scholarly journals Full characterization of a phase-locked DUV double pulse generated in an all-reflective shaping setup working under grazing incidence in a broad spectral range

2021 ◽  
Vol 128 (1) ◽  
Author(s):  
S. Hartwell ◽  
A. Azima ◽  
C. Haunhorst ◽  
M. Kazemi ◽  
M. Namboodiri ◽  
...  
Keyword(s):  
Spectral Range ◽  
Pulse Shaping ◽  
Near Infrared ◽  
Grazing Incidence ◽  
Ultrashort Laser Pulses ◽  
Double Pulse ◽  
Spectral Amplitude ◽  
Present Contribution ◽  
Frequency Spectra ◽  
Time Frequency

AbstractControlling the temporal and spectral properties of ultrashort laser pulses in the visible and near-infrared spectral range by means of a femtosecond pulse-shaping device is a powerful tool with many applications in ultrafast spectroscopy. A major and successful concept is known as the 4f design, which has a symmetric zero-dispersion-compressor geometry. Most 4f pulse shapers rely on using transmissive optics in their beam path limiting the operational wavelength ranges. In the present contribution, we use an all-reflective shaping setup to generate a phase-locked 266 nm double pulse to benchmark its performance in the limit of short wavelengths. The setup comprises the complete spectral amplitude and phase diagnostics for quantitative analysis of the pulse properties before and after the shaper using the technique of frequency-resolved optical gating. The measured time–frequency spectra are in good agreement with optical simulations. The geometry and hardware of the device including the optical components are designed, such that all harmonics of the deep UV pulses travel the same path, giving the instrument the ability to work with soft X-ray pulses, under vacuum conditions, down to the few-nanometer wavelength scale.

Sparse spectral attributes to detect hydrocarbons: A case study from deep dolomite reservoirs

2019 ◽  
Vol 7 (3) ◽  
pp. T701-T711
Author(s):  
Jianhu Gao ◽  
Bingyang Liu ◽  
Shengjun Li ◽  
Hongqiu Wang
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Spectral Amplitude ◽  
Data Set ◽  
Frequency Spectra ◽  
Hydrocarbon Production ◽  
Hydrocarbon Detection ◽  
Time Frequency ◽  
Gas Bearing

Hydrocarbon detection is always one of the most critical sections in geophysical exploration, which plays an important role in subsequent hydrocarbon production. However, due to the low signal-to-noise ratio and weak reflection amplitude of deep seismic data, some conventional methods do not always provide favorable hydrocarbon prediction results. The interesting dolomite reservoirs in Central Sichuan are buried over an average depth of 4500 m, and the dolomite rocks have a low porosity below approximately 4%, which is measured by well-logging data. Furthermore, the dominant system of pores and fractures as well as strong heterogeneity along the lateral and vertical directions lead to some difficulties in describing the reservoir distribution. Spectral decomposition (SD) has become successful in illuminating subsurface features and can also be used to identify potential hydrocarbon reservoirs by detecting low-frequency shadows. However, the current applications for hydrocarbon detection always suffer from low resolution for thin reservoirs, probably due to the influence of the window function and without a prior constraint. To address this issue, we developed sparse inverse SD (SISD) based on the wavelet transform, which involves a sparse constraint of time-frequency spectra. We focus on investigating the applications of sparse spectral attributes derived from SISD to deep marine dolomite hydrocarbon detection from a 3D real seismic data set with an area of approximately [Formula: see text]. We predict and evaluate gas-bearing zones in two target reservoir segments by analyzing and comparing the spectral amplitude responses of relatively high- and low-frequency components. The predicted results indicate that most favorable gas-bearing areas are located near the northeast fault zone in the upper reservoir segment and at the relatively high structural positions in the lower reservoir segment, which are in good agreement with the gas-testing results of three wells in the study area.

scholarly journals Pathological and physiological high-frequency oscillations in focal human epilepsy

2013 ◽  
Vol 110 (8) ◽  
pp. 1958-1964 ◽  
Author(s):  
Andrew Matsumoto ◽  
Benjamin H. Brinkmann ◽  
S. Matthew Stead ◽  
Joseph Matsumoto ◽  
Michal T. Kucewicz ◽  
...  
Keyword(s):  
High Frequency ◽  
Motor Task ◽  
High Amplitude ◽  
Spectral Amplitude ◽  
Support Vector ◽  
Frequency Spectra ◽  
Time Frequency ◽  
Human Epilepsy ◽  
High Frequency Oscillations ◽  
Measuring Frequency

High-frequency oscillations (HFO; gamma: 40–100 Hz, ripples: 100–200 Hz, and fast ripples: 250–500 Hz) have been widely studied in health and disease. These phenomena may serve as biomarkers for epileptic brain; however, a means of differentiating between pathological and normal physiological HFO is essential. We categorized task-induced physiological HFO during periods of HFO induced by a visual or motor task by measuring frequency, duration, and spectral amplitude of each event in single trial time-frequency spectra and compared them to pathological HFO similarly measured. Pathological HFO had higher mean spectral amplitude, longer mean duration, and lower mean frequency than physiological-induced HFO. In individual patients, support vector machine analysis correctly classified pathological HFO with sensitivities ranging from 70–98% and specificities >90% in all but one patient. In this patient, infrequent high-amplitude HFO were observed in the motor cortex just before movement onset in the motor task. This finding raises the possibility that in epileptic brain physiological-induced gamma can assume higher spectral amplitudes similar to those seen in pathologic HFO. This method if automated and validated could provide a step towards differentiating physiological HFO from pathological HFO and improving localization of epileptogenic brain.

Metastructures for the Giant Enhancement of Raman Scattering in the Near Infrared Spectral Range

JETP Letters ◽  
2020 ◽  
Vol 112 (1) ◽  
pp. 31-36
Author(s):  
V. I. Kukushkin ◽  
V. E. Kirpichev ◽  
E. N. Morozova ◽  
V. V. Solov’ev ◽  
Ya. V. Fedotova ◽  
...  
Keyword(s):  
Raman Scattering ◽  
Spectral Range ◽  
Near Infrared ◽  
Infrared Spectral Range ◽  
Infrared Spectral

scholarly journals Deep Learning Approach for Vibration Signals Applications

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3929
Author(s):  
Han-Yun Chen ◽  
Ching-Hung Lee
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Parameters Optimization ◽  
Frequency Spectra ◽  
Vibration Signals ◽  
Bearing Faults ◽  
Time Frequency ◽  
And Performance ◽  
The One ◽  
Short Time

This study discusses convolutional neural networks (CNNs) for vibration signals analysis, including applications in machining surface roughness estimation, bearing faults diagnosis, and tool wear detection. The one-dimensional CNNs (1DCNN) and two-dimensional CNNs (2DCNN) are applied for regression and classification applications using different types of inputs, e.g., raw signals, and time-frequency spectra images by short time Fourier transform. In the application of regression and the estimation of machining surface roughness, the 1DCNN is utilized and the corresponding CNN structure (hyper parameters) optimization is proposed by using uniform experimental design (UED), neural network, multiple regression, and particle swarm optimization. It demonstrates the effectiveness of the proposed approach to obtain a structure with better performance. In applications of classification, bearing faults and tool wear classification are carried out by vibration signals analysis and CNN. Finally, the experimental results are shown to demonstrate the effectiveness and performance of our approach.

scholarly journals Atmospheric Compensation of PRISMA Data by Means of a Learning Based Approach

2021 ◽  
Vol 13 (15) ◽  
pp. 2967
Author(s):  
Nicola Acito ◽  
Marco Diani ◽  
Gregorio Procissi ◽  
Giovanni Corsini
Keyword(s):  
Spectral Range ◽  
Near Infrared ◽  
Random Noise ◽  
Synthetic Data ◽  
Regression Function ◽  
Short Wave ◽  
Hyperspectral Data ◽  
Electromagnetic Spectrum ◽  
Great Opportunity ◽  
Atmospheric Compensation

Atmospheric compensation (AC) allows the retrieval of the reflectance from the measured at-sensor radiance and is a fundamental and critical task for the quantitative exploitation of hyperspectral data. Recently, a learning-based (LB) approach, named LBAC, has been proposed for the AC of airborne hyperspectral data in the visible and near-infrared (VNIR) spectral range. LBAC makes use of a parametric regression function whose parameters are learned by a strategy based on synthetic data that accounts for (1) a physics-based model for the radiative transfer, (2) the variability of the surface reflectance spectra, and (3) the effects of random noise and spectral miscalibration errors. In this work we extend LBAC with respect to two different aspects: (1) the platform for data acquisition and (2) the spectral range covered by the sensor. Particularly, we propose the extension of LBAC to spaceborne hyperspectral sensors operating in the VNIR and short-wave infrared (SWIR) portion of the electromagnetic spectrum. We specifically refer to the sensor of the PRISMA (PRecursore IperSpettrale della Missione Applicativa) mission, and the recent Earth Observation mission of the Italian Space Agency that offers a great opportunity to improve the knowledge on the scientific and commercial applications of spaceborne hyperspectral data. In addition, we introduce a curve fitting-based procedure for the estimation of column water vapor content of the atmosphere that directly exploits the reflectance data provided by LBAC. Results obtained on four different PRISMA hyperspectral images are presented and discussed.

scholarly journals Satellite monitoring of different vegetation types by differential optical absorption spectroscopy (DOAS) in the red spectral range

2007 ◽  
Vol 7 (1) ◽  
pp. 69-79 ◽  
Author(s):  
T. Wagner ◽  
S. Beirle ◽  
T. Deutschmann ◽  
M. Grzegorski ◽  
U. Platt
Keyword(s):  
Optical Absorption ◽  
Absorption Spectroscopy ◽  
Spectral Range ◽  
Near Infrared ◽  
Vegetation Type ◽  
Differential Optical Absorption Spectroscopy ◽  
Trace Gas ◽  
Satellite Monitoring ◽  
Optical Absorption Spectroscopy ◽  
Vegetation Types

Abstract. A new method for the satellite remote sensing of different types of vegetation and ocean colour is presented. In contrast to existing algorithms relying on the strong change of the reflectivity in the red and near infrared spectral region, our method analyses weak narrow-band (few nm) reflectance structures (i.e. "fingerprint" structures) of vegetation in the red spectral range. It is based on differential optical absorption spectroscopy (DOAS), which is usually applied for the analysis of atmospheric trace gas absorptions. Since the spectra of atmospheric absorption and vegetation reflectance are simultaneously included in the analysis, the effects of atmospheric absorptions are automatically corrected (in contrast to other algorithms). The inclusion of the vegetation spectra also significantly improves the results of the trace gas retrieval. The global maps of the results illustrate the seasonal cycles of different vegetation types. In addition to the vegetation distribution on land, they also show patterns of biological activity in the oceans. Our results indicate that improved sets of vegetation spectra might lead to more accurate and more specific identification of vegetation type in the future.

Quantum efficiency of silicon photodiodes in the near-infrared spectral range

2003 ◽  
Vol 42 (22) ◽  
pp. 4415 ◽  
Author(s):  
Chris Hicks ◽  
Mark Kalatsky ◽  
Richard A. Metzler ◽  
Alexander O. Goushcha
Keyword(s):  
Quantum Efficiency ◽  
Spectral Range ◽  
Near Infrared ◽  
Infrared Spectral Range ◽  
Infrared Spectral
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