Abstract
The MATLAB instrumental tools accelerate development of application owing to the integration of such different plan tools like the language for working with matrixes, visual modelling, automatic generation of the software code and additional packages for various knowledge areas into one environment. Its powerful language of matrix computations is natural for representation of signals and development of algorithms for digital processing of signals. Additional packages of applied MATLAB (toolboxes) software and Simulink blocks are the richest sources of ready functions, basic blocks for construction of models and visual tools for work with signals. This ensures a wonderful base for user’s own algorithms and software. As an integral part of MATLAB products package for signal processing, Simulink allows fast development, modelling and testing of digital processing of signals using interactive visual modelling with the help of diagrams. Simulink helps analyse the work of algorithms at the earliest stages of software development.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
National Instruments White Paper, Signal generation in Labview (2016). http://www.ni.com/white-paper/4087/en/
M. Dehghan, M. Abbaszadeh, A. Mohebbib, Error estimate for the numerical solution of fractional reaction-subdiffusion process based on a meshless method. J. Comput. Appl. Math. 280, 14–36 (2015)
R. Ferré, Discrete convolution with modulo operations. Appl. Math. Lett. 4(5), 13–17, ISSN 0893-9659 (1991). https://doi.org/10.1016/0893-9659(91)90135-I
M. Sławomir, Combination of the meshless finite difference approach with the Monte Carlo random walk technique for solution of elliptic problems. Comput. Math. Appl. 76(4), 854–876, ISSN 0898-1221 (2018). https://doi.org/10.1016/j.camwa.2018.05.025
R. Sarmiento, F. Tobajas, V. de Armas, R. Esper-ChaÃn, J.F. López, J.A. Montiel-Nelson, A. Núñez, A Cordic processor for FFT computation and its implementation using gallium arsenide technology. IEEE Trans. Very Large Scale Integr. VLSI Syst. 6(1), 18–30 (1998). http://www.iuma.ulpgc.es/~lopez/journals/IEEE%20TVLSI%2098.pdf
R.B. Abdessalem, S. Nejati, L.C. Briand, T. Stifter, Testing advanced driver assistance systems using multi-objective search and neural networks, in Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering (ASE 2016) (USA, 2016), pp. 63–74. https://doi.org/10.1145/2970276.2970311
J.Y. Cao, Z.Q. Wang, A new numerical scheme for the space fractional diffusion equation, in Frontiers of Manufacturing Science and Measuring Technology IV. Applied Mechanics and Materials, vol. 599 (Trans Tech Publications, 2014), pp. 1305–1308. https://doi.org/10.4028/www.scientific.net/AMM.599-601.1305
X. Li, H. Li, B. Wu, A new numerical method for variable order fractional functional differential equations. Appl. Math. Lett. 68, 80–86, ISSN 0893-9659 (2017). https://doi.org/10.1016/j.aml.2017.01.001
H. Liao, P. Lyu, S. Vong, Y. Zhao, Stability of fully discrete schemes with interpolation-type fractional formulas for distributed-order subdiffusion equations. Numer. Algorithms 75(4), 845–878 (2016). https://doi.org/10.1007/s11075-016-0223-7
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2019 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Singh, D., Singh, M., Hakimjon, Z. (2019). Requirements of MATLAB/Simulink for Signals. In: Signal Processing Applications Using Multidimensional Polynomial Splines. SpringerBriefs in Applied Sciences and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-13-2239-6_6
Download citation
DOI: https://doi.org/10.1007/978-981-13-2239-6_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-2238-9
Online ISBN: 978-981-13-2239-6
eBook Packages: EngineeringEngineering (R0)