A wearable nanoscale heart sound sensor based on P(VDF-TrFE)/ZnO/GR and its application in cardiac disease detection

• Yi Luo,
• Jian Liu,
• Jiachang Zhang,
• Yu Xiao,
• Ying Wu and
• Zhidong Zhao

Beilstein J. Nanotechnol. 2023, 14, 819–833, doi:10.3762/bjnano.14.67

• analyzing heart sounds mainly involves three parts: signal preprocessing, feature extraction, and classification recognition. The classification methods of heart sound signals can be divided into several types, including BP neural network, support vector machines (SVMs), Gaussian mixture models, wavelet

• feature that mimics the sensitivity of sound signals of different frequencies in the human ear, based on the hearing mechanism. Extracting MFCC features is useful for modeling heart sound signals. The wavelet feature extraction method uses “db6 wavelet decomposition” to generate seven feature vectors. The

A chemiresistive sensor array based on polyaniline nanocomposites and machine learning classification

• Jiri Kroutil,
• Alexandr Laposa,
• Ali Ahmad,
• Jan Voves,
• Vojtech Povolny,
• Ladislav Klimsa,
• Marina Davydova and
• Miroslav Husak

Beilstein J. Nanotechnol. 2022, 13, 411–423, doi:10.3762/bjnano.13.34

• data by principal component analysis to be a highly accurate method reach to 99% of the classification of six different gases. Keywords: feature extraction; gas sensor; pattern recognition; sensor array; Introduction The control and monitoring of toxic gaseous substances, such as ammonia, nitrogen

Reducing molecular simulation time for AFM images based on super-resolution methods

• Zhipeng Dou,
• Jianqiang Qian,
• Yingzi Li,
• Rui Lin,
• Jianhai Wang,
• Peng Cheng and
• Zeyu Xu

Beilstein J. Nanotechnol. 2021, 12, 775–785, doi:10.3762/bjnano.12.61

• to reconstruct the complete image. The flow chart of the proposed SRCNN reconstruction method. First, the low-resolution simulated image is obtained, then the SRCNN method is used to reconstruct the high-resolution image. The SRCNN consists of three layers to implement feature extraction, non-linear

Nanogenerator-based self-powered sensors for data collection

• Yicheng Shao,
• Maoliang Shen,
• Yuankai Zhou,
• Xin Cui,
• Lijie Li and
• Yan Zhang

Beilstein J. Nanotechnol. 2021, 12, 680–693, doi:10.3762/bjnano.12.54

• uses a principal component analysis (PCA) algorithm for feature extraction and a support vector machine (SVM) algorithm for gesture recognition, with a recognition accuracy of 98.63% and recognition time of less than 1s. The front-end sensor could be replaced by a more advanced self-powered pressure

Automated image segmentation-assisted flattening of atomic force microscopy images

• Yuliang Wang,
• Tongda Lu,
• Xiaolai Li and
• Huimin Wang

Beilstein J. Nanotechnol. 2018, 9, 975–985, doi:10.3762/bjnano.9.91

• , whereby methods including thresholding [31][32], circle Hough transform [33], and clustering [30] can be applied. Recently, Wang et al. proposed a contour expansion method for feature extraction in AFM height images [3][34]. The method achieves an accurate localization and optimized boundary detection for