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Filtering by: Creator Li, Xin Remove constraint Creator: Li, Xin Committee Member Raich, Raviv Remove constraint Committee Member: Raich, Raviv

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  • High efficiency wideband low-power delta-sigma modulators

    Creator:
    Lee, Sang Hyeon
    Abstract:
    Delta-sigma analog-to-digital converters traditionally have been used for low speed, high resolution applications such as measurements, sensors, voice and audio systems. Through continued device scaling in CMOS technology and architectural and circuit level design innovations, they have even become popular for wideband, high dynamic range applications such as wired and...
    Resource Type:
    Dissertation
    Full Text:
    -Yao Ke, Wei Li, Meng Xin, Qadeer Khan, Ramin Zanbaghi, Peter Kurahashi, Rob Gregoire, Dave Gubbins

  • Multi-instance multi-label learning : algorithms and applications to bird bioacoustics

    Creator:
    Briggs, Forrest
    Abstract:
    We consider the problem of supervised classification of bird species from audio recordings in a real-world acoustic monitoring scenario (i.e. audio data is collected in the field with an omnidirectional microphone, without human supervision). Obtaining better data about bird activity can assist conservation efforts, and improve our understanding of their...
    Resource Type:
    Dissertation

  • Wideband and high accuracy delta sigma modulation data converter

    Creator:
    Jung, Youngho
    Abstract:
    Nowadays, needs for wideband and high accuracy analog-to-digital converter are increasing rapidly in manifold applications such as wireless communication, digital video and other consumer electronics. Besides, low power consumption is required to have longer battery life in portable systems. CMOS technology scaling and innovative modulator topology make the implementation much...
    Resource Type:
    Dissertation
    Full Text:
    Yu, Yan Wang, Weilun Shen, Tong Tao, Wang Tao, Wei Li, Po-Yao Ke, Jiaming Lin, Chia-Hung Chen

  • Linearity enhancement techniques for data converters

    Creator:
    Cao, Jinzhou
    Abstract:
    Data converters are essential interface circuits between the analog world that people live in and the digital processors that people live with. Linearity, which often is a tradeoff against other performance criteria, is one of the major performance demands from applications for both analog-to-digital converts (ADC) and digital-to-analog converters (DAC)....
    Resource Type:
    Dissertation
    Full Text:
    brilliant and generous fellow graduate students. I especially thank Xin Meng for providing an ADC base

  • Modeling, Fabrication, and Characterization of Magnetic Thin Films for Integrated Inductor and MRAM Applications

    Creator:
    Buford, Benjamin
    Abstract:
    Magnetic thin films have potential to improve devices such as on-chip inductors, and enable new technologies such as magnetic random access memory (MRAM). The use of magnetic cores in on-chip inductors is typically limited to applications well under 1 GHz. At higher frequencies, the performance of the magnetic core is...
    Resource Type:
    Dissertation

  • Position estimation in indoor localization with trilateration

    Creator:
    Su, Zhenqiang
    Abstract:
    Triangulateration uses geometric distance to estimate the location of user by employing techniques like received signal strength (RSS), time-of-arrival (TOA), time-difference-of-arrival (TDOA) and image processing. Radio frequency (RF) signal positioning using TOA or TDOA techniques generally requires timing synchronization of the anchors and/or the anchors and targets. If the desired...
    Resource Type:
    Dissertation

  • Learning with Partial Supervision for Clustering and Classification

    Creator:
    Pei, Yuanli
    Abstract:
    In the field of machine learning, clustering and classification are two fundamental tasks. Traditionally, clustering is an unsupervised method, where no supervision about the data is available for learning; classification is a supervised task, where fully-labeled data are collected for training a classifier. In some scenarios, however, we may not...
    Resource Type:
    Dissertation
    Full Text:
    Nguyen, Xingyi Li, Thi Kim Phung Lai, Gaole Jin, and Qi Lou. Thank you for discussing interesting ideas

  • Leveraging Structures of the Data in Deep Learning

    Creator:
    Li, Xingyi
    Abstract:
    The performance of deep learning frameworks could be significantly improved through considering the particular underlying structures for each dataset. In this thesis, I summarize our three work about boosting the performance of deep learning models through leveraging structures of the data. In the first work, we theoretically justify that, for...
    Resource Type:
    Dissertation
    Full Text:
    , Xinyao Wang, Neale Ratzlaff, Xin Li, Bob DeBortoli, Hung Nguyen, Weikun Han, Wenxuan Wu, Zehuan Chen

  • Predicting Properties of Bio-jet Fuels using Machine Learning and ATR-FTIR Spectroscopy

    Creator:
    Mayer, Morgan A.
    Abstract:
    High-potential molecules derived from biomass sources may suitably replace or supplement traditional nonrenewable hydrocarbon fuels to reduce pollution and fuel processing costs. Due to expensive and time-consuming property testing, models that predict key properties from optical data would initially vet potential additives before investment and bench-scale testing. Attenuated Total Reflection...
    Resource Type:
    Masters Thesis