China
Africa
Explore chapters and articles related to this topic
Blackbody Radiation and Light
Published in Juan Bisquert, The Physics of Solar Cells, 2017
However, the spectral flux refers to the number of photons or photon energy per second in an interval in a small range of wavelengths (ν, ν + dν) around a single frequency ν. The spectral quantity ϕE(ν,s)=dΦE(0,ν,s)dνdescribes the energy flux (power). The radiant flux density is called the spectral irradiance Eν (Sizmann et al., 1991). The spectral photon flux is ϕph(ν,s)=dΦph(0,ν,s)dν
Blackbody Radiation and Light
Published in Juan Bisquert, The Physics of Solar Energy Conversion, 2020
However, the spectral flux refers to the number of photons or photon energy per second in an interval in a small range of wavelengths (ν, ν + dν) around a single frequency ν. The spectral quantity ϕE(ν,s)=dΦE(0,ν,s)dν
Spectral feature and optimization- based actor-critic neural network for arrhythmia classification using ECG signal
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2020
Anoop Vylala, Bipin Plakkottu Radhakrishnan
The spectral flux evaluates the temporal variation of the logarithmically-scaled ECG signal across the adjacent frames. It is defined as the squared difference between the normalized magnitudes of successive spectral distributions and here, the normalization is based on the total energy in the window. In other words, the spectral flux is the measure of the amount of the local spectral change. The spectral flux is given as,
Hybrid optimization enabled deep learning model for Parkinson's disease classification
Published in The Imaging Science Journal, 2023
M. K. Dharani, R. Thamilselvan
The several spectrum properties, including the spectral centroid, spectral spread, tonal power ratio, pitch chroma, fluctuation index, spectral flux, spectral kurtosis, spectral roll-off, MSE, spectral bandwidth, and spectral flatness, are extracted from the filtered speech signal.
Hybrid hunt-based deep convolutional neural network for emotion recognition using EEG signals
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2022
Sujata Bhimrao Wankhade, Dharmpal Dronacharya Doye
The spectral features are generated from the sample EEG signal directly, which enables characterization of the EEG signal in the frequency domain instead of the time domain in the form of the spectral variance, spectral mean, spectral skewness, spectral entropy, and spectral kurtosis. The spectral mean, variance, and entropy are evaluated as in Eqs. (31), (33), and (34), respectively, where the individual time instances of the original signal are considered rather than using the frequency bands of the sample signal. Moreover, the spectral mean, variance, and entropy define the variation and central tendency of the spectrum. The spectral features, such as s spectral variance, spectral mean, spectral skewness, spectral entropy, and spectral kurtosis (Tanner et al. 2005; Lerch 2012) are denoted as, and respectively. The spectral skewness's significance is that the EEG signal's stability is analyzed, where the location in the frequency domain is determined. Moreover, the complementary information based on the power spectral density (PSD) is revealed, ensuring effective signal analysis. The spectral kurtosis is defined based on the kurtosis of the perplex irregular variable at the frequency bin which is modeled as, where and is the conjugate of the complex random variable and denotes the order cumulant. The spectral skewness represents the peakedness of the signal, or in other words, skewness denotes the symmetry of the spectrum around the arithmetic mean. The spectral flux is a measure that characterizes the dynamic changes in the spectral information of the signal, and is represented as, where represents a Time-Frequency Domain (TFD) of size of the analytic associate of a real signal of length and is the predetermined time duration between two slices of a TFD.