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Articles 1 - 6 of 6
Full-Text Articles in Other Computer Engineering
Adapting Deep Learning For Underwater Acoustic Communication Channel Modeling, Li Wei
Adapting Deep Learning For Underwater Acoustic Communication Channel Modeling, Li Wei
Dissertations, Master's Theses and Master's Reports
The recent emerging applications of novel underwater systems lead to increasing demand for underwater acoustic (UWA) communication and networking techniques. However, due to the challenging UWA channel characteristics, conventional wireless techniques are rarely applicable to UWA communication and networking. The cognitive and software-defined communication and networking are considered promising architecture of a novel UWA system design. As an essential component of a cognitive communication system, the modeling and prediction of the UWA channel impulse response (CIR) with deep generative models are studied in this work.
Firstly, an underwater acoustic communication and networking testbed is developed for conducting various simulations and …
An Experimental Study Towards Underwater Propulsion System Using Structure Borne Traveling Waves, Shreyas Suhas Gadekar
An Experimental Study Towards Underwater Propulsion System Using Structure Borne Traveling Waves, Shreyas Suhas Gadekar
Dissertations, Master's Theses and Master's Reports
The method of generating steady-state structure-borne traveling waves underwater in an infinite media creates abundant opportunities in the field of propulsive applications, and they are gaining attention from several researchers. This experimental study provides a framework for harnessing traveling waves in a 1D beam immersed under quiescent water using two force input methods and providing a motion to an object floating on the surface of the water.
In this study, underwater traveling waves are tailored using structural vibrations at five different frequencies in the range of 10Hz to 300Hz. The resulting fluid motion provides a propulsive thrust that moves a …
High-Performance Spectral Methods For Computer-Aided Design Of Integrated Circuits, Zhiqiang Zhao
High-Performance Spectral Methods For Computer-Aided Design Of Integrated Circuits, Zhiqiang Zhao
Dissertations, Master's Theses and Master's Reports
Recent research shows that by leveraging the key spectral properties of eigenvalues and eigenvectors of graph Laplacians, more efficient algorithms can be developed for tackling many graph-related computing tasks. In this dissertation, spectral methods are utilized for achieving faster algorithms in the applications of very-large-scale integration (VLSI) computer-aided design (CAD)
First, a scalable algorithmic framework is proposed for effective-resistance preserving spectral reduction of large undirected graphs. The proposed method allows computing much smaller graphs while preserving the key spectral (structural) properties of the original graph. Our framework is built upon the following three key components: a spectrum-preserving node aggregation and …
Anomaly Inference Based On Heterogeneous Data Sources In An Electrical Distribution System, Yachen Tang
Anomaly Inference Based On Heterogeneous Data Sources In An Electrical Distribution System, Yachen Tang
Dissertations, Master's Theses and Master's Reports
Harnessing the heterogeneous data sets would improve system observability. While the current metering infrastructure in distribution network has been utilized for the operational purpose to tackle abnormal events, such as weather-related disturbance, the new normal we face today can be at a greater magnitude. Strengthening the inter-dependencies as well as incorporating new crowd-sourced information can enhance operational aspects such as system reconfigurability under extreme conditions. Such resilience is crucial to the recovery of any catastrophic events. In this dissertation, it is focused on the anomaly of potential foul play within an electrical distribution system, both primary and secondary networks as …
Performance Comparison Of Binarized Neural Network With Convolutional Neural Network, Lopamudra Baruah
Performance Comparison Of Binarized Neural Network With Convolutional Neural Network, Lopamudra Baruah
Dissertations, Master's Theses and Master's Reports
Deep learning is a trending topic widely studied by researchers due to increase in the abundance of data and getting meaningful results with them. Convolutional Neural Networks (CNN) is one of the most popular architectures used in deep learning. Binarized Neural Network (BNN) is also a neural network which consists of binary weights and activations. Neural Networks has large number of parameters and overfitting is a common problem to these networks. To overcome the overfitting problem, dropout is a solution. Randomly dropping some neurons along with its connections helps to prevent co-adaptations which finally help in reducing overfitting. Many researchers …
Design Automation For Carbon Nanotube Circuits Considering Performance And Security Optimization, Lin Liu
Design Automation For Carbon Nanotube Circuits Considering Performance And Security Optimization, Lin Liu
Dissertations, Master's Theses and Master's Reports
As prevailing copper interconnect technology advances to its fundamental physical limit, interconnect delay due to ever-increasing wire resistivity has greatly limited the circuit miniaturization. Carbon nanotube (CNT) interconnects have emerged as promising replacement materials for copper interconnects due to their superior conductivity. Buffer insertion for CNT interconnects is capable of improving circuit timing of signal nets with limited buffer deployment. However, due to the imperfection of fabricating long straight CNT, there exist significant unidimensional-spatially correlated variations on the critical CNT geometric parameters such as the diameter and density, which will affect the circuit performance.
This dissertation develops a novel timing …