Property of Self-Similarity Between Baseband and Modulated Signals

Liu, Shuai; Bai, Weiling; Srivastava, Gautam; Machado, J. A. Tenreiro

doi:10.1007/s11036-019-01358-9

Cited by 34 publications

(4 citation statements)

References 18 publications

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“…In the future, the explainability of our proposed AI model can be used in patient monitoring [38] and health big data [39]. Some novel network improvement and signal 60 RCBO [7] 6L-CNN [8] CSSNet [11] COVNet [12] DeCovNet [13] 7L-CCD [14] FCONet [15] PSSPNN(Ours) RN-50 [9] RN-18 [10] MAF1 (%) 15 Computational and Mathematical Methods in Medicine processing techniques may help our AI model in future researches, such as filters [40,41], fuzzy [42,43], edge computing [44], knowledge-aid [45,46], autofocus [47], graph integration, and cross-domain knowledge exploitation [48][49][50].…”

Section: 3mentioning

confidence: 99%

PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation

Wang

Zhang

Cheng

et al. 2021

Computational and Mathematical Methods in Medicine

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Aim. COVID-19 has caused large death tolls all over the world. Accurate diagnosis is of significant importance for early treatment. Methods. In this study, we proposed a novel PSSPNN model for classification between COVID-19, secondary pulmonary tuberculosis, community-captured pneumonia, and healthy subjects. PSSPNN entails five improvements: we first proposed the n-conv stochastic pooling module. Second, a novel stochastic pooling neural network was proposed. Third, PatchShuffle was introduced as a regularization term. Fourth, an improved multiple-way data augmentation was used. Fifth, Grad-CAM was utilized to interpret our AI model. Results. The 10 runs with random seed on the test set showed our algorithm achieved a microaveraged F1 score of 95.79%. Moreover, our method is better than nine state-of-the-art approaches. Conclusion. This proposed PSSPNN will help assist radiologists to make diagnosis more quickly and accurately on COVID-19 cases.

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Section: 3mentioning

confidence: 99%

PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation

Wang

Zhang

Cheng

et al. 2021

Computational and Mathematical Methods in Medicine

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“…If the view zooms in to a molecule scale, then the picture will show many curves formed by the arrangement of the molecules. What's more, the self-similarity is especially effective to deal with some nonlinear variables [7]. For example, the relation of self-similarity between baseband and modulated signals can be deduced [7].…”

Section: Introductionmentioning

confidence: 99%

“…What's more, the self-similarity is especially effective to deal with some nonlinear variables [7]. For example, the relation of self-similarity between baseband and modulated signals can be deduced [7]. To make it general, the concept of fractals can be applicate in many different fields and can help people to solve different problems.…”

Section: Introductionmentioning

confidence: 99%

The principle and state-of-art applications of self-similar structure

Junpei Cui

2023

Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023)

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The self-similarity structure is very common in people's real life. For example, on the decorations of the walls, on the art works, even used by scientists to data prediction. This research first introduced some common methods to calculate the dimensions of fractals. Then illustrated some natural fractals and how to represent them with matrix transformations and matrix multiplications. Besides, this paper introduced some properties of each transformation. Afterwards, this study analyzed a classic 2D self-similar structure called Koch Curve and used the method demonstrated before better illustrated how the dimension of an artificial fractal can be calculated. According the analysis, there are more method to calculate the fractal dimension. However, only the simplest method will be introduced in this article. At last, this study described the process to applicate the fractal knowledge on coastline calculating. Moreover, there is also some discussion about the limitations the fractals and its outlooks. Overall, these results shed light on guiding further exploration of the self-similar structure applications and generating the chaos matter.

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“…calculated, and then the estimated value of the corresponding crosstalk component can be subtracted from the received signal of each channel to cancel the crosstalk[21], and the new estimated value of the received signal can be obtained as follows:Digital interference signal filtering on laser interface for optical fiber communication…”

mentioning

confidence: 99%

Digital interference signal filtering on laser interface for optical fiber communication

Zhang¹,

Gadekallu

2022

EAI Endorsed Scal Inf Syst

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INTRODUCTION: Fiber laser communication is a communication method that uses laser and fiber medium to realize data transmission and information output OBJECTIVES: In order to reduce the signal interference of optical fiber communication laser interface and ensure the communication quality of optical fiber network. A filtering method of optical fiber communication laser interface interference signal based on digital filtering technology is designed. METHODS: In this paper, the interface model of optical fiber communication network is firstly constructed, and the interface noise signal is input into the digital filter bank. The digital quadrature filtering method and the least square algorithm are used to separate the denoised signals to reduce the crosstalk between the signals in the channel. In this way, the crosstalk component in the signal can be filtered out, and a better filtering processing effect of the laser interface interference signal can be achieved. RESULTS: The results of peak signal-to-noise ratio are above 25, which effectively filters the interference signal in the signal, and retains the effective signal completely. The intelligibility of optical fiber communication network in signal communication is above 0.94, and the highest value is 0.986. The distortion degree are all below 0.025, and the minimum value is 0.004. The communication bit error rate are all below 0.001, which ensures the communication quality of the network. CONCLUSION: The experimental results show that the signal noise reduction effect of the proposed method is good, which provides a reliable basis for filtering and separating interference signals of optical fiber communication laser interface.

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Property of Self-Similarity Between Baseband and Modulated Signals

Cited by 34 publications

References 18 publications

PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation

PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation

The principle and state-of-art applications of self-similar structure

Digital interference signal filtering on laser interface for optical fiber communication

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