Read the abstract, see who wrote it and who cites it, and get the citation in APA, MLA, Chicago or Harvard.
About 10,000 papers for “Signal processing”
Uwe Ligges, Tom Short, Paul Kienzle · 2006
A set of signal processing functions originally written for 'Matlab' and 'Octave'. Includes filter generation utilities, filtering functions, resampling routines, and visualization of filter models. It also includes interpolation functions.
Cited by 51Open access
Peter Lang, Axel Pinz · 2008
Abstract – Within a hybrid vision / inertial tracking system proper calibration of the sensors and their relative pose is essential. We present a new method for 3-axis inertial sensor calibration based on model fitting and a method to find the rotation between vision and inertial system based on rotation differences. We achieve a coordinate system rotation mismatch of < 1 ° with respect to mechanical setup and sensor performance.
Cited by 0
Robert J. Purdy, Peter E. Blankenship, Charles Edward Muehe et al. · 2010
■ This article recounts the development of radar signal processing at Lincoln Laboratory. The Laboratory’s significant efforts in this field were initially driven by the need to provide detected and processed signals for air and ballistic missile defense systems. The first processing work was on the Semi-Automatic Ground Environment (SAGE) air-defense system, which led to algorithms and techniques for detection of aircraft in the presence of clutter. This work was quickly followed by processing efforts in ballistic missile defense, first in surface-acoustic-wave technology, in concurrence with the initiation of radar measurements at the Kwajalein Missile Range, and then by exploitation of the newly evolving technology of digital signal processing, which led to important contributions for ballistic missile defense and Federal Aviation Administration applications. More recently, the Laboratory has pursued the computationally challenging application of adaptive processing for the suppression of jamming and clutter signals. This article discusses several important programs in these areas.
Cited by 0
Fingerprinting · 2009
Cited by 0
2014
The Levinson-Durbin block solves the nth-order system of linear equations for the particular case where R is a Hermitian, positive-definite, Toeplitz matrix and b is identical to the first column of R shifted by one element and with the opposite sign. The input to the block, r = [r(1) r(2)... r(n+1)], can be a 1-D or 2-D vector (row or column). It contains lags 0 through n of an autocorrelation sequence, which appear in the matrix R. The block can output the polynomial coefficients, A, the reflection coefficients, K, and the prediction error power, P, in various combinations. The Output(s) parameter allows you to enable the A and K outputs by selecting one of the following settings: A — Port A outputs A=[1 a(2) a(3)... a(n+1)], the solution to the Levinson-Durbin equation. A has the same dimension as the input. The elements of the output can also be viewed as the coefficients of an nth-order autoregressive (AR) process (see below). K — Port K outputs K=[k(1) k(2)... k(n)], which contains n reflection coefficients, and has the same dimension as the input, less one element. (A scalar input causes an error when you select K.) Reflection coefficients are useful for realizing a lattice representation of the AR process described below. A and K — The block outputs both representations at their respective ports. (A scalar input causes an error when you select A and K.) Both A and K are always 1-D vectors. The prediction error power, P, (a scalar), is output when you select the Output prediction error power (P) 1 of 8 1/25/06 1:04 PMLevinson-Durbin:: Blocks (Signal Processing Blockset)
Cited by 0
Past Year Exam Question · UTPedia (Universiti Teknologi Petronas) · 2025
Cited by 0
Sushma Sharma, H. Sanath Kumar, Charul Thareja · Journal of Advance Research in Electrical & Electronics Engineering (ISSN 2208-2395) · 2014
This paper provides a survey of digital signal processing over analog signal processing. Initially digital signal processing is developed to replace limited application based analog signal processing (ASP) of high cost. This paper describes the comparison of analog signal processing (ASP) and digital signal processing, technology under digital signal processing , application of digital signal processing, new technology of digital signal processing (DSP). This paper also focuses on the future scope of digital signal processing (DSP).
Cited by 0Open access
2011
Cited by 0
Liang Wang, Jianxin Zhao, Richard Mortier · Undergraduate topics in computer science · 2022
Cited by 0
Albert Y. Kim A, Caren Marzban A, Donald B. Percival B · 2010
Cited by 0
Rune Brincker, Carlos E. Ventura · 2015
The purpose of this chapter is to provide guidance on how to estimate the correlation functions and the spectral density functions that are needed to interpret test data. It provides insight into what is important to know when processing test data and what are the important issues that should be considered before the estimating these functions is started. All the steps involved in proper signal processing are discussed.
Cited by 0
Bourne, J. B., Stern, Richard M., Jr. · DSpace@MIT (Massachusetts Institute of Technology) · 2010
Cited by 0Open access
2023
Cited by 0Open access
Eli Fieldsteel · 2024
Abstract Signal processing is a cornerstone of electronic music projects, capable of adding a sense of space, depth, cohesion, and other types of musical enrichment through the use of effects. This chapter introduces signal processing effects from both technical and creative perspectives, with the goal of clarifying essential techniques for incorporating signal processing algorithms into musical projects. The chapter begins with an overview of signal flow in a digital audio context, relating it to inserts, auxiliary sends, and other common-practice ideas found throughout analog hardware and DAW software, and compares these ideas to SuperCollider's signal routing tools, particularly busses, groups, order of execution, addActions, and input/output UGens. The chapter introduces the process of accessing, monitoring, and processing live microphone signals, and transitions into techniques for crafting a variety of signal processors, including echoes, flanger and chorus effects, comb filters, harmonizers and pitch-shifters, reverberators, and real-time granulators.
Cited by 0
T. N. Johnson · Springer theses · 2025
Cited by 0
2020
Cited by 0
Byrne, Charles L. · 2014
Signal Processing: A Mathematical Approach is designed to show how many of the mathematical tools the reader knows can be used to understand and employ signal processing techniques in an applied environment. Assuming an advanced undergraduate- or graduate-level understanding of mathematics-including familiarity with Fourier series, matrices, probab
Cited by 0Open access
Sandra Dorothee Starke (3826375), Chris Baber (3826372) · Figshare · 2017
<p>Maxima detection in the signals derived from optical motion capture (top) and an accelerometer (bottom). Each maximum (red circle) marks the start of a new sawing cycle, the time elapsed between maxima defines the sawing cycle duration.</p>
Cited by 0Open access
Isaac Skog, Skog Isaac · 2008
Cited by 0
Alexander G. Parlos, See Profile, Kyusung Kim et al. · 2016
Cited by 0