BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Department of Electrical & Computer Engineering - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Department of Electrical & Computer Engineering
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:20190310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20191103T060000
END:STANDARD
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
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201102T090000
DTEND;TZID=America/New_York:20201102T100000
DTSTAMP:20260506T180149
CREATED:20201026T214743Z
LAST-MODIFIED:20201026T214743Z
UID:4537-1604307600-1604311200@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Ya Guo
DESCRIPTION:PhD Proposal Review: Power Optimization and Management of PV Grid-Connected Microgrid in Energy Market \nYa Guo \nLocation: Zoom Link \nAbstract: Microgrids can integrate renewable energy resources (RES)\, such as photovoltaics (PV) and wind energy generation\, with the main power grid to provide reliable\, secure and affordable energy. Fortunately\, the electricity markets have evolved to facilitate RES participation. One major challenge lies in how to manage power and energy flow within grid-connected microgrid system\, to optimize financial gains while maintaining high reliability. This becomes challenging since electricity trading policies and tariffs vary by utility companies from area to area. Furthermore\, RES are mostly intermittent sources. Adding additional energy storage systems (ESS) into microgrids becomes a vital solution to mitigating the energy production intermittency\, as well as providing energy backup in emergency. Battery ESS (BESS) are deployed on a large scale in grid-connected installations worldwide. Optimal operation of the energy storage system also becomes important for microgrid end-users to ensure that they will at least recover BESS operating cost. Moreover\, there always exist uncertainties in RES power generation\, load power consumption\, and even dynamic electricity pricing. It is vital to deal with the forecasted errors in real-time. Developing proper uncertainty characterization can better facilitate the whole system power management to limit the negative influences of these uncertainties.\nIn this research\, dynamic programming (DP) algorithm is proposed to forecast the global optimal solution to power flow dispatch of PV grid-connected microgrid. Various electricity pricing structures\, including fixed pricing\, time-of-use (TOU) pricing and real-time pricing (RTP) are explored for customers in different areas. The battery nonlinear charging/discharging degradation model is also exploited for system power optimization. The objective is to achieve the minimum microgrid system operation cost\, in other words\, the maximum economic benefits for end-users. Besides\, this research proposes power control methods to implement forecasted optimal power schedule\, as well as dealing with errors among forecast and real-time PV\, load and RTP. Rule-based (RB) algorithm is also studied as a baseline for comparison. Moreover\, uncertainty characterization for PV\, load and dynamic pricing will be developed using Monte Carlo Simulation (MCS)\, and stochastic optimization approach will be explored in cooperation with these uncertainties.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-ya-guo/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201105T120000
DTEND;TZID=America/New_York:20201105T130000
DTSTAMP:20260506T180149
CREATED:20201023T211519Z
LAST-MODIFIED:20201023T211519Z
UID:4533-1604577600-1604581200@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Aziz Kocanaogullari
DESCRIPTION:PhD Dissertation Defense: Active Recursive Bayesian Classification (Querying and Stopping) for Event Related Potential Driven Brain Computer Interface Systems \nAziz Kocanaogullari \nLocation: Remote (contact akocanaogullari@ece.neu.edu) \nAbstract: Recursive Bayesian classification (RBC) requires optimal latent variable estimation in the presence of noisy observation to achieve real-time sequential decision making. Active RBC introduced in this dissertation attempts to effectively select queries that lead to more informative observations to rapidly reduce uncertainty until a confident decision is made. Accordingly\, active RBC includes the following fundamental components:(S)A stopping criterion based on the posterior probability to stop evidence collection;(Q)a querying step to decide how to collect further evidence from relevant sources to benefit speed and accuracy objectives of RBC;(C)a classification objective based on the posterior distribution and loss values attributed to each true label and decision option pair to determine the optimal decision once the stopping criterion has been satisfied.\nThis dissertation specifically focuses on optimizing querying (Q) and stopping (S) for RBC. Conventional stopping criterion design methodologies lack insight of the RBC geometry and evolution of the posterior probability vector. Additionally\, conventional active querying methods stagger due to misleading prior information. In this case\, the system uses time inefficiently to overcome the provided belief by querying most likely candidates a number of iterations. Furthermore\, in contrast to inference and querying being coactive\, typically the optimality objectives are designed separately.\nAn electroencephalography (EEG)-based brain computer interface (BCI) system specifically de-signed for typing is used as a testbed for active RBC. BCI systems provide a communication pathway between the user and the environment both in medical and non-medical domains. EEG signals are widely used with promising performance to estimate user intent in BCI systems. BCI typing systems are epitomes of RBC driven systems as repeated evidence collection is mandated due to highly variable EEG signals given a particular user intent (latent variable hidden in the brain). However\, in many cases\, EEG-based communication staggers and lacks accuracy and speed due to inefficient RBC.\nTo increase the performance of RBC\, motivated by information theoretic approaches to coding and active learning this dissertation contributes to the literature in three folds: (i) A complete analysis of stopping criterion and geometrical description of the RBC problem is provided. Motivated by the posterior motion a stopping criterion design is proposed. Moreover\, an early stopping scheme with one step ahead prediction is shown to make a decision with marginal accuracy deficit. (ii)Influenced by the posterior motion\, a new query selection objective is proposed. This querying mechanism is shown to result in rapid and accurate inference in scenarios in which the recursive inference starts with a misleading (or adversarial) prior probability distribution for the latent variable of interest (e.g. user attempting to type a letter/word that is unlikely according to the language model). (iii) Querying and stopping approaches are taken together into consideration and an experimental study specifically on BCI typing is presented. Additionally\, the dissertation shows it is possible to reformulate RBC with Rényi entropy measures solidifying the connection between stopping and querying objective design. All contributions are verified using a BCI typing system “BCIPy” with simulations and human-in-the-loop experiments.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-aziz-kocanaogullari/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201109T150000
DTEND;TZID=America/New_York:20201109T160000
DTSTAMP:20260506T180149
CREATED:20201105T220658Z
LAST-MODIFIED:20201105T220658Z
UID:4557-1604934000-1604937600@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Carlos Bocanegra
DESCRIPTION:PhD Proposal Review: A Systems Approach to Spectrum Sharing and Multi-antenna Operation for Emerging Networks \nCarlos Bocanegra \nLocation: TBD \nAbstract: The demands on wireless connectivity and sensing are ever-increasing\, fueled by both emerging applications and an exponential growth in the number of connected devices. Availability of new spectrum in the sub-6GHz bands is limited\, which motivates research on innovative ways to utilize the current available spectrum and explore the use of additional spectrum beyond the 6GHz threshold.\nThis thesis explores three promising techniques with focus on the Physical (L1) and Link (L2) layers. Approach 1 concerns spectrum sharing in the sub-6GHz band\, where wireless standards are granted opportunistic access within unlicensed spectrum to increase their usable bandwidth. Approach 2 concerns the design of massive multi-antenna systems\, through which devices can benefit from beamforming gains at transmission and diversity gains at reception. Approach 3 concerns the use of very high frequency bands (VHFB)\, or so called millimeter-wave bands. Each of these approaches\, however\, has its own set of challenges\, such as fairness in channel access\, interference management\, and optimal beamforming user-specified quality-of-service\, respectively.\nFor spectrum sharing as described in Approach 1\, this thesis presents E-Fi\, an interference-evasion mechanism that allows Wi-Fi devices to survive opportunistic in-band LTE transmissions. The main contribution is to achieve this without any cooperation between these two\, using Almost Blank Subframes (ABS). E-Fi ensures fair channel access while reusing existing Wi-Fi standards\, i.e.\, Wi-Fi Direct\, and thus incurring minimal deployment costs.\nFor Approach 2\, this thesis introduces two multi-antenna frameworks\, a decentralized one for cellular- and a centralized one for IoT-oriented applications\, respectively. For the former\, it presents NetBeam\, a reconfigurable system of distributed 3D beamformers (3DBF). While NetBeam uses 3DBF to tackle multi-user interference in 3D multi-user deployments\, it enforces Machine Learning and efficient antenna selection strategies to deliver the individual required SINR levels to users. As a centralized multi-antenna system\, it presents RFGo\, a privacy-preserving self-checkout system using passive Radio Frequency ID (RFID) tags. RFGo achieves fast tag discovery using a custom-built RFID reader\, which simultaneously decodes a tag’s response from multiple carrier-level synchronized antennas. RFGo achieves reliable tag detection by means of a neural network that accurately discriminates products within the checkout area from those laying outside of it.\nIn the proposed work that covers Approach 3\, this thesis describes an outline of an algorithmic framework for millimeter-wave communications that efficiently allocates antenna elements from Base Stations (BS) to users for hybrid beamforming\, while considering their individual traffic demands. We propose to trade-off flexible array geometries (that allows to limit interference to specific regions) versus the irregularity that results in the sidelobes.\nIn summary\, this thesis tackles complex challenges in the future 5G and beyond wireless networks through a combination theory\, algorithm design and experimental implementation.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-carlos-bocanegra/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201112T130000
DTEND;TZID=America/New_York:20201112T140000
DTSTAMP:20260506T180149
CREATED:20201103T200924Z
LAST-MODIFIED:20201103T200924Z
UID:4546-1605186000-1605189600@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Yaoshen Yuan
DESCRIPTION:PhD Proposal Review: Enhancements for Monte Carlo Light Modeling Method and Its Applications in Near-infrared-based Brain Techniques \nYaoshen Yuan \nLocation: Microsoft Teams 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. The goal of this proposal is to focus on solving the challenges mentioned above. \nTo 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. For 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. Lastly\, 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. 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. \n 
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-yaoshen-yuan/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201113T090000
DTEND;TZID=America/New_York:20201113T100000
DTSTAMP:20260506T180149
CREATED:20201103T210724Z
LAST-MODIFIED:20201103T210724Z
UID:4551-1605258000-1605261600@ece.northeastern.edu
SUMMARY:Electrical and Computer Engineering Webinar
DESCRIPTION:Join faculty staff and current students to learn more about graduate school options in Electrical + Computer Engineering \nFriday\, November 13 \n9:00 AM EST \nhttps://us02web.zoom.us/webinar/register/WN_sBbUcJBJQ_eroL2ll-mjbQ
URL:https://ece.northeastern.edu/event/electrical-and-computer-engineering-webinar-2/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201113T140000
DTEND;TZID=America/New_York:20201113T150000
DTSTAMP:20260506T180149
CREATED:20201110T024923Z
LAST-MODIFIED:20201110T024923Z
UID:4570-1605276000-1605279600@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Yumin Liu
DESCRIPTION:PhD Proposal Review: 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 addressseveral 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 method 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.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-yumin-liu/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201124T140000
DTEND;TZID=America/New_York:20201124T150000
DTSTAMP:20260506T180149
CREATED:20201103T210959Z
LAST-MODIFIED:20201103T210959Z
UID:4552-1606226400-1606230000@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Joseph Robinson
DESCRIPTION:PhD Dissertation Defense: Automatic Face Understanding: Recognizing Families in Photos \nJoseph Robinson \nLocation: Zoom Link \nAbstract: Visual kinship recognition has an abundance of practical uses. For this\, we built the largest database for kinship recognition\, FIW. Built entirely in-house with no cost using a semi-automatic labeling scheme. Specifically\, we first aligned faces detected in family photos with names in the corresponding text metadata to mine the label proposals with high confidence. The remaining data were labeled using a novel clustering algorithm that used label proposals as side information to guide more accurate clusters. Great savings in time and human input was had. Statistically\, FIW shows enormous gains over its predecessors. We have several benchmarks in kinship verification\, family classification\, tri-subject verification\, and large-scale search & retrieval. We also trained CNNs on FIW and deployed the model on the renowned KinWild I and II to gain state-of-the-art (SOTA). Most recently\, we further augmented FIW with multimedia (MM) for 200 of its 1\,000 families- a labeled collection we dubbed FIW-MM. Now\, video dynamics\, audio\, and text captions can be used in the decision making of kinship recognition systems. \nFIW continues to pave the way for this research track: (1) advanced SOTA (e.g.\, marginalized denoising auto-encoder based on metric learning that preserves intrinsic structures of kin-data and encapsulates discriminating information in learned features); (2) introduced generative models to predict a child’s appearance from a parent pair (i.e.\, proposed an adversarial autoencoder conditioned on age and gender to map between facial appearance and these higher-level features for control of age and gender); (3) designed evaluations with benchmarks to support challenges\, workshops\, and tutorials at top tier conferences (e.g.\, CVPR\, MM\, FG\, ICME)\, and a premiere Kaggle Competition. We expect FIW will significantly impact research and reality. \nAdditionally\, we tackled the classic problem of facial landmark localization in images. This is a task that has been in focus for decades\, and many solutions have been proposed. However\, there are revamped interests in pushing facial landmark detection technologies to handle more challenging data with deep networks now prevailing throughout machine learning. A majority of these networks have objectives based on L1 or L2 norms\, which inherit several disadvantages. First of all\, the locations of landmarks are determined from generated heatmaps (i.e.\, confidence maps) from which predicted landmark locations (i.e.\, the means) get penalized without accounting for the spread: a high scatter corresponds to low confidence and vice-versa. To address this\, we introduced a LaplaceKL objective that penalizes for low confidence. Another issue is a dependency on labeled data\, which is expensive to collect and susceptible to error. We addressed both issues by proposing an adversarial training framework that leverages unlabeled data to improve model performance. Our method claims SOTA on renowned benchmarks. Furthermore\, our model is robust with a reduced size: 1/8 the number of channels (i.e.\, 0.0398 MB) is comparable to state-of-that-art in real-time on a CPU. Thus\, our method is of high practical value to real-life applications. \nFinally\, we built the Balanced Faces in the Wild (BFW) dataset to serve as a proxy to measure bias across ethnicity and gender subgroups\, allowing us to characterize FR performances per subgroup. We show performances are non-optimal when a single score threshold is used to determine whether sample pairs are genuine or imposter. Furthermore\, actual performance ratings vary greatly from the reported across subgroups. Thus\, claims of specific error rates only hold for populations matching that of the validation data. We mitigate the imbalanced performances using a novel domain adaptation learning scheme on the facial encodings extracted using SOTA deep nets. Not only does this technique balance performance\, but it also boosts the overall performance. A benefit of the proposed is to preserve identity information in facial features while removing demographic knowledge in the lower dimensional features. The removal of demographic knowledge prevents future potential biases from being injected into decision making. Additionally\, privacy concerns are satisfied by this removal. We explore why this works qualitatively with hard samples. We also show quantitatively that subgroup classifiers can no longer learn from the encodings mapped by the proposed. \n 
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-joseph-robinson/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201125T120000
DTEND;TZID=America/New_York:20201125T130000
DTSTAMP:20260506T180149
CREATED:20201112T213551Z
LAST-MODIFIED:20201112T213551Z
UID:4572-1606305600-1606309200@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Aykut Onol
DESCRIPTION:PhD Dissertation Defense: Planning of Contact-Interaction Trajectories Using Numerical Optimization \nAykut Onol \nLocation: Zoom Link \nAbstract: Dynamic multi-contact behaviors\, such as locomotion and item manipulation\, remain to be a challenge for today’s robotic systems. This is primarily due to the discontinuous and non-smooth dynamics introduced by contacts. For mobile manipulators (e.g.\, humanoids) to become useful for dangerous\, dirty\, and dull tasks\, such as those in disaster response\, they need to be capable of interacting with their cluttered\, constrained\, and changing environments. It is therefore essential to develop methods that would enable robots to plan and execute contact-rich motions in dynamic surroundings.\nIn this dissertation research\, we investigate the planning of contact-interaction trajectories and utilize numerical optimal control techniques to solve this problem in a generalizable and computationally-tractable way. We develop a contact-implicit trajectory optimization framework for the automatic discovery of dynamic contact-rich behaviors given only a high-level goal\, i.e.\, the desired configuration of the environment. A variable smooth contact model is introduced to improve the convergence of gradient-based optimization without compromising the physical fidelity of resulting motions. This is achieved by employing smooth virtual forces that act as a decoupled relaxation of the rigid-body contact model. Second\, we develop a sequential convex optimization procedure that provides reliable convergence characteristics while solving this non-convex problem. Third\, a penalty loop approach is proposed to generalize this method to a wide range of robotic applications.\nIn addition to these\, we develop a novel Coulomb friction model and an on-the-fly contact constraint activation method using state-triggered constraints\, STCs. STCs are a more modular alternative to complementarity constraints which are widely used to model discrete aspects in contact-related problems. Our extensive simulation experiments demonstrate that STCs hold immense promise to efficiently model a broad range of discrete elements in the planning and control of contact-interaction trajectories. As a result\, this dissertation presents methods that enable the planning of dynamic contact-rich behaviors for different robots and tasks without requiring any parameter tuning or tailored initial guess.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-aykut-onol/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201130T093000
DTEND;TZID=America/New_York:20201130T103000
DTSTAMP:20260506T180149
CREATED:20201121T024753Z
LAST-MODIFIED:20201123T205506Z
UID:4591-1606728600-1606732200@ece.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Sila Deniz Calisgan
DESCRIPTION:MS Thesis Defense: MEMS Infrared Resonant Detectors With Near-Zero Power Readout For Miniaturized Low Power Systems \nSila Deniz Calisgan \nLocation: Online \nAbstract: The demand for low-cost and low-power microsystems for spectrally-selective IR sensing has been rising with the proliferation of Internet of Things (IoT) for applications such as security surveillance and natural disaster monitoring. As a result\, there is a need for low-power\, high sensitivity IR sensors with minimum deployment and maintenance cost that can detect trace levels of chemicals. This thesis reports on the first experimental demonstrations of passive integrated microsystems based on transmission spectroscopy using narrowband uncooled microelectromechanical resonant infrared (IR) detectors. Moreover\, the MEMS-CMOS integrated microsystem can turn itself ON to quantify the intensity of infrared radiation when an above-threshold IR signature is present\, but otherwise remain dormant with near-zero standby power consumption. The proposed sensor system combines the unique advantage of two recently developed technologies\, namely\, the zero-power nature of micromechanical photoswitches (MPs) and the high resolution of aluminum nitride (AlN) MEMS resonant infrared detectors\, to achieve an unprecedented IR sensing capability. Thanks to the spectral selectivity enabled by the plasmonically enhanced thermo-mechanical transduction in MEMS structures\, the proposed sensor system is capable of discriminating the spectral content of incoming IR radiation for the identification of events of interest. The prototype presented here is automatically powered up by the MP when the incoming IR radiation exceeds 440 nW showing a high IR detection resolution in active state and a near-zero power consumption (~3 nW) in standby. The ultrathin plasmonic absorber with narrow bandwidth (FWHM<17% ) and near-perfect IR absorption (η>92%) coupled with the high IR detection capability ( NEP~ 463 pW/√Hz) of the AlN resonator was exploited for a filter-free spectroscopic chemical sensor based on uncooled AlN resonant IR detectors with a minimum concentration detection limit of <0.01% (Benzonitrile in Hexane).
URL:https://ece.northeastern.edu/event/ece-ms-thesis-defense-sila-deniz-calisgan/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201130T130000
DTEND;TZID=America/New_York:20201130T140000
DTSTAMP:20260506T180149
CREATED:20201123T204938Z
LAST-MODIFIED:20201123T204938Z
UID:4592-1606741200-1606744800@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Berkan Kadioglu
DESCRIPTION:PhD Proposal Review: Sample Complexity of Pairwise Ranking Regression \nBerkan Kadioglu \nLocation: Zoom \nAbstract: We consider a rank regression setting\, in which a dataset of $N$ samples with features in $\mathbb{R}^d$ is ranked by an oracle via $M$ pairwise comparisons.\nSpecifically\, there exists a latent total ordering of the samples; when presented with a pair of samples\, a noisy oracle identifies the one ranked higher w.r.t. the underlying total ordering. A learner observes a dataset of such comparisons\, and wishes to regress sample ranks from their features.\nWe show that to learn the model parameters with $\epsilon > 0$ accuracy\, it suffices to conduct $M \in \Omega(dN\log^3 N/\epsilon^2)$ comparisons uniformly at random when $N$ is $\Omega(d/\epsilon^2)$. \n 
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-berkan-kadioglu/
END:VEVENT
END:VCALENDAR