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DTSTART;TZID=America/New_York:20220627T140000
DTEND;TZID=America/New_York:20220627T150000
DTSTAMP:20260423T033940
CREATED:20221103T183838Z
LAST-MODIFIED:20221103T183838Z
UID:5916-1656338400-1656342000@ece.northeastern.edu
SUMMARY:Xiaolong Ma's PhD Dissertation Defense
DESCRIPTION:“Towards Efficient Deep Neural Network Execution with Model Compression and Platform-specific Optimization” \nAbstract: \nDeep learning or deep neural network (DNN)\, as one of the most powerful machine learning techniques\, has become the fundamental element and core enabler of the artificial intelligence. Many incredible\, bleeding-edge applications\, such as community/shared virtual reality experiences and self-driving cars\, will crucially rely on the ubiquitous availability and real-time executability of the high-quality deep learning models. Among the variety of the AI-associated platforms\, mobile and embedded computing devices have become key carriers of deep learning to facilitate the widespread of machine intelligence. In this talk\, I will first focus on a compression-compilation co-design method that deploy a unique sparse model on an off-the-shelf mobile device with real-time execution speed. This method advances the state-of-the-art by introducing a new dimension\, fine-grained pruning patterns inside the coarse-grained structures\, revealing a previously unknown point in the design space. The designed patterns are interpretable\, and can be obtained by a fully automatic pattern-aware pruning framework that achieves pattern library extraction\, pattern assignment (pruning) and weight training simultaneously. With the higher accuracy enabled by fine-grained pruning patterns\, the unique insight is to use the compiler to re-gain and guarantee high hardware efficiency. We take a step forward by considering a more practical scenario\, that the deployment-execution mode for AI tasks no longer satisfy the user preference\, and enabling edge training becomes inevitable since it promotes much better personalized intelligent services while strengthen users’ privacy by avoiding data egress from their devices. To this end\, I will demonstrate my approaches that use sparsity to achieve fast and efficient training on the edge devices. I will evaluate the static lottery ticket sparse training\, and then demonstrate a high-accuracy and low-cost dynamic sparse training framework that makes the edge training possible. It successfully incorporates the pattern-based sparsity into sparse training\, and also exploit the data-level sparsity to further improve the acceleration. I will conclude by using our sparse training method on a distributed training scenario\, which demonstrates the state-of-the-art accuracy and great flexibility for modern AI model training. \nCommittee: \nProf. Yanzhi Wang (Advisor) \nProf. Xue Lin \nProf. David Kaeli
URL:https://ece.northeastern.edu/event/xiaolong-mas-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220616T120000
DTEND;TZID=America/New_York:20220616T130000
DTSTAMP:20260423T033940
CREATED:20221103T183733Z
LAST-MODIFIED:20221103T183733Z
UID:5914-1655380800-1655384400@ece.northeastern.edu
SUMMARY:Hussein Hussein's PhD Proposal Review
DESCRIPTION:“Parametric Circuits for Enhanced Sensing and RF Signal Processing” \nAbstract: \nMassive deployments of wireless sensor nodes (WSNs) that continuously detect physical\, biological or chemical parameters are needed to truly benefit from the unprecedented possibilities opened by the Internet‑of‑Things (IoT). Just recently\, new sensors with higher sensitivities have been demonstrated by leveraging advanced on‑chip designs and microfabrication processes. Yet\, WSNs using such sensors require energy to transmit the sensed information. Consequently\, they either contain batteries that need to be periodically replaced or energy harvesting circuits whose low efficiencies prevent a frequent and continuous sensing\, even impacting the maximum range of communication. Here\, we discuss a new battery-less and harvester-free remote sensing tag\, namely the subharmonic tag (SubHT)\, leveraging unique nonlinear characteristics to fundamentally break any previous paradigms for passive WSNs. SubHT can sense and transmit information without requiring supplied or harvested DC power. Also\, it transmits the sensed information at a difference frequency from the one of its interrogation signal\, rendering its reader immune from multi-path\, from clutter and from its own self‑interference. Also\, even though SubHT may not require any advanced and expensive manufacturing\, its unique nonlinear response enables extraordinary high sensitivities and dynamic ranges that can even surpass those achieved by the most advanced on-chip sensors. More interestingly\, SubHT can be even configured to operate in a “threshold sensing” mode\, making it able to respond to any interrogation signal only when the sensed parameter has exceeded a remotely reprogrammable threshold\, as well as to memorize any violation in a sensed parameter without requiring any memory components. In this talk\, the first SubHT prototypes for temperature sensing will be showcased. Even more\, we will show how including high quality factor (Q) resonators in a SubHT’s network allows to implement even more functionalities\, such as the long-range identification or tracking of any items or localization and navigation in a GPS denied environment. Yet\, the dynamics exploited by SubHT can also be leveraged to address various needs along radio-frequency (RF) chains. In this regard\, we show how the SubHT’s nonlinear dynamics can be leveraged to build components\, such as parametric filters\, frequency selective limiters and signal to noise enhancers\, that improve the stability of RF frequency synthesizers and instinctually suppress co-site or self-interferes\, paving an unprecedented path towards integrated radios with improved performance and longer battery-life time. \nCommittee: \nProf. Cristian Cassella (Advisor)\nProf. Marvin Onabajo\nProf. Matteo Rinaldi\nProf. Andrea Alù
URL:https://ece.northeastern.edu/event/hussein-husseins-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220608T093000
DTEND;TZID=America/New_York:20220608T103000
DTSTAMP:20260423T033940
CREATED:20221103T183939Z
LAST-MODIFIED:20221103T183939Z
UID:5918-1654680600-1654684200@ece.northeastern.edu
SUMMARY:Ziyue Xu's PhD Proposal Review
DESCRIPTION:“High Efficiency RF Energy Harvesting and Power Management Circuits Techniques for IoT Application” \nAbstract: \nAs the number of Internet of Things (IoT) devices is continuing to grow\, there is a need that a significant percentage these devices operate at ultra-low power (ULP) levels\, either using harvested energy or using a small battery with a long lifetime. Energy harvesting techniques can help to achieve long lifetimes\, but the system should be able to operate efficiently with a small amount of harvested energy and often from low voltages. Energy harvesting from solar\, thermal\, vibration\, and radio-frequency (RF) are increasingly being used to realize batteryless operation for IoT and biomedical applications. A typical multi-input energy harvesting system including multiple energy transducers\, maximum power point tracking (MPPT)\, matching network (MN)\, and DC-DC converter. Solar cells and thermoelectric generators have a few mV to hundreds of mV open-circuit voltage that require maximum power tracking to make sure the optimal power extraction is achieved. The piezoelectric transducer is modeled as AC source with internal resistance from 10s Ω to kΩ that requires AC-DC conversion\, known as rectification to better use the energy. And the following DC-DC regulation stage is to regulate the output voltage to deal with the sudden change of the load or the input voltage drop. Among these techniques\, RF energy harvesting system is particularly promising for biomedical and IoT devices where other sources are not readily available. Several of these applications are utilizing widely used WiFi and Bluetooth low-energy (BLE) communication standards. These applications along with the wirelessly-powered neural implantable medical devices (n-IMD) for neural stimulation and recording are also benefiting from ultra-low power (ULP) circuits and systems design advancements. Since the available RF power decreases rapidly with distance\, it is desirable to design rectifiers that are able to operate with low incident power. This Ph.D. proposal presents a simplified design approach and analysis of RF energy harvesting rectifiers for different design objectives. The proposal also includes the design of a new self-biased gate (SBG) rectifier with a non-linear gate biasing technique. At lower power levels\, the SBG rectifier drops the entirety of output voltage to create a higher gate bias. However\, to address the issue of leakage at higher input power levels\, the gate-biasing technique drops only a fraction of the output voltage. This approach helps to realize high efficiency across input power range. The fully integrated\, high-efficiency SBG-based RF energy harvesting circuit can also provide a high output voltage of 9.3 V with a 30% end-to-end efficiency (PHE). Further\, to enhance the available RF energy to a remotely located RF energy receiver\, the proposal presents a highly efficient distributed RF beamforming technique. To improve the power delivery in the downstream power management circuits\, a boost converter architecture that can reduce switching noise injection by changing its switching frequency is also presented. The associated power management system includes a boost converter operating in DCM\, FVC and a digital control loop. The system is capable of providing a stable 1V supply for RF receiver front-ends with very low performance impact. \n  \nCommittee Members: \nProf. Aatmesh Shrivastava (Advisor) \nProf. Marvin Onabajo \nProf. Nian X. Sun
URL:https://ece.northeastern.edu/event/ziyue-xus-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:20220422T180000
DTEND;TZID=America/New_York:20220422T200000
DTSTAMP:20260423T033940
CREATED:20220419T174430Z
LAST-MODIFIED:20220419T174645Z
UID:5548-1650650400-1650657600@ece.northeastern.edu
SUMMARY:ECELEBRATION
DESCRIPTION:Honoring Diversity in ECE – a celebration of Electrical and Computer Engineering students\, faculty\, and staff with marginalized identities! Register as an honoree to be awarded a certificate\, or as an observer. Let’s reset and re-connect to build a stronger community rooted in mutual respect and empathy for one another. Monday\, April 18th is the last day to register for Friday’s banquet.
URL:https://ece.northeastern.edu/event/ecelebration/
LOCATION:Curry Student Center\, 360 Huntington Ave.\, Boston\, MA\, 02115\, United States
ORGANIZER;CN="Diversity%2C Equity%2C and Inclusion":MAILTO:diversity@northeastern.edu
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:20220304T110000
DTEND;TZID=America/New_York:20220304T120000
DTSTAMP:20260423T033940
CREATED:20220216T200737Z
LAST-MODIFIED:20220222T235806Z
UID:5468-1646391600-1646395200@ece.northeastern.edu
SUMMARY:MathWorks Week: AI for Humans
DESCRIPTION:Join MathWorks engineers and Northeastern faculty for these insightful seminars in Climate Change\, Quantum Computing\, and AI. \n\nAI for Humans\nFriday\, March 4\, 11 am – 12 pm\nRegister: MathWorksNUSymposiumAI.eventbrite.com \nFundamentals of AI\nNeha Sardesai\, MathWorks \nHow to apply machine learning and deep learning to images and signals. You’ll see how MATLAB® provides an environment to apply advanced techniques without requiring coding or experience in machine learning and deep learning. \nInvariant Representation Learning for Human Pose Estimation withSmall Data\nSarah Ostadabbas\, Professor\, Dept. of Electrical and Computer Engineering \nDescriptions of the state-of-the-art representation learning algorithms for visual perception tasks in the contexts of human pose estimation\, especially when we are facing problems where data collection or labeling is expensive (i.e. Small Data domains). \nMachine learning for retina image analysis for Retinopathy ofPrematurity (ROP) severity assessment.\nDeniz Erdogmus\, Professor\, Dept. of Electrical and Computer Engineering \nDiscussion of the use of active learning\, deep learning\, and Siamese neural networks to develop deep neural network models for automated retina image analysis to diagnose and assess the severity of retinopathy of prematurity in babies born prematurely.
URL:https://ece.northeastern.edu/event/mathworks-week-at-northeastern-university-2022-03-04/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220303T150000
DTEND;TZID=America/New_York:20220303T160000
DTSTAMP:20260423T033940
CREATED:20220216T200737Z
LAST-MODIFIED:20220222T235653Z
UID:5466-1646319600-1646323200@ece.northeastern.edu
SUMMARY:MathWorks Week: Chemistry\, Deep Learning and Quantum Computing
DESCRIPTION:Join MathWorks engineers and Northeastern faculty for these insightful seminars in Climate Change\, Quantum Computing\, and AI. \n\nChemistry\, Deep Learning and Quantum Computing\nThursday\, March 3\, 3 pm – 4 pm\nRegister: MathWorksNUSymposiumQuantumComputing.eventbrite.com \nGraph Neural Networks for Chemistry Using MATLAB\nHossein Jooya\, MathWorks \nMATLAB’s new features in handling chemical structures\, from small organic molecules to proteins will be demonstrated. Graph-convolutional (GC) and graph-attention (GA) networks are explained with various examples from toxicity prediction to molecular optimization. Attendees will have access to the shared code modules and can adapt them for their own research with hand-in-hand support from MathWorks technical team. \nPhotonic Quantum Technologies\nSunil Mittal\, Professor\, Dept. of Electrical and Computer Engineering \nThis talk will discuss the generation\, manipulation\, and measurements of quantum states of light\, such as entangled photons\, for applications in photonic quantum computation\, quantum communications\, and sensing. \nDo You Trust Your Quantum Computers with Correct Answers?\nDevesh Tiwari\, Professor\, Dept. of Electrical and Computer Engineering \nNoisy Intermediate-Scale Quantum (NISQ) machines are increasingly being used to develop quantum algorithms and establish use cases for quantum computing. These devices\, however\, are highly error-prone and produce output which can be far from the correct output of the quantum algorithm. This talk will discuss some promising approaches towards estimating the correct program output on erroneous quantum devices.
URL:https://ece.northeastern.edu/event/mathworks-week-at-northeastern-university-2022-03-03/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220301T103000
DTEND;TZID=America/New_York:20220301T113000
DTSTAMP:20260423T033940
CREATED:20220216T200737Z
LAST-MODIFIED:20220222T235523Z
UID:5464-1646130600-1646134200@ece.northeastern.edu
SUMMARY:MathWorks Week: Climate\, Energy and the Built Environment
DESCRIPTION:Join MathWorks engineers and Northeastern faculty for these insightful seminars in Climate Change\, Quantum Computing\, and AI. \n\nClimate\, Energy and the Built Environment\nTuesday\, March 1\, 10:30 am – 11:30 am\nRegister: MathWorksNUSymposiumClimate.eventbrite.com \nInsights of climate changes from the Common Era: an Artificial Intelligence view\nJianghao Wang\, MathWorks \nThe rapid global warming seen in observations over the past 150 years shows nearly global coherence\, the spatiotemporal coherence of climate epochs earlier in the Common Era (the past 2\,000 years)\, however\, has yet to be robustly tested. Understanding how the climate system works and how historical temperature changes shed light on the study of anthropogenic climate change. \nModeling the Stochastic Dynamics of Rotating Wind Turbine Blades\nLuca Caracoglia\, Professor\, Dept. of Civil and Environmental Engineering \nThis presentation describes the results of recent research activities\, examining the dynamic modeling of wind turbine blades under the influence of various sources of input error and noise. The presentation will focus on the flutter phenomenon. Flutter is a flow-induced dynamic instability that results from the coupling between flap-wise bending mode and torsional mode of the rotating blade. \nLocating Damage in Structural Systems\nDennis Bernal\, Professor\, Dept. of Civil and Environmental Engineering \nThis presentation outlines the basic ideas behind some techniques used to localize damage applicable in cases where the structure is large\, and the number of sensors is small. Visual inspection has been the traditional procedure used to check the condition of structural systems but there is significant interest in devising ways to replace or enhance this approach by incorporating information from sensors.
URL:https://ece.northeastern.edu/event/mathworks-week-at-northeastern-university/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220223T173000
DTEND;TZID=America/New_York:20220223T183000
DTSTAMP:20260423T033940
CREATED:20220223T201859Z
LAST-MODIFIED:20220223T201859Z
UID:5500-1645637400-1645641000@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Miead Tehrani-Moayyed
DESCRIPTION:PhD Proposal Review: RF Channel Models for Static and Mobile Scenarios: From Simulations to Models for Large-scale Emulations \nMiead Tehrani-Moayyed \nLocation: ISEC 432 \nAbstract: The extremely high data rates provided by communications at higher frequency bands\, e.g.\, millimeter waves (mmWave)\, can help address the unprecedented demands of next-generation wireless networks. However\, as several impairments limit wireless coverage at higher frequencies\, accurate models of wireless scenarios and testing at scale are needed to show actual potential and to realize the promises that the new wireless technologies can bring forth. Large-scale accurate simulations and wireless networks emulators are now a time and cost-effective solution to perform these tests in a lab before deployment in the field. This dissertation work focuses on modeling\, calibration\, and validation of realistic RF scenarios for wireless network emulation at scale.\nThe contributions of our work include (i) investigating the characteristic of the wireless channel at higher frequencies (mmWave) and the performance evaluation of mmWave communications on top of the recently released NR standard for 5G cellular networks\, and (ii) a framework to create RF scenarios for emulators like \emph{Colosseum} starting from rich forms of input\, like those obtained by ray-tracers or via real-field measurements.\n(i) We derive channel propagation models via ray-tracing simulations for mmWave transmissions with applications to vehicle-to-everything (V2X) communications. We analyze aspects related to blockage modeling\, the effects of antenna beamwidth\, beam alignment\, and multipath fading in urban scenarios and emphasize the importance of capturing diffuse scattered rays for improved large-scale and small-scale radio channel propagation models. Furthermore\, we compare the performance of mmWave 5G NR with the 4G long-term evolution (LTE) standard on a realistic environment and show the impact of MIMO technology to improve the performance of 5G NR cellular networks. As transmitted radio signals are received as clusters of multipath rays\, identifying these clusters provides better spatial and temporal characteristics of the channel. We deal with the clustering process and its validation across a wide range of frequencies in the mmWave spectrum below 100 GHz. We analyze how the clustering solution changes with narrower-beam antennas\, and we provide a comparison of the cluster characteristics for different types of antennas.\n(ii) Our framework to model wireless scenarios for large-scale emulators optimally scales down the large set of RF data in input to the fewer parameters allowed by the emulator by using efficient clustering techniques and channel impulse response re-sampling. We demonstrate the effectiveness of the proposed framework through modeling realistic scenarios for Colosseum starting from the rich input from a commercial-grade ray-tracing software: Wireless Insite by Remcom. We propose to finish our investigation (a)~by introducing ways of dealing with mobility in emulated scenarios\, and to perform adequate channel sounding to validate them\, and (b)~by indicating ways to provide input to the emulator through actual wireless measurements in the field. Particularly\, as campaigns in the field provide measurements for a sparse set of locations\, we plan to use deep learning techniques to “interpolate” channel parameters for a larger set of locations\, determining the trade-offs for achieving desired accuracy and reasonable computational requirements.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-miead-tehrani-moayyed/
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:20220216T133000
DTEND;TZID=America/New_York:20220216T143000
DTSTAMP:20260423T033940
CREATED:20220214T210524Z
LAST-MODIFIED:20220214T210524Z
UID:5459-1645018200-1645021800@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Yuanyuan Li
DESCRIPTION:PhD Proposal Review: Submodularity in Cache Networks \nYuanyuan Li \nLocation: Zoom Link \nAbstract: As information-based demand surges\, distributed network services\, e.g.\, cache networks\, play an important role to mitigate network traffic. Cache networks are a natural abstraction for many applications\, including information-centric networks\, content delivery networks\, cloud computing\, and edge/wireless IoT. How to allocate resources (routing\, placing items in caches\, flow control\, etc.) in cache networks is a crucial problem\, as resources (storage space\, and bandwidths) are usually limited. Resource allocation in networks has been traditionally approached through classic convex optimization. However\, simple problems becomes combinotorial in cache networks\, which leads to NP-hardness. Enlightened by several works studying cache networks\, we identify a useful property\, submodularity\, which is the key to approximation algorithms solving those NP hard resource allocation problem in cache networks.\nLeveraging submodularity\, we study a cache network\, in which intermediate nodes equipped with caches can serve content requests\, from different angles.\nFirst\, we model this network as a universally stable queuing system\, in which packets carrying identical responses are consolidated before being forwarded downstream. We refer to resulting queues as M/M/1c or counting queues\, as consolidated packets carry a counter indicating the packet’s multiplicity. Cache networks comprising such queues are hard to analyze; we propose two approximations: one via M/M/∞ queues\, and one based on M/M/1c queues under the assumption of Poisson arrivals. We show that\, in both cases\, the problem of jointly determining (a) content placements and (b) service rates admits a poly-time\, 1-1/e approximation algorithm. We also show that our analysis\, with respect to both algorithms and associated guarantees\, extends to (a) counting queues over items\, rather than responses\, as well as to (b) queuing at nodes and edges\, as opposed to just edges.\nSecond\, we refer to the cost reduction enabled by caching as the caching gain\, and the product of the caching gain of a content request and its request rate as caching gain rate. We aim to study \emph{fair} content allocation strategies through a utility-driven framework\, where each request achieves a utility of its caching gain rate\, and consider a family of α-fair utility functions to capture different degrees of fairness. The resulting problem is an NP-hard problem with a non-decreasing submodular objective function. Submodularity allows us to devise a deterministic allocation strategy with an optimality guarantee factor arbitrarily close to 1-1/e. When 0 < α ≤ 1\, we further propose a randomized strategy that attains an improved optimality guarantee\, (1-1/e)^(1-α)\, in expectation.\nThird\, We study a cache network under arbitrary adversarial request arrivals. We propose a distributed online policy based on the online tabular greedy algorithm. Our distributed policy achieves sublinear (1-1/e)-regret\, also in the case when update costs cannot be neglected.\nFinally\, we propose an experimental design network paradigm\, wherein learner nodes train possibly different Bayesian linear regression models via consuming data streams generated by data source nodes over a network. We formulate this problem as a social welfare optimization problem in which the global objective is defined as the sum of experimental design objectives of individual learners\, and the decision variables are the data transmission strategies subject to network constraints. We first show that\, assuming Poisson data streams\, the global objective is a continuous DR-submodular function. We then propose a Frank-Wolfe type algorithm that outputs a solution within a 1-1/e factor from the optimal. Our algorithm contains a novel gradient estimation component which is carefully designed based on Poisson tail bounds and sampling.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-yuanyuan-li/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220215T140000
DTEND;TZID=America/New_York:20220215T150000
DTSTAMP:20260423T033940
CREATED:20220209T213529Z
LAST-MODIFIED:20220209T213529Z
UID:5439-1644933600-1644937200@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Stella Banou
DESCRIPTION:PhD Proposal Review: Coupling Methods for Wireless Intra-body Communication and Sensing \nStella Banou \nLocation: 432 ISEC \nAbstract: Advances in miniaturized bio-compatible Internet of Things (IoT) device design and wireless connectivity have resulted in rapid strides towards realizing the vision of connected health and ubiquitous monitoring of physiological conditions. Core enablers of this capability are wearable and implanted IoT devices\, albeit with limitations arising from their low energy storage and computational power. This thesis goes beyond the RF-only communication standards by exploring alternate communication modalities that are more amenable for inter- and intra-body communication. In summary\, this thesis explores the conductive and radiating nature of the human body as a channel for three non-RF coupling communication methods – Galvanic\, Magnetic and Capacitive coupling.\nIn part I\, an implementation of Galvanic Coupling-based beamforming is presented for implant to wearable communication. The key idea here is to exploit the conductivity of human tissue and transmit weak electrical signals by coupling them via electrodes to muscle tissue in a way that concentrates energy at the receiver location. In part II\, we focus on realizing a relay network of IoT devices for both implant-implant and implant to on-skin sensor communication using Magnetic Resonance Coupling. The advantage of this method over Galvanic Coupling is that the former reduces attenuation when signals pass through human tissue. In part III\, we enhance the scope of the connected health paradigm to now include sensing for proximity and for automated encouraging of healthy habits that mitigate the spread of communicable diseases using Capacitive Coupling.\nAs part of proposed work\, we will design a novel human antenna field to sense and communicate with other IoT devices in the near field – within 2.5 meters\, also using Capacitive Coupling. This will complete the full cycle of data flow\, from implanted to wearable devices and finally connect the body network to the computational cloud for the next generation of IoT-enabled healthcare.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-stella-banou/
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:20220215T090000
DTEND;TZID=America/New_York:20220215T100000
DTSTAMP:20260423T033940
CREATED:20220214T210441Z
LAST-MODIFIED:20220214T210441Z
UID:5457-1644915600-1644919200@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Abhimanyu Venkatraman Sheshashayee
DESCRIPTION:PhD Proposal Review: Wake-up Radio-enabled Wireless Networking: Measurements and Evaluation of Data Collection Techniques in Static and Mobile Scenarios \nAbhimanyu Venkatraman Sheshashayee \nLocation: 432 ISEC \nAbstract: Multi-hop Wireless Networks such as Wireless Sensor Networks and similar networks that enable most applications of the Internet of Things\, are comprised of wirelessly communicating nodes that are powered by batteries. In many relevant scenarios\, it is inconvenient or impossible to replenish or replace the batteries of these nodes\, which limits the operational lifespan of the network. One of the most significant sources of power consumption comes from idle listening on the node’s main radio. This can be ameliorated by Wake-up Radio (WuR) technology: Nodes keep their main radio off while listening for a signal via an ultra-low-power auxiliary radio used only for wake-up purposes. When the appropriate signal is received\, the node turns its main radio on\, conducts the necessary exchange of packets\, and then turns off its main radio. This strategy allows for a considerable reduction in power consumption.\nThis dissertation studies data collection approaches that leverage WuR technology to maximize the lifespan of multi-hop networks for data gathering via routing and via a Mobile Data Collector (MDC). We analyze contemporary WuR technology\, isolating the main criticalities of the state-of-the-art\, including range and data rates. We use a prototype with highly desirable characteristics to conduct experiments to measure its effective communication range\, in both static and mobile scenarios. We then examine the application of WuR technology to data collection scenarios based on multi-hop routing. We devise new techniques and evaluate the effects of different WuR characteristics on the performance of routing\, considering for the first time what the network performance could be if we could overcome the limitation of current WuRs.\nThe remainder of the dissertation will focus on mobile data collection protocols and approaches. We are conducting a comprehensive survey of mobile data collection protocols. We plan to execute exhaustive simulation-based experiments with selected protocols applied to various scenarios. We will evaluate the performance of those protocols and determine how their features influence their performance. We will use the information gleaned from our investigations to develop a novel mobile data collection protocol that effectively utilizes WuR technology to maximize network lifespan. The effectiveness of our protocol will be evaluated using both simulations and physical experiments\, sporting an ad hoc testbed of WuR-enabled nodes and a quad-rotor drone for the MDC.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-abhimanyu-venkatraman-sheshashayee/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220214T100000
DTEND;TZID=America/New_York:20220214T110000
DTSTAMP:20260423T033940
CREATED:20220210T013147Z
LAST-MODIFIED:20220210T013147Z
UID:5441-1644832800-1644836400@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Mengshu Sun
DESCRIPTION:PhD Proposal Review: Deep Learning Acceleration on Edge Devices with Algorithm/System Co-Design \nMengshu Sun \nLocation: Zoom Link \nAbstract: As deep learning has succeeded in a broad range of applications in recent years\, there is an increasing trend towards deploying deep neural networks (DNNs) on edge devices such as FPGAs and mobiles. However\, there exists a significant gap between the extraordinary accuracy of state-of-the-art DNNs and the efficient implementations on edge devices\, due to their limited resources to DNNs with high computation and memory intensity. With the target of simultaneously accelerating the inference and maintaining the accuracy of DNNs\, I investigate efficient implementation of deep learning on low-power and resource-constrained devices in this dissertation\, leveraging algorithm/system co-design techniques that incorporate hardware-friendly DNN compression algorithms with system design optimizations. \nIn the first part of this dissertation\, I explore the DNN compression algorithms leveraging weight pruning and quantization techniques. As for weight pruning\, novel structured and fined-grained sparsity schemes are proposed and obtained with the reweighted regularization pruning algorithm\, and then incorporated into acceleration frameworks on both FPGAs and mobiles to make the acceleration rate of sparse models approach the pruning rate of GFLOPs for the unpruned models. As for quantization\, intra-layer mixed precision/scheme weight quantization is proposed to boost utilization of heterogeneous FPGA resources and therefore improving the FPGA throughput\, by assigning multiple precisions and/or multiple schemes at the filter level within each layer and maintaining the same ratio of filters with different quantization assignments across all the layers. \nIn the second part of this dissertation\, I study the system implementations\, proposing an automatic DNN acceleration framework to generate DNN accelerators to satisfy a target frame rate (FPS). Unlike previous approaches that start from model quantization and then optimizing the FPS for hardware implementations\, this automatic framework will provide an estimation of the FPS with the FPGA resource utilization analysis and performance analysis modules\, and the bit-width is reduced until the target FPS is met and the ratio is automatically determined to guide the quantization process and the accelerator implementation on hardware. A resource utilization model is developed to overcome the difficulty in estimating the LUT consumption\, and a novel computing engine for DNNs is designed with various optimization techniques in support of DNN compression to improve the computation parallelism and resource utilization efficiency.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-mengshu-sun/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220210T113000
DTEND;TZID=America/New_York:20220210T123000
DTSTAMP:20260423T033940
CREATED:20220210T215423Z
LAST-MODIFIED:20220210T215423Z
UID:5443-1644492600-1644496200@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Giuseppe Michetti
DESCRIPTION:PhD Proposal Review: IoT Front-Ends enhanced by Time-Variant RF-MEMS based Circuits \nGiuseppe Michetti \nLocation: Zoom \nAbstract: Implementation of cheap\, scalable radio frequency (RF) front ends in the context of the Internet of Things and 5G devices calls for reconfigurable and spectrally efficient components and circuits operating at RF. In the 4G era\, micro-electro-mechanical systems (MEMS) based on piezoelectric resonators have dominated the filter market for mobile radios\, due to their selectively narrow bandwidth (BW)\, small footprint\, and for their capability to be mass-produced with standard CMOS techniques.\nFor succeeding in the 5G era\, micro-acoustic technologies need to take on the challenge of large data-rates and potentially novel RF front-end architectures. To this end\, I introduce spatio-temporal modulation as a powerful tool to enrich the state-of-the-art of RF front-ends\, and I demonstrate how this can be effectively used to fundamentally increase the performance of high-quality factor microsystems operating at RF.\nFor the case of full-duplex systems\, a nonreciprocal filter structure is proposed\, together with its modeling\, optimization strategies\, and experimental demos at 1GHz and 2.5GHz. Starting from this novel modulation scheme\, MEMS devices are used in place of other resonant technologies\, to enable a filter that features strong nonreciprocal propagation at low power consumption (10s of uW) and high linearity (>30dBm).\nFor the case of half-duplex systems\, a novel modulated filter architecture is introduced and modeled showing its capability of real-time BW control\, as well as to fundamentally extend the BW limited of MEMS filters\, typically associated with their limited piezoelectric coupling coefficient (k¬t2)\, without the need of lossy tunable components. Unprecedented BW tuning ratio (3:1) is experimentally demonstrated at VHF (300MHz) using commercial off-the-shelf resonators\, within a compact footprint\, large absolute BW\, and at a reduced fabrication complexity.\nTo cast this device into next-generation mobile radios\, custom-built MEMS devices are developed and characterized for these filter architectures. MEMS device designs for these architectures are proposed\, leveraging the novel Sc- doped AlN thin-films technology recently added to the Northeastern portfolio of microfabrication capabilities. \n 
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-giuseppe-michetti/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220209T120000
DTEND;TZID=America/New_York:20220209T130000
DTSTAMP:20260423T033940
CREATED:20220208T001833Z
LAST-MODIFIED:20220208T001833Z
UID:5413-1644408000-1644411600@ece.northeastern.edu
SUMMARY:ECE Seminar: Derya Aksaray
DESCRIPTION:ECE Seminar: Reinforcement Learning for Dynamical Systems with Temporal Logic Specifications \nDerya Aksaray \nLocation: 442 Dana or Zoom Link \nAbstract: In many applications\, dynamical systems such as drones\, mobile robots\, or autonomous cars need to achieve complex specifications on their trajectories which may include spatial (e.g.\, regions of interest)\, temporal (e.g.\, time bounds)\, and logical (e.g.\, priority\, preconditions\, concurrency among tasks) requirements. As these specifications become more complex\, encoding them via algebraic equations become intractable. Alternatively\, such specifications can be compactly expressed and used in control synthesis by utilizing the framework of temporal logics. In this talk\, I will address the problem of learning optimal control policies for satisfying temporal logic (TL) specifications in the face of uncertainty. Standard reinforcement learning (RL) algorithms\, which aim to maximize the expected sum of discounted rewards\, are not directly applicable when the objective is to satisfy a TL specification. To overcome this limitation\, I will formulate an approximate problem that can be solved via reinforcement learning and present the suboptimality bound of the proposed solution. Then\, I will consider the case where a TL specification is given as the constraint rather than the objective and present a novel approach for satisfying the TL constraint with a desired probability throughout the learning process. I will motivate this part by multi-use of autonomous systems\, e.g.\, a drone executing a pick-up and delivery mission as its primary task (constraint) while learning to fly over regions of interest (aerial monitoring) as its secondary task (objective). Finally\, I will conclude my talk by discussing some future directions toward the resilience and safety of autonomous systems with complex specifications. \nBio: Derya Aksaray is currently an Assistant Professor in the Department of Aerospace Engineering and Mechanics at the University of Minnesota (UMN). Before joining UMN\, she held post-doctoral researcher positions at the Massachusetts Institute of Technology from 2016-2017 and at Boston University from 2014-2016. She received her Ph.D. degree in Aerospace Engineering from the Georgia Institute of Technology in 2014. Her research interests lie primarily in the areas of control theory\, formal methods\, and machine learning with applications to autonomous systems and aerial robotics.
URL:https://ece.northeastern.edu/event/ece-seminar-derya-aksaray/
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:20220209T103000
DTEND;TZID=America/New_York:20220209T120000
DTSTAMP:20260423T033940
CREATED:20220209T213347Z
LAST-MODIFIED:20220209T213347Z
UID:5436-1644402600-1644408000@ece.northeastern.edu
SUMMARY:ECE Seminar: Qiushi Guo
DESCRIPTION:ECE Seminar: Emergent Active Photonic Platforms for Next-generation Mid-infrared and Ultrafast Photonics \nQiushi Guo \nLocation: 442 Dana or Zoom Link \nAbstract: As two basic properties of light\, wavelength and timescale are central to numerous photonic applications. Compared to visible and near-infrared\, the longer wavelength mid-infrared spectral regime contains unique thermal visual information and chemical fingerprints of the environment.  On a different front\, femtosecond light sources and systems can enable ultrafast information processing\, sensing\, and computing. Yet\, current chip-scale photonic devices and systems are facing tremendous challenges in detecting\, generating\, and processing light of long wavelength and ultrashort timescale. Overcoming these challenges requires new materials and clever device architectures\, and these technologies stand poised to revolutionize fields such as biomedical sensing\, free-space communication\, and photonic computing in both classical and quantum domains. \nIn this talk\, I will show that by engineering the carrier and nonlinear dynamics in emergent active photonic materials\, we can detect photons beyond the regimes accessible to conventional laser sources and detectors\, and process information in an ultrafast manner. In the first half of my talk\, I will first briefly introduce the discovery of black phosphorus (BP) mid-infrared photonics\, highlighting the world’s first BP mid-infrared detectors with high internal gain\, as well as BP’s electrically tunable spectral response due to its unique bandgap tunability. Then\, I will discuss a new strategy for detecting longer wavelength mid-infrared radiations at 12 µm. This is achieved by harnessing the intrinsic mid-infrared plasmons in large-scale graphene. \nThe second half of my talk will cover my recent work on integrated lithium niobate (LN) ultrafast photonics in both classical and quantum domains. I will discuss the realization of ultra-strong nonlinear optical interactions and dynamics in dispersion-engineered and quasi-phase-matched integrated LN devices\, which have enabled 100 dB/cm optical parametric amplification\, ultra-wide bandwidth quantum squeezing\, as well as femtosecond and femtojoule all-optical switching. Finally\, I will outline promising pathways toward realizing chip-scale ultrafast light sources and microsystems for on-chip spectroscopic sensing\, mid-infrared free-space communication\, coherent all-optical computing\, and next-generation thermal vision technologies. \nBio: Dr. Qiushi Guo is currently a postdoctoral scholar at the California Institute of Technology with Prof. Alireza Marandi. He received his Ph.D. in Electrical Engineering from Yale University in Dec. 2019\, advised by Prof. Fengnian Xia. He received his M.S. degree in Electrical Engineering from the University of Pennsylvania in 2014\, and his B.S. degree in Electrical Engineering from Xi’an Jiaotong University in 2012. Qiushi is the winner of the 2021 Henry Prentiss Becton Graduate Prize for his exceptional research achievements at Yale University. His research interests include integrated nonlinear and quantum photonics\, mid-infrared photonics\, and 2-D materials optoelectronics. He has published 36 peer-reviewed research papers in leading scientific journals with citations more than 2700 times. He is serving on the editorial board of the journal Micromachines.
URL:https://ece.northeastern.edu/event/ece-seminar-qiushi-guo/
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:20220208T110000
DTEND;TZID=America/New_York:20220208T122000
DTSTAMP:20260423T033940
CREATED:20220208T001931Z
LAST-MODIFIED:20220208T001931Z
UID:5416-1644318000-1644322800@ece.northeastern.edu
SUMMARY:ECE Seminar: Sunwoo Lee
DESCRIPTION:ECE Seminar: Autonomous Microsystems Based on Heterogeneously Integrated CMOS for Biological Big Data \nSunwoo Lee \nLocation: 442 Dana or Zoom Link \nAbstract: Minimally invasive and chronic physiological monitoring can provide an effective means of disease prevention and early detection while the cumulative big data can unveil hidden patterns in our physiology. Yet\, current physiological monitoring tools are often bulky\, invasive\, and expensive\, limiting their sensitivity and applicability. In this talk\, I will discuss autonomous microsystems based on heterogeneously integrated CMOS\, a platform on which ideal physiological sensors and actuators can be built.\nA micro-scale optoelectronically transduced electrode (MOTE)\, an exemplary microsystem I have designed and built for tetherless neural recording\, is powered and communicates optically through a vertically integrated AlGaAs micro-scale light emitting diode (µLED)\, eliminating the needs for a battery or a RF coil; the MOTE is smaller than a human hair (~60 µm × 30 µm × 330 µm) and weighs about one 1 µg (cf. a grain of sand is about 670 µg). I will review the unique challenges and considerations in developing such heterogeneous systems in terms of device fabrication\, circuit design\, integration\, and handling/manipulation.\nWhile the MOTE is designed for neural recording\, its design methodologies can also be used to monitor other physiological parameters such as temperature\, pH\, glucose-level\, etc. I will introduce future autonomous microsystems with expanded modalities and how to interface them with existing wearables. As such microsystems become more accessible\, the resulting biological big data will help enable personalized healthcare and produce a physiological ‘digital twin’ (like the architectural digital twins of select cities) that can add a new dimension to epidemiological and aging studies. \nBio: Sunwoo Lee (Member\, IEEE) received the B.S. degree in Electrical and Computer Engineering from Cornell University\, Ithaca\, NY in 2010\, and the M.S. and Ph.D. degrees in Electrical Engineering from Columbia University\, New York\, NY in 2012 and 2016\, respectively\, working on graphene synthesis and graphene-based nano-electro-mechanical systems for signal processing and sensing applications. In 2016\, he joined the Molnar Group in the School of Electrical and Computer Engineering at Cornell University as a post-doctoral researcher and has been working on heterogeneously integrated CMOS for physiological monitoring. Sunwoo was a recipient of Qualcomm Innovation Fellowship (QInF) 2012 as well as QInF 2013\, and a recipient of Pi-Star Award for Young Researcher Presentation at CARBONHAGEN 2015.
URL:https://ece.northeastern.edu/event/ece-seminar-sunwoo-lee/
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:20220131T130000
DTEND;TZID=America/New_York:20220131T140000
DTSTAMP:20260423T033940
CREATED:20220128T024437Z
LAST-MODIFIED:20220128T024437Z
UID:5401-1643634000-1643637600@ece.northeastern.edu
SUMMARY:ECE Seminar: Michael Everett
DESCRIPTION:ECE Seminar: Deployable Learning Machines: From cost-to-go estimation to certification \nMichael Everett \nLocation: 442 Dana and Zoom Link \nAbstract: Autonomous robots have the potential to transform our everyday lives\, yet most of these systems struggle outside of the lab or carefully designed warehouses. This talk will first describe our work toward a new generation of robots that learn to handle the highly dynamic and uncertain nature of human environments. In particular\, I will highlight the importance of obtaining accurate cost-to-go models\, which we show can be learned from self-play or aerial imagery for a variety of applications\, from navigation among pedestrians to last-mile delivery. The talk will then dive into the challenges of certifying the safety and robustness properties of machines that learn. I will describe our work that uses convex relaxations and set partitioning to simplify the analysis of highly nonlinear neural networks used across AI. These analysis tools led to the first framework for deep reinforcement learning that is certifiably robust to adversarial attacks and noisy sensor data. The tools also enable reachability analysis — the calculation of all states that a system could reach in the future — for systems that employ neural networks in the feedback loop\, which provides another notion of safety for learning machines that interact with uncertain environments. Finally\, I will discuss my long-term vision that aims to spark a new era of learning machines that can be deployed in any environment without human supervision. \nBio: Michael Everett is currently a Research Scientist in the Department of Aeronautics and Astronautics at the Massachusetts Institute of Technology (MIT). He received the S.B.\, S.M.\, and Ph.D. degrees in mechanical engineering in 2015\, 2017\, and 2020\, respectively\, at MIT. His research lies at the intersection of machine learning\, robotics\, and control theory. His papers have been recognized as one of the Editors’ Top 5 Articles of 2021 in IEEE Access\, Best Paper Award on Cognitive Robotics at IROS 2019\, Best Student Paper Award and Finalist for Best Paper Award on Cognitive Robotics at IROS 2017\, and Finalist for Best Multi-Robot Systems Paper Award at ICRA 2017. He has been interviewed live on the air by BBC Radio and his team’s robots were featured by Today Show and the Boston Globe.
URL:https://ece.northeastern.edu/event/ece-seminar-michael-everett/
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:20220127T110000
DTEND;TZID=America/New_York:20220127T120000
DTSTAMP:20260423T033940
CREATED:20220125T231649Z
LAST-MODIFIED:20220125T231649Z
UID:5397-1643281200-1643284800@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Meruyert Assylbekova
DESCRIPTION:PhD Proposal Review: Aluminum Nitride and Scandium-doped Aluminum Nitride Materials and Devices for Beyond 6 GHz Communication \nMeruyert Assylbekova \nLocation: Zoom Link \nAbstract: With 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 life­changing 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 Front­End (RFFE) architectures. 5G smartphones are expected to carry over the legacy sub-­6 GHz bands\, which translates into an increased number of filters.\nIn 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.\nWhile 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.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-meruyert-assylbekova/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220124T130000
DTEND;TZID=America/New_York:20220124T140000
DTSTAMP:20260423T033940
CREATED:20220118T232834Z
LAST-MODIFIED:20220118T232834Z
UID:5383-1643029200-1643032800@ece.northeastern.edu
SUMMARY:ECE Seminar: Nathan Lazarus
DESCRIPTION:ECE Seminar: Stretchable Magnetics for Soft Robotics \nNathan Lazarus \nLocation: Zoom Link \nAbstract: Recent innovations in making robots from softer biofriendly materials have opened broad new applications ranging from medicine to agriculture. Due to the reliance of much of the field on pneumatic actuation\, heavy and rigid pumps\, and control circuitry for driving pressure chambers have become a major limitation for fully soft\, untethered soft robots. In my talk\, I will discuss all aspects of creating soft electromagnets\, inductors and power circuits for electromagnetic actuation and power management in stretchable systems. Using unconventional materials like room temperature liquid metals and ferrofluids\, we demonstrate record performance for a stretchable inductor. These stretchable inductors are then used to create flexible and stretchable pumps with flow rates nearly two orders of magnitude higher than past demonstrations in the literature and integrated into a simple soft robot demonstrator. \nBio: Nathan Lazarus has worked extensively in areas ranging from mixed signal electronics to MEMS fabrication\, with his Ph.D. at Carnegie Mellon culminating in 2012 with the demonstration of the highest recorded fractional sensitivity to date for a capacitive chemical sensor topology integrated with CMOS electronics. Since joining US Army Research Laboratory in May 2012\, Dr. Lazarus’s research has focused on stretchable power electronics\, soft robotics and 3D printing. He has received numerous awards including ARL’s Honorary Award for Engineering and the Rookie of the Year Excellence in Federal Career Award (Gold) from the Baltimore Federal Executive Board. In 2019\, Dr. Lazarus was selected for the Presidential Early Career Award for Scientists and Engineers (PECASE)\, the highest honor given by the US government for researchers beginning their independent research careers.
URL:https://ece.northeastern.edu/event/ece-seminar-nathan-lazarus/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220120T153000
DTEND;TZID=America/New_York:20220120T163000
DTSTAMP:20260423T033940
CREATED:20220111T201529Z
LAST-MODIFIED:20220111T201529Z
UID:5372-1642692600-1642696200@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Hamed Mohebbi Kalkhoran
DESCRIPTION:PhD Proposal Review: Machine Learning Approaches for Classification of Myriad Underwater Acoustic Events Over Continental-shelf Scale Regions with Passive Ocean Acoustic Waveguide Remote Sensing \nHamed Mohebbi Kalkhoran \nLocation: Zoom Link \nAbstract: Underwater acoustic data contain a myriad of sound sources that include bioacoustics related to marine life such as marine mammals and fishes; man-made such as ships\, sonar\, and airguns; as well as natural geophysical processes such as earthquake\, hurricane\, and volcanic eruption. Among underwater acoustic events\, marine mammal vocalization classification is one of the most challenging problems due to their transient broadband calls\, high variation in the calls of a specie (intra-class variation)\, and high similarity between the calls of some species. In this thesis\, we investigate machine learning approaches for classifying marine mammal vocalizations for real-time applications. We utilize acoustic data from a 160-element coherent hydrophone array and employ the passive ocean acoustic waveguide remote sensing technique to enable sensing and detections over instantaneous wide areas more than 100 km in diameter from the array. A variety of computational accelerating approaches\, combining hardware and software\, that make the methods desirable for real-time applications are also developed.\nHumpback whale behavior\, population distribution and structure can be inferred from long term underwater passive acoustic monitoring of their vocalizations. Here we employ machine learning approaches to classify humpback whale vocalizations into song and non-song calls. We use wavelet signal denoising and coherent array processing to enhance the signal-to-noise ratio. To build features vector for every time sequence of the beamformed signals\, we employ Bag of Words approach to time-frequency features. Finally\, we apply Support Vector Machine (SVM)\, Neural Networks\, and Naive Bayes to classify the acoustic data and compare their performances. Best results are obtained using Mel Frequency Cepstrum Coefficient (MFCC) features and SVM which leads to 94% accuracy and 72.73% F1-score for humpback whale song versus non-song vocalization classification.\nTo classify a large variety of whale species by their calls\, we extracted time-frequency features from Power Spectrogram Density (PSD) of the beamformed signals. Then we used these features to train three classifiers\, which are SVM\, Neural Networks\, and Random forest to classify six whale species: Fin\, Sei\, Blue\, Minke\, Humpback\, and general Odontocetes. Best results were obtained with Random forest classifier\, which achieved 95% accuracy\, and 85% F1 score. To detect transient sound sources\, first we applied Per-Channel Energy Normalization (PCEN) on the PSD of the beamformed signals. We applied thresholding on the PCEN data followed by morphological image opening to find potential sound sources and reduce noisy detections. Then we applied connected component analysis to obtain the final detected sounds for each bearing. To estimate the Direction of Arrival (DoA) of detected sounds\, we applied non-maximum suppression (NMS)\, which is widely used in object detection applications in computer vision\, on the detected sounds. We used mean power of each detected sound as the scores for NMS. To speed up the data processing\, we investigated a variety of accelerating approaches\, such as analyzing the effect of floating point precision\, applying parallel processing\, and implementing fast algorithms to run on GPU.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-hamed-mohebbi-kalkhoran/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220120T110000
DTEND;TZID=America/New_York:20220120T120000
DTSTAMP:20260423T033940
CREATED:20220106T194246Z
LAST-MODIFIED:20220106T194246Z
UID:5368-1642676400-1642680000@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Vedant Sumaria
DESCRIPTION:PhD Proposal Review: Exploring Micro-Machined Glass Shell Resonators For Sensor Application \nVedant Sumaria \nLocation: Zoom \nAbstract: Optical resonators have been playing an important role in modern optics. They are fundamental in any laser device\, etalon for optical filtering\, accurate measurement for non-linear optics. Bulk optical resonators that use two or more mirrors are usually used in all branches of modern linear and non-linear optics. There are many limitations in using such systems because they cannot provide high performance (high quality (Q) factor) and their size\, weight\, and alignment\, creates stability problems. To solve these problems\, there was an emerging class of miniaturized dielectric cavity based optical resonators that exploited the light confinement phenomenon through internal reflection. These resonators have a circular symmetry\, and they sustain modes known as the Whispering Gallery Modes (WGM) that is nothing but electromagnetic waves that circulate and are confined within the structure. Fabrication of these dielectric optical resonators is simpler and comparatively inexpensive. They demonstrate higher mode stability and higher performance. \nIn this proposal review\, I will discuss the working principles of a WGM resonator and study the various loss mechanisms to improve the quality factor. Further I will discuss the fabrication of on chip glass-blown microspherical shell resonators. These on-chip spherical glass shells are micrometers to millimeters in diameter with ultra-smooth surfaces and micrometer wall thicknesses which can sustain optical resonance modes with high Q-factors up to 50 million. Further we discuss various methods used to etch the backside silicon to create a liquid core optical resonator. This etching leads to increase in the surface roughness leading to loss of resonance. We optimized etching methods and parameters to keep the resonance as high as 18 million. By etching the silicon resonator’s temperature sensitivity is improved from -1.15 GHz/K to 2.23 GHz/K. This optical WGM sensor is then novel biosensor consisting of a chip-scale whispering gallery mode resonators with High-Q factor and a micro-caloric system. The silicon released shell resonator is elastically coupled to a kapton tubing system. Temperature change in the system induces thermal expansion and thermorefractive changes which can be sensitively monitored through changes in the optical resonance characteristics. We demonstrate a measurement resolution less than 10mK and a method of measuring temperature change to eliminate background noise that shows a great potential for detection of various biomolecules such as urea. We also discuss the possibility to use the sensor as an extremely sensitive IR sensor. Finally\, we talk about the future work in immobilization of urease and glucose oxidase to test for analytes like urea and glucose with concentrations in micro-mole.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-vedant-sumaria/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220114T140000
DTEND;TZID=America/New_York:20220114T150000
DTSTAMP:20260423T033940
CREATED:20220118T193826Z
LAST-MODIFIED:20220118T193826Z
UID:5377-1642168800-1642172400@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Andac Demir
DESCRIPTION:PhD Dissertation Defense: Automated Bayesian Network Exploration for Nuisance-Robust Inference \nAndac Demir \nLocation: Zoom Link \nAbstract: A fundamental challenge in the analysis of physiological signals is learning useful features that are robust to nuisance factors e.g.\, inter-subject \& inter-session variability\, and achieve the highest nuisance-invariant classification performance. Towards resolving this problem\, we introduce 3 frameworks: AutoBayes\, which is an AutoML approach to conduct neural architecture search for research prototyping\, and GNN based frameworks: EEG-GNN and EEG-GAT.\nThe ultimate goal of the AutoBayes framework is to identify the conditional relationship between a physiological dataset\, associated task labels\, nuisance variations and potential latent variables in order to robustly infer the task labels invariant of nuisance factors. Nuisance factors in the case of physiological datasets could be variations in subjects or sessions\, but we only focus on subject variations in the experiments. AutoBayes enumerates all plausible Bayesian networks between data\, labels\, nuisance variations and potential latent variables\, detects and prunes the unnecessary edges according to Bayes-Ball Algorithm\, and then trains the resulting DNN architectures for different hyperparameter configurations in an adversarial / non-adversarial or a variational / non-variational setting to achieve the highest validation performance. Instead of hyperparameter tuning for model optimization\, AutoBayes concentrates on the architecture search of plausible Bayesian networks\, and achieves state-of-the-art performance across several physiological datasets. Furthermore\, we ensemble several Bayesian networks by stacking their posterior probability vectors in a higher level learning space\, train a shallow MLP as a meta learner\, and measure the task and nuisance classification performance on a hold-out dataset. We observe that exploration of different inference Bayesian networks has a significant benefit in improving the robustness of the machine learning pipeline\, and the parallel activity of vast assemblies of different Bayesian network models significantly reduces variation across subjects in the cross-validation setting.\nIn the second part of the dissertation\, we benchmark the performance of EEG-GNN and EEG-GAT against the AutoBayes framework. CNN’s have been frequently used to extract subject-invariant features from EEG for classification tasks\, but this approach holds the underlying assumption that electrodes are equidistant analogous to pixels of an image and hence fails to explore/exploit the complex functional neural connectivity between different electrode sites. We overcome this limitation by tailoring the concepts of convolution and pooling applied to 2D grid-like inputs for the functional network of electrode sites. Furthermore\, we develop various GNN models that project electrodes onto the nodes of a graph\, where the node features are represented as EEG channel samples collected over a trial\, and nodes can be connected by weighted/unweighted edges according to a flexible policy formulated by a neuroscientist. The empirical evaluations show that our proposed GNN-based framework\, EEG-GNN\, outperforms standard CNN classifiers across ErrP and RSVP datasets\, as well as allowing neuroscientific interpretability and explainability to deep learning methods tailored to EEG related classification problems. Besides that\, EEG-GAT employs multi-head attention mechanism in conjunction with the GNN architecture to learn the graph topology of observations instead of utilizing a graph shift operator that is heuristically constructed by a domain expert. This implicitly allows the exploration of the functional neural connectivity peculiar to a cognitive task between pairs of EEG electrode sites as well as EEG channel selection\, which is critical for reducing computational cost\, and designing portable EEG headsets.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-andac-demir/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220111T110000
DTEND;TZID=America/New_York:20220111T170000
DTSTAMP:20260423T033940
CREATED:20220110T194722Z
LAST-MODIFIED:20220110T194722Z
UID:5370-1641898800-1641920400@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Sungho Kang
DESCRIPTION:PhD Dissertation Defense: Plasmonically Enhanced Infrared Sensing Microsystems \nSungho Kang \nLocation: Zoom Link \nAbstract: Infrared (IR) spectroscopic sensing has become a key technique in multidisciplinary environments such as military applications\, industrial safety control\, and smart homes\, by providing an accurate and non-disruptive analysis of the target objects. Recently the demand for high performance and compact IR spectroscopy systems has been steadily growing due to the advent of Internet of Things and the burgeoning development of miniaturized sensors. The key challenge lies in realizing high performance IR detectors that have low noise\, high IR throughput\, and spectral sensitivity in a miniaturized form factor. This challenge has been tackled in the study of micro-electromechanical sensing systems and metamaterial absorbers\, in which the ultra-high resolution sensing capability and the near-perfect IR absorption properties can be simultaneously exploited in a minimized footprint. The metal-insulator-metal (MIM) IR absorbers\, in particular\, are characterized by the near-unity absorptance with lithographically tunable peak absorption wavelength and spectral selectivity in an ultra-thin form factor\, suitable for the implementation of miniaturized spectroscopic IR microsystems. The exceptional IR absorption characteristics realized by the MIM IR absorbers and their sub-wavelength form factor allow for seamless integration with the existing IR sensing microsystem and the unprecedented IR sensing performance for the next generation IoT sensing solutions. In this defense\, novel development of miniaturized IR spectroscopic sensor and maintenance-free wireless human sensors based on the two key technologies are presented: (1) multispectral resonant IR detector array and (2) plasmonically-enhanced long-wavelength infrared micromechanical photoswitch. This study shows that the demonstrated technologies can replace the traditional IR sensors with the new generation IR sensing microsystems that are characterized by their high performance\, compact form factor\, power efficiency and low cost.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-sungho-kang/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211220T110000
DTEND;TZID=America/New_York:20211220T120000
DTSTAMP:20260423T033940
CREATED:20211216T002527Z
LAST-MODIFIED:20211216T002527Z
UID:5353-1639998000-1640001600@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Anahita Moradmand
DESCRIPTION:PhD Proposal Review: Robust Observer Structures and Control Design for Linear and Nonlinear Dynamical Systems with Applications \nAnahita Moradmand \nLocation: Zoom Link \nAbstract: This thesis focuses on special class of observers and controllers for different and seperable nonlinear systems. In the first step\, a robust fault detection approach using observers for linear systems is proposed where we combine the unknown inout observer UIO and an extended proportional integral observer PIO\, which has a fading term for robust fault detection. The integrated observer is called proportional integral fading unknown input observer (PIFUIO).\nFurthermore\, we extend our result to nonlinear systems with special structures as the design of nonlinear observer had limitation for general types of nonlinear systems. In the second step\, analysis and design of positive systems is considered whereby positive stabilization and the design of positive unknown input observer (PUIO) are introduced. Also\, the robust stability analysis of this class of systems is studied in which the robust stability is formulated in terms of LMI. the class of separable positive nonlinear systems is also analyzed and the design of observer and controller are provided. Finally\, we extend our desgin from Lipschitz type nonlinearity to state-dependent type by focusing on interconnected systems where we propose a distributed control architecture to take advantage of the global performance similar to centralized control and leverage the benefits of decentralized control.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-anahita-moradmand/
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DTSTART;TZID=America/New_York:20211216T150000
DTEND;TZID=America/New_York:20211216T160000
DTSTAMP:20260423T033940
CREATED:20211216T002630Z
LAST-MODIFIED:20211216T002630Z
UID:5355-1639666800-1639670400@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Jinghan Zhang
DESCRIPTION:PhD Dissertation Defense: Domain Design Space Exploration: Designing a Unified Platform for a Domain of Streaming Applications \nJinghan Zhang \nLocation: ISEC 362 or Zoom Link \nAbstract: Many demanding streaming applications share functional and structural similarities with other applications in their respective domain\, e.g.\, video analytics\, software-defined radio\, and radar. This opens the opportunity for specialization to achieve the needed efficiency and/or performance.\nPlatforms integrating many accelerators (ACCs) is a primary approach for efficient\, high-performance stream computing.\nHowever\, designing one platform for each application is not economical due to the high costs of nonrecurring engineering (NRE) and time-to-market (TTM).\nTo this end\, the concept of domain platforms is proposed\, which takes advantage of similarities across applications and designs one unified platform to accelerate a domain of applications instead of focusing on a single reference application.\nThis dissertation approaches designing domain platforms from a function-level (kernel-level) acceleration through a heterogeneous ACC-rich platform\, where each ACC is specialized to accelerate a particular function.\nThere is a great challenge to select ACCs allocated in the domain platform\, considering the large design space and performance balance across many applications.\nHowever\, current Design Space Exploration (DSE) tools only focus on an individual application in isolation (e.g.\, one particular vision flow) for allocating a platform\, but not a set of similar applications.\nThis dissertation introduces Greedy Guided Mutation (GGM) to speed up the mutation in the GIDE algorithm\, which calculates an ACC score according to current allocation to guide mutation.\nAlternatively\, Rapid Domain Platform Performance Prediction (RDP^3) methods are introduced to replace a large number of the slow platform assessment in domain DSE\, which avoids the complex application binding exploration.\nIn the experiments\, GGM reduces 84.8% of exploration time with a 0.23% loss of the final OpenVX domain platform’s performance.\nRDP^3 using a machine learning method yields an even more significant speedup\, saving 90.8% of exploration time with only 0.0003% performance loss.\nDmDSE is a milestone to broaden DSE scope from individual applications to the domain level. It tremendously pushes the domain platform design from manually and engineering experience guided into a general automatic process.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-jinghan-zhang/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211216T093000
DTEND;TZID=America/New_York:20211216T103000
DTSTAMP:20260423T033940
CREATED:20211216T015218Z
LAST-MODIFIED:20211216T015218Z
UID:5357-1639647000-1639650600@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Jaehyeon Ryu
DESCRIPTION:PhD Proposal Review: Engineering Functional Nanomesh for Advanced Neuroelectronics \nJaehyeon Ryu \nLocation: Zoom Link \nAbstract: Transparent electronics have emerged as promising platforms for neural interfacing by enabling simultaneous electrophysiological recording and optical measurements. Also\, there are high demands for stretchable devices due to their low modulus and compatible interface with irregular and soft neural tissue. However\, current transparent\, stretchable approaches are usually limited by their scalability for neuroelectronic applications. Here\, I present multi-functional nanomesh as an approach to achieve stretchable\, transparent microelectrode arrays (MEAs) with excellent scalability. By stacking mechanical supporting polymer\, gold\, and conductive polymer in a nanomesh structure on elastomer substrate\, multilayer nanomesh-based MEAs show excellent stretchability\, transparency\, and electrochemical properties with single neuron scale dimensions. The performance of these multi-functional nanomesh-based MEAs has been characterized through bench testing\, and I plan to perform in vivo validation in the remaining period of my thesis. These highly stretchable and transparent multilayer nanomesh MEAs are promising for applications ranging from neuroscience to biomedical devices.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-jaehyeon-ryu/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211210T134500
DTEND;TZID=America/New_York:20211210T144500
DTSTAMP:20260423T033940
CREATED:20211202T021501Z
LAST-MODIFIED:20211202T021536Z
UID:5329-1639143900-1639147500@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Leonardo Bonati
DESCRIPTION:PhD Proposal Review: Softwarized Approaches for the Open RAN of NextG Cellular Networks \nLeonardo Bonati \nLocation: 532 ISEC \nAbstract: The 5th (5G) and 6th generations (6G) of cellular networks\, also known as NextG\, will bring unprecedented flexibility to the wireless cellular ecosystem. Because of its typically closed and rigid market\, the telco industry has incurred high costs and non-trivial obstacles in delivering those new services and functionalities to satisfy the requirements and the demand of NextG networks. To break this trend the industry is now moving towards open architectures based on softwarized approaches\, which will afford network operators flexible control and unprecedented adaptability to heterogeneous conditions\, including traffic and application requirements. Now\, by simply expressing a high-level intent\, operators will be able to instantiate bespoke services on-demand on a generic hardware infrastructure\, and to adapt such services to the current network conditions. Through disaggregation\, network elements will split their functionalities across multiple components—possibly provided by different vendors—interconnected through well-defined open interfaces. The separation of control functions from the hardware fabric\, and the introduction of standardized control interfaces\, will ultimately enable definition and use of softwarized control loops\, which will bring embedded intelligence and real-time analytics to effectively realizing the vision of autonomous and self-optimizing networks.\nThis dissertation work focuses on the design\, prototyping and experimental evaluation of softwarized approaches for the new open Radio Access Network (RAN) of NextG cellular networks. We analyze the architectural enablers\, challenges and requirements for a programmatic zero-touch control of the very many network elements and propose practical solutions for its realization. We prototype solutions by leveraging open-source software implementations of cellular protocol stacks and frameworks\, and heterogeneous virtualization technologies\, including the srsRAN and OpenAirInterface cellular implementations\, and the O-RAN framework. The contributions of this work include (i) the first demonstration of O-RAN data-driven control loops in a large-scale experimental testbed using open-source\, programmable RAN and RAN Intelligent Controller (RIC) components through xApps of our design\, and (ii) CellOS\, a zero-touch cellular operating system that automatically generates and executes distributed control programs for simultaneous optimization of heterogeneous control objectives on multiple network slices starting from a high-level intent expressed by the operators. The effectiveness of our solutions in achieving superior control and performance of the RAN is demonstrated on state-of-the-art experimental facilities\, including software-defined radio-based laboratory setups and open access experimental wireless platforms\, such as Colosseum\, Arena\, and the POWDER-RENEW platform from the U.S. PAWR program.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-leonardo-bonati/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211210T110000
DTEND;TZID=America/New_York:20211210T120000
DTSTAMP:20260423T033940
CREATED:20211207T203514Z
LAST-MODIFIED:20211207T203514Z
UID:5339-1639134000-1639137600@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Michele Pirro
DESCRIPTION:PhD Proposal Review: Scandium-doped Aluminum Nitride for new MEMS technologies \nMichele Pirro \nLocation: 432 ISEC \nZoom Link Meeting ID: 938 0086 0379 https://northeastern.zoom.us/u/abZS2SmtT1 \nAbstract: The increasing demand for data is pushing the MEMS industry to more performant and area-efficient systems to be used in IOT nodes as sensors and RF-components. In this market\, AlN plays a pivotal role thanks to the piezoelectric properties accompanied with good stability over power and temperature in miniaturized devices. In fact\, AlN is already present in different commercial MEM systems\, such as duplexers\, ultrasound generators\, energy harvesters and so on\, proving a mature mass-production process flow. The required more stringent specifications in terms of bandwidth\, losses and efficiency are pushing towards piezoelectric materials with higher coupling coefficient\, but still in a compatible post-CMOS process flow. Luckily\, recent works showed how it is possible to enhance the piezoelectric effect by doping AlN with Scandium\, allowing up to 400% increase in the d33 piezoelectric coefficient. The enhanced acoustic transduction along with the recent demonstration of ferroelectric switching and the post-IC compatibility\, is making Sc-doped AlN a new material with the potential not only to replace AlN\, but also to integrate different functionalities within the same component. Academy and industry all over the world are actively researching the actual potential of the material but there is still a lack of information on high-Sc concentration\, which would allow lower-voltage switching along with higher d33. This work has the main objective to show Sc-concentration > 28% and their piezo/ferroelectric response for a new class of microelectromechanical devices.\nThe proposal will discuss the advance in the process flow of high Sc- concentrations\, showing the impact of the deposition parameters on the material properties. Thin films with good crystallinity on IC-substrate and enhanced d33 are reported\, along with first attempts to resonator-devices. An in-depth ferroelectric characterization will show how coercive field and leakage current are the main limiting factors the material is facing to integrate its memory effect. For this purpose\, the work will present how tuning of Sc-concentration\, substrate-rf and bulk stress can ease these limiting factor\, opening to new acoustic devices with memory functionalities. The last part will focus on the co-integration of acoustic properties with ferroelectric switching for tunable filters and ultrasonic generators in post-IC compatible substrates.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-michele-pirro/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211209T150000
DTEND;TZID=America/New_York:20211209T160000
DTSTAMP:20260423T033940
CREATED:20211208T011849Z
LAST-MODIFIED:20211208T011849Z
UID:5348-1639062000-1639065600@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Arjun Singh
DESCRIPTION:PhD Dissertation Defense: Design\, Modeling\, and Operation of Plasmonic Devices for Smart Communication Systems in the Terahertz Band \nArjun Singh \nLocation: 332 ISEC or Teams Link \nAbstract: The terahertz (THz) band is an attractive spectral resource for realizing future communication systems\, with the potential of supporting very high-speed data rates and increasingly dense networks. However\, the lack of a well-developed technology that operates at these frequencies has remained a challenge for the scientific community. The very high propagation losses at THz frequencies and the decimating impact of everyday objects on THz wave propagation necessitate an up-haul of the conventional communication link\, with smart control over the radiation\, propagation\, and detection of THz signals. Additionally\, device physics at THz frequencies\, among them the very high gain requirement and large electrical size\, may render the often-held assumptions of the propagation model invalid. An interdisciplinary approach spanning device design and operation\, and wireless propagation and signal processing is required.\nTo this end\, the proposed research herein addresses the facilitation of an end-to-end communication link with graphene plasmonics as the cornerstone of the fundamental device physics. The devices designed can be utilized to effectively overcome the limited communication distance –The grand challenge of the THz band. Different from other undertakings\, every attempt is made to ac-knowledge and accommodate the complex trade-offs in the design process. First\, a novel graphene based plasmonic array architecture is proposed\, explained\, and modeled. The fundamental radiating element of the array architecture\, called the plasmonic front-end\, consists of a self-sufficient plasmonic source\, a plasmonic modulator that acts as a phase controller\, and a plasmonic nano-antenna for effective radiation. The designed array is compact and provides complete beamsteering support\, with a new tailored algorithm developed for beamforming weight selection. Numerical evaluations and full-wave finite difference frequency domain (FDFD) simulations with COMSOL Multi-physics are utilized to verify array operation. Exploiting these properties\, a multi-beam array design is presented next\, where orthogonal spatial filters are utilized to provide support for spatial multiplexing towards the realization of ultra-massive MIMO (UM-MIMO). Taking this further\, the design considerations of an interleaved plasmonic array are presented\, in which the beamsteering capability is utilized to simultaneously achieve radio frequency interference (RFI) mitigation with channel capacity maximization for multi-user scenarios. Additionally\, to realize the vision of a smart communication system with a programmable wireless environment\, a hybrid reflectarray is presented. The fundamental element is modeled as a jointly designed and integrated metal-graphene patch. Numerical and simulation results are utilized to demonstrate the attractive properties of the reflectarray as compared to other proposed counterparts\, including an independence from the incoming angle of the impinging wave\, dynamic phase control capability\, and strong reflection efficiency. The requirements of a THz communication link and their impact on the common communication protocols are considered next. It is shown that certain scenarios may render regular array operation invalid\, motivating codebook designs that function in the massive near-field Fresnel zone of electrically large THz devices. Numerical simulations and theoretical analysis are presented to highlight their potential in improving system performance and capacity while reducing the system complexity. Finally\, the significant milestones in the fabrication process of these devices are also presented.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-arjun-singh/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211207T140000
DTEND;TZID=America/New_York:20211207T150000
DTSTAMP:20260423T033940
CREATED:20211130T003827Z
LAST-MODIFIED:20211130T003827Z
UID:5321-1638885600-1638889200@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Sara Banian
DESCRIPTION:PhD Dissertation Defense: Content-Aware AI-Driven Design Assistance Frameworks for Graphic Design Layouts \nSara Banian \nLocation: Zoom Link \nAbstract: Designing user interfaces (UIs) for mobile interaction is widespread but still challenging. It is important for the overall user satisfaction and application success. During the design process\, designers express their requirements through images describing the UI’s layout\, structure\, and content. Designers\, however\, encounter key challenges throughout the design process. For example\, searching for inspiring design examples is challenging because current search systems rely on only text-based queries and do not consider the UI structure and content. Furthermore\, these systems often focus on overall page-level layout over individual UI components. Also\, creating wireframe templates is difficult for many designers as it necessitates an understanding of different design guidelines. Therefore\, it is critical to support designers by developing effective design tools to help them be more productive and creative.\nIn this dissertation\, I aim to explore how to develop design assistance methodologies to augment the process of UI layout design\, with a particular focus on visual search and layout generation. Specifically\, for this exploration\, I seek to investigate the use of advanced deep learning models in the context of mobile UI layout design. Processing layouts differs from processing pixel-level images in that it necessitates processing both the semantic (e.g.\, labels) and spatial (e.g.\, coordinates) content of the layout to model the data properly. To achieve this\, I explore the design problems from both the data and the model side. First\, I present a large-scale UI dataset that accurately specifies the interface’s view hierarchy (i.e.\, UI components and their location). Second\, I contribute the VINS framework\, which is composed of three systems LayVis\, CompVis\, and TransVis that addresses layout-based visual search\, component-based visual search\, and layout generation\, respectively.\nFirst\, I introduce LayVis\, an object-detection layout-based retrieval model. It takes as input a UI image and retrieves visually similar design examples. Next\, I introduce CompVis\, a component-based visual search system to easily retrieve individual UI components via convolutional neural networks (CNNs). Specifically\, for a given query\, the system allows to retrieve (1) text label synonyms\, (2) similar UI components\, and (3) design examples containing such components. Finally\, I present TransVis\, a transformer-based generative framework that investigate how to generate UI layouts according to user specifications and following design practices. It specifically models UI layouts as an ordered sequence of elements based on spatial and semantic relationships for (1) generating complete UI layouts\, (2) auto-completing existing UI layouts seamlessly\, and (3) supporting many design elements per layout.\nOverall\, the work presented in this dissertation contributes to augmenting the UI layout design. Through quantitative and qualitative evaluation of VINS\, we conclude the following: (1) Advanced deep learning models can aid in the development of design assistance methodologies for layout design; and (2) Designers perceive the use of VINS inspiring and useful. Such insights\, combined with the open-sourced large-scale dataset\, can help the research community develop more effective AI-based data-driven design tools. This work presents future opportunities to investigate different deep learning models within the context of layout design and how designers interact with these AI-based models.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-sara-banian/
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