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DTSTART;TZID=America/New_York:20210603T110000
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DTSTAMP:20260511T230317
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UID:26088-1622718000-1622721600@coe.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://coe.northeastern.edu/event/ece-phd-proposal-review-mehdi-nasrollahpourmotlaghzanjani/
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DTSTART;TZID=America/New_York:20210603T150000
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UID:26116-1622732400-1622736000@coe.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://coe.northeastern.edu/event/ece-phd-dissertation-defense-yumin-liu/
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