Modeling and simulation of FIR filter using distributed arithmetic algorithm on FPGA

Rai, Amrita; Roy, Ajay; Qamar, Shamimul; Saif, Abdulelah G. F.; Hamoda, Magdi Mohammad; Azeem, Abdul; Mohammed, Salman Arafath

doi:10.1007/s11042-024-18637-7

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…The LMS algorithm requires many matrix and vector operations, and the FPGA's ability to perform multiple computational tasks, weighted updates, and error calculations in parallel ensures smooth processing of the data stream through the filter design, significantly accelerating the data processing rate [33]. In addition, the reconfigurability of FPGAs allows researchers to tailor the hardware logic to the LMS filter requirements, which not only helps to optimize the computational tasks but also allows the logic to be adjusted to the characteristics of the input data, which in turn improves the processing efficiency [34].…”

Section: Dslms Adaptive Filtermentioning

confidence: 99%

Design and Implementation of DSLMS Algorithm Based Photoelectric Detection of Weak Signals

Wang,

Song

et al. 2024

Applied Sciences

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Accurately extracting weak signals is extremely important for overall performance and application in optoelectronic imaging and optical communication systems. While weak signals are susceptible to noise, adaptive filtering is a commonly used noise removal method. Still, its convergence speed is slow, the steady-state error is large, and the anti-interference ability is weak. To solve the above problems, this paper proposes a new type of variable-step-length adaptive filtering algorithm (DSLMS) based on the minutiae function, which effectively reduces the noise component in error through its combination with the pair cancelation system, utilizing the low correlation property of the noise signal, to improve the anti-noise interference ability of the adaptive filter. Using FPGA and Matlab (2018b) for experimental verification, the results show that this algorithm shows significant advantages in noise suppression, accelerated algorithm convergence, and low steady-state error, and it has effectiveness and application potential for the optoelectronic detection of weak signal processing.

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Section: Dslms Adaptive Filtermentioning

confidence: 99%