Modified Filter Equation with Improved Fuzzy Logic System Based Directional Median Filter for Mixed Noise

Kaurav, Bhageerath Singh; Markam, Karuna; Sahoo, Pooja

doi:10.35940/ijitee.g8946.0510721

Cited by 2 publications

(3 citation statements)

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“…Modulation recognition can be viewed as a multiclassification problem, where the original signal is observed at the receiver [22]. The general expression for the received baseband signal is ( )…”

Section: A Signal Modelmentioning

confidence: 99%

“…The number of iterations is set to 100; the batch size is set to 512; the optimizer selects adaptive moment estimation; the default learning rate is 0.001; the dropout rate is set to 0.5. FIGURE.9 showns the modulation signal classification accuracy of RCTLNet, KNN [26], CNN [27], ResNet [15], LSTM [16] and CNN-LSTM [19].…”

Section: B Settingsmentioning

confidence: 99%

“…At the same time, traditional denoising algorithms can suppress the noises by applying appropriately designed filters and soft threshold values, such as the wavelet-based denoising algorithms [14] and the filter-based denoising algorithm [15]. However, traditional denoising algorithms might not work well in practical systems.…”

Section: Introductionmentioning

confidence: 99%

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Modulation Signal Classification Algorithm Based on Denoising Residual Convolutional Neural Network

Guo

Wang

2022

IEEE Access

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Traditional denoising algorithms are easy to lose signal details, resulting in low recognition accuracy of modulated signals. A algorithm for modulation signal classification based on denoising residual Convolutional Neural Network (DRCNet) is proposed. DRCNet inserts a soft threshold function as a nonlinear transformation layer into the deep architecture to build a soft threshold learning network (STLNet). STLNet obtains an appropriate threshold according to the signal-to-noise ratio of the input signal samples, which is used to remove useless noise features, thereby effectively denoising the signal.This paper builds a serial network (RCTLNet) connected by Convolutional Neural Networks and Long-Short-Term Memory Networks. RCTLNet uses the convolutional neural network to extract the spatial features of the signal samples, and uses the long and short-term memory network to extract the temporal features of the signal samples. And then realize the classification and identification of the modulated signal. Experiments show that the recognition accuracy of the proposed modulated signal classification model is 92%, which is 7% higher than that of the Convolutional Long-Short Term Memory Network (CNN-LSTM).

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Section: A Signal Modelmentioning

confidence: 99%