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DTSTART:20210314T070000
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DTSTART;TZID=America/New_York:20230303T100000
DTEND;TZID=America/New_York:20230303T110000
DTSTAMP:20260522T070145
CREATED:20230223T162329Z
LAST-MODIFIED:20230223T162329Z
UID:35977-1677837600-1677841200@coe.northeastern.edu
SUMMARY:Guanying Sun's PhD Dissertation Defense
DESCRIPTION:“Optimizing Reconstruction for Mm-Wave Body Scanner Imaging” \nCommittee: \nProf. Carey Rappaport (Advisor) \nProf. Edwin Marengo \nProf. Jose Martinez-Lorenzo \nAbstract: \nIn the past decades\, due to evolving threats\, passenger screening has become an important secure measure at airport and other secure locations. Numerous passenger screening techniques have been developed by researchers in both academia and industry to detect threats from explosives and weapons. Among these developments\, the multistatic mm-wave radar Advanced Imaging Technology (AIT) system was developed at Northeastern University. A problem with this system is the sidelobes from its physical limitations\, such as the finite aperture extent and the violation of the Nyquist sampling criterion by the sparse array. Therefore\, it is important to suppress the sidelobes so that to improve the quality of the reconstruction image. In this proposal\, we investigate two categories of methods\, one is based on post-processing\, and the other is based on system configuration optimization. In the former category four methods are developed\, while in the latter two methods are proposed. \nIn the first category\, the first method is the phase coherence method which is designed to weight the coherent sum based on the phase diversity of the reconstructed solutions for different transmitters. In this method\, two ways are considered to construct the Phase Coherence Factor (PCF). The first way is to use the information of wrapped phase\, and the second way is to use the information of unwrapped phase\, which is more intuitive than the first way. The second method is the coherence factor related method. Three coherence-factor based methods are analyzed and then incorporated into the imaging procedure of our nearfield millimeter-wave radar security scanning system. The third method is the SNR-dependent coherence factor method\, which takes SNR into consideration when forming the coherence factor. This method can generate better results than the pure coherence-factor based methods by choosing a proper set of parameters. The fourth method is the block-weighting algorithm where the neighbor weight amplifies bright areas and attenuates dark areas\, while the block keeps the influence local. The effectiveness of these methods has been verified with both simulation and measurement data. \nIn the second category\, the first method is optimizing receiver positions via PSF-based multi-objective optimization. Two metrics for measuring image quality of the PSF are proposed and defined as objective functions. The solution-selection metric is introduced to select the desired solution from the numerous Pareto-optimal solutions. Simulation shows that the optimized receiver design generates images with lower sidelobe level than the uniform receiver design. The second method is the dual-frequency radar design\, where a dual frequency\, wideband antenna array is designed by combining a high frequency subarray with a low frequency subarray. The image of the dual frequency array is obtained by multiplying the images of the two subarrays. We analyzed the amplitude of the PSF theoretically and proposed a criterion for the selection of dual frequency array design. The system imaging simulation shows that the grating lobes are significantly reduced for the dual frequency array with fewer radar modules/elements than the conventional array. This design will make the new generation system superior to the conventional scanning system.
URL:https://coe.northeastern.edu/event/guanying-suns-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230302T090000
DTEND;TZID=America/New_York:20230302T100000
DTSTAMP:20260522T070145
CREATED:20230223T162222Z
LAST-MODIFIED:20230223T162222Z
UID:35974-1677747600-1677751200@coe.northeastern.edu
SUMMARY:Matthew Schinault's PhD Proposal Review
DESCRIPTION:“Development of A Large-Aperture 160-Element Coherent Hydrophone Array System for Instantaneous Wide Area Ocean Acoustic Sensing” \nAbstract: \nA large aperture coherent hydrophone towed array system comprising of 160 elements and an aperture length of 192 meters has been developed for real-time instantaneous wide-area ocean acoustic remote sensing and monitoring. The design and manufacture of these arrays requires a multidisciplinary approach to achieve acoustic performance capable for detection\, classification\, localization and tracking. Drawing from disciplines such as material science\, electrical engineering\, mechanical engineering\, hydrodynamics\, oceanography\, bioacoustics and signal processing. Due to the cost and complexity of towed array technology\, development of large aperture towed arrays has been limited at the university level. With military\, oil and gas exploration as the chief technology developers and users. The military and commercial focus is narrow and does not allow for scientific study\, resulting in significant gaps in the way we understand ocean acoustics around the globe. Here we model\, design\, fabricate and field test a broadband array for general ocean sensing that is configured to support a wide range of research to include study of marine mammals\, fish shoals\, geophysical processes\, surface or subsea man-made craft\, seismic surveying and the various challenges associated with detection\, classification and localization of underwater sound sources. \nHere\, we present the design process\, beginning with modeling and measurement of piezoelectric material properties. This allows us to perform finite element analysis\, estimating beampatterns and frequency response with a hydrophone electrical model. A pressure to voltage input model of the hydrophone is used to obtain the voltage levels produced to then configure amplification\, gain and filter stages providing a system level transfer function from analog to digital conversion. The array performance with a delay and sum beamformer is estimated for a broad range of frequencies\, with beamforming above half-lambda spacing. The components of the mechanical tow package are modeled to inform array construction estimating vibration and flow noise. A turbulent boundary layer model for flow noise estimation and environmental noise model determines the gains and cutoff frequencies necessary for performance. The comprehensive performance model is compared with a parameter estimation from test data to quantify array performance. \nTowed arrays are subject to environmental extremes\, with time at sea being costly. To increase the reliability\, the array is designed using field replaceable pressure tolerant components including hydrophones\, pre-amplifiers\, power modules\, telemetry and analog to digital conversion units. All components are verified by pressure chamber testing to ensure operation at depth. This large aperture array was able to be made without specialized facilities by utilizing modular interchangeable array interconnects allowing for conventional array populating and oil-filling methods with aperture lengths that are serviceable onboard research vessels. Array design\, fabrication and assembly was performed on-site at Northeastern University in Boston\, Massachusetts. Examples of passive acoustic data from array deployment during a sea trial in the U.S. Northeast coast are presented illustrating array capabilities. \nCommittee: \nProf. Purnima Ratilal Makris (Advisor)\nProf. Marvin Onabajo\nProf. Yongmin Liu\nDr. Alessandra Tesei
URL:https://coe.northeastern.edu/event/matthew-schinaults-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230227T130000
DTEND;TZID=America/New_York:20230227T140000
DTSTAMP:20260522T070145
CREATED:20230223T162432Z
LAST-MODIFIED:20230223T162432Z
UID:35980-1677502800-1677506400@coe.northeastern.edu
SUMMARY:Yu Yin's PhD Proposal Review
DESCRIPTION:Committee: \nProf. Yun Fu (Advisor) \nProf. Sarah Ostadabbas \nProf. Ming Shao \nAbstract:\nThe community has long enjoyed the benefits of synthesizing data\, as it provides a reliable and controllable source for training machine learning models while reducing the need for data collection from the real world. Human face and body synthesis are especially appealing to research communities\, where model fairness and ethical deployment are critical concerns. However\, generating digit humans that are convincing\, realistic-looking\, identity-preserving\, and high-quality are still challenging in 2D and 3D image synthesis.\nThis dissertation investigates the potential for understanding human behavior by recreating it\, and can be broadly divided into three sections. (1) In Section one\, we explore the 2D image generation models and their interaction with face applications (i.e.\, landmark localization and face recognition tasks). Specifically\, super-resolution (SR) and landmark localization of tiny faces are highly correlated tasks. To this end\, we propose joint frameworks that enable face alignment and SR to benefit from one another\, hence enhancing the performance of both tasks. Moreover\, we demonstrate that face frontalization provides an effective and efficient way for face data augmentation and further improves face recognition performance in extreme pose scenarios. (2) In Section two\, we explore the 3D parametric generation models and how they support human body pose and shape estimation. Advancing technology to monitor our bodies and behavior while sleeping and resting is essential for healthcare. However\, keen challenges arise from our tendency to rest under blankets. To mitigate the negative effects of blanket occlusion\, we use an attention-based restoration module to explicitly reduce the uncertainty of occluded parts by generating uncovered modalities\, which further update the current estimation via a cyclic fashion. (3) In Section three\, we explore the 3D Nerf-based Generative models in generating high-quality images with consistent 3D geometry. We propose a universal method to surgically fine-tune these NeRF-GAN models in order to achieve high-fidelity animation of real subjects only by a single image.
URL:https://coe.northeastern.edu/event/yu-yins-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230215T113000
DTEND;TZID=America/New_York:20230215T123000
DTSTAMP:20260522T070145
CREATED:20230210T160554Z
LAST-MODIFIED:20230210T160554Z
UID:35734-1676460600-1676464200@coe.northeastern.edu
SUMMARY:Yiyue Jiang's PhD Proposal Review
DESCRIPTION:“FPGA-based Accelerator of Neural Networks for Digital Predistortion” \nCommittee: \nProf. Miriam Leeser (Advisor) \nProf. John Dooley \nProf. Stefano Basagni \nAbstract: \nPower Amplifiers (PAs) are an essential part of wireless communications. \nAs wireless standards evolve and become more demanding\,  the requirements for PAs change as well.  Specifically\, PAs need to balance linearity and energy efficiency while adhering to 5G wireless standards and beyond. PA behaviors differ based on several criteria\, including the type of PA\, power levels\, and the environment. To overcome the nonlinear behavior of a PA\, a flexible system to achieve digital predistortion (DPD) is required that can rapidly adapt to its environment. \nIn many situations\, traditional methods such as the memory polynomial model cannot adapt to all these factors. Neural networks have been used for some years in RF and microwave engineering. Early work demonstrated the suitability of neural networks to model complicated active device characteristics. Current neural network based DPD systems all do the training offline and are therefore not real-time systems. To reduce the cost to upgrade hardware and to provide more flexibility to different power amplifiers’ linearization needs\, a specific neural network based reconfigurable\, adaptive\, and real-time digital predistortion system is proposed. This system targets Zynq All Programmable System on Chip (SoC) devices which feature an ARM processor and FPGA together with RF frontend on the same chip. The system proposed in this research combines real-time DPD with on-chip training. Furthermore\, most research on FPGA based inference accelerators targets classification problems with probability output. There is no accelerator working on the signal processing problem focusing on sample-by-sample output. Our proposed system is optimized in both algorithm and implementation targeting sample-by-sample processing with high accuracy and real-time efficiency. \n 
URL:https://coe.northeastern.edu/event/yiyue-jiangs-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230203T150000
DTEND;TZID=America/New_York:20230203T170000
DTSTAMP:20260522T070145
CREATED:20230201T150236Z
LAST-MODIFIED:20230201T150236Z
UID:35550-1675436400-1675443600@coe.northeastern.edu
SUMMARY:Kubra Alemdar's PhD Proposal Review
DESCRIPTION:“Overcoming and Engineering Wireless Signals for Communication and Computation” \nAbstract: \nThe phenomenal growth of connected devices\, especially rapid expansion of IoT networks and the increasing demand for wireless services are the main driving forces for the evolution of wireless technologies. However\, the realization of such technologies requires a radical transformation of existing infrastructures to satisfy the needs of changing wireless environments. The main limitation in delivering these systems stems from a huge diversity in their demands and constraints. To address this limitation\, this dissertation shows how wireless signals and their interaction with and within wireless propagation domain can be used as communication or computational tools that enable us to achieve certain novel tasks. Specifically\, we build i) cross-functionality architectures to engineer the wireless channel to a) enable the operation of emerging technologies\, and b) demonstrate a new paradigm for computing with wireless signals\, and ii) intelligently shape the wireless channel to create reliable communication links. \nThis dissertation presents an experimentally validated software-hardware system to deliver three key contributions: We present a physical layer solution for distributed networks that provides over-the-air (OTA) clock synchronization\, called as RFCLOCK\, to overcome the hurdle of implementing fine-grained synchronization for emerging technologies. We first develop the theory for such precision synchronization and second implement it in a custom-design\, which is compatible with commercial-off-the-shelf (COTS) software-defined radios (SDRs). We compare the performance of RFClock with popular wired and GPS-based hardware solutions\, both in terms of clock performance\, as well as impact on distributed beamforming. \nNext\, we propose an RIS-based (reconfigurable reflecting surface) spatio-temporal approach to enhance the link reliability for IoTs where sensors are small-factor designs with single-antenna in rich multipath environment. We demonstrate the design of RIS and how it can effectively perturb the environment\, generating multiple wireless propagation channels and achieving performance of multi-antenna receiver in a Single-Input Single-Output (SISO) link. We compare the performance of the system with multi-antenna receiver in terms of channel hardening and outage probability. \nFinally\, we propose AirFC\, a system harnessing the capability of OTA computation to run inference on a neural network (NN) consisting of a set of fully connected layers (FC) by leveraging multi-antenna systems. We experimentally demonstrate and validate that such computation is accurate enough when compared to its digital counterpart. \nAs part of proposed research ahead\, we will address the challenges of realizing RIS-assisted communication in non-stationary conditions where the wireless channel can abruptly change due to the dynamic environment. We will first demonstrate the conditions in which conventional channel estimation methods cannot be utilized. We will then propose a learning method to create directional beams through reflections from RIS towards target locations without estimating the channel. \nLocation: 632 ISEC \nCommittee: \nProf. Kaushik Chowdhury (Advisor) \nProf. Marvin Onabajo \nProf. Josep Jornet
URL:https://coe.northeastern.edu/event/kubra-alemdars-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230202T103000
DTEND;TZID=America/New_York:20230202T113000
DTSTAMP:20260522T070145
CREATED:20230126T154948Z
LAST-MODIFIED:20230126T155022Z
UID:35236-1675333800-1675337400@coe.northeastern.edu
SUMMARY:Amani Al-shawabka's PhD Proposal Review
DESCRIPTION:“Channel-and-Adversary-Resilient Radio Fingerprinting through Data-Driven Approaches at Scale” \nCommittee: \nProf. Tommaso Melodia (Advisor)\nProf. Kaushik Chowdhury\nProf. Francesco Restuccia \nAbstract: \nRadio fingerprinting authenticates wireless devices by leveraging tiny hardware-level imperfections inevitably present in the radio circuitry. This way\, devices can be directly identified at the physical layer– thus avoiding energy-expensive upper-layer cryptography that resource-limited embedded devices may not be able to afford. Recent advances have proven that employing deep learning algorithms can achieve fingerprinting accuracy levels that were impossible to achieve by traditional low-dimensional algorithms. Still\, the wireless research community lacks an exhaustive understanding of the challenges associated with developing robust\, reliable\, and channel-resilient radio fingerprinting through deep-learning approaches for practical applications. Key challenges are the non-stationarity of the wireless channel\, and the dynamic effects introduced by the operational environment\, which significantly limit fingerprinting applications by obscuring the hardware impairments associated with the transmitted waveform.\nIn this thesis\, we (i) develop a full-fledged\, systematic investigation to quantify the impact of the wireless channel by providing a first-of-its-kind evaluation on deep-learning-based fingerprinting algorithms\, examining the worst-case scenario (employing devices with identical radio circuitry) and at scale; (ii) develop large-scale open datasets for radio fingerprinting collected in diverse\, rich\, channel conditions and environments\, and using different technologies\, including WiFi and LoRa; (iii) identify conditions where hardware impairments are still detectable; and (iv) design\, implement\, and benchmark new data-driven algorithms to counter the degradation introduced by the wireless channel. Notably\, we propose a generalized\, real-time channel- and adversary-resilient data-driven approach to authenticate wireless devices at scale in practical scenarios. To the best of our knowledge\, our work for the first time improves the fingerprinting accuracy of the worst-case scenario with up to 4x and 6.3x for WiFi and LoRa technologies\, respectively.
URL:https://coe.northeastern.edu/event/amani-al-shawabkas-phd-proposal-review-2/
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:20221216T140000
DTEND;TZID=America/New_York:20221216T150000
DTSTAMP:20260522T070145
CREATED:20221215T160806Z
LAST-MODIFIED:20221215T160806Z
UID:34803-1671199200-1671202800@coe.northeastern.edu
SUMMARY:Ali Al Qaraghuli's PhD Proposal Review
DESCRIPTION:“Terahertz Communication for Space Systems” \nAbstract: \nWith the ultimate vision of ubiquitous egalitarian worldwide coverage driven by the rapid proliferation of high-speed satellite networks\, private companies are launching satellites into orbit at unprecedented rates. The main goal of such networks enabled by dense constellations in space is to build a more robust telecommunications infrastructure and provide worldwide internet access to users on Earth. Similarly\, small satellites are included in the vision of non-terrestrial networks (NTN) for 6G networks which promise for more connectivity on Earth. The significant projected traffic in the orbital uplink\, downlink\, and crosslink communication will demand more spectrum to suit more users and satisfy the need for higher data rates. Similarly\, the push towards using smaller satellites in the form of CubeSats will require the hardware to be more compact than ever. This introduces the terahertz band (0.1-10THz) as a candidate technology to satisfy both large bandwidth and device compactness requirements due to the smaller wavelength of terahertz signals. These two advantages\, however\, come at the cost of high propagation losses and impose the use of very high-gain directional antennas\, leading to limitations in constellation network design. This proposal evaluates terahertz communication in space in contrast to competitor technologies such as microwaves and free-space optical communication\, and establishes the feasibility of terahertz networks in space. Next\, the areas of research and innovation required to realize terahertz space communication systems are identified and explored. Finally\, advancements in those areas are presented\, and the next steps are identified to transform terahertz space communication systems into reality. \n  \nCommittee: \nProf. Josep Jornet (advisor) \nProf. Tommaso Melodia \nProf. Kaushik Chowdhury
URL:https://coe.northeastern.edu/event/ali-al-qaraghulis-phd-proposal-review/
LOCATION:432 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
GEO:42.3396156;-71.0886534
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221209T120000
DTEND;TZID=America/New_York:20221209T133000
DTSTAMP:20260522T070145
CREATED:20221130T212737Z
LAST-MODIFIED:20221130T212840Z
UID:34621-1670587200-1670592600@coe.northeastern.edu
SUMMARY:Alexey Tazin's PhD Dissertation Defense
DESCRIPTION:“Composition of UML Class Diagrams Using Category Theory and External Constraints” \nAbstract:\nIn large software development projects there is always a need for refactoring and optimization of the design. Usually software designs are represented using UML diagrams (e.g class diagrams). A software engineering team may create multiple versions of class diagrams satisfying some external constraints. In some cases\, subdiagrams of the developed diagrams can be selected and combined into one diagram. It is difficult to perform this task manually since manual process is very time consuming\, is prone to human errors\, and is not manageable for large projects. In this dissertation we present an algorithmic support for automating the generation of composed diagrams\, where the composed diagram satisfies a given collection of external constraints and is optimal with respect to a given objective function. The composition of diagrams is based on the colimit operation from category theory. The developed approach was verified experimentally by generating random external constraints (expressed in SPARQL and OWL)\, generating random class diagrams using these external constraints\, generating composed diagrams that satisfy these external constraints\, and computing class diagram metrics for each composed diagram. \nCommittee: \nProf. Mieczyslaw Kokar (Advisor) \nProf. David Kaeli \nDr. Jeff Smith
URL:https://coe.northeastern.edu/event/alexey-tazins-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221209T110000
DTEND;TZID=America/New_York:20221209T130000
DTSTAMP:20260522T070145
CREATED:20221130T213204Z
LAST-MODIFIED:20221130T213204Z
UID:34626-1670583600-1670590800@coe.northeastern.edu
SUMMARY:Bin Sun's PhD Dissertation Defense
DESCRIPTION:“Factorization guided Lightweight Neural Networks for Visual Analysis” \nCommittee: \nProf. Yun Fu (Advisor) \nProf. Ming Shao \nProf. Lili Su \nAbstract: \nDeep learning has become popular in recent years primarily due to powerful computing devices such as GPUs. However\, many applications such as face alignment\, image classification\, and gesture recognition need to be deployed to multimedia devices\, smartphones\, or embedded systems with limited resources. Thus\, there is an urgent need for high-performance but memory-efficient deep learning models. For this\, we design several lightweight deep learning models for different tasks with factorization strategies. \nSpecifically\, we constructed a lightweight face alignment model by proposing a factorization-based deep convolution module named Depthwise Separable Block (DSB) and a light but practical module based on the spatial configuration of the faces. Experiments on four popular datasets verify that Block Mobilenet has better overall performance with less than 1MB storage size.\nBesides the face analysis application\, we also explored a general\, lightweight deep learning module for image classification with low-rank pointwise residual (LRPR) convolution\, called LRPRNet. Essentially\, LRPR aims at using a low-rank approximation to factorize the pointwise convolution while keeping depthwise convolutions as the residual module to rectify the LRPR module. Moreover\, our LRPR is quite general and can be directly applied to many existing network architectures. \nDue to the success of the factorization strategy on image-based data\, we extended factorization on time sequence data for Sign Language Recognition (SLR). We achieved the first rank in the challenge of SLR with the help of our proposed novel Separable Spatial-Temporal Convolution Network (SSTCN)\, which divides a 3D convolution on joint features into several stages \, which help the SSTCN achieve higher accuracy with fewer parameters. \nWe also tried to factorize the features for single image super resolution (SISR). Factorization on features will reduce the feature size in order to reduce the computation costs. However\, the reduction of the spatial size is counter-intuitive for the super resolution task. With our exploration\, we demonstrated a network named Hybrid Pixel-Unshuffled Network (HPUN)\, which factorized the features to achieve the lightweight purpose while keeping high performance. Specifically\, we utilized pixel-unshuffle operation to factorize the input features. After the factorization\, we improved the performance by the grouped convolution\, max-pooling\, and self-residual. The experiments on popular benchmarks showed that the factorization strategy could achieve SOTA performance on SISR.
URL:https://coe.northeastern.edu/event/bin-suns-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221208T140000
DTEND;TZID=America/New_York:20221208T160000
DTSTAMP:20260522T070145
CREATED:20221202T151226Z
LAST-MODIFIED:20221202T151226Z
UID:34671-1670508000-1670515200@coe.northeastern.edu
SUMMARY:Chuangtang Wang's PhD Proposal Review
DESCRIPTION:“All-optical Control of Magnetization in Nanostructures” \nCommittee: \nProf. Yongmin Liu (Advisor) \nProf. Don Heiman \nProf. Nian X. Sun \nAbstract:\nThe switching of magnetization by a femtosecond laser within several picoseconds has recently gained substantial attention\, because it promises next-generation\, energy-efficient\, and high-rate data storage technology. One of the most intriguing demonstrations is the helicity-dependent switching (HD-AOS) of a ferromagnet\, in which the magnetization states can be deterministically written and erased using left- and right-circularly polarized light. However\, the challenge is to realize a single-pulse HD-AOS. Controlling the spin angular momentum transfer from light to magnetic materials in nanostructures is the key to advance this field.\nIn my thesis research work\, I will study the all-optical control of magnetization in different nanostructures\, aiming to better understand the underlying mechanisms of HD-AOD and accelerate the technology development. Firstly\, helicity-driven magnetization dynamics in heavy metal/ferromagnet Au(Pt)/Co bilayer by the optical spin transfer torque (OSTT) is experimentally explored. The wavelength-dependent measurement of OSTT reveals that the quantum efficiency of OSTT strongly depends on the interface electronic structure and pump energy. The Inverse Faraday effect (IFE)\, which is believed to be the driving mechanism of HD-AOS\, is subsequently investigated in an Au thin film. The dependence of IFE on photon energy implies that the orbital angular momentum contribution to IFE is dominated by the excitation of laser pulses. To the best of our knowledge\, it is the first demonstration of this phenomenon. Lastly\, I will discuss our recent results on plasmonics-enhanced all-optical control of magnetization. Light can be tightly confined in plasmonic structures\, which can potentially enable low-energy and high-density magnetic data storage.
URL:https://coe.northeastern.edu/event/chuangtang-wangs-phd-proposal-review/
LOCATION:138 ISEC\, 360 Huntington Ave\, 138 ISEC\, Boston\, MA\, 02115\, United States
GEO:42.3401758;-71.0892797
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221208T110000
DTEND;TZID=America/New_York:20221208T120000
DTSTAMP:20260522T070145
CREATED:20221130T213045Z
LAST-MODIFIED:20221130T213045Z
UID:34624-1670497200-1670500800@coe.northeastern.edu
SUMMARY:Danlin Jia's PhD Dissertation Defense
DESCRIPTION:“Towards Performance and Cost-efficiency for Data-intensive Applications in Distributed Data Processing Systems” \nAbstract: \nData-intensive science (DIS) has experienced a significant boom in the past decade. The emerging technologies of data-intensive services and infrastructures contribute to DIS’s development and raise challenges. An ecosystem has been constructed considering performance\, scalability\, sustainability\, and reliability to provide a high-quality service to DIS applications. The ecosystem consists of services exposed to users for application deployment and infrastructures to support data storage\, transfer\, and management from the system’s perspective. DIS applications share typical features\, such as memory and I/O intensity. Thus\, addressing the bottlenecks triggered by memory-intensive or I/O-intensive workloads in services and infrastructures is essential to improve the performance and cost-efficiency of the whole ecosystem. In this dissertation\, we investigate the characteristics of various DIS applications and design new resource allocation and scheduling schemes for the services and infrastructures in the DIS ecosystem. \nWe first investigate memory optimization in DIS ecosystems. In-memory data analytic frameworks are proposed to cache critical intermediate data in memory instead of in storage drives. Apache Spark is a commonly adopted in-memory data analytic framework with two memory managers\, Static and Unified. However\, the static memory manager lacks flexibility. In contrast\, the unified memory manager puts heavy pressure on the garbage collection of the Java Virtual Machine on which Spark resides. To address these issues\, we propose a new learning-based bidirectional usage-bounded memory allocation scheme to support dynamic memory allocation considering both memory demands and latency introduced by garbage collection. Distributed data-processing workloads in container-based virtualization take advantage of resource sharing\, fast delivery\, and excellent portability of containerization but also suffer from resource competition and performance interference. This inevitably induces performance degradation and significantly long latency\, even worse when over-provisioning. Motivated by this problem\, we design an efficient memory allocation scheme (RITA) for containerized parallel systems to improve data processing latency. RITA monitors applications’ memory usage and cache characteristics and dynamically re-allocates memory resources. \nWe also propose I/O optimizations for DIS applications and infrastructures. Distributed Deep Learning (DDL) accelerates DNN training by distributing training workloads across multiple computation accelerators\, e.g.\, GPUs. Although a surge of research has been devoted to optimizing DDL training\, the impact of data loading on GPU usage and training performance has been relatively under-explored. When multiple DDL applications are deployed\, the lack of a practical and efficient technique for data-loader allocation incurs GPU idleness and degrades the training throughput. In this dissertation\, we thus investigate the impact of data-loading on the global training throughput and design a resource allocator that uses the data-loading rate as a knob to reduce the GPU idleness. Finally\, designs and optimizations on disaggregated storage systems supported by cutting-edge storage and network techniques emerge dramatically. Disaggregated storage systems can scale resources independently and provide high-quality services for hyper-scale architectures. The traditional congestion control mechanism relieves congestion by limiting the data-sending rate of senders. However\, such a design scarifies the storage drive’s performance as data are generated but stalled on storage host nodes if network congestion happens. To solve this issue\, we design a storage-side rate control mechanism to mitigate network congestion while avoiding sacrificing I/O performance. \nCommittee: \nProf. Ningfang Mi (Advisor) \nProf. Xue Lin \nProf. David Kaeli
URL:https://coe.northeastern.edu/event/danlin-jias-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221206T160000
DTEND;TZID=America/New_York:20221206T173000
DTSTAMP:20260522T070145
CREATED:20221205T210005Z
LAST-MODIFIED:20221205T210005Z
UID:34698-1670342400-1670347800@coe.northeastern.edu
SUMMARY:Md Navid Akbar's PhD Dissertation Defense
DESCRIPTION:“Inference from Brain Imaging: Incorporating Domain Knowledge and Latent Space Modeling” \nAbstract:\n\nBrain imaging can probe the anatomy (structural) of our brain\, or its function (functional). A particular imaging modality (unimodal) generally provides only a particular insight into human health. Transcranial magnetic stimulation (TMS)\, though still in its infancy as a brain imaging modality\, is such a functional\, unimodal technique. TMS helps model human motor-cortical mapping\, using corresponding muscle activity captured by surface electromyography (EMG)\, but it necessitates a reliable data-driven model. Earlier works have modeled the causal direction only (from cortical representation to muscles)\, or the inverse direction (from muscles to cortical representation)\, with simple statistical regression. We modeled this motor-cortical mapping bi-directionally in this dissertation\, using deep learning. We first modeled TMS-induced 3D electric field (E-field) in a brain to causal multi-muscle activation picked up by EMG\, in a regression task using a convolutional neural network (CNN) autoencoder. By fusing neuroscience domain knowledge (e.g.\, an empirical neural response profile)\, we reduced 14% squared error\, compared to the baseline model that did not contain this. We then designed our novel inverse imaging CNN model\, to reconstruct physiologically meaningful E-field distributions (in the image domain) from a given set of muscle activations (in the sensor domain). By adopting variational inference in the CNN model\, to learn the underlying latent space better\, we were able to reduce 13% in squared error over our purely CNN baseline. \nDiagnosis with brain imaging is often incomplete with a unimodal technique\, and having multiple sources (multimodal) may be advantageous. Successful multimodal fusion can provide more holistic information\, compared to its constituents. One relevant example is the classification of late post-traumatic seizure (LPTS). Previous works in this space have tackled LPTS classification with either unimodal functional imaging\, or non-machine learning (ML) structural modeling. In this dissertation\, we first undertook the ML classification of binary LPTS: with unimodal\, structural brain imaging\, namely diffusion magnetic resonance imaging (dMRI). By incorporating interpretable domain knowledge (post-traumatic lesion volume compensation)\, we improved 7% in the mean area under the curve (AUC) over the standard technique in literature. Finally\, we classified LPTS for a larger sample of subjects\, utilizing multimodal imaging\, including functional MRI (fMRI) and electroencephalography (EEG). Following unsupervised imputation for any missing modality within the subjects\, we introduced our novel multimodal fusion algorithm\, which attempts to leverage the underlying structure of the multivariate information. We found that our proposed algorithm improved by 7% in AUC performance\, over a naive Bayesian estimator that can handle missing data intrinsically.\nCollectively\, the work presented here demonstrated that incorporating domain knowledge in the modeling pipeline successfully improved inference. Similar improvements were also observed by learning and leveraging the possible underlying latent structure of the given information\, and adapting the models accordingly. \n\n\n\nCommittee:\n\nProf. Deniz Erdogmus (Advisor) \nProf. Mathew Yarossi (Co-advisor)\nProf. Dominique Duncan\nProf. Sarah Ostadabbas
URL:https://coe.northeastern.edu/event/md-navid-akbars-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221129T140000
DTEND;TZID=America/New_York:20221129T153000
DTSTAMP:20260522T070145
CREATED:20221121T202209Z
LAST-MODIFIED:20221121T202209Z
UID:34504-1669730400-1669735800@coe.northeastern.edu
SUMMARY:Prof. Hui Guan -  "Towards accurate and efficient edge computing via multi-task learning "
DESCRIPTION:“Towards accurate and efficient edge computing via multi-task learning ” \n\nAbstract: \n\n\nAI-powered applications increasingly adopt Deep Neural Networks (DNNs) for solving many prediction tasks\, leading to more than one DNNs running on resource-constrained devices. Supporting many models simultaneously on a device is challenging due to the linearly increased computation\, energy\, and storage costs. An effective approach to address the problem is multi-task learning (MTL) where a set of tasks are learned jointly to allow some parameter sharing among tasks. MTL creates multi-task models based on common DNN architectures and has shown significantly reduced inference costs and improved generalization performance in many machine learning applications. In this talk\, we will introduce our recent efforts on leveraging MTL to improve accuracy and efficiency for edge computing. The talk will introduce multi-task architecture design systems that can automatically identify resource-efficient multi-task models with low inference costs and high task accuracy. \n\n\nBio:\n \n\n\n\nHui Guan is an Assistant Professor in the College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst\, the flagship campus of the UMass system. She received her Ph.D. in Electrical Engineering from North Carolina State University in 2020. Her research lies in the intersection between machine learning and systems\, with an emphasis on improving the speed\, scalability\, and reliability of machine learning through innovations in algorithms and programming systems. Her current research focuses on both algorithm and system optimizations of deep multi-task learning and graph machine learning.
URL:https://coe.northeastern.edu/event/prof-hui-guan-towards-accurate-and-efficient-edge-computing-via-multi-task-learning/
LOCATION:442 Dana\, 360 Huntington Ave\, 442 DA\, Boston\, MA\, 02115\, United States
GEO:42.3387508;-71.0923044
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=442 Dana 360 Huntington Ave 442 DA Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave\, 442 DA:geo:-71.0923044,42.3387508
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221129T100000
DTEND;TZID=America/New_York:20221129T130000
DTSTAMP:20260522T070145
CREATED:20221103T210151Z
LAST-MODIFIED:20221103T210151Z
UID:34212-1669716000-1669726800@coe.northeastern.edu
SUMMARY:Research Presentations On Bendable Electronics and Sustainable Technologies (BEST)
DESCRIPTION:Professor Ravinder Dahiya will be joining Northeastern’s ECE Department on January 2023. Please join us for an interactive mini-symposium featuring projects from the BEST Lab directed by Professor Dahiya. \n  \nThe presenters are: \nDr. Dhayalan Shakthivel\, Research Associate\, Inorganic Nanowires for Flexible and Large Area Electronics \nDr. Gaurav Khandelwal\, Post-doc\, Functional Materials based Triboelectric Nanogenerators for Selfpowered Sensors and Systems \nDr. Fengyuan Liu\, Post-doc\, “Hebbian-like” learning in electronic skin \nDr. Abhishek S. Dahiya\, Research Associate\, Towards energy autonomous electronic skin using sustainable technologies \nAyoub Zumeit\, PhD candidate\, Inorganic nanostructures-based high-performance flexible electronics \nAdamos Christou\, PhD candidate\, Novel Technologies for High-Performance Printed Electronics \nRadu Chirila\, PhD candidate\, Electronic Skin and Holographic Systems for Socially Intelligent Robots \nJoão Neto\, PhD candidate\, Hardware building for neuromorphic electronic skin \nLuca De Pamphilis\, PhD candidate\, Nanowire-based electronic layers for flexible neuromorphic devices \nMake sure to RSVP & specify inperson or virtual attendance. See you soon!
URL:https://coe.northeastern.edu/event/research-presentations-on-bendable-electronics-and-sustainable-technologies-best/
LOCATION:442 Dana\, 360 Huntington Ave\, 442 DA\, Boston\, MA\, 02115\, United States
GEO:42.3387508;-71.0923044
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=442 Dana 360 Huntington Ave 442 DA Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave\, 442 DA:geo:-71.0923044,42.3387508
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221128T120000
DTEND;TZID=America/New_York:20221128T140000
DTSTAMP:20260522T070145
CREATED:20221121T162045Z
LAST-MODIFIED:20221121T162045Z
UID:34492-1669636800-1669644000@coe.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://coe.northeastern.edu/event/xuanyi-zhaos-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221128T100000
DTEND;TZID=America/New_York:20221128T110000
DTSTAMP:20260522T070145
CREATED:20221109T185206Z
LAST-MODIFIED:20221109T185206Z
UID:34258-1669629600-1669633200@coe.northeastern.edu
SUMMARY:Can Qin's PhD Proposal Review
DESCRIPTION:“Transfer Learning across Domains\, Tasks and Models” \nAbstract: \nThe big data stands as a cornerstone of deep learning\, which has significantly improved a wide range of machine learning and computer vision tasks. Despite such a great success\, data collection is time-consuming and costly\, considering manual efforts and privacy restrictions. Transfer learning is a promising direction toward data-efficient AI by leveraging acquired data and pre-trained models as guidance. This dissertation focus on the feature and model transfer across different domains and tasks\, which can be roughly summarized into three sections. (1) Section One focuses on Unsupervised Domain Adaptation (UDA) without any labels in the target domain. The technical challenge of UDA is the distribution mismatch across domains. I have presented a hierarchical alignment model as the solution. (2) Section Two extends UDA into semi-supervised domain adaptation (SSDA) with minimal target-domain labels\, which is useful and effortless to acquire. To avoid overfitting toward labeled data\, I have proposed structural regularization verified on different classification benchmarks. (3) Section Three mainly explores the model transfer\, including teacher-student knowledge distillation and heterogeneous models infusion with a high potential for model compression and enhancement. \nCommittee: \nProf. Raymond Fu (Advisor) \nProf. Octavia Camps \nProf. Huaizu Jiang
URL:https://coe.northeastern.edu/event/can-qins-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221122T110000
DTEND;TZID=America/New_York:20221122T120000
DTSTAMP:20260522T070145
CREATED:20221103T173322Z
LAST-MODIFIED:20221103T173322Z
UID:34197-1669114800-1669118400@coe.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://coe.northeastern.edu/event/mahshid-asris-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221118T110000
DTEND;TZID=America/New_York:20221118T120000
DTSTAMP:20260522T070145
CREATED:20221115T182757Z
LAST-MODIFIED:20221115T182757Z
UID:34375-1668769200-1668772800@coe.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://coe.northeastern.edu/event/phd-dissertation-defense-shivang-aggarwal/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221117T133000
DTEND;TZID=America/New_York:20221117T144000
DTSTAMP:20260522T070145
CREATED:20221109T220229Z
LAST-MODIFIED:20221109T220229Z
UID:34261-1668691800-1668696000@coe.northeastern.edu
SUMMARY:Distinguished Seminar - David Horsley
DESCRIPTION:“Systems Based on Ultrasonic MEMS: Commercialization and Future Directions “ \nDavid Horsley  \nProfessor\, University of California\, Davis Adjunct Professor\, University of California\, Berkeley  \n  \nAbstract \nThe increasing maturity of thin-film piezoelectric materials and the MEMS manufacturing ecosystem has enabled the rapid development of sensor systems based on piezoelectric micromachined ultrasonic transducers (PMUTs). In this talk\, I will describe work by my research group over the last decade to develop and commercialize PMUT-based systems for consumer electronics applications\, starting with air-coupled PMUTs used for time-of-flight (ToF) range-finding and human presence sensing. These ToF sensors were commercialized by my startup\, Chirp Microsystems (now part of TDK)\, and are used today in various products such as smart-locks\, robot vacuum cleaners\, and laptops. We subsequently developed an ultrasonic fingerprint sensor based on the monolithic integration of PMUTs with CMOS that is used for biometric authentication in consumer products today. A common feature of the ToF sensor and the fingerprint sensor is that they are systems that combine MEMS\, integrated circuits\, and algorithms. The ability to realize a complete ultrasonic system on chip (SoC) opens new research opportunities in areas such as portable medical imaging systems for point-of care ultrasound (POCUS) as well as wearable ultrasonic devices. \nBiography \nDavid A. Horsley received his PhD in Mechanical Engineering from the University of California\, Berkeley\, in 1998. He is a Professor of Mechanical and Aerospace Engineering at the University of California\, Davis\, and an Adjunct Professor of Mechanical Engineering at the University of California\, Berkeley\, where he is co-director of the Berkeley Sensor and Actuator Center (BSAC). He is also co-founder and CTO of Chirp Microsystems Inc. (a TDK Group Company)\, a manufacturer of ultrasonic sensors using MEMS technology. Dr. Horsley was Co-Chair of the 2016 IEEE Sensors Conference\, Co-Chair of the 2017 Transducers Research Foundation Napa Microsystems Workshop\, and Co-Chair of the 2020 IEEE MEMS Conference. Dr. Horsley is an IEEE Fellow\, a Fellow of the National Academy of Inventors\, is a recipient of the National Science Foundation’s CAREER Award\, the UC Davis Outstanding Junior Faculty Award\, the 2016 NSF I/UCRC Association’s Schwarzkopf Award for Technological Innovation\, and the 2018 East Bay Innovation Award. He has authored or co-authored over 200 scientific papers and holds over 30 patents.
URL:https://coe.northeastern.edu/event/distinguished-seminar-david-horsley/
LOCATION:136 ISEC\, 360 Huntington Ave\, 136 ISEC\, Boston\, MA\, 02115\, United States
GEO:42.3401758;-71.0892797
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221115T140000
DTEND;TZID=America/New_York:20221115T150000
DTSTAMP:20260522T070145
CREATED:20221103T144043Z
LAST-MODIFIED:20221115T154305Z
UID:34078-1668520800-1668524400@coe.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://coe.northeastern.edu/event/raana-sabri-khiavis-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221109T120000
DTEND;TZID=America/New_York:20221109T130000
DTSTAMP:20260522T070145
CREATED:20221107T182254Z
LAST-MODIFIED:20221107T182254Z
UID:34237-1667995200-1667998800@coe.northeastern.edu
SUMMARY:Bernard Herrera-Soukup PhD Dissertation Defense
DESCRIPTION:“Ferroelectric Micro-machined Ultrasonic Transducers for Biomedical and Processing In-Sensor Applications” \nAbstract: \nPiezoelectric Micromachined Ultrasonic Transducers (PMUTs) are Micro Electro-Mechanical Systems (MEMS) devices that have become an established technology in applications such as range-finding\, fingerprint sensing and imaging due to their capability of ultrasonic transduction in a miniaturized footprint\, easily amenable to create large arrays. However\, their application space still remains quite open. PMUTs are well fitted to applications in liquid media\, such as implantable and underwater devices\, due to their inherent acoustic matching and wide bandwidth. Thus\, in the first part of the dissertationl\, we explore novel applications such as PMUT-based intra-body and underwater networking\, power transfer\, source localization\, wide-band matching and duplexing. \nAluminum Nitride (AlN) has been the material of choice for our PMUTs due to its biocompatibility and possibility of single-chip integration with supporting CMOS circuitry. Scandium doping of AlN thin films has recently been demonstrated to increase piezoelectric coupling coefficients while introducing ferroelectric properties in the material. However\, a simultaneous use of both capabilities has not been demonstrated in the state-of-the-art. The ability of having distinct ferroelectric states\, that alter the mechanical performance of the devices\, allows for Processing-In-Sensor features and provides the building blocks for neuromorphic signal processing capabilities. The second part of the dissertation explores the AlScN material integration into novel Ferroelectric Micromachined Ultrasonic Transducers (FMUTs) and their emerging application space. \n  \nCommittee: \nProf. Matteo Rinaldi (Advisor)\nProf. Tommaso Melodia\nProf. Cristian Cassella
URL:https://coe.northeastern.edu/event/bernard-herrera-soukup-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:20221108T153000
DTEND;TZID=America/New_York:20221108T163000
DTSTAMP:20260522T070145
CREATED:20221103T173536Z
LAST-MODIFIED:20221103T173536Z
UID:34204-1667921400-1667925000@coe.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://coe.northeastern.edu/event/giuseppe-michettis-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:20221107T123000
DTEND;TZID=America/New_York:20221107T140000
DTSTAMP:20260522T070145
CREATED:20221103T151749Z
LAST-MODIFIED:20221103T151749Z
UID:34189-1667824200-1667829600@coe.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://coe.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:20260522T070145
CREATED:20221103T173638Z
LAST-MODIFIED:20221103T173638Z
UID:34206-1667397600-1667401200@coe.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://coe.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:20260522T070145
CREATED:20221103T173443Z
LAST-MODIFIED:20221103T173443Z
UID:34202-1667390400-1667394000@coe.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://coe.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:20260522T070145
CREATED:20221103T173401Z
LAST-MODIFIED:20221103T173401Z
UID:34200-1666962000-1666965600@coe.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://coe.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:20260522T070145
CREATED:20221103T173221Z
LAST-MODIFIED:20221103T173221Z
UID:34195-1666609200-1666612800@coe.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://coe.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:20260522T070145
CREATED:20221103T151700Z
LAST-MODIFIED:20221103T151700Z
UID:34186-1666267200-1666270800@coe.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://coe.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:20260522T070145
CREATED:20221103T151510Z
LAST-MODIFIED:20221103T151510Z
UID:34182-1666011600-1666015200@coe.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://coe.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:20260522T070145
CREATED:20221103T151427Z
LAST-MODIFIED:20221103T151427Z
UID:34176-1665759600-1665763200@coe.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://coe.northeastern.edu/event/meruyert-assylbekovas-phd-dissertation-defense/
END:VEVENT
END:VCALENDAR