Estimation Of Attenuation Coefficient Research Articles

Do attenuation coefficients based on luminescence bleaching fronts reflect true light attenuation in rocks?

The emerging technique of rock surface luminescence dating employs a double-exponential fitting dating model based on the Bouguer-Lambert law. The accuracy of exposure age determination is sensitive to the light attenuation coefficient, μ, which is usually derived from fitting the luminescence profile as a function of depth into the rock. Alternatively, μ might be determined by direct measurement using a spectrophotometer. However, their directly measured values of μ were significantly larger than those estimated from field luminescence bleaching data. Here, we aim to confirm these previous results by measuring the luminescence-depth profiles of both IRSL and IRPL signals from granite rock exposed to different light sources, including sunlight, SOL2 and IR LEDs under controlled conditions. We also measured monochromatic light (blue LED: (470 ± 30) nm, green LED: (520 ± 30) nm, and infrared LED: (850 ± 30) nm) attenuation in granite rock slices of varying thickness (0.62–2.60 mm) using a high-sensitivity photodiode. Finally, we use a camera-based measurement system and a Risø Luminescence Imager to investigate spatial variation in transmitted light as a function of slice thickness. The spatial correlation between direct light transmission and luminescence emission is then investigated, to investigate the underlying reasons for the difference between direct measurement and luminescence-based estimates of μ. We find that light attenuation through a granite rock slice is very heterogeneous; most light penetrates through localized low attenuation light paths corresponding to transmission through the most transparent grains, presumably quartz. The light penetration and escape into the surrounding matrix are assumed to be influenced by the surface area of quartz grains. As a result, transmissive light paths result in a significant area of feldspar grains at depth in the slice being exposed to more light than might be expected from average attenuation measurement. When the incident light predominantly travels through feldspar minerals, simultaneously stimulating luminescence from within any potassium feldspar (K-feldspar), the attenuation coefficients derived from the luminescence-depth profiles tend to be smaller than the average of 1.18 ± 0.07 (n = 79) irrespective of the kinetic model used for fitting. Thus, luminescence-based attenuation coefficient estimates appear to be affected by the distribution of light attenuation across and within the rock slice, making such estimates preferable to directly measured (average) estimates of light attenuation coefficients. These insights are important for our understanding of the significance of light attenuation coefficients in rock samples; they contribute to the reassessment of parameters and measurement methods in dating models and so to improvements in accuracy of rock surface luminescence dating techniques.

The Use of Noninvasive Velacur® for Discriminating between Volunteers and Patients with Chronic Liver Disease: A Feasibility Study.

Nonalcoholic fatty liver disease is the leading cause of chronic liver disease globally and can progress to cirrhosis, liver failure, and liver cancer. Current AASLD, AGA, and ADA guidelines recommend assessment for liver fibrosis in all patients with NAFLD. Serum biomarkers for fibrosis, while widely available, have notable limitations. Imaging-based noninvasive testing for liver fibrosis/cirrhosis is more accurate and is becoming more widespread. We evaluated the feasibility of a novel shear wave absolute vibroelastography (S-WAVE) modality called Velacur® for assessing liver stiffness measurement (LSM) for fibrosis and attenuation coefficient estimation (ACE) in differentiating patients with chronic liver disease from normal healthy controls. Fifty-four healthy controls and 89 patients with NAFLD or cured HCV with a prior known LSM of >8 kPa were enrolled, and all subjects were evaluated with FibroScan® and Velacur®. Velacur® was able to discriminate patients with increased liver stiffness as determined by a FibroScan® score of >8 kPa from healthy controls with an AUC of 0.938 (0.88-0.96). For assessment of steatosis in NAFLD patients only, Velacur® could identify patients with steatosis from healthy controls with an AUC of 0.831 (0.777-0.880). The Velacur® scan quality assessment was superior in healthy controls, as compared to patients, and the scan quality, as assessed by the quality factor (QF) and interquartile range (IQR)/median, was affected by BMI. Velacur® was safe and well tolerated by patients, and there were no adverse events. Velacur® assessment of liver stiffness measurement and liver attenuation is comparable to results obtained by FibroScan® and is an alternative technology for monitoring liver fibrosis progression in patients with chronic liver disease. This trial is registered with NCT03957070.

BCC and Immunocryosurgery scar differentiation through computational resolution-enhanced OCT images and skin optical attenuation: A proof-of-concept study.

Monitoring medical therapy remains a challenging task across all non-surgical skin cancer treatment modalities. In addition, confirmation of residual tumours after treatment is essential for the early detection of potential relapses. Optical coherence tomography (OCT), a non-invasive method for real-time cross-sectional imaging of living tissue, is a promising imaging approach for assessing relatively flat, near-surface skin lesions, such as those that occur in most basal cell carcinomas (BCCs), at the time of diagnosis. However, the skin's inherent property of strong light scattering impedes the implementation of OCT in these cases due to the poor image quality. Furthermore, translating OCT's optical parameters into practical use in routine clinical settings is complicated due to substantial observer subjectivity. In this retrospective pilot study, we developed a workflow based on the upscale of the OCT images resolution using a deep generative adversarial network and the estimation of the skin optical attenuation coefficient. At the site of immunocryosurgery-treated BCC, the proposed methodology can extract optical parameters and discriminate objectively between tumour foci and scar tissue.

MB-DECTNet: a model-based unrolling network for accurate 3D dual-energy CT reconstruction from clinically acquired helical scans

Objective. Over the past several decades, dual-energy CT (DECT) imaging has seen significant advancements due to its ability to distinguish between materials. DECT statistical iterative reconstruction (SIR) has exhibited potential for noise reduction and enhanced accuracy. However, its slow convergence and substantial computational demands render the elapsed time for 3D DECT SIR often clinically unacceptable. The objective of this study is to accelerate 3D DECT SIR while maintaining subpercentage or near-subpercentage accuracy. Approach. We incorporate DECT SIR into a deep-learning model-based unrolling network for 3D DECT reconstruction (MB-DECTNet), which can be trained end-to-end. This deep learning-based approach is designed to learn shortcuts between initial conditions and the stationary points of iterative algorithms while preserving the unbiased estimation property of model-based algorithms. MB-DECTNet comprises multiple stacked update blocks, each containing a data consistency layer (DC) and a spatial mixer layer, with the DC layer functioning as a one-step update from any traditional iterative algorithm. Main results. The quantitative results indicate that our proposed MB-DECTNet surpasses both the traditional image-domain technique (MB-DECTNet reduces average bias by a factor of 10) and a pure deep learning method (MB-DECTNet reduces average bias by a factor of 8.8), offering the potential for accurate attenuation coefficient estimation, akin to traditional statistical algorithms, but with considerably reduced computational costs. This approach achieves 0.13% bias and 1.92% mean absolute error and reconstructs a full image of a head in less than 12 min. Additionally, we show that the MB-DECTNet output can serve as an initializer for DECT SIR, leading to further improvements in results. Significance. This study presents a model-based deep unrolling network for accurate 3D DECT reconstruction, achieving subpercentage error in estimating virtual monoenergetic images for a full head at 60 and 150 keV in 30 min, representing a 40-fold speedup compared to traditional approaches. These findings have significant implications for accelerating DECT SIR and making it more clinically feasible.

Deep-learning-based methods of attenuation correction for SPECT and PET

Attenuation correction (AC) is essential for quantitative analysis and clinical diagnosis of single-photon emission computed tomography (SPECT) and positron emission tomography (PET). In clinical practice, computed tomography (CT) is utilized to generate attenuation maps (μ-maps) for AC of hybrid SPECT/CT and PET/CT scanners. However, CT-based AC methods frequently produce artifacts due to CT artifacts and misregistration of SPECT-CT and PET-CT scans. Segmentation-based AC methods using magnetic resonance imaging (MRI) for PET/MRI scanners are inaccurate and complicated since MRI does not contain direct information of photon attenuation. Computational AC methods for SPECT and PET estimate attenuation coefficients directly from raw emission data, but suffer from low accuracy, cross-talk artifacts, high computational complexity, and high noise level. The recently evolving deep-learning-based methods have shown promising results in AC of SPECT and PET, which can be generally divided into two categories: indirect and direct strategies. Indirect AC strategies apply neural networks to transform emission, transmission, or MR images into synthetic μ-maps or CT images which are then incorporated into AC reconstruction. Direct AC strategies skip the intermediate steps of generating μ-maps or CT images and predict AC SPECT or PET images from non-attenuation-correction (NAC) SPECT or PET images directly. These deep-learning-based AC methods show comparable and even superior performance to non-deep-learning methods. In this article, we first discussed the principles and limitations of non-deep-learning AC methods, and then reviewed the status and prospects of deep-learning-based methods for AC of SPECT and PET.

List-mode quantitative joint reconstruction of activity and attenuation maps in Time-of-Flight PET

Quantitative PET imaging requires accurate determination of patient-specific attenuation maps, which remains challenging on hybrid PET/MRI systems owing to the lack of a direct relationship between MR image intensity and attenuation coefficients. The aim of the present study is to develop a list-mode based algorithm for accurate and robust attenuation correction of PET data using time-of-flight (TOF) emission information. We analyze and address the challenges of list-mode emission-based maximum-likelihood joint estimation of activity and attenuation (LM-MLAA) in state-of-the-art PET imaging. The proposed method exploits a rapid on-the-fly system matrix calculation algorithm based on elliptic integrals while updating the attenuation map from accumulating list-mode coincidences to achieve accelerated image reconstruction. The scattering compensation is incorporated inside it using an iterative approach in such that the current estimation of attenuation map used on a course grid sampling scattering points to make an estimate of scattering. The performance of the proposed LM-MLAA approach was evaluated on Monte Carlo simulations of a phantom at different time resolutions. The contrast and noise for hot and cold regions on reconstructed images at different time resolutions were analysed. The estimated attenuation map exhibits resilience against noise, effectively eliminates high-frequency cross-talk even in the absence of prior information on attenuation coefficients, and enables discrimination among different anatomical regions in the reconstructed image. The error in the mean estimated attenuation coefficients after 50 iterations was ∼ 2% in water and ∼ -14% in Teflon regions for TOF resolutions corresponding to those of most current commercial PET systems (∼ 500 ps). The proposed LM-MLAA framework can be used for joint reconstruction of activity and attenuation maps from list-mode emission data as standalone or a complementary approach to existing in multimodality imaging such as PET/MRI, where direct measurement attenuation maps is not possible.

Lidar attenuation coefficient in the global oceans: insights from ICESat-2 mission.

The attenuation coefficient of natural waters plays a significant role in our understanding of hydrology from both the oceanographic and biological point of view. The advent of near-continuous observations by sophisticated space-based lidars now offers an unprecedented opportunity to characterize attenuation coefficients over open oceans on global and regional scales. At present, however, literature reports of lidar-derived attenuation coefficient estimates (klidar, m-1) in oceanic waters are very limited. In this study, we present a global survey of klidar derived from ATLAS/ICESat-2 nighttime measurements. Our results augment the existing passive sensor ocean color data set with a new diurnal component and extend the record to now include previously unavailable polar nighttime observations. The values of ATLAS measured klidar at 532 nm are between 0.045 and 0.39 m-1 with the higher values (>0.15 m-1) correlated with coastal waters and sea ice covered oceans. The average klidar in clearest oligotrophic ocean gyres is ∼0.058 ± 0.012 m-1 at 532 nm. The results reported here demonstrate the feasibility of using ATLAS/ICESat-2 lidar measurements for global klidar studies, which will in turn provide critical insights that enable climate models to correctly describe the amount of light present under sea ice, and for heat deposition studies in the upper ocean.

Accuracy and precision of depth-resolved estimation of attenuation coefficients in optical coherence tomography.

Parametric imaging of the attenuation coefficient using optical coherence tomography (OCT) is a promising approach for evaluating abnormalities in tissue. To date, a standardized measure of accuracy and precision of by the depth-resolved estimation (DRE) method, as an alternative to least squares fitting, is missing. We present a robust theoretical framework to determine accuracy and precision of the DRE of . We derive and validate analytical expressions for the accuracy and precision of determination by the DRE using simulated OCT signals in absence and presence of noise. We compare the theoretically achievable precisions of the DRE method and the least-squares fitting approach. Our analytical expressions agree with the numerical simulations for high signal-to-noise ratios and qualitatively describe the dependence on noise otherwise. A commonly used simplification of the DRE method results in a systematic overestimation of the attenuation coefficient in the order of , where is the pixel stepsize. When , is reconstructed with higher precision by the depth-resolved method compared to fitting over the length of an axial fitting range . We derived and validated expressions for the accuracy and precision of DRE of . A commonly used simplification of this method is not recommended as being used for OCT-attenuation reconstruction. We give a rule of thumb providing guidance in the choice of estimation method.

Depth-resolved attenuation coefficient estimation from optical coherence tomography data in case of incomplete signal attenuation in the imaging depth range

Attenuation coefficient estimation from optical coherent tomography (OCT) data is one of the emerging methods for additional information extraction from the OCT data. With the reasonable assumptions of uniform proportion of the light, scattered backwards, relative to the light, scattered in all directions and the assumption of complete light attenuation within the imaging depth range, the attenuation coefficient can be estimated in every pixel of the OCT data volume, i.e. with the depth resolution. In the present paper the numerically effective method for lifting the second assumption was proposed. With numerical simulations and experiments it was shown, that the proposed method allows attenuation coefficient estimation even if OCT signal was not completely attenuated within the imaging depth range. Since the proposed method lifts one of the requirements for depth-resolved attenuation coefficient estimation, it allows the extension of the depth-resolved attenuation estimation method to the new applications.

Feasibility of a novel shield of nuclear radiation with W–Ni–Fe–Co and La–Bi alloys alternative to Pb and ordinary concrete absorbers

Preventing people and electronic apparatuses from high-energetic nuclear radiation photons has been a significant concern. Due to disadvantages of lead, many studies have been carried out on different materials for several decades. The purpose of this investigation is to exhibit a novel shield design of alloys formed by the combination of W, La and Bi contents as a desired replacement for conventional lead-based absorbers. Photon shielding performances of the investigated alloys have been studied by estimation of mass attenuation coefficient, half-value layer by applying Monte Carlo GEANT4 simulation code and XCOM computer software. Moreover, the EBFs of the alloys were calculated by geometric progression (G-P) fitting formula for photon energy 0.015–10 MeV up to 40 mfp penetration depth. Consequently, the shielding performance of W-3 alloy shows that it is better by a factor of 1.52 and 8.29 than Pb and OC materials, respectively.

Improved Ultrasound Attenuation Estimation with Non-uniform Structure Detection and Removal.

Accurate detection of liver steatosis is important for liver disease management. Ultrasound attenuation coefficient estimation (ACE) has great potential in quantifying liver fat content. The ACE methods commonly assume uniform tissue characteristics. However, in vivo tissues typically contain non-uniform structures, which may bias the attenuation estimation and lead to large standard deviations. Here we propose a series of non-uniform structure detection and removal (NSDR) methods to reduce the impact from non-uniform structures during ACE analysis. The effectiveness of NSDR was validated through phantom and in vivo studies. In a pilot clinical study, ACE with NSDR provided more robust in vivo performance as compared with ACE without NSDR, indicating its potential for in vivo applications.

Validation of Remote-Sensing Algorithms for Diffuse Attenuation of Downward Irradiance Using BGC-Argo Floats

Estimates of the diffuse attenuation coefficient (Kd) at two different wavelengths and band-integrated (PAR) were obtained using different published algorithms developed for open ocean waters spanning in type from explicit-empirical, semi-analytical and implicit-empirical and applied to data from spectral radiometers on board six different satellites (MODIS-Aqua, MODIS-Terra, VIIRS–SNPP, VIIRS-JPSS, OLCI-Sentinel 3A and OLCI-Sentinel 3B). The resultant Kds were compared to those inferred from measurements of radiometry from sensors on board autonomous profiling floats (BGC-Argo). Advantages of BGC-Argo measurements compared to ship-based ones include: 1. uniform sampling in time throughout the year, 2. large spatial coverage, and 3. lack of shading by platform. Over 5000 quality-controlled matchups between Kds derived from float and from satellite sensors were found with values ranging from 0.01 to 0.67 m−1. Our results show that although all three algorithm types provided similarly ranging values of Kd to those of the floats, for most sensors, a given algorithm produced statistically different Kd distributions from the two others. Algorithm results diverged the most for low Kd (clearest waters). Algorithm biases were traced to the limitations of the datasets the algorithms were developed and trained with, as well as the neglect of sun angle in some algorithms. This study highlights: 1. the importance of using comprehensive field-based datasets (such as BGC-Argo) for algorithm development, 2. the limitation of using radiative-transfer model simulations only for algorithm development, and 3. the potential for improvement if sun angle is taken into account explicitly to improve empirical Kd algorithms. Recent augmentation of profiling floats with hyper-spectral radiometers should be encouraged as they will provide additional constraints to develop algorithms for upcoming missions such as NASA’s PACE and SBG and ESA’s CHIME, all of which will include a hyper-spectral radiometer.

Estimation of linear and mass attenuation coefficients of soy-lignin bonded Rhizophora spp. particleboard as a potential phantom material using caesium-137 and cobalt-60.

In this study, linear and mass attenuation coefficients of fabricated particleboards intended for use as phantom material were estimated using 137Cs and 60Co radiation sources. Particleboards made of Rhizophora spp. wood trunk bonded with soy flour and lignin were fabricated at a target density of 1.0gcm-3, with and without gloss finish coating. Elemental composition of the particleboards was obtained by means of energy dispersive X-ray (EDX) spectroscopy. Experimental setups were simulated via the GATE Monte Carlo (MC) package, with particle histories of 1 × 106-1 × 107. Linear and mass attenuation coefficients obtained from measurements and GATE simulations were compared and discussed. The percentage differences between the measured and simulated linear and mass attenuation coefficients of the samples were reasonably small (2.05-4.88% for 137Cs and 3.24-5.38% for 60Co). It is shown that all the particleboards have the potential to be used as phantom materials as the attenuation coefficients measured were in good agreement with those of water (calculated with XCOM) and with those simulated with the GATE toolkit. The use of gloss finish coating also did not show any significant effect on the attenuation coefficient of the phantom material. Verification of experimental results via GATE simulations has been shown crucial in providing reliable data for energy transmission studies. Based on the results achieved in this study, it is concluded that the studied material-Rhizophora spp. wood trunk bonded with soy flour and lignin including gloss finish coating-can be used in radiation dosimetry studies.

Attenuation Coefficient Estimation for PET/MRI With Bayesian Deep Learning Pseudo-CT and Maximum-Likelihood Estimation of Activity and Attenuation.

A major remaining challenge for magnetic resonance-based attenuation correction methods (MRAC) is their susceptibility to sources of magnetic resonance imaging (MRI) artifacts (e.g., implants and motion) and uncertainties due to the limitations of MRI contrast (e.g., accurate bone delineation and density, and separation of air/bone). We propose using a Bayesian deep convolutional neural network that in addition to generating an initial pseudo-CT from MR data, it also produces uncertainty estimates of the pseudo-CT to quantify the limitations of the MR data. These outputs are combined with the maximum-likelihood estimation of activity and attenuation (MLAA) reconstruction that uses the PET emission data to improve the attenuation maps. With the proposed approach uncertainty estimation and pseudo-CT prior for robust MLAA (UpCT-MLAA), we demonstrate accurate estimation of PET uptake in pelvic lesions and show recovery of metal implants. In patients without implants, UpCT-MLAA had acceptable but slightly higher root-mean-squared-error (RMSE) than Zero-echotime and Dixon Deep pseudo-CT when compared to CTAC. In patients with metal implants, MLAA recovered the metal implant; however, anatomy outside the implant region was obscured by noise and crosstalk artifacts. Attenuation coefficients from the pseudo-CT from Dixon MRI were accurate in normal anatomy; however, the metal implant region was estimated to have attenuation coefficients of air. UpCT-MLAA estimated attenuation coefficients of metal implants alongside accurate anatomic depiction outside of implant regions.

Multi-band bottom index: A novel approach for coastal environmental monitoring using hyperspectral data

Hyperspectral remote sensing has potential for improving shallow-water environmental monitoring, including benthic cover mapping and estimation of bathymetry and water optical properties. Detailed spectral information provided by hyperspectral sensors is particularly required to discriminate benthic covers that have similar spectral patterns (e.g., coral and algae), which cannot be achieved by multispectral sensors with a limited number of wavebands. An important process in such applications is separation of the bottom reflection from the absorption and scattering caused by the water column. Recently, inversion of a semi-analytical radiative transfer model using airborne hyperspectral data has become popular for simultaneous estimation of benthic cover, bathymetry, and water optical properties. However, it requires non-trivial settings and ancillary information, and generally is not user friendly. A cost-effective water-column correction method based on two-band combination has also been popular for multi-band remote sensing; however, it is not suited to hyperspectral data, because of the combinatorial proliferation of many hyperspectral wave bands. To fill the gap between the two approaches, in this paper, a new cost-effective method for water-column correction optimal for hyperspectral data is proposed. The method, called the multi-band bottom index (MBI), incorporates empirical radiative transfer theory and a traditional spectral enhancement technique (i.e., logarithmic residual method). A case study was conducted for the coral reef habitats around Ishigaki Island, using free hyperspectral satellite imagery (Hyperion). The result shows that our MBI approach has more robust performance in the estimation of the water attenuation coefficient than the existing water-column correction approach. The derived MBI map successfully mitigates water-column effects and enhances the spectral pattern of benthic materials. Comparison with in-situ spectra in the literature revealed that the overall spectral pattern of the MBI is reasonable for the benthic categories of coral, seagrass, and sandy bottom. The MBI approach is also promising for application to recently launched hyperspectral satellite sensors, such as HISUI, DESIS, and PRISMA.

Estimation of a sediment attenuation coefficient using mid-frequency bottom-interacting signals in Jinhae Bay, Southeast Korea.

The inversion results of geoacoustic parameters using bottom-interacting signals in Jinhae Bay, South Korea, were presented in March 2019 by Kwon, Choi, Ryang, Son, and Jung [J. Acoust. Soc. Am. 145, 1205-1211 (2019)]. At site 1 in this study area, where the surficial sediment was dominated by mud, bottom-loss estimates were typical of losses in sediment with a sound speed lower than that of the water column. In this case, because most of the energy incident on the bottom at the angle of intromission is transmitted into the sediment, scattering may dominate the bottom-path energy, which can lead to an underestimate of bottom loss and an overestimate of the sediment attenuation coefficient. Here, we attempt to provide an accurate estimate of the sediment attenuation coefficient by comparing the difference in amplitudes between the bottom-reflected and sub-bottom-reflected signals over a frequency band of several kHz. To verify the inversion results, measurements of sediment attenuation at the site were also taken by repeatedly transmitting and receiving continuous acoustic waves of 120-180 kHz using four stake transducers embedded in the sediment. The inversion results are in reasonable agreement with the sediment attenuation coefficients measured in situ.

Reverberation clutter signal suppression in ultrasound attenuation estimation using wavelet-based robust principal component analysis

Objective. Ultrasound attenuation coefficient estimation (ACE) has diagnostic potential for clinical applications such as quantifying fat content in the liver. Previously, we have proposed a system-independent ACE technique based on spectral normalization of different frequencies, called the reference frequency method (RFM). This technique does not require a well-calibrated reference phantom for normalization. However, this method may be vulnerable to severe reverberation clutter introduced by the body wall. The clutter superimposed on liver echoes may bias the estimation. Approach. We proposed to use robust principal component analysis, combined with wavelet-based sparsity promotion, to suppress the severe reverberation clutters. The capability to mitigate the reverberation clutters was validated through phantom and in vivo studies. Main Results. In the phantom studies with added reverberation clutters, higher normalized cross-correlation and smaller mean absolute errors were attained as compared to RFM results without the proposed method, demonstrating the capability to reconstruct tissue signals from reverberations. In a pilot patient study, the correlation between ACE and proton density fat fraction (PDFF), a measurement of liver fat by MRI as a reference standard, was investigated. The proposed method showed an improvement of the correlation (coefficient of determination, R = 0.82) as compared with the counterpart without the proposed method (R = 0.69). Significance: The proposed method showed the feasibility of suppressing the reverberation clutters, providing an important basis for the development of a robust ACE with large reverberation clutters.

Estimation of water attenuation coefficient by imaging modeling of the backscattered light with the pulsed laser range-gated imaging system

Compared to conventional floodlighted imaging systems, a pulsed laser range-gated imaging system can get a real-time high performance underwater image when the distance of target is known. Otherwise, optical properties of the water, mainly the attenuation coefficient of the water, should be obtained to automatically set the parameters of the imaging system. Typically, special instrument is required to measure the attenuation coefficient of the water. In this work, a water attenuation coefficient estimation method is proposed merely with the pulsed laser range-gated imaging system. The imaging model of the backscattered light of the pulsed laser range-gated imaging system is built through the light propagation theory. As a result, the water attenuation coefficient is calculated by nonlinear estimation method. Experiments under different water conditions are designed and carried out to verify the proposed method. Results show that the relative errors between the estimation and the reference are about 1% for the water in the water tank and seawater pool, and about 4% for the towing boat tank, respectively.

Ultrasonic Measurement of Bolt Axial Stress Using the Energy Ratio of Multiple Echoes

The reliability and stability of a mechanical system generally counts on the effective detection and control of the stress state of its bolted connections. In this paper, a novel bolt axial stress evaluation method based on the scattering attenuation of polycrystal using the energy ratio of multiple echoes is proposed. Above all, the longitudinal-incident attenuation coefficients for preload cubic crystalline materials in the Rayleigh regime are introduced. In view of the estimation of frequency-dependent attenuation coefficients are prone to suffer from the disturbance of the cylindrical guided waves, the energy ratios of the first two back-end echoes within the signals are applied instead. Subsequently, a stress measurement experiment is conducted to validate the proposed method, in which the accurate energies of the echoes are evaluated using the S transform. The results reveal that, as long as the probe is firmly bonded, the proposed method can achieve better sensitivity and reliability than that of the existing methods using TOF (Time of flight), especially for short or full-threaded bolts.

An evaluation of remote sensing algorithms for the estimation of diffuse attenuation coefficients in the ultraviolet bands.

In this study, six algorithms (both empirical and semi-analytical) developed for the estimation of Kd in the ultraviolet (UV) domain (specifically 360, 380, and 400 nm) were evaluated from a dataset of 316 stations covering oligotrophic ocean and coastal waters. In particular, the semi-analytical algorithm (Lee et al. 2013) used remote sensing reflectance in these near-blue UV bands estimated from a recently developed deep learning system as the input. For Kd(380) in a range of 0.018 - 2.34 m-1, it is found that the semi-analytical algorithm has the best performance, where the mean absolute relative difference (MARD) is 0.19, and the coefficient of determination (R2) is 0.94. For the empirical algorithms, the MARD values are 0.23-0.90, with R2 as 0.70-0.92, for this evaluation dataset. For a VIIRS and in situ matchup dataset (N = 62), the MARD of Kd(380) is 0.21 (R2 as 0.94) by the semi-analytical algorithm. These results indicate that a combination of deep learning system and semi-analytical algorithms can provide reliable Kd(UV) for past and present satellite ocean color missions that have no spectral bands in the UV, where global Kd(UV) products are required for comprehensive studies of UV radiation on marine primary productivity and biogeochemical processes in the ocean.