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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210128T123000
DTEND;TZID=America/New_York:20210128T133000
DTSTAMP:20260428T002832
CREATED:20210125T194810Z
LAST-MODIFIED:20210125T194810Z
UID:4703-1611837000-1611840600@ece.northeastern.edu
SUMMARY:ECE Seminar: Seungmoon Song
DESCRIPTION:Seminar Title: Toward predictive simulation of human movement – for assistive devices and rehabilitation treatment  \nLocation: Zoom Link \nAbstract: I will present my research towards predictive simulations of human movement for assistive devices and rehabilitation treatment. First\, I will talk about a neuromechanical control model based on simple reflexes. The model can generate diverse locomotion behaviors\, react to perturbations similarly to humans\, and explain why walking performance declines with age. However\, as the model was focused on low-level motor control primarily for steady locomotion behaviors\, extending and verifying the model for more complex movements and reactions is necessary for producing reliable predictions for novel scenarios. In the later part\, I will present recent projects on conducting a human experiment with gait assistive exoskeletons and using deep reinforcement learning to developing complex control models. In the experimental study\, we found using human-in-the-loop optimization that it is possible to substantially increase self-selected walking speed with ankle exoskeletons. Regarding deep reinforcement learning\, we organized the Learn to Move competition\, where participants developed controllers for a human musculoskeletal simulation model. The competition has been organized at the NeurIPS conference since 2017 and has attracted over 1300 teams from around the world. At last\, I will discuss my plan of incorporating rigorous experimental validations and advanced computational techniques toward neuromechanical models that could change the way we design rehabilitation treatment and study human movement. \nSpeaker Bio: Seungmoon Song is a postdoctoral researcher in the Mechanical Engineering Department of Stanford University. He is also a recipient of an NIH K99 award and the lead organizer of the NeurIPS: Learn to Move competition. His research focuses on modeling the neuromechanics of human movement and applying it to rehabilitation and robotics. As a postdoc\, he is working on improving human walking performance with exoskeleton assistance using human-in-the-loop optimization. During his Ph.D. at the Robotics Institute of Carnegie Mellon University\, he proposed a reflex-based control model that could explain various aspects of human locomotion including diverse locomotion behaviors of healthy adults\, responses to unexpected disturbances\, and performance degradation in aging.
URL:https://ece.northeastern.edu/event/ece-seminar-seungmoon-song/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210127T150000
DTEND;TZID=America/New_York:20210127T170000
DTSTAMP:20260428T002832
CREATED:20210126T230728Z
LAST-MODIFIED:20210126T230728Z
UID:4707-1611759600-1611766800@ece.northeastern.edu
SUMMARY:AIX Seminar
DESCRIPTION:Please join us for our first AIX Seminar. \nLocation: Zoom Link \nSeminar 1 title: Allostasis and Interoception: Brain-Body Interactions and Implications for Robotics \nSpeakers: Dr. Karen Quigley\, Northeastern University\, and Dr. Erin Reilly\, Veteran Affairs \nAbstract: For much of the history of psychology\, sensation\, perception\, action\, emotion\, and cognition were studied as if they were separate\, biologically-defined faculties — they are not. A prominent current neuroscientific perspective (and variants thereof) suggest that a brain runs an internal\, predictive model or simulation of itself in the world. This model supports all functions achieved by a brain\, and in this view\, predictions constitute the internal model. Our lab has marshaled neuroanatomical evidence that predictions arise from visceromotor control regions in the brain to support anticipated action and other metabolically-costly functions such as learning. Collectively\, these anticipatory regulatory processes are called allostasis. Allostasis is the major task of a brain\, which utilizes 20% of the energetic budget of a human. A brain also requires a body\, which is the effector by which the brain supports maintenance of its own energetic needs. The internal model also is modified by prediction error arising from unanticipated inputs from both exteroceptive (e.g.\, vision) and interoceptive (e.g.\, viscerosensory) sources. Interoceptive sensations provide critical information to the brain about the status of the body\, enabling motor and visceromotor actions that can most efficiently support the brain’s energetic needs. Understanding these biological realities can bring new ideas to both the design of robots\, and also to our understanding of how to optimize humans-robot interactions. \n  \nSeminar 2 title: Improving Interaction using Intelligence \nSpeaker: Dr. Jaime Ruiz\, University of Florida \nAbstract: Adding intelligence to user interfaces provides unique opportunities to improve the way users interact with computing systems. In this talk\, I will give a broad overview of the types of projects undertaken by may lab. I will also highlight several projects that aim to use neration of multimodal interfaces.
URL:https://ece.northeastern.edu/event/aix-seminar/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210127T130000
DTEND;TZID=America/New_York:20210127T142000
DTSTAMP:20260428T002832
CREATED:20210120T012431Z
LAST-MODIFIED:20210120T012431Z
UID:4693-1611752400-1611757200@ece.northeastern.edu
SUMMARY:ECE Seminar: Dr. Jun Xiao
DESCRIPTION:Seminar Title: 2D Materials For Next-Generation Information Technology: From Functional Material Miniaturization To Energy-Efficient Phase Engineering \nLocation: Zoom Link \nAbstract: The emergence of artificial intelligence and 5G technology is transforming our world with novel applications such as the Internet of Things\, smart manufacturing\, and AI-empowered medical care. However\, this information revolution sets a massive demand for information capacity and energy supply. Such a big challenge urges innovations in device engineering and its material building blocks to boost information capacity and reduce energy consumption. In this talk\, I will focus on the exciting progress of the emergent 2D layered materials and their device engineering in this direction. First\, I will introduce our discovery of intrinsic 2D out-of-plane ferroelectricity in semiconducting In2Se3\, which holds great promise for ferroelectric device miniaturization. I will then present our electrostatic doping control innovation as a new energy-efficient mechanism for structural phase engineering in layered materials. I will further show how we utilize such technology to invent the non-volatile Berry curvature memory\, a new type of energy-efficient quantum devices. Inspired by these findings and techniques\, I will also briefly discuss the exciting future opportunities of leveraging the structure-property relationship and light-matter interactions in layered quantum materials and devices to boost the translation of novel quantum notions into technological advantages for energy-efficient neuromorphic computing\, robust quantum processing\, and biosensing. \nSpeaker Bio: Dr. Jun Xiao is a postdoctoral scholar working with Prof. Aaron Lindenberg in the Department of Materials Science & Engineering and Prof. Tony Heinz in the Department of Applied Physics at Stanford University. He earned his Ph.D. in Applied Science and Technology from UC Berkeley (2018) under Prof. Xiang Zhang’s supervision. His research experience and interests focus on leveraging quantum materials and devices for energy-efficient neuromorphic engineering\, robust quantum computing\, THz sensing\, and high-throughput manufacturing.
URL:https://ece.northeastern.edu/event/ece-seminar-dr-jun-xiao/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210126T130000
DTEND;TZID=America/New_York:20210126T142000
DTSTAMP:20260428T002832
CREATED:20210120T002827Z
LAST-MODIFIED:20210120T002827Z
UID:4689-1611666000-1611670800@ece.northeastern.edu
SUMMARY:ECE Seminar: Dr. Subhanshu Gupta
DESCRIPTION:Seminar Title: The Coming Together of Silicon Circuits and AI for Next Generation Wireless Communications\, IOT\, and Quantum Applications \nLocation: Zoom Link \nAbstract: The ubiquity of silicon-based devices around us have paved the way for fast communications\, personalized healthcare\, and terabits/sec computing unthinkable few decades ago. The question of what’s next for silicon-based circuits and systems gets interesting with the end (or as some say\, slowing) of technology scaling but the emphasis on wireless infrastructure\, internet-of-things\, and quantum computing leveraging advances in Artificial Intelligence (AI) has brought forward several new fundamental challenges. This talk will harness recent research in silicon-based circuits and systems and AI to bridge the fundamental gap in the underlying physics of large-scale wireless communications and cryogenics harmoniously with the outside environment. The first part of the talk will present reconfigurable spatial signal processors for large-scale antenna arrays that can achieve unprecedented resolution both in near-field and far-field. Introducing discretetime delay compensating techniques with large range-to-resolution ratios and AI-optimized radio frontend solutions\, we will demonstrate high data-rates with wide modulated bandwidths suited to 5G/Beyond-5G wireless communications. The second part of the talk will present AI optimizers to solve practical issues in well-known high-speed and high-resolution superconducting/quantum circuits for the first time. We will look into the design of an energy-efficient and low-latency optimizer that greatly reduces the calibration time and enabling heterogeneous cryogenic platforms coupling speed and energy-efficiency of Josephson Junctions with area-efficiency of CMOS. The third part of the talk will present silicon-based systems-on-chip that enables large-scale IoT networks combining advances in self powered radios with energy harvesters tapping into the surrounding environments. We will conclude this talk with custom integrated cryoelectronics and multi-antenna testbeds for modeling and design of high-speed cryoelectronic processors and spatial signal processors with diverse spatial functions such as beam training and RFI cancellation for future quantum computing and distributed antenna arrays of tomorrow. \nSpeaker Bio: Dr. Subhanshu Gupta received the B.E. degree from the National Institute of Technology (NIT) at Tiruchirappalli\, Tiruchirappalli\, India\, in 2002\, and the M.S. and Ph.D. degrees from the University of Washington\, Seattle\, WA\, USA\, in 2006 and 2010\, respectively. He is currently an Assistant Professor of electrical engineering and computer science with Washington State University\, Pullman\, WA\, USA. He has held industrial positions at Maxlinear (Irvine\, CA) where he worked on wideband transceivers for SATCOM and infrastructure applications. Subhanshu is a recipient of the National Science Foundation CAREER Award in 2020\, the Department of Defense DURIP award in 2021\, and the Cisco Faculty Research Award in 2017. He and his group has also been nominated and awarded multiple student awards including Analog Devices Outstanding Student Designer Award in 2008\, the IEEE RFIC Symposium Best Student Paper Award (third place in 2011 and nominee in 2020)\, and the IEEE Applied Superconductivity Conference (nominee in 2020). Subhanshu serves as an Associate Editor for the IEEE Transactions on Circuits and Systems – I for the term 2020-21 and also served as a guest editor for IEEE Design & Test of Computers in 2019. His research interests include large-scale phased arrays and wideband transceivers\, low-power time-domain circuits and systems\, and statistical hardware optimization for next-generation wireless communications\, internet-of-things\, and quantum applications.
URL:https://ece.northeastern.edu/event/ece-seminar-dr-subhanshu-gupta/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210125T123000
DTEND;TZID=America/New_York:20210125T133000
DTSTAMP:20260428T002832
CREATED:20210114T234911Z
LAST-MODIFIED:20210114T234911Z
UID:4685-1611577800-1611581400@ece.northeastern.edu
SUMMARY:ECE Seminar: Kris Dorsey
DESCRIPTION:Seminar Title: Challenges and opportunities in design in tunable\, soft mechanical sensors. \nLocation: Zoom Link \nAbstract: Physically-soft mechanical sensors are poised to unlock exciting new applications in wearable devices\, robotics\, and human-machine interfaces. Typically with these sensors\, tuning their properties through the device geometry is a challenge. A promising development in soft mechanical sensors is hierarchically-patterned structures within the sensor\, which enables both deformation selectivity and the ability to tune\, and potentially reconfigure\, sensing properties. Dorsey will discuss challenges and recent work related to designing and fabricating hierarchically-patterned sensors\, including origami-patterned sensors. Dorsey will also present work in enhancing the stability and mechanical selectivity of stretchable sensors\, and discuss applications for such sensors in wearable healthcare applications and soft robotics.    \nSpeaker Bio: \nKris Dorsey is an assistant professor of engineering in the Picker Engineering Program at Smith College. She was a President’s Postdoctoral Fellow at the University of California\, Berkeley and University of California\, San Diego. Dr. Dorsey graduated from Carnegie Mellon University with a Ph.D. in Electrical and Computer Engineering and earned her Bachelors of Science in Electrical and Computer Engineering from Olin College. She founded The MicroSMITHie Lab at Smith College to investigate micro- and miniature-scale sensor design and to prepare undergraduates for graduate study in engineering. Her current research interests include novel morphology soft sensors\, stability concerns for soft-material sensors\, and sensors for soft robots and wearable medical devices.  Dr. Dorsey has co-authored several publications on hyper–elastic strain sensors\, novel soft lithography processes\, and the stability of gas chemical sensors. In 2019\, she received the NSF CAREER award.  
URL:https://ece.northeastern.edu/event/ece-seminar-kris-dorsey/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210121T150000
DTEND;TZID=America/New_York:20210121T160000
DTSTAMP:20260428T002832
CREATED:20210114T234813Z
LAST-MODIFIED:20210114T234813Z
UID:4683-1611241200-1611244800@ece.northeastern.edu
SUMMARY:ECE Seminar: Mojtaba Sharifi
DESCRIPTION:Seminar Title: Research Background and Experience in Medical Robotics\,Human-Robot Interaction\, and Collaborative/Assistive Devices \nLocation: Zoom Link \nAbstract: In this talk\, Mojtaba Sharifi will go over the research projects he has done in the field of Medical Robotics\, Human-Robot Interaction (HRI)\, and Collaborative/Assistive Robotics during the past ten years. His presentation is organized in three sections\, which cover his research achievements chronologically from his MSc to the current Postdoc position. The first one is devoted to his main research area during the MSc and Ph.D. programs on the “Control of HRI: Medical Robotic and Tele-Robotic Systems”. After that\, he will touch upon his recent contribution made on the “Interaction Learning and Autonomy for Collaborative Robots and Assistive Exoskeletons”\, during the postdoctoral research. The last part of this presentation is dedicated to his past and ongoing projects on the “Human Musculoskeletal Modeling & Soft Exoskeletons for Safe HRI”\, for biomedical applications. Throughout this presentation\, the theoretical and experimental aspects of these studies will be elaborated on.   \n Biography: Mojtaba Sharifi received the B.Sc. degree in Mechanical Engineering from Shiraz University\, Shiraz\, Iran\, in 2010 and the M.Sc. degree in Mechanical Engineering from Sharif University of Technology\, Tehran\, Iran\, in 2012. He conducted a collaborative project in the Telerobotic and Biorobotic Systems Lab of the University of Alberta\, Canada\, from 2015 to 2016 as a visiting doctoral researcher. Then\, he earned a Ph.D. degree in the School of Mechanical Engineering at Sharif University of Technology\, Tehran\, Iran\, in 2017. Mojtaba also performed an interdisciplinary research project on the design and fabrication of new soft robotic actuators in 2019 as a research associate at the University College London\, UK. He has published more than 40 papers and chapters in high-quality journals\, conferences\, and books on his interdisciplinary theoretical-experimental research. His research interests include the design and implementation of autonomous control systems\, physical human-robot interaction (pHRI)\, medical robotics (rehabilitation\, surgery\, and imaging)\, control of musculoskeletal systems\, impedance control and learning\, haptics\, collaborative– and tele-robotics\, soft robotics\, wearable\, and assistive mechatronic systems (exoskeleton and prosthesis). Mojtaba is the recipient of a postdoctoral fellowship award\, working at the Department of Electrical and Computer Engineering and the Department of Medicine\, University of Alberta\, Canada. He is now investigating new autonomous control policies employing adaptive learning rules for the Central Pattern Generation (CPG) to update and personalize the human locomotion\, which is to be tracked by a lower-limb powered exoskeleton with optimized torque and FES inputs. He is also leading a project that aims to design\, fabricate\, and implement soft robotic systems for safely assisting people with upper-limb weakness.  
URL:https://ece.northeastern.edu/event/ece-seminar-mojtaba-sharifi/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210121T113000
DTEND;TZID=America/New_York:20210121T123000
DTSTAMP:20260428T002832
CREATED:20210119T210023Z
LAST-MODIFIED:20210119T210032Z
UID:4687-1611228600-1611232200@ece.northeastern.edu
SUMMARY:ECE Seminar: Paolo Santi
DESCRIPTION:Seminar Title: IoT: An Enabling Technology for Designing Better Cities \nLocation: Zoom Link \nAbstract:  IoT is rapidly evolving into an enabling technology with countless potential applications. In this seminar\, we will explore how IoT technology can help in the design of better cities\, starting from the re-design of city systems and infrastructures (mobility\, power grid\, etc.) based on large- scale data acquisition. We will highlight the challenges related to systems where real-time actuation is a need\, as well as those related to design problems where actuation occurs on a longer time scale\, such as urban infrastructure planning. We will then show how IoT technology can be used also to gain a deeper understanding of how humans interact with existing urban systems. This deeper comprehension of human behavior is key to design systems that are not only “algorithmically” efficient\, but that also conform to fundamental human behavioral patterns. \nBio: Paolo Santi is Principal Research Scientist at MIT Senseable City Lab and Research Director at the Istituto di Informatica e Telematica\, CNR\, Pisa\, Italy. Dr. Santi holds a “Laurea” degree and the PhD in computer science from the University of Pisa\, Italy. Dr. Santi is a member of the IEEE Computer Society and has recently been recognized as\nDistinguished Scientist by the Association for Computing Machinery. His research interest is in the modeling and analysis of complex systems ranging from wireless multi hop\nnetworks to sensor and vehicular networks and\, more recently\, smart mobility and intelligent transportation systems. In these fields\, he has contributed more than 160 scientific papers and two books.
URL:https://ece.northeastern.edu/event/ece-seminar-paolo-santi/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210119T123000
DTEND;TZID=America/New_York:20210119T133000
DTSTAMP:20260428T002832
CREATED:20210113T231029Z
LAST-MODIFIED:20210113T231029Z
UID:4674-1611059400-1611063000@ece.northeastern.edu
SUMMARY:ECE Seminar: Maria Kyrarini
DESCRIPTION:Seminar Title: Robot Learning from Demonstrations for Human-Robot Synergy \nLocation: Zoom Link \nAbstract: Imagine a world where robots support and assist us in our everyday professional and personal life. To achieve a successful Human-Robot Synergy\, robots will need to learn new tasks from humans seamlessly\, to act on the new knowledge\, and easily adapt to new situations and people around them. Robot Learning from Demonstrations (RLfD) is a method used to enhance the ability of robots to be easily teachable by people\, a vital ability for a successful Human-Robot Synergy. RLfD enables non-expert users to ‘program’ a robot by simply guiding the robot through a task. However\, current research in RLfD tends to disconnect low-level motor control and high-level symbolic reasoning capabilities. In this talk\, I will present a novel RLfD framework\, which enhances a robot’s abilities to learn and perform the sequences of actions for object manipulation tasks (high-level learning) and\, simultaneously\, learn and adapt the necessary trajectories for object manipulation (low-level learning). Then\, I will present a ‘hands-free’ human-robot interaction modality that enables individuals with severe motor impairments\, such as quadriplegia\, to teach a robot an assistive manipulation task. I will discuss how the presented RLfD framework was evaluated in a dual-arm industrial robot for assembly tasks and in an assistive robotic manipulator for providing a drink. The experimental results demonstrate the potential of the developed robot learning framework to enable continuous human-robot synergy in industrial and assistive applications. Finally\, I will conclude the talk with a brief discussion of my ongoing work and future research plans. \nSpeaker Bio: Maria Kyrarini is a postdoctoral research fellow at the University of Texas at Arlington under the advisement of Professor Dr. Fillia Makedon. She is also the assistant director of the Heracleia Human-Centered Computing Lab. In 2019\, Maria received her Ph.D. in Engineering from the University of Bremen under the supervision of Professor Dr.-Eng. Axel Gräser. The title of her Ph.D. thesis is: “Robot learning from human demonstrations for human-robot synergy”. Before that\, she received her M.Eng. degree in Electrical and Computer Engineering and her M.Sc. degree in Automation Systems both from the National Technical University of Athens (NTUA) in 2012 and 2014\, respectively. Her primary research interests are in the fields of Robot Learning from Human Demonstrations\, Human-Robot Interaction\, and Assistive Robotics with a special focus on Enhancing Human Performance.
URL:https://ece.northeastern.edu/event/ece-seminar-maria-kyrarini/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210115T110000
DTEND;TZID=America/New_York:20210115T120000
DTSTAMP:20260428T002832
CREATED:20210114T214057Z
LAST-MODIFIED:20210114T214057Z
UID:4682-1610708400-1610712000@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Lorenzo Bertizzolo
DESCRIPTION:PhD Proposal Review: Software-Defined Wireless Networking for 5G and Beyond: From Indoor Cells to Distributed Aerial Swarms \nLorenzo Bertizzolo \nLocation: MS Teams Link \nAbstract: While Software-defined Networking is a consolidated and widely adopted concept in fixed infrastructure\, its adoption to the wireless domain has been limited by some fundamental challenges. Different from wired deployments\, wireless stacks are characterized by tight inter-dependencies among their protocol stack layers (known as vertical coupling) and the nodes sharing the wireless channel (horizontal coupling). These effects combined undermine the implementation of i) Control plane / data plane separation\, and ii) Control of multiple data planes from a separate controller; the two founding principles of SDN. Recent developments in spectrum access technology\, however\, made it possible to reconfigure multiple layers of the wireless stack at once. This paved the way for the development of cross-layer algorithms toward the implementation of control plane / data plane separation for wireless. Moreover\, cross-layer algorithms can be employed together with distributed control theory to implement distributed and scalable control for wireless networks\, this way overcoming the difficulties of implementing separate control for multiple data planes.\nThis proposal exploits full-stack programmability to propose\, design\, and implement new cross-layer algorithms that reconfigure the wireless stack at multiple layers and in real-time. Through the systematic use of cross-layer optimization\, closed-loop control\, and dynamic network adaptation\, this proposal contributes to the development of a wide range of technological innovations for spectrum access\, to bring the benefits of Software-defined networking to the wireless domain. We present a closed-loop PHY/MAC cross-layer control algorithm to enable spectrally-efficient OFDM spectrum access in Wi-Fi populated bands. Then\, we exploit the technological innovations of a 5G Open-RAN infrastructure and propose a control system that enables broadband 5G connectivity for aerial cellular users that dynamically adapts to the changing network conditions like the time-changing distribution of pedestrian users in the surrounding. At millimeter-wave frequencies\, we propose a cross-domain control algorithm that reduces the initial access latency in standalone high-frequency systems and obtains higher spectral efficiency for aerial links. Finally\, we empower the SDN paradigm to bring network management to distributed aerial swarms. Through full-stack software programmability and programmable motion control\, we implement scalable wireless network management for distributed aerial swarms.\nWe conclude the proposal with an overview of the requirements and design principles for next-generation wireless testing platforms to support software-programmable spectrum access. \n 
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-lorenzo-bertizzolo/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210113T110000
DTEND;TZID=America/New_York:20210113T120000
DTSTAMP:20260428T002832
CREATED:20210112T001703Z
LAST-MODIFIED:20210112T001703Z
UID:4667-1610535600-1610539200@ece.northeastern.edu
SUMMARY:ECE Seminar: Elahe Soltanaghaei
DESCRIPTION:Location: Zoom Link \nSeminar Title: Sensing the Physical World using Pervasive Wireless Infrastructure \nAbstract: The promise of IoT and emerging applications such as smart cities\, autonomous vehicles\, and mixed reality that are tightly coupled\nwith the physical environment pushes the demand for high-fidelity sensing. Meanwhile\, we are also seeing advances in wireless technologies such as Millimeter-wave and Massive MIMO systems that can transform the role of wireless networks from a pure communication medium to a pervasive sensing infrastructure. Elahe’s research investigates the synergy of wireless and sensing by applying signal processing and machine learning techniques to low-level RF signals. This talk will focus on how to map the natural interactions of wireless signals with the environment into physical and behavioral measurements for human sensing\, device localization and object tracking. She will then discuss her ongoing research on designing an RF-equivalent of optical retro-reflectors for automotive applications and will conclude with her\nroadmap toward omni-present sensing for the wireless embedded systems of the future. \nSpeaker Bio: Elahe Soltanaghaei is a postdoctoral researcher at Carnegie Mellon University in the Wireless\, Sensing\, and Embedded Systems\n(WiSE) lab. She received her PhD in Computer Science from University of Virginia. Her research spans the areas of wireless sensing and networking with applications in IoT and Cyber-Physical Systems. Reflecting the multidisciplinary nature of her research\, her work has been published in premier conferences and journals in the areas of mobile computing\, wireless networks\, and energy and infrastructure. She is the recipient of 2020 ACM SIGMOBILE Dissertation Award\, 2019 EECS Rising Stars\, and 2019 N2-Women Young Researcher Fellowship.
URL:https://ece.northeastern.edu/event/ece-seminar-elahe-soltanaghaei/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210112T100000
DTEND;TZID=America/New_York:20210112T110000
DTSTAMP:20260428T002832
CREATED:20201222T022618Z
LAST-MODIFIED:20201222T022618Z
UID:4647-1610445600-1610449200@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Haoqing Li
DESCRIPTION:PhD Proposal Review: Robust Processing against Interferences in GNSS Navigation \nHaoqing Li \nLocation: Zoom Link \nAbstract: Satellite-based navigation is prevalent as positioning applications among our lives\, how-ever\, this high reliance brings potential threats when different interferences and jamming signals are considered. Jamming devices\, although illegal in many countries\, can be easily to get. Those devices can broadcast high-power jamming signals in Global Navigation Satellite System (GNSS) frequency band to destroy receiver’s performance. While jamming signals are illegal and we may get rid of it with the power of law\, other kinds of interferences will cannot even be avoided. Distance Measuring Equipment (DME) signal is applied to measure the distance between aircraft and ground station\, significant in aircraft transport but interference in GNSS processing. Besides\, the GNSS signal itself can also be a interference after reflection and refraction. Since we couldn’t simply re-move those from the source\, methods to mitigate influences of interferences is necessary for stable performance of receiver. There are three main blocks in GNSS receiver: acquisition block\, tracking block and positioning block\, where influence of interferences could be eliminate to get an accurate Position\, Velocity\, and Time (PVT) solution. In this article\, robust statistics processing is applied as one of the interference mitigation methods. This method aims to lower influence of outliers\, which is the presence of many kinds of interferences in either time domain or transformed domain. Robust statistics processing can be used in pre-correlation in both acquisition block and tracking block\, while a robust Kalman filter is designed in positioning block to get rid of interferences. Deep learning\, achieving extraordinary performance in many application domains\, also provides improvement to tracking block against multipath problem. A deep neural network is built to substitute the whole tracking loop to bring robustness to receiver.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-haoqing-li/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210108T140000
DTEND;TZID=America/New_York:20210108T150000
DTSTAMP:20260428T002832
CREATED:20210107T213951Z
LAST-MODIFIED:20210107T213951Z
UID:4660-1610114400-1610118000@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Sungho Kang
DESCRIPTION:PhD Proposal Review: Metamaterial Absorbers for Infrared Sensing Microsystems \nSungho Kang \nLocation: Zoom \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 proposal\, novel development of zero-power long-wavelength IR (LWIR) detector and miniaturized IR spectroscopic sensor based on the two key technologies are presented: (1) plasmonically-enhanced LWIR micromechanical photoswitch and (2) multispectral resonant IR detector array.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-sungho-kang/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201217T140000
DTEND;TZID=America/New_York:20201217T150000
DTSTAMP:20260428T002832
CREATED:20201210T000522Z
LAST-MODIFIED:20201210T000522Z
UID:4619-1608213600-1608217200@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Kaidi Xu
DESCRIPTION:PhD Proposal Review: Towards Empirical Implementation and Theoretical Analysis in Adversarial Machine Learning \nKaidi Xu \nLocation: Zoom Link \nAbstract: Deep learning or deep neural networks (DNNs) have achieved extraordinary performance in many application domains such as image classification\, object detection and recognition\, natural language processing and medical image analysis. 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\nonto 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 dissertation\, we present the mechanism of adversarial machine learning in both empirical and theoretical ways. Specifically\, we first introduce a uniform adversarial attack generation framework\, structured attack (StrAttack)\, 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 powerful framework\, Expectation over Transformation (EoT). Utilize EoT with Thin Plate Spline (TPS) transformation\, we can generate Adversarial T-shirts\, a robust physical adversarial example for evading person detectors even if it could undergo non-rigid deformation due to a moving person’s pose changes.\nThird\, we stand on the defense side and propose the first adversarial training method based on Graph Neural Network.\nFinally\, 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.\nLiRPA studies the adversarial example in a theoretical way and can guarantee the test accuracy of a model by given perturbation constraints.\nIn the future\, we plan to study a novel patch transformer network to truthfully model real-world physical transformations empirically. In addition\, at the formal robustness direction\, we plan to explore the complete verification\, that given sufficient time\, the verifier should give a definite “yes/no” answer for a property under verification. Our LiRPA framework combining with GPUs may accelerate this procedure.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-kaidi-xu/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201216T140000
DTEND;TZID=America/New_York:20201216T150000
DTSTAMP:20260428T002832
CREATED:20201210T000756Z
LAST-MODIFIED:20201210T000756Z
UID:4621-1608127200-1608130800@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Amirreza Farnoosh
DESCRIPTION:PhD Proposal Review: 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 proposal\, we are learning the underlying representation of these data in an unsupervised manner\, tailored towards several emerging applications\, namely indoor navigation and mapping\, neuroscience hypothesis testing\, and time series segmentation and forecasting.\nAs such\, (1) we present 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 introduce 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 present 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.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-amirreza-farnoosh/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201216T140000
DTEND;TZID=America/New_York:20201216T150000
DTSTAMP:20260428T002832
CREATED:20201205T015159Z
LAST-MODIFIED:20201205T015159Z
UID:4614-1608127200-1608130800@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Pu Zhao
DESCRIPTION:PhD Proposal Review: 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.\nWith a deeper understanding of the DNN robustness\, we further explore the deployment problem of DNN models on edge devices with limited resources. To 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-proposal-review-pu-zhao/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201216T100000
DTEND;TZID=America/New_York:20201216T110000
DTSTAMP:20260428T002832
CREATED:20201214T194420Z
LAST-MODIFIED:20201214T194420Z
UID:4631-1608112800-1608116400@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Hongjia Li
DESCRIPTION:PhD Proposal Review: Automation Design and DNN Acceleration Algorithms: From Software Implementation to Hardware Physical Design \nHongjia Li \nLocation: Zoom Link \nAbstract: Deep learning has been growing at a fast pace in recent years and has been expanded into many application fields\, with a wide range from image recognition\, object detection to medical applications. Meanwhile\, edging devices such as 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 these devices.\nIn this proposal review\, I will dive into some aspects of DNN acceleration methods\, including model compression techniques and software implementation optimizations. The goal is to achieve an unprecedented\, real-time performance of large-scale neural network inference on edging devices. Additionally\, an efficient physical design automation design is introduced for Adiabatic Quantum-Flux-Parametron (AQFP) circuits\, meeting the unique features and constraints.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-hongjia-li/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201216T100000
DTEND;TZID=America/New_York:20201216T110000
DTSTAMP:20260428T002832
CREATED:20201210T000641Z
LAST-MODIFIED:20201210T000641Z
UID:4620-1608112800-1608116400@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Majid Sabbagh
DESCRIPTION:PhD Proposal Review: The perils of shared computing: A hardware security perspective \nMajid Sabbagh \nLocation: Teams Link \nAbstract: The enormous computation power of modern processors and accelerators has rendered them shared computing resources for multiple users and applications\, both in the cloud and on the edge. Despite software techniques for security such as virtualization and containers\, recently a new attack surface is emerging that pertains to the hardware vulnerabilities of shared computing resources\, posing serious threats to shared computing.\nFault attacks (FAs) and Side-Channel Attacks (SCAs) are two hardware-oriented attacks that target the system implementations. FAs aim to tamper the integrity of application execution through different fault injection methods\, to compromise the data or disrupt computation at run-time. SCAs exploit the information leakage of sensitive applications in physical parameters\, such as power consumption\, electromagnetic emanations\, and timing\, to breach the confidentiality of the application. \nIn this dissertation\, we introduce a new class of FAs against Graphics Processing Units (GPUs)\, called overdrive fault attacks. We discover the security vulnerability of GPU’s voltage-frequency scaling (VFS) mechanism\, a common feature to balance power consumption and performance. An out-of-specification configuration of GPU voltage and frequency can be set by an adversary on the host CPU\, through the software interfaces to GPU’s power management units. This setting will cause timing violations for the computation and result in silent data corruptions (SDCs). We apply the overdrive fault attacks on two common victim applications. One is cryptographic applications accelerated by GPU. We launch a differential fault analysis (DFA) attack on an AES kernel running on an AMD RX 580 GPU and successfully recover the secret key. The other victim is deep neural network (DNN) inference. In modern GPUs that support multiple kernels\, the adversary is able to track the execution of the victim DNN through shared resources and control the timing of fault injections precisely. We launch a successful attack on a convolutional neural network kernel running on an NVIDIA RTX 2080 SUPER GPU with misclassifications. We further study the characteristics of fault injections and the fault propagation through the network.\nWe evaluate a timing side-channel attack called Prime+Probe attack on Central Processing Units (CPUs) and propose a Side-Channel Attack DEtection Tool (SCADET). SCADET is a methodology and a tool that analyzes an x86 program’s memory accesses. It records and analyzes the memory accesses using dynamic binary instrumentation by running the program in a controlled environment to accurately identify the malicious access patterns corresponding to the Prime+Probe attack.\nFinally\, I propose an FPGA-based RISC-V processor prototype as an evaluation platform for various cache timing attacks and transient attacks\, and implement a taint tracking-based countermeasure against transient attacks. For the first phase\, we have ported spectre v1 and v2 and return-stack-buffer attack to the SonicBOOM RISC-V processor.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-majid-sabbagh/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201216T093000
DTEND;TZID=America/New_York:20201216T103000
DTSTAMP:20260428T002832
CREATED:20201214T194558Z
LAST-MODIFIED:20201214T194558Z
UID:4632-1608111000-1608114600@ece.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Jinghan Zhang
DESCRIPTION:MS Thesis Defense: Allocating One Common Accelerator-Rich Platform for Many Streaming Applications \nJinghan Zhang \nLocation: 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 (e.g. heterogeneous computing) to achieve the needed efficiency and/or performance. However\, current Design Space Exploration (DSE) focuses on an individual application in isolation (e.g. one particular vision flow)\, but not a set of similar applications. Hence\, optimizations that occur due to considering multiple applications simultaneously are missed. New DSE methodologies and tools are needed with a broader scope of application sets instead of individual applications.\nThis thesis introduces a novel Domain DSE approach focusing on streaming applications. Key contributions are: (1) a formalized method to extract the functional and structural similarities of domain applications\, (2) domain application generation to provide enough synthetic domains as study cases\, (3) a rapid platform performance estimation and comparison at two abstraction levels: Domain Score (DS) and Analytic Performance Estimation (APE) model\, (4) a methodology to evaluate a platform’s benefit for a set of applications\, and (5) two novel algorithms\, Dynamic Score Selection (DSS) and GenetIc Domain Exploration (GIDE)\, for hardware/software partitioning of a domain-specific platform to maximize the throughput across domain applications (under certain constraints).\nThis thesis demonstrates DSS’s and GIDE’s benefits using OpenVX applications and synthetic domains. The DSS and GIDE generated domain-specific platforms improve performance over application-specific platforms by 58%\, and 75% for OpenVX\, as well as by 23% and 48% for synthetic applications. GIDE’s platforms reach 99.8% (OpenVX) and 97.6% (synthetic) throughput of the domain optimal platform obtained through exhaustive search.
URL:https://ece.northeastern.edu/event/ece-ms-thesis-defense-jinghan-zhang/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201215T110000
DTEND;TZID=America/New_York:20201215T120000
DTSTAMP:20260428T002832
CREATED:20201214T194726Z
LAST-MODIFIED:20201214T194726Z
UID:4633-1608030000-1608033600@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Arjun Singh
DESCRIPTION:PhD Proposal Review: Design\, Modeling and Operation of Plasmonic Devices for Smart Communication Systems in the Terahertz Band \nArjun Singh \nLocation: Teams Link \nAbstract: The terahertz (THz) band is an attractive spectral resource for future communication systems\, for 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. To overcome these obstacles\, novel plasmonic devices that exploit the attractive properties of graphene have been proposed. However\, there are several challenges\, such as low output power and high reflection losses\, that are not yet addressed. The objective of the proposed research herein is to facilitate an end-to-end communication link with graphene plasmonics as the cornerstone of the fundamental device physics. The devices designed can be utilized at both the communication endpoints\, as well as across the channel\, to effectively overcome the limited communication distance – The grand challenge of the THz band.\nTo this end\, a graphene-based plasmonic array architecture is first 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. The array designed through an integration of these front-ends 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. The array is also demonstrated to provide a strong effective isotropic radiated power (EIRP)\, that increases exponentially with array size. To mitigate the negative effects of the channel environment\, such as unwanted blockages and high path losses for simpler devices\, 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 proposed reflectarray as compared to other proposed counterparts\, including independence from the incoming angle of the impinging wave\, dynamic phase control capability\, and a strong reflection efficiency. The unique design properties of the plasmonic array\, as well as the hybrid reflectarray\, open the option of incorporating techniques such as multi-beam beamforming design and interleaved\, independent arrays\, to boost the channel capacity.\nAs a part of the proposed work\, the impact of the design properties of these devices on the communications link will be investigated by developing the fundamental problem and considering all trade-offs. The undertaking will be significantly more robust and conclusive than those that have been performed previously\, both due to the consideration of a complete end to end link\, as well as the incorporation of the characteristics of the device design model. Finally\, preliminary fabrication results in the realization of these devices are presented\, and the roadmap ahead is outlined.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-arjun-singh/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201212T103000
DTEND;TZID=America/New_York:20201212T113000
DTSTAMP:20260428T002832
CREATED:20201207T214335Z
LAST-MODIFIED:20201207T214335Z
UID:4617-1607769000-1607772600@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Ning Liu
DESCRIPTION:PhD Dissertation Defense: Real-World Applicable Deep Learning Techniques: From Efficient Modeling to Automated Model Optimization \nNing Liu \nLocation: Zoom Link \nAbstract: Recently\, deep neural networks (DNNs) have been widely studied and achieved tremendous success in a variety of real-world applications\, such as computer vision\, medical diagnosis and machine translation. Deep reinforcement learning (DRL)\, as an emerging powerful deep learning technique\, combines DNNs with reinforcement learning into an interactive system. DRL opens up many new applications in domains such as healthcare\, robotics and smart grids. With the rapid evolution of IT infrastructures\, cloud computing has been witnessed as the prevailing computing paradigm. The underlying infrastructure of cloud computing relies on a large amount of data centers. The energy efficiency issue from “cloud” becomes more crucial and calls for more attentions.\nIn this dissertation\, to solve the real-world energy efficiency problems\, we take advantage of the deep learning and deep reinforcement learning techniques for efficient modeling of “cloud” applications. We present a DNN-based power management framework for regulation service and a novel DRL-based hierarchical framework for solving the overall resource allocation and power management problem. On the other hand\, the powerful DNNs themselves are massive\, consuming tremendous energy. Therefore\, we explore the efficiency on deep neural networks. We propose an automatic model pruning framework to reduce the storage and computation requirements and accelerate inference. Our framework outperforms the prior work on automatic model compression by up to 33× in pruning rate (120× reduction in the actual parameter count) under the same accuracy. Significant inference speedup has been observed from the proposed framework on actual measurements on smartphone.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-ning-liu/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201211T150000
DTEND;TZID=America/New_York:20201211T160000
DTSTAMP:20260428T002832
CREATED:20201207T214155Z
LAST-MODIFIED:20201207T214227Z
UID:4615-1607698800-1607702400@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Qifan Li
DESCRIPTION:PhD Dissertation Defense: Development of Magnetodielectric Materials with Low Loss and High Snoek’s Product for Microwave Applications \nQifan Li \nLocation: Teams Link \nAbstract: Exhibiting both relative magnetic permeability and electric permittivity greater than unity\, magnetodielectric materials have been attracting great attention in both academia and industry for next-generation communication\, sensing\, and radar applications. It is always of great interest for researchers to tailor the magnetic properties of magnetodielectric materials for high permeability\, low magnetic loss and large Snoek’s product towards higher-frequency applications.\nHexagonal ferrites form an important group of magnetodielectric materials. Besides the six best known hexagonal structures\, i.e.\, M-\, W-\, X-\, Y-\, Z- and U-type hexaferrites\, some unique hexagonal structures\, named 18H hexaferrites\, were discovered in 1970s. For the first time\, the dynamic magnetic properties and their temperature dependence of polycrystalline Mg-Zn 18H hexaferrites at microwave frequencies are investigated. Owing to a remarkably low damping coefficient\, the frequency dispersion of complex permeability reveals a narrow and strong resonance. The Mg-Zn 18H hexaferrites show excellent loss tangent of 0.07 at 3 and 4 GHz. Accordingly\, narrow FMR linewidths in the range of 486-660 Oe are measured. The temperature dependence of the damping coefficient is 0.0004 /°C\, indicating a small variation of the intrinsic loss with temperature. These results are the best performance among the polycrystalline microwave ferrites reported so far for the S- and C-band applications.\nMagnetodielectric composites\, prepared by dispersing magnetic particles homogenously in an electrically insulating matrix\, are another type of magnetodielectric materials. It is crucial to predict the effective magnetic properties of the multi-phase mixture. A modified effective medium theory is proposed by extending the traditional formulas with the effects of particle-size distribution and clustering of inclusions. Its accuracy is verified by two kinds of magnetodielectric composites over wide ranges of both particle concentration and frequency.\nThe magnetic properties of microwave ferrites are strongly affected by their polycrystalline microstructure\, which is mainly controlled by the sintering process. The two-step sintering technique is systematically studied for the preparation of hexaferrites. With optimal combinations of sintering temperatures in each step\, significant reduction in magnetic loss and enhancement in Snoek’s product are achieved with uniform and fine-grained structures.\nPrecise measurement of broadband permeability and permittivity is crucial to develop advanced magnetodielectric materials. A straightforward\, explicit and noniterative method is proposed by eliminating the error from the direct measurement of sample position in the standard Nicolson-Ross-Weir method. Based on the results from two kinds of magneto-dielectric materials measured in two sets of test fixtures of different geometries\, this method is theoretically and experimentally proven to have high and position-independent accuracy over a wide frequency range.\nFinally\, a patch antenna on Mg-18H magnetodielectric substrate is designed to operate at 3.6 GHz for 5G wireless communication. Benefiting from the large refractive index of the magnetodielectric material\, the size of the patch antenna is significantly reduced. Moreover\, compared to the dielectric substrate providing the same miniaturization factor\, magnetodielectric antennas exhibit significant advantages for larger bandwidth and gain.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-qifan-li/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201211T140000
DTEND;TZID=America/New_York:20201211T150000
DTSTAMP:20260428T002832
CREATED:20201210T000912Z
LAST-MODIFIED:20201210T000912Z
UID:4622-1607695200-1607698800@ece.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Xinan Huang
DESCRIPTION:MS Thesis Defense: Exploring Effectiveness of Naive Spatio-Temporal Exploits for Depth Completion \nXinan Huang \nLocation: Zoom Link \nAbstract: With an increasing need for usable depth for autonomous navigation systems such as self-driving cars\, depth completion is becoming an increasingly studied subject. RGB data provide much-needed aid in providing good recreation of dense depth maps from sparse LiDAR output. Yet\, these data are also provided in sequential form. And thus for this thesis\, we aim to explore how effective using network layers that exploit Spatio-temporal features would be in achieving higher depth completion accuracy. We propose adding 3D convolutional layers and ConvGRU layers to a preexisting depth completion network and perform ablation studies on the effectiveness of these methods. We were able to verify that naive approaches are able to garner improvements quantitatively and qualitatively\, but training results show that additional geometric constraints would perhaps boost such exploits even further for better depth completion results.
URL:https://ece.northeastern.edu/event/ece-ms-thesis-defense-xinan-huang/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201210T090000
DTEND;TZID=America/New_York:20201210T100000
DTSTAMP:20260428T002832
CREATED:20201207T234717Z
LAST-MODIFIED:20201207T234717Z
UID:4618-1607590800-1607594400@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Chenyang Zhu
DESCRIPTION:PhD Dissertation Defense: Remote Monitoring of Multiple Ships over Instantaneous Continental-shelf Scale Region with a Large-aperture Coherent Hydrophone Array \nChenyang Zhu \nLocation: Zoom Link \nAbstract: Multiple mechanized ocean vessels\, including both surface ships and submerged vehicles\, can be simultaneously monitored over instantaneous continental-shelf scale regions >10\,000 km 2 via passive ocean acoustic waveguide remote sensing. A large-aperture densely-sampled coherent hydrophone array system is employed in the Norwegian Sea in Spring 2014 to provide directional sensing in 360 degree horizontal azimuth and to significantly enhance the signal-to-noise ratio (SNR) of ship-radiated underwater sound\, which improves ship detection ranges by roughly two orders of magnitude over that of a single hydrophone. Here\, 30 mechanized ocean vessels spanning ranges from nearby to over 150 km from the coherent hydrophone array\, are detected\, localized and classified. The vessels are comprised of 20 identified commercial ships and 10 unidentified vehicles present in 8 h/day of POAWRS observation for two days. The underwater sounds from each of these ocean vessels received by the coherent hydrophone array are dominated by narrowband signals that are either constant frequency tonals or have frequencies that waver or oscillate slightly in time. The estimated bearing-time trajectory of a sequence of detections obtained from coherent beamforming are employed to determine the horizontal location of each vessel using the Moving Array Triangulation (MAT) technique. For commercial ships present in the region\, the estimated horizontal positions obtained from passive acoustic sensing are verified by Global Positioning System (GPS) measurements of the ship locations found in historical AIS database. We provide time-frequency characterizations of the underwater sounds radiated from the commercial ships and the unidentified vessels. The time-frequency features along with the bearing-time trajectory of the detected signals are applied to simultaneously track and distinguish these vessels.\nNext\, three approaches for simultaneous ship long-range automatic detection\, acoustic signature characterization\, and bearing-time trajectory estimation have been developed and applied\, each focusing on a different aspect of a ship’s radiated underwater sound received on a large-aperture densely-sampled coherent hydrophone array. (i) Ships narrowband machinery tonal sound is analyzed via temporal coherence using Mean Magnitude-Squared Coherence (MMSC) calculations. (ii) Ships broadband cavitation noise amplitude modulated by propeller rotation is examined using Cyclic Spectral Coherence (CSC) analysis that provides estimates for propeller blade pass rotation frequency\, shaft rotation frequency\, and hence the number of propeller blades. (iii) Mean power spectral densities averaged across specific broad bandwidths are calculated to detect and compare output sound pressure levels from acoustically energetic ships. Each of these techniques are applied after coherent beamforming of the received acoustic signals on a coherent hydrophone array\, leading to significantly enhanced signal-to-noise ratios for simultaneous detection and characterization of multiple ships over continental-shelf scale regions. The approaches are illustrated by application to\nroughly two hours of acoustic recordings of a 160-element coherent hydrophone array deployed in the Norwegian Sea during an experiment in February 2014. Six ocean vessels are simultaneously detected and their acoustic signatures characterized\, located at a variety of bearings and ranges out to 200 km from the coherent hydrophone array\, with speeds ranging from 0.5 knots to 13 knots\, verified by Global Positioning System (GPS) information from Automatic Identification System (AIS) database. Hybrid usage of the three methods provide a robust approach for ship characterization in terms of machinery tonal sound signature\, propeller rotation signature\, and ship broadband energetics that can be employed for efficient ship classification. The CSC approach is demonstrated to be also useful for automatic detection and bearing-time estimation of repetitive marine mammal vocalizations present in coherent hydrophone array recordings\, providing estimates of inter-pulse-train and inter-pulse intervals from CSC spectra cyclic fundamental and first recurring peak frequencies respectively.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-chenyang-zhu/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201208T150000
DTEND;TZID=America/New_York:20201208T160000
DTSTAMP:20260428T002832
CREATED:20201203T223238Z
LAST-MODIFIED:20201203T223238Z
UID:4606-1607439600-1607443200@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Sheng Lin
DESCRIPTION:PhD Dissertation Defense: Platform-specific Model Compression for Deep Neural Networks with Joint Methods \nSheng Lin \nLocation: Zoom Link \nAbstract: Deep learning has delivered its powerfulness in many application domains\, especially in computer vision\, natural language processing and speech recognition. As the backbone of deep learning\, deep neural networks (DNNs) consist of multiple layers of various types with hundreds to thousands of neurons. Embedded platforms are now becoming essential for deep learning deployment due to their portability\, versatility\, and energy efficiency. The large model size of DNNs\, while providing excellent accuracy\, also burdens the hardware platforms with intensive computation and storage. To consider the requirements of specific tasks\, many researchers have investigated reducing DNN model size for efficient implementation in hardware devices with reasonable accuracy prediction. However\, it lacks a systemic investigation on platform-specific DNN acceleration frameworks. \nIn this dissertation\, we present several software-hardware co-design techniques to speed up the DNN algorithm on specific platforms. At the software level\, we present joint model compression techniques for DNN model training and inference with reasonable accuracy performance. At the hardware level\, these algorithms and methods are targeting storage reduction\, low power consumption\, efficient inference\, and data security. By using joint methods to optimize different types of networks\, the targeted hardware platforms can reduce asymptotic complexity of both computation and storage\, making our approach distinguished from existing approaches. First\, we present a Fast Fourier Transform-based DNN model for inference phase on embedded platforms. Second\, we build a framework for two most commonly used model compression techniques\, low-bit linear weight quantization and its combination with different weight pruning methods. Third\, we apply quantization techniques for the always-on keyword spotting system and eliminate the energy-consuming ADC with an energy-efficient analog processing circuit. Finally\, we propose a federated learning framework to protect user’s data privacy while reducing overall communication cost during the training process.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-sheng-lin/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201208T140000
DTEND;TZID=America/New_York:20201208T150000
DTSTAMP:20260428T002832
CREATED:20201202T012637Z
LAST-MODIFIED:20201202T012637Z
UID:4605-1607436000-1607439600@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Raffaele Guida
DESCRIPTION:PhD Dissertation Defense: Remotely Rechargeable Embedded Platforms for Next Generation IoT Systems in Critical Environments \nRaffaele Guida \nLocation: Teams Meeting \nAbstract: In the near future\, a new generation of miniaturized\, multi-function and smart wireless devices for Internet of Things (IoT) systems\, designed for real-time monitoring and with real-time reconfiguration will be deployed in critical and challenging environments\, e.g.\, underwater and inside the human body. These futuristic IoT platforms can now be realized thanks to advances in low-power electronics and wireless communications. However\, the need for long-term and reliable power supply\, together with the need to support innovative functions\, impose new powering requirements that cannot be satisfied by traditional batteries. Batteries have in fact a major impact on the size and lifetime of the device\, and often need to be replaced through complex\, expensive and non-scalable procedures. For example\, powering of Internet of Underwater Things (IoUT) devices in deep water remains one of the main challenges\, since these systems are typically powered by batteries that need to be recharged through difficult and expensive operations.\nFurthermore\, existing medical implants do not provide at once the miniaturized end-to-end sensing-computation-communication-recharging capabilities to implement Implantable Internet of Medical Things (IIoMT) applications.\nThis dissertation fills the existing research gaps by presenting innovative designs of battery-less devices remotely rechargeable through ultrasonic wireless power transfer. Specifically\, two major systems are presented\, U-Verse – the first FDA-compliant IIoMT platform packing sensing\, computation\, communication\, and recharging circuits into a penny-scale platform – and the first IoUT battery-less sensor node that can be wirelessly recharged through ultrasonic waves.\nU-Verse uses a single miniaturized transducer for data exchange and for wireless charging. To predict U-Verse’s performance\, a mathematical model of its charging efficiency is derived and experimentally validated. A matching circuit to maximize the amount of power transferred from the outside is proposed\, and the design of a full-fledged cm-scale printed circuit board (PCB) is presented. Extensive experimental evaluation indicates that U-Verse (i) is able to recharge a 330mF and 15F energy storage unit – several orders of magnitude higher than existing work – respectively under 20 and 60 minutes at a depth of 5cm; (ii) achieves stored charge duration of up to 610 and 40 hours in case of battery and supercapacitor energy storage\, respectively. Finally\, U-Verse is demonstrated through (i) a closed-loop application where a periodic sensing/actuation task sends data via ultrasounds through real porcine meat; and (ii) a real-time reconfigurable pacemaker. As for the underwater sensor node\, the architecture of an underwater platform capable of extracting electrical energy from ultrasonic waves is first introduced. Then\, the interfacing of the system with an underwater communication unit is illustrated. The design of a prototype where the storage unit is realized with a batch of supercapacitors is also discussed. Experimental results show that the harvested energy is sufficient to provide the sensor node with the power necessary to perform a sensing operation and power a modem for ultrasonic communications. Given the reduced attenuation of ultrasonic waves in water\, the proposed approach proves to cover longer distances with less transmission power than alternative solutions. Last\, the overall operating efficiency of the system is evaluated.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-raffaele-guida/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201207T160000
DTEND;TZID=America/New_York:20201207T170000
DTSTAMP:20260428T002832
CREATED:20201130T195726Z
LAST-MODIFIED:20201130T195726Z
UID:4600-1607356800-1607360400@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Trinayan Baruah
DESCRIPTION:PhD Proposal Review: Improving the Virtual Memory Efficiency of GPUs \nTrinayan Baruah \nLocation: Zoom Link \nAbstract: GPUs have been adopted widely based their ability to exploit data-level parallelism found in modern-day applications\, ranging from high performance computing to machine learning. This widespread adoption has\, in part\, been accelerated by the development of more intuitive high-level programming languages\, efficient runtimes and drivers\, and easier mechanisms to manage data movement. Modern day GPUs and multi-GPU systems utilize virtual memory systems\, enabling programmers to access large address spaces that are beyond the physical memory limits a GPU. There mechanisms have built in mechanisms for memory translation\, sparing the programmer from having to reason about complex data-movement operations. Virtual memory support on a GPU includes both hardware and software support. At the hardware level\, Translation Lookaside Buffers (TLBs) are used to cache translations close to the compute units. At the software level\, the programming model supports a unified memory model which automates the movement of pages across multiple devices in a a system. Despite the improvements in programmability\, due to the inefficiency in existing TLB mechanisms for TLB management and page migration\, the performance of current virtual memory support on GPUs is sub-optimal.\nIn this dissertation\, we first identify the key challenges in virtual memory support for GPUs today. We then propose mechanisms to reduce the bottlenecks arising from virtual memory management at both a hardware level and at the runtime level. This allows GPUs to fully enjoy the benefits of virtual memory\, while ensuring high performance. We also develop simulation tools that enable researchers to explore new and novel virtual memory features in future single GPU and multi-GPU systems.\nTo enhance hardware support for virtual memory on a GPU\, we explore a mechanism that enables prefetching of page-table entries into the GPUs TLBs\, thereby reducing the number of TLB misses and improving performance. We also leverage the fact that many page-table entries can be shared across different GPU cores. We design a low-cost interconnect that enables sharing of page-table entries across the GPU cores. To improve the performance of unified memory on multi-GPU systems\, we propose a hardware/software mechanism that monitors accesses to each page\, and uses this information when making page-migration decisions. We also propose mechanisms to reduce the cost of TLB shootdowns on the GPU during page-migration in NUMA multi-GPU systems. \n 
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-trinayan-baruah/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201204T150000
DTEND;TZID=America/New_York:20201204T160000
DTSTAMP:20260428T002832
CREATED:20201123T205204Z
LAST-MODIFIED:20201123T205204Z
UID:4593-1607094000-1607097600@ece.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Yuxuan Cai
DESCRIPTION:MS Thesis Defense: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design \nYuxuan Cai \nLocation: Zoom Link \nAbstract: The rapid development and wide utilization of object detection techniques have aroused attention on both accuracy and speed of object detectors. However\, the current state-of-the- art object detection works are either accuracy-oriented using a large model but leading to high latency or speed-oriented using a lightweight model but sacrificing accuracy. In this work\, we propose YOLObile framework\, a real-time object detection on mobile devices via compression compilation co-design. A novel block-punched pruning scheme is proposed for any kernel size. To improve computational efficiency on mobile devices\, a GPU-CPU collaborative scheme is adopted along with advanced compiler-assisted optimizations. Experimental results indicate that our pruning scheme achieves 14× compression rate of YOLOv4 with 49.0 mAP. Under our YOLObile framework\, we achieve 17 FPS inference speed using GPU on Samsung Galaxy S20. By incorporating our proposed GPU-CPU collaborative scheme\, the inference speed is increased to 19.1 FPS\, and outperforms the original YOLOv4 by 5× speedup.
URL:https://ece.northeastern.edu/event/ece-ms-thesis-defense-yuxuan-cai/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201202T140000
DTEND;TZID=America/New_York:20201202T150000
DTSTAMP:20260428T002832
CREATED:20201130T201126Z
LAST-MODIFIED:20201130T201212Z
UID:4601-1606917600-1606921200@ece.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Kathan Vyas
DESCRIPTION:MS Thesis Defense: Data-Efficient analysis of Human Behavior by Spatio-Temporal Pose Generation and Inference \nKathan Vyas \nLocation: Zoom Link  \nPasscode: 474462 \nAbstract: Identifying human pose over time provides critical information towards understanding human behavior and their physical interaction with the environment surrounding them. In the past few decades\, the human pose estimation topic has witnessed groundbreaking research in the computer vision field thanks to the powerful deep learning models. These models are trained using several thousands of labeled sample images if not more. Such extensive data requirement posed a fundamental problem for domains (i.e. Small Data domains)\, in which data collection or labeling is expensive or limited due to privacy or security concerns such as healthcare. In this thesis\, we present a data-efficient learning pipeline to address small data problem in a healthcare-related human pose estimation application. In particular\, we infer spatio-temporal human poses to analyze typical vs. atypical behaviors in children with Autism spectrum disorder (ASD). To mitigate data limitation\, we propose two thrusts in our learning pipeline. The first thrust is a data-efficient machine learning approach\, in which a pre-trained (on adult pose images) pose estimation model with deep structure is fine-tuned on a small set of children pose videos\, provided to us by our collaborators. We implement a non-linear particle filter interpolation to deal with any missing body keypoints in the estimated poses and employ a novel PoTion (pose motion) based temporal aggregation technique to evaluate poses over time. The second thrust is a synthetic data augmentation approach\, in which we build a framework to create synthetic 3D humans with articulated bodies in order to render more pose images/videos in our application contexts. We use a novel 3D registration approach based on RANSAC and implement iterative closest point (ICP) to obtain 3D meshes from the scanned point clouds from both adult and kid mannequins\, which is then rigged and articulated in the Blender to generate our human avatars. We then infuse these avatars in various synthetic environments to create contexts similar to the target application\, which is a kid with both typical and atypical behaviors in a home-like environment.
URL:https://ece.northeastern.edu/event/ece-ms-thesis-defense-kathan-vyas/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201202T100000
DTEND;TZID=America/New_York:20201202T110000
DTSTAMP:20260428T002832
CREATED:20201119T022728Z
LAST-MODIFIED:20201119T022728Z
UID:4580-1606903200-1606906800@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Bilgehan Donmez
DESCRIPTION:PhD Dissertation Defense: Topology Error Detection in Power System State Estimation \nBilgehan Donmez \nLocation: Teams Link \nAbstract: Growth of renewable energy\, changes in weather patterns\, and increases in cyber- and physical-attacks are examples of recent challenges in power system operation. To keep up with these rapid transformations\, it is imperative to improve the tools used in modern-day control centers.\nAs the centerpiece of system operations\, improvements in state estimation (SE) accuracy would result in better situational awareness for system operators. The state estimate can often be compromised when there are errors in the assumed topology of the network. Therefore\, topology error detection plays a key role in SE. In the first part of this dissertation\, topology errors in the external systems\, which are the neighboring control areas\, are investigated. When a subset of measurements coming from an external area is lost\, some parts of the system can become unobservable. Since SE cannot be carried out for the unobservable portion of the system\, the topology of the external system cannot be tracked in its usual way. This dissertation offers a computationally efficient external line outage detection algorithm that uses only the internal bus phase angles\, any available phasor measurement units (PMUs)\, and the pre-contingency system topology of the system. Coupled with a post-verification step\, this method is shown to be effective in detecting external line outages.\nThe second part of the dissertation focuses on topology errors in the internal system. The conventional SE implementations use the simplified bus-branch (BB) electrical network provided by the topology processor (TP). When the status of circuit breakers are not reported correctly to the TP\, the electrical equivalent it creates will be inaccurate. Therefore\, topology errors usually result in SE convergence problems or yield significantly biased estimates. To properly detect these types of errors\, rather than using the typical BB representation\, the network model is expanded to include circuit breakers and other switching devices in substations. SE is then reformulated to work with this detailed node-breaker (NB) model.\nAlthough the expansion of the model introduces operational and computational challenges\, several strategies are employed to counter these issues. The proposed innovations include the formulations of two separate equality-constrained SE algorithms\, the development of optimal meter placement algorithms\, and utilization of parallel processing. As demonstrated through the simulations conducted\, the methods developed in this dissertation are practical enough for adaptation to real-world systems.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-bilgehan-donmez/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201202T100000
DTEND;TZID=America/New_York:20201202T110000
DTSTAMP:20260428T002832
CREATED:20201117T013509Z
LAST-MODIFIED:20201117T013509Z
UID:4575-1606903200-1606906800@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Leili Hayati
DESCRIPTION:PhD Dissertation Defense: Ceramic Magnetic Wires at Wireless Communication Frequencies \nLeili Hayati \nLocation: Online \nAbstract: Ferrite magnetic devices play an important role in modern wireless telecommunication systems. They generally require permanent magnets in order to magnetically polarize the ferrite material component used in these devices. The permanent magnets are bulky and take up most of the size and weight of a magnetic circuit. The aim of this research is to do away with permanent magnet bias circuits as utilized in circulators and ferrite planar devices\, especially in wireless communication systems operating below 2 GHz. Recently\, ferromagnetic nanowires (NWs) have been embedded into porous templates\, are used to design various microwave magnetic and electronics devices. The main advantage of magnetic NWs is that in zero magnetic field\, the microwave absorption frequency can be easily tuned over a large range of frequencies. Clearly\, the metallic nature of the magnetic NWs contributed to the high loss. It is expected that insulating magnetic NWs will improve the insertion loss sufficiently to produce viable ferrite devices at wireless communication frequencies below 2GHz and at higher frequencies. There are no pure insulating magnetic materials. However\, there are ferrites that are nearly insulating and are ferrimagnetic. Their saturation magnetization is much lower than the metallic ferromagnetic counterpart. This is a desirable property for magnetic device operating below 2 GHz. Of all the ferrite materials yttrium iron garnet (YIG) exhibits the lowest FMR linewidth ever measured and low saturation magnetization. In this work\, an array of high-purity YIG NWs embedded in a porous silicon membrane\, were synthesized using sol-gel method and the magnetic properties of the pure YIG Nanoparticles and the composite substrate were characterized by utilizing vibrating sample magnetometer (VSM) technique. From the ferromagnetic resonance (FMR) spectra\, it has been found that the measurements are characterized by a uniaxial magnetic anisotropy energy due to the high aspect ratio of the NWs. Based on the magnetic parameters of the composite substrate and characterizing YIG NWs\, a coplanar waveguide was designed by HFSS software. By applying a small external magnetic field and changing the internal magnetic H field by ±8%\, the phase of S21 parameter shifts up to 30̊ degrees near 1.7GHz.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-leili-hayati/
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