scholarly journals BATSE evidence for GRB spectral features

1998 ◽  
Author(s):  
M. S. Briggs ◽  
D. L. Band ◽  
R. D. Preece ◽  
G. N. Pendleton ◽  
W. S. Paciesas ◽  
...  
Keyword(s):  
Spectral Features

Dialect Identification Using Tonal and Spectral Features in Two Dialects of Ao

2018 ◽  
Author(s):  
Moakala Tzudir ◽  
Priyankoo Sarmah ◽  
S R Mahadeva Prasanna
Keyword(s):  
Spectral Features

Correlations of TCS and EELS spectral features for graphite, ZnSe(111) and CdTe(110)

1989 ◽  
Vol 213 (2-3) ◽  
pp. A218
Author(s):  
A. Dittmar-Wituski ◽  
M. Naparty ◽  
J. Skonieczny
Keyword(s):  
Spectral Features

scholarly journals Raman spectroscopic detection of interleukin-10 and angiotensin converting enzyme

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Shuo Zhang ◽  
Frederieke A. M. van der Mee ◽  
Roel J. Erckens ◽  
Carroll A. B. Webers ◽  
Tos T. J. M. Berendschot
Keyword(s):  
Angiotensin Converting Enzyme ◽  
Biomedical Applications ◽  
Interleukin 10 ◽  
Spectral Features ◽  
Converting Enzyme ◽  
Raman Spectroscopic ◽  
Non Invasive ◽  
Confocal Raman ◽  
First Time ◽  
Enzyme Characteristic

AbstractIn this report we present a confocal Raman system to identify the unique spectral features of two proteins, Interleukin-10 and Angiotensin Converting Enzyme. Characteristic Raman spectra were successfully acquired and identified for the first time to our knowledge, showing the potential of Raman spectroscopy as a non-invasive investigation tool for biomedical applications.

scholarly journals A Study of Features and Deep Neural Network Architectures and Hyper-Parameters for Domestic Audio Classification

2021 ◽  
Vol 11 (11) ◽  
pp. 4880
Author(s):  
Abigail Copiaco ◽  
Christian Ritz ◽  
Nidhal Abdulaziz ◽  
Stefano Fasciani
Keyword(s):  
Network Architecture ◽  
Single Channel ◽  
Classification Performance ◽  
Network Size ◽  
Directed Acyclic Graphs ◽  
Spectral Features ◽  
Audio Classification ◽  
Resource Requirements ◽  
Efficient Alternative ◽  
Computational Resources

Recent methodologies for audio classification frequently involve cepstral and spectral features, applied to single channel recordings of acoustic scenes and events. Further, the concept of transfer learning has been widely used over the years, and has proven to provide an efficient alternative to training neural networks from scratch. The lower time and resource requirements when using pre-trained models allows for more versatility in developing system classification approaches. However, information on classification performance when using different features for multi-channel recordings is often limited. Furthermore, pre-trained networks are initially trained on bigger databases and are often unnecessarily large. This poses a challenge when developing systems for devices with limited computational resources, such as mobile or embedded devices. This paper presents a detailed study of the most apparent and widely-used cepstral and spectral features for multi-channel audio applications. Accordingly, we propose the use of spectro-temporal features. Additionally, the paper details the development of a compact version of the AlexNet model for computationally-limited platforms through studies of performances against various architectural and parameter modifications of the original network. The aim is to minimize the network size while maintaining the series network architecture and preserving the classification accuracy. Considering that other state-of-the-art compact networks present complex directed acyclic graphs, a series architecture proposes an advantage in customizability. Experimentation was carried out through Matlab, using a database that we have generated for this task, which composes of four-channel synthetic recordings of both sound events and scenes. The top performing methodology resulted in a weighted F1-score of 87.92% for scalogram features classified via the modified AlexNet-33 network, which has a size of 14.33 MB. The AlexNet network returned 86.24% at a size of 222.71 MB.

scholarly journals Tension Monitoring of Toothed Belt Drives Using Interval-Based Spectral Features

2020 ◽  
Vol 53 (2) ◽  
pp. 738-743
Author(s):  
Moritz Fehsenfeld ◽  
Johannes Kühn ◽  
Mark Wielitzka ◽  
Tobias Ortmaier
Keyword(s):  
Spectral Features ◽  
Belt Drives ◽  
Toothed Belt
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