In this paper, a nonlinear low-pass filter is presented, which produces significantly less phase lag than linear and some nonlinear filters. The proposed filter employs a saturation function to enhance the linear filter's performance. The gain and phase responses of the filter are derived analytically using a modified describing function, and the efficiency of the proposed method is examined through numerical examples. Based on the required cut-off frequency and noise to signal ratio, a rule of thumb is given to set the filter's parameters. In the frequency domain, simulation results show that the filter's gain response is near 0dB in the pass-band, and the noise attenuation rate is -40dB∕dec, while the phase lag is three times lesser compared to 2 order Butterworth low-pass filter. Moreover, comparing with Jin et al.'s parabolic sliding mode filter and feed-forwarded parabolic sliding mode filter the gain and phase of the proposed filter are closer to zero in the pass-band and before cut-off frequency. Furthermore, the filter's performance is also evaluated in case of different noise color and concluded that the proposed filter is superior to linear and nonlinear filters in case of white, blue, or purple noise Finally, the filter's effectiveness and the tuning guideline are validated by simulating a precision motion control system in the discrete-time domain.
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http://dx.doi.org/10.1016/j.isatra.2020.11.013 | DOI Listing |
Comput Methods Programs Biomed
January 2025
Guizhou Province International Science and Technology Cooperation Base for Precision Imaging Diagnosis and Treatment, Key Laboratory of Advanced Medical Imaging and Intelligent Computing of Guizhou Province, Department of Radiology, Guizhou Provincial People's Hospital, Guizhou 550002, China. Electronic address:
Background And Objective: Accurate segmentation of the prostate region in magnetic resonance imaging (MRI) is crucial for prostate-related diagnoses. Recent studies have incorporated Transformers into prostate region segmentation to better capture long-range global feature representations. However, due to the computational complexity of Transformers, these studies have been limited to processing single slices.
View Article and Find Full Text PDFMicrob Genom
January 2025
Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia.
Phylogenetic analyses are crucial for understanding microbial evolution and infectious disease transmission. Bacterial phylogenies are often inferred from SNP alignments, with SNPs as the fundamental signal within these data. SNP alignments can be reduced to a 'strict core' by removing those sites that do not have data present in every sample.
View Article and Find Full Text PDFChaos
January 2025
Classe di Scienze, Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126 Pisa, Italy.
Modeling how a shock propagates in a temporal network and how the system relaxes back to equilibrium is challenging but important in many applications, such as financial systemic risk. Most studies, so far, have focused on shocks hitting a link of the network, while often it is the node and its propensity to be connected that are affected by a shock. Using the configuration model-a specific exponential random graph model-as a starting point, we propose a vector autoregressive (VAR) framework to analytically compute the Impulse Response Function (IRF) of a network metric conditional to a shock on a node.
View Article and Find Full Text PDFMethodsX
June 2025
Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Tamil Nadu 600062, India.
The disease affects the optic nerve and represents the principle reasons of irreversible vision loss, mostly asymptomatic and uncontrolled. Consequently, early and accurate diagnosis is critical to prevent or reduce its effect, however, conventional diagnostic techniques often fail to provide concrete results. In this regard, we present a new approach built on Generative Adversarial Networks (GAN) and MobileNetV2 pretrained architecture for diagnosing glaucoma.
View Article and Find Full Text PDFJ Appl Stat
June 2024
Graduate School, Department of Urban Big Data Convergence, University of Seoul, Seoul, South Korea.
Clustering is an essential technique that groups similar data points to uncover the underlying structure and features of the data. Although traditional clustering methods such as -means are widely utilized, they have limitations in identifying nonlinear clusters. Thus, alternative techniques, such as kernel -means and spectral clustering, have been developed to address this issue.
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