scholarly journals Channel Noise Optimization of Polar Codes Decoding Based on a Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ming Yan ◽  
Xingrui Lou ◽  
Yan Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Channel Coding ◽  
Belief Propagation ◽  
High Reliability ◽  
Correlation Coefficients ◽  
Channel Noise ◽  
Accurate Estimation ◽  
Polar Codes ◽  
Decoding Algorithm

Polar code has the characteristics of simple coding and high reliability, and it has been used as the control channel coding scheme of 5G wireless communication. However, its decoding algorithm always encounters problems of large decoding delay and high iteration complexity when dealing with channel noise. To address the above challenges, this paper proposes a channel noise optimized decoding scheme based on a convolutional neural network (CNN). Firstly, a CNN is adopted to extract and train the colored channel noise to get more accurate estimation noise, and then, the belief propagation (BP) decoding algorithm is used to decode the polar codes based on the output of the CNN. To analyze and verify the performance of the proposed channel noise optimized decoding scheme, we simulate the decoding of polar codes with different correlation coefficients, different loss function parameters, and different code lengths. The experimental results show that the CNN-BP concatenated decoding can better suppress the colored channel noise and significantly improve the decoding gain compared with the traditional BP decoding algorithm.

scholarly journals Using a One-Dimensional Convolutional Neural Network on Visible and Near-Infrared Spectroscopy to Improve Soil Phosphorus Prediction in Madagascar

2021 ◽  
Vol 13 (8) ◽  
pp. 1519
Author(s):  
Kensuke Kawamura ◽  
Tomohiro Nishigaki ◽  
Andry Andriamananjara ◽  
Hobimiarantsoa Rakotonindrina ◽  
Yasuhiro Tsujimoto ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Near Infrared ◽  
Soil Phosphorus ◽  
Predictive Ability ◽  
Available P ◽  
Accurate Estimation ◽  
One Dimensional ◽  
P Content ◽  
Soil Available P

As a proximal soil sensing technique, laboratory visible and near-infrared (Vis-NIR) spectroscopy is a promising tool for the quantitative estimation of soil properties. However, there remain challenges for predicting soil phosphorus (P) content and availability, which requires a reliable model applicable for different land-use systems to upscale. Recently, a one-dimensional convolutional neural network (1D-CNN) corresponding to the spectral information of soil was developed to considerably improve the accuracy of soil property predictions. The present study investigated the predictive ability of a 1D-CNN model to estimate soil available P (oxalate-extractable P; Pox) content in soils by comparing it with partial least squares (PLS) and random forest (RF) regressions using soil samples (n = 318) collected from natural (forest and non-forest) and cultivated (upland and flooded rice fields) systems in Madagascar. Overall, the 1D-CNN model showed the best predictive accuracy (R2 = 0.878) with a highly accurate prediction ability (ratio of performance to the interquartile range = 2.492). Compared to the PLS model, the RF and 1D-CNN models indicated 4.37% and 23.77% relative improvement in root mean squared error values, respectively. Based on a sensitivity analysis, the important wavebands for predicting soil Pox were associated with iron (Fe) oxide, organic matter (OM), and water absorption, which were previously known wavelength regions for estimating P in soil. These results suggest that 1D-CNN corresponding spectral signatures can be expected to significantly improve the predictive ability for estimating soil available P (Pox) from Vis-NIR spectral data. Rapid and accurate estimation of available P content in soils using our results can be expected to contribute to effective fertilizer management in agriculture and the sustainable management of ecosystems. However, the 1D-CNN model will require a large dataset to extend its applicability to other regions of Madagascar. Thus, further updates should be tested in future studies using larger datasets from a wide range of ecosystems in the tropics.

Improved NSC decoding algorithm for polar codes based on multi-in-one neural network

2020 ◽  
Vol 86 ◽  
pp. 106758
Author(s):  
Xiumin Wang ◽  
Jun Li ◽  
Zhuoting Wu ◽  
Jinlong He ◽  
Yue Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Polar Codes ◽  
Decoding Algorithm

scholarly journals Prediction of sgRNA on-target activity in bacteria by deep learning

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Lei Wang ◽  
Juhua Zhang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Prediction Models ◽  
Correlation Coefficients ◽  
Predictive Performance ◽  
Coding Region ◽  
A Genome ◽  
Wide Scale ◽  
Target Activity

Abstract Background One of the main challenges for the CRISPR-Cas9 system is selecting optimal single-guide RNAs (sgRNAs). Recently, deep learning has enhanced sgRNA prediction in eukaryotes. However, the prokaryotic chromatin structure is different from eukaryotes, so models trained on eukaryotes may not apply to prokaryotes. Results We designed and implemented a convolutional neural network to predict sgRNA activity in Escherichia coli. The network was trained and tested on the recently-released sgRNA activity dataset. Our convolutional neural network achieved excellent performance, yielding average Spearman correlation coefficients of 0.5817, 0.7105, and 0.3602, respectively for Cas9, eSpCas9 and Cas9 with a recA coding region deletion. We confirmed that the sgRNA prediction models trained on prokaryotes do not apply to eukaryotes and vice versa. We adopted perturbation-based approaches to analyze distinct biological patterns between prokaryotic and eukaryotic editing. Then, we improved the predictive performance of the prokaryotic Cas9 system by transfer learning. Finally, we determined that potential off-target scores accumulated on a genome-wide scale affect on-target activity, which could slightly improve on-target predictive performance. Conclusions We developed convolutional neural networks to predict sgRNA activity for wild type and mutant Cas9 in prokaryotes. Our results show that the prediction accuracy of our method is improved over state-of-the-art models.

scholarly journals HiCNN2: Enhancing the Resolution of Hi-C Data Using an Ensemble of Convolutional Neural Networks

Genes ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 862
Author(s):  
Tong Liu ◽  
Zheng Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Mean Squared Error ◽  
Correlation Coefficients ◽  
Chromatin Interactions ◽  
Squared Error ◽  
Genome Wide

We present a deep-learning package named HiCNN2 to learn the mapping between low-resolution and high-resolution Hi-C (a technique for capturing genome-wide chromatin interactions) data, which can enhance the resolution of Hi-C interaction matrices. The HiCNN2 package includes three methods each with a different deep learning architecture: HiCNN2-1 is based on one single convolutional neural network (ConvNet); HiCNN2-2 consists of an ensemble of two different ConvNets; and HiCNN2-3 is an ensemble of three different ConvNets. Our evaluation results indicate that HiCNN2-enhanced high-resolution Hi-C data achieve smaller mean squared error and higher Pearson’s correlation coefficients with experimental high-resolution Hi-C data compared with existing methods HiCPlus and HiCNN. Moreover, all of the three HiCNN2 methods can recover more significant interactions detected by Fit-Hi-C compared to HiCPlus and HiCNN. Based on our evaluation results, we would recommend using HiCNN2-1 and HiCNN2-3 if recovering more significant interactions from Hi-C data is of interest, and HiCNN2-2 and HiCNN if the goal is to achieve higher reproducibility scores between the enhanced Hi-C matrix and the real high-resolution Hi-C matrix.

scholarly journals A Robust Morpheme Sequence and Convolutional Neural Network-Based Uyghur and Kazakh Short Text Classification

Information ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 387 ◽  
Author(s):  
Sardar Parhat ◽  
Mijit Ablimit ◽  
Askar Hamdulla
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Text Classification ◽  
High Reliability ◽  
Short Text ◽  
Low Resource ◽  
Linguistic Resources ◽  
Text Corpora ◽  
A Value ◽  
Text Content

In this paper, based on the multilingual morphological analyzer, we researched the similar low-resource languages, Uyghur and Kazakh, short text classification. Generally, the online linguistic resources of these languages are noisy. So a preprocessing is necessary and can significantly improve the accuracy. Uyghur and Kazakh are the languages with derivational morphology, in which words are coined by stems concatenated with suffixes. Usually, terms are used as the representation of text content while excluding functional parts as stop words in these languages. By extracting stems we can collect necessary terms and exclude stop words. Morpheme segmentation tool can split text into morphemes with 95% high reliability. After preparing both word- and morpheme-based training text corpora, we apply convolutional neural network (CNN) as a feature selection and text classification algorithm to perform text classification tasks. Experimental results show that the morpheme-based approach outperformed the word-based approach. Word embedding technique is frequently used in text representation both in the framework of neural networks and as a value expression, and can map language units into a sequential vector space based on context, and it is a natural way to extract and predict out-of-vocabulary (OOV) from context information. Multilingual morphological analysis has provided a convenient way for processing tasks of low resource languages like Uyghur and Kazakh.

scholarly journals Computed tomography angiography-based deep learning method for treatment selection and infarct volume prediction in anterior cerebral circulation large vessel occlusion

2021 ◽  
Vol 10 (11) ◽  
pp. 205846012110603
Author(s):  
Lasse Hokkinen ◽  
Teemu Mäkelä ◽  
Sauli Savolainen ◽  
Marko Kangasniemi
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Convolutional Neural Network ◽  
Computed Tomography Angiography ◽  
Time Window ◽  
Infarct Volume ◽  
Correlation Coefficients ◽  
Late Time ◽  
Large Vessel Occlusion ◽  
Vessel Occlusion

Background Computed tomography perfusion (CTP) is the mainstay to determine possible eligibility for endovascular thrombectomy (EVT), but there is still a need for alternative methods in patient triage. Purpose To study the ability of a computed tomography angiography (CTA)-based convolutional neural network (CNN) method in predicting final infarct volume in patients with large vessel occlusion successfully treated with endovascular therapy. Materials and Methods The accuracy of the CTA source image-based CNN in final infarct volume prediction was evaluated against follow-up CT or MR imaging in 89 patients with anterior circulation ischemic stroke successfully treated with EVT as defined by Thrombolysis in Cerebral Infarction category 2b or 3 using Pearson correlation coefficients and intraclass correlation coefficients. Convolutional neural network performance was also compared to a commercially available CTP-based software (RAPID, iSchemaView). Results A correlation with final infarct volumes was found for both CNN and CTP-RAPID in patients presenting 6–24 h from symptom onset or last known well, with r = 0.67 ( p < 0.001) and r = 0.82 ( p < 0.001), respectively. Correlations with final infarct volumes in the early time window (0–6 h) were r = 0.43 ( p = 0.002) for the CNN and r = 0.58 ( p < 0.001) for CTP-RAPID. Compared to CTP-RAPID predictions, CNN estimated eligibility for thrombectomy according to ischemic core size in the late time window with a sensitivity of 0.38 and specificity of 0.89. Conclusion A CTA-based CNN method had moderate correlation with final infarct volumes in the late time window in patients successfully treated with EVT.

Improved Belief Propagation Decoding Algorithm for Short Polar Codes

2017 ◽  
Vol 96 (1) ◽  
pp. 1437-1449 ◽  
Author(s):  
Shajeel Iqbal ◽  
Adnan Ahmed Hashmi ◽  
GoangSeog Choi
Keyword(s):  
Belief Propagation ◽  
Polar Codes ◽  
Decoding Algorithm
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