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DTSTART;TZID=America/New_York:20220120T110000
DTEND;TZID=America/New_York:20220120T120000
DTSTAMP:20260423T020108
CREATED:20220106T194246Z
LAST-MODIFIED:20220106T194246Z
UID:5368-1642676400-1642680000@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Vedant Sumaria
DESCRIPTION:PhD Proposal Review: Exploring Micro-Machined Glass Shell Resonators For Sensor Application \nVedant Sumaria \nLocation: Zoom \nAbstract: Optical resonators have been playing an important role in modern optics. They are fundamental in any laser device\, etalon for optical filtering\, accurate measurement for non-linear optics. Bulk optical resonators that use two or more mirrors are usually used in all branches of modern linear and non-linear optics. There are many limitations in using such systems because they cannot provide high performance (high quality (Q) factor) and their size\, weight\, and alignment\, creates stability problems. To solve these problems\, there was an emerging class of miniaturized dielectric cavity based optical resonators that exploited the light confinement phenomenon through internal reflection. These resonators have a circular symmetry\, and they sustain modes known as the Whispering Gallery Modes (WGM) that is nothing but electromagnetic waves that circulate and are confined within the structure. Fabrication of these dielectric optical resonators is simpler and comparatively inexpensive. They demonstrate higher mode stability and higher performance. \nIn this proposal review\, I will discuss the working principles of a WGM resonator and study the various loss mechanisms to improve the quality factor. Further I will discuss the fabrication of on chip glass-blown microspherical shell resonators. These on-chip spherical glass shells are micrometers to millimeters in diameter with ultra-smooth surfaces and micrometer wall thicknesses which can sustain optical resonance modes with high Q-factors up to 50 million. Further we discuss various methods used to etch the backside silicon to create a liquid core optical resonator. This etching leads to increase in the surface roughness leading to loss of resonance. We optimized etching methods and parameters to keep the resonance as high as 18 million. By etching the silicon resonator’s temperature sensitivity is improved from -1.15 GHz/K to 2.23 GHz/K. This optical WGM sensor is then novel biosensor consisting of a chip-scale whispering gallery mode resonators with High-Q factor and a micro-caloric system. The silicon released shell resonator is elastically coupled to a kapton tubing system. Temperature change in the system induces thermal expansion and thermorefractive changes which can be sensitively monitored through changes in the optical resonance characteristics. We demonstrate a measurement resolution less than 10mK and a method of measuring temperature change to eliminate background noise that shows a great potential for detection of various biomolecules such as urea. We also discuss the possibility to use the sensor as an extremely sensitive IR sensor. Finally\, we talk about the future work in immobilization of urease and glucose oxidase to test for analytes like urea and glucose with concentrations in micro-mole.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-vedant-sumaria/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220114T140000
DTEND;TZID=America/New_York:20220114T150000
DTSTAMP:20260423T020108
CREATED:20220118T193826Z
LAST-MODIFIED:20220118T193826Z
UID:5377-1642168800-1642172400@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Andac Demir
DESCRIPTION:PhD Dissertation Defense: Automated Bayesian Network Exploration for Nuisance-Robust Inference \nAndac Demir \nLocation: Zoom Link \nAbstract: A fundamental challenge in the analysis of physiological signals is learning useful features that are robust to nuisance factors e.g.\, inter-subject \& inter-session variability\, and achieve the highest nuisance-invariant classification performance. Towards resolving this problem\, we introduce 3 frameworks: AutoBayes\, which is an AutoML approach to conduct neural architecture search for research prototyping\, and GNN based frameworks: EEG-GNN and EEG-GAT.\nThe ultimate goal of the AutoBayes framework is to identify the conditional relationship between a physiological dataset\, associated task labels\, nuisance variations and potential latent variables in order to robustly infer the task labels invariant of nuisance factors. Nuisance factors in the case of physiological datasets could be variations in subjects or sessions\, but we only focus on subject variations in the experiments. AutoBayes enumerates all plausible Bayesian networks between data\, labels\, nuisance variations and potential latent variables\, detects and prunes the unnecessary edges according to Bayes-Ball Algorithm\, and then trains the resulting DNN architectures for different hyperparameter configurations in an adversarial / non-adversarial or a variational / non-variational setting to achieve the highest validation performance. Instead of hyperparameter tuning for model optimization\, AutoBayes concentrates on the architecture search of plausible Bayesian networks\, and achieves state-of-the-art performance across several physiological datasets. Furthermore\, we ensemble several Bayesian networks by stacking their posterior probability vectors in a higher level learning space\, train a shallow MLP as a meta learner\, and measure the task and nuisance classification performance on a hold-out dataset. We observe that exploration of different inference Bayesian networks has a significant benefit in improving the robustness of the machine learning pipeline\, and the parallel activity of vast assemblies of different Bayesian network models significantly reduces variation across subjects in the cross-validation setting.\nIn the second part of the dissertation\, we benchmark the performance of EEG-GNN and EEG-GAT against the AutoBayes framework. CNN’s have been frequently used to extract subject-invariant features from EEG for classification tasks\, but this approach holds the underlying assumption that electrodes are equidistant analogous to pixels of an image and hence fails to explore/exploit the complex functional neural connectivity between different electrode sites. We overcome this limitation by tailoring the concepts of convolution and pooling applied to 2D grid-like inputs for the functional network of electrode sites. Furthermore\, we develop various GNN models that project electrodes onto the nodes of a graph\, where the node features are represented as EEG channel samples collected over a trial\, and nodes can be connected by weighted/unweighted edges according to a flexible policy formulated by a neuroscientist. The empirical evaluations show that our proposed GNN-based framework\, EEG-GNN\, outperforms standard CNN classifiers across ErrP and RSVP datasets\, as well as allowing neuroscientific interpretability and explainability to deep learning methods tailored to EEG related classification problems. Besides that\, EEG-GAT employs multi-head attention mechanism in conjunction with the GNN architecture to learn the graph topology of observations instead of utilizing a graph shift operator that is heuristically constructed by a domain expert. This implicitly allows the exploration of the functional neural connectivity peculiar to a cognitive task between pairs of EEG electrode sites as well as EEG channel selection\, which is critical for reducing computational cost\, and designing portable EEG headsets.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-andac-demir/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220111T110000
DTEND;TZID=America/New_York:20220111T170000
DTSTAMP:20260423T020108
CREATED:20220110T194722Z
LAST-MODIFIED:20220110T194722Z
UID:5370-1641898800-1641920400@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Sungho Kang
DESCRIPTION:PhD Dissertation Defense: Plasmonically Enhanced Infrared Sensing Microsystems \nSungho Kang \nLocation: Zoom Link \nAbstract: Infrared (IR) spectroscopic sensing has become a key technique in multidisciplinary environments such as military applications\, industrial safety control\, and smart homes\, by providing an accurate and non-disruptive analysis of the target objects. Recently the demand for high performance and compact IR spectroscopy systems has been steadily growing due to the advent of Internet of Things and the burgeoning development of miniaturized sensors. The key challenge lies in realizing high performance IR detectors that have low noise\, high IR throughput\, and spectral sensitivity in a miniaturized form factor. This challenge has been tackled in the study of micro-electromechanical sensing systems and metamaterial absorbers\, in which the ultra-high resolution sensing capability and the near-perfect IR absorption properties can be simultaneously exploited in a minimized footprint. The metal-insulator-metal (MIM) IR absorbers\, in particular\, are characterized by the near-unity absorptance with lithographically tunable peak absorption wavelength and spectral selectivity in an ultra-thin form factor\, suitable for the implementation of miniaturized spectroscopic IR microsystems. The exceptional IR absorption characteristics realized by the MIM IR absorbers and their sub-wavelength form factor allow for seamless integration with the existing IR sensing microsystem and the unprecedented IR sensing performance for the next generation IoT sensing solutions. In this defense\, novel development of miniaturized IR spectroscopic sensor and maintenance-free wireless human sensors based on the two key technologies are presented: (1) multispectral resonant IR detector array and (2) plasmonically-enhanced long-wavelength infrared micromechanical photoswitch. This study shows that the demonstrated technologies can replace the traditional IR sensors with the new generation IR sensing microsystems that are characterized by their high performance\, compact form factor\, power efficiency and low cost.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-sungho-kang/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211220T110000
DTEND;TZID=America/New_York:20211220T120000
DTSTAMP:20260423T020108
CREATED:20211216T002527Z
LAST-MODIFIED:20211216T002527Z
UID:5353-1639998000-1640001600@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Anahita Moradmand
DESCRIPTION:PhD Proposal Review: Robust Observer Structures and Control Design for Linear and Nonlinear Dynamical Systems with Applications \nAnahita Moradmand \nLocation: Zoom Link \nAbstract: This thesis focuses on special class of observers and controllers for different and seperable nonlinear systems. In the first step\, a robust fault detection approach using observers for linear systems is proposed where we combine the unknown inout observer UIO and an extended proportional integral observer PIO\, which has a fading term for robust fault detection. The integrated observer is called proportional integral fading unknown input observer (PIFUIO).\nFurthermore\, we extend our result to nonlinear systems with special structures as the design of nonlinear observer had limitation for general types of nonlinear systems. In the second step\, analysis and design of positive systems is considered whereby positive stabilization and the design of positive unknown input observer (PUIO) are introduced. Also\, the robust stability analysis of this class of systems is studied in which the robust stability is formulated in terms of LMI. the class of separable positive nonlinear systems is also analyzed and the design of observer and controller are provided. Finally\, we extend our desgin from Lipschitz type nonlinearity to state-dependent type by focusing on interconnected systems where we propose a distributed control architecture to take advantage of the global performance similar to centralized control and leverage the benefits of decentralized control.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-anahita-moradmand/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211216T150000
DTEND;TZID=America/New_York:20211216T160000
DTSTAMP:20260423T020108
CREATED:20211216T002630Z
LAST-MODIFIED:20211216T002630Z
UID:5355-1639666800-1639670400@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Jinghan Zhang
DESCRIPTION:PhD Dissertation Defense: Domain Design Space Exploration: Designing a Unified Platform for a Domain of Streaming Applications \nJinghan Zhang \nLocation: ISEC 362 or Zoom Link \nAbstract: Many demanding streaming applications share functional and structural similarities with other applications in their respective domain\, e.g.\, video analytics\, software-defined radio\, and radar. This opens the opportunity for specialization to achieve the needed efficiency and/or performance.\nPlatforms integrating many accelerators (ACCs) is a primary approach for efficient\, high-performance stream computing.\nHowever\, designing one platform for each application is not economical due to the high costs of nonrecurring engineering (NRE) and time-to-market (TTM).\nTo this end\, the concept of domain platforms is proposed\, which takes advantage of similarities across applications and designs one unified platform to accelerate a domain of applications instead of focusing on a single reference application.\nThis dissertation approaches designing domain platforms from a function-level (kernel-level) acceleration through a heterogeneous ACC-rich platform\, where each ACC is specialized to accelerate a particular function.\nThere is a great challenge to select ACCs allocated in the domain platform\, considering the large design space and performance balance across many applications.\nHowever\, current Design Space Exploration (DSE) tools only focus on an individual application in isolation (e.g.\, one particular vision flow) for allocating a platform\, but not a set of similar applications.\nThis dissertation introduces Greedy Guided Mutation (GGM) to speed up the mutation in the GIDE algorithm\, which calculates an ACC score according to current allocation to guide mutation.\nAlternatively\, Rapid Domain Platform Performance Prediction (RDP^3) methods are introduced to replace a large number of the slow platform assessment in domain DSE\, which avoids the complex application binding exploration.\nIn the experiments\, GGM reduces 84.8% of exploration time with a 0.23% loss of the final OpenVX domain platform’s performance.\nRDP^3 using a machine learning method yields an even more significant speedup\, saving 90.8% of exploration time with only 0.0003% performance loss.\nDmDSE is a milestone to broaden DSE scope from individual applications to the domain level. It tremendously pushes the domain platform design from manually and engineering experience guided into a general automatic process.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-jinghan-zhang/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211216T093000
DTEND;TZID=America/New_York:20211216T103000
DTSTAMP:20260423T020108
CREATED:20211216T015218Z
LAST-MODIFIED:20211216T015218Z
UID:5357-1639647000-1639650600@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Jaehyeon Ryu
DESCRIPTION:PhD Proposal Review: Engineering Functional Nanomesh for Advanced Neuroelectronics \nJaehyeon Ryu \nLocation: Zoom Link \nAbstract: Transparent electronics have emerged as promising platforms for neural interfacing by enabling simultaneous electrophysiological recording and optical measurements. Also\, there are high demands for stretchable devices due to their low modulus and compatible interface with irregular and soft neural tissue. However\, current transparent\, stretchable approaches are usually limited by their scalability for neuroelectronic applications. Here\, I present multi-functional nanomesh as an approach to achieve stretchable\, transparent microelectrode arrays (MEAs) with excellent scalability. By stacking mechanical supporting polymer\, gold\, and conductive polymer in a nanomesh structure on elastomer substrate\, multilayer nanomesh-based MEAs show excellent stretchability\, transparency\, and electrochemical properties with single neuron scale dimensions. The performance of these multi-functional nanomesh-based MEAs has been characterized through bench testing\, and I plan to perform in vivo validation in the remaining period of my thesis. These highly stretchable and transparent multilayer nanomesh MEAs are promising for applications ranging from neuroscience to biomedical devices.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-jaehyeon-ryu/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211210T134500
DTEND;TZID=America/New_York:20211210T144500
DTSTAMP:20260423T020108
CREATED:20211202T021501Z
LAST-MODIFIED:20211202T021536Z
UID:5329-1639143900-1639147500@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Leonardo Bonati
DESCRIPTION:PhD Proposal Review: Softwarized Approaches for the Open RAN of NextG Cellular Networks \nLeonardo Bonati \nLocation: 532 ISEC \nAbstract: The 5th (5G) and 6th generations (6G) of cellular networks\, also known as NextG\, will bring unprecedented flexibility to the wireless cellular ecosystem. Because of its typically closed and rigid market\, the telco industry has incurred high costs and non-trivial obstacles in delivering those new services and functionalities to satisfy the requirements and the demand of NextG networks. To break this trend the industry is now moving towards open architectures based on softwarized approaches\, which will afford network operators flexible control and unprecedented adaptability to heterogeneous conditions\, including traffic and application requirements. Now\, by simply expressing a high-level intent\, operators will be able to instantiate bespoke services on-demand on a generic hardware infrastructure\, and to adapt such services to the current network conditions. Through disaggregation\, network elements will split their functionalities across multiple components—possibly provided by different vendors—interconnected through well-defined open interfaces. The separation of control functions from the hardware fabric\, and the introduction of standardized control interfaces\, will ultimately enable definition and use of softwarized control loops\, which will bring embedded intelligence and real-time analytics to effectively realizing the vision of autonomous and self-optimizing networks.\nThis dissertation work focuses on the design\, prototyping and experimental evaluation of softwarized approaches for the new open Radio Access Network (RAN) of NextG cellular networks. We analyze the architectural enablers\, challenges and requirements for a programmatic zero-touch control of the very many network elements and propose practical solutions for its realization. We prototype solutions by leveraging open-source software implementations of cellular protocol stacks and frameworks\, and heterogeneous virtualization technologies\, including the srsRAN and OpenAirInterface cellular implementations\, and the O-RAN framework. The contributions of this work include (i) the first demonstration of O-RAN data-driven control loops in a large-scale experimental testbed using open-source\, programmable RAN and RAN Intelligent Controller (RIC) components through xApps of our design\, and (ii) CellOS\, a zero-touch cellular operating system that automatically generates and executes distributed control programs for simultaneous optimization of heterogeneous control objectives on multiple network slices starting from a high-level intent expressed by the operators. The effectiveness of our solutions in achieving superior control and performance of the RAN is demonstrated on state-of-the-art experimental facilities\, including software-defined radio-based laboratory setups and open access experimental wireless platforms\, such as Colosseum\, Arena\, and the POWDER-RENEW platform from the U.S. PAWR program.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-leonardo-bonati/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211210T110000
DTEND;TZID=America/New_York:20211210T120000
DTSTAMP:20260423T020108
CREATED:20211207T203514Z
LAST-MODIFIED:20211207T203514Z
UID:5339-1639134000-1639137600@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Michele Pirro
DESCRIPTION:PhD Proposal Review: Scandium-doped Aluminum Nitride for new MEMS technologies \nMichele Pirro \nLocation: 432 ISEC \nZoom Link Meeting ID: 938 0086 0379 https://northeastern.zoom.us/u/abZS2SmtT1 \nAbstract: The increasing demand for data is pushing the MEMS industry to more performant and area-efficient systems to be used in IOT nodes as sensors and RF-components. In this market\, AlN plays a pivotal role thanks to the piezoelectric properties accompanied with good stability over power and temperature in miniaturized devices. In fact\, AlN is already present in different commercial MEM systems\, such as duplexers\, ultrasound generators\, energy harvesters and so on\, proving a mature mass-production process flow. The required more stringent specifications in terms of bandwidth\, losses and efficiency are pushing towards piezoelectric materials with higher coupling coefficient\, but still in a compatible post-CMOS process flow. Luckily\, recent works showed how it is possible to enhance the piezoelectric effect by doping AlN with Scandium\, allowing up to 400% increase in the d33 piezoelectric coefficient. The enhanced acoustic transduction along with the recent demonstration of ferroelectric switching and the post-IC compatibility\, is making Sc-doped AlN a new material with the potential not only to replace AlN\, but also to integrate different functionalities within the same component. Academy and industry all over the world are actively researching the actual potential of the material but there is still a lack of information on high-Sc concentration\, which would allow lower-voltage switching along with higher d33. This work has the main objective to show Sc-concentration > 28% and their piezo/ferroelectric response for a new class of microelectromechanical devices.\nThe proposal will discuss the advance in the process flow of high Sc- concentrations\, showing the impact of the deposition parameters on the material properties. Thin films with good crystallinity on IC-substrate and enhanced d33 are reported\, along with first attempts to resonator-devices. An in-depth ferroelectric characterization will show how coercive field and leakage current are the main limiting factors the material is facing to integrate its memory effect. For this purpose\, the work will present how tuning of Sc-concentration\, substrate-rf and bulk stress can ease these limiting factor\, opening to new acoustic devices with memory functionalities. The last part will focus on the co-integration of acoustic properties with ferroelectric switching for tunable filters and ultrasonic generators in post-IC compatible substrates.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-michele-pirro/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211209T150000
DTEND;TZID=America/New_York:20211209T160000
DTSTAMP:20260423T020108
CREATED:20211208T011849Z
LAST-MODIFIED:20211208T011849Z
UID:5348-1639062000-1639065600@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Arjun Singh
DESCRIPTION:PhD Dissertation Defense: Design\, Modeling\, and Operation of Plasmonic Devices for Smart Communication Systems in the Terahertz Band \nArjun Singh \nLocation: 332 ISEC or Teams Link \nAbstract: The terahertz (THz) band is an attractive spectral resource for realizing future communication systems\, with the potential of supporting very high-speed data rates and increasingly dense networks. However\, the lack of a well-developed technology that operates at these frequencies has remained a challenge for the scientific community. The very high propagation losses at THz frequencies and the decimating impact of everyday objects on THz wave propagation necessitate an up-haul of the conventional communication link\, with smart control over the radiation\, propagation\, and detection of THz signals. Additionally\, device physics at THz frequencies\, among them the very high gain requirement and large electrical size\, may render the often-held assumptions of the propagation model invalid. An interdisciplinary approach spanning device design and operation\, and wireless propagation and signal processing is required.\nTo this end\, the proposed research herein addresses the facilitation of an end-to-end communication link with graphene plasmonics as the cornerstone of the fundamental device physics. The devices designed can be utilized to effectively overcome the limited communication distance –The grand challenge of the THz band. Different from other undertakings\, every attempt is made to ac-knowledge and accommodate the complex trade-offs in the design process. First\, a novel graphene based plasmonic array architecture is proposed\, explained\, and modeled. The fundamental radiating element of the array architecture\, called the plasmonic front-end\, consists of a self-sufficient plasmonic source\, a plasmonic modulator that acts as a phase controller\, and a plasmonic nano-antenna for effective radiation. The designed array is compact and provides complete beamsteering support\, with a new tailored algorithm developed for beamforming weight selection. Numerical evaluations and full-wave finite difference frequency domain (FDFD) simulations with COMSOL Multi-physics are utilized to verify array operation. Exploiting these properties\, a multi-beam array design is presented next\, where orthogonal spatial filters are utilized to provide support for spatial multiplexing towards the realization of ultra-massive MIMO (UM-MIMO). Taking this further\, the design considerations of an interleaved plasmonic array are presented\, in which the beamsteering capability is utilized to simultaneously achieve radio frequency interference (RFI) mitigation with channel capacity maximization for multi-user scenarios. Additionally\, to realize the vision of a smart communication system with a programmable wireless environment\, a hybrid reflectarray is presented. The fundamental element is modeled as a jointly designed and integrated metal-graphene patch. Numerical and simulation results are utilized to demonstrate the attractive properties of the reflectarray as compared to other proposed counterparts\, including an independence from the incoming angle of the impinging wave\, dynamic phase control capability\, and strong reflection efficiency. The requirements of a THz communication link and their impact on the common communication protocols are considered next. It is shown that certain scenarios may render regular array operation invalid\, motivating codebook designs that function in the massive near-field Fresnel zone of electrically large THz devices. Numerical simulations and theoretical analysis are presented to highlight their potential in improving system performance and capacity while reducing the system complexity. Finally\, the significant milestones in the fabrication process of these devices are also presented.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-arjun-singh/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211207T140000
DTEND;TZID=America/New_York:20211207T150000
DTSTAMP:20260423T020108
CREATED:20211130T003827Z
LAST-MODIFIED:20211130T003827Z
UID:5321-1638885600-1638889200@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Sara Banian
DESCRIPTION:PhD Dissertation Defense: Content-Aware AI-Driven Design Assistance Frameworks for Graphic Design Layouts \nSara Banian \nLocation: Zoom Link \nAbstract: Designing user interfaces (UIs) for mobile interaction is widespread but still challenging. It is important for the overall user satisfaction and application success. During the design process\, designers express their requirements through images describing the UI’s layout\, structure\, and content. Designers\, however\, encounter key challenges throughout the design process. For example\, searching for inspiring design examples is challenging because current search systems rely on only text-based queries and do not consider the UI structure and content. Furthermore\, these systems often focus on overall page-level layout over individual UI components. Also\, creating wireframe templates is difficult for many designers as it necessitates an understanding of different design guidelines. Therefore\, it is critical to support designers by developing effective design tools to help them be more productive and creative.\nIn this dissertation\, I aim to explore how to develop design assistance methodologies to augment the process of UI layout design\, with a particular focus on visual search and layout generation. Specifically\, for this exploration\, I seek to investigate the use of advanced deep learning models in the context of mobile UI layout design. Processing layouts differs from processing pixel-level images in that it necessitates processing both the semantic (e.g.\, labels) and spatial (e.g.\, coordinates) content of the layout to model the data properly. To achieve this\, I explore the design problems from both the data and the model side. First\, I present a large-scale UI dataset that accurately specifies the interface’s view hierarchy (i.e.\, UI components and their location). Second\, I contribute the VINS framework\, which is composed of three systems LayVis\, CompVis\, and TransVis that addresses layout-based visual search\, component-based visual search\, and layout generation\, respectively.\nFirst\, I introduce LayVis\, an object-detection layout-based retrieval model. It takes as input a UI image and retrieves visually similar design examples. Next\, I introduce CompVis\, a component-based visual search system to easily retrieve individual UI components via convolutional neural networks (CNNs). Specifically\, for a given query\, the system allows to retrieve (1) text label synonyms\, (2) similar UI components\, and (3) design examples containing such components. Finally\, I present TransVis\, a transformer-based generative framework that investigate how to generate UI layouts according to user specifications and following design practices. It specifically models UI layouts as an ordered sequence of elements based on spatial and semantic relationships for (1) generating complete UI layouts\, (2) auto-completing existing UI layouts seamlessly\, and (3) supporting many design elements per layout.\nOverall\, the work presented in this dissertation contributes to augmenting the UI layout design. Through quantitative and qualitative evaluation of VINS\, we conclude the following: (1) Advanced deep learning models can aid in the development of design assistance methodologies for layout design; and (2) Designers perceive the use of VINS inspiring and useful. Such insights\, combined with the open-sourced large-scale dataset\, can help the research community develop more effective AI-based data-driven design tools. This work presents future opportunities to investigate different deep learning models within the context of layout design and how designers interact with these AI-based models.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-sara-banian/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211203T100000
DTEND;TZID=America/New_York:20211203T110000
DTSTAMP:20260423T020108
CREATED:20211202T020751Z
LAST-MODIFIED:20211202T020751Z
UID:5325-1638525600-1638529200@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Murphy Wonsick
DESCRIPTION:PhD Dissertation Defense: Supervisory Control for Humanoid Robots Through Virtual Reality Interfaces \nMurphy Wonsick \nLocation: ISEC 655 \nAbstract: Recent advancements in robotics have allowed robots to become capable enough to be used in a wide variety of domains that are dangerous for humans to operate in\, such as disaster relief operations\, exploration of extraterrestrial planets\, bomb disposal\, or nuclear decommissioning efforts. However\, current supervisory control interfaces that allow humans to explore and interact in these environments through remote presence and teleoperation are complex and often require expert operators. Virtual reality provides a medium to create immersive and easy-to-use teleoperation interfaces. Virtual reality allows operators to visualize and interact with 3D data in a 3D environment that is not possible with traditional interfaces that make use of 2D devices\, such as monitors\, keyboards\, mice\, tablets\, and/or game controllers. Yet\, development of supervisory control virtual reality interfaces for robot operation is still very limited. Most present work in virtual reality interfaces focuses on direct teleoperation and not on high-level control that supervisory control interfaces can provide. In this dissertation\, we focus on developing virtual reality supervisory control interfaces for remote robot operation. We specifically focus on high degree-of-freedom robots\, such as humanoid robots or mobile manipulator robots\, as they are the most suited types of robots for remote operation. To accomplish this\, we first look to better understand and define humanoid robot capabilities using NASA’s humanoid robot\, Valkyrie. Following\, we synthesize the current state-of-the-art supervisory control interfaces for humanoid robots to create our own supervisory control interface using traditional devices. We then use this information to create a virtual reality supervisory control interface for Valkyrie. Finally\, we look to improve virtual reality interfaces for robot operation through a user-centered design approach to inform future development on virtual reality interfaces.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-murphy-wonsick/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211202T150000
DTEND;TZID=America/New_York:20211202T160000
DTSTAMP:20260423T020108
CREATED:20211202T020920Z
LAST-MODIFIED:20211202T020920Z
UID:5327-1638457200-1638460800@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Tong Jian
DESCRIPTION:PhD Proposal Review: Robust Sparsified Deep Learning \nTong Jian \nLocation: Zoom Link \n(ID: 75807284369\, Passcode: 463BXOZk) \nAbstract: In this thesis\, we investigate and address robustness concerns about DNN-based real-life applications on resource constrained systems\, environment adaptation\, and adversarial learning\, respectively. We propose a means of compressing a Radio Frequency (RF) deep neural network architecture through weight pruning\, and provide a systems-level analysis of the implementation of such a pruned architecture at resource-constrained edge devices. In particular\, we jointly train and sparsify neural networks tailored to edge hardware implementations. Under only negligible accuracy loss (less than 1%)\, we can achieve at most 27.2x pruning rate for 50-device classification. We demonstrate the efficacy of our approach over multiple edge hardware platforms and our method yields significant inference speedups\, 11.5x on the FPGA and 3x on the smartphone\, as well as high efficiency.\nFurthermore\, we propose a new learn-prune-share (LPS) algorithm for achieving robustness to environment adaptation in the field of lifelong learning. Our method maintains a parsimonious neural network model and achieves exact no forgetting by splitting the network into task-specific partitions via an ADMM-based weight pruning strategy. Moreover\, a novel selective knowledge sharing scheme is integrated seamlessly into the ADMM optimization framework to address knowledge reuse. We show that our approach achieves significant improvement over the state-of-the-art methods on multiple real-life datasets.\nFinally\, we investigate the HSIC bottleneck as regularizer (HBaR) as a means to enhance adversarial robustness. We show that the HSIC bottleneck enhances robustness to adversarial attacks both theoretically and experimentally. In particular\, we prove that the HSIC bottleneck regularizer reduces the sensitivity of the classifier to adversarial examples. Our experiments on multiple benchmark datasets and architectures demonstrate that incorporating an HSIC bottleneck regularizer attains competitive natural accuracy and improves adversarial robustness\, both with and without adversarial examples during training.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-tong-jian/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211201T153000
DTEND;TZID=America/New_York:20211201T163000
DTSTAMP:20260423T020108
CREATED:20211123T011210Z
LAST-MODIFIED:20211123T011210Z
UID:5299-1638372600-1638376200@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Zulqarnain Qayyum Khan
DESCRIPTION:PhD Proposal Review: Interpretable Machine Learning for Affective Neuroscience and Psychophysiology \nZulqarnain Qayyum Khan \nLocation: Zoom Link \nAbstract: In this thesis\, we leverage Machine Learning to investigate questions of interest in affective psychophysiology and neuroscience . We argue for and apply appropriate existing methods where possible and analyze the results they provide. Where existing methods fail to provide an answer we propose and build new models. We demonstrate the use of Hierarchical Clustering to investigate autonomic nervous system reactivity during an active coping stressor task\, revealing physiological indices of challenge and threat. Similarly\, we leverage Dirichlet Process Gaussian Mixture Modelling (DP-GMM) to reveal the variation in affective experience during a context-aware experience sampling study and to investigate the relationship between emotional granularity and cardiorespiratory physiological activity using resting state data for participants in the same study. We propose and develop Neural Topographic Factor Analysis (NTFA)\, a novel factor analysis model for fMRI data with a deep generative prior that teases apart participant and stimulus driven variation and commonalities and learns a latent space that can shed light on important neuroscientific phenomenon such as individual variation and degeneracy.\nBased on the work we have already done\, we propose three further lines of research that we intend to include in this thesis. First\, NTFA can essentially be viewed as a family of models\, where appropriate modifications can be made depending on what questions are needed to be answered. Leveraging this\, we propose explicitly adapting NTFA to tackle the question of degeneracy in neural responses. This involves introducing another latent space which can be used to capture and visualize the interaction of each participant with each stimulus in a given fMRI study. The arrangement of inferred embeddings in this latent space can then suggest presence or absence of different types of degeneracy in neural responses among participants in response to the presented stimuli. Second\, during the course of this interdisciplinary research we realized that there is a need for a comprehensive work that sheds light on the assumptions and limitations of some of the most popular machine learning methods used commonly in the sciences (specially psychology)\, and provide recommendations on how researchers can be more mindful of the underlying assumptions machine learning methods make. This can then equip users of ML methods to draw more appropriate conclusions from the results they get. We intend to include this in our thesis. Third\, continuing along the same lines\, there is also a need for better explanation models for the increasingly complicated ML models in use today. This is especially true in health sciences where the knowledge of why an ML model made a particular decision is almost as important as that decision being accurate. To this end we propose a theoretical work that ties the reliability of explanation models to the robustness of the models they are trying to explain.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-zulqarnain-qayyum-khan/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211201T120000
DTEND;TZID=America/New_York:20211201T130000
DTSTAMP:20260423T020108
CREATED:20211124T225107Z
LAST-MODIFIED:20211124T225107Z
UID:5307-1638360000-1638363600@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Flavius Pop
DESCRIPTION:PhD Dissertation Defense: Intrabody Communication for Real-Time Monitoring of Implanted Medical Devices based on Piezoelectric Micromachined Ultrasonic Transducers \nFlavius Pop \nLocation: Zoom Link \nAbstract: Nowadays when we think about medical devices and patient monitoring\, we can easily imagine ourselves laying down in a hospital bad\, wires coming out of everywhere\, and being looked after by nurses and physicians. Scary and not that comfortable! For this reason\, medical wearable devices are becoming more popular for at-home monitoring and transmitting data back to the hospital. Sometimes wearables are not enough\, this is why Implanted Medical Devices (IMDs) are still required to monitor many vital signs (blood flow\, insulin level\, neurons reading etc.) and act upon these readings (nerve stimulation\, heart defibrillation\, insulin pumping etc.). In order to be minimally invasive\, reduce the risk of infection and rejection from the body\, and last a long time (avoiding any further surgery) the IMDs require robust wireless communication technology to communicate with the external world. In this presentation I am going to show how we can implement an ultrasonic wireless communication link based on Piezoelectric Micromachined Ultrasonic Transducers (pMUTs) arrays. PMUT arrays can be integrated with existing IMDs\, used for wireless power charging\, and can enable communication links for receiving and transmitting data. During the first part of the presentation I will show the modeling and design of the pMUT arrays\, followed by the fabrication process and the device’s characterization for system level validation. At this point\, the communication link is implemented with arrays implanted in a tissue phantom and the channel is characterized at several distances. During the second part of the presentation I will show novel techniques to improve the ultrasonic communication link such as duplexing matching networks for bandwidth definition and direct modulation for implantation depth increase and direct bitstream feeding. In the future I envision that the number of IMDs are going to increase\, and therefore I developed a scanning protocol that will allow medical doctors to find all implanted devices. This is the equivalent of an “ultrasonic stethoscope”. Given the small form-factor of the IMDs these will have little to no space for a battery\, limiting the operation lifetime. For this reason\, I developed an Ultrasonic Wakeup Receiver (UWuRx) based and on the direct modulation system and on a Micromachined Electro-Mechanical System (MEMS) switch which allows for near zero-power consumption in the idle state. This UWuRx enabled on-demand device usability and limited the idle power consumption\, which leads to battery life extension.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-flavius-pop/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211201T090000
DTEND;TZID=America/New_York:20211201T100000
DTSTAMP:20260423T020108
CREATED:20211124T225155Z
LAST-MODIFIED:20211129T200240Z
UID:5309-1638349200-1638352800@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Kai Li
DESCRIPTION:PhD Dissertation Defense: Reconfigurable and Intelligent Wireless Charging Surfaces \nKai Li \nLocation: 232 ISEC \nAbstract: Reconfigurable intelligent surfaces (RISs) have received significant attention for theirpotential to transform the environments by intelligently reconfiguring the surfaces\, infrastructures\,and engineering the electrical and magnetic fields. On the other hand\, while wireless power transfer has advanced\, there has been limited progress on increasing the charging coverage\, such as charging over large surfaces\, multi-device charging\, and automation. This dissertation aims to address these challenges and design and develop first-of-its-kindtheory and practice to transform ordinary surfaces into contactless\, intelligent\, and multi-devicewireless chargers. First\, the combination between magnetic resonance and the so-called concept of‘energy hopping’ across wireless inter-connected coils turns a large surface into a programmablewireless charging surface. The magnetic fields are carefully shaped on the fly over the surface\,enabling them to distribute energy efficiently at multiple locations on demand and charge differenttypes of devices. Two frameworks are developed: SoftCharge can deliver 23 W up-to 20cm over a larger surface\, and iSurface enables the creation of arbitrary and configurable power spots and power flow paths over 2D and 3D resonator surfaces. Inspired by the strong coupled magnetic resonance wireless power transfer\, two intelligentsurface sensing frameworks\, SoftSense\, and iSense\, are introduced that create collaborative surface-based object sensing and tracking using networked coils. SoftSense allows detection of the type of object and where it is placed on a large surface. iSense enables robot tracking over large surfaces.We validate our design on real sensing prototypes\, and experimental results show that each sensing coil only consumes few milliwatts and has 90% accuracy for velocity estimation.Combined with meta-surface\, we extended the intelligent charging surfaces to enhances safety\, end-to-end power transfer efficiency\, and customized power pattern over the surface.Toward this\, we design and develop a new system call meta-resonance wireless power transfer system that consists of power distribution layer and meta-resonance layer\, along with a new theory and prototype for fine-tuned and controllable power amplifying\, power blocking and normal power passing over the surface. We aim to create customized pattern and different application from portable devices(phone\, tablet) to medical devices\, and industrial devices with high safety and high power transfer efficiency.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-kai-li-2/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211130T120000
DTEND;TZID=America/New_York:20211130T130000
DTSTAMP:20260423T020108
CREATED:20211130T004002Z
LAST-MODIFIED:20211130T004002Z
UID:5323-1638273600-1638277200@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Sara Garcia Sanchez
DESCRIPTION:PhD Proposal Review: Learning and Shaping the Wireless Environment: An Integrated View of Sensing\, Computing and Communication \nSara Garcia Sanchez \nLocation: Microsoft Teams \nAbstract: The explosive growth in Internet of Things (IoT) deployments and anticipated data volumes that will be generated within future autonomous vehicles require collecting and processing large amounts of data\, generally transmitted over the wireless channel. In this context\, conventional permanent deployments limited to leverage the existing wireless environment\, often fall short of meeting the required capacity demand. To address this limitation\, this dissertation takes a hands-on approach to equip communication systems with technology to perceive and collaborate with and within the environment. Specifically\, we build (i) accurate physics-oriented predictive models and multimode sensing techniques to gain awareness of the existing channel\, as well as (ii) novel multidisciplinary approaches to intelligently modify the wireless channel towards the communication link benefit.\nIn this dissertation\, we first prove that combining wireless channel modelling\, multimode sensing and robotics provides significant link performance gains. To this extent\, we adopt a systems approach to study how millimeter wave (mmWave) radio transmitters on Unmanned Aerial Vehicles (UAVs) provide high throughput links under typical hovering conditions. Based on sensing and modelling efforts\, we propose techniques to exploit the information contained in the spatial and angular domains of empirically collected data from GPS\, cameras and RF signals. We demonstrate hovering impact mitigation by (i) selecting near-to-optimum transmission parameters as compared to the mmWave standard IEEE 802.11ad and (ii) proposing corrective coordinated actions at the UAVs from the robotic controls. These methods achieve mmWave beam-tracking and robust link deployment under event(s) impacting link performance\, such as hovering or blockage in the light of sight between transmitter and receiver.\nThen\, this dissertation experimentally demonstrates how the wireless environment can be interactively programmed through the use of Reconfigurable Intelligent Surfaces (RIS) to partially offload computation into the wireless domain. In particular\, we propose AirNN\, a system capable of realizing analog over-the-air convolutions\, accurately enough to substitute their digital equivalent in a Convolutional Neural Network (CNN).\nAs proposed future work\, this dissertation will explore innovative uses of the RIS technology in MIMO systems for 6G and beyond. Specifically\, we will investigate (i) how the use of RIS helps overcome environmental limitations of a highly spatially correlated MIMO system\, and (ii) whether the use of RIS can enable the use of MIMO techniques with a single antenna at the receiver.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-sara-garcia-sanchez-2/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211130T120000
DTEND;TZID=America/New_York:20211130T130000
DTSTAMP:20260423T020108
CREATED:20211129T194635Z
LAST-MODIFIED:20211129T194635Z
UID:5318-1638273600-1638277200@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Sara Garcia Sanchez
DESCRIPTION:PhD Proposal Review: Learning and Shaping the Wireless Environment: An Integrated View of Sensing\, Computing and Communication \nSara Garcia Sanchez \nLocation: TBA \nAbstract: The explosive growth in Internet of Things (IoT) deployments and anticipated data volumes that will be generated within future autonomous vehicles require collecting and processing large amounts of data\, generally transmitted over the wireless channel. In this context\, conventional permanent deployments limited to leverage the existing wireless environment\, often fall short of meeting the required capacity demand. To address this limitation\, this dissertation takes a hands-on approach to equip communication systems with technology to perceive and collaborate with and within the environment. Specifically\, we build (i) accurate physics-oriented predictive models and multimode sensing techniques to gain awareness of the existing channel\, as well as (ii) novel multidisciplinary approaches to intelligently modify the wireless channel towards the communication link benefit.\nIn this dissertation\, we first prove that combining wireless channel modelling\, multimode sensing and robotics provides significant link performance gains. To this extent\, we adopt a systems approach to study how millimeter wave (mmWave) radio transmitters on Unmanned Aerial Vehicles (UAVs) provide high throughput links under typical hovering conditions. Based on sensing and modelling efforts\, we propose techniques to exploit the information contained in the spatial and angular domains of empirically collected data from GPS\, cameras and RF signals. We demonstrate hovering impact mitigation by (i) selecting near-to-optimum transmission parameters as compared to the mmWave standard IEEE 802.11ad and (ii) proposing corrective coordinated actions at the UAVs from the robotic controls. These methods achieve mmWave beam-tracking and robust link deployment under event(s) impacting link performance\, such as hovering or blockage in the light of sight between transmitter and receiver.\nThen\, this dissertation experimentally demonstrates how the wireless environment can be interactively programmed through the use of Reconfigurable Intelligent Surfaces (RIS) to partially offload computation into the wireless domain. In particular\, we propose AirNN\, a system capable of realizing analog over-the-air convolutions\, accurately enough to substitute their digital equivalent in a Convolutional Neural Network (CNN).\nAs proposed future work\, this dissertation will explore innovative uses of the RIS technology in Multiple Input Multiple Output (MIMO) systems for 6G and beyond. Specifically\, we will investigate (i) how the use of RIS helps overcome environmental limitations of a highly spatially correlated MIMO channels\, and (ii) whether the use of RIS can enable the use of MIMO techniques with a single antenna at the receiver.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-sara-garcia-sanchez/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211104T100000
DTEND;TZID=America/New_York:20211104T110000
DTSTAMP:20260423T020108
CREATED:20211028T184322Z
LAST-MODIFIED:20211028T184322Z
UID:5275-1636020000-1636023600@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Cooper Loughlin
DESCRIPTION:PhD Proposal Review: Unsupervised Machine Learning Approaches to Sequential Data Analysis \nCooper Loughlin \nLocation: Remote \nAbstract: Analysis of sequential data is central to many fields of science and engineering. Often\, sequences are collections of observations made over time and space with little or no contextual information. The goal of analysis may be to evaluate relationships\, identify unusual observations\, or forecast future behavior based on historical data. Unsupervised modeling of sequences (e.g.\, time series) can illuminate the underlying structure of the data and enable analysis. \nIn this proposal\, we discuss a statistical model for multivariate time series and an associated inference algorithm. We develop a preliminary model for a particularly challenging class of multivariate time series where the observations are counts (non-negative integers) that are nonuniformly sampled in time. We develop a state space model and inference algorithm based on Monte Carlo integration and Expectation-Maximization. This preliminary work highlights some key challenges still to be addressed. In particular\, continuously variable sampling intervals\, computational complexity of sampling\, and long-term dependencies among observations are properties of real data that are not handled well by the preliminary model. Recent developments in unsupervised sequence modeling using deep learning techniques are introduced including variational auto-encoders\, recurrent neural networks\, and ordinary differential equation recurrent neural networks. We propose utilizing these deep learning techniques to improve the state of the art in sequential data analysis.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-cooper-loughlin/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211103T103000
DTEND;TZID=America/New_York:20211103T110000
DTSTAMP:20260423T020108
CREATED:20211019T180502Z
LAST-MODIFIED:20211022T000104Z
UID:5243-1635935400-1635937200@ece.northeastern.edu
SUMMARY:Electrical & Computer Engineering
DESCRIPTION:Please join faculty and graduate admissions staff at a webinar discussing the Electrical and Computer Engineering departmental program offerings and experiential learning opportunities in the Graduate School of Engineering.
URL:https://ece.northeastern.edu/event/electrical-computer-engineering/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211103T100000
DTEND;TZID=America/New_York:20211103T110000
DTSTAMP:20260423T020108
CREATED:20211004T174453Z
LAST-MODIFIED:20211004T174453Z
UID:5220-1635933600-1635937200@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Jared Miller
DESCRIPTION:PhD Proposal Review: Nonlinear and Time-Delay Systems Analysis using Occupation Measures \nJared Miller \nLocation: Zoom Link \nAbstract: Techniques to analyze nonlinear systems include peak and reachable set estimation. The reachable set of a system is the set of states accessible by trajectories of a dynamical system at specified times given initial conditions. The peak estimation problem finds extreme values of a state function along trajectories. Examples of peak estimation include finding the maximum height of a wave\, voltage on a power line\, speed of a vehicle\, and infection rate of an epidemic. These problems may be posed as infinite dimensional linear programs (LP) in occupation measures\, where occupation measures are Borel measures that contain all information about trajectories. Under mild assumptions\, a sequence of Linear Matrix Inequalities (LMI) in increasing degree will converge from outside to the LP optimum\, which is in turn equal to the true optimum of the program in trajectories.\nThe first part of this thesis expands upon the occupation measure formulation for peak estimation. The safety of trajectories with respect to an unsafe set may be quantified by measuring the constraint violation (safety margins)\, which is a maximum peak estimation problem. The distance of closest approach between trajectories and an unsafe set may be bounded through a modification of the peak estimation problem. Peak estimation may be applied to dynamics possessing a broad class of uncertainties\, which includes the data-driven setting of black-box polynomial dynamics. A modular MATLAB toolbox is developed to solve and interpret these variations on peak estimation problems.\nThe second part of this thesis introduces an occupation measure framework for analysis and control of time-delay systems. The evolution of time delay systems depends on present and past values of the state. Some instances of time delay systems with their associated delays include epidemic models (incubation period)\, population dynamics (gestation time)\, and fluid modeling (transport time of fluid moving in a pipe). An occupation measure framework is developed to define weak solutions over a finite time interval of nonlinear time-delay systems with a finite number of bounded discrete delays. Applications of this time-delay weak solution include optimal control (including dead-time)\, peak estimation\, and reachable set estimation of time delay systems.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-jared-miller/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211029T110000
DTEND;TZID=America/New_York:20211029T120000
DTSTAMP:20260423T020108
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:20260423T020108
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:20260423T020108
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:20260423T020108
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:20260423T020108
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/
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DTSTART;TZID=America/New_York:20211006T110000
DTEND;TZID=America/New_York:20211006T120000
DTSTAMP:20260423T020108
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/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210927T110000
DTEND;TZID=America/New_York:20210927T120000
DTSTAMP:20260423T020108
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/
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DTSTART;TZID=America/New_York:20210920T130000
DTEND;TZID=America/New_York:20210920T140000
DTSTAMP:20260423T020108
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/
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DTSTART;TZID=America/New_York:20210916T160000
DTEND;TZID=America/New_York:20210916T170000
DTSTAMP:20260423T020108
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/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210914T140000
DTEND;TZID=America/New_York:20210914T150000
DTSTAMP:20260423T020108
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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