scholarly journals Speckle Noise Detection and Removal for Laser Speech Measurement Systems

2021 ◽  
Vol 11 (21) ◽  
pp. 9870
Author(s):  
Yahui Wang ◽  
Wenxi Zhang ◽  
Zhou Wu ◽  
Xinxin Kong ◽  
Hongxin Zhang
Keyword(s):  
Linear Prediction ◽  
Impulsive Noise ◽  
Large Fraction ◽  
Speckle Noise ◽  
Measured Signal ◽  
Noise Detection ◽  
Signal Ratio ◽  
Removal Method ◽  
Wide Range ◽  
Lp Model

Laser speech measurement is a new sound capture technology based on Laser Doppler Vibrometry (LDV). It avoids the need for contact, is easily concealed and is ideal for remote speech acquisition, which has led to its wide-scale adoption for military and security applications. However, lasers are easily affected by complex detection environments. Thus, speckle noise often appears in the measured speech, seriously affecting its quality and intelligibility. This paper examines all of the characteristics of impulsive noise in laser measured speech and proposes a novel automatic impulsive noise detection and removal method. This method first foregrounds noise using decorrelation based on a linear prediction (LP) model that improves the noise-to-signal ratio (NSR) of the measured signal. This makes it possible to detect the position of noise through a combination of the average short-time energy and kurtosis. The method not only precisely locates small clicks (with a duration of just a few samples), but also finds the location of longer bursts and scratches (with a duration of up to a hundred samples). The located samples can then be replaced by more appropriate samples whose coding is based on the LP model. This strategy avoids unnecessary processing and obviates the need to compromise the quality of the relatively large fraction of samples that are unaffected by speckle noise. Experimental results show that the proposed automatic speckle noise detection and removal method outperforms other related methods across a wide range of degraded audio signals.

scholarly journals Line-of-Sight Winds and Doppler Effect Smearing in ACE-FTS Solar Occultation Measurements

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 680
Author(s):  
Chris D. Boone ◽  
Johnathan Steffen ◽  
Jeff Crouse ◽  
Peter F. Bernath
Keyword(s):  
Atmospheric Chemistry ◽  
Doppler Effect ◽  
Line Of Sight ◽  
Measured Signal ◽  
Mixing Ratio ◽  
Model Calculations ◽  
Doppler Shifts ◽  
Solar Occultation ◽  
Wide Range ◽  
Wind Profiles

Line-of-sight wind profiles are derived from Doppler shifts in infrared solar occultation measurements from the Atmospheric Chemistry Experiment Fourier transform spectrometers (ACE-FTS), the primary instrument on SCISAT, a satellite-based mission for monitoring the Earth’s atmosphere. Comparisons suggest a possible eastward bias from 20 m/s to 30 m/s in ACE-FTS results above 80 km relative to some datasets but no persistent bias relative to other datasets. For instruments operating in a limb geometry, looking through a wide range of altitudes, smearing of the Doppler effect along the line of sight can impact the measured signal, particularly for saturated absorption lines. Implications of Doppler effect smearing are investigated for forward model calculations and volume mixing ratio retrievals. Effects are generally small enough to be safely ignored, except for molecules having a large overhang in their volume mixing ratio profile, such as carbon monoxide.

scholarly journals Towards Reduced CNNs for De-Noising Phase Images Corrupted with Speckle Noise

Photonics ◽  
2021 ◽  
Vol 8 (7) ◽  
pp. 255
Author(s):  
Marie Tahon ◽  
Silvio Montresor ◽  
Pascal Picart
Keyword(s):  
Phase Error ◽  
Speckle Noise ◽  
Experimental Conditions ◽  
Benchmark Data ◽  
Wide Range ◽  
Phase Images ◽  
Different Depths ◽  
Phase Data ◽  
Do So

Digital holography is a very efficient technique for 3D imaging and the characterization of changes at the surfaces of objects. However, during the process of holographic interferometry, the reconstructed phase images suffer from speckle noise. In this paper, de-noising is addressed with phase images corrupted with speckle noise. To do so, DnCNN residual networks with different depths were built and trained with various holographic noisy phase data. The possibility of using a network pre-trained on natural images with Gaussian noise is also investigated. All models are evaluated in terms of phase error with HOLODEEP benchmark data and with three unseen images corresponding to different experimental conditions. The best results are obtained using a network with only four convolutional blocks and trained with a wide range of noisy phase patterns.

scholarly journals Assessing land–ocean connectivity via submarine groundwater discharge (SGD) in the Ria Formosa Lagoon (Portugal): combining radon measurements and stable isotope hydrology

2016 ◽  
Vol 20 (8) ◽  
pp. 3077-3098 ◽  
Author(s):  
Carlos Rocha ◽  
Cristina Veiga-Pires ◽  
Jan Scholten ◽  
Kay Knoeller ◽  
Darren R. Gröcke ◽  
...  
Keyword(s):  
Submarine Groundwater Discharge ◽  
Large Fraction ◽  
Groundwater Discharge ◽  
Point Sources ◽  
Total N ◽  
Ria Formosa ◽  
Basin Scale ◽  
Wide Range ◽  
Dominant Feature ◽  
Submarine Groundwater

Abstract. Natural radioactive tracer-based assessments of basin-scale submarine groundwater discharge (SGD) are well developed. However, SGD takes place in different modes and the flow and discharge mechanisms involved occur over a wide range of spatial and temporal scales. Quantifying SGD while discriminating its source functions therefore remains a major challenge. However, correctly identifying both the fluid source and composition is critical. When multiple sources of the tracer of interest are present, failure to adequately discriminate between them leads to inaccurate attribution and the resulting uncertainties will affect the reliability of SGD solute loading estimates. This lack of reliability then extends to the closure of local biogeochemical budgets, confusing measures aiming to mitigate pollution.Here, we report a multi-tracer study to identify the sources of SGD, distinguish its component parts and elucidate the mechanisms of their dispersion throughout the Ria Formosa – a seasonally hypersaline lagoon in Portugal. We combine radon budgets that determine the total SGD (meteoric + recirculated seawater) in the system with stable isotopes in water (δ2H, δ18O), to specifically identify SGD source functions and characterize active hydrological pathways in the catchment. Using this approach, SGD in the Ria Formosa could be separated into two modes, a net meteoric water input and another involving no net water transfer, i.e., originating in lagoon water re-circulated through permeable sediments. The former SGD mode is present occasionally on a multi-annual timescale, while the latter is a dominant feature of the system. In the absence of meteoric SGD inputs, seawater recirculation through beach sediments occurs at a rate of  ∼  1.4  ×  106 m3 day−1. This implies that the entire tidal-averaged volume of the lagoon is filtered through local sandy sediments within 100 days ( ∼  3.5 times a year), driving an estimated nitrogen (N) load of  ∼  350 Ton N yr−1 into the system as NO3−. Land-borne SGD could add a further  ∼  61 Ton N yr−1 to the lagoon. The former source is autochthonous, continuous and responsible for a large fraction (59 %) of the estimated total N inputs into the system via non-point sources, while the latter is an occasional allochthonous source capable of driving new production in the system.

Comparison of Several Acoustic Modeling Techniques for Speech Emotion Recognition

2016 ◽  
Vol 7 (1) ◽  
pp. 58-68 ◽  
Author(s):  
Imen Trabelsi ◽  
Med Salim Bouhlel
Keyword(s):  
Emotion Recognition ◽  
Linear Prediction ◽  
Recognition Rate ◽  
Gaussian Mixture ◽  
Speech Emotion Recognition ◽  
Support Vector ◽  
Emotional States ◽  
Wide Range ◽  
Leibler Divergence ◽  
Perceptual Linear Prediction

Automatic Speech Emotion Recognition (SER) is a current research topic in the field of Human Computer Interaction (HCI) with a wide range of applications. The purpose of speech emotion recognition system is to automatically classify speaker's utterances into different emotional states such as disgust, boredom, sadness, neutral, and happiness. The speech samples in this paper are from the Berlin emotional database. Mel Frequency cepstrum coefficients (MFCC), Linear prediction coefficients (LPC), linear prediction cepstrum coefficients (LPCC), Perceptual Linear Prediction (PLP) and Relative Spectral Perceptual Linear Prediction (Rasta-PLP) features are used to characterize the emotional utterances using a combination between Gaussian mixture models (GMM) and Support Vector Machines (SVM) based on the Kullback-Leibler Divergence Kernel. In this study, the effect of feature type and its dimension are comparatively investigated. The best results are obtained with 12-coefficient MFCC. Utilizing the proposed features a recognition rate of 84% has been achieved which is close to the performance of humans on this database.

scholarly journals Edge Enhancing Accelerated Diffusion Model for Speckle Denoising in Medical Imagery

2019 ◽  
Vol 29 ◽  
pp. 01009
Author(s):  
Arundhati Bagchi Misra ◽  
Chartese Jones ◽  
Hyeona Lim
Keyword(s):  
Medical Images ◽  
Lagrange Equation ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Total Variation Minimization ◽  
Pde Model ◽  
Medical Imagery ◽  
Wide Range ◽  
Automated Processing ◽  
Processing Techniques

Speckle noise occurs in a wide range of medical images due to sampling and digital degradation. Removing speckle noise from medical images is the key for further automated processing techniques like segmentation, and can help the clinicians with better diagnosis and therapy. We consider partial differential equation (PDE)-based denoising model which is a modified Euler-Lagrange equation derived from the total variation minimization functional with additional speckle noise constraints. The new PDE model is designed and optimized to rectify speckle noise and enhance edges present in medical imagery. Wealso develop the efficicient and stable discretization techniques for the corresponding speckle denoising model. The method is tested for several types of images including ultrasound images, and it is compared favorably to the conventional denoising model.

scholarly journals Terahertz Time-Domain Polarimetry in Reflection for Film Characterization

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3352
Author(s):  
Sandrine van Frank ◽  
Elisabeth Leiss-Holzinger ◽  
Michael Pfleger ◽  
Christian Rankl
Keyword(s):  
Optical Properties ◽  
Time Domain ◽  
Terahertz Spectroscopy ◽  
Single Layer ◽  
Measured Signal ◽  
Terahertz Time Domain Spectroscopy ◽  
Wide Range ◽  
Reflection Geometry ◽  
Layer Thin Film ◽  
Good Agreement

Terahertz time-domain spectroscopy is a useful technique to characterize layered samples and thin films. It gives access to their optical properties and thickness. Such measurements are done in transmission, which requires access to the sample from opposite sides. In reality this is not always possible. In such cases, reflection measurements are the only option, but they are more difficult to implement. Here we propose a method to characterize films in reflection geometry using a polarimetric approach based on the identification of Brewster angle and modeling of the measured signal to extract the refractive index and thickness of the sample. The technique is demonstrated experimentally on an unsupported single layer thin film sample. The extracted optical properties and thickness were in good agreement with established transmission terahertz spectroscopy measurements. The new method has the potential to cover a wide range of applications, both for research and industrial purposes.

scholarly journals Investigation on Blending Effects of Gasoline Fuel with N-Butanol, DMF, and Ethanol on the Fuel Consumption and Harmful Emissions in a GDI Vehicle

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1845 ◽  
Author(s):  
Haifeng Liu ◽  
Xichang Wang ◽  
Diping Zhang ◽  
Fang Dong ◽  
Xinlu Liu ◽  
...  
Keyword(s):  
Steady State ◽  
Fuel Consumption ◽  
Large Fraction ◽  
Direct Injection ◽  
Transient State ◽  
Gasoline Direct Injection ◽  
Nox Emissions ◽  
Fuel Blends ◽  
Wide Range ◽  
Oxygenated Fuel

The effects of three kinds of oxygenated fuel blends—i.e., ethanol-gasoline, n-butanol-gasoline, and 2,5-dimethylfuran (DMF)-gasoline-on fuel consumption, emissions, and acceleration performance were investigated in a passenger car with a chassis dynamometer. The engine mounted in the vehicle was a four-cylinder, four-stroke, turbocharging gasoline direct injection (GDI) engine with a displacement of 1.395 L. The test fuels include ethanol-gasoline, n-butanol-gasoline, and DMF-gasoline with four blending ratios of 20%, 50%, 75%, and 100%, and pure gasoline was also tested for comparison. The original contribution of this article is to systemically study the steady-state, transient-state, cold-start, and acceleration performance of the tested fuels under a wide range of blending ratios, especially at high blending ratios. It provides new insight and knowledge of the emission alleviation technique in terms of tailoring the biofuels in GDI turbocharged engines. The results of our works showed that operation with ethanol–gasoline, n-butanol–gasoline, and DMF–gasoline at high blending ratios could be realized in the GDI vehicle without any modification to its engine and the control system at the steady state. At steady-state operation, as compared with pure gasoline, the results indicated that blending n-butanol could reduce CO2, CO, total hydrocarbon (THC), and NOX emissions, which were also decreased by employing a higher blending ratio of n-butanol. However, a high fraction of n-butanol increased the volumetric fuel consumption, and so did the DMF–gasoline and ethanol–gasoline blends. A large fraction of DMF reduced THC emissions, but increased CO2 and NOX emissions. Blending n-butanol can improve the equivalent fuel consumption. Moreover, the particle number (PN) emissions were significantly decreased when using the high blending ratios of the three kinds of oxygenated fuels. According to the results of the New European Drive Cycle (NEDC) cycle, blending 20% of n-butanol with gasoline decreased CO2 emissions by 5.7% compared with pure gasoline and simultaneously reduced CO, THC, NOX emissions, while blending ethanol only reduced NOX emissions. PN and particulate matter (PM) emissions decreased significantly in all stages of the NEDC cycle with the oxygenated fuel blends; the highest reduction ratio in PN was 72.87% upon blending 20% ethanol at the NEDC cycle. The high proportion of n-butanol and DMF improved the acceleration performance of the vehicle.

scholarly journals Age, Activity and Rotation in Mid and Late-Type M Dwarfs from MEarth

2013 ◽  
Vol 9 (S302) ◽  
pp. 176-179
Author(s):  
Andrew A. West ◽  
Kolby L. Weisenburger ◽  
Jonathan Irwin ◽  
David Charbonneau ◽  
Jason Dittmann ◽  
...  
Keyword(s):  
Dimensional Space ◽  
Large Fraction ◽  
Rotation Period ◽  
Magnetic Activity ◽  
Rotating Stars ◽  
Late Type ◽  
M Dwarfs ◽  
Magnetic Field Generation ◽  
Rotation Periods ◽  
Wide Range

AbstractUsing spectroscopic observations and photometric light curves of 280 nearby M dwarfs from the MEarth exoplanet transit survey, we examine the relationships between magnetic activity (quantified by Hα emission), rotation period, and stellar age (derived from three-dimensional space velocities). Although we have known for decades that a large fraction of mid-late-type M dwarfs are magnetically active, it was not clear what role rotation played in the magnetic field generation (and subsequent chromospheric heating). Previous attempts to investigate the relationship between magnetic activity and rotation in mid-late-type M dwarfs were hampered by the limited number of M dwarfs with measured rotation periods (and the fact that vsini measurements only probe rapid rotation). However, the photometric data from the MEarth survey allows us to probe a wide range of rotation periods for hundreds of M dwarf stars (from less than one to over 100 days). Over all M spectral types we find that magnetic activity decreases with longer rotation periods, including late-type, fully convective M dwarfs. We find that the most magnetically active (and hence, most rapidly rotating) stars are consistent with a kinematically young population, while slow-rotators are less active or inactive and appear to belong to an older, dynamically heated stellar population.

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