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DTSTART:20210314T070000
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DTSTART:20211107T060000
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221128T120000
DTEND;TZID=America/New_York:20221128T140000
DTSTAMP:20260422T234137
CREATED:20221121T212045Z
LAST-MODIFIED:20221121T212045Z
UID:5973-1669636800-1669644000@ece.northeastern.edu
SUMMARY:Xuanyi Zhao's PhD Proposal Review
DESCRIPTION:“AlN/AlScN based Micro Acoustic Metamaterials for Radio Frequency Applications of the Next Generations” \nAbstract: \nIn the last two decades‚ micro-acoustic resonators (μARs) have played a key role in integrated 1G-to-4G radios‚ providing the technological means to achieve compact radio frequency (RF) filters with low loss and moderate fractional bandwidths (BW<4%). More specifically‚ Aluminum Nitride (AlN) based filters have populated the front-end of most commercial mobile transceivers due to the good dielectric‚ piezoelectric and thermal properties exhibited by AlN thin-films and because their fabrication process is compatible with the one used for any Complementary Metal Oxide Semiconductor (CMOS) integrated circuits (ICs). Nevertheless‚ the rapid growth of 5G and the abrupt technological leap expected with the development of sixth-generation (6G) communication systems are expected to severely complicate the design of future radio front-ends by demanding Super-High-Frequency (SHF) filtering components with much larger fractional bandwidths than achievable today. In the meantime\, as more acoustic filters replying on bulk waves which requests the devices to be physically-suspended to operate\, thermal related nonlinearity has been a challenge which requests new designs to enhance the thermal linearity thus power handling for these acoustic components. Even more‚ the recent invention of on-chip nonreciprocal components‚ like the circulators and isolators recently built in slightly different CMOS technologies‚ has provided concrete means to double the spectral efficiency of current radios by enabling the adoption of full-duplex communication schemes. Nevertheless‚ for such schemes to be really usable in wireless systems‚ self-interference cancellation networks including wideband‚ low-loss and large group delay lines are needed. Yet‚ the current on-chip delay lines that are also manufacturable through CMOS processes‚ which rely on the piezoelectric excitation of Surface Acoustic Waves (SAWs) or Lamb Waves in piezoelectric thin films‚ have their bandwidth and insertion-loss severely limited by the relatively low electromechanical coupling coefficient exhibited by their input and output transducers. As a results‚ these components are hardly usable to form any desired self-interference cancelation networks. In order to overcome these challenges‚ only recently‚ new classes of microacoustic resonators and delay lines exploiting the high piezoelectric coefficient of Aluminum Scandium Nitride (AlScN) thin films and the exotic dispersive features of acoustic metamaterials (AMs) have been emerging. These devices rely on forests of locally resonant piezoelectric rods to generate unique modal distributions‚ as well as unconventional wave propagation features that cannot be found in conventional SAW and Lamb wave counterparts. In this presentation‚ the design‚ fabrication and performance of the first microacoustic metamaterials (μAMs) based resonators and delay lines will be showcased. Moreover\, AMs based reflectors are invented and demonstrated providing new improving the linearity and power handling of the AlScN μARs. In addition to reviewing the current status of our work\, we will propose several further explorations of using our AlN/AlScN based AMs in RF applications of the next generations. \nCommittee: \nProf. Cristian Cassella (advisor) \nProf. Matteo Rinaldi \nDr. Jeronimo Segovia-Fernandez
URL:https://ece.northeastern.edu/event/xuanyi-zhaos-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221122T110000
DTEND;TZID=America/New_York:20221122T120000
DTSTAMP:20260422T234137
CREATED:20221103T213322Z
LAST-MODIFIED:20221103T213322Z
UID:5942-1669114800-1669118400@ece.northeastern.edu
SUMMARY:Mahshid Asri's Proposal Review
DESCRIPTION:“Development of Anomaly Detection and Characterization Algorithms Using Wideband Radar Image Processing for Security Applications” \nAbstract:\nDetection and characterization of suspicious body-worn objects is necessary for safe and effective personnel screening. In airports\, developing a precise system that can distinguish threats and explosives from objects like money belt can reduce the pat-down significantly while maintaining effective security.\nThis work proposes two main algorithms which are developed for different millimeter-wave radar systems. The first project is a material characterization algorithm designed for a 30 GHz wideband multi bi-static radar system used for passenger screening in airports. The proposed algorithm can automatically distinguish lossless materials from lossy ones and calculate their thickness and permittivities. Starting from the radar reconstructed image showing a cross-section of the body\, we extract the nominal body contour using Fourier series\, separate body and object responses\, categorize the object as lossy or lossless based on the depression and protrusion of the body contour\, and finally predict possible values for the object’s permittivity and thickness. Our resulting classification is good\, implying fewer nuisance alarms at check points. The second project is a metal detection algorithm designed to monitor pedestrians walking along a sidewalk for large\, concealed metallic objects. Finite Difference Frequency Domain and SAR algorithms are used to simulate the images produced by this 6 GHz wideband radar system. \nCommittee: \nProf. Carey Rappaport (Advisor) \nProf. Charles DiMarzio \nProf. Edwin Marengo
URL:https://ece.northeastern.edu/event/mahshid-asris-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221118T110000
DTEND;TZID=America/New_York:20221118T120000
DTSTAMP:20260422T234137
CREATED:20221115T232757Z
LAST-MODIFIED:20221115T232757Z
UID:5962-1668769200-1668772800@ece.northeastern.edu
SUMMARY:PhD Dissertation Defense Shivang Aggarwal
DESCRIPTION:Location: ISEC 332 \n“Towards Reliable\, High Capacity mmWave Wireless LANs for Mobile Devices” \nAbstract: \nThe IEEE 802.11ad standard\, with its 14 GHz of unlicensed spectrum around 60 GHz\, is touted as one of the key technologies for building the next generation of WLANs that will enable high throughput demanding mobile applications. However\, there have been serious concerns regarding the susceptibility of mmWave links to mobility and blockage as well as smartphone energy consumption at Gigabit scale data rates. \nIn this dissertation\, first\, through extensive measurement campaigns with commercial off-the-shelf (COTS) devices as well as a highly configurable software-defined radio (SDR) based testbed\, we characterize the performance and energy efficiency of mobile devices operating in 60 GHz WLANs and identify problems that prevent wide adoption of the mmWave technology in such devices. Then\, using the insights from these measurement campaigns\, we design solutions to tackle these problems and prototype them for real-world evaluation.\nThis dissertation makes the following contributions:\n(i) We extensively study the performance and power consumption of 802.11ad on commercial smartphones. We focus on the specific aspects affected by unique smartphone features\, e.g.\, antenna placement or user mobility patterns\, and compare the performance against that achieved by 802.11ad laptops in previous studies. We also compare 802.11ad against its main competitors 802.11ac and 802.11ax. Overall\, our results show that 802.11ad is better able to address the needs of emerging bandwidth-intensive applications in smartphones than its 5 GHz counterparts. At the same time\, we identify several key research directions towards realizing its full potential.\n(ii) We extensively study the two main link adaptation mechanisms in 802.11ad\, rate adaptation (RA) and beamforming. We undertake a large measurement campaign using an SDR-based testbed giving us complete access to the PHY and MAC layers. We look at the two link adaptation mechanisms separately and study the effectiveness of a few RA and beamforming heuristics. Further\, look at the interaction between the two link adaptation mechanisms\, specifically\, which mechanism should be triggered when and in what order. We design a practical\, standard-compliant link adaptation framework that leverages ML and PHY layer information to determine when to trigger link adaptation and which adaptation mechanism to use.\n(iii) To address the issues with mmWave link reliability\, we explore the use of multiple frequency bands to couple the performance of 802.11ad with the reliability of legacy WiFi. To accomplish this\, we develop a Multipath TCP (MPTCP) scheduler to efficiently use both interfaces simultaneously in order to achieve a higher overall throughput as well as seamlessly switch to a single interface when the other one fails. Further\, we port MPTCP to a dual-band (5 GHz/60 GHz) smartphone\, evaluate its power consumption\, and provide recommendations towards the design of an energy-aware MPTCP scheduler.\n(iv) To enable high user QoE\, and maintain that in the face of ever-changing network conditions\, applications such as virtual reality (VR) and video streaming perform quality adaptation. A key component of quality adaptation is throughput prediction. Thus\, we extensively study the predictability of the network throughput of an 802.11ad WLAN in downloading data to an 802.11ad- enabled mobile device under varying mobility patterns and orientations of the mobile device.\n(v) With a dramatic increase in throughput requirements of applications and AP-user density in the near future\, multi-user multi-stream communication in the 60 GHz band is required. To this end\, the IEEE 802.11ay standard\, successor to the current 802.11ad standard\, includes support for simultaneous transmission over multiple data streams. Using an SDR-based testbed\, we extensively study the performance of SU- and MU-MIMO in 60 GHz WLANs in multiple environments\, analyze the performance in each environment\, identify the factors that affect it\, and compare it against the performance of SISO. Finally\, we propose two heuristics that perform both beam and user selection with low overhead while outperforming previously proposed approaches \nCommittee:\nProf. Dimitrios Koutsonikolas (Advisor)\nProf. Kaushik Chowdhury\nProf. Tommaso Melodia
URL:https://ece.northeastern.edu/event/phd-dissertation-defense-shivang-aggarwal/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221115T140000
DTEND;TZID=America/New_York:20221115T150000
DTSTAMP:20260422T234137
CREATED:20221103T184043Z
LAST-MODIFIED:20221115T204305Z
UID:5920-1668520800-1668524400@ece.northeastern.edu
SUMMARY:Raana Sabri Khiavi's PhD Proposal Review
DESCRIPTION:“Theory and design of spatiotemporally-modulated metasurfaces for comprehensive control of light” \nAbstract: \nPhotonic metasurfaces are key platforms for manipulating almost all properties of light such as amplitude\, phase\, polarization\, wave vector\, pulse shape\, and orbital angular momentum in a sub-wavelength dimension. They are capable of providing unprecedented modulation of wavefront through imparting spatial or temporal variation on the incoming wave. Recently\, considerable efforts have been devoted to design active metasurfaces that enable real-time tuning and post-fabrication control of the optical response. Toward achieving this goal\, electro-optically tunable materials such as doped semiconductors\, multiple-quantum-wells (MQWs)\, and atomically thin sheets are incorporated into the building blocks of the geometrically-fixed metasurfaces. Despite the significant progress in this field\, there has been several limitations imparted to the optical response of such so-called quasi-static metasurfaces. Remarkably\, the strong resonant dispersion in such metasurfaces leads to narrow spectral and angular bandwidths. In addition\, the co-varying amplitude and phase response as well as the limited phase modulation give rise to undesired artefacts manifested on their output profiles. The slow response time to the external stimuli is another drawback that restricts the performance of the metasurfaces. Introducing time into the external stimulus of the metasurfaces\, as an additional degree of freedom\, offers a way out to surmount the obstacles facing the quasi-static metasurfaces. Modulation in time enables myriad of exotic space-time scattering phenomena\, where possibility to break the reciprocity and generation/manipulation of the sideband scattered signals offer the most appealing functionalities. The objective of this work is to investigate the less explored mechanisms for yielding reconfigurable plasmonic metasurfaces in both space and time. Several realizations of quasi-static and time-modulated devices integrated with the electro-optical materials such as indium-tin-oxide (ITO) with the potential wide phase modulation is presented. It has been shown that time-modulated metasurfaces are superior to their quasi-static counterparts in terms of providing access to the dispersionless modulation-induced phase shift spanning over 2π as well as the constant amplitude at the sidebands. Novel and unique applications of space-time photonic metasurfaces by spatiotemporal manipulation of light for all-angle\, broadband beam steering\, suppressing the undesired sidelobes\, high speed continuous beam scanning\, and dispersionless dynamic wavefront engineering are studied. \nCommittee: \nProf. Hossein Mosallaei (Advisor) \nProf. Charles DiMarzio \nProf. Siddhartha Ghosh
URL:https://ece.northeastern.edu/event/raana-sabri-khiavis-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221108T153000
DTEND;TZID=America/New_York:20221108T163000
DTSTAMP:20260422T234137
CREATED:20221103T213536Z
LAST-MODIFIED:20221103T213536Z
UID:5948-1667921400-1667925000@ece.northeastern.edu
SUMMARY:Giuseppe Michetti's PhD Dissertation Defense
DESCRIPTION:“RF Front-End Components based on Linear-Time-Variant Modulation of Piezoelectric MEMS Resonators” \nAbstract: \nThroughout the last decade\, radio frequency (RF) components for over-the-air communication and sensing have been subject to sustained market pressure to adapt to the novel trends such as spectrum sharing\, programmability\, and low-power operation. When these features are required in chip-scale RF hardware\, innovative solutions are necessary as conventional materials and techniques become bottlenecks for next-generation radios. In this work\, we explore advanced wave manipulation circuital techniques such as Linear-Time-Variant (LTV) networks in conjunction with high-performance RF passives based on Micro-Electro-Mechanical Systems (MEMS) to address some of these challenges. Leveraging the unique spectral characteristic of RF MEMS resonators\, we show some components based on LTV concepts\, for novel RF systems with advanced spectral efficiency and real-time reconfigurability. \nUsing AlN and ScAlN thin film MEMS resonators as building blocks\, we propose a design technique for MEMS-based LTV Circulators and Self Interference Cancelers\, enabling chip-scaled RF full-duplex systems to enable efficient use of the RF spectrum with up to 47.5 dB cancellation in an 8 % bandwidth (BW) at 450 MHz. We introduce and validate experimentally MEMS-based LTV BW-tunable filters with high linearity (>30 dBm)\, and 5:1 BW tunability\, designed for several bands from 100 MHz to 2.7 GHz for emerging paradigms such as software-defined-radios and cooperative networks. We also introduce MEMS-based near-zero energy RF front-end for the Internet-of-Things (IoT)\, implementing RF energy harvesting to power up a resonant Wake-Up Receiver circuit\, with an experimental demonstration at (800 MHz) for deployment in remote sensor networks and emerging IoT wearable applications. \nAlong with the experimental validation of the proposed components\, analytical and numerical tools are also discussed for future development and research. \nCommittee: \nProf. Matteo Rinaldi (Advisor) \nProf. Cristian Cassella \nProf. Andrea Alù
URL:https://ece.northeastern.edu/event/giuseppe-michettis-phd-dissertation-defense/
LOCATION:432 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
GEO:42.3396156;-71.0886534
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221107T123000
DTEND;TZID=America/New_York:20221107T140000
DTSTAMP:20260422T234137
CREATED:20221103T191749Z
LAST-MODIFIED:20221103T191749Z
UID:5938-1667824200-1667829600@ece.northeastern.edu
SUMMARY:Tianyu Dai's PhD Dissertation Defense
DESCRIPTION:“Robust Data-Driven Control” \nAbstract: \nDuring the last two decades\, data-driven control (DDC) has attracted growing attention in the control community. Unlike model-based control (MBC)\, which 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 crucial 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. \nRobust data-driven control (RDDC) as a branch of DDC has developed rapidly in recent years\, focusing on the data-driven controller design for the state space model. It aims to solve the following problem: given a single trajectory of noisy data and a few priors of the model structure\, how to design a robust state feedback controller to stabilize the system with unknown dynamics\, and in addition\, to meet some performance criteria. By robust\, we mean the learned controller can stabilize all possible systems residing in the set compatible with the noisy data. \nThis dissertation aims to summarize our contributions to the RDDC field. We focus on the L_infinity bounded noise\, and 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. In the dissertation\, we propose RDDC algorithms for linear\, switched\, and nonlinear systems with process noise\, extend results for error-in-variables (a more general case)\, and discuss a worst-case optimal estimation of the trajectory of a switched linear system. \nCommittee: \nProf. Mario Sznaier (Advisor) \nProf. Octavia Camps\nProf. Bahram Shafai \nProf. Eduardo Sontag
URL:https://ece.northeastern.edu/event/tianyu-dais-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221102T140000
DTEND;TZID=America/New_York:20221102T150000
DTSTAMP:20260422T234137
CREATED:20221103T213638Z
LAST-MODIFIED:20221103T213638Z
UID:5950-1667397600-1667401200@ece.northeastern.edu
SUMMARY:Kai Huang's PhD Dissertation Defense
DESCRIPTION:“Partitioning Data Across Multiple\, Network Connected FPGAs with High Bandwidth Memory to Accelerate Non-streaming Applications” \nAbstract:\nField Programmable Gate Arrays (FPGAs) are increasingly used in cloud computing to increase the run time of various applications. Flexibility\, efficiency and lower power enable FPGAs to be important components in modern data centers. Applications such as Secure Function Evaluation (SFE)\, graph processing\, and machine learning are increasingly mapped to FPGA-based adaptable cloud computing platforms. However\, due to resource limitations\, it is difficult to map applications to only one FPGA. Applications with a streaming data processing pattern can be mapped to a multiple-FPGA platform where the FPGAs are connected in a 1-D or ring topology\, thus communications overhead can be pipelined with computations. The communication\, merely passing data from boards to boards\, will not significantly affect the system performance if the bandwidth is sufficient. In a more general processing pattern involving non-streaming applications\, each FPGA is responsible for only a portion of the computation and the FPGAs must keep exchanging data during the run time of the application. The communication cost can be the bottleneck of such a system. The challenge is how to map and parallelize these applications to a multi-FPGA cloud computing platform in such a way that communication is minimized and speedup is maximized.\nIn this research\, we build a framework to map garbled circuit applications\, an implementation of SFE\, to a cloud computing platform that has FPGA cards attached to computing nodes. The FPGAs on the node are able to communicate directly through the network. The framework consists of two parts: hardware design and software preprocessing. The hardware design integrates with the Xilinx UDP network stack enabling the capability to exchange data through the network and thus bypassing the processor and its software stack. The framework also takes advantage of High Bandwidth Memory (HBM) for high off-chip memory throughput. The levels of memory hierarchy available on the FPGA are used for caching both local data and incoming and outgoing network data. Preprocessing will generate the reordered batches of each layer needed for processing\, efficient memory allocation and final memory layout. We also applied an effective partitioning algorithm to schedule executions to different FPGAs to minimize the communication between FPGAs. By generating different size of problems from the EMP-toolkit\, we can demonstrate that this hardware-software co-design framework achieves nearly optimal two times speedup on a two-FPGA setup compared to a one-FPGA implementation. We explore extremely large examples that cannot be mapped to one-FPGA\, proving that it is achievable to map large examples of billions of operations to this distributed heterogeneous system. \nCommittee: \nProf. Miriam Leeser(advisor) \nProf. Stratis Ioannidis(co-advisor) \nProf. Mieczyslaw Kokar
URL:https://ece.northeastern.edu/event/kai-huangs-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221102T120000
DTEND;TZID=America/New_York:20221102T130000
DTSTAMP:20260422T234137
CREATED:20221103T213443Z
LAST-MODIFIED:20221103T213443Z
UID:5946-1667390400-1667394000@ece.northeastern.edu
SUMMARY:Yuexi Zhang's PhD Proposal Review
DESCRIPTION:“Human Body and Activity Analysis” \nAbstract: \nHuman-related applications such as person detection\, human pose estimations and human activity recognition\, that always draw a lot of attentions in computer vision community. In this proposal\, we discuss several related topics that we are interested in\, and demonstrate how we improve the existing methods. The first problem we consider is video-based human pose estimation. For most general approaches\, researchers focus on collecting human poses from each frame independently and then associate them based on matching or tracking methods. However\, such the pipeline usually relies on complex computations and also consumes running time. To overcome such shortages\, we propose a light weighted network with the unsupervised training strategy\, that aims to reduce running time but remaining the performance. The next problem we explore is about cross-view action recognition (CVAR). The goal of CVAR is to recognize a human action when observed from a previously unseen viewpoint. This is important for some applications such as surveillance systems where is not practical or feasible to collect large amounts of training data when adding a new camera. In this case\, it requires methods that are able to generate reliable view-invariant information trained with given viewpoints and recognize the action from an unseen viewpoint. In general\, most approaches rely on 3D data\, but using 2D data is still under-discovered. Besides\, the performance of those approaches using only 2D data is far worse than 3D approaches. Therefore\, we propose a simple yet efficient CVAR framework that takes 2D data as input and close the performance gap between 3D and 2D input. The last problem we investigate is online action detection and we are interested in detecting action start at current stage. Online action start detection problem is to detect an action startpoint as soon as it occurs with its action category in untrimmed\, streaming videos\, and it has potential applications such as early alert generation in surveillance systems. The typical approaches usually heavily rely on frame-level annotations and also they are limited to pre-defined action categories. Therefore\, we propose a novel yet simple design\, 3D MLP-mxier based architecture that aims to detect the taxonomy-free action start without using frame-level annotations. \n  \nCommittee: \nDr. Octavia Camps(Advisor) \nDr. Mario Sznaier \nDr. Sarah Ostadabbas
URL:https://ece.northeastern.edu/event/yuexi-zhangs-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221028T130000
DTEND;TZID=America/New_York:20221028T140000
DTSTAMP:20260422T234137
CREATED:20221103T213401Z
LAST-MODIFIED:20221103T213401Z
UID:5944-1666962000-1666965600@ece.northeastern.edu
SUMMARY:Guillem Reus Muns' PhD Proposal Review
DESCRIPTION:Location: ISEC 332 \n“AI for communications and sensing in RF environments” \nAbstract: \nThe recent growth of Internet of Things (IoT)\, as well as other new revolutionary applications utilizing wireless spectrum are leading the way towards realization of next generation wireless systems that jointly utilize communications and sensing. However\, such systems offer many degrees of freedom\, and optimizing them for a specific task is difficult to accomplish with deterministic and classical approaches. For this reason\, data-driven and AI-based methods have been pursued actively by the research community\, as they are able to find solutions that often come close to or exceed the performance of the deterministic counterparts with a fractional execution complexity. This thesis presents\, through real systems and with experimental validation\, our progressive efforts in three broad areas\, where AI enables the operation of aerial and terrestrial systems that combine sensing and communications. This dissertation explores the following key use cases with distinct contributions made in each: \ni) Sensing-aided communications for air and ground systems. First\, we present a UAV communication method that defines constellation points in space that map to transmitter frequency bands and are detected at the Base Station using millimeter wave sensors. Second\, we explore alternative vehicle-to-infrastructure mmWave beamforming methods\, leveraging a) vehicle position and velocity estimation using in-band standard compliant 802.11ad radar and b) camera images and GPS location information.\nii) Signal classification using communication signals\, where we propose a) a UAV classification method using uniquely UAV-transmitted signals and b) an RF fingerprinting technique that improves class separation by combining triplet loss with regular classification techniques.\niii) ‘AirFC’\, an over-the-air computation method that implements fully connected neural networks inference leveraging multi-antenna systems. \nFinally\, the proposed work will address challenges in the CBRS band\, where a tiered structure is implemented to access the spectrum. Hence\, continuous sensing is needed to make sure that radar (tier 1) is not interfered by cellular systems (tier 2). Here\, we propose reusing the already existing cellular infrastructure to act as a radar detector\, which enhances their functionality to go beyond that of regular wireless communications. \nCommittee: \nProf. Kaushik Chowdhury (Advisor) \nProf. Hanumant Singh \nProf. Stratis Ioannidis
URL:https://ece.northeastern.edu/event/guillem-reus-muns-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221024T110000
DTEND;TZID=America/New_York:20221024T120000
DTSTAMP:20260422T234137
CREATED:20221103T213221Z
LAST-MODIFIED:20221103T213221Z
UID:5940-1666609200-1666612800@ece.northeastern.edu
SUMMARY:Yixuan He's PhD Proposal Review
DESCRIPTION:Committee: \nProf. Yong-Bin Kim. Advisor \nProf. Marvin Onabajo \nProf. Lombardi Fabrizio \n  \nAbstract: \nIn order to match the needs of powerful neural networks and meet the hard constraints from hardware\, binary neural networks are treated as hardware-friendly deep learning algorithms due to the fact that it can achieve similar inference accuracy with fewer computing resources comparing to traditional convolutional neural networks. As for its VLSI implementations\, the computing-in-memory (CIM) technology has been proved to solve the memory-wall bottleneck problem shown in traditional von Neumann machine and can be a perfect choice to implement neural networks with binary data. Therefore\, this work proposes a novel time-domain computing-in-memory core that implements XNOR-and-accumulate of binary neural networks with all-digital elements. This new technique uses 8T-SRAM cells to perform XNOR operations inside memory array and accumulates the related XNOR output values in time-domain with specialized racing structures and delay lines. The circuit is built and simulated in Cadence using Samsung 65nm CMOS technology with 1V power supply. The results show correct functionality\, 2730 GOPS throughput and 431 TOPS/W power efficiency. With further exploration\, the time-domain computation can be a new candidate in the field of in-memory-computing for deep learning applications since it has its own superiorities in terms of throughput\, power efficiency in comparison to other mixed-signal or traditional digital methods.
URL:https://ece.northeastern.edu/event/yixuan-hes-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221020T120000
DTEND;TZID=America/New_York:20221020T130000
DTSTAMP:20260422T234137
CREATED:20221103T191700Z
LAST-MODIFIED:20221103T191700Z
UID:5936-1666267200-1666270800@ece.northeastern.edu
SUMMARY:Neset Unver Akmandor's PhD Proposal Review
DESCRIPTION:“Improving Computational Efficiency of Motion Planning Algorithms for Mobile and Time-Dependent Robotic Tasks in Dynamic Environments” \nAbstract: \nRobots will become a part of our lives at home as personal assistants. Although their current functionality is highly restricted to specific tasks and environments\, their practicality encourages robotics engineers for further advancement. Especially\, mobile robots with manipulation capabilities have a huge potential to support humans in physically demanding workplaces\, such as warehouses and hospitals. Considering the complexity of the human level tasks and the dynamic settings\, the state-of-the-art robot motion planning methods need to be improved in terms of their computational efficiency. To contribute on closing the gap\, this proposal presents three novelties whose applications focus on mobile robots in dynamic environments. First\, we introduce a reactive navigation framework in 3D workspaces. The proposed approach does not rely on the global map information and achieves fast navigation by employing motion primitives and their heuristic evaluations on the-fly. Second\, we present a Deep Reinforcement Learning based navigation approach in which we define the occupancy observations as heuristic evaluations of motion primitives\, rather than using raw sensor data. It utilizes occupancy observations in different data structures to analyze their effects on both training process and navigation performance. We train and test our methodology on two different robots within challenging physics-based simulation environments including static and dynamic obstacles. Finally\, we propose a computationally efficient framework for trajectory planning for robots with high degrees-of freedom while adapting its system model\, constraints and time-dependent target state using the latest information from the dynamic environment. \n  \nCommittee: \nDr. Taskin Padir (Advisor)Dr. Pau ClosasDr. Michael EverettDr. Erdal Kayacan
URL:https://ece.northeastern.edu/event/neset-unver-akmandors-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221017T130000
DTEND;TZID=America/New_York:20221017T140000
DTSTAMP:20260422T234137
CREATED:20221103T191510Z
LAST-MODIFIED:20221103T191510Z
UID:5934-1666011600-1666015200@ece.northeastern.edu
SUMMARY:Sila Deniz Calisgan's PhD Dissertation Defense
DESCRIPTION:“ADVANCEMENTS ON ZERO STANDBY POWER MEMS SENSORS” \nAbstract: \nDue to the fast development of the internet of things\, and unattended wireless sensor networks\, the number of connected devices worldwide is expected to increase exponentially in the future. In order to maintain such large networks of physical and virtual objects\, there is a need for sensors\, actuators and devices with dimensions and power consumption that are orders of magnitude smaller than the state-of-the-art. Currently no existing technology could enable the implementation of large-scale wireless sensor networks in remote locations due to the prohibitive cost associated with installation and maintenance. The fundamental technical challenge lies in the continuous power consumption of state-of-the-art sensor technologies: Commercially available sensors are not smart enough to identify targets of interest without consuming any power and rely on active electronics to detect and discriminate signal of interest. Therefore\, they consume power continuously to monitor the environment even when there is no relevant data to be detected\, which results in a short battery lifetime limited to very few months. This dissertation presents improvements on a new class of zero-power microsystems that fundamentally break the paradigm\, with zero-power consumption\, until awakened by a specific physical signature. This approach is applied to multiple sensing modalities. In particular\, I have experimentally demonstrated zero-power wireless sensors triggered by different physical and chemical quantities such as: infrared radiation; radio frequency signals; acoustic signals and volatile organic chemicals. The capabilities of the zero-power sensors result in a nearly unlimited duration of operation\, with a groundbreaking impact on the proliferation of the internet of things. \n  \nCommittee: \nProf. Matteo Rinaldi (Advisor)Prof. Marilyn MinusProf. Srinivas TadigadapaProf. Zhenyun Qian
URL:https://ece.northeastern.edu/event/sila-deniz-calisgans-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221014T150000
DTEND;TZID=America/New_York:20221014T160000
DTSTAMP:20260422T234137
CREATED:20221103T191427Z
LAST-MODIFIED:20221103T191427Z
UID:5932-1665759600-1665763200@ece.northeastern.edu
SUMMARY:Meruyert Assylbekova's PhD Dissertation Defense
DESCRIPTION:“Aluminum Nitride and Scandium-doped Aluminum Nitride materials and devices for beyond 6 GHz communication” \nAbstract: \nWith almost all of the sub-¬6 GHz spectrum now being allocated\, current bandwidth shortage has motivated the exploration of untapped frequencies beyond 6 GHz for future broadband wireless communication. Shift to higher frequency spectra is expected to deliver a significant performance improvement in network capacity\, data rates\, latency\, and coverage. These refinements will enable the development of new life¬changing technologies such as Vehicle to Everything (V2V to V2X)\, ubiquitous Internet of Things (IoT)\, and Augmented and Virtual reality (AR and VR). Among a variety of novel 5G applications\, the implementation of 5G mobile broadband imposes especially demanding specifications on Radio Frequency Front¬End (RFFE) architectures. 5G smartphones are expected to carry over the legacy sub-¬6 GHz bands\, which translates into an increased number of filters. In this context\, the first part of this work will introduce lithographically defined Aluminum Nitride (AlN) piezoelectric microacoustic resonators as a promising solution for the implementation of future minituarized adaptive RFFEs. While AlN has been a material of choice for acoustic filters for over two decades\, future technologies are calling for a material with superior piezoelectric strength. It has been shown that the piezoelectric activity of AlN can be enhanced by partially substituting Al with Sc to form AlScN. Thus\, the second part of this work will explore material properties of AlScN along with the challenges that need to be addressed to take full advantage of its piezoelectric and ferroelectric strength. Last\, AlScN resonators and filters will be demonstrated as promising candidates for the future beyond 6GHz technologies. \nCommittee: \nProf. Matteo Rinaldi (advisor) \nProf. Nicol McGruer \nProf. Cristian Cassella
URL:https://ece.northeastern.edu/event/meruyert-assylbekovas-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220929T090000
DTEND;TZID=America/New_York:20220929T220000
DTSTAMP:20260422T234137
CREATED:20221103T185118Z
LAST-MODIFIED:20221103T185118Z
UID:5930-1664442000-1664488800@ece.northeastern.edu
SUMMARY:Priyangshu Sen's PhD Dissertation Defense
DESCRIPTION:Location: ISEC 532 \n“Physical Layer Design for Ultrabroadband Terahertz Communications: From Theory to Experiments” \nAbstract: \nTerahertz (THz)-band (0.1 THz to 10 THz) communication is envisioned as a key technology to meet the demand for faster\, more ubiquitous wireless communication networks for the sixth generation (6G) of wireless systems and beyond. For many years\, the lack of compact\, fast and efficient ways to generate\, modulate\, detect and demodulate THz signals has limited the feasibility of such communication systems. Recent progress within different device technologies is finally closing the terahertz technology gap and enabling\, for the first time\, experimental wireless research in the THz band. \nThis thesis presents the first steps towards advancing the development and bridging the gap between theoretical and experimental THz communication research. At the core of this work\, the TeraNova platform\, i.e\, the first testbed for ultra-broadband wireless communications at THz frequencies in the world\, is designed and built. In terms of hardware\, the platform consists of multiple sets of analog front-ends at three different frequencies between 100 GHz and 1.05 THz and three different digital signal processing back-ends\, able to manipulate tens of GHz of bandwidth. In terms of software\, tailored framing\, time synchronization\, channel estimation\, and single and multi-carrier modulation techniques are implemented in guided by the experimental characterization of the THz hardware and the THz channel. Moreover\, implementation details and early experimental results to demonstrate the platform’s capabilities/limitations are reported. The platform is then used to demonstrate several milestones in the field\, including the first true THz link in the first absorption-defined window above 1 THz (i.e.\, 1-1.05 THz) and the longest multi-kilometer link (2.01 Km) at the 200-240 GHz band. Further\, Knowing the peculiarities of the THz band and the available device technology in the frequency range\, innovative solutions are proposed. Based on the observed behaviors\, M-ary amplitude and phase-shift keying is presented to simultaneously overcome the limitations due to peak to average power ratio (PAPR) and reduce the effective symbol error rate (SER)\, both while using a high-order modulation scheme. Based on the unique molecular absorption at THz frequencies\, two innovative modulation schemes are presented to make the most of the THz channel. First\, to not only overcome but exploit the distance-dependent bandwidth of the THz band\, hierarchical bandwidth modulations are proposed as a suitable candidate for a single transmitter and multiple receiver (STMR) system. Second\, to reliably communicate even in the presence of absorption peaks\, chirp spread spectrum (CSS) based communication is investigated. Specifically\, Chirp-Spread Binary Phase-Shift Keying (CS-BPSK) is proposed over traditional Binary Chirp Spread Spectrum (BCSS) to obtain better BER. Moreover\, beyond the physics\, current spectrum allocations break down the otherwise very large bands into narrow sub-sets to accommodate sensing users. Spectrum sharing is needed to make the most out of the spectral resources. Therefore\, the capability of the direct sequence spread spectrum (DSSS) is explored to illustrate the performance by acknowledging the coexistence between active and passive users. Further\, channel sounding is conducted in diverse indoor and outdoor scenarios to understand the channel statistics and design reliable communication links. As the last contribution in this dissertation\, the channel model and statistics are explored for an ultra-broadband outdoor channel in different weather conditions. Further\, the channel metrics are explored in various indoor scenarios with different structural and geometrical aspects\, occupancy\, and antenna gain at 130 GHz. For this purpose\, a fully tailored signal processing back-end for sliding correlator type channel sounder is developed\, which is capable of capturing multipath profiles to describe the ultra-broadband nature of the link. \nIn a nutshell\, this dissertation presents the technologies and the results\, highlights the challenges\, and defines a path to move forward with innovative solutions toward practical THz ultra-broadband and long-distance communication systems. \nCommittee: \nProf. Josep Jornet (Advisor) \nProf. Tommaso Melodia \nProf. Milica Stojanovic \nProf. Kaushik Chowdhury
URL:https://ece.northeastern.edu/event/priyangshu-sens-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220926T143000
DTEND;TZID=America/New_York:20220926T153000
DTSTAMP:20260422T234137
CREATED:20221103T184922Z
LAST-MODIFIED:20221103T184922Z
UID:5926-1664202600-1664206200@ece.northeastern.edu
SUMMARY:Michele Pirro's PhD Dissertation Defense
DESCRIPTION:“AlScN material characterization for MEMS applications” \nAbstract: \nThe 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. 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.\nI will discuss the advance in the process flow of high Sc-concentrations\, showing the impact of the deposition parameters on the material properties with focus on the ferroelectric behavior. Effect of RF powers on the substrate and on the target are also analyzed\, demonstrating the possibility to properly optimize the AlScN deposition. Last\, I will present MEM devices which exploit the enhanced piezoelectric activity (high frequency resonators and pmut) and the ferroelectric properties (impedance with memory)\, confirming the potential of the material for new multi-functionalities MEMS. \nCommittee: \nProf. Matteo Rinaldi (Advisor) \nProf. Cristian Cassella \nProf. Siddhartha Ghosh
URL:https://ece.northeastern.edu/event/michele-pirros-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220923T120000
DTEND;TZID=America/New_York:20220923T130000
DTSTAMP:20260422T234137
CREATED:20221103T181851Z
LAST-MODIFIED:20221103T181851Z
UID:5882-1663934400-1663938000@ece.northeastern.edu
SUMMARY:Eric Robinson's PhD Proposal Review
DESCRIPTION:“Techniques for the Modelling\, Design\, and Fabrication of Ultra-Wideband Dipole Arrays”\nAbstract: \nA novel set of techniques are proposed which advance the state of the art for the modelling\, design\, and fabrication of ultra-wideband dipole arrays. First\, existing techniques and relevant topics in the field are introduced and summarized. These include equivalent circuit and Green’s Function models for the impedance of the infinite dipole array. Challenges for the realization of arrays are discussed\, including finite array effects\, common-mode effects\, and the limitations of different fabrication techniques. Several relevant recent innovations by the author are presented to form the foundation of the proposed work. \nFirst\, a new lossy transmission line model for the infinite dipole array impedance is described which results in highly accurate predictions across wide bandwidths and for large scan angles. The accuracy of the model is demonstrated via comparisons to full-wave simulations\, and the model is used to rapidly design a wide-scanning\, ultra-wideband array to demonstrate its value. Next\, a new technique developed by the author is described for physically realizing complex dipole array geometries with integrated dielectrics. A tightly-coupled array is achieved by 3D-printing an array of elements featuring internal through-cavities in the shape of the radiating elements. The internal surfaces of these cavities are then metallized via a copper deposition\, producing an array of conductive elements within a dielectric shell\, resulting in improved mechanical rigidity\, improved scan performance\, and increased inter-element capacitance for ultrawide bandwidth. \nBuilding on these results\, additional research is proposed for completion of the dissertation. First\, the lossy transmission line model will be extended to other relevant cases\, including the unbalanced feed-structure-fed dipole array and the hybrid slot-dipole array. Second\, a new model will be implemented to describe the effects of surface waves on the active impedance of the tightly-coupled dipole array. Mitigating techniques will be proposed based on the results of this model\, enabling the realization of finite arrays which better conform to the predicted performance of the infinite array. Finally\, each of the aforementioned models and techniques will be leveraged towards the design and fabrication of a notional array for an imaging application\, demonstrating their practical value for array design. \nCommittee: \nProf. Carey Rappaport (Advisor) \nProf. Josep Jornet\nProf. Edwin Marengo Fuentes \nDr. Ian McMichael
URL:https://ece.northeastern.edu/event/eric-robinsons-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220922T130000
DTEND;TZID=America/New_York:20220922T140000
DTSTAMP:20260422T234137
CREATED:20221103T184721Z
LAST-MODIFIED:20221103T184721Z
UID:5924-1663851600-1663855200@ece.northeastern.edu
SUMMARY:Justin Crabb's PhD Proposal Review
DESCRIPTION:“Multiphysics Simulation of Graphene Transistors for On-Chip Plasmonic THz Signal Generation and Modulation” \nAbstract: \nTerahertz communication is envisioned as a key technology not only for the next generation of macro-scale networks (e.g.\, 6G+)\, but also for transformative networking applications at the nanoscale (e.g.\, wireless nanosensor networks and wireless networks on chip). This proposal focuses on the development of a multiphysics simulation platform for a plasmonic THz nanogenerator with on-chip modulation. The in-house developed finite-element-method platform\, which self-consistently solves the hydrodynamic and Maxwell’s equations\, is utilized to provide extensive numerical results demonstrating the device’s functionality along with ultra-wide bandwidth and high modulation index capabilities. \nFirst\, a comprehensive theory of the Dyakonov-Shur (DS) plasma instability in current-biased graphene transistors is presented. Using the hydrodynamic approach\, equations describing the DS instability in the two-dimensional electron fluid in graphene at arbitrary values of electron drift velocity are derived. These non-linear equations together with Maxwell’s equations are used for numerical analysis of the spatial and temporal evolution of the graphene electron system after the DS instability is triggered by random current fluctuations. Conditions necessary for the onset of the DS instability and the properties of the final stationary state of the graphene electron system are analyzed. \nNext\, a detailed numerical analysis of the DS plasma instability in the DC current-biased graphene transistor with the gate shifted with respect to the middle of the transistor conducting channel is presented. The geometric asymmetry is shown to be sufficient to trigger the DS instability in the two-dimensional electron gas in the transistor channel. Sustained plasma oscillations in the instability endpoint are demonstrated and the properties of these oscillations are analyzed for different positions of the gate and at different values of other physical and geometric FET parameters. The obtained results show the possibility of designing a tunable on-chip source of THz electromagnetic radiation based on the graphene transistor with a shifted gate. \nFollowing\, the on-chip THz nano-generator with amplitude and frequency modulation capabilities is presented. The proposed device uses and leverages the tunability of the Dyakonov-Shur instability for the growth and modulation of plasmonic oscillations in the two-dimensional electron gas channel of the graphene transistor. \n  \nCommittee: \nProf. Josep Jornet (Advisor) \nProf. Tommaso Melodia \nProf. Matteo Rinaldi \nProf. Hossein Mosallaei
URL:https://ece.northeastern.edu/event/justin-crabbs-phd-proposal-review/
LOCATION:432 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
GEO:42.3396156;-71.0886534
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=432 ISEC 360 Huntington Ave Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave:geo:-71.0886534,42.3396156
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220825T140000
DTEND;TZID=America/New_York:20220825T150000
DTSTAMP:20260422T234137
CREATED:20221103T181955Z
LAST-MODIFIED:20221103T181955Z
UID:5884-1661436000-1661439600@ece.northeastern.edu
SUMMARY:Tarik Kelestemur's PhD Dissertation Defense
DESCRIPTION:Location: ISEC 532 \n“Combining Classical and Learning-based Methods for Visual and Tactile Manipulation” \nAbstract: \nRobots that operate in dynamic and ever-changing environments need to make sense of their surroundings and act in them safely and efficiently. This requires the integration of multiple sensory modalities such as visual and tactile. Humans can naturally fuse different feedbacks from the environment or substitute them with one another to perform everyday tasks. For example\, to use a computer mouse\, we first locate the mouse using vision and then use touch feedback from our fingers to precisely localize the buttons. Ideally\, we would like robots to have human-level perception and control of the environment to achieve various tasks. This dissertation address two significant problems toward this overarching goal. \nThe first problem we consider in this dissertation is figuring out how to use tactile information in conjunction with visual feedback. Robotic manipulators that interact with objects and environments are often equipped with visual sensors such as RGB and depth cameras. They estimate the state of their environment using these sensors and act upon the estimated state. While a large body of previous work has shown that we can achieve impressive results only with visual sensors\, more precise and delicate tasks require touch information which gives direct feedback from the environment. To this end\, we propose methods for efficiently combining the tactile and visual information to leverage the advantages of these modalities.\nThe second problem we investigate is how to build visual and tactile manipulation methods that can generalize over the different novel environments and objects. The rise of deep learning has enabled robots to solve challenging perception and control problems using visual and tactile observations while generalizing to novel objects and environments. However\, a common issue among deep learning-based methods is that these methods usually work only within the distribution of the training data and do not perform well when they are presented with unseen examples. Furthermore\, they cannot distinguish whether they are dealing with in or out-of-distribution data. We propose to address this issue by combining well-established and principled algorithmic priors with the generalization capabilities of deep learning. \nIn the first part of this dissertation\, we investigate the problem of pose estimation of the robotic grippers with respect to the environment and objects. The proposed framework introduces a learnable Bayes filter that can estimate the position of a gripper in a single image of the environment. We learn the observation and motion models of the Bayes filter using modern neural network architectures and use recursive belief updates for tracking the position of the gripper over time. Later\, the belief estimation is used as an input to policies where the aim is to solve manipulation tasks using tactile feedback. In the second part\, we look at the problem of estimating shapes from partial observations. We propose a method called DeepGPIS that combines a powerful deep learning-based implicit shape representation with a non-parametric inference approach model for implicit surfaces (GPIS) which allows us to generate complete shapes of novel objects and estimate their predictive uncertainties. \nCommittee: \nProf. Taskin Padir (Advisor) \nProf. Robert Platt (Advisor) \nProf. David Rosen (Advisor)
URL:https://ece.northeastern.edu/event/tarik-kelestemurs-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220822T150000
DTEND;TZID=America/New_York:20220822T160000
DTSTAMP:20260422T234137
CREATED:20221103T182044Z
LAST-MODIFIED:20221103T182044Z
UID:5886-1661180400-1661184000@ece.northeastern.edu
SUMMARY:Hamed Mohebbi Kalkhoran's PhD Dissertation Defense
DESCRIPTION:“Machine learning approaches for classification of myriad underwater acoustic events over continental-shelf scale regions with passive ocean acoustic waveguide remote sensing” \nAbstract: \nUnderwater acoustic data contain a myriad of sound sources. Among underwater acoustic events\, marine mammal vocalization classification is one of the most challenging problems due to their transient broadband calls\, high variation in the calls of a specie\, and high similarity between the calls of some species. Here\, we developed machine learning approaches for classifying marine mammal vocalizations for real-time applications. We utilize acoustic data from a 160-element coherent hydrophone array and employ the passive ocean acoustic waveguide remote sensing technique to enable sensing and detections over instantaneous wide areas more than 100 km in diameter from the array. A variety of computational accelerating approaches\, combining hardware and software\, that make the methods desirable for real-time applications are also developed. \nThe humpback whale vocalizations can be divided into two classes\, song and non-song calls. Here we use wavelet signal denoising and coherent array processing to enhance the signal-to-noise ratio. To build features vector for every time sequence of the beamformed signals\, we employ Bag of Words approach to time-frequency features. Finally\, we apply Support Vector Machine (SVM)\, Neural Networks\, and Naive Bayes to classify the acoustic data and compare their performances. Best results are obtained using Mel Frequency Cepstrum Coefficient (MFCC) features and SVM which leads to 94% accuracy and 72.73% F1-score for humpback whale song versus non-song vocalization classification. \nTo classify a large variety of whale species vocalizations\, we extracted time-frequency features from Power Spectrogram Density (PSD) of the beamformed signals. Then we used these features to train three classifiers\, which are SVM\, Neural Networks\, and Random forest to classify six whale species: Fin\, Sei\, Blue\, Minke\, Humpback\, and general Odontocetes. We also trained a set of Convolutional Neural Networks (CNN) to detect and classify each of these six whale vocalization categories directly using Per-Channel Energy Normalization (PCEN) spectrograms. Best results were obtained with Random forest classifier\, which achieved 95% accuracy\, and 85% F1 score. To detect transient sound sources\, first we applied PCEN on the PSD of the beamformed signals. We applied thresholding on the PCEN data followed by morphological image opening to find potential sound sources and reduce noisy detections. Then we applied connected component analysis to obtain the final detected sounds for each bearing. To estimate the Direction of Arrival (DoA) of detected sounds\, we applied non-maximum suppression (NMS)\, which is widely used in object detection applications in computer vision\, on the detected sounds. We used mean power of each detected sound as the scores for NMS. To speed up the data processing\, we investigated a variety of accelerating approaches\, such as analyzing the effect of floating point precision\, applying parallel processing\, and implementing fast algorithms to run on GPU. During an experiment in the U.S. Northeast coast on board the US research vessel RV Endeavor in September 2021\, we utilized the software and hardware advances developed here to record underwater acoustic data using Northeastern University in-house fabricated large aperture 160- element coherent hydrophone array with sampling frequency of 100 kHz per element. \nCommittee: \nProf. Purnima Ratilal (Advisor) \nProf. Themistoklis Sapsis \nProf. Devesh Tiwari
URL:https://ece.northeastern.edu/event/hamed-mohebbi-kalkhorans-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220817T110000
DTEND;TZID=America/New_York:20220817T120000
DTSTAMP:20260422T234137
CREATED:20221103T182252Z
LAST-MODIFIED:20221103T182252Z
UID:5890-1660734000-1660737600@ece.northeastern.edu
SUMMARY:Mithun Diddi's PhD Dissertation Defense
DESCRIPTION:“Multiple UAVs for Synchronous – Shared Tasks and Long-term Autonomy” \nAbstract: \nThis thesis focuses on the use of multiple unmanned aerial vehicles(UAVs) in a distributed framework from a systems perspective to synchronously perform shared tasks such as aerial beamforming and coordinated mapping and to enhance the reliability of performing periodic (mapping) tasks at remote locations for long-term autonomous(LTA) missions. We present an autonomy stack for multiple\, heterogeneous UAVs with a simulation framework. We implemented the end-to-end pipelines for perception and communication applications involving multiple UAVs. \nRepeated deployments in harsh-weather\, real-world locations are challenging and are limited by the need for human operators. These infrastructure-poor\, remote locations pose unique challenges to long-term autonomous missions. In these locations\, harvesting power onboard using solar panels may be a viable alternative for recharging batteries.\nIn the first part of the thesis\, we focus on hardware architecture for UAVs to enable reliable LTA missions with minimal human intervention. We developed a Size\, Weight\, and Power(SWaP) constrained Smart charging stack to minimize hotel loads seen during the recharging process and enable efficient charging of batteries. This leads to the design of a standalone\, solar rechargeable quadcopter.\nReal-world applications such as reconstructing a dynamic scene from multiple viewpoints and distributed aerial beamforming require multiple robots(agents) to coordinate and synchronously act to accomplish shared tasks. These tasks require spatially distant\, multiple UAVs to have time\, phase\, and frequency synchronization. We demonstrate a Synchronous UAV(S-UAV) architecture for wireless synchronization based on GPS disciplined oscillators and the associated software framework needed for temporal registration of data across multiple UAVs.\nWe have built four S-UAVs and demonstrate the ability to 3D reconstruct a dynamic scene from overlapping viewpoints. Dynamic baseline camera arrays formed using multiple S-UAVs are used to synchronously capture a dynamic environment with people moving around. A single-time instance of synchronously captured images of the scene is used to 3D reconstruct the dynamic environment while preserving static scene assumptions of Structure from Motion(SFM). \nIn the second part of the thesis\, we focus on multi UAV autonomy framework for real-world applications of UAVs in perception\, wireless communications\, and reliable LTA missions. We present ‘Simplenav\,’ a navigation stack for heterogeneous\, multiple UAVs\, and ‘OctoRosSim\,’ a computationally lightweight multi-UAV simulation framework for validating the multi-UAV planning and autonomy pipeline. We demonstrate this framework with novel applications of end-to-end autonomy pipelines developed for a coordinated swarm of UAVs. \nCommittee: \nProf. Hanumant Singh (Advisor) \nProf. Kaushik Chowdhury \nProf. Taskin Padir
URL:https://ece.northeastern.edu/event/mithun-diddis-phd-dissertation-defense/
LOCATION:432 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
GEO:42.3396156;-71.0886534
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=432 ISEC 360 Huntington Ave Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave:geo:-71.0886534,42.3396156
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220815T140000
DTEND;TZID=America/New_York:20220815T150000
DTSTAMP:20260422T234137
CREATED:20221103T182152Z
LAST-MODIFIED:20221103T182152Z
UID:5888-1660572000-1660575600@ece.northeastern.edu
SUMMARY:Nikita Mirchandani's PhD Dissertation Defense
DESCRIPTION:“Ultra-Low Power and Robust Analog Computing Circuits and System Design Framework for Machine Learning Applications” \nAbstract: \nAs the scaling of CMOS transistors has almost halted\, performance gains of digital systems have also started to stagnate. There is a renewed interest in alternate computing techniques such as in-memory computing\, hybrid computing\, approximate computing\, and analog computing. In particular\, analog computing has reemerged as a promising alternative to save power and improve performance specifically for machine-learning (ML) applications. Power and chip area efficiency make analog computing highly appealing for implementing deep learning algorithms on-chip\, computing circuits for the internet-of-things (IoT) devices\, and implantable and wearable biomedical devices. However\, compared to digital computing\, analog computing methods have not nearly been utilized to their fullest potential due to longstanding challenges related to reliability\, programmability\, power consumption\, and high susceptibility to variations. \nThe subject of this dissertation research is to develop robust ultra-low power analog hardware suitable for machine learning applications. First\, a robust analog design methodology is presented to address issues of variability in analog circuits. A constant transconductance design technique using switched capacitor circuits is presented. The design approach is then applied to build circuits for ML applications. An analog vector matrix multiplier (VMM) is designed to be used in the convolutional layer in an ML analog computing vision hardware platform. Computing circuits are tested as part of an image classification DNN algorithm on the MNIST dataset and can achieve a classification accuracy of 96.1%.\nThe design approach is also used to design an analog computing system architecture for detection of seizures using EEG signals. A conventional EEG monitoring system includes an analog front-end (AFE)\, ADC\, digital filtering stage\, EEG feature extraction engine\, and SVM classification. Such systems suffer from high power and chip area requirements. The corresponding analog architecture is composed of AFE amplifiers to provide gain for the incoming signal. The AFE is followed by an analog filtering stage\, where spectral power from each of the bands is used as a feature for seizure classification. The output of each filter is applied to a corresponding feature extraction circuit to continuously monitor the onset of a seizure in an ultra-lower power mode with sub-threshold analog processing. The system level architecture is first modeled to obtain classification accuracy of seizures. Simulation times for the design of such complex analog systems can be prohibitively long\, particularly when the impacts of nonidealities such as noise\, nonlinearity\, and device mismatches have to be considered at the system level. The simulation time is reduced by building accurate models of the analog blocks for faster simulations. The analog models help to define the required specifications for each block in order to achieve a specified system-level classification accuracy.\nInfrastructure circuits like oscillators and voltage regulators for the proposed SoC are presented. A 254 nW 21 kHz on-chip RC oscillator with 21.5 ppm/oC temperature stability is presented to provide stable clock source for the proposed SoC. Finally\, novel lightweight hardware security primitives are described to equip individual IoT device with side-channel resistant crypto-implementations\, and unique ID or key \ngeneration. \nCommittee: \nProf. Aatmesh Shrivastava (Advisor) \nProf. Marvin Onabajo \nProf. Yong-Bin Kim
URL:https://ece.northeastern.edu/event/nikita-mirchandanis-phd-dissertation-defense/
LOCATION:432 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
GEO:42.3396156;-71.0886534
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=432 ISEC 360 Huntington Ave Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave:geo:-71.0886534,42.3396156
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220805T093000
DTEND;TZID=America/New_York:20220805T103000
DTSTAMP:20260422T234137
CREATED:20221103T182421Z
LAST-MODIFIED:20221103T182421Z
UID:5894-1659691800-1659695400@ece.northeastern.edu
SUMMARY:Mahdiar Sadeghi's PhD Dissertation Defense
DESCRIPTION:“Model-based decision making in life sciences” \nAbstract: \nMathematical models are key tools in rational decision-making processes. A “good” model is expected to reproduce experimental observations\, which enables predictions outside the previous experimental settings. The accuracy of predictions depends on the assumptions used to model the system. The objective of this study is to explore possible approaches to deploy models in order to generate new hypotheses in life sciences. A few biological systems relevant to protein translation\, chemotherapy\, immunotherapy\, and epidemics are considered. Models are analyzed numerically/analytically to optimize a new decision/control. In protein translation processes\, it is discovered that no switching policy is better than constant rates to maximize ribosome flow. In a particular experimental setting of chemotherapy\, a new dosing plan for chemotherapy is identified and predicted to result in maximum shrinkage of the tumor volume. In immunotherapy\, key features of binding kinetics of T-cell engagers in pre-clinical experiments are discussed. Moreover\, epidemic models under social distancing guidelines are studied. Considering a single-interval social distancing based on the start time and the duration of the social distancing shows a surprising linear relationship. Some of the results presented in this dissertation are shown to be valid in multiple applications. \n  \nCommittee: \nProf. Eduardo Sontag (Advisor)Dr. Irina KarevaProf. Carey RappaportProf. Bahram ShafaiProf. Mark Niedre
URL:https://ece.northeastern.edu/event/mahdiar-sadeghis-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220804T140000
DTEND;TZID=America/New_York:20220804T150000
DTSTAMP:20260422T234137
CREATED:20221103T182334Z
LAST-MODIFIED:20221103T182334Z
UID:5892-1659621600-1659625200@ece.northeastern.edu
SUMMARY:Tong Jian's PhD Dissertation Defense
DESCRIPTION:“Robust Sparsified Deep Learning” \nAbstract: \nThis dissertation studies robustness issues around DNN deployments on resource constrained systems\, under both environmental and adversarial input adaptation. We propose a means of compressing a Radio Frequency deep neural network architecture through weight pruning\, and provide a systems-level analysis of the implementation of such a pruned architecture at resource-constrained edge devices. In particular\, we jointly train and sparsify neural networks tailored to edge hardware implementations. \nNext\, we propose a new learn-prune-share (LPS) algorithm for achieving robustness to environment adaptation in the field of lifelong learning. Our method maintains a parsimonious neural network model and achieves exact no forgetting by splitting the network into task-specific partitions via a weight pruning strategy optimized by the Alternating Direction Methods of Multipliers (ADMM). Moreover\, a novel selective knowledge sharing scheme is integrated seamlessly into the ADMM optimization framework to address knowledge reuse.\nFurthermore\, we investigate the Hilbert-Schmidt Information Bottleneck as regularizer (HBaR) as a means to enhance adversarial robustness. We show that the Hilbert-Schmidt Information bottleneck enhances robustness to adversarial attacks both theoretically and experimentally. In particular\, we prove that the HSIC bottleneck regularizer reduces the sensitivity of the classifier to adversarial examples. \nFinally\, we propose a novel framework Pruning-without-Adversarial-training (PwoA) for the purpose of achieving adversarial robustness on resource-constrained systems. PwoA can efficiently prune a previously trained robust neural network while maintaining adversarial robustness\, without further generating adversarial examples. We leverage concurrent self-distillation and pruning to preserve knowledge in the original model as well as regularizing the pruned model via the HBaR. \nCommittee: \nProf. Stratis Ioannidis (Advisor) \nProf. Jennifer Dy\nProf. Kaushik Chowdhury \nProf. Yanzhi Wang
URL:https://ece.northeastern.edu/event/tong-jians-phd-dissertation-defense/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220727T143000
DTEND;TZID=America/New_York:20220727T153000
DTSTAMP:20260422T234137
CREATED:20221103T182600Z
LAST-MODIFIED:20221103T182600Z
UID:5898-1658932200-1658935800@ece.northeastern.edu
SUMMARY:Kimia Shayestehfard's PhD Proposal Review
DESCRIPTION:“Permutation Invariant Graph Learning” \nAbstract:\nGraphs are widely used in many areas such as biology\, engineering\, and social sciences to model sets of objects and their interactions and relationships. Tasks addressed by applying machine learning to graphs\, known as graph learning\, include node and graph classification\, edge prediction\, transfer learning\, and generative modeling/distribution sampling\, to name a few.\nDue to high prevalence and multitude of applications of graphs across different fields\, graph neural networks have been developed in the past few years. Graph neural networks have shown tremendous success at producing node embeddings that capture structural and relational information of a graph and are discriminative for downstream tasks. However\, graph learning algorithms still deal with a major challenge\, namely\, the lack of permutation invariance: In a dataset of sampled graphs\, nodes may be ordered arbitrarily\, and aligning them is combinatorial and computationally expensive. Moreover\, many graph distance algorithms do not satisfy metric properties\, which can significantly hamper the fidelity of the downstream tasks. In this work we address the challenges posed by permutation invariance via combining fast and tractable metric graph alignment methods with graph neural networks. We propose a tractable\, non-combinatorial method for solving the graph transfer learning problem by combining classification and embedding losses with a continuous\, convex penalty motivated by tractable graph distances. We demonstrate that our method successfully predicts labels across graphs with almost perfect accuracy; in the same scenarios\, training embeddings through standard methods leads to predictions that are no better than random. Furthermore\, we propose a framework that combines fast and tractable graph alignment methods with a family of deep generative models and are thus invariant to node permutations. These models can be learned by solving convex optimization problems. Our experiments demonstrate that our models successfully learn graph distributions\, outperforming competitors by at least 66% in two relevant performance scores and improve the computation time up to 20 times over existing metric graph alignment methods. \nCommittee: \nProf. Stratis Ioannidis (Advisor) \nProf. Dana Brooks (Advisor) \nProf. Tina Eliassi-Rad
URL:https://ece.northeastern.edu/event/kimia-shayestehfards-phd-proposal-review/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220721T143000
DTEND;TZID=America/New_York:20220721T153000
DTSTAMP:20260422T234137
CREATED:20221103T182855Z
LAST-MODIFIED:20221103T182855Z
UID:5902-1658413800-1658417400@ece.northeastern.edu
SUMMARY:Siyue Wang's PhD Dissertation Defense
DESCRIPTION:“Towards Robust and Secure Deep Learning Models and Beyond” \nAbstract: \nModern science and technology witness the breakthroughs of deep learning during the past decades. Fueled by the rapid improvements of computational resources\, learning algorithms\, and massive amounts of data\, deep neural networks (DNNs) have played a dominant role in many real world applications. Nonetheless\, there is a spring of bitterness mingling with this remarkable success – recent studies have revealed the limitations of DNNs which raise safety and reliability concerns of its widespread usage: 1) the robustness of DNN models under adversarial attacks and facing instability problems of edge devices\, and 2) the protection and verification of intellectual properties of well-trained DNN models.In this dissertation\, we first investigate how to build robust DNNs under adversarial attacks\, where deliberately crafted small perturbations added to the clean inputs can lead to wrong prediction results with high confidence. We approach the solution by incorporating stochasticity into DNN models. We propose multiple schemes to harden the DNN models when facing adversarial threats\, including Defensive Dropout (DD)\, Hierarchical Random Switching (HRS)\, and Adversarially Trained Model Switching (AdvMS). Besides\, we also propose a stochastic fault-tolerant training scheme that can generally improve the robustness of DNNs when facing the instability problem on DNN accelerators without focusing on optimizations for individual devices.The second part of this dissertation focuses on how to effectively protect the intellectual property for DNNs and reliably identify their ownership. We propose Characteristic Examples (C-examples) for effectively fingerprinting DNN models\, featuring high-robustness to the well-trained DNN and its derived versions (e.g. pruned models) as well as low-transferability to unassociated models. To better perform functionality verification of DNNs implemented on edge devices for on-device inference applications\, we also propose Intrinsic Examples. Intrinsic Examples as fingerprinting of DNN can detect adversarial third-party attacks that embed misbehaviors through re-training. The generation process of our fingerprints does not intervene with the training phase and no additional data are required from the training/testing set. \nCommittee: \nProf. Xue Lin (Advisor)Prof. Yunsi FeiProf. Yanzhi Wang
URL:https://ece.northeastern.edu/event/siyue-wangs-phd-dissertation-defense/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220713T110000
DTEND;TZID=America/New_York:20220713T120000
DTSTAMP:20260422T234137
CREATED:20221103T183115Z
LAST-MODIFIED:20221103T183115Z
UID:5906-1657710000-1657713600@ece.northeastern.edu
SUMMARY:Leonardo Bonati's PhD Dissertation
DESCRIPTION:“Softwarized Approaches for the Open RAN of NextG Cellular Networks” \nAbstract: \nThe 5th and 6th generations of cellular networks (5G and 6G)\, also known as NextG\, will bring unprecedented flexibility to the wireless cellular ecosystem. Because of a typically closed and rigid market\, the telco industry has incurred high costs and non-trivial obstacles for delivering new services and functionalities that satisfy the requirements and the demands of NextG networks. To break this trend the industry is now moving toward open architectures based on softwarized approaches\, which afford network operators flexible control and unprecedented adaptability to heterogeneous conditions\, including traffic and application requirements. Now\, by simply expressing a high-level intent\, operators will be able to instantiate bespoke services on-demand on a generic hardware infrastructure\, and to adapt such services to the current network conditions. Through disaggregation\, network elements will split their functionalities across multiple components—possibly provided by different vendors—interconnected through well-defined open interfaces. The separation of control functions from the hardware fabric\, and the introduction of standardized control interfaces\, will ultimately enable the definition and use of softwarized control loops\, which will bring embedded intelligence and real-time analytics to effectively realizing the vision of autonomous and self-optimizing networks.\nThis dissertation work focuses on the design\, prototyping and experimental evaluation of softwarized approaches for the Open Radio Access Network (RAN) of NextG cellular networks. We analyze the architectural enablers\, challenges\, and requirements for a programmatic zero-touch control of the very many network elements and propose practical solutions for its realization. We prototype solutions by leveraging open-source software implementations of cellular protocol stacks and frameworks\, and heterogeneous virtualization technologies\, including the srsRAN and OpenAirInterface cellular implementations\, and the O-RAN framework. The contributions of this work include (i) the first demonstration of O-RAN data-driven control loops in a large-scale experimental testbed using open-source\, programmable RAN and RAN Intelligent Controller (RIC) components through xApps of our design; (ii) CellOS\, a zero-touch cellular operating system that automatically generates and executes distributed control programs for simultaneous optimization of heterogeneous control objectives on multiple network slices starting from a high-level intent expressed by the operators; (iii) OpenRAN Gym\, the first publicly-available research platform for the design\, prototyping\, and experimentation at scale of data-driven O-RAN solutions\, and (iv) OrchestRAN\, a network intelligence orchestration framework for Open RAN that automates the deployment of data-driven inference and control solutions. The effectiveness of our solutions in achieving superior control and performance of the RAN is demonstrated at scale on state-of-the-art experimental facilities\, including software-defined radio-based laboratory setups and open access experimental wireless platforms\, such as Colosseum\, Arena\, and the POWDER and COSMOS platforms from the U.S. PAWR program.
URL:https://ece.northeastern.edu/event/leonardo-bonatis-phd-dissertation/
LOCATION:432 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
GEO:42.3396156;-71.0886534
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=432 ISEC 360 Huntington Ave Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave:geo:-71.0886534,42.3396156
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220712T153000
DTEND;TZID=America/New_York:20220712T163000
DTSTAMP:20260422T234137
CREATED:20221103T183319Z
LAST-MODIFIED:20221103T183319Z
UID:5910-1657639800-1657643400@ece.northeastern.edu
SUMMARY:Zulqarnain Qayyum Khan's PhD Dissertation Defense
DESCRIPTION:“Interpretable Machine Learning for Affective Psychophysiology and Neuroscience” \nAbstract: \nIn this thesis\, we leverage existing Machine Learning (ML) models where appropriate and develop novel models to advance the understanding of affective psychophysiology and neuroscience. Additionally\, considering the increased use of ML as a toolbox\, we highlight underlying assumptions and limitations of basic ML methods to help better contextualize the conclusions drawn from application of ML in this domain. Similarly\, given the increasingly opaque ML models\, the resulting rise of methods to explain these models\, and the importance of explainability to interdisciplinary research\, we investigate theoretical properties of these explainers.\nAffective pyschophysiology research typically uses supervised analyses which leave little room for exploration. Studies of motivated performance tasks often focus on two states of threat and challenge\, exhibiting somewhat inconsistent physiological properties. Using unsupervised analysis of physiology data\, we find evidence for the presence of a third state for the first time\, that may help explain these inconsistencies. Similarly\, prototypical view of emotion often searches for consistency and specificity\, as opposed to constructionist account of emotion which proposes emotion categories as populations of situation-specific variable instances. In results supportive of this constructionist view\, we find large variability in both the number and nature of clusters in unsupervised analyses of ambulatory physiological data. Similarly\, in functional neuroimaging a largely unsolved challenge is to develop models that appropriately account for the commonalities and variations among participants and stimuli\, scale to large amounts of data\, and reason about uncertainty in an unsupervised manner. Such models are needed to investigate important neuroscientific phenomena such as individual variation and degeneracy. We develop Neural Topographic Factor Analysis (NTFA)\, a novel ML model for fMRI data with a deep generative prior that teases apart participant and stimulus driven variation and commonalities\, and demonstrate its potential in investigating individual variation and degeneracy.\nWe further utilize this interdisciplinary research experience to shed light on assumptions and limitations of some of the basic ML methods commonly used in the sciences (especially psychological science). These methods are often used as software packages. We argue that researchers need to be more mindful of their underlying assumptions when drawing conclusions. Along the same lines\, ML methods themselves are becoming increasingly blackbox\, making it harder to reason about underlying assumptions. This has led to an increased focus on explainers\, which provide interpretability to ML methods that is critical for interdisciplinary research. The theoretical properties of these explainers\, however\, remain understudied. We further the research in this direction by defining explainer astuteness as a measure of robustness and theoretically demonstrate that smooth classifiers lend themselves to more astute explanations. \nCommittee: \nProf. Jennifer Dy (Advisor)\nProf. Lisa Feldman Barrett\nProf. Dana Brooks\nProf. Karen Quigley\nProf. Octavia Camps
URL:https://ece.northeastern.edu/event/zulqarnain-qayyum-khans-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220711T100000
DTEND;TZID=America/New_York:20220711T230000
DTSTAMP:20260422T234137
CREATED:20221103T183012Z
LAST-MODIFIED:20221103T183012Z
UID:5904-1657533600-1657580400@ece.northeastern.edu
SUMMARY:Bengisu Ozbay's PhD Proposal Review
DESCRIPTION:“Fast Identification via Subspace Clustering and Applications to Dynamic and Geometric Scene Understanding” \nAbstract: \nMore and more data is needed in order to build new machine learning and computer vision techniques. Using human operators to identify these vast datasets would be too expensive\, hence the use of unsupervised learning has grown more common. Piecewise linear or affine models can be used in a broad range of applications connected to system identification and computer vision.\nIn this proposal\, we suggest an efficient method that only requires singular value decomposition of matrices whose size is unaffected by the total number of points. This method only has to be performed (number of clusters) times. We discovered that it is feasible to find the polynomials that represent the hyperplanes by doing a singular value decomposition (SVD) on the empirical moments matrix containing the data. In this approach\, the notion of using polynomials and Christoffel functions to conduct SVDs in order to partition data into sets\, each of which originates from a different cluster\, is central. Data may be segmented and then the parameters of each group can be extracted using application-specific techniques. In particular\, the problems that are taken into consideration in this proposal include identification of Auto-regressive with Extra Input (SARX) models\, affine linear subspace clustering\, two-view motion segmentation\, and identification of a group of nonlinear systems known as Wiener systems.\nThis proposal is structured as follows: to begin with\, we offer a semi-algebraic clustering framework for locating reliable subsets from the data\, which belongs in a union of varieties and segments the data sequentially using Christoffel polynomials. We employ this strategy for switched system identification and affine subspace clustering challenges. In both instances\, the data resides in linear affine varieties. To expand the given approach beyond linear affine arrangements\, we reformulate it for quadratic surfaces and further apply it to the two-view motion segmentation task. Finally\, using this suggested semi-algebraic formulation\, we are able to detect a class of nonlinearities\, namely Wiener systems with an even nonlinearity\, which is indeed an NP-hard issue.
URL:https://ece.northeastern.edu/event/bengisu-ozbays-phd-proposal-review/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220707T130000
DTEND;TZID=America/New_York:20220707T140000
DTSTAMP:20260422T234137
CREATED:20221103T183515Z
LAST-MODIFIED:20221103T183515Z
UID:5912-1657198800-1657202400@ece.northeastern.edu
SUMMARY:Alexandria Will-Cole's PhD Proposal Review
DESCRIPTION:“Morphology\, Magnetism\, and Transport in Nanomaterials and Nanocomposites” \nAbstract: \nMagnetic thin film materials and bilayer composites enable unprecedented new applications\, ranging from magnetic-based microelectromechanical systems (magnetoelectric sensors\, ultracompact magnetoelectric antennas\, etc.)\, terahertz emitters\, to spin-orbit-torque driven magnetic memories. Here we focus on two subdisciplines within magnetics – magnetoelectrics and spintronics heterostructures. \nThe first aspect of the talk is focused on magnetoelectrics. Strain-mediated magnetoelectric coupling (i.e.\, voltage/electric field control of magnetism\, or magnetic field control of electrical polarization) in bilayer composites has received heightened attention in the research community for applications in memory\, motors\, sensors\, communication etc. The composite ME effect is dependent on the magnetostrictive effect (magnetic-mechanical coupling) and the piezoelectric effect (electrical-mechanical coupling)\, and therefore to improve the composites each constituent phase needs to be optimal. Here we demonstrate the feasibility of machine learning\, specifically Bayesian Optimization methods\, to optimize ferromagnetic materials\, specifically (Fe100−y Gay)1−xBx (x=0–21 & y=9–17) and (Fe100−y Gay)1−xCx (x=1–26 and y=2–18) to demonstrate optimization of structure-property relationships\, specifically the compositional effect on magnetostriction and ferromagnetic resonance linewidth. Following the materials optimization study\, we present voltage control of ultrafast demagnetization in ME heterostructure of (Fe81Ga19)88B12/ Pb(Mg1/3Nb2/3)O3–PbTiO3. Previous studies implement multiple strategies to tune ultrafast demagnetization namely via the laser pump wavelength\, fluence\, polarization\, and pulse duration as these control the total absorbed energy into the film. Here we present an alternate strategy to tune ultrafast demagnetization with application of an electric field in the ME heterostructure to induce magnetic axis rotation. Additionally\, we studied magnetic anisotropy changes and E-field tuning behavior following ultrafast demagnetization. \nThe second aspect of this talk is focused on spintronics heterostructures\, namely ferromagnetic (FM)/topological insulator (TI) or ferrimagnetic insulator (FI)/topological insulator (TI) bilayer composites\, and TI sputter growth and characterization. Bilayer FM/TI and FI/TI heterostructures are promising for spintronic memory applications due to their low switching energy and therefore power efficiency. TIs have been grown with molecular beam epitaxy (oriented\, epitaxial films) and RF magnetron sputtering (amorphous to crystalline oriented films) and have demonstrated large spin-to-charge conversion efficiencies. However\, the reactivity of TIs with FM films is often overlooked in the spin-orbit-torque literature\, even though there are reports that it is energetically favorable for topological insulators to react with metals and form interfacial layers. Here we present the interfacial reaction and antiferromagnetic phase formation between MBE-grown Sb2Te3 and sputtered Ni80Fe20 films. Since FM/TI interfaces are highly reactive and form novel interfacial phases\, which can encourage spin memory loss\, it is critical to explore heterostructures with cleaner interfaces. Recently\, we synthesized chemically stable Y3Fe5O12/Bi2Te3 films\, which should have a chemically sharp interface. We present preliminary structural and magnetic characterization\, followed by proposed experiments to study proximity induced magnetization in these bilayer composites. Concurrent to our investigation spintronic heterostructures\, we seek to optimize sputter deposition of TIs. However\, sputtering TIs requires enhanced control over defects/stoichiometry as these influence bulk transport. We present preliminary results and propose experiments to elucidate structure-transport relationships\, such that we can provide strategies to controllably suppress bulk conduction to access topologically protected surface states. \nCommittee:\nProf. Nian X. Sun (advisor)\nProf. Don Heiman (co-advisor)\nProf. Yongmin Liu\nDr. A. Gilad Kusne\nDr. Todd Monson
URL:https://ece.northeastern.edu/event/alexandria-will-coles-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220707T130000
DTEND;TZID=America/New_York:20220707T140000
DTSTAMP:20260422T234137
CREATED:20221103T183228Z
LAST-MODIFIED:20221103T183228Z
UID:5908-1657198800-1657202400@ece.northeastern.edu
SUMMARY:Sara Garcia Sanchez's PhD Dissertation Defense
DESCRIPTION:“Learning and Shaping the Wireless Environment: An Integrated View of Sensing\, Computing and Communication” \nAbstract: \nThe explosive growth in Internet of Things (IoT) deployments and anticipated data volumes that will be generated within future autonomous devices require collecting and processing large amounts of data\, generally transmitted over the wireless channel. Rigid infrastructure deployment that does not adapt to the changing wireless environment is not well suited to handle these new demands. To address this limitation\, this dissertation takes a hands-on approach to equip communication systems with technology to learn from\, interact with and actuate within the environment. Specifically\, we build (i) accurate physics-based predictive models and multimodal sensing techniques to gain awareness of the existing channel\, as well as (ii) novel multidisciplinary approaches to intelligently shape the wireless channel towards enhancing the communication link. \nWe first prove that combining wireless channel modeling\, multimodal sensing and robotics provides significant link performance gains. To this extent\, we adopt a systems approach to study how millimeter wave (mmWave) radio transmitters on Unmanned Aerial Vehicles (UAVs) provide high throughput links under typical hovering conditions. Based on sensing and modeling efforts\, we propose techniques to exploit the information contained in the spatial and angular domains of empirically collected data from GPS\, cameras and RF signals. We demonstrate how to mitigate the impact of hovering by (i) selecting near-to-optimum transmission parameters as compared to the mmWave standard IEEE 802.11ad\, and (ii) proposing corrective coordinated actions at the UAVs from the robotic controls. These methods achieve mmWave beam-tracking and robust link deployment under event(s) impacting link performance\, such as hovering or blockage in the light of sight between transmitter and receiver.\nFinally\, we experimentally demonstrate how the wireless environment can be interactively shaped through the use of Reconfigurable Intelligent Surfaces (RIS). First\, we propose AirNN\, a system capable of partially offloading computation into the wireless domain by realizing analog convolutions with over-the-air computation. We demonstrate that such computation is accurate enough to substitute its digital equivalent in a Convolutional Neural Network (CNN). Second\, we propose a RIS-based spatio-temporal signal modification approach for channel hardening (i.e.\, ensure low power fluctuations in the received signal) in a Single-Input Single-Output link and under rich multipath\, which is common for IoT 5G+ deployments. We prove that our approach achieves channel hardening similar to a classical Single-Input Multiple-Output (SIMO) system while only using a single antenna element at the receiver end. \nAll the above theoretical advances are validated with rigorous analysis and experimentation. \nCommittee: \nProf. Kaushik Chowdhury (Advisor) \nProf. Stefano Basagni \nProf. Josep Jornet
URL:https://ece.northeastern.edu/event/sara-garcia-sanchezs-phd-dissertation-defense/
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