BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Department of Electrical & Computer Engineering - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://ece.northeastern.edu
X-WR-CALDESC:Events for Department of Electrical & Computer Engineering
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20200308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20201101T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20210314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20211107T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20220313T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20221106T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210603T110000
DTEND;TZID=America/New_York:20210603T120000
DTSTAMP:20260517T135342
CREATED:20210526T201737Z
LAST-MODIFIED:20210526T201737Z
UID:4960-1622718000-1622721600@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Mehdi Nasrollahpourmotlaghzanjani
DESCRIPTION:PhD Proposal Review: RFICs for Biomedical Magnetic and Magnetoelectric Microsystems \nMehdi Nasrollahpourmotlaghzanjani \nLocation: Zoom Links \nAbstract: Design and analysis of the advanced biomedical circuit and systems in wide variety of applications has emerged a significant interest. Not only in different engineering disciplines\, but also in a variety of applications such as neuroscience\, COVID-19\, etc. In this study\, we are proposing an implantable device\, handheld device for detecting different diseases and the RFIC design for the ME antenna and sensor evaluations.\nFirst\, we show and miniaturized implantable device for deep brain implantation that provides wireless power transfer efficiency (PTE) of 1 to 2 orders of magnitude higher than the reported micro-coils for brain stimulation. The proposed device will simultaneously measure the as magnetic field activity when neurons are firing. In the second part we will go over the RFIC design for the bio-implant devices\, evaluation of the ME antennas for communication purposes and the circuit interface to measure the ME and GMI sensors. For final part\, we will discuss the handheld device design for early diagnosis of different diseases such as\, lung cancer\, Alzheimer\, Covid-19\, etc through exhaled breath on the molecularly imprinted polymer (MIP) gas sensors.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-mehdi-nasrollahpourmotlaghzanjani/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210603T150000
DTEND;TZID=America/New_York:20210603T160000
DTSTAMP:20260517T135342
CREATED:20210602T213220Z
LAST-MODIFIED:20210602T213220Z
UID:4970-1622732400-1622736000@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Yumin Liu
DESCRIPTION:PhD Dissertation Defense: Learning from Spatio-Temporal Data with Applications in Climate Science \nYumin Liu \nLocation: Zoom Link \nAbstract: Climate change is one of the major challenges to human beings and many other species in our time. In the recent decade\, the number of disasters related to climate change such as wildfires\, storms\, floods and droughts are increasing\, and the casualty and economic losses caused by them are larger compared to those of decades ago. This calls for better and efficient ways to predict climate change in order to better prepare and reduce losses. Predicting climate change involves using historical observational data and model simulated data\, both of which usually involve multiple locations and timestamps and are spatio-temporal. With the rapid development and progress of machine learning\, these methods have achieved several impactful contributions in many domains; we would like to translate its impact to climate science.\nIn this thesis we address several problems in climate science. This challenging complex domain enable us to develop\, innovate\, adapt\, and advance machine learning in the following ways. 1) We develop a multi-task learning method to estimate relationships between tasks and learn the basis tasks in different locations especially for nearby locations which may share similar climate patterns. This method assumes that the weights of an observed task is a linear combination of several latent basis tasks and that the task relationships can be learnt by imposing a regularized precision matrix. 2) We propose a nonparameteric mixture of sparse linear regression models to cluster and identify important climate models for prediction. This model incorporates Dirichlet Process (DP) to automatically determine the number of clusters\, imposes Markov Random Field (MRF) constraints to guarantee spatio-temporal smoothness\, and selects a subset of global climate models (GCMs) that are useful for prediction within each spatio-temporal cluster with a spike-and-slab prior. We derive an effective Gibbs sampling method for this model. 3) We adapt image super resolution methods to climate downscaling — increasing spatial resolution for climate variables for local impact analysis. The proposed method is called YNet which is a novel deep convolutional neural network (CNN) with skip connections and fusion capabilities to perform downscaling for climate variables on multiple GCMs directly rather than on reanalysis data. 4) We use saliency map method to discover dependencies among climate variables. We propose the concept of cyclical saliency map (Cyclic-SM) which are meaningful in climate context and more robust to noise as compared to ordinary saliency maps. We show that Cyclic-SMs can reveal relevant spatial regions for prediction. We demonstrate the effectiveness of this method in climate downscaling\, ENSO index prediction and river flow prediction.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-yumin-liu/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210608T100000
DTEND;TZID=America/New_York:20210608T110000
DTSTAMP:20260517T135342
CREATED:20210602T213048Z
LAST-MODIFIED:20210602T213048Z
UID:4968-1623146400-1623150000@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Lorenzo Bertizzolo
DESCRIPTION:PhD Dissertation Defense: Software-Defined Wireless Networking for 5G and Beyond: From Indoor Cells to Non-Terrestrial UAV Networks \nLorenzo Bertizzolo \nLocation: Microsoft Teams \nAbstract: While Software-defined Networking (SDN) is a consolidated and widely adopted concept in fixed infrastructure\, its adoption to the wireless domain has been limited by some fundamental challenges. Unlike traditional fixed infrastructure\, which relies entirely on fiber communications\, wireless networks suffer from unpredictable access and backhaul operations. Therefore\, they ask for new architectural solutions to implement SDN. A prerequisite to implementing SDN for wireless is to adopt programmable radio hardware and software-based implementation of the wireless protocol stack. Then\, it is necessary to adopt control APIs that expose the wireless protocols’ internal operations to external control. Finally\, to bridge the gap between SDN theory and implementation\, this thesis proposes a series of architectural solutions that provide the communication infrastructure and the architectural control innovations necessary to implement SDN control and optimization on real wireless systems. Specifically\, this thesis proposes two different architectural solutions. For fixed Radio Access Networks (RANs) that benefit from a low-latency and reliable backhaul infrastructure\, we propose to implement a centralized control approach similar to SDN for fixed infrastructure. Here a central controller collects network state information of a distributed RAN\, solves a network control problem\, and distributes the solutions to the individual wireless nodes. On the other hand\, for infrastrcuture-less RANs\, which implement both the access and the backhaul in wireless\, we propose a new architectural solution that moves the control logic to the edge of the networks\, to the very wireless nodes.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-lorenzo-bertizzolo/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210610T130000
DTEND;TZID=America/New_York:20210610T140000
DTSTAMP:20260517T135342
CREATED:20210510T185954Z
LAST-MODIFIED:20210510T185954Z
UID:4931-1623330000-1623333600@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Andac Demir
DESCRIPTION:PhD Proposal Review: 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 2 frameworks: AutoBayes\, which is an AutoML approach to conduct neural architecture search for research prototyping\, and a GNN based framework: EEG-GNN.\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 proposal\, we benchmark the performance of EEG-GNN 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 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. Another practical advantage of our GNN-based framework is that it can also be used in EEG channel selection\, which is critical for reducing computational cost\, and designing portable EEG headsets.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-andac-demir/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210610T153000
DTEND;TZID=America/New_York:20210610T163000
DTSTAMP:20260517T135342
CREATED:20210609T174644Z
LAST-MODIFIED:20210609T174644Z
UID:4974-1623339000-1623342600@ece.northeastern.edu
SUMMARY:PhD Proposal Review: Bernard Herrera
DESCRIPTION:PhD Proposal Review: Ferroelectric Micromachined Ultrasonic Transducers for Intra-body and In-memory Sensing Applications \nBernard Herrera \nLocation: Teams \nAbstract: Piezoelectric Micromachined Ultrasonic Transducers (PMUTs) are Micro Electro-Mechanical Systems (MEMS) devices that have become an established technology in applications such as range-finding\, fingerprint sensing and imaging due to their capability of ultrasonic transduction in a miniaturized footprint\, easily amenable to create large arrays. However\, their application space still remains quite open. PMUTs are well fitted to applications in liquid media\, such as implantable and underwater devices\, due to their inherent acoustic matching and wide bandwidth. Thus\, in the first part of this proposal\, we explore novel applications such as PMUT-based intra-body networking\, power transfer\, source localization\, wide-band matching and duplexing.\nAluminum Nitride (AlN) has been the material of choice for our PMUTs due to its biocompatibility and possibility of single-chip integration with supporting CMOS circuitry. Scandium doping of AlN thin films has recently been demonstrated to increase piezoelectric coupling coefficients while introducing ferroelectric properties in the material. However\, a simultaneous use of both capabilities has not been demonstrated in the state-of-the-art. The ability of having distinct ferroelectric states\, that alter the mechanical performance of the devices\, allows for in-memory sensing and actuation features and provides the building blocks for neuromorphic signal processing capabilities. The second part of the proposal explores the AlScN material integration into novel Ferroelectric Micromachined Ultrasonic Transducers (FMUTs) and their emerging application space.
URL:https://ece.northeastern.edu/event/phd-proposal-review-bernard-herrera/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210611T103000
DTEND;TZID=America/New_York:20210611T113000
DTSTAMP:20260517T135342
CREATED:20210609T174749Z
LAST-MODIFIED:20210609T174749Z
UID:4976-1623407400-1623411000@ece.northeastern.edu
SUMMARY:PhD Proposal Review: Rashida Nayeem
DESCRIPTION:PhD Proposal Review: Human Strategies in the Control of Complex Objects: A Task-Dynamic Approach with Clinical Applications \nRashida Nayeem \nLocation: Zoom Link \nPasscode: 247537 \nAbstract: Functional interaction with objects – tool use – is essential in daily living and is regarded as the foundation of our evolutionary advantage. Humans effortlessly interact with a variety of objects\, including those with complex internal dynamics. Even the simple action of picking up a cup of coffee to drink is a mechanically intricate process: the hand applies a force to the cup\, and indirectly to the liquid\, which exerts forces back on the hand. Reacting to and mitigating these dynamics in real time is difficult due to long sensorimotor delays and ubiquitous noise in the human sensorimotor system. Hence\, prediction is necessary to preempt undesired ‘sloshing’. But prediction of this nonlinear and potentially chaotic object is extremely difficult. Hence\, this research tests the hypothesis that humans learn to control the object to make dynamics simpler––or more predictable. Inspired by the task of transporting a ‘cup of coffee\,’ a series of experiments use a virtual and a real testbed that model the task as a cup-and-ball system. In all experiments\, human subjects move the cup with a rolling ball inside. Aim-1 investigates the effect of linearization on human control strategies in a 2D virtual task. Aim-2 examines if humans exploit initial conditions to facilitate predictable dynamics in the 2D virtual task. This is tested in subjects that are provided with either full sensory information\, or when deprived of visual or haptic information. Aim-3 examines how subjects explore and prepare the cup-and-ball in the same 2D task and a novel 3D virtual task introducing planar cup movements. The question is how subjects explore and transport objects that have different dynamic properties\, either unknown or indicated by the visual cues. The analysis adopts a task-dynamic approach that affords principled hypothesis-testing by parsing the complex dynamics into execution and result variables\, with minimal assumptions about the human controller. Aim-4 takes the insights from this basic research to a clinical context\, testing patients with stroke in this functional task. A real version of the cup-and-ball task was created to quantitatively assess severity and recovery of motor impairment in patients after stroke. Using the same analysis methods\, the objective is to sensitively assess impairment in the context of a functional skill.
URL:https://ece.northeastern.edu/event/phd-proposal-review-rashida-nayeem/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210614T090000
DTEND;TZID=America/New_York:20210614T100000
DTSTAMP:20260517T135342
CREATED:20210602T181602Z
LAST-MODIFIED:20210602T181602Z
UID:4966-1623661200-1623664800@ece.northeastern.edu
SUMMARY:Robotics at Northeastern: A personal view of Autonomy Underwater\, On Land and with Aerial Systems
DESCRIPTION:This session will cover interconnected nature of autonomous vehicles on land\, underwater\, and in the air. Using examples from research at Northeastern\, it explores the interconnections of systems design\, application-driven robotics\, and the fundamental algorithms associated with simultaneous localization and mapping (SLAM)\, machine learning (ML)\, and computer vision and imaging as it applies to a variety of scientific and commercial settings. \nThis webinar will be lead by ECE Faculty Member Dr. Hanumant Singh. Event logistics are below: \nDate: June 14\, 2021 \nTime: 9:00am EST \nRegistration \n 
URL:https://ece.northeastern.edu/event/robotics-at-northeastern-a-personal-view-of-autonomy-underwater-on-land-and-with-aerial-systems/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210618T090000
DTEND;TZID=America/New_York:20210618T100000
DTSTAMP:20260517T135342
CREATED:20210614T181136Z
LAST-MODIFIED:20210614T181136Z
UID:4983-1624006800-1624010400@ece.northeastern.edu
SUMMARY:PhD Dissertation Defense: Trinayan Baruah
DESCRIPTION:PhD Dissertation Defense: Improving the Virtual Memory Efficiency of GPUs \nTrinayan Baruah \nLocation: Zoom Link \nAbstract: GPUs have been adopted widely based their ability to exploit data-level parallelism found in modern-day applications\, ranging from high performance computing to machine learning. This widespread adoption has\, in part\, been accelerated by the development of more intuitive high-level programming languages\, efficient runtimes and drivers\, and easier mechanisms to manage data movement. Modern day GPUs and multi-GPU systems utilize virtual memory systems\, enabling programmers to access large address spaces that are beyond the physical memory limits a GPU. There mechanisms have built in mechanisms for memory translation\, sparing the programmer from having to reason about complex data-movement operations. Virtual memory support on a GPU includes both hardware and software support. At the hardware level\, translation lookaside buffers~(TLBs) are used to cache translations close to the compute units. At the software level\, the programming model supports a unified memory model which automates the movement of pages across multiple devices in a a system. Despite the improvements in programmability\, due to inefficiencies existing virtual memory management mechanisms\, including TLB management and page migration policies\, the performance obtained on today’s GPUs is sub-optimal.\nIn this dissertation\, we first identify the key challenges in virtual memory support for GPUs today. We then propose mechanisms to reduce the bottlenecks arising from virtual memory support at both a hardware level and at the runtime level. This allows GPUs to fully enjoy the benefits of virtual memory\, while ensuring high performance. We also develop simulation tools that enable researchers to explore new and novel virtual memory features in future single GPU and multi-GPU systems.\nTo enhance hardware support for virtual memory on a GPU\, we explore a mechanism that enables prefetching of page-table entries into the GPUs TLBs\, thereby reducing the number of TLB misses and improving performance. We also leverage the fact that many page-table entries can be shared across different GPU cores. We design a low-cost interconnect that enables sharing of page-table entries across the GPU cores. To improve the performance of unified memory on multi-GPU systems\, we propose a hardware/software mechanism that monitors accesses to each page\, and uses this information when making page-migration decisions. We also propose mechanisms to reduce the cost of TLB shootdowns on the GPU during page-migration in NUMA multi-GPU systems.
URL:https://ece.northeastern.edu/event/phd-dissertation-defense-trinayan-baruah/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210622T130000
DTEND;TZID=America/New_York:20210622T140000
DTSTAMP:20260517T135342
CREATED:20210617T174637Z
LAST-MODIFIED:20210617T174637Z
UID:4990-1624366800-1624370400@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Majid Sabbagh
DESCRIPTION:PhD Dissertation Defense: The Perils of Shared Computing: A Hardware Security Perspective \nMajid Sabbagh \nLocation: Microsoft Teams Link \nAbstract: Modern processors and hardware accelerators\, in the cloud or on the edge\, are capable of running multiple workloads from different users concurrently. Despite software techniques for security such as virtualization and containers\, a new attack surface is emerging that pertains to the hardware vulnerabilities of shared computing resources\, posing serious threats to shared computing. Fault attacks (FAs)\, Side-Channel Attacks (SCAs)\, and Transient-Execution Attacks (TEA) are three hardware-oriented attacks that target the system implementations. FAs aim to tamper the integrity of application execution through different fault injection methods\, to compromise the data or disrupt computation at run-time. SCAs exploit the information leakage of sensitive applications in physical parameters\, such as power consumption\, electromagnetic emanations\, and timing\, to breach the confidentiality of the application. TEAs exploit transient hardware operations such as speculative execution in Central Processing Units (CPUs) to tap on sensitive data temporarily and retrieve them from implicative microarchitectural states.\nIn this dissertation\, we investigate the three kinds of attacks that all exploit vulnerabilities due to shared computing. We first introduce a new non-invasive FA against Graphics Processing Units (GPUs)\, called overdrive fault attacks. We discover the security vulnerability of GPU’s voltage-frequency scaling (VFS) mechanism\, a common feature to balance power consumption and performance. An out-of-specification configuration of GPU voltage and frequency can be set by an adversary on the host CPU\, through the software interfaces to GPU’s power management units. This setting will cause timing violations for the computation and result in silent data corruptions (SDCs). We apply the overdrive fault attacks on two common victim applications. One is cryptographic applications accelerated by GPU. We launch a differential fault analysis (DFA) attack on an AES kernel running on an AMD RX 580 GPU and successfully recover the secret key. The other victim is convolutional neural network (CNN) inference. We thoroughly characterize fault injections and propagation in a CNN on a GPU and analyze the controllability of the attack. We successfully launch an end-to-end misclassification attack during CNN inferences with careful timing control.\nWe then evaluate a timing side-channel attack called Prime+Probe attack on CPUs and propose a Side-Channel Attack DEtection Tool (SCADET). SCADET is a methodology and a tool that operates on an x86 program’s binary. It records and analyzes the program’s memory accesses using dynamic binary instrumentation by running the program in a controlled environment to accurately identify the malicious access patterns demonstrated by the Prime+Probe attack.\nFinally\, we introduce an efficient hardware-level taint-tracking defense against the most prominent TEAs\, the speculative execution attacks. We take a secure-by-design approach and propose a mechanism called Secure Speculative Execution via RISC-V Open Hardware Design (SSE-RV)\, based on the latest Berkeley Out-of-Order Machine (SonicBOOM). We prototype our SSE-RV processor on an FPGA running a Linux operating system. Our results show that we can protect against Spectre-v1\, v2\, and v5. Our defense scheme is general and can be extended to protect against other transient execution attacks.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-majid-sabbagh/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210622T140000
DTEND;TZID=America/New_York:20210622T150000
DTSTAMP:20260517T135342
CREATED:20210614T211638Z
LAST-MODIFIED:20210614T211638Z
UID:4986-1624370400-1624374000@ece.northeastern.edu
SUMMARY:PhD Dissertation Defense: Ala Tokhmpash
DESCRIPTION:PhD Dissertation Defense: Fractional Order Derivative in Circuits\, Systems\, and Signal Processing with Specific Application to Seizure Detection \nAla Tokhmpash \nLocation: Zoom Link \nAbstract: Epilepsy is a chronic brain disease that affects around 50 million people worldwide. This disease is characterized by recurrent seizures\, which are brief episodes of involuntary movement that may involve a part or the entire body and are sometimes accompanied by loss of consciousness. It is the third most common neurological disorder in the United States\, only after Alzheimer’s disease and stroke. Patients suffering from epilepsy\, a brain disorder\, can have more than one type of seizure. Seizure detection systems can be life-changing for patients with epileptic seizures. By accurately identifying the periods in which seizure occurrence has a higher chance of happening we can help epileptic patients live a more normal life. Prior works on automated seizure detection overwhelmingly either rely solely entirely on domain knowledge\, or instead use a black box deep learning model. This thesis aims to integrate machine learning techniques with available seizure detection methods to improve detection performance. In this process\, we take advantage of mathematical tools provided by fractional-order derivatives as well as fuzzy entropy concepts. Specifically\, 1) we show the effectiveness of fractional order methods (FOM) in representing signals with long-range dependencies 2) using case studies in control and power systems\, we further examine the performance of FOM in the presence of parameter uncertainty. 3) using two publicly available data sets of brain signals from multiple patients\, we develop a cohesive framework to leverage FOM for extracting features that can be then used by statistical learning methods. 4) following recent works in this field\, we generalize the notion of entropy to include the fractional-order case. Combined with the fuzzy sets describing the uncertainty in data\, we leverage fractional fuzzy entropy as a robust descriptor of the state of brain signals. Through these case studies\, we demonstrate a significant increase in performance accuracy compared to models that do not consider FOM.
URL:https://ece.northeastern.edu/event/phd-dissertation-defense-ala-tokhmpash/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210629T110000
DTEND;TZID=America/New_York:20210629T120000
DTSTAMP:20260517T135342
CREATED:20210623T211312Z
LAST-MODIFIED:20210623T211312Z
UID:5005-1624964400-1624968000@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Yaoshen Yuan
DESCRIPTION:PhD Dissertation Defense: Enhancing Monte Carlo Light Modeling Methods for the Development of Near-infrared Based Brain Techniques \nYaoshen Yuan \nLocation: Zoom Link \nAbstract: Studying light propagation in biological tissues is critical for developing biophotonics techniques and its applications. Monte Carlo (MC) method\, a stochastic solver for radiative transfer equation\, has been recognized as the gold standard for modeling light propagation in turbid media. However\, due to the stochastic nature of MC method\, millions even billions of photons are usually required to achieve accurate results using MC method\, leading to a long computational time even with the acceleration using graphical processing units (GPU).\nFurthermore\, due to the rapid advances in multi-scale optical imaging techniques such as optical coherence tomography (OCT) and multiphoton microscopy (MPM)\, there is an increasing need to model light propagation in extremely complex tissues such as vessel networks. The mesh-based Monte Carlo (MMC) is usually superior than the voxel-based MC method for such modeling since unlike grid-like voxels\, tetrahedral meshes can represent arbitrary shapes with curved boundaries. However\, the mesh density can be excessively high when the tissue structure is extremely complex\, resulting in high computational costs and memory demand.\nThe goal of this proposal is to focus on solving the challenges mentioned above. To tackle the first challenge\, we came up with a filtering approach with GPU acceleration to improve the signal-to-noise ratio (SNR) of the results while keeping the simulated photons low. The adaptive non-local means (ANLM) filter is selected to suppress the stochastic noise in MC results because 1) the filtering process on each voxel is mutually independent\, making it possible for parallel computing; 2) it has high performance in denoising and a strong capacity in edge-preserving.\nFor the second problem\, a novel method\, implicit mesh-based Monte Carlo (iMMC)\, was proposed to significantly reduce the mesh density. The iMMC utilizes the edge\, node and face of the tetrahedral mesh to model tissue structures with shapes of cylinder\, sphere and thin layer. The typical applications for edge\, node and face-based iMMC are vessel networks\, porous media and membranes\, respectively.\nLastly\, we applied MC simulations and aforementioned filter on segmented brain models derived from MRI neurodevelopmental atlas to estimate the light dosage for transcranial photobiomodulation (t-PBM)\, a technique for treating major depressive disorder using near infrared\, across lifespan. The iMMC simulation was also applied to evaluate the impact of human hair on the brain sensitivity for functional near-infrared spectroscopy (fNIRS). Furthermore\, a new approach that can improve the penetration depth in optical brain imaging as well as PBM is proposed. In this approach\, the possibility of placing light sources in head cavities is investigated using MC simulations. The preliminary results demonstrate a better performance in deep brain monitoring compared to the standard transcranial approach using 10-20 EEG positioning system.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-yaoshen-yuan-2/
END:VEVENT
END:VCALENDAR