Progress in neural network based techniques for signal integrity analysis–a survey

Goay, Chan Hong; Aziz, Azniza Abd; Ahmad, Nur Syazreen; Goh, Patrick

doi:10.11591/eei.v8i1.1405

Cited by 4 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A neural network can predict numerical values correctly, and it can prevent overfitting easily. ANN is much suitable in several areas, including natural language and image processing, prediction as well as emotion recognition [17][18][19]. Figure 1.…”

Section: Background Of the Study 21 Sparkmentioning

confidence: 99%

A smart method for spark using neural network for big data

Rahman

Hossen

Sultana

et al. 2021

IJECE

View full text Add to dashboard Cite

Apache spark, famously known for big data handling ability, is a distributed open-source framework that utilizes the idea of distributed memory to process big data. As the performance of the spark is mostly being affected by the spark predominant configuration parameters, it is challenging to achieve the optimal result from spark. The current practice of tuning the parameters is ineffective, as it is performed manually. Manual tuning is challenging for large space of parameters and complex interactions with and among the parameters. This paper proposes a more effective, self-tuning approach subject to a neural network called Smart method for spark using neural network for big data (SSNNB) to avoid the disadvantages of manual tuning of the parameters. The paper has selected five predominant parameters with five different sizes of data to test the approach. The proposed approach has increased the speed of around 30% compared with the default parameter configuration.

show abstract

Section: Background Of the Study 21 Sparkmentioning

confidence: 99%

A smart method for spark using neural network for big data

Rahman

Hossen

Sultana

et al. 2021

IJECE

View full text Add to dashboard Cite

show abstract

“…The artificial neural network (ANN) is one of the most popular machine learning methods for circuit modelling. Besides being used for various microwave and RF circuit modelling [6]- [7], the ANN has also been used for various signal integrity modelling tasks involving high-speed channels [8], [9]. These include modelling of crosstalk [10], eye diagram [11]- [12], and transient responses [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Transmission line impulse response modelling using machine learning techniques

Lim,

How,

Goay

et al. 2023

IJ-AI

View full text Add to dashboard Cite

<p>Conventional methods of circuit simulation such as full-wave electromagnetic ﬁeldsolvers can be very slow. Machine learning is an emerging technology in modelling, simulation, optimization, and design that present attractive alternatives to the conventional methodologies because they can be trained with a small amount of data, and then used to perform fast circuit predictions within the same design space. In this paper, we present applications of machine learning techniques for the modelling of transmission lines from their impulse reponses. The standard multilayer perceptron (MLP) neural network and the gaussian process (GP) regression techniques are demonstrated, and<br />both models are successfully implemented to model the impulse responses of transmission lines with great accuracies. We show that the GP outperforms the MLP in terms of prediction accuracies and that the GP is more data efﬁcient than the MLP. This is beneﬁcial considering that each training sample is expensive, making the GP a good candidate for the task, compared to the more popular MLP.</p>

show abstract

“…A neural model with appropriate generalization can provide precise answers even when testing it with inputs that have never been experienced before in the training set [8], also DL offer high prediction performance compared to other ML methods such as support vector machine (SVM) and random forest (RF) [9]. In recurrent neural networks (RNN) with long short-term memory (LSTM), the impermanent correlations of the input data can be learned [10], which consists of blocks of memory that allows retaining input information for a long period [9].…”

Section: Introductionmentioning

confidence: 99%

An optimized RNN-LSTM approach for parkinson’s disease early detection using speech features

2021

View full text Add to dashboard Cite

Parkinson's disease (PD) is the second most common neurodegenerative disorder disease right after Alzheimer's and the most common movement disorder for elderly people. It is characterized as a progressive loss of muscle control, which leads to trembling characterized by uncontrollable shaking, or (tremors) in different parts of the body. In recent years, deep learning (DL) models achieved significant progress in automatic speech recognition, however, limited studies addressed the problem of distinguishing people with PD for further clinical diagnosis. In this paper, an approach for the early detection of patients with PD using speech features was proposed, a recurrent neural network (RNN) with long short-term memory (LSTM) is applied with the batch normalization layer and adaptive moment estimation (ADAM) optimization algorithm used after the network hidden layers to improve the classification performance. The proposed approach is applied with 2 benchmark datasets of speech features for patients with PD and healthy control subjects. The proposed approach achieved an accuracy of 95.8% and MCC=92.04% for the testing dataset. In future work, we aim to increase the voice features that will be worked on and consider using handwriting kinematic features.

show abstract

Progress in neural network based techniques for signal integrity analysis–a survey

Cited by 4 publications

References 15 publications

A smart method for spark using neural network for big data

A smart method for spark using neural network for big data

Transmission line impulse response modelling using machine learning techniques

An optimized RNN-LSTM approach for parkinson’s disease early detection using speech features

Contact Info

Product

Resources

About