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Filtering by: Creator Schorr%2C G. S. Remove constraint Creator: Schorr%2C G. S. Academic Affiliation Electrical Engineering and Computer Science Remove constraint Academic Affiliation: Electrical Engineering and Computer Science Language English [eng] Remove constraint Language: English [eng]

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  • Learning MDP action models via discrete mixture trees

    Creator:
    Wynkoop, Michael S.
    Abstract:
    This thesis addresses the problem of learning dynamic Bayesian network (DBN) models to support reinforcement learning. It focuses on learning regression tree models of the conditional probability distributions of the DBNs. Existing algorithms presume that the stochasticity in the domain can be modeled as a deterministic function with additive noise....
    Resource Type:
    Masters Thesis
    Full Text:
    OF Michael S. Wynkoop for the degree of Masters of Science in Computer Science presented on June 9

  • Simulator-Defined MDP Planning with Applications in Natural Resource Management

    Creator:
    Alkaee Taleghan, Majid
    Abstract:
    This work is inspired by problems in natural resource management centered on the challenge of invasive species. Computing optimal management policies for maintaining ecosystem sustainable is challenging. Many ecosystem management problems can be formulated as MDP (Markov Decision Process) planning problems. In a simulator-defined MDP, the Markovian dynamics and rewards...
    Resource Type:
    Dissertation
    Full Text:
    G. Dietterich This work is inspired by problems in natural resource management centered on the

  • Activity recognition in desktop environments

    Creator:
    Shen, Jianqiang
    Abstract:
    Knowledge workers are struggling in the information flood. There is a growing interest in intelligent desktop environments that help knowledge workers organize their daily life. Intelligent desktop environments allow the desktop user to define a set of “activities” that characterize the user’s desktop work. These environments then attempt to identify...
    Resource Type:
    Dissertation
    Full Text:
    i s i n g s t u d e n t s h e n p r o j e c t T a s k T r a c e r c o m m e n t s o n t h e s i

  • Machine learning methods for public policy : simulation, optimization, and visualization

    Creator:
    McGregor, Sean
    Abstract:
    Society faces many complex management problems, particularly in the area of shared public resources such as ecosystems. Existing decision making processes are often guided by personal experience and political ideology rather than state-of-the-art scientific understanding. This dissertation envisions a future in which multiple stakeholders are provided with computational tools for...
    Resource Type:
    Dissertation
    Full Text:
    Methods for Public Policy: Simulation, Optimization, and Visualization Abstract approved: Thomas G

  • Analysis and design on low-power multi-Gb/s serial links

    Creator:
    Hu, Kangmin
    Abstract:
    High speed serial links are critical components for addressing the growing demand for I/O bandwidth in next-generation computing applications, such as many-core systems, backplane and optical data communications. Due to continued process scaling and circuit innovations, today's CMOS serial link transceivers can achieve tens of Gb/s per pin. However, most...
    Resource Type:
    Dissertation
    Full Text:
    2018 2020 2022 2024 100 101 102 Year D at e ra te (G b/ s) 2008 2010 2012 2014 2016

  • Design techniques for low-power multi-GS/s analog-to-digital converters

    Creator:
    Jiang, Tao
    Abstract:
    Ultra-high-speed (>10GS/s), medium-resolution (5~6bit), low-power (<50mW) analog-to-digital converter can find it application in the areas of digital oscilloscopes and next-generation serial link receivers. There are several challenges to enable a successful design, however. First, the time-interleaved architecture is required in order to achieve over 10GS/s sampling rate, with the trade-off...
    Resource Type:
    Dissertation
    Full Text:
    Design Techniques for Low-Power Multi-GS/s Analog-to

  • Low-Power 12-bit Quasi-Passive Segmented DAC Implemented in 130nm CMOS Technology

    Creator:
    Pierce, Alexander G.
    Abstract:
    This thesis work proposes a low-power 12-bit digital-to-analog converter (DAC) designed in a 130nm process. The DAC to be presented is of a segmented design where the architecture is split into pipelined and thermometer coded segments. This allows for high linearity and low distortion while maintaining high speed and low...
    Resource Type:
    Masters Thesis
    Full Text:
    expressed as: 𝐹𝑜𝑀-CB$%1%$ = 𝑆𝑁𝑅 + 10𝑙𝑜𝑔)' Q 𝑓/ 2R 𝑃91//183#%9 S (3.1.10) For audio

  • A comparison of substrate noise coupling in heavily doped and lightly doped substrates for mixed-signal circuits

    Creator:
    Ayers, James S.
    Abstract:
    An analysis of substrate noise coupling in mixed-signal circuits has been performed in the TSMC 0.25 [mu]m lightly doped and heavily doped CMOS processes. Methods to minimize noise coupling in both the chip design and board design phases are presented along with techniques for accurate circuit simulation of noise coupling....
    Resource Type:
    Masters Thesis
    Full Text:
    AN ABSTRACT OF THE THESIS OF James S. Ayers for the degree of

  • A 400Mb/s 850nm FSO Link based on CMOS Integrated Receiver for Hybrid WiFi and Optical Femtocells

    Creator:
    Chen, Siyuan
    Abstract:
    The increasing demand for higher data-rates is challenging to satisfy with spectrum-deficient indoor WiFi networks. A novel hybrid WiFi Free-Space Optical (WIFO) system has been proposed to enhance the wireless capacity of indoor WiFi networks. In this thesis, an integrated optical wireless receiver is designed and integrated in 65-nm CMOS...
    Resource Type:
    Masters Thesis
    Full Text:
    /s 850nm FSO Link based on CMOS Integrated Receiver for Hybrid WiFi and Optical Femtocells Abstract

  • Model-based approximation methods for reinforcement learning

    Creator:
    Wang, Xin
    Abstract:
    The thesis focuses on model-based approximation methods for reinforcement learning with large scale applications such as combinatorial optimization problems. First, the thesis proposes two new model-based methods to stablize the value–function approximation for reinforcement learning. The first one is the BFBP algorithm, a batch-like reinforcement learning process which iterates between...
    Resource Type:
    Dissertation
    Full Text:
    Methods for Reinforcement Learning. Abstract approved: Thomas G Dietterich The thesis focuses on model