Adaptive algorithms for non-Gaussian noise environments: the order statistic least mean square algorithms

Marketing profit optimization and preventing the crops' infections are a critical issue. This requires crops recognition and classification based on their characteristics and different features. The current work proposed a recognition/classification system that applied to differentiate between fresh (healthy) from rotten crops as well as to identify each crop from the other based on their common feature vectors. Consequently, image processing is employed to perform the statistical measurements of each crop. ImageJ software was employed to analyze the desired crops to extract their features. These extracted features are used for further crops recognition and classification using the Least Mean Square Error (LMSE) algorithm in Matlab. Another classification method based on Bag of Features (BoF) technique is employed to classify crops into classes, namely healthy and rotten. The experimental results are applied of databases for orange, mango, tomato and potatoes. The achieved recognition (classification) rate by using the LMSE for all datasets (healthy and rotten) has 100%. However, after adding 10%, 20%, and 30% Gaussian noise, the obtained the average recognition rates were 85%, 70%, and 25%; respectively. Moreover, the classification (healthy and rotten) using BoF achieved accuracies of 100%, 88%, 94%, and 75% for potatoes, mango, orange, and tomato; respectively. Furthermore, the classification for all the healthy datasets achieved accuracy of 88%.

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Adaptive Order-Statistic Filters for optimal non-Gaussian noise suppression

2011 Western New York Image Processing Workshop ◽

10.1109/wnyipw.2011.6122886 ◽

2011 ◽

Author(s):

Manuel Fernandez

Keyword(s):

Order Statistic ◽

Gaussian Noise ◽

Noise Suppression ◽

Non Gaussian

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A time domain random flicker band-limited Gaussian noise jamming algorithm for LMS-based GPS

MATEC Web of Conferences ◽

10.1051/matecconf/202133607010 ◽

2021 ◽

Vol 336 ◽

pp. 07010

Author(s):

Tao Wu ◽

Jixiang Wu ◽

Hui Wang ◽

Ligang Cui ◽

Yinsong Yang

Keyword(s):

Time Domain ◽

Gaussian Noise ◽

Mean Square ◽

Least Mean Square ◽

Performance Gain ◽

Gps Receiver ◽

Gps Receivers ◽

Average Performance ◽

Band Limited

Global position systems (GPS) receiver based on least mean square (LMS) could resist the interference by null jamming direction. Considering that the existing band-limited Gaussian noise jamming signals were easily suppressed by LMS-based GPS receivers, a time domain random flicker band-limited Gaussian noise jamming algorithm was proposed to improve its performance. By disturbing the convergence of LMS, it could achieve the purpose of suppressing the LMS-based GPS. Simulation result shows that the proposed algorithm has an average performance gain of 2.7dB~4.6dB under different number of interferences compared with band-limited Gaussian noise.

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A Kernel Recursive Maximum Versoria-Like Criterion Algorithm for Nonlinear Channel Equalization

Symmetry ◽

10.3390/sym11091067 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1067 ◽

Cited By ~ 3

Author(s):

Qishuai Wu ◽

Yingsong Li ◽

Wei Xue

Keyword(s):

Adaptive Filtering ◽

Impulse Noise ◽

Channel Equalization ◽

Mean Square ◽

Least Mean Square ◽

Mixed Mean ◽

Non Gaussian ◽

Kernel Least Mean Square ◽

Nonlinear Adaptive Filtering ◽

Better Than

In this paper, a kernel recursive maximum Versoria-like criterion (KRMVLC) algorithm has been constructed, derived, and analyzed within the framework of nonlinear adaptive filtering (AF), which considers the benefits of logarithmic second-order errors and the symmetry maximum-Versoria criterion (MVC) lying in reproducing the kernel Hilbert space (RKHS). In the devised KRMVLC, the Versoria approach aims to resist the impulse noise. The proposed KRMVLC algorithm was carefully derived for taking the nonlinear channel equalization (NCE) under different non-Gaussian interferences. The achieved results verify that the KRMVLC is robust against non-Gaussian interferences and performs better than those of the popular kernel AF algorithms, like the kernel least-mean-square (KLMS), kernel least-mixed-mean-square (KLMMN), and Kernel maximum Versoria criterion (KMVC).

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