scholarly journals Channel Sounding and Scene Classification of Indoor 6G Millimeter Wave Channel Based on Machine Learning

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 843
Author(s):  
Liang Yin ◽  
Ruonan Yang ◽  
Yuliang Yao
Keyword(s):  
Machine Learning ◽  
Millimeter Wave ◽  
High Frequency ◽  
Signal Propagation ◽  
Classification Model ◽  
Machine Learning Method ◽  
Learning Method ◽  
Scene Classification ◽  
Channel Sounding ◽  
Wave Channel

Millimeter wave, especially the high frequency millimeter wave near 100 GHz, is one of the key spectrum resources for the sixth generation (6G) mobile communication, which can be used for precise positioning, imaging and large capacity data transmission. Therefore, high frequency millimeter wave channel sounding is the first step to better understand 6G signal propagation. Because indoor wireless deployment is critical to 6G and different scenes classification can make future radio network optimization easy, we built a 6G indoor millimeter wave channel sounding system using just commercial instruments based on time-domain correlation method. Taking transmission and reception of a typical 93 GHz millimeter wave signal in the W-band as an example, four indoor millimeter wave communication scenes were modeled. Furthermore, we proposed a data-driven supervised machine learning method to extract fingerprint features from different scenes. Then we trained the scene classification model based on these features. Baseband data from receiver was transformed to channel Power Delay Profile (PDP), and then six fingerprint features were extracted for each scene. The decision tree, Support Vector Machine (SVM) and the optimal bagging channel scene classification algorithms were used to train machine learning model, with test accuracies of 94.3%, 86.4% and 96.5% respectively. The results show that the channel fingerprint classification model trained by machine learning method is effective. This method can be used in 6G channel sounding and scene classification to THz in the future.

scholarly journals Single Image De-Raining using Supervised CNN Model

2021 ◽  
Vol 9 (10) ◽  
pp. 244-250
Author(s):  
Dr. Geeta Hanji
Keyword(s):  
Machine Learning ◽  
High Frequency ◽  
Accurate Result ◽  
Supervised Machine Learning ◽  
Machine Learning Method ◽  
Learning Method ◽  
Single Image ◽  
Elapsed Time ◽  
Difficult Issue

Abstract: An image captured in rain reduces the visibility quality of image which affects the analytical task like detecting objects and classifying pictures. Hence, image de-raining became important in last few years. Since pictures taken in rain include rain streaks of all sizes, single image de-raining is becoming much difficult issue to solve, which may flow in different direction and the density of each rain streak is different. Rain streaks have a varied effect on various areas of picture, and hence it becomes important for removing rain streak from rainy pictures as rainy images tend to lose its high frequency information; previously many methods were proposed for this purpose but they failed to provide accurate results. Hence we have studied and implemented a supervised machine learning method using convolutional neural network (CNN) algorithm to get more accurate result of rain streak removal from an image captured during rain and in less elapsed time by preserving high rated information of image during removal of rain streak. Keywords: CNN, elapsed time, single image de-raining, supervised machine learning, rain streaks.

Verification of a classification prediction method for the development of musical expression in early childhood using a machine learning method based on 3D motion capture data

2020 ◽  
Vol 7 (9) ◽  
pp. 338-358
Author(s):  
Mina Sano
Keyword(s):  
Machine Learning ◽  
Early Childhood ◽  
Statistical Analysis ◽  
Classification Model ◽  
Machine Learning Method ◽  
Learning Method ◽  
Musical Expression ◽  
3D Motion ◽  
Model Training ◽  
Classification Prediction

It is widely acknowledged that children take developmental steps in performing musical expression through body movement dynamics. The author presents an approach to verify the method of classification prediction by machine learning using multiple classifiers to evaluate and classify the developmental degree of musical expressions in early childhood. The author addresses this potential solution by showing statistical analysis of full-body 3D motion captured data using such statistical analysis, and applies machine learning measures to predict developmental degrees of musical expression. In 2016, the author extracted the feature quantities of movement based on the results of the movement analysis of the musical expressions in the MEB program regarding 3-year-old, 4-year-old, and 5-year-old children (n=76). The developmental degree of musical expression was classified into three stages based on video analysis of the musical expression of each child. The 2016 training data as the feature quantity of movement (factors) and the three-stage evaluation (categorical dependent variables) were used as the model training for machine learning. Based on the results in 2017 with 2018, the classification model training results were applied to the acquired data (n=87) in 2019. The result of sensitivity analysis showed that the moving average acceleration of pelvis, the moving distance of right foot and the moving distance of right hand had a strong influence on the development of musical expression in early childhood. From those analysis results, the appropriateness of the machine learning method using decision trees for classifying the developmental degree of musical expression in early childhood was verified.

scholarly journals Application of Radiomics Analysis Based on CT Combined With Machine Learning in Diagnostic of Pancreatic Neuroendocrine Tumors Patient’s Pathological Grades

2021 ◽  
Vol 10 ◽  
Author(s):  
Tao Zhang ◽  
YueHua Zhang ◽  
Xinglong Liu ◽  
Hanyue Xu ◽  
Chaoyue Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neuroendocrine Tumors ◽  
Feature Selection Method ◽  
Area Under The Curve ◽  
Classification Model ◽  
Machine Learning Method ◽  
Learning Method ◽  
Pancreatic Neuroendocrine Tumors ◽  
Adaptive Boosting ◽  
Strong Ability

PurposeTo evaluate the value of multiple machine learning methods in classifying pathological grades (G1,G2, and G3), and to provide the best machine learning method for the identification of pathological grades of pancreatic neuroendocrine tumors (PNETs) based on radiomics.Materials and MethodsA retrospective study was conducted on 82 patients with Pancreatic Neuroendocrine tumors. All patients had definite pathological diagnosis and grading results. Using Lifex software to extract the radiomics features from CT images manually. The sensitivity, specificity, area under the curve (AUC) and accuracy were used to evaluate the performance of the classification model.ResultOur analysis shows that the CT based radiomics features combined with multi algorithm machine learning method has a strong ability to identify the pathological grades of pancreatic neuroendocrine tumors. DC + AdaBoost, DC + GBDT, and Xgboost+RF were very valuable for the differential diagnosis of three pathological grades of PNET. They showed a strong ability to identify the pathological grade of pancreatic neuroendocrine tumors. The validation set AUC of DC + AdaBoost is 0.82 (G1 vs G2), 0.70 (G2 vs G3), and 0.85 (G1 vs G3), respectively.ConclusionIn conclusion, based on enhanced CT radiomics features could differentiate between different pathological grades of pancreatic neuroendocrine tumors. Feature selection method Distance Correlation + classifier method Adaptive Boosting show a good application prospect.

Speech Organ Contour Extraction Using Real-Time MRI and Machine Learning Method

2019 ◽  
Author(s):  
Hironori Takemoto ◽  
Tsubasa Goto ◽  
Yuya Hagihara ◽  
Sayaka Hamanaka ◽  
Tatsuya Kitamura ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Machine Learning Method ◽  
Learning Method ◽  
Contour Extraction
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