Discriminating varieties of tea plant based on Vis/NIR spectral characteristics and using artificial neural networks

<p>Seismic networks record vibrations that are captured by their stations. These vibrations can be coming from various sources, such as tectonic tremors, quarry blasts and anthropogenic sources. Separation of earthquakes from other sources may require human intervention and it can be a labor-intensive work. In case of lack of such a separation, seismic hazard may be miscalculated. Our goal is to discriminate earthquakes from quarry blasts by using artificial neural networks. To do that, we used two different databases for earthquake signals and quarry blasts. Neither of them have data from our study of interest, which is North-East of Italy. We used three channel signals from the stations as an input for the neural networks. Signals are used as both time series and their spectral characteristics and are fed to the neural networks with this information. We then separated earthquakes from quarry blasts in North-East Italy by using our algorithm. We conclude that our algorithm is able to discriminate earthquakes from quarry blasts with high accuracy and the database can be used in different regions with different earthquake and quarry blast sources in a large variety of distances.</p>

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Correlated EEG Signals Simulation Based on Artificial Neural Networks

International Journal of Neural Systems ◽

10.1142/s0129065717500083 ◽

2017 ◽

Vol 27 (05) ◽

pp. 1750008 ◽

Cited By ~ 2

Author(s):

Nikola M. Tomasevic ◽

Aleksandar M. Neskovic ◽

Natasa J. Neskovic

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Spectral Characteristics ◽

Measurement Data ◽

Eeg Signals ◽

Mental Load ◽

Novel Approach ◽

Eeg Data ◽

Artificial Neural ◽

Eeg Recordings

In recent years, simulation of the human electroencephalogram (EEG) data found its important role in medical domain and neuropsychology. In this paper, a novel approach to simulation of two cross-correlated EEG signals is proposed. The proposed method is based on the principles of artificial neural networks (ANN). Contrary to the existing EEG data simulators, the ANN-based approach was leveraged solely on the experimentally acquired EEG data. More precisely, measured EEG data were utilized to optimize the simulator which consisted of two ANN models (each model responsible for generation of one EEG sequence). In order to acquire the EEG recordings, the measurement campaign was carried out on a healthy awake adult having no cognitive, physical or mental load. For the evaluation of the proposed approach, comprehensive quantitative and qualitative statistical analysis was performed considering probability distribution, correlation properties and spectral characteristics of generated EEG processes. The obtained results clearly indicated the satisfactory agreement with the measurement data.

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Improved real-time bio-aerosol classification using artificial neural networks

Atmospheric Measurement Techniques ◽

10.5194/amt-11-6259-2018 ◽

2018 ◽

Vol 11 (11) ◽

pp. 6259-6270 ◽

Cited By ~ 5

Author(s):

Maciej Leśkiewicz ◽

Miron Kaliszewski ◽

Maksymilian Włodarski ◽

Jarosław Młyńczak ◽

Zygmunt Mierczyk ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Real Time ◽

Spectral Characteristics ◽

Operating Characteristics ◽

New Era ◽

Real Time Analysis ◽

Fluorescence Characteristics ◽

Aerosol Classification ◽

Artificial Neural

Abstract. Air pollution has had an increasingly powerful impact on the everyday life of humans. More and more people are aware of the health problems that may result from inhaling air which contains dust, bacteria, pollens or fungi. There is a need for real-time information about ambient particulate matter. Devices currently available on the market can detect some particles in the air but cannot classify them according to health threats. Fortunately, a new type of technology is emerging as a promising solution. Laser-based bio-detectors are characterizing a new era in aerosol research. They are capable of characterizing a great number of individual particles in seconds by analyzing optical scattering and fluorescence characteristics. In this study we demonstrate the application of artificial neural networks (ANNs) to real-time analysis of single-particle fluorescence fingerprints acquired using BARDet (a Bio-AeRosol Detector). A total of 48 different aerosols including pollens, bacteria, fungi, spores, and nonbiological substances were characterized. An entirely new approach to data analysis using a decision tree comprising 22 independent neural networks was discussed. Applying confusion matrices and receiver operating characteristics (ROC) analysis the best sets of ANNs for each group of similar aerosols were determined. As a result, a very high accuracy of aerosol classification in real time was achieved. It was found that for some substances that have characteristic spectra, almost each particle can be properly classified. Aerosols with similar spectral characteristics can be classified as specific clouds with high probability. In both cases the system recognized aerosol type with no mistakes. In the future, it is planned that performance of the system may be determined under real environmental conditions, involving characterization of fluorescent and nonfluorescent particles.

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