deep learning models. For this\, we design several lightweight deep learning models for different tasks with factorization strategies. \nSpecifically\, we constructed a lightweight face alignment model by proposing a factorization-based deep convolution module named Depthwise Separable Block (DSB) and a light but practical module based on the spatial configuration of the faces. Experiments on four popular datasets verify that Block Mobilenet has better overall performance with less than 1MB storage size. Besides the face analysis application\, we also explored a general\, lightweight deep learning module for image classification with low-rank pointwise residual (LRPR) convolution\, called LRPRNet. Essentially\, LRPR aims at using a low-rank approximation to factorize the pointwise convolution while keeping depthwise convolutions as the residual module to rectify the LRPR module. Moreover\, our LRPR is quite general and can be directly applied to many existing network architectures.\nDue to the success of the factorization strategy on image-based data\, we extended factorization on time sequence data for Sign Language Recognition (SLR). We achieved the first rank in the challenge of SLR with the help of our proposed novel Separable Spatial-Temporal Convolution Network (SSTCN)\, which divides a 3D convolution on joint features into several stages \, which help the SSTCN achieve higher accuracy with fewer parameters.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-bin-sun/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210927T110000
DTEND;TZID=America/New_York:20210927T120000
DTSTAMP:20260422T164757
CREATED:20210920T183529Z
LAST-MODIFIED:20210920T183529Z
UID:5177-1632740400-1632744000@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Tianyu Dai
DESCRIPTION:PhD Proposal Review: Data-Driven Control and Estimation \nTianyu Dai \nLocation: Zoom Link \nAbstract: During the last two decades\, data-driven control (DDC) has attracted growing attention in the control community. Unlike model-based control (MBC) that first uses the collected data to identify the system\, then designs the controller according to the certainty equivalence principle\, DDC skips the system identification (SYSID) step and leads to a control law directly from data. One important feature of DDC is that some fundamental limitations of MBC such as uncertainty versus robustness\, inevitable modeling error\, and possible expensive cost of SYSID are avoided in the DDC framework. These benefits enable the researcher to design controllers with better performance and accuracy. \nThe aim of this proposal is to summarize our contributions to the DDC field. We mainly discuss the following problem: given a single trajectory of noisy data and a few priors of the model structure\, how to design a state feedback controller to stabilize the system with unknown dynamics and in addition\, to meet some performance criteria. The main idea hinges on duality theory to establish the connection between two sets\, one compatible with the noisy data\, and the second satisfying some design properties such as stability or optimality. Our main results show that for all possible systems compatible with the data\, the data-driven control law can be obtained by solving a convex optimization problem. \nThis proposal is organized as follows: to start with\, we propose a DDC framework for switched linear systems relying on the Farkas’ lemma to search for a common polyhedral control Lyapunov function using the theory of moments. Then to reduce the computational burden\, we provide another method called data-driven quadratic stabilization control for linear systems that is based on quadratic Lyapunov function. To deal with nonlinear system\, we first design data-driven controllers for polynomial systems using the dual Lyapunov theorem. Then to handle general nonlinearities\, we propose a method based on state-dependent representations. Finally\, a data-driven estimator is proposed that gives the worst-case optimal estimation of the trajectory of a switched linear system.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-tianyu-dai/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210920T130000
DTEND;TZID=America/New_York:20210920T140000
DTSTAMP:20260422T164757
CREATED:20210920T174407Z
LAST-MODIFIED:20210920T174931Z
UID:5172-1632142800-1632146400@ece.northeastern.edu
SUMMARY:Distinguished Lecture: Dr. Elliot Eichen
DESCRIPTION:The Institute for the Wireless Internet of Things is pleased to host a distinguished lecture on Real-time Geo-spatial Spectrum Sharing by Dr. Elliot Eichen. \nWhen: Monday\, September 20th\, 1pm. \nLocation: Zoom Link \nAbstract: New technology and new applications for wireless communications have created competition for frequency bands traditionally allocated to remote sensing and defense applications. Competition for spectrum is particularly intense in mm (and sub-mm) wave bands where the requirements for 5G/6G transmissions overlap with measurements made by passive radiometers (Earth Exploration Satellite Services – EESS) that are used for weather forecasting and as baseline data for climate models. Real-time Geospatial Spectrum Sharing (RGSS) enables EESS radiometers and 5G/6G networks to gracefully share spectrum by modifying network traffic during the time window (~ 10s of msec) that a base station (gNB) and its connected endpoints (UEs) are within the effective field of view (eFOV) of a radiometer. RGSS is based on existing network infrastructure rather than Monte-Carlo network simulations (the ITU model); it can provide better isolation between 5G/6G transmissions and EESS radiometers than the ITU’s hardware-based Out-of-Band (OOB) emission limits (e.g.\, -32 dBW/200MHz-gNB and -29 dBW/200MHz-UE) in dense urban environments\, while simultaneously enabling carriers to create larger cell sizes and use network repeaters in suburban and rural settings. In addition\, RGSS can adapt to changes in network or remote sensing technology by modifying the underlying network or EESS ecosystem descriptions (schemas). \nIn this talk\, we show that RGSS: \n\nCan prevent 5G/6G transmissions from corrupting EESS measurement data\nHas sufficient geolocation accuracy to provide a realistic solution\, based on experimental confirmation of predicted measurement pixels vs. actual measurement pixels\nApplies to all mm-wave and submm-wave bands (e.g.\, a single system can be used for all bands\, such as 24\, 51\, and 90 GHz\, although the modification time windows for each band may be different)\nEnables carriers to optimize network performance by geography and time of day\, rather than designing for the worst-case scenario across the entire network (i.e.\, avoids the ” one size fits all” OOB emissions model)\nIncludes the effect of massive Multiple-Input Multiple-Output (MIMO) beamforming antennas\nIs commensurate with existing 5G architectures and deployment models\, and\nProvides a simple mechanism to test and police compliance compared with over the air TRP OOB measurements.\n\nBio: Elliot Eichen retired as Director of R&D at Verizon in 2017\, after a 35-year career (except for 2½ years on staff at MIT) at GTE Labs\, GTE/BBN\, and Verizon Labs. From 2018-2019\, he was an IEEE-USA/AAAS congressional fellow\, which is where he became interested in spectrum management and the overlap between 5G/6G and EESS passive sensors. Dr. Eichen received a Ph.D. in Optics from The University of Arizona\, and a B.S in Physics from SUNY Stony Brook. His contributions to the technical community include associate editor of IEEE Photonics Technology Letters\, committee chair of Optical Fiber Communications (OFC)\, chair of the IEEE/OSA Optical Amplifier Conference\, Visiting Industry Professor at Tufts University\, and adjunct faculty at NEU. He has more than 40 peer-reviewed publications and about 60 US patents.
URL:https://ece.northeastern.edu/event/distinguished-lecture-dr-elliot-eichen/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210916T160000
DTEND;TZID=America/New_York:20210916T170000
DTSTAMP:20260422T164757
CREATED:20210908T194439Z
LAST-MODIFIED:20210908T194439Z
UID:5158-1631808000-1631811600@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Nirjhar Bhattacharjee
DESCRIPTION:PhD Proposal Review: Sputtered Topological Insulator/Ferromagnet Heterostructures for Energy Efficient Spintronic Device Applications \nNirjhar Bhattacharjee \nLocation: Zoom Link \nAbstract: Topological insulators (TI) are Van der Waals (VdW) layered materials which possess enormous spin-orbit coupling (SOC) strength and spin-momentum locked robust surface states. TIs in presence of time-reversal symmetry breaking magnetic order can also exhibit chiral quantum anomalous (QAH) or Axion insulator edge channels. These and myriad of other material properties predicted\, and achievable utilizing TIs\, magnetic-TIs (MTI) and TI based heterostructures can open the path towards realization of a diverse class of energy efficient spintronic devices for information processing and storage. Crystalline oriented TIs which possess topologically nontrivial properties are grown using molecular beam epitaxy (MBE) which is incompatible with industrial CMOS processes. Magnetron sputtering\, on the other hand is the CMOS industry stand thin film growth technique because of the advantages of high throughput\, large area\, and high quality thin film growth capability. In this work\, first the growth of high quality TI: Bi2Te3 thin films using CMOS compatible magnetron sputtering process is introduced. Next\, room temperature characterization of magnetic and SOT properties of TI/ferromagnet (FM) heterostructures will be presented. Finally\, fascinating magnetic properties of material systems with FM species intercalated in TIs will be reported which can possibly house exotic quantum material phases. \nBy varying process temperature between 20-250ºC\, growth of Bi2Te3 with stoichiometric composition and varying crystalline order from disordered to highly c-axis oriented VdW layered films were obtained. Using X-ray diffraction (XRD) and high-resolution transmission electron microscopy (HRTEM) imaging\, the crystalline property of the TI film was confirmed. Further\, coupling the sputtered TI films with ferromagnetic (FM) thin films surprisingly showed a giant enhancement in Gilbert damping with c-axis oriented TI which is crucial for energy efficient SOT-MRAM devices. This suggested enhancement in spin-orbit coupling strength for c-axis oriented TI thin films compared to disordered ones. Formation of interface layers because of elemental diffusion has been reported in literature. But\, literature reports on SOT characteristics have largely assumed atomically sharp interfaces between TI and FM layers. We observed crystalline order dependent interface thickness and composition in Bi2Te3/Ni80Fe20 heterostructures because of diffusion of Ni across the interface. An enhancement in damping-line SOT in crystalline ordered Bi2Te3 was observed. The spin-charge conversion efficiency was however found to be larger for granular and lowest for polycrystalline disordered Bi2Te3 samples considering the interface layers. Further\, with the intercalation of Ni in Bi2Te3\, emergence of an antiferromagnetic VdW phase was observed in Ni-intercalated Bi2Te3 interface. This AFM VdW interface resulted in a large spontaneous exchange bias in Bi2Te3/Ni80Fe20 and Bi2Te3/NiZn-Ferrite heterostructures at temperatures below ~63 K which is higher than the transition temperatures of MTIs reported in literature. Structural and chemical characterization of the Ni-intercalated Bi2Te3 showed evidence of formation of Ni-Te bonds and indicated towards formation of MTI compounds. These results open new avenues for experimental exploration of fascinating high-temperature QAH and other topologically nontrivial material phases in interfaces of industrial CMOS process compatible sputter-grown TI/FM heterostructures. Understanding the properties of these TI based material systems can lead to realization of robust energy efficient spintronic devices.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-nirjhar-bhattacharjee/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210914T140000
DTEND;TZID=America/New_York:20210914T150000
DTSTAMP:20260422T164757
CREATED:20210908T184332Z
LAST-MODIFIED:20210908T184332Z
UID:5151-1631628000-1631631600@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Vageeswar Rajaram
DESCRIPTION:PhD Dissertation Defense: Near-Zero Power Microelectromechanical Sensors for Large-Scale IoT Sensor Networks \nVageeswar Rajaram \nLocation: ISEC 432 \nAbstract: The Internet-of-things revolution has ushered the development of sensing technologies aimed towards establishing large-scale remote sensor networks to monitor the environment continuously and with high spatial resolution. However\, with existing sensor technologies this goal has so far been limited in terms of scalability (i.e.\, the number of sensors in a network\, areal coverage and spatial granularity). A major impeding factor is sensor power consumption: state-of-the-art remote sensor technologies need to be actively powered (i.e.\, by a battery) to continuously monitor the environment for an object of interest\, even at standby (when it is not present). This is because all signals collected by the sensor from the environment need to be processed by active signal conditioning circuits to distinguish a signal of interest from other signals. Therefore\, in applications where an event or signal of interest occurs only occasionally\, most of the battery is drained by processing irrelevant signals. The result is that as the sensor network scales up\, so do the costs and labor associated with the sensors’ battery replacements. This makes it unfeasible to deploy and maintain large numbers of sensors for any application and greatly limits the scale of sensor networks. Extremely low power consumption therefore is critical in enabling large sensor networks by reducing or even eliminating costs associated with frequent battery replacements.\nThis work describes the development of a revolutionary new sensing platform aimed at creating sensors with battery lifetimes limited only by the self-discharge of the battery itself (>10 years). The ultimate goal for the technology is to enable maintenance-free sensor nodes for truly large-scale “deploy-and-forget” sensor networks. In particular\, this work details the development of novel infrared sensors based on micro-electro-mechanical photoswitches that are capable of detecting and distinguishing specific infrared signatures associated with objects of interest (hot gases\, fire\, human body\, etc.) while remaining dormant with near-zero power consumption at standby. This unique sensor technology aims to break the paradigm of requiring a power supply to perform sensing by instead relying on the energy contained in the infrared signals emitted by the object of interest itself to perform its detection. This dissertation presents a comprehensive summary of the sensor’s design\, its capabilities\, and the various technical developments that have led this technology to evolve from a concept to a prototype near-zero power wireless infrared sensor with orders of magnitude lesser power consumption compared to the state-of-the-art.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-vageeswar-rajaram/
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:20210910T140000
DTEND;TZID=America/New_York:20210910T150000
DTSTAMP:20260422T164757
CREATED:20210908T194338Z
LAST-MODIFIED:20210908T194338Z
UID:5156-1631282400-1631286000@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Wenqian Liu
DESCRIPTION:PhD Dissertation Defense: Explainable Efficient Models for Computer Vision Applications \nWenqian Liu \nLocation: Zoom Link \nAbstract: State of the art deep learning based models\, such as Convolutional Neural Networks (CNNs) and generative models\, achieve impressive results\, but with their great performance comes great complexity and opacity\, huge parametric spaces and little explainability. The criticality of model explainability and output interpretability\, manifests clearly in real-time critical decision making processes and human-centred applications\, such as in healthcare\, security and insurance. Explainability and interpretability are tackled in this thesis\, as intrinsic qualities in the model architecture as well as post-hoc improvement on existing models. In the area of frame prediction in video sequences\, we introduce DYAN\, a novel network with very few parameters\, that is easy to train and produces accurate high quality predictions. Another key aspect of DYAN is interpretability\, as its encoder-decoder architecture is designed following concepts from systems identification theory and exploits the dynamics-based invariants of the data. We also introduce KW-DYAN\, an extension of DYAN that tackles the issue of time lagging in video predictions\, by implementing a novel way of quantifying prediction timeliness and proposing a new recurrent network for adaptive temporal sequence prediction. The experimental results show the reduced lagging across datasets\, while also performing well in other metrics. In this thesis we also propose the first technique to visually explain VAEs by means of gradient-based attentions\, with methods to generate visual attentions from the learned latent space\, and also demonstrate such attention explanations serve more than just explaining VAEs. We show how these attention maps can be used to localize anomalies in images\, conducting state-of-the-art performance on multiple datasets. We also apply our technique for skin image anomaly detection and diagnosis and achieve competitive quantitative and qualitative results.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-wenqian-liu/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210903T110000
DTEND;TZID=America/New_York:20210903T120000
DTSTAMP:20260422T164757
CREATED:20210825T175854Z
LAST-MODIFIED:20210825T175854Z
UID:5137-1630666800-1630670400@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Nikita Mirchandani
DESCRIPTION:PhD Proposal Review: Ultra-Low Power and Robust Analog Computing Circuits and System Design Framework for Machine Learning Applications \nNikita Mirchandani \nLocation: Zoom Link \nAbstract: As 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. Analog computing has better area and power efficiency when compared to their digital counterpart. 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.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-nikita-mirchandani/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210830T093000
DTEND;TZID=America/New_York:20210830T103000
DTSTAMP:20260422T164757
CREATED:20210819T215252Z
LAST-MODIFIED:20210819T215252Z
UID:5129-1630315800-1630319400@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Keng Chen
DESCRIPTION:PhD Proposal Review: Design of Accurate and Responsive Power Management Integrated Circuits for Microprocessor Systems \nKeng Chen \nLocation: Zoom Link \nAbstract: As technology improves\, the processors used in data centers are becoming increasingly powerful. Since the number of complementary metal-oxide semiconductor (CMOS) transistors integrated into these processors continues to rise\, it becomes more challenging to design the power management integrated circuits to accurately regulate the supply voltages and ensure fast operational speeds for the processors. Furthermore\, with multi-core technology being extensively used in processor design\, there is more than just one voltage rail per chip that needs to be precisely regulated. In this research\, circuit design methods will be developed to improve the regulation accuracy\, both of the output voltage and converter switching frequency\, which are proposed for an integrated buck regulator designed for point of load (POL) applications. In order to ensure both accuracy and fast speed of operation\, a constant-on-time (COT) architecture was selected for this integrated buck regulator design approach. To provide accurate output voltage regulation\, the on-chip feedback sensing network and the bandgap reference circuit need to exhibit high accuracy. For this reason\, a highly accurate bandgap reference generation circuit with 5.8 ppm/°C – 13.5 ppm/°C over a wide operational region (-40 °C to 150 °C) has been designed. This bandgap reference circuit has a current-mode architecture and provides a 1.16V reference voltage with a 3.3V supply. A multi-section curvature compensation method is proposed to alleviate the nonlinear temperature-dependent error from the bipolar junction transistor’s base-emitter voltage. The two operational amplifiers utilized in this bandgap reference design to generate proportional-to-absolute-temperature (PTAT) and complementary-to-absolute-temperature (CTAT) current sources share one auxiliary auto-zero amplifier to ensure low input-referred offset voltage. Within many voltage regulators\, the feedback sensing network consists of multiple operational amplifiers\, and a major error source is the impact of the input-referred offset voltages of these amplifiers. \nThis research introduces the utilization of two different input-referred offset voltage correction methods for multiple amplifiers. The multi-amplifier system under investigation is used for feedback sensing during voltage regulation. It contains an instrumentation amplifier consisting of three folded cascode stages\, and an additional amplifier configured as a unity-gain buffer for a reference voltage. The first method in this work alleviates voltage offsets in a 4-amplifier system based on a shared auxiliary amplifier correction circuit that switches between different target amplifiers. The second method applies a chopping-based auto-zero procedure to cancel the input-referred offset voltage of the same system. Since chopping causes voltage ripples\, a correction circuit with a successive approximation register (SAR) analog-to-digital converter is used to reduce the output ripples. Both proposed methods can achieve less than 1mV feedback sensing error in voltage regulator applications.\nFor the constant-on-time buck regulator itself\, a new frequency locking loop to regulate the on-time pulse is also proposed in this work. The system shows less than 1% frequency shift over a wide programmable operational frequency range (400 KHz to 2 MHz). This frequency accuracy will further benefit the constant-on-time circuit to meet electromagnetic interference requirements without harming the fast-transient performance.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-keng-chen/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210825T103000
DTEND;TZID=America/New_York:20210825T113000
DTSTAMP:20260422T164757
CREATED:20210818T000348Z
LAST-MODIFIED:20210823T174620Z
UID:5115-1629887400-1629891000@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Apoorve Mohan
DESCRIPTION:PhD Dissertation Defense: Rethinking the Choice between Static and Dynamic Provisioning for Centralized Bare-Metal Deployments \nApoorve Mohan \nLocation: Zoom Link \nAbstract: Technological inertia leads many organizations to continue to employ static provisioning strategies for bare-metal deployments. This results in architectural and runtime inefficiencies. However\, most performance and security-sensitive organizations currently employ static provisioning strategies to run and manage their workloads on physical servers (also known as bare-metal servers). This thesis demonstrates how recent technological advances enable dynamic provisioning strategies to improve aggregate bare-metal resource efficiency in centralized bare-metal deployments. Conceptually\, this dissertation has four parts. First\, it introduces a new system architecture for dynamic bare-metal provisioning. The proposed architecture reduces overhead and operational complexity and improves fault tolerance and performance. To achieve these improvements\, it decouples the provisioned state from the bare-metal servers. Second\, the thesis identifies the performance and operational issues due to intrusive software introspection strategies employed in existing bare-metal deployments. We present a dynamic bare-metal provisioning-based system to enable non-intrusive software introspection of bare-metal deployments to mitigate these issues. Many organizations have realized the cost benefits of consolidating their computing infrastructure over managing multiple\, relatively smaller compute facilities. \nOver the past decade\, virtual infrastructure solutions have become the obvious choice in consolidated (i.e.\, single-tenant managed or centralized) deployments for many organizations due to the scalability\, availability\, and cost benefits it offers. However\, most performance and security-sensitive organizations such as medical companies and hospitals\, financial institutions\, federal agencies run their workloads directly on physical (i.e.\, bare-metal) infrastructure. They are unwilling to tolerate the performance unpredictability and security risks due to co-location and performance overhead or vulnerabilities due to a large code base of the complex virtualization stack. In addition\, even if an organization uses virtual infrastructure\, bare-metal infrastructure is used to set up virtualization software stack and when their workloads require direct and exclusive access to hardware components (e.g.\, InfiniBand\, RAID\, FPGAs\, GPUs) that are difficult to virtualize. Such organizations invest enormous sums of money to buy or rent bare-metal servers and set up and manage bare-metal clusters. Thus\, it is imperative that these organizations efficiently operate and utilize the bare-metal clusters they set up to maximize their investment returns. However\, despite the technological advances over the past decade\, organizations continually employ static provisioning strategies in bare-metal deployments that contribute to poor aggregate bare-metal resource efficiency. Thesis Statement: This thesis demonstrates how dynamic provisioning can mitigate observed inefficiencies in centralized bare-metal deployments. The proposed dynamic provisioning strategies leverages existing storage disaggregation and fault-tolerance technologies to improve aggregate bare-metal resource efficiency. By applying it to four real-world scenarios\, this thesis demonstrates the effectiveness of dynamic provisioning.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-apoorve-mohan/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210813T130000
DTEND;TZID=America/New_York:20210813T140000
DTSTAMP:20260422T164757
CREATED:20210812T222754Z
LAST-MODIFIED:20210812T222754Z
UID:5112-1628859600-1628863200@ece.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Hafiyya Malik
DESCRIPTION:MS Thesis Defense: Studies for Improving Multi-channel Confocal Fluorescence Imagining in a Multi-modal Microscope \nHafiyya Malik \nLocation: Zoom Link \nMeeting ID: 988 8423 1362 Passcode: 399185 \nAbstract: The goal of this engineering thesis was to implement confocal fluorescence microscopy in an upgraded legacy electro-optical microscopy system for characterization of the viability of cells in biofilms. This application necessitated a 2-channel fluorescence measurement with the possible addition of confocal reflectance for context. Two major areas were addressed. First was the potential for using a silicon photomultiplier (SiPM) detector to replace the usual vacuum photomultiplier employed in confocal fluorescence microscope. A SiPM development board was purchased and tested to determine its performance at different light levels and in the presence of background light. The SiPM has the potential to be a more robust and reliable detector\, with the one disadvantage of a smaller area than some vacuum photomultipliers. The second area was the alignment of a long optical path that must include optics for complementary modes of imaging and allow for automated measurement across two spectral channels without loss of alignment. Alignment procedures were developed to simplify the process of alignment and a sliding stage was used to maintain alignment of two dichroic filters when changing channels. Based on this work\, multi-channel confocal fluorescence imaging in conjunction with confocal reflectance and bright-field microscope will be possible.
URL:https://ece.northeastern.edu/event/ece-ms-thesis-defense-hafiyya-malik/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210812T113000
DTEND;TZID=America/New_York:20210812T123000
DTSTAMP:20260422T164757
CREATED:20210809T173706Z
LAST-MODIFIED:20210810T185007Z
UID:5101-1628767800-1628771400@ece.northeastern.edu
SUMMARY:ECE Teaching Presentation: Mohammad Fanaei
DESCRIPTION:Title: Introduction to Python Programming with Applications in Computational Problem Solving. \nMohammad Fanaei \nLocation: Zoom Link \nAbstract: Python is a high-level\, interpreted programming language\, which supports multiple programming paradigms\, including functional and object-oriented programming. In this introductory learning conversation\, we will introduce the conditional execution of code\, loops\, and function definitions in Python. We will then apply the newly learned concepts to implement several numerical analysis methods and a Monte-Carlo simulation. The lecture will involve peer learning and assessment and assumes no background knowledge of Python programming. \nBio: Dr. Mohammad Fanaei received his Ph.D. degree in Electrical Engineering from West Virginia University\, Morgantown\, WV\, in 2016\, and his M.Sc. and B.Sc. degrees\, both in Electrical Engineering\, from Isfahan University of Technology\, Iran\, in 2008 and 2005\, respectively. Over the last six years\, Dr. Fanaei has worked at three different universities\, as an assistant professor of Electrical and Robotics & Mechatronic Systems Engineering at the University of Detroit Mercy (from 2016 to 2017 and from 2018 to present)\, as an assistant professor of Electrical Engineering at Bucknell University (from 2017 to 2018) and in the Iron Range Engineering Program in the Department of Integrated Engineering at Minnesota State University Mankato (from 2015 to 2016). Dr. Fanaei’s research interests are in the broad areas of the design\, analysis\, and evaluation of machine learning and deep learning technologies enabling connected\, automated\, and autonomous driving systems\, as well as the applications of stochastic signal processing in wireless communication systems and sensor networks. His teaching interests include embedded systems\, digital design\, wireless networks\, cryptology and network security\, communication systems\, stochastic signal processing\, and digital signal processing.
URL:https://ece.northeastern.edu/event/ece-teaching-presentation-mohammad-fanaei/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210810T100000
DTEND;TZID=America/New_York:20210810T110000
DTSTAMP:20260422T164757
CREATED:20210727T192945Z
LAST-MODIFIED:20210727T192945Z
UID:5077-1628589600-1628593200@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Pu Zhao
DESCRIPTION:PhD Dissertation Defense: Towards Robust Image Classification with Deep learning and Real-Time DNN Inference on Mobile \nPu Zhao \nLocation: Zoom Link \nAbstract:  As the rapidly increasing popularity of deep learning\, deep neural networks (DNN) have become the fundamental and essential building blocks in various applications such as image classification and object detection. However\, there are two main issues which potentially limit the wide application of DNNs: 1) the robustness of DNN models raises security concerns\, and 2) the large computation and storage requirements of DNN models lead to difficulties for its wide deployment on popular yet resource-constrained devices such as mobile phones.\nTo investigate the DNN robustness\, we explore the DNN attack\, robustness evaluation and defense. More specifically\, for DNN attack\, we achieve various attack goals (e.g. adversarial examples and fault sneaking attacks) with different algorithms (e.g. alternating direction method of multipliers (ADMM) and natural gradient descent (NGD) attacks) under various conditions (white-box and black-box attacks). For robustness evaluation\, we propose a fast evaluation method to obtain the model perturbation bound such that any model perturbation within the bound does not alter the model classification outputs or incur model mis-behaviors. For the DNN defense\, we investigate the defense performance with model connection techniques and successfully mitigate the fault sneaking and backdoor attacks. With a deeper understanding of the DNN robustness\, we further explore the deployment problem of DNN models on edge devices with limited resources.\nTo satisfy the storage and computation limitation on edge devices\, we adopt model pruning to remove the redundancy in models\, thus reducing the storage and computation during inference. Besides\, as some applications have real-time requirements with high inference speed sensitivities such as object detection on autonomous cars\, we further try to implement real-time DNN inference for various DNN applications on mobile devices with pruning and compiler optimization. To summary\, we mainly investigate the DNN robustness and implement real-time DNN inference on the mobile.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-pu-zhao/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210806T140000
DTEND;TZID=America/New_York:20210806T150000
DTSTAMP:20260422T164757
CREATED:20210730T184341Z
LAST-MODIFIED:20210730T184341Z
UID:5091-1628258400-1628262000@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Mohammadreza Sharif
DESCRIPTION:PhD Dissertation Defense: Human-in-the-loop Prosthetic Robot Hand Control through Contextual Adaptation \nMohammadreza Sharif \nLocation: ISEC136 or Zoom Link \nAbstract: Despite fifty years of research on prosthetic robot hands\, this technology is yet to be fully acknowledged by amputees as well as manufacturers\, due to lack of robustness and intuitive control. Our goal is to enhance the control robustness and intuitiveness through integrating the context\, i.e. knowledge of environment and task\, with human bio-signals\, e.g. hand trajectory. Although this solution has already been studied in the literature\, no unified framework is proposed for multi-modal information fusion. This research is aimed at introducing a novel framework for human-in-the-loop prosthetic robot hand control. We propose our solution in two parts\, (1) grasp inference and (2) end-to-end actuator control. For (1)\, we propose a model-based and a model-free grasp inference framework. Our model-based method is based on particle filters method. With knowledge of context hard-coded into the solution per se\, our particle-filter-based framework can incorporate any input signal using a proper weight function. In our model-free method we use hidden Markov mode to learn the grasp-inference task directly from human hand transport trajectories. For (2)\, we propose a reinforcement learning (RL) framework which learns to control robot actuator directly from the context with less information hard-coded into the solution. We leverage imitation learning (IL) besides RL to overcome challenging exploration in the problem. To provide invariance to the human hand transport trajectories\, we first provide a solution based on synthesized trajectories based on a human motion model. Later\, we adopt an over-sampling technique for real human hand transport trajectories\, to serve as a means of data augmentation. This research provides a step forward in more rigorous frameworks for multi-modal information fusion for prosthetic robot hand control and grasp inference through model- /data-based methods. \n 
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-mohammadreza-sharif/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210804T130000
DTEND;TZID=America/New_York:20210804T140000
DTSTAMP:20260422T164757
CREATED:20210727T225554Z
LAST-MODIFIED:20210727T225554Z
UID:5081-1628082000-1628085600@ece.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Hooman Barati Sedeh
DESCRIPTION:MS Thesis Defense: Space-Time Graphene Metasurfaces \nHooman Barati Sedeh \nLocation: Zoom Link \nAbstract: The unprecedented growth of the data exchanged between wireless devices and the rapid emergence of high-quality wireless services have raised the demand for communication bandwidth and data transmission rates. This has motivated the migration of wireless networks toward the utilization of carrier waves with higher frequencies beyond the millimeter-wave band. Terahertz (THz) band is envisioned as one of the enabling technologies for future generations of wireless communication mobile networks (such as 6G) to address the needs of high-speed and bandwidth-intensive applications. THz communications are expected to provide broadband services to several wireless devices in the internet of things applications. The recent migration from internet-of-things to the internet of nano-things imposes certain constraints in terms of size\, weight\, and power (SWaP) on the THz antennas responsible for communications\, calling for the development of smart antennas with adaptive response capable of establishing multiple active data links through multi-beam scanning in a multiple-input\, multiple-output (MIMO) network while meeting the demands on the capacity and SWaP. Moreover\, provisioning a reliable communication channel that ensures the security of its users’ information remains vitally essential for the next generation of communication networks.\nMetasurfaces\, consisting of subwavelength elements\, are poised to enable improved free-space optical communications with low SWaP thanks to their small form factor and capability to provide unprecedented control over the wavefront of electromagnetic waves at the subwavelength scale. The recent investigations of active metasurfaces have aimed toward overcoming the fixed response of conventional metasurfaces and developing smart antenna systems with adaptive beamforming capabilities that can point the beam toward the desired users in real-time through pixelated control over the phase of the scattered wave. Moreover\, the adaptive communication by such quasi-static tunable metasurfaces can be secured by encrypting the transmitted data via holography to impose restrictions on the data access from an adversary. Despite the fruitful progress in this area\, quasi-static active metasurface face several challenges to meet the high demands on the capacity of communication due to their reliance on resonant phase shift accumulations which limit the operation bandwidth and hinders the scalability in terms of the number of channels in the account of non-trivial coupling effects between resonant unit cells. Furthermore\, these metasurfaces cannot be used for covert communication as they do not allow for engineering the spectral content of scattered light.\nThis thesis explores the roles of space and time in active metasurfaces for establishing adaptive and secure multichannel communication at low-THz frequency regime. As the primary goal of this work is twofold\, we will tackle each problem separately. At first\, we propose a technique for adaptive multichannel communication through simultaneous and independent multifrequency multibeam scanning via a single time-modulated metasurface consisting of graphene micro-patch antennas whose Fermi energy levels are modulated by radio-frequency biasing signals. To this aim\, we divide the metasurface aperture into interleaved orthogonally modulated sub-array antennas with distinct modulation frequencies\, rendering a shared aperture in space-time. The higher-order frequency harmonics generated by the sub-arrays in such a space-time shared-aperture metasurface are mutually orthogonal in the sense that they do not yield an observable interference pattern and can be separated by spectral filtering. A distinct constant progressive modulation phase delay is then adopted in each sub-array to independently scan its corresponding higher-order frequency harmonics via dispersionless modulation-induced phase gradient with minimal sidelobe level and full angle-of-view over a wide bandwidth. In the second part of this work\, we will propose another technique for establishing active secure communication links over single and multiple orthogonal frequency channels via a metasurface that consists of graphene micro-ribbons. To this aim\, the Fermi energy level of each graphene micro-ribbons is modulated via pseudo-random radio-frequency biasing signals whose DC offsets are adjusted to tilt the reflected beam toward the predefined direction by imposing a spatial phase gradient profile across the surface\, while their waveforms are engineered to expand the incident wave spectrum into a noise-like spectrum with a near-zero power spectral density via random modulation of the reflection phase of each element with respect to its offset phase. This permits for addressing a legitimate mobile user in real-time who can retrieve the incident signal via synchronous demodulation with the pseudo-random key of the metasurface while camouflaging the signal from the adversary by lowering the probability of detection and spectral encryption. The approach is then extended to enable multi-channel secure communication by dividing the metasurface into interleaved sub-arrays modulated with orthogonal pseudo-random keys\, which provides simultaneous and independent control over multiple beams with non-overlapping spread spectra which can be retrieved by independent legitimate users while rejecting unwarranted access by eavesdroppers as well as other users.
URL:https://ece.northeastern.edu/event/ece-ms-thesis-defense-hooman-barati-sedeh/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210804T100000
DTEND;TZID=America/New_York:20210804T110000
DTSTAMP:20260422T164757
CREATED:20210729T182123Z
LAST-MODIFIED:20210729T182123Z
UID:5085-1628071200-1628074800@ece.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Owen McElhinney
DESCRIPTION:MS Thesis Defense: On the Application of Spline Functions to Problems in Hyperspectral Imaging \nOwen McElhinney \nLocation: Zoom Link \nAbstract: Hyperspectral Imaging (HSI) is a rapidly growing topic in the field of remote sensing. Hyperspectral cameras trade off a reduction in the spatial resolution of modern imaging for a higher spectral resolution. This allows for the detection of surface materials using the principles of spectroscopy.\nThis work will investigate the application of a class of functions called splines to three different problems in the field of HSI. Splines are a special class of data-fitting functions that guarantee continuity. Common data-fitting techniques like polynomial and piecewise-polynomial fitting are unable to match the complexity of HSI data. Splines provide a robust fitting procedure that matches the physical reality of material spectra. They can additionally be used to smooth data\, calculate derivatives\, interpolate between points\, and more.\nThe first problem will look at the smoothing of noisy data for detecting small materials. When objects are smaller than the pixel\, the observed spectrum will mix the target with background materials. The problem of unmixing removes the influence of these background materials from observed data. If the object is too small\, the estimates from unmixing will be dominated by error. In this scenario\, splines will be used to smooth out these random variations.\nThe second problem will use splines to introduce a new solution to the problem of Temperature Emissivity Separation (TES). The physical quantity captured by the camera is radiance. For the detection of materials\, ground reflectance or emissivity are desired. TES is the process by which ground radiance is converted to material emissivity. Splines will be used to replace estimated roughness in this problem with an analytical solution.\nThe third and final application looks at using splines to detect gases without relying on image statistics. Gas features are sharp and only impact a narrow window of the spectrum. This application attempted to use splines to detect these sharp features by looking at the difference between the collected data and an interpolation across the feature.\nThe first two applications yielded interesting and useful results. The third application yielded some interesting conclusions about the general problem and improved methods for using splines in this space.
URL:https://ece.northeastern.edu/event/ece-ms-thesis-defense-owen-mcelhinney/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210804T100000
DTEND;TZID=America/New_York:20210804T110000
DTSTAMP:20260422T164757
CREATED:20210727T225426Z
LAST-MODIFIED:20210727T225426Z
UID:5079-1628071200-1628074800@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Amirreza Farnoosh
DESCRIPTION:PhD Dissertation Defense: Unsupervised Learning of Low-Dimensional Dynamical Representations from Spatiotemporal Data \nAmirreza Farnoosh \nLocation: Zoom Link \nAbstract: Ever-improving sensing technologies offer a fast and accurate collection of large-scale spatiotemporal data\, recorded from multimodal sensors of heterogeneous natures\, in various application domains\, ranging from medicine and biology to robotics and traffic control. In this dissertation\, we propose frameworks for learning the underlying representation of these data in an unsupervised manner\, tailored towards several emerging applications\, namely indoor navigation and mapping\, neuroscience hypothesis testing\, time series forecasting\, 3D motion segmentation\, and human action recognition.\nAs such\, (1) we developed an unsupervised framework for real-time depth and view-angle estimation from an inertially augmented video recorded from an indoor scene by employing geometric-based machine learning and deep learning models. (2) We introduced a hierarchical deep generative factor analysis framework for temporal modeling of neuroimaging datasets. Our model approximates high dimensional data by a product between time-dependent weights and spatially-dependent factors which are in turn represented in terms of lower dimensional latents. This framework can be extended to perform clustering in the low dimensional temporal latent or perform factor analysis in the presence of a control signal. (3) We developed a deep switching dynamical system for dynamical modeling of multidimensional time-series data. Specifically\, we employ a deep vector auto-regressive latent model switched by a chain of discrete latents to capture higher-order multimodal latent dependencies. This results in a flexible model that (i) provides a collection of potentially interpretable states abstracted from the process dynamics\, and (ii) performs short- and long-term vector time series prediction in a complex multi-relational setting. (4) We developed a dynamical deep generative latent model for segmentation of 3D pose data over time that parses the meaningful intrinsic states in the dynamics of these data and enables a low-level dynamical generation and segmentation of skeletal movements. Our model encodes highly correlated skeletal data into a set of few spatial basis of switching temporal processes in a low-dimensional latent framework. We extended this model for human action recognition by decoding from these low-dimensional latents to the motion data and their associated action labels.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-amirreza-farnoosh/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210802T140000
DTEND;TZID=America/New_York:20210802T150000
DTSTAMP:20260422T164757
CREATED:20210729T185822Z
LAST-MODIFIED:20210729T185822Z
UID:5087-1627912800-1627916400@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Yulun Zhang
DESCRIPTION:PhD Dissertation Defense: Deep Convolutional Neural Network for Image Restoration and Synthesis \nYulun Zhang \nLocation: Zoom Link \nAbstract: Image restoration and synthesis with deep learning play a fundamental role in the computer vision community. They are widely used on mobile devices (e.g.\, smartphones) or lead to billion-dollar startups. However\, how to design efficient deep convolutional neural networks (CNNs) to extract higher-quality deep CNN features for better image restoration and synthesis is still challenging. In this dissertation talk\, I will describe my recent works to enhance CNN features in the channel dimension or/and the spatial dimensions. First\, for image restoration\, I will briefly introduce our proposed residual dense network. Then\, I will introduce the residual in residual (RIR) structure to train very deep super-resolution networks. Such an RIR structure could also make the network learn more high-frequency information\, being critical for high-resolution output. Attention mechanism (e.g.\, channel attention and spatial attention) is further explored to highlight the features. Second\, for image synthesis\, I will introduce multimodal style transfer via graph cuts. I visualize the deep features and find the multimodal style representation. I then formulate the style matching problem as an energy minimization one\, which could be solved via graph cuts. As a result\, the transferred features contain spatially semantic information\, providing more visually pleasing stylized results. Besides\, we investigate image synthesis about texture hallucination with large scaling factors. We propose an efficient high-resolution hallucination network for very large scaling factors.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-yulun-zhang/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210729T150000
DTEND;TZID=America/New_York:20210729T160000
DTSTAMP:20260422T164757
CREATED:20210727T191933Z
LAST-MODIFIED:20210727T191933Z
UID:5075-1627570800-1627574400@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Weite Zhang
DESCRIPTION:PhD Proposal Review: High Sensing-capacity Multi-dimensional-coded Millimeter-wave MIMO Imaging System \nWeite Zhang \nLocation: Microsoft Teams Link \nAbstract: Millimeter-wave (mm-wave) MIMO imaging systems have been explored to use more and more complicated radar waveforms to achieve advanced multiplexing and high-performance imaging. As the complexity of the radar waveform increases\, conventional systems inevitably suffer from higher design difficulty and cost. In spite of the radar waveform design\, existing mm-wave imaging systems are still suboptimal due to the fact that the sensing matrix is not tailored properly to achieve its maximum capacity\, which often results in large mutual information between successive measurements\, and limited imaging performance.\nAs the first contribution of this proposal\, high sensing-capacity mm-wave MIMO imaging systems with multi-dimensional-coding are built. In the first prototype\, a 70-77 GHz frequency-modulated continuous wave (FMCW) MIMO imaging system with massive channels is studied. To enhance the sensing-capacity\, a compressive reflector antenna (CRA) is added to perform randomized spatial wavefront coding to increase the measurement diversity. Both static and on-the-move experiments are carried out to show the functionality of the imaging system. In the second prototype\, an 81-86 GHz software-defined mm-wave MIMO imaging system is designed\, which makes use of cost-effective software-defined radios (SDRs) with mm-wave mixers. Due to the baseband flexibility of SDRs\, efficient orthogonal frequency-division multiplexing (OFDM) with binary phase coding is designed as the radar waveform to achieve simultaneous MIMO transmission\, where high receiving signal-to-noise ratio and spectrum efficiency are achieved. Again\, a CRA is designed and applied to increase the measurement diversity. Primary simulation and experimental results show good imaging performance with reduced side lobe effect.\nAs the second contribution of this proposal\, a material characterization method is developed\, which is vital in some important mm-wave imaging applications\, such as security screening\, where both object profile and material information are required for potential threats prediction. Specifically\, a Geometrical Optics (GO) forward model based on a reflectarray imaging system is developed. The GO forward model can be adapted to any other imaging systems as long as their geometrical configurations are known. Both simulations and experiments are performed to show the effectiveness and efficiency of the proposed material characterization method\, where the complex relative permittivity as well as a more accurate shape of the object is retrieved.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-weite-zhang/
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DTSTART;TZID=America/New_York:20210728T140000
DTEND;TZID=America/New_York:20210728T150000
DTSTAMP:20260422T164757
CREATED:20210727T191651Z
LAST-MODIFIED:20210727T191651Z
UID:5073-1627480800-1627484400@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Bahare Azari
DESCRIPTION:PhD Proposal Review: Circular-Symmetric Correlation Layer based on FFT \nBahare Azari \nLocation: Zoom Link \nAbstract: Planar convolutional neural networks\, widely known as CNNs\, have been exceptionally successful in many computer vision and machine learning tasks\, such as object detection\, tracking\, and classification. The convolutional layers in CNN are characterized by pattern-matching filters that can identify motifs in the signal residing on a 2D plane. However\, there exists various applications in which we have signals lying on a curved manifold or an arbitrary collection of coordinates\, e.g.\, temperature and climate data on the surface of the (spherical) earth\, and 360-panoramic images acquired from LiDAR. In these applications\, we usually need our network to be equivariant/invariant to various transformations of the input\, i.e.\, as we transform the input according to a certain action of a group\, the output is respectively transformed (equivariance)\, or remains unchanged (invariance). The convolution layers are empirically known to be invariant to small translations of their input image\, but they are not completely immune to relatively large translations Hence\, they may fail on the tasks that requires invariance to a specific transformation\, and and on the data that includes a wide range of that transformation. \nIn this work we consider equivariant/invariant tasks on 360-panoramic data. For a systematic treatment of analyzing the 360-panoramic data\, we propose a circular-symmetric correlation Layer (CCL) based on the formalism of roto-translation equivariant correlation on the continuous group constructed of the unit circle and the real line. We implement this layer efficiently using the well-known Fast Fourier Transform (FFT) and discrete cosine transform (DCT) algorithm. We discuss how the FFT yields the exact calculation of the correlation along the panoramic direction due to the circular symmetry and guarantees the invariance with respect to circular shift. The DCT provides an improved approximation with respect to transnational symmetry compared to what we observe in CNNs. We demonstrated the invariance analysis of networks built with CCL on two benchmark datasets comparing the equivariance of neural networks adopting CCL layers and regular CNN. Then\, we showcase the performance analysis of a general network equipped with CCL on recognition and classification tasks\, such as panoramic scene change detection\, 3D object classification\, LIDAR Semantic Segmentation.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-bahare-azari/
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DTSTART;TZID=America/New_York:20210723T100000
DTEND;TZID=America/New_York:20210723T110000
DTSTAMP:20260422T164757
CREATED:20210707T005406Z
LAST-MODIFIED:20210707T005406Z
UID:5032-1627034400-1627038000@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Mahmoud Ibrahim
DESCRIPTION:PhD Dissertation Defense: Low-Power Integrated Circuit Design for Wireless Devices in the Internet of Things \nMahmoud Ibrahim \nLocation:  Zoom \nAbstract: Numerous integrated sensing devices are under development for wireless medical diagnostic and monitoring applications. However\, the data rates of wireless devices connected to the Internet of Things are limited and strongly depend on the available power. This research addresses the need for circuit-level design methods to enable higher data rates with lower power consumption in order to facilitate the proliferation of wireless devices that can overcome the speed-power conundrum. The potential applications include continuous-time monitoring of physiological signals\, where increased data rates imply the ability to exchange more information during the same time\, more accurate data\, and/or data from a greater number of sites associated with each wireless node.\nAn energy-efficient binary frequency shift keying (BFSK) transmitter architecture for biomedical applications is introduced as the first part of this dissertation research. To achieve low power consumption with higher data rates\, the novel transmitter architecture leverages image rejection techniques to generate each of the two tones of the transmitted BFSK signal while keeping the phase-locked loop (PLL) oscillator frequency unchanged\, and thus maintaining low PLL power and overall transmitter power. A fabricated prototype chip in 130nm complementary metal-oxide-semiconductor (CMOS) technology achieves data rates up to 10 Mbps while consuming 180 µW with up to -20 dBm output power according to Medical Implant Communication System (MICS) band requirements. The measurement results confirm state-of-the-art energy-efficient performance with 18 pJ/bit.\nAs a natural continuation of the first part of this research\, a complementary receiver architecture is described in the second part of this dissertation to provide full transceiver capabilities. The new receiver design approach takes advantage of the transmitted signal characteristics by using both the frequency information and phase information to demodulate the received digital bits. This design method results in improved sensitivity with reduced power consumption through relaxed receiver block specification requirements. The custom-designed receiver circuits include a new low-noise amplifier (LNA) topology for energy-efficient antenna impedance matching\, and a single mixer circuit that realizes the signal down-conversion with differential in-phase and quadrature-phase baseband output signals to circumvent the complexity associated with two mixers and to save power. Measurement results of the fabricated receiver in 65nm CMOS technology show a sensitivity of -82 dBm with an input signal at 10 Mbps centered around 416 MHz. With a power consumption of 610 µW and an energy efficiency of 61 pJ/bit\, this receiver architecture displays state-of-the-art performance with respect to data rate\, power and sensitivity compared to other receivers in the same frequency range.\nIn addition to the new transmitter and receiver architectures\, a large-signal transconductance linearization technique is presented as part of this dissertation research to extend the dynamic range of analog baseband filters. Furthermore\, a low-power sinusoidal signal generation technique is introduced and analyzed\, which is a versatile and essential component of the transmitter design approach.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-mahmoud-ibrahim/
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DTSTART;TZID=America/New_York:20210719T113000
DTEND;TZID=America/New_York:20210719T123000
DTSTAMP:20260422T164757
CREATED:20210713T212719Z
LAST-MODIFIED:20210713T212719Z
UID:5046-1626694200-1626697800@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Berkan Kadioglu
DESCRIPTION:PhD Dissertation Defense: An Analysis of Algorithms with Discrete Choice Models \nBerkan Kadioglu \nLocation: Zoom Link \nAbstract: In the first half of our work\, we consider a rank regression setting\, in which a dataset of $N$ samples with features in $\mathbb{R}^d$ is ranked by an oracle via $M$ pairwise comparisons.\nSpecifically\, there exists a latent total ordering of the samples; when presented with a pair of samples\, a noisy oracle identifies the one ranked higher w.r.t. the underlying total ordering.\nA learner observes a dataset of such comparisons\, and wishes to regress sample ranks from their features.\nWe show that to learn the model parameters with $\epsilon > 0$ accuracy\, it suffices to conduct $M \in \Omega(dN\log^3 N/\epsilon^2)$ comparisons uniformly at random when $N$ is $\Omega(d/\epsilon^2)$.\nCompared to learning from class labels\, learning from comparison labels has two advantages: First\, comparison labels reveal both inter and intra-class information\, where class labels only contain the former.\nSecond\, comparison labels also exhibit lower variability across different labelers.\nThis has been observed experimentally in multiple domains\, including medicine \citep{campbell2016plus\,kalpathy2016plus\, stewart2005absolute} and recommendation systems \citep{schultz2004learning\,zheng2009mining\,brun2010towards\, koren2011ordrec}\, and is due to the fact that humans often find it easier to make relative\, rather than absolute\, judgements.\nMany works focusing on empirically learning comparison labels show excellent performance in practice \citep{tian2019severity\,yildiz2019classification}.\nOur work provides a theoretical foundation for analyzing and understanding this empirical performance.\nMoreover\, we extend the problem we initially study to a harder setting.\nWe do this by moving from pairwise comparisons to multi-way comparisons.\nFurthermore\, we study an online variant of the previous problem where the goal is to maintain high user engagement throughout the learning period.\nThis of course\, indirectly leads to the goal of learning parameters of the discrete choice model as accurately as possible\, fast.\nThis new problem is directly related to a setting in which a retailer recommends products to customers.\nA common problem in many recommendation tasks is to simultaneously learn the utilities of items to be recommended and maintain high user engagement.\nWe are generally constrained by a limit on the total number of items to be recommended at a time for an unknown time horizon.\nRecently\, bandit algorithms have been proposed for this setting where the multinomial logit model is assumed.\nBounds on error metrics are provided for upper confidence and Thompson sampling based algorithms.\nIn our paper\, we propose a variational inference based Thompson sampling algorithm and identify the required properties to achieve $\tilde O(D^{3/2}\sqrt T)$ worst-case regret.\nThrough extensive experiments we show that our method performs much better than the recently proposed \emph{TSMNL} algorithm in many error metrics.\nWe further accelerate our algorithm to be used in practical settings.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-berkan-kadioglu/
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DTSTART;TZID=America/New_York:20210713T100000
DTEND;TZID=America/New_York:20210713T110000
DTSTAMP:20260422T164757
CREATED:20210706T174832Z
LAST-MODIFIED:20210706T174832Z
UID:5023-1626170400-1626174000@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Maher Kachmar
DESCRIPTION:PhD Dissertation Defense: Active Resource Partitioning and Planning for Storage Systems using Time Series Forecasting and Machine Learning Techniques \nMaher Kachmar \nLocation: Zoom \nAbstract: In today’s enterprise storage systems\, supported data services such as snapshot delete or drive rebuild can result in tremendous performance overhead if executed inline along with heavy foreground IO\, often leading to missing Service Level Objectives (SLOs). Moreover\, new classes of data services\, such as thin provisioning\, instant volume snapshots\, and data reduction features make capacity planning and drive wear-out prediction quiet challenging. Having enough free storage pool capacity available ensures that the storage system operates in favorable conditions during heavy foreground IO cycles. This enables the storage system to defer background work to a future idle cycle. Static partitioning of storage systems resources such as CPU cores or memory caches may lead to missing data reduction rate (DRR) guarantees. However\, typical storage system applications such as Virtual Desktop Infrastructure (VDI) or web services follow a repetitive workload pattern that can be learned and/or forecasted. Learning these workload pattern allows us to address several storage system resource partitioning and planning challenges that may not be overcome with traditional manual tuning and primitive feedback mechanism.\nFirst\, we propose a priority-based background scheduler that learns this pattern and allows storage systems to maintain peak performance and meet service level objectives (SLOs) while supporting a number of data services. When foreground IO demand intensifies\, system resources are dedicated to service foreground IO requests. Any background processing that can be deferred is recorded to be processed in future idle cycles\, as long as our forecaster predicts that the storage pool has remaining capacity. A smart background scheduler can adopt a resource partitioning model that allows both foreground and background IO to execute together\, as long as foreground IOs are not impacted\, harnessing any free cycles to clear background debt. Using traces from VDI and web services applications\, we show how our technique can out-perform a static policy that sets fixed limits on the deferred background debt and reduces SLO violations from 54.6% (when using a fixed background debt watermark)\, to only 6.2% when employing our dynamic smart background scheduler.\nSecond\, we propose a smart capacity planning and recommendation tool that ensures the right number of drives are available in the storage pool in order to meet both capacity and performance constraints\, without over-provisioning storage. Equipped with forecasting models that characterize workload patterns\, we can predict future storage pool utilization and drive wear-outs. Similarly\, to meet SLOs\, the tool recommends expanding pool space in order to defer more background work through larger debt bins. Overall\, our capacity planning tool provides a day/hour countdown for the next Data Unavailability/Data Loss (DU/DL) event\, accurately predicting DU/DL events to cover a future 12-hour time window.\nMoreover\, supported services such as data deduplication are becoming a common feature adopted in the data center\, especially as new storage technologies mature. Static partitioning of storage system resources\, memory caches\, may lead to missing SLOs\, such as the Data Reduction Rate (DRR) or IO latency. Lastly\, we propose a Content-Aware Learning Cache (CALC) that uses online reinforcement learning models (Q-Learning\, SARSA and Actor-Critic) to actively partition the storage system cache between a deduplicated data digest cache\, content cache\, and address-based data cache to improve cache hit performance\, while maximizing data reduction rates. Using traces from popular storage applications\, we show how our machine learning approach is robust and can out-perform an iterative search method for various data-sets and cache sizes. Our content-aware learning cache improves hit rates by 7.1% when compared to iterative search methods\, and 18.2\% when compared to traditional LRU-based data cache implementation.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-maher-kachmar/
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DTSTART;TZID=America/New_York:20210707T170000
DTEND;TZID=America/New_York:20210707T180000
DTSTAMP:20260422T164757
CREATED:20210706T175131Z
LAST-MODIFIED:20210706T175131Z
UID:5027-1625677200-1625680800@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Kaidi Xu
DESCRIPTION:PhD Dissertation Defense: Can We Trust AI? Towards Practical Implementation and Theoretical Analysis in Trustworthy Machine Learning \nKaidi Xu \nLocation: Zoom Link \nAbstract: Deep learning has achieved extraordinary performance in many application domains recently. It has been well accepted that DNNs are vulnerable to adversarial attacks\, which raises concerns of DNNs in security-critical applications and may result in disastrous consequences. Adversarial attacks are usually implemented by generating adversarial examples\, i.e.\, adding sophisticated perturbations onto benign examples\, such that adversarial examples are classified by the DNN as target (wrong) labels instead of the correct labels of the benign examples. The adversarial machine learning aims to study this phenomenon and leverage it to build robust machine learning systems and explain DNNs.\nIn this talk\, I will present the mechanism of adversarial machine learning in both empirical and theoretical ways. Specifically\, a uniform adversarial attack generation framework\, structured attack (StrAttack) is introduced\, which explores group sparsity in adversarial perturbations by sliding a mask through images aiming for extracting key spatial structures. Second\, we discuss the feasibility of adversarial attacks in the physical world and introduce a convincing framework\, Expectation over Transformation (EoT). Utilize EoT with Thin Plate Spline (TPS) transformation\, we can generate Adversarial T-shirts\, a powerful physical adversarial patch for evading person detectors even if it could undergo non-rigid deformation due to a moving person’s pose changes. Third\, we stand on the defense side and design the first adversarial training method based on Graph Neural Network. Finally\, we introduce Linear relaxation-based perturbation analysis (LiRPA) for neural networks\, which computes provable linear bounds of output neurons given a certain amount of input perturbation. LiRPA studies the adversarial example in a theoretical way and can guarantee the test accuracy of a model by given perturbation constraints. The generality\, flexibility\, efficiency and ease-of-use of our proposed framework facilitate the adoption of LiRPA based provable methods for other machine learning problems beyond robustness verification
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-kaidi-xu/
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DTSTART;TZID=America/New_York:20210707T140000
DTEND;TZID=America/New_York:20210707T150000
DTSTAMP:20260422T164757
CREATED:20210706T175010Z
LAST-MODIFIED:20210706T175010Z
UID:5025-1625666400-1625670000@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Xiaolong Ma
DESCRIPTION:PhD Proposal Review: Towards Efficient Deep Neural Network Execution with Model Compression and Platform-specific Optimization \nXiaolong Ma \nLocation: Zoom \nAbstract: Deep learning or deep neural networks (DNNs) have become the fundamental element and core enabler of ubiquitous artificial intelligence. Recently\, with the emergence of a spectrum of high-end mobile devices\, many deep learning applications that formerly required desktop-level computation capability are being transferred to these devices. However\, executing DNN inference is still challenging considering the high computation and storage demands\, specifically\, if real-time performance with high accuracy is needed. Weight pruning of DNNs is proposed\, but existing schemes represent two extremes in the design space: non-structured pruning is fine-grained\, accurate\, but not hardware friendly; structured pruning is coarse-grained\, hardware-efficient\, but with higher accuracy loss. To solve the problem\, we propose a compression-compilation co-optimization framework\, which includes 1) a new dimension\, fine-grained pruning patterns inside the coarse-grained structures that achieves accuracy enhancement and preserve the structural regularity that can be leveraged for hardware acceleration\, 2) a pattern-aware pruning framework that achieves pattern library extraction\, pattern selection\, pattern and connectivity pruning and weight training simultaneously\, and 3) a set of thorough architecture-aware compiler/code generation-based optimizations\, i.e.\, filter kernel reordering\, compressed weight storage\, register load redundancy elimination\, and parameter auto-tuning for real-time execution of the mainstream DNN applications on the mobile platforms. Evaluation results demonstrate that our framework outperforms three state-of-the-art end-to-end DNN frameworks\, TensorFlow Lite\, TVM\, and Alibaba Mobile Neural Network with speedup up to 44.5x\, 11.4x\, and 7.1x\, respectively\, with no accuracy compromise. Real-time inference of representative large-scale DNNs (e.g.\, VGG-16\, ResNet-50) can be achieved using mobile devices.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-xiaolong-ma/
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DTSTART;TZID=America/New_York:20210701T110000
DTEND;TZID=America/New_York:20210701T120000
DTSTAMP:20260422T164757
CREATED:20210623T211449Z
LAST-MODIFIED:20210623T211449Z
UID:5007-1625137200-1625140800@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Xianfeng Liang
DESCRIPTION:PhD Dissertation Defense: RF Magnetoelectric Microsystems \nXianfeng Liang \nLocation: Zoom Link \nAbstract: Multiferroic materials are the materials that inherently exhibit two or more ferroic properties\, such as ferroelectricity\, ferromagnetism and ferroelasticity\, etc. Magnetoelectric (ME) materials with coupled magnetization and electric polarization have attracted intense interests recently due to the realization of strong ME coupling and their key roles inME applications. Since the revival of thin-film ME heterostructures with giant ME coefficients\, a variety of multifunctional ME devices\, such as sensors\, inductors\, filters\, antennas etc. have been developed. Exciting progress has been made on novel ME materials and devices because of their high-performance ME coupling.\nIn this dissertation\, we will first show the properties of magnetostrictive (FeGaC and SmFe) and piezoelectric (ZnO)thin-film materials that are necessary for realizing strong ME coupling. A systematic investigation of the soft magnetism\, the change of modulus of elasticity with magnetization (delta-E effect)\, and microwave properties was carried out on FeGaC and SmFe thin films. We successfully developed the magnetostrictive FeGaC thin films with low coercive field of less than 1 Oe\, high saturation magnetization\, narrow ferromagnetic resonance (FMR) linewidth\, and an ultra-low Gilbert damping constant of 0.0027. A record high piezomagnetic coefficient of 9.71 ppm/Oe\, high saturation magnetostriction constant of 81.2 ppm\, and large delta-E effect of -120 GPa at 500 nm were achieved. ZnO films with high c-axis crystal orientation was also achieved by carefully optimizing the sputtering process parameters. These properties make them attractive materials for magnetoelectric and other voltage tunable RF/microwave device applications.\nAfter presenting the magnetostrictive and piezoelectric thin films and their static and dynamic properties\, we introduce the radio frequency (RF) ME microsystems. Mechanically driven antennas have been demonstrated to be the most effective method to miniaturize antennas compared to state-of-the-art compact antennas.The ME antennas based on a released magnetostrictive/piezoelectric heterostructure rely on electromechanical resonance instead of electromagnetic wave resonance\, which results in an antenna size as small as one-thousandth of an electromagnetic wavelength. Due to the strong ME coupling in thin-film ME heterostructures\, we proposed the ultra-compact MEMS ME antennas and improved their performance by using anchor designs\, array structure\, and SMR structure. These miniaturized robustME antennas can be implemented in numerous real-world applications such as internet of things\, wearable and bio-implantable devices\, smart phones\, wireless communication systems\, etc. The ME antennas\, with an overall dimension of 700 m×700 m (L×W)\, were designed to operate at a resonant frequency of 2 GHz and experimentally demonstrated a gain of -18.85 dBi. Furthermore\, we demonstrated highly sensitive integrated RF giant magnetoimpedance (GMI)sensors based on amplitude and phase sensitive mechanisms. The amplitude and phase magnetic noise levels were demonstrated to be 810pT /√Hz at 1000 Hz and 100pT /√Hz\, respectively.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-xianfeng-liang/
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