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Introduction to Random Signals and Noise

TLDR
This text provides a clear introduction to the fundamentals of stochastic processes and their practical applications to random signals and noise, including analogue, discrete-time and bandpass signals in both time and frequency domains.
Abstract
Random signals and noise are present in many engineering systems and networks. Signal processing techniques allow engineers to distinguish between useful signals in audio, video or communication equipment, and interference, which disturbs the desired signal. With a strong mathematical grounding, this text provides a clear introduction to the fundamentals of stochastic processes and their practical applications to random signals and noise. With worked examples, problems, and detailed appendices, Introduction to Random Signals and Noise gives the reader the knowledge to design optimum systems for effectively coping with unwanted signals. Key features: • Considers a wide range of signals and noise, including analogue, discrete-time and bandpass signals in both time and frequency domains. • Analyses the basics of digital signal detection using matched filtering, signal space representation and correlation receiver. • Examines optimal filtering methods and their consequences. • Presents a detailed discussion of the topic of Poisson processed and shot noise.

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Journal ArticleDOI

The Utilization of Artificial Neural Networks for Multisensor System Integration in Navigation and Positioning Instruments

TL;DR: An INS/GPS integration method based on artificial neural networks (ANN) to fuse uncompensated INS measurements and differential GPS (DGPS) measurements is suggested and two different architectures: the position update architecture (PUA) and the position and velocity PUA (PVUA).
Journal ArticleDOI

Procedural noise using sparse Gabor convolution

TL;DR: This paper introduces a noise based on sparse convolution and the Gabor kernel that enables all of these properties of noise, and introduces setup-free surface noise, a method for mapping noise onto a surface, complementary to solid noise, that maintains the appearance of the noise pattern along the object and does not require a texture parameterization.
Journal ArticleDOI

Radio Frequency Interference Excision Using Spectral‐Domain Statistics

TL;DR: The ability of the algorithm to discriminate RFI against the temporally and spectrally complex radio emission produced during solar radio bursts is demonstrated.
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LC-MSsim – a simulation software for liquid chromatography mass spectrometry data

TL;DR: LC-MSsim generates simulated LC-MS data sets and incorporates models for peak shapes and contaminations, which means algorithm developers can match the results of feature detection and alignment algorithms against the simulated ion lists and meaningful error rates can be computed.
Journal ArticleDOI

Simulation-based comparison of noise effects in wavelength modulation spectroscopy and direct absorption TDLAS

TL;DR: In this paper, a simulative investigation of noise effects in WMS and direct absorption diode laser absorption spectroscopy is presented, with the goal of estimating the necessary ADC resolution for each technique.
References
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Book

Probability, Random Variables and Random Signal Principles

TL;DR: 1 Probability 2 The Random Variable 3 Operations on one Random Variable--Expectation 4 Multiple Random Variables 5 Operations of Multiple Randomvariables 6 Random Processes-Temporal Characteristics 7 Random processes-Spectral Characteristics 8 Linear Systems with Random Inputs 9 Optimum Linear Systems 10 Some Practical Applications of the Theory.
Book

Random Signals: Detection Estimation and Data Analysis

TL;DR: Review of Probability and Random Variables Random Processes and Sequences Response of Systems to Random Inputs Special Classes of Random Processs Signal Detection Linear Minimum MSE Filtering Statistics Estimating Parameters of Random processes from Data.