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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230222T173000
DTEND;TZID=America/New_York:20230222T183000
DTSTAMP:20260425T234038
CREATED:20230210T194123Z
LAST-MODIFIED:20230214T230832Z
UID:6103-1677087000-1677090600@ece.northeastern.edu
SUMMARY:Engineers Week: Fireside Chat – Break the Mold! Think Beyond Technology to Make an Impact in Unimaginable Ways
DESCRIPTION:Featuring Award-Winning Engineer and Commentator Dr. Shini Somara and Dean Gregory Abowd \nThink about engineering in a completely different way. Engineering is all around us and involves technology and beyond to solve the complex challenges of the world. Engineering is for everyone\, and everyone is for engineering! Dr. Somara has been featured on Crash Course\, BBC World\, Discovery Channel\, and more. Get ready for an engaging\, out-of-the-box session! \nWhen: Wed.\, February 22\, 5:30 p.m. – 6:30 p.m. (doors open at 5 p.m.) \nReception to follow with refreshments – opportunity to meet and network with Dr. Somara and Dean Abowd \nWhere: 17th Floor of East Village \nWho: For engineers and non-engineers (undergraduate\, graduate\, and high school students) \nRegister at: https://neweek.sites.northeastern.edu/
URL:https://ece.northeastern.edu/event/break-the-mold-think-beyond-technology-to-make-an-impact-in-unimaginable-ways/
LOCATION:East Village\, 17th floor\, 360 Huntington Ave\, East Village 17th floor\, Boston\, MA\, 02115\, United States
GEO:42.3394629;-71.0885286
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230221T173000
DTEND;TZID=America/New_York:20230221T193000
DTSTAMP:20260425T234038
CREATED:20230211T010858Z
LAST-MODIFIED:20230211T010858Z
UID:6120-1677000600-1677007800@ece.northeastern.edu
SUMMARY:Engineers Week: Cookies with the Dean
DESCRIPTION:Celebrating our COE students! Opportunity to meet and talk to Dean Gregory Abowd. Enjoy snacks (popcorn\, pretzels\, various desserts\, hot chocolate)\, free swag\, and photo booths! \nWhen: Tuesday\, February 21\, 5:30-7:30 p.m. \nWhere: Robinson Quad Bamboo & Industry Tents (near Mugar Life Sciences Building – 330 Huntington Ave) \nWho: COE students
URL:https://ece.northeastern.edu/event/engineers-week-cookies-with-the-dean/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230215T113000
DTEND;TZID=America/New_York:20230215T123000
DTSTAMP:20260425T234038
CREATED:20230210T210554Z
LAST-MODIFIED:20230210T210554Z
UID:6101-1676460600-1676464200@ece.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://ece.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:20260425T234038
CREATED:20230201T200236Z
LAST-MODIFIED:20230201T200236Z
UID:6078-1675436400-1675443600@ece.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://ece.northeastern.edu/event/kubra-alemdars-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230202T103000
DTEND;TZID=America/New_York:20230202T123000
DTSTAMP:20260425T234038
CREATED:20230117T234535Z
LAST-MODIFIED:20230117T234535Z
UID:6055-1675333800-1675341000@ece.northeastern.edu
SUMMARY:Qing Jin's PhD Proposal Review
DESCRIPTION:“Decoupling Efficiency-Performance Optimization for Modern Neural Networks” \nCommittee:\n\nProf. Yanzhi Wang (Advisor)\nProf. David R. Kaeli\nProf. Sunil Mittal\nProf. Jennifer Dy \n\nAbstract:\n\nDeep learning has achieved remarkable success in a variety of modern applications\, but this success is often accompanied by inefficiency in terms of storage and inference speed\, which can hinder their practical use on resource-constrained hardware. Developing highly efficient neural networks that maintain high prediction accuracy is crucial and challenging. This dissertation explores the potential for simultaneously achieving high efficiency and high prediction accuracy in neural networks\, and can be broadly divided into three sections. (1) In Section One\, we explore the implementation of highly efficient generative adversarial networks (GANs) capable of generating high-quality images within a predefined computational budget. The key challenge lies in identifying the optimal architecture for the generative model while simultaneously preserving the quality of the generated images from the compressed model\, despite its reduced computational cost. To achieve this\, we propose a novel neural architecture search (NAS) algorithm and a new knowledge distillation technique. (2) In Section Two\, we explore the challenge of quantizing discriminative models without relying on high-precision multiplications. To address this issue\, we present an innovative approach to determine the optimal fixed-point formats for both weights and activations based on their statistical properties. Our results demonstrate that high accuracy in quantized neural networks can be achieved without the need for high-precision multiplications. (3) In Section Three\, we delve into the challenge of training neural networks for innovative computing platforms\, specifically processing-in-memory (PIM) systems. Through a detailed mathematical derivation of the backward propagation algorithm\, we facilitate the training of quantized models on these platforms. Additionally\, through a thorough theoretical analysis of training dynamics\, we ensure convergence and propose a systematic solution for quantizing neural networks on PIM systems.
URL:https://ece.northeastern.edu/event/qing-jins-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230202T103000
DTEND;TZID=America/New_York:20230202T113000
DTSTAMP:20260425T234038
CREATED:20230126T204948Z
LAST-MODIFIED:20230126T205022Z
UID:6070-1675333800-1675337400@ece.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://ece.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
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:20230130T090000
DTEND;TZID=America/New_York:20230130T100000
DTSTAMP:20260425T234038
CREATED:20230125T213940Z
LAST-MODIFIED:20230125T213940Z
UID:6065-1675069200-1675072800@ece.northeastern.edu
SUMMARY:Sai Geetha Seri's PhD Proposal Review
DESCRIPTION:“Advancing Passive Ocean Acoustic Waveguide Remote Sensing for Detection of Fish Sounds\, Seismo-Acoustic Airgun Signals\, and Marine Mammal Vocalizations including Instrumentation Enhancements” \nCommittee: \nProf Purnima Ratilal Makris (Advisor)\nProf Josep M Jornet\nDr Nils Olav Handegard \nAbstract: \nUnderwater passive acoustic monitoring is important for understanding the marine environment\, since many ocean entities produce sound that can travel long ranges especially at low frequencies. For instance\, sound plays a vital role in the communication\, navigation\, and behavior of many marine biological organisms. Human activities in the ocean\, such as shipping\, offshore piling\, and energy prospecting\, generate a wide range and levels of sound. Natural environmental processes\, such as the passage of a hurricane and offshore seismicity are sources of underwater sound. In this thesis\, the instantaneous wide-area Passive Ocean Acoustic Waveguide Remote Sensing (POAWRS) technology implemented with a coherent hydrophone array is developed further and enhanced in a number of ways. First\, the automatic detection and analysis of man-made seismo-acoustic airgun signals employed in offshore geophysical and energy exploration surveys is investigated. Next\, the POAWRS technique is applied successfully for the first time toward the analysis and identification of sounds from some oceanic fish species in the wild using an eight-element prototype hydrophone array. Probability of Detection (PoD) regions are quantified separately for both the seismo-acoustic signals and fish sounds to provide an understanding of the horizontal spatial propagation extent of the acoustic signals from these sources. Finally\, we demonstrate significant enhancements in monitoring marine mammal sounds to include real-time capability and over a wider frequency range via a new in-house developed and fabricated 160-element coherent hydrophone array system. Here\, data from three distinct receiver array systems are analyzed\, presenting a technological evolution in the sensor systems utilized to implement and advance the POAWRS approach for ocean sensing. Development and integration of data acquisition approaches for both acoustic and non-acoustic sensors contained in the in-house developed array are discussed\, including design challenges and solutions.
URL:https://ece.northeastern.edu/event/sai-geetha-seris-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230111T100000
DTEND;TZID=America/New_York:20230111T120000
DTSTAMP:20260425T234038
CREATED:20230104T212321Z
LAST-MODIFIED:20230104T212321Z
UID:6042-1673431200-1673438400@ece.northeastern.edu
SUMMARY:Yukui Luo's PhD Proposal Review
DESCRIPTION:“Securing FPGA as a Shared Cloud-Computing Resource: Threats and Mitigations” \nAbstract:\nWith the widespread adoption of cloud computing\, the demand for programmable hardware acceleration devices\, such as field-programmable gate array (FPGA)\, has increased. To further improve the performance of FPGA-enabled cloud computing\, one promising technology is to virtualize the hardware resources of an FPGA device\, which allows multiple users to share the same FPGA. This solution can provide on-demand instances at the FPGA resource and time levels\, significantly improving the utilization and energy efficiency of the FPGA devices. However\, due to the hardware reconfigurability of FPGA\, current virtualization methods for multi-tenant GPU and TPU instances are incompatible with multi-tenant FPGA virtualization.We define the threat model for multi-tenant FPGA and discuss the security issues related to Confidentiality\, Data Integrity\, and Availability. Based on an analysis of potential attacks\, we present our latest research results and propose two future research directions for mitigations: (1) a multi-tenant FPGA plug-to-play obfuscation module and (2) a hardware-software co-designed multi-tenant FPGA virtualization system\, which includes a hypervisor and a smart multi-tenant FPGA platform.\n\n\nCommittee:\n\nProf. Xiaolin Xu (Advisor) \nProf. Yunsi Fei\nProf. Xue Lin
URL:https://ece.northeastern.edu/event/yukui-luos-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221215T090000
DTEND;TZID=America/New_York:20221215T110000
DTSTAMP:20260425T234038
CREATED:20221213T011124Z
LAST-MODIFIED:20221213T011124Z
UID:6023-1671094800-1671102000@ece.northeastern.edu
SUMMARY:Daniel Uvaydov's PhD Proposal Review
DESCRIPTION:“Real-Time Spectrum Sensing for Inference and Control”\n\nAbstract:\nSpectrum sensing can enable the next generation of wireless applications ranging from opportunistic spectrum access to cognitive radio networks. The key unaddressed challenges of spectrum sensing are that (i) it has to be performed with extremely low latency over varying bandwidths and must guarantee strict real-time processing constraints; (ii) its underlying algorithms need to be extremely accurate\, and flexible enough to work with different wireless bands and protocols to find application in real-world settings. We address these challenges in multiple wireless applications by utilizing Deep Learning techniques as the main vehicle of spectrum sensing for both inference and control. By leveraging mechanisms such as data augmentation\, channel attention\, voting\, and segmentation we are able to push beyond the capabilities of existing Deep Learning techniques and create generalizable spectrum sensing algorithms. Furthermore we deploy different spectrum sensing solutions in real testbeds for over the air evaluations and applicable proof-of-concepts.\n\n\nCommittee:\n\nProf. Tommaso Melodia (Advisor) \nProf. Francesco Restuccia\nProf. Kaushik Chowdhury
URL:https://ece.northeastern.edu/event/daniel-uvaydovs-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:20221209T120000
DTEND;TZID=America/New_York:20221209T133000
DTSTAMP:20260425T234038
CREATED:20221201T022737Z
LAST-MODIFIED:20221201T022840Z
UID:6006-1670587200-1670592600@ece.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://ece.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:20260425T234038
CREATED:20221201T023204Z
LAST-MODIFIED:20221201T023204Z
UID:6010-1670583600-1670590800@ece.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://ece.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:20260425T234038
CREATED:20221202T201226Z
LAST-MODIFIED:20221202T201226Z
UID:6014-1670508000-1670515200@ece.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://ece.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:20260425T234038
CREATED:20221201T023045Z
LAST-MODIFIED:20221201T023045Z
UID:6008-1670497200-1670500800@ece.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://ece.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:20260425T234038
CREATED:20221206T020005Z
LAST-MODIFIED:20221206T020005Z
UID:6017-1670342400-1670347800@ece.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://ece.northeastern.edu/event/md-navid-akbars-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221205T100000
DTEND;TZID=America/New_York:20221205T110000
DTSTAMP:20260425T234038
CREATED:20221201T022546Z
LAST-MODIFIED:20221201T022546Z
UID:6004-1670234400-1670238000@ece.northeastern.edu
SUMMARY:Ramtin Khalili's PhD Dissertation Defense
DESCRIPTION:Abstract: \nState estimation is a critical application in energy management systems. Due to the increased penetration of inverter-based resources\, installed advanced infrastructure at all voltage levels\, and unconventional loads like electric vehicle charging stations\, a three-phase state estimator formulation is essential. The first issue is the convoluted formulation and modeling techniques that are required in three-phase systems studies. Moreover\, the size of network matrices expanded\, which makes the analysis computationally costly. This dissertation addresses this by proposing a new decoupled state estimation method. The idea is to exploit the linearity of measurement equations\, decompose the three-phase coupled equations into three independent modal measurement equations\, perform the state estimation independently for each mode\, and finally reconstruct the three-phase quantities. This method is applicable to both radial and meshed three-phase networks. Furthermore\, multi-phase structures can be handled by the new estimator\, which makes the approach practical when monitoring mixed-phase feeder sections is of interest. \nWhile utilities are investing in expanding the grid and installing more PMUs\, there might not be enough PMUs to make the network observable in all networks\, especially at lower voltage levels. So\, PMU-based linear state estimators are not always feasible. On the other hand\, SCADA measurements are available with adequate redundancy in most networks. However\, SCADA-based state estimation is nonlinear\, which brings various problems like divergence issues and significant CPU times. The computational complexity will be even worse if the three-phase state estimation is formulated based on SCADA measurements due to their nonlinear nature\, which makes modal decoupling impossible. So\, a new linear formulation has been proposed for both the positive-sequence and three-phase networks based on conventional measurements. This approach converts the nonlinear recursive problem into an iterative linear state estimation problem. \nThe inherent assumption in most of the state estimators is a perfect network model. However\, network parameter errors are susceptible to errors that can bias the state estimation solution. This can deceive the existing bad data tools as parameter errors appear as if multiple interacting measurement errors occur locally. So\, a two-stage method is proposed for parameter error identification and correction for large three-phase networks. A systematic PMU placement strategy is also proposed to ensure the detectability of parameter errors. The benefits of multi-area state estimation are demonstrated for the deregulated power grids for monitoring the local and boundary areas. It has also shown promising results in increasing the efficiency of state estimation using a distributed framework. Parameter and measurement errors can remain undetected as a result of weakened measurement redundancy on the boundaries. However\, boundary errors in the area boundaries will be detected due to measurement consolidation at the coordination level. \nCommittee:\nProf. Ali Abur (Advisor)\nProf. Bahram Shafai\nProf. Mahshid Amirabadi
URL:https://ece.northeastern.edu/event/ramtin-khalilis-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221202T080000
DTEND;TZID=America/New_York:20221202T170000
DTSTAMP:20260425T234038
CREATED:20220824T182336Z
LAST-MODIFIED:20220824T182336Z
UID:5782-1669968000-1670000400@ece.northeastern.edu
SUMMARY:First Year Engineering Expo
DESCRIPTION:Please come to the Curry Student Center indoor quad and pit on Friday\, December 2nd to see Northeastern’s First-Year Engineering Students’ inventive projects\, games\, and exhibits. \nStudents will showcase original board games\, interactive projects geared to teach children sustainability concepts\, and prolific prototypes to help solve a wide range of problems. \nEach project applies the engineering concepts introduced this past semester\, which includes the Engineering Design Process\, Solidworks\, AutoCAD\, Programming with C++ and Matlab\, and controlling microelectronics with Arduino.
URL:https://ece.northeastern.edu/event/first-year-engineering-expo-3/
LOCATION:Curry Student Center\, 360 Huntington Ave.\, Boston\, MA\, 02115\, United States
GEO:42.3394629;-71.0885286
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Curry Student Center 360 Huntington Ave. Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave.:geo:-71.0885286,42.3394629
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221129T140000
DTEND;TZID=America/New_York:20221129T153000
DTSTAMP:20260425T234038
CREATED:20221122T012209Z
LAST-MODIFIED:20221122T012209Z
UID:5975-1669730400-1669735800@ece.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://ece.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:20260425T234038
CREATED:20221104T010151Z
LAST-MODIFIED:20221104T010151Z
UID:5952-1669716000-1669726800@ece.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://ece.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:20260425T234038
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:20260425T234038
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:20260425T234038
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:20260425T234038
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:20260425T234038
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
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:20260425T234038
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/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221104T130000
DTEND;TZID=America/New_York:20221104T160000
DTSTAMP:20260425T234038
CREATED:20221021T181420Z
LAST-MODIFIED:20221021T181420Z
UID:5850-1667566800-1667577600@ece.northeastern.edu
SUMMARY:NanoSI Workshop
DESCRIPTION:The NSF sponsored Nano Systems Innovation (NanoSI) workshop will conduct a deep dive into infrastructure requirements to enable a unique national infrastructure for piezoelectric and hetero-integrated nano systems. The virtual workshop will bring together researchers\, government\, industry and foundry partners to identify the emerging needs for adaptable infrastructure that can address national research priorities in advanced hetero integration to post-CMOS and more than Moore devices. The workshop will concentrate on planning for infrastructure that can close the gap between the local research and prototyping capabilities of the universities with advanced semiconductor manufacturing activities with the ultimate goal of reducing the time for innovation and transition of the new foundational nano-system technologies that are going to be the at root of our nation’s economic strength\, national security and technological standing in the years to come. The workshop will explore the community’s preferred pathways for accessing and engagement with the future infrastructure\, management plans\, and ideas to strengthen the community by extending access to underrepresented groups. Furthermore\, the workshop will identify strategies to leverage the national facility to educate an experienced future workforce for semiconductor and advanced nanomanufacturing industries in the United States. \nThe NanoSI virtual workshop will include pre-recorded 8-minute presentations given by multiple stakeholders from academia\, industry and government highlighting technological areas of interest and providing multiple perspectives on the value proposition of a national infrastructure for piezoelectric and hetero-integrated nano systems. These short presentations will be made available to the workshop attendees through the password-protected event web page by Friday October 28th\, 2022. Taking inputs from this asynchronous session of the workshop\, the organizers will prepare a report and present it to the attendees during a three-hours synchronous virtual session that will take place on Friday November 4th\, 2022 at 1 – 4 pm EST. The report presentation will be followed by breakout discussion sessions focused on providing feedback and addressing outstanding questions. \nRegister Now
URL:https://ece.northeastern.edu/event/nanosi-workshop/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221103T133000
DTEND;TZID=America/New_York:20221103T143000
DTSTAMP:20260425T234038
CREATED:20221103T184634Z
LAST-MODIFIED:20221103T184634Z
UID:5922-1667482200-1667485800@ece.northeastern.edu
SUMMARY:Lin Deng's PhD Proposal Review
DESCRIPTION:“On-chip and multiplexed metasurfaces for light manipulation” \nAbstract: \nMetasurfaces\, which consist of two-dimensional subwavelength nanostructures that can locally manipulate the proprieties of light including amplitude\, phase\, and polarization\, provide an unprecedented means to control optical waves in a prescribed manner. Different functionalities\, such as structured light\, holograms\, and flat lenses\, have been realized by metasurfaces. It is a pressing need to develop on-chip and multiplexed metasurfaces to further advance the practical applications of metasurfaces. \nIn this proposal review\, I will discuss two metasurfaces designed with compact size and the ability to multiplex various information channels. The first one can realize mode conversion and wavefront shaping by integrating a C-shape metallic metasurface on top of a planar waveguide. By controlling the orientation of each C-shape nanoantenna\, we can achieve mode conversion and focusing effect for the cross-polarized electric fields inside the waveguide. We demonstrated the design and simulation results of 16 scenarios of wideband transverse magnetic (TM) to transverse electric (TE) modes conversion with mode purity up to 98% as well as on-chip lenses at the wavelength of 1550 nm. The second device is to realize the precise control of the amplitude and phase at multiple channels in response to different incident angles and output polarization states by a single planar metasurface. With the help of the genetic algorithm\, we designed and demonstrated all-dielectric metasurfaces composed of silicon nano-blocks that can produce multiple complex structured light beams when taking the angle of incidence and the polarization states into consideration. Our research is expected to substantially benefit the development of mode division multiplexing (MDM) as well as polarization-division multiplexing (PDM). \nCommittee:\nProf. Yongmin Liu (Advisor)
URL:https://ece.northeastern.edu/event/lin-dengs-phd-proposal-review/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221102T140000
DTEND;TZID=America/New_York:20221102T150000
DTSTAMP:20260425T234038
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/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221102T120000
DTEND;TZID=America/New_York:20221102T130000
DTSTAMP:20260425T234038
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/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221028T130000
DTEND;TZID=America/New_York:20221028T140000
DTSTAMP:20260425T234038
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/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221024T110000
DTEND;TZID=America/New_York:20221024T120000
DTSTAMP:20260425T234038
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/
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