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Electrical and Electronics

Electrical Engineering Theses and Dissertations

2020

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Full-Text Articles in Engineering

On-Chip Nonreciprocal Components For Full-Duplex Communications And Gaussian Regulated Gate Driver For Electromagnetic Interference Reduction, Chang Yang Oct 2020

On-Chip Nonreciprocal Components For Full-Duplex Communications And Gaussian Regulated Gate Driver For Electromagnetic Interference Reduction, Chang Yang

Electrical Engineering Theses and Dissertations

This dissertation is comprised of two unrelated design endeavors. The first one is about two CMOS nonreciprocal components: 1) an isolator and 2) a circulator. To make the components compact enough for the next generation communication systems with wide bandwidth, both components operate at 100 GHz band for full-duplex transceivers for ultra-high-data-rate millimeter-wave wireless communication. The proposed nonreciprocal structures are based on a time-domain modulation by signals at around 1/6 of the RF frequencies and spatial duplexing over the RF signal paths, demonstrating over 45 dB isolation in a bandwidth of 1.5 GHz over the tuning range of 85-110 GHz. …


Model-Based And Data-Driven Situational Awareness For Distribution System Monitoring And Control, Ying Zhang Aug 2020

Model-Based And Data-Driven Situational Awareness For Distribution System Monitoring And Control, Ying Zhang

Electrical Engineering Theses and Dissertations

Electric power systems are undergoing a dramatic change. The penetration of distributed energy resources (DERs) such as wind turbine generators and photovoltaic panels is turning a traditional power system into the active distribution network. Power system situational awareness, which provides critical information for system monitoring and control, is being challenged by multiple sources of uncertainties such as random meter errors, stochastic power output of DERs, and imprecise network parameters. On the other hand, cyber-physical power system operation is vulnerable to cyberattacks against effective state estimation, such as false data injection attacks (FDIAs). To construct next-generation smart grids, this dissertation develops …


A 2.56 Gbps Serial Wireline Transceiver That Supports An Auxiliary Channel And A Hybrid Line Driver To Compensate Large Channel Loss, Xiaoran Wang Aug 2020

A 2.56 Gbps Serial Wireline Transceiver That Supports An Auxiliary Channel And A Hybrid Line Driver To Compensate Large Channel Loss, Xiaoran Wang

Electrical Engineering Theses and Dissertations

Serial transceiver links are widely used for high-speed point-to-point communications. This dissertation describes two transceiver link designs for two different applications.

In serial wireline communications, security is an increasingly important factor to concern. Securing an information processing system at the application and system software layers is regarded as a necessary but incomplete defense against the cyber security threats. In this dissertation, an asynchronous serial transceiver that is capable of transmitting and receiving an auxiliary data stream concurrently with the primary data stream is described. The transceiver instantiates the auxiliary data stream by modulating the phase of the primary data without …


Learning Deep Architectures For Power Systems Operation And Analysis, Mahdi Khodayar Aug 2020

Learning Deep Architectures For Power Systems Operation And Analysis, Mahdi Khodayar

Electrical Engineering Theses and Dissertations

With the rapid increase in size and computational complexities of power systems, the need for powerful computational models to capture strong patterns from energy datasets is emerged. In this thesis, we provide a comprehensive review on recent advances in deep neural architectures that lead to significant improvements in classification and regression problems in the area of power engineering. Furthermore, we introduce our novel deep learning methodologies proposed for a large variety of applications in this area. First, we present the interval deep probabilistic modeling for wind speed forecasting. Incorporating the Rough Set Theory into deep neural networks, we create an …