Parallel Computing for Real-time Signal Processing and Control

Tokhi, M. O.; Shaheed, M. H.

doi:10.1007/978-1-4471-0087-4

Cited by 23 publications

(3 citation statements)

References 22 publications

(31 reference statements)

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“…As a result, the speedup of adopting SIMD can be theoretically defined as N times faster than a single processor. This is commonly used in applications such as image processing, matrix manipulation and sorting [34]. Vector processors are an example of a SIMD implementation.…”

Section: A Background and Selection Of Parallel Architecturementioning

confidence: 99%

“…However, this causes MIMD to demand more computational resources than its SIMD counterpart. Although the performance of MIMD may be weaker than SIMD, it is more popular amongst architect designers due to its better economy, robust design constraints and application flexibility [33], [34]. Furthermore, SIMD's demand for specialized hardware such as vector processors make it more difficult to implement.…”

Section: A Background and Selection Of Parallel Architecturementioning

confidence: 99%

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Exploring parallel processing for wide area measurement data applications

Peng

Mead

Nair

2011

2011 IEEE Power and Energy Society General Meeting

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In this paper the potential of parallelizing real-time monitoring applications using mainstream multi-core processors is explored. In particular, the focus is on implementing and assessing the oscillatory monitoring application. The objective is to evaluate the practicality of using parallel processing to achieve faster computational speed. In this paper, two monitoring techniques with different mathematical formulations are used. They are the block-processing Prony Analysis and recursive Extended Complex Kalman Filter. Both techniques are implemented to demonstrate different aspects of parallel processing. Prony Analysis is used to show the potential of operating multiple algorithms concurrently while ECKF demonstrates the possibility of parallelizing the decomposed subroutines. Comparisons with the corresponding sequential algorithms are carried out using synthetic signals in Visual C++. Simulation results show that parallel processing is able to reduce the computation time.

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Section: A Background and Selection Of Parallel Architecturementioning

confidence: 99%

Section: A Background and Selection Of Parallel Architecturementioning

confidence: 99%

Exploring parallel processing for wide area measurement data applications

Peng

Mead

Nair

2011

2011 IEEE Power and Energy Society General Meeting

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show abstract

“…Some of these [Elliot and Nelson, 1986], [Elliott et al, 1987], [Chaplin and Smith, 1981], [Tokhi and Leitch, 1991], [Tokhi and Hossain, 1994], [Tokhi and Hossain 2002] an adaptive control algorithm [Tokhi, et al, 2002], [Tokhi and Hossain, 1994].…”

Section: Introductionmentioning

confidence: 99%

Comparative Performance of Intelligent Algorithms for System Identification and Control

Madkour¹,

Dahal²,

Yu³

2008

Journal of Intelligent Systems

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This paper presents an investigation into the comparative performance of intelligent system identification and control algorithms within the framework of an active vibration control (AVC) system. Evolutionary Genetic algorithms (GAs) and Adaptive Neuro-Fuzzy Inference system (ANFIS) algorithms are used to develop mechanisms of an AVC system, where the controller is designed on the basis of optimal vibration suppression using the plant model. A simulation platform of a flexible beam system in transverse vibration using finite difference (FD) method is considered to demonstrate the capabilities of the AVC system using GAs and ANFIS. MATLAB GA tool box for GAs and Fuzzy Logic tool box for ANFIS function are used to design the AVC system. The system is then implemented, tested and its performance assessed for GAs and ANFIS based algorithms. Finally a comparative performance of the algorithms in implementing systemidentification and corresponding AVC system using GAs and ANFIS is presented and discussed through a set of experiments.

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Digital Real‐Time Data Processing with Embedded Systems

Bosse

Lehmhus

2017

Material‐Integrated Intelligent Systems ‐ Technology and Applications

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Parallel Computing for Real-time Signal Processing and Control

Cited by 23 publications

References 22 publications

Exploring parallel processing for wide area measurement data applications

Exploring parallel processing for wide area measurement data applications

Comparative Performance of Intelligent Algorithms for System Identification and Control

Digital Real‐Time Data Processing with Embedded Systems

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