Spectral Structure of Sets of Integers

2004 ◽  
pp. 83-96 ◽  
Author(s):  
Ben Green
Keyword(s):  
Spectral Structure

Effect of Fluctuation of Intensity and Spectral Structure of Light on the Growth and Energetics of Young Fish

2003 ◽  
Vol 39 (2) ◽  
pp. 95-106 ◽  
Author(s):  
A. S. Konstantinov ◽  
V. S. Vechkanov ◽  
V. A. Kuznetsov ◽  
A. B. Ruchin
Keyword(s):  
Spectral Structure ◽  
Young Fish

PHYSICAL INTERPRETATION OF WAVE BREAKING CRITERIA

2017 ◽  
Author(s):  
Sergey Kuznetsov ◽  
Sergey Kuznetsov ◽  
Yana Saprykina ◽  
Yana Saprykina ◽  
Boris Divinskiy ◽  
...  
Keyword(s):  
Nonlinear Wave ◽  
Wave Breaking ◽  
Second Harmonic ◽  
Vertical Axis ◽  
Breaking Waves ◽  
Wave Transformation ◽  
Spectral Structure ◽  
Similarity Parameter ◽  
Nonlinear Harmonics ◽  
The Waves

On the base of experimental data it was revealed that type of wave breaking depends on wave asymmetry against the vertical axis at wave breaking point. The asymmetry of waves is defined by spectral structure of waves: by the ratio between amplitudes of first and second nonlinear harmonics and by phase shift between them. The relative position of nonlinear harmonics is defined by a stage of nonlinear wave transformation and the direction of energy transfer between the first and second harmonics. The value of amplitude of the second nonlinear harmonic in comparing with first harmonic is significantly more in waves, breaking by spilling type, than in waves breaking by plunging type. The waves, breaking by plunging type, have the crest of second harmonic shifted forward to one of the first harmonic, so the waves have "saw-tooth" shape asymmetrical to vertical axis. In the waves, breaking by spilling type, the crests of harmonic coincides and these waves are symmetric against the vertical axis. It was found that limit height of breaking waves in empirical criteria depends on type of wave breaking, spectral peak period and a relation between wave energy of main and second nonlinear wave harmonics. It also depends on surf similarity parameter defining conditions of nonlinear wave transformations above inclined bottom.

Thermodynamic Green’s Functions and Spectral Structure

2018 ◽  
Author(s):  
Norman J. Morgenstern Horing
Keyword(s):  
Green’S Function ◽  
Green's Function ◽  
Green's Functions ◽  
Green’S Functions ◽  
Spectral Weight ◽  
Statistical Thermodynamic ◽  
Spectral Structure ◽  
Imaginary Time ◽  
Translational Invariance ◽  
The One

Multiparticle thermodynamic Green’s functions, defined in terms of grand canonical ensemble averages of time-ordered products of creation and annihilation operators, are interpreted as tracing the amplitude for time-developing correlated interacting particle motions taking place in the background of a thermal ensemble. Under equilibrium conditions, time-translational invariance permits the one-particle thermal Green’s function to be represented in terms of a single frequency, leading to a Lehmann spectral representation whose frequency poles describe the energy spectrum. This Green’s function has finite values for both t>t′ and t<t′ (unlike retarded Green’s functions), and the two parts G1> and G1< (respectively) obey a simple proportionality relation that facilitates the introduction of a spectral weight function: It is also interpreted in terms of a periodicity/antiperiodicity property of a modified Green’s function in imaginary time capable of a Fourier series representation with imaginary (Matsubara) frequencies. The analytic continuation from imaginary time to real time is discussed, as are related commutator/anticommutator functions, also retarded/advanced Green’s functions, and the spectral weight sum rule is derived. Statistical thermodynamic information is shown to be embedded in physical features of the one- and two-particle thermodynamic Green’s functions.

scholarly journals Data Augmentation and Spectral Structure Features for Limited Samples Hyperspectral Classification

2021 ◽  
Vol 13 (4) ◽  
pp. 547
Author(s):  
Wenning Wang ◽  
Xuebin Liu ◽  
Xuanqin Mou
Keyword(s):  
Classification Accuracy ◽  
Data Augmentation ◽  
Classification Problem ◽  
Classification Performance ◽  
Spectral Structure ◽  
Limited Sample ◽  
Sample Classification ◽  
Training Samples ◽  
Traditional Classification ◽  
Hyperspectral Classification

For both traditional classification and current popular deep learning methods, the limited sample classification problem is very challenging, and the lack of samples is an important factor affecting the classification performance. Our work includes two aspects. First, the unsupervised data augmentation for all hyperspectral samples not only improves the classification accuracy greatly with the newly added training samples, but also further improves the classification accuracy of the classifier by optimizing the augmented test samples. Second, an effective spectral structure extraction method is designed, and the effective spectral structure features have a better classification accuracy than the true spectral features.

scholarly journals Extended High Frequencies Provide Both Spectral and Temporal Information to Improve Speech-in-Speech Recognition

2020 ◽  
Vol 24 ◽  
pp. 233121652098029
Author(s):  
Allison Trine ◽  
Brian B. Monson
Keyword(s):  
Speech Recognition ◽  
Pure Tone ◽  
Recognition Performance ◽  
Low Frequency ◽  
Exploratory Analysis ◽  
Temporal Information ◽  
Significant Benefit ◽  
Head Orientation ◽  
Spectral Structure ◽  
High Frequencies

Several studies have demonstrated that extended high frequencies (EHFs; >8 kHz) in speech are not only audible but also have some utility for speech recognition, including for speech-in-speech recognition when maskers are facing away from the listener. However, the contribution of EHF spectral versus temporal information to speech recognition is unknown. Here, we show that access to EHF temporal information improved speech-in-speech recognition relative to speech bandlimited at 8 kHz but that additional access to EHF spectral detail provided an additional small but significant benefit. Results suggest that both EHF spectral structure and the temporal envelope contribute to the observed EHF benefit. Speech recognition performance was quite sensitive to masker head orientation, with a rotation of only 15° providing a highly significant benefit. An exploratory analysis indicated that pure-tone thresholds at EHFs are better predictors of speech recognition performance than low-frequency pure-tone thresholds.

On exponential ergodicity and spectral structure for birth-death processes

1973 ◽  
Vol 5 (01) ◽  
pp. 9-11
Author(s):  
Herman Callaert ◽  
Julian Keilson
Keyword(s):  
Spectral Structure ◽  
Exponential Ergodicity ◽  
Birth Death

Are Rising Sounds Always Louder? Influences of Spectral Structure and Intensity-Region on Loudness Sensitivity to Intensity-Change Direction

2015 ◽  
Vol 101 (6) ◽  
pp. 1083-1093 ◽  
Author(s):  
Emmanuel Ponsot ◽  
Sabine Meunier ◽  
Abbes Kacem ◽  
Jacques Chatron ◽  
Patrick Susini
Keyword(s):  
Intensity Change ◽  
Spectral Structure ◽  
Change Direction ◽  
Intensity Region
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