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DTSTART;TZID=America/New_York:20230901T130000
DTEND;TZID=America/New_York:20230901T140000
DTSTAMP:20260427T182849
CREATED:20230816T150411Z
LAST-MODIFIED:20230816T150432Z
UID:37858-1693573200-1693576800@coe.northeastern.edu
SUMMARY:Mostafa Abedi PhD Proposal Review
DESCRIPTION:Title: Power-Efficient and Security-Enhancing Techniques for Ultra-low Power IoT  Devices \nCommittee Members: \nProf. Aatmesh Shrivastava (Advisor)\nProf. Marvin Onabajo\nProf. Nian X. Sun \nAbstract:\nInternet-of-things (IoT) devices often rely on ambient energy sources such as photovoltaic (PV) cells and thermoelectric generators (TEGs) for their operation. Minimizing power loss through ambient energy harvesting optimization can significantly extend the battery life or support battery-free sensor nodes in IoT devices. A maximum power point tracking (MPPT) circuit is often used for impedance matching to maximize energy transfer efficiency. This research proposes an ultra-low power\, high-tracking efficiency MPPT circuit based on Hill-Climbing (HC) algorithm suitable for micro-power DC harvesters. The proposed system employs a modified version of the hill-climbing algorithm. In case of input power changes and consequent deviation of the harvester from the MPP\, an integrated Power Change Detector (PCD) is proposed to reactivate the MPPT circuit. The PCD detects changes in input power and activates the MPPT circuit\, enabling automatic activation and resulting in substantial power savings. Furthermore\, due to the proposed power estimation technique\, the MPPT is not dependent on the internal structure of the energy source\, and its tracking efficiency is unrelated to the conversion ratio of the converter. This approach enables us to achieve a peak tracking efficiency of over 99.9\%. To adjust the input power of the harvester to track the maximum power point\, we propose a new\, efficient Pulse Width Modulation (PWM) circuit. This circuit exhibits a wide duty cycle range\, low power consumption\, linearity\, and robustness against variations. \nThis research also focuses on increasing the security of IoT devices. In the past\, chip fabrication was mostly done internally by semiconductor firms. Now\, it is more collaborative\, pulling in designs from various sources and having a few factories produce them. This new way of working means that companies that only handle design might face more challenges like the threat of hardware Trojans (HT) being added either during the design phase or production. With that in mind\, we introduce a different circuit design approach. We aim to find these Trojans\, particularly the newer analog Trojans. The idea is to boost the security of IoT devices by detecting these issues early. In addition\, to improve the security of IoT systems\, we propose an ultra-low power energy monitoring system (EMS) to detect and mitigate denial-of-sleep (DoSL) attacks. In this project\, we explore a new method of defense against DoSL attacks by monitoring energy consumption. We will implement a low-power system to monitor the lifetime of the IoT node by continuously evaluating the harvested\, stored\, and consumed energy in the node.
URL:https://coe.northeastern.edu/event/mostafa-abedi-phd-proposal-review/
LOCATION:532 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230818T143000
DTEND;TZID=America/New_York:20230818T153000
DTSTAMP:20260427T182849
CREATED:20230817T142938Z
LAST-MODIFIED:20230817T142938Z
UID:37895-1692369000-1692372600@coe.northeastern.edu
SUMMARY:Xu Yizhe MS Thesis Defense
DESCRIPTION:Title:\nIntegration of Polyimide Flexible PCB Wings in Northeastern’s Aerobat \nLocation:\nRoom: ISEC 532\, Teams link \nCommittee Members:\nProf. Alireza Ramezani(Advisor)\nProf. Rifat Sipahi \nAbstract:\nThe principal aim of this Master’s thesis is to propel the optimization of the membrane wing structure of the Northeastern Aerobat through origami techniques and enhancing its capacity for secure hovering within confined spaces. Bio-inspired drones offer distinctive capabilities that pave the way for innovative applications\, encompassing wildlife monitoring\, precision agriculture\, search and rescue operations\, as well as the augmentation of residential safety. The evolved noise-reduction mechanisms of birds and insects prove advantageous for drones utilized in tasks like surveillance and wildlife observation\, ensuring operation devoid of disturbances. Traditional flying drones equipped with rotary or fixed wings encounter notable constraints when navigating narrow pathways. While rotary and fixed-wing systems are conventionally harnessed for surveillance and reconnaissance\, the integration of onboard sensor suites within micro aerial vehicles (MAVs) has garnered interest in vigilantly monitoring hazardous scenarios in residential settings. Notwithstanding the agility and commendable fault tolerance exhibited by systems such as quadrotors in demanding conditions\, their inflexible body structures impede collision tolerance\, necessitating operational spaces free of collisions. Recent years have witnessed an upsurge in integrating soft and pliable materials into the design of such systems; however\, the pursuit of aerodynamic efficiency curtails the utilization of excessively flexible materials for rotor blades or propellers. This thesis introduces a guard design incorporating feedback-driven stabilizers\, enabling stable hovering flights within Northeastern’s Robotics-Inspired Study and Experimentation (RISE) cage.
URL:https://coe.northeastern.edu/event/xu-yizhe-ms-thesis-defense/
LOCATION:532 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230809T110000
DTEND;TZID=America/New_York:20230809T120000
DTSTAMP:20260427T182849
CREATED:20230802T192340Z
LAST-MODIFIED:20230802T192340Z
UID:37696-1691578800-1691582400@coe.northeastern.edu
SUMMARY:Nasim Soltani PhD Proposal
DESCRIPTION:Title: Deep Learning for the Physical Layer: From Signal Classification to Decoding \nLocation: ISEC 532 \nCommittee Members:\nProf. Kaushik Chowdhury (Advisor)\nProf. Stratis Ioannidis\nProf. Robert Nowak \nAbstract:\nThe growth in wireless spectrum usage has created new physical layer applications and intensified the importance of the existing ones. Physical layer applications ranging from device authentication to signal decoding and interpretation are traditionally handled by deterministic signal processing algorithms. Such algorithms\, while effective\, often require long sequences of data for decision making\, or need approximations of the environmental conditions\, such as noise models\, which may not be always correct in practical conditions. For these reasons\, traditional algorithms are not suitable for making quick decisions on the high rate wireless data with higher noise and interference that is a result of crowded spectrum. To this end\, deep learning-based methods have been explored extensively by the researchers to substitute for the traditional signal processing algorithms for the physical layer. This thesis explores novel methods in this area in the following parts: \nPart I – Signal classification: In this part\, we look at two distinct problems of waveform classification and Radio Frequency (RF) fingerprinting. In the first problem\, we study two use cases of modulation classification on edge devices\, followed by waveform classification and spectrum localization in the Citizen Broadband Radio Service (CBRS) band. In the second problem\, we look at RF fingerprinting that is classifying received signals in terms of subtle impairments that each transmitter leaves in its emitted waveform\, due to its hardware manufacturing imperfections. We propose methods to overcome the wireless channel effect for RF fingerprinting in both stationary transmitters on a large scale dataset (i.e.\, 5k WiFi devices)\, and identical hovering Unmanned Aerial Vehicles (UAVs) that transmit proprietary signals. \nPart II – Signal decoding: In this part\, we introduce our design of a modular machine learning (ML)-aided Orthogonal Frequency Division Multiplexing (OFDM) receiver that improves the bit error rate (BER) of the traditional receiver. We show how a neural network-based demapper block can be used for secure data transmission. Furthermore\, we show how an ML-aided receiver can provide the possibility of reducing communication overhead by obviating the need for the first field of preamble in WiFi signals. We show that reducing the preamble length contributes to higher throughput in WiFi networks\, without BER degradation. \nPart III – As the proposed work\, we will explore the use of active learning for smart sampling of training sets in wireless communications tasks. Active learning reduces the labeling overhead that is often performed using the compute-intensive traditional signal processing algorithms\, by intelligently selecting the most informative training samples to be labeled instead of labeling the whole set. We will also design an ML-life cycle control scheme to monitor and update the performance of an ML-aided 5G receiver\, when deployed in the field with varying environmental conditions.
URL:https://coe.northeastern.edu/event/nasim-soltani-phd-proposal/
LOCATION:532 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
END:VEVENT
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DTSTART;TZID=America/New_York:20230725T130000
DTEND;TZID=America/New_York:20230725T140000
DTSTAMP:20260427T182849
CREATED:20230721T142252Z
LAST-MODIFIED:20230721T142322Z
UID:37567-1690290000-1690293600@coe.northeastern.edu
SUMMARY:Batool Salehihikouei Phd Proposal Review
DESCRIPTION:Title:\nLeveraging Deep Learning on Multimodal Sensor Data for Wireless Communication: From mmWave Beamforming to Digital Twins \nCommittee Members:\nProf. Kaushik Chowdhury (Advisor)\nProf. Hanumant Singh\nProf. Josep Jornet\nDr. Mark Eisen \nAbstract:\nWith the widespread Internet of Things (IoT) devices\, a wide variety of sensors are now present in different environments. For example\, self-driving vehicles and automated warehouses depend on sensor information for navigation and management of the robots\, respectively. In this dissertation\, we present a paradigm\, where these sensors are re-purposed to assist network management in wireless communication\, especially when classic approaches fall short to provide the required quality of service (QoS). This thesis presents data-driven and AI-based methods\, where the multimodal sensor information is used for beamforming at the mmWave band\, and envisions a systematic framework for joint optimization of the navigation and network management in factory floor environments. In particular\, the contributions in this dissertation are as follows. First\, we present deep learning fusion algorithms\, where the inputs from a multitude of sensor modalities such as GPS (Global Positioning System)\, camera\, and LiDAR (Light Detection and Ranging) are combined towards predicting the optimum beam at the mmWave band. We prove that fusing the multimodal sensor data improves the prediction accuracy compared to using single modalities. Second\, we study the trade-off between the accuracy and cost of different learning strategies for multimodal beamforming. In this regard\, we make a case for using federated learning for beamforming at the mmWave band and demonstrate that it is the most successful learning strategy\, with respect to the communication overhead. Finally\, we take measures to further optimize the computation and communication overhead\, by incorporating a pruning strategy tailored to the disturbed nature of the federated learning systems. In the proposed research work\, we suggest using digital twins to overcome the challenges of scarcity of data and close-world assumption in deep learning algorithms. A digital twin is a replica of a real world entity\, which is typically used for studying the impact of any configuration settings in a safe\, digital environment. In this dissertation\, we propose using digital twins for generating training data for multimodal beamforming\, in unseen scenarios. Moreover\, we study a robotic industrial setting\, where the path planning policy is continuously updated by monitoring the dynamics of the real world\, constructing the digital twin\, and updating the policy.
URL:https://coe.northeastern.edu/event/batool-salehihikouei-phd-proposal-review/
LOCATION:532 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230420T080000
DTEND;TZID=America/New_York:20230420T170000
DTSTAMP:20260427T182849
CREATED:20230420T183901Z
LAST-MODIFIED:20230420T183901Z
UID:36789-1681977600-1682010000@coe.northeastern.edu
SUMMARY:Chenghao Wang's MS Thesis Defense
DESCRIPTION:“Legged Walking on Inclined Surfaces” \nCommittee Members:\nProf. Alireza Ramezani(Advisor)\nProf. Miriam Leeser\nProf. Bahram Shafai \nAbstract:\nThe main contributions of this MS Thesis are centered around taking steps towards successful multi-modal demonstrations using Northeastern’s legged-aerial robot\, Husky Carbon. This work discusses the challenges involved in achieving multi-modal locomotion such as trotting-hovering and thruster-assisted incline walking and reports progress made towards overcoming these challenges. Animals like birds use a combination of legged and aerial mobility\, as seen in Chukars’s wing-assisted incline running (WAIR)\, to achieve multi-modal locomotion. Chukars use forces generated by their flapping wings to manipulate ground contact forces and traverse steep slopes and overhangs. Husky’s design takes inspiration from birds such as Chukars. This MS thesis presentation outlines the mechanical and electrical details of Husky’s legged and aerial units. The thesis presents simulated incline walking using a high-fidelity model of the Husky Carbon over steep slopes of up to 45 degrees.
URL:https://coe.northeastern.edu/event/chenghao-wangs-ms-thesis-defense/
LOCATION:532 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230412T150000
DTEND;TZID=America/New_York:20230412T170000
DTSTAMP:20260427T182849
CREATED:20230405T135342Z
LAST-MODIFIED:20230405T135342Z
UID:36489-1681311600-1681318800@coe.northeastern.edu
SUMMARY:IER Open House
DESCRIPTION:Following our success with last year’s Open House\, we are again opening the lab up to visitors! This year\, demos will be held on the 5th floor of ISEC by Yingzi Lin\, Ilya Vidrin\, Alireza Ramezani\, Taskin Padir\, Kris Dorsey\, Rob Platt\, and Hanu Singh. In Richards Hall\, Dagmar Sternad\, CJ Hasson\, and Max Shepherd will be hosting demos as well.\n\n\nThe flyer for this event is attached. Please reach out to Noah (n.smith@northeastern.edu) with any questions! Make sure to register here: https://www.eventbrite.com/e/institute-for-experiential-robotics-open-house-tickets-603775106597
URL:https://coe.northeastern.edu/event/ier-open-house/
LOCATION:532 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221207T130000
DTEND;TZID=America/New_York:20221207T140000
DTSTAMP:20260427T182849
CREATED:20221129T184142Z
LAST-MODIFIED:20221129T184142Z
UID:34605-1670418000-1670421600@coe.northeastern.edu
SUMMARY:12/7 IER Seminar Series: Steve Dorton - "Trust Dynamics with AI in High-Consequence Work Systems"
DESCRIPTION:Trust Dynamics with AI in High-Consequence Work Systems \nWednesday\, 12/7/2022 from 1 – 2 pm \nISEC 532 & Zoom\nZoom: https://northeastern.zoom.us/j/96750528112?pwd=Z3M0b1Z0QTZFaWM3QzZ5bC92SjFUZz09 \nSteve Dorton \nPrincipal Scientist for Sensemaking\, Decision Making\, and AI \nThe MITRE Corporation \nAbstract: \nArtificial Intelligence (AI) is often viewed as the means by which intelligence analysts will cope with the ever-increasing deluge of data from various sources. The best AI is moot\, however\, if analysts cannot trust the outputs of the AI to inform high-consequence decision making. A naturalistic study was performed to understand how intelligence professionals gain and lose trust in AI “in the wild.” The study assessed various trust factors proposed in the literature and identified various themes from interviews with intelligence professionals. We will discuss how to apply these findings to engineer more trustworthy AI for high-consequence decision applications. \nBio: \nSteve Dorton is a Principal Scientist for Sensemaking\, Decision Making\, and AI at the MITRE Corporation. His research generally falls at the intersection of the social and computational sciences\, focusing on how intelligent systems can help and harm human cognition in national security contexts. He also holds an adjunct lecturer appointment in the University of Maryland School of Public Policy\, where he teaches social\, ethical\, and policy considerations for AI and big data.
URL:https://coe.northeastern.edu/event/12-7-ier-seminar-series-steve-dorton-trust-dynamics-with-ai-in-high-consequence-work-systems/
LOCATION:532 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
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