Digital Signal Processing Ntu Reddit
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Graph Representation Learning
Author: William L. Hamilton
language: en
Publisher: Springer Nature
Release Date: 2022-06-01
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.
RF and Digital Signal Processing for Software-Defined Radio
Understand the RF and Digital Signal Processing Principles Driving Software-defined Radios!Software-defined radio (SDR) technology is a configurable, low cost, and power efficient solution for multimode and multistandard wireless designs. This book describes software-defined radio concepts and design principles from the perspective of RF and digital signal processing as performed within this system. After an introductory overview of essential SDR concepts, this book examines signal modulation techniques, RF and digital system analysis and requirements, Nyquist and oversampled data conversion techniques, and multirate digital signal processing.. KEY TOPICS•Modulation techniquesMaster analog and digital modulation schemes•RF system-design parametersExamine noise and link budget analysis and Non-linear signal analysis and design methodology•Essentials of baseband and bandpass sampling and gain controlIF sampling architecture compared to traditional quadrature sampling, Nyquist zones, automatic gain control, and filtering•Nyquist sampling converter architecturesAnalysis and design of various Nyquist data converters•Oversampled data converter architecturesAnalysis and design of continuous-time and discrete-time Delta-Sigma converters•Multirate signal processing Gain knowledge of interpolation, decimation, and fractional data rate conversion*Offers readers a powerful set of analytical and design tools*Details real world designs*Comprehensive coverage makes this a must have in the RF/Wireless industry
Altmetrics for Research Outputs Measurement and Scholarly Information Management
This book constitutes the refereed proceedings of the International Workshop on Altmetrics for Research Outputs Measurements and Scholarly Information Management, AROSIM 2018, held in Singapore, in January 2018. The 7 revised full papers presented together with two keynote papers and one introduction paper were carefully reviewed and selected from 20 submissions. The workshop will investigate how social media based metrics along with traditional and non-traditional metrics can advance the state-of-the-art in measuring research outputs.