Jitter Decomposition of High-Speed Data Signals From Jitter Histograms With a Pole–Residue Representation Using Multilayer Perceptron Neural Networks

Ku, Chin Kui; Goay, Chan Hong; Ahmad, Nur Syazreen; Goh, Patrick

doi:10.1109/temc.2019.2936000

Cited by 22 publications

(9 citation statements)

References 21 publications

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“…The MLP uses a supervised learning procedure named back propagation for training. After completing the calculations in the hidden layer, the output layer can finally provide the desired estimation for n input samples as follow [104], [105]:…”

Section: ) Multilayer Perceptron (Mlp)mentioning

confidence: 99%

Data Mining Applications to Fault Diagnosis in Power Electronic Systems: A Systematic Review

Moradzadeh

Mohammadi‐Ivatloo

Pourhossein

et al. 2022

IEEE Trans. Power Electron.

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Section: ) Multilayer Perceptron (Mlp)mentioning

confidence: 99%

Data Mining Applications to Fault Diagnosis in Power Electronic Systems: A Systematic Review

Moradzadeh

Mohammadi‐Ivatloo

Pourhossein

et al. 2022

IEEE Trans. Power Electron.

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“…The logger device is a real-time measurement device running low latency optimized firmware, such that measurements are taken with maximum precision and speed. For this application, the speed is critical to perform the most accurate measurements with minimal time jitter and, therefore, to minimise the distortions of the recorded reference behaviour [49].…”

Section: Data Acquisitionmentioning

confidence: 99%

Self-Protected Virtual Sensor Network for Microcontroller Fault Detection

Sternharz

Skackauskas

Elhalwagy

et al. 2022

Sensors

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This paper introduces a procedure to compare the functional behaviour of individual units of electronic hardware of the same type. The primary use case for this method is to estimate the functional integrity of an unknown device unit based on the behaviour of a known and proven reference unit. This method is based on the so-called virtual sensor network (VSN) approach, where the output quantity of a physical sensor measurement is replicated by a virtual model output. In the present study, this approach is extended to model the functional behaviour of electronic hardware by a neural network (NN) with Long-Short-Term-Memory (LSTM) layers to encapsulate potential time-dependence of the signals. The proposed method is illustrated and validated on measurements from a remote-controlled drone, which is operated with two variants of controller hardware: a reference controller unit and a malfunctioning counterpart. It is demonstrated that the presented approach successfully identifies and describes the unexpected behaviour of the test device. In the presented case study, the model outputs a signal sample prediction in 0.14 ms and achieves a reconstruction accuracy of the validation data with a root mean square error (RMSE) below 0.04 relative to the data range. In addition, three self-protection features (multidimensional boundary-check, Mahalanobis distance, auxiliary autoencoder NN) are introduced to gauge the certainty of the VSN model output.

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“…In TLC, the TLC method is used to establish a time-domain expression of jitter including RJ, sinusoidal jitter (SJ), and duty cycle distortion (DCD), the TLC with a zero-argument is the variance of the period jitter, and RMS of RJ can also be expressed by the TLC. Two jitter decomposition method based on the neural network are CNN-based Jitter Decomposition [16] and ANN-based jitter decomposition [17]. CNN increases the computational cost and cannot be well applied to practical circuit testing, and ANN's training data are generated directly from the dual-Dirac model, there is no large cost associated with this process.…”

Section: Related Workmentioning

confidence: 99%

PointNet-Based Jitter Decomposition on Point Cloud of Jitter Histogram

Ren¹,

Fu²,

Zhou³

et al. 2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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Jitter is one of the key factors affecting bit error rate (BER) on high-speed links. A novel method of jitter decomposition by PointNet using 2D point cloud of jitter histogram is proposed for decomposing the time interval error (TIE) jitter into deterministic jitter (DJ) and random jitter (RJ). The proposed method uses transform-Net (T-Net) and multi-layer perceptron (MLP) to learn global point cloud features and uses average pooling to aggregate information from all the points. Experimental results show that the proposed approach can decompose jitter into DJ and RJ, and is better than the traditional jitter decomposition method i.e. time lag correlation (TLC) and Time-Domain PLL Jitter Decomposition (T-D PLL). In addition, results show that the performance of the proposed method is better than CNN, PointRNN, and PointANN.

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Jitter Decomposition of High-Speed Data Signals From Jitter Histograms With a Pole–Residue Representation Using Multilayer Perceptron Neural Networks

Cited by 22 publications

References 21 publications

Data Mining Applications to Fault Diagnosis in Power Electronic Systems: A Systematic Review

Data Mining Applications to Fault Diagnosis in Power Electronic Systems: A Systematic Review

Self-Protected Virtual Sensor Network for Microcontroller Fault Detection

PointNet-Based Jitter Decomposition on Point Cloud of Jitter Histogram

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