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DTSTART;TZID=America/New_York:20230721T140000
DTEND;TZID=America/New_York:20230721T153000
DTSTAMP:20260421T224842
CREATED:20230718T175222Z
LAST-MODIFIED:20230718T175222Z
UID:6433-1689948000-1689953400@ece.northeastern.edu
SUMMARY:Daniel Uvaydov's PhD Dissertation Defense
DESCRIPTION:Title: Real-Time Spectrum Sensing for Inference and Control \nCommittee Members: \nProf. Tommaso Melodia (Advisor) \nProf. Kaushik Choudhury \nProf. Francesco Restuccia \nAbstract: \nThrough growing cellular innovations\, the usage and congestion of the wireless spectrum is increasing at incredible speeds. High demand and limited supply pose a resource issue known as the “spectrum crunch”. With the high diversity of users sharing a large portion of the spectrum to request and receive diverse services\, spectrum coordination becomes very difficult. Large scale device synchronization for spectrum coordination requires high overhead and more wireless transmissions further reducing spectrum resources. However\, by monitoring the spectrum\, otherwise known as spectrum sensing\, we can develop mechanisms where users can opportunistically take action based on the current state of the spectrum\, without need for direct coordination between devices. Spectrum sensing can enable the next generation of wireless applications ranging from opportunistic spectrum access to cognitive radio networks. The key unaddressed challenges of spectrum sensing are that (i) it requires very extensive and diverse datasets; (ii) it has to be performed with extremely low latency over varying bandwidths and must guarantee strict real-time processing constraints; (iii) its underlying algorithms need to be extremely accurate\, and flexible enough to work with different wireless bands and protocols to find application in real-world settings. This dissertation focuses on addressing these challenges in multiple wireless applications by utilizing Deep Learning (DL) techniques as the main vehicle of spectrum sensing for both inference and control. Algorithmic spectrum sensing has generally been model-based which limits its performance in diverse settings and environments\, for this reason we explore data-driven spectrum sensing algorithms. Mainly\, this work takes a holistic approach to address spectrum sensing problems from multiple directions with the overarching goal of developing the core building blocks for the next generation of intelligent\, AI-driven\, efficient spectrum sharing systems. By leveraging mechanisms such as data augmentation\, channel attention\, voting\, and segmentation we are able to push beyond the capabilities of existing DL techniques and create generalizable spectrum sensing algorithms. Furthermore we deploy different spectrum sensing solutions in real testbeds for over the air evaluations and applicable proof-of-concepts. The contributions of this work includes (i) multiple datasets and implementations for DL enabled spectrum sensing with applications in radio frequency and underwater; (ii) a method for tackling the core issue of dataset generation in supervised learning algorithms for spectrum sensing via a novel data augmentation technique; (iii) a study into one of the first ever semi-unsupervised approaches for wideband multi-class spectrum sensing.
URL:https://ece.northeastern.edu/event/daniel-uvaydovs-phd-dissertation-defense/
LOCATION:432 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
GEO:42.3396156;-71.0886534
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230413T160000
DTEND;TZID=America/New_York:20230413T170000
DTSTAMP:20260421T224842
CREATED:20230405T174232Z
LAST-MODIFIED:20230405T174232Z
UID:6246-1681401600-1681405200@ece.northeastern.edu
SUMMARY:Hussein Hussein’s PhD Dissertation Defense
DESCRIPTION:“Parametric Circuits for Enhanced Sensing and RF Signal Processing” \nCommittee Members: \nProf. Cristian Cassella (Advisor) \nProf. Marvin Onabajo \nProf. Matteo Rinaldi \nProf. Andrea Alù \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.
URL:https://ece.northeastern.edu/event/hussein-husseins-phd-dissertation-defense/
LOCATION:432 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
GEO:42.3396156;-71.0886534
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230202T103000
DTEND;TZID=America/New_York:20230202T113000
DTSTAMP:20260421T224842
CREATED:20230126T204948Z
LAST-MODIFIED:20230126T205022Z
UID:6070-1675333800-1675337400@ece.northeastern.edu
SUMMARY:Amani Al-shawabka's PhD Proposal Review
DESCRIPTION:“Channel-and-Adversary-Resilient Radio Fingerprinting through Data-Driven Approaches at Scale” \nCommittee: \nProf. Tommaso Melodia (Advisor)\nProf. Kaushik Chowdhury\nProf. Francesco Restuccia \nAbstract: \nRadio fingerprinting authenticates wireless devices by leveraging tiny hardware-level imperfections inevitably present in the radio circuitry. This way\, devices can be directly identified at the physical layer– thus avoiding energy-expensive upper-layer cryptography that resource-limited embedded devices may not be able to afford. Recent advances have proven that employing deep learning algorithms can achieve fingerprinting accuracy levels that were impossible to achieve by traditional low-dimensional algorithms. Still\, the wireless research community lacks an exhaustive understanding of the challenges associated with developing robust\, reliable\, and channel-resilient radio fingerprinting through deep-learning approaches for practical applications. Key challenges are the non-stationarity of the wireless channel\, and the dynamic effects introduced by the operational environment\, which significantly limit fingerprinting applications by obscuring the hardware impairments associated with the transmitted waveform.\nIn this thesis\, we (i) develop a full-fledged\, systematic investigation to quantify the impact of the wireless channel by providing a first-of-its-kind evaluation on deep-learning-based fingerprinting algorithms\, examining the worst-case scenario (employing devices with identical radio circuitry) and at scale; (ii) develop large-scale open datasets for radio fingerprinting collected in diverse\, rich\, channel conditions and environments\, and using different technologies\, including WiFi and LoRa; (iii) identify conditions where hardware impairments are still detectable; and (iv) design\, implement\, and benchmark new data-driven algorithms to counter the degradation introduced by the wireless channel. Notably\, we propose a generalized\, real-time channel- and adversary-resilient data-driven approach to authenticate wireless devices at scale in practical scenarios. To the best of our knowledge\, our work for the first time improves the fingerprinting accuracy of the worst-case scenario with up to 4x and 6.3x for WiFi and LoRa technologies\, respectively.
URL:https://ece.northeastern.edu/event/amani-al-shawabkas-phd-proposal-review-2/
LOCATION:432 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
GEO:42.3396156;-71.0886534
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=432 ISEC 360 Huntington Ave Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave:geo:-71.0886534,42.3396156
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221215T090000
DTEND;TZID=America/New_York:20221215T110000
DTSTAMP:20260421T224842
CREATED:20221213T011124Z
LAST-MODIFIED:20221213T011124Z
UID:6023-1671094800-1671102000@ece.northeastern.edu
SUMMARY:Daniel Uvaydov's PhD Proposal Review
DESCRIPTION:“Real-Time Spectrum Sensing for Inference and Control”\n\nAbstract:\nSpectrum sensing can enable the next generation of wireless applications ranging from opportunistic spectrum access to cognitive radio networks. The key unaddressed challenges of spectrum sensing are that (i) it has to be performed with extremely low latency over varying bandwidths and must guarantee strict real-time processing constraints; (ii) its underlying algorithms need to be extremely accurate\, and flexible enough to work with different wireless bands and protocols to find application in real-world settings. We address these challenges in multiple wireless applications by utilizing Deep Learning techniques as the main vehicle of spectrum sensing for both inference and control. By leveraging mechanisms such as data augmentation\, channel attention\, voting\, and segmentation we are able to push beyond the capabilities of existing Deep Learning techniques and create generalizable spectrum sensing algorithms. Furthermore we deploy different spectrum sensing solutions in real testbeds for over the air evaluations and applicable proof-of-concepts.\n\n\nCommittee:\n\nProf. Tommaso Melodia (Advisor) \nProf. Francesco Restuccia\nProf. Kaushik Chowdhury
URL:https://ece.northeastern.edu/event/daniel-uvaydovs-phd-proposal-review/
LOCATION:432 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
GEO:42.3396156;-71.0886534
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221108T153000
DTEND;TZID=America/New_York:20221108T163000
DTSTAMP:20260421T224842
CREATED:20221103T213536Z
LAST-MODIFIED:20221103T213536Z
UID:5948-1667921400-1667925000@ece.northeastern.edu
SUMMARY:Giuseppe Michetti's PhD Dissertation Defense
DESCRIPTION:“RF Front-End Components based on Linear-Time-Variant Modulation of Piezoelectric MEMS Resonators” \nAbstract: \nThroughout the last decade\, radio frequency (RF) components for over-the-air communication and sensing have been subject to sustained market pressure to adapt to the novel trends such as spectrum sharing\, programmability\, and low-power operation. When these features are required in chip-scale RF hardware\, innovative solutions are necessary as conventional materials and techniques become bottlenecks for next-generation radios. In this work\, we explore advanced wave manipulation circuital techniques such as Linear-Time-Variant (LTV) networks in conjunction with high-performance RF passives based on Micro-Electro-Mechanical Systems (MEMS) to address some of these challenges. Leveraging the unique spectral characteristic of RF MEMS resonators\, we show some components based on LTV concepts\, for novel RF systems with advanced spectral efficiency and real-time reconfigurability. \nUsing AlN and ScAlN thin film MEMS resonators as building blocks\, we propose a design technique for MEMS-based LTV Circulators and Self Interference Cancelers\, enabling chip-scaled RF full-duplex systems to enable efficient use of the RF spectrum with up to 47.5 dB cancellation in an 8 % bandwidth (BW) at 450 MHz. We introduce and validate experimentally MEMS-based LTV BW-tunable filters with high linearity (>30 dBm)\, and 5:1 BW tunability\, designed for several bands from 100 MHz to 2.7 GHz for emerging paradigms such as software-defined-radios and cooperative networks. We also introduce MEMS-based near-zero energy RF front-end for the Internet-of-Things (IoT)\, implementing RF energy harvesting to power up a resonant Wake-Up Receiver circuit\, with an experimental demonstration at (800 MHz) for deployment in remote sensor networks and emerging IoT wearable applications. \nAlong with the experimental validation of the proposed components\, analytical and numerical tools are also discussed for future development and research. \nCommittee: \nProf. Matteo Rinaldi (Advisor) \nProf. Cristian Cassella \nProf. Andrea Alù
URL:https://ece.northeastern.edu/event/giuseppe-michettis-phd-dissertation-defense/
LOCATION:432 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
GEO:42.3396156;-71.0886534
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=432 ISEC 360 Huntington Ave Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave:geo:-71.0886534,42.3396156
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220922T130000
DTEND;TZID=America/New_York:20220922T140000
DTSTAMP:20260421T224842
CREATED:20221103T184721Z
LAST-MODIFIED:20221103T184721Z
UID:5924-1663851600-1663855200@ece.northeastern.edu
SUMMARY:Justin Crabb's PhD Proposal Review
DESCRIPTION:“Multiphysics Simulation of Graphene Transistors for On-Chip Plasmonic THz Signal Generation and Modulation” \nAbstract: \nTerahertz communication is envisioned as a key technology not only for the next generation of macro-scale networks (e.g.\, 6G+)\, but also for transformative networking applications at the nanoscale (e.g.\, wireless nanosensor networks and wireless networks on chip). This proposal focuses on the development of a multiphysics simulation platform for a plasmonic THz nanogenerator with on-chip modulation. The in-house developed finite-element-method platform\, which self-consistently solves the hydrodynamic and Maxwell’s equations\, is utilized to provide extensive numerical results demonstrating the device’s functionality along with ultra-wide bandwidth and high modulation index capabilities. \nFirst\, a comprehensive theory of the Dyakonov-Shur (DS) plasma instability in current-biased graphene transistors is presented. Using the hydrodynamic approach\, equations describing the DS instability in the two-dimensional electron fluid in graphene at arbitrary values of electron drift velocity are derived. These non-linear equations together with Maxwell’s equations are used for numerical analysis of the spatial and temporal evolution of the graphene electron system after the DS instability is triggered by random current fluctuations. Conditions necessary for the onset of the DS instability and the properties of the final stationary state of the graphene electron system are analyzed. \nNext\, a detailed numerical analysis of the DS plasma instability in the DC current-biased graphene transistor with the gate shifted with respect to the middle of the transistor conducting channel is presented. The geometric asymmetry is shown to be sufficient to trigger the DS instability in the two-dimensional electron gas in the transistor channel. Sustained plasma oscillations in the instability endpoint are demonstrated and the properties of these oscillations are analyzed for different positions of the gate and at different values of other physical and geometric FET parameters. The obtained results show the possibility of designing a tunable on-chip source of THz electromagnetic radiation based on the graphene transistor with a shifted gate. \nFollowing\, the on-chip THz nano-generator with amplitude and frequency modulation capabilities is presented. The proposed device uses and leverages the tunability of the Dyakonov-Shur instability for the growth and modulation of plasmonic oscillations in the two-dimensional electron gas channel of the graphene transistor. \n  \nCommittee: \nProf. Josep Jornet (Advisor) \nProf. Tommaso Melodia \nProf. Matteo Rinaldi \nProf. Hossein Mosallaei
URL:https://ece.northeastern.edu/event/justin-crabbs-phd-proposal-review/
LOCATION:432 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
GEO:42.3396156;-71.0886534
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=432 ISEC 360 Huntington Ave Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave:geo:-71.0886534,42.3396156
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220817T110000
DTEND;TZID=America/New_York:20220817T120000
DTSTAMP:20260421T224842
CREATED:20221103T182252Z
LAST-MODIFIED:20221103T182252Z
UID:5890-1660734000-1660737600@ece.northeastern.edu
SUMMARY:Mithun Diddi's PhD Dissertation Defense
DESCRIPTION:“Multiple UAVs for Synchronous – Shared Tasks and Long-term Autonomy” \nAbstract: \nThis thesis focuses on the use of multiple unmanned aerial vehicles(UAVs) in a distributed framework from a systems perspective to synchronously perform shared tasks such as aerial beamforming and coordinated mapping and to enhance the reliability of performing periodic (mapping) tasks at remote locations for long-term autonomous(LTA) missions. We present an autonomy stack for multiple\, heterogeneous UAVs with a simulation framework. We implemented the end-to-end pipelines for perception and communication applications involving multiple UAVs. \nRepeated deployments in harsh-weather\, real-world locations are challenging and are limited by the need for human operators. These infrastructure-poor\, remote locations pose unique challenges to long-term autonomous missions. In these locations\, harvesting power onboard using solar panels may be a viable alternative for recharging batteries.\nIn the first part of the thesis\, we focus on hardware architecture for UAVs to enable reliable LTA missions with minimal human intervention. We developed a Size\, Weight\, and Power(SWaP) constrained Smart charging stack to minimize hotel loads seen during the recharging process and enable efficient charging of batteries. This leads to the design of a standalone\, solar rechargeable quadcopter.\nReal-world applications such as reconstructing a dynamic scene from multiple viewpoints and distributed aerial beamforming require multiple robots(agents) to coordinate and synchronously act to accomplish shared tasks. These tasks require spatially distant\, multiple UAVs to have time\, phase\, and frequency synchronization. We demonstrate a Synchronous UAV(S-UAV) architecture for wireless synchronization based on GPS disciplined oscillators and the associated software framework needed for temporal registration of data across multiple UAVs.\nWe have built four S-UAVs and demonstrate the ability to 3D reconstruct a dynamic scene from overlapping viewpoints. Dynamic baseline camera arrays formed using multiple S-UAVs are used to synchronously capture a dynamic environment with people moving around. A single-time instance of synchronously captured images of the scene is used to 3D reconstruct the dynamic environment while preserving static scene assumptions of Structure from Motion(SFM). \nIn the second part of the thesis\, we focus on multi UAV autonomy framework for real-world applications of UAVs in perception\, wireless communications\, and reliable LTA missions. We present ‘Simplenav\,’ a navigation stack for heterogeneous\, multiple UAVs\, and ‘OctoRosSim\,’ a computationally lightweight multi-UAV simulation framework for validating the multi-UAV planning and autonomy pipeline. We demonstrate this framework with novel applications of end-to-end autonomy pipelines developed for a coordinated swarm of UAVs. \nCommittee: \nProf. Hanumant Singh (Advisor) \nProf. Kaushik Chowdhury \nProf. Taskin Padir
URL:https://ece.northeastern.edu/event/mithun-diddis-phd-dissertation-defense/
LOCATION:432 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
GEO:42.3396156;-71.0886534
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=432 ISEC 360 Huntington Ave Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave:geo:-71.0886534,42.3396156
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220815T140000
DTEND;TZID=America/New_York:20220815T150000
DTSTAMP:20260421T224842
CREATED:20221103T182152Z
LAST-MODIFIED:20221103T182152Z
UID:5888-1660572000-1660575600@ece.northeastern.edu
SUMMARY:Nikita Mirchandani's PhD Dissertation Defense
DESCRIPTION:“Ultra-Low Power and Robust Analog Computing Circuits and System Design Framework for Machine Learning Applications” \nAbstract: \nAs the scaling of CMOS transistors has almost halted\, performance gains of digital systems have also started to stagnate. There is a renewed interest in alternate computing techniques such as in-memory computing\, hybrid computing\, approximate computing\, and analog computing. In particular\, analog computing has reemerged as a promising alternative to save power and improve performance specifically for machine-learning (ML) applications. Power and chip area efficiency make analog computing highly appealing for implementing deep learning algorithms on-chip\, computing circuits for the internet-of-things (IoT) devices\, and implantable and wearable biomedical devices. However\, compared to digital computing\, analog computing methods have not nearly been utilized to their fullest potential due to longstanding challenges related to reliability\, programmability\, power consumption\, and high susceptibility to variations. \nThe subject of this dissertation research is to develop robust ultra-low power analog hardware suitable for machine learning applications. First\, a robust analog design methodology is presented to address issues of variability in analog circuits. A constant transconductance design technique using switched capacitor circuits is presented. The design approach is then applied to build circuits for ML applications. An analog vector matrix multiplier (VMM) is designed to be used in the convolutional layer in an ML analog computing vision hardware platform. Computing circuits are tested as part of an image classification DNN algorithm on the MNIST dataset and can achieve a classification accuracy of 96.1%.\nThe design approach is also used to design an analog computing system architecture for detection of seizures using EEG signals. A conventional EEG monitoring system includes an analog front-end (AFE)\, ADC\, digital filtering stage\, EEG feature extraction engine\, and SVM classification. Such systems suffer from high power and chip area requirements. The corresponding analog architecture is composed of AFE amplifiers to provide gain for the incoming signal. The AFE is followed by an analog filtering stage\, where spectral power from each of the bands is used as a feature for seizure classification. The output of each filter is applied to a corresponding feature extraction circuit to continuously monitor the onset of a seizure in an ultra-lower power mode with sub-threshold analog processing. The system level architecture is first modeled to obtain classification accuracy of seizures. Simulation times for the design of such complex analog systems can be prohibitively long\, particularly when the impacts of nonidealities such as noise\, nonlinearity\, and device mismatches have to be considered at the system level. The simulation time is reduced by building accurate models of the analog blocks for faster simulations. The analog models help to define the required specifications for each block in order to achieve a specified system-level classification accuracy.\nInfrastructure circuits like oscillators and voltage regulators for the proposed SoC are presented. A 254 nW 21 kHz on-chip RC oscillator with 21.5 ppm/oC temperature stability is presented to provide stable clock source for the proposed SoC. Finally\, novel lightweight hardware security primitives are described to equip individual IoT device with side-channel resistant crypto-implementations\, and unique ID or key \ngeneration. \nCommittee: \nProf. Aatmesh Shrivastava (Advisor) \nProf. Marvin Onabajo \nProf. Yong-Bin Kim
URL:https://ece.northeastern.edu/event/nikita-mirchandanis-phd-dissertation-defense/
LOCATION:432 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
GEO:42.3396156;-71.0886534
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220713T110000
DTEND;TZID=America/New_York:20220713T120000
DTSTAMP:20260421T224842
CREATED:20221103T183115Z
LAST-MODIFIED:20221103T183115Z
UID:5906-1657710000-1657713600@ece.northeastern.edu
SUMMARY:Leonardo Bonati's PhD Dissertation
DESCRIPTION:“Softwarized Approaches for the Open RAN of NextG Cellular Networks” \nAbstract: \nThe 5th and 6th generations of cellular networks (5G and 6G)\, also known as NextG\, will bring unprecedented flexibility to the wireless cellular ecosystem. Because of a typically closed and rigid market\, the telco industry has incurred high costs and non-trivial obstacles for delivering new services and functionalities that satisfy the requirements and the demands of NextG networks. To break this trend the industry is now moving toward open architectures based on softwarized approaches\, which afford network operators flexible control and unprecedented adaptability to heterogeneous conditions\, including traffic and application requirements. Now\, by simply expressing a high-level intent\, operators will be able to instantiate bespoke services on-demand on a generic hardware infrastructure\, and to adapt such services to the current network conditions. Through disaggregation\, network elements will split their functionalities across multiple components—possibly provided by different vendors—interconnected through well-defined open interfaces. The separation of control functions from the hardware fabric\, and the introduction of standardized control interfaces\, will ultimately enable the definition and use of softwarized control loops\, which will bring embedded intelligence and real-time analytics to effectively realizing the vision of autonomous and self-optimizing networks.\nThis dissertation work focuses on the design\, prototyping and experimental evaluation of softwarized approaches for the Open Radio Access Network (RAN) of NextG cellular networks. We analyze the architectural enablers\, challenges\, and requirements for a programmatic zero-touch control of the very many network elements and propose practical solutions for its realization. We prototype solutions by leveraging open-source software implementations of cellular protocol stacks and frameworks\, and heterogeneous virtualization technologies\, including the srsRAN and OpenAirInterface cellular implementations\, and the O-RAN framework. The contributions of this work include (i) the first demonstration of O-RAN data-driven control loops in a large-scale experimental testbed using open-source\, programmable RAN and RAN Intelligent Controller (RIC) components through xApps of our design; (ii) CellOS\, a zero-touch cellular operating system that automatically generates and executes distributed control programs for simultaneous optimization of heterogeneous control objectives on multiple network slices starting from a high-level intent expressed by the operators; (iii) OpenRAN Gym\, the first publicly-available research platform for the design\, prototyping\, and experimentation at scale of data-driven O-RAN solutions\, and (iv) OrchestRAN\, a network intelligence orchestration framework for Open RAN that automates the deployment of data-driven inference and control solutions. The effectiveness of our solutions in achieving superior control and performance of the RAN is demonstrated at scale on state-of-the-art experimental facilities\, including software-defined radio-based laboratory setups and open access experimental wireless platforms\, such as Colosseum\, Arena\, and the POWDER and COSMOS platforms from the U.S. PAWR program.
URL:https://ece.northeastern.edu/event/leonardo-bonatis-phd-dissertation/
LOCATION:432 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
GEO:42.3396156;-71.0886534
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=432 ISEC 360 Huntington Ave Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave:geo:-71.0886534,42.3396156
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220608T093000
DTEND;TZID=America/New_York:20220608T103000
DTSTAMP:20260421T224842
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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DTSTART;TZID=America/New_York:20220223T173000
DTEND;TZID=America/New_York:20220223T183000
DTSTAMP:20260421T224842
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
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=432 ISEC 360 Huntington Ave Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave:geo:-71.0886534,42.3396156
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DTSTART;TZID=America/New_York:20220215T140000
DTEND;TZID=America/New_York:20220215T150000
DTSTAMP:20260421T224842
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
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=432 ISEC 360 Huntington Ave Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave:geo:-71.0886534,42.3396156
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DTSTART;TZID=America/New_York:20210914T140000
DTEND;TZID=America/New_York:20210914T150000
DTSTAMP:20260421T224842
CREATED:20210908T184332Z
LAST-MODIFIED:20210908T184332Z
UID:5151-1631628000-1631631600@ece.northeastern.edu
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/
LOCATION:432 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
GEO:42.3396156;-71.0886534
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=432 ISEC 360 Huntington Ave Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave:geo:-71.0886534,42.3396156
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