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DTSTART;TZID=America/New_York:20220223T173000
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DTSTAMP:20260427T185938
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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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220215T140000
DTEND;TZID=America/New_York:20220215T150000
DTSTAMP:20260427T185938
CREATED:20220209T213529Z
LAST-MODIFIED:20220209T213529Z
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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/
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DTSTART;TZID=America/New_York:20210914T140000
DTEND;TZID=America/New_York:20210914T150000
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CREATED:20210908T184332Z
LAST-MODIFIED:20210908T184332Z
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SUMMARY:ECE PhD Dissertation Defense: Vageeswar Rajaram
DESCRIPTION:PhD Dissertation Defense: Near-Zero Power Microelectromechanical Sensors for Large-Scale IoT Sensor Networks \nVageeswar Rajaram \nLocation: ISEC 432 \nAbstract: The Internet-of-things revolution has ushered the development of sensing technologies aimed towards establishing large-scale remote sensor networks to monitor the environment continuously and with high spatial resolution. However\, with existing sensor technologies this goal has so far been limited in terms of scalability (i.e.\, the number of sensors in a network\, areal coverage and spatial granularity). A major impeding factor is sensor power consumption: state-of-the-art remote sensor technologies need to be actively powered (i.e.\, by a battery) to continuously monitor the environment for an object of interest\, even at standby (when it is not present). This is because all signals collected by the sensor from the environment need to be processed by active signal conditioning circuits to distinguish a signal of interest from other signals. Therefore\, in applications where an event or signal of interest occurs only occasionally\, most of the battery is drained by processing irrelevant signals. The result is that as the sensor network scales up\, so do the costs and labor associated with the sensors’ battery replacements. This makes it unfeasible to deploy and maintain large numbers of sensors for any application and greatly limits the scale of sensor networks. Extremely low power consumption therefore is critical in enabling large sensor networks by reducing or even eliminating costs associated with frequent battery replacements.\nThis work describes the development of a revolutionary new sensing platform aimed at creating sensors with battery lifetimes limited only by the self-discharge of the battery itself (>10 years). The ultimate goal for the technology is to enable maintenance-free sensor nodes for truly large-scale “deploy-and-forget” sensor networks. In particular\, this work details the development of novel infrared sensors based on micro-electro-mechanical photoswitches that are capable of detecting and distinguishing specific infrared signatures associated with objects of interest (hot gases\, fire\, human body\, etc.) while remaining dormant with near-zero power consumption at standby. This unique sensor technology aims to break the paradigm of requiring a power supply to perform sensing by instead relying on the energy contained in the infrared signals emitted by the object of interest itself to perform its detection. This dissertation presents a comprehensive summary of the sensor’s design\, its capabilities\, and the various technical developments that have led this technology to evolve from a concept to a prototype near-zero power wireless infrared sensor with orders of magnitude lesser power consumption compared to the state-of-the-art.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-vageeswar-rajaram/
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