Semi-Supervised Learning Based on Hybrid Neural Network for the Signal Integrity Analysis

2020 ◽  
Vol 67 (10) ◽  
pp. 1934-1938 ◽  
Author(s):  
Siyu Chen ◽  
Jienan Chen ◽  
Tingrui Zhang ◽  
Shuwu Wei
Keyword(s):  
Neural Network ◽  
Supervised Learning ◽  
Signal Integrity ◽  
Hybrid Neural Network ◽  
Integrity Analysis

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

2019 ◽  
Vol 8 (1) ◽  
pp. 276-282
Author(s):  
Chan Hong Goay ◽  
Azniza Abd Aziz ◽  
Nur Syazreen Ahmad ◽  
Patrick Goh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Signal Integrity ◽  
Circuit Modeling ◽  
Modeling Tool ◽  
Simulation Tools ◽  
The Neural Network ◽  
Data Rates ◽  
Recent Advancement ◽  
Integrity Analysis

With the increase in data rates, signal integrity analysis has become more time and memory intensive. Simulation tools such as 3D electromagnetic field solvers can be accurate but slow, whereas faster models such as design equations and equivalent circuit models lack accuracy. Artificial neural networks (ANNs) have recently gained popularity in the RF and microwave circuit modeling community as a new modeling tool. This has in turn spurred progress towards applications of neural networks in signal integrity. A neural network can learn from a set of data generated during the design process. It can then be used as a fast and accurate modeling tool to replace conventional approaches. This paper reviews the recent advancement of neural networks in the area of signal integrity modeling. Key advancements are considered, particularly those that assist the ability of the neural network to cope with an increasing number of inputs and handle large amounts of data.

A hybrid neural network goal attain optimization for failed sensor(s) radiation pattern in linear array

2019 ◽  
Vol 4 (4) ◽  
pp. 101-109
Author(s):  
Navaamsini Boopalan ◽  
Agileswari K. Ramasamy ◽  
Farrukh Hafiz Nagi
Keyword(s):  
Neural Network ◽  
Radiation Pattern ◽  
Array Signal Processing ◽  
Linear Array ◽  
Signal To Noise Ratio ◽  
Beam Pattern ◽  
Sensor Failure ◽  
Signal To Noise ◽  
Hybrid Neural Network ◽  
Noise Ratio

Array sensors are widely used in various fields such as radar, wireless communications, autonomous vehicle applications, medical imaging, and astronomical observations fault diagnosis. Array signal processing is accomplished with a beam pattern which is produced by the signal's amplitude and phase at each element of array. The beam pattern can get rigorously distorted in case of failure of array element and effect its Signal to Noise Ratio (SNR) badly. This paper proposes on a Hybrid Neural Network layer weight Goal Attain Optimization (HNNGAO) method to generate a recovery beam pattern which closely resembles the original beam pattern with remaining elements in the array. The proposed HNNGAO method is compared with classic synthesize beam pattern goal attain method and failed beam pattern generated in MATLAB environment. The results obtained proves that the proposed HNNGAO method gives better SNR ratio with remaining working element in linear array compared to classic goal attain method alone. Keywords: Backpropagation; Feed-forward neural network; Goal attain; Neural networks; Radiation pattern; Sensor arrays; Sensor failure; Signal-to-Noise Ratio (SNR)

scholarly journals Multi-Scale Aggregation Graph Neural Networks Based on Feature Similarity for Semi-Supervised Learning

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 403
Author(s):  
Xun Zhang ◽  
Lanyan Yang ◽  
Bin Zhang ◽  
Ying Liu ◽  
Dong Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Supervised Learning ◽  
Structured Data ◽  
Similarity Matrix ◽  
Scale Feature ◽  
Multi Scale ◽  
Adjacent Node ◽  
Feature Similarity ◽  
The Mean ◽  
Graph Neural Networks

The problem of extracting meaningful data through graph analysis spans a range of different fields, such as social networks, knowledge graphs, citation networks, the World Wide Web, and so on. As increasingly structured data become available, the importance of being able to effectively mine and learn from such data continues to grow. In this paper, we propose the multi-scale aggregation graph neural network based on feature similarity (MAGN), a novel graph neural network defined in the vertex domain. Our model provides a simple and general semi-supervised learning method for graph-structured data, in which only a very small part of the data is labeled as the training set. We first construct a similarity matrix by calculating the similarity of original features between all adjacent node pairs, and then generate a set of feature extractors utilizing the similarity matrix to perform multi-scale feature propagation on graphs. The output of multi-scale feature propagation is finally aggregated by using the mean-pooling operation. Our method aims to improve the model representation ability via multi-scale neighborhood aggregation based on feature similarity. Extensive experimental evaluation on various open benchmarks shows the competitive performance of our method compared to a variety of popular architectures.

scholarly journals A neural network-based algorithm for high-throughput characterisation of viscoelastic properties of flowing microcapsules

Soft Matter ◽  
2021 ◽  
Author(s):  
Tao Lin ◽  
Zhen Wang ◽  
Wen Wang ◽  
Yi Sui
Keyword(s):  
Neural Network ◽  
High Throughput ◽  
Viscoelastic Properties ◽  
Dynamic Deformation ◽  
Membrane Elasticity ◽  
Hybrid Neural Network ◽  
High Throughput Method

We have developed a high-throughput method, by combining a hybrid neural network with a mechanistic capsule model, to predict membrane elasticity and viscosity of microcapsules from their dynamic deformation in a branched microchannel.

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