Realizing the Effective Detection of Tumor in Magnetic Resonance Imaging using Cluster-Sparse Assisted Super-Resolution

Recently, significant research has been done in Super-Resolution (SR) methods for augmenting the spatial resolution of the Magnetic Resonance (MR) images, which aids the physician in improved disease diagnoses. Single SR methods have drawbacks; they fail to capture self-similarity in non-local patches and are not robust to noise. To exploit the non-local self-similarity and intrinsic sparsity in MR images, this paper proposes the use of Cluster-Sparse Assisted Super-Resolution. This SR method effectively captures similarity in non-locally positioned patches by training on clusters of patches using a self-adaptive dictionary. This method of training also leads to better edge and texture detection. Experiments show that using Cluster-Sparse Assisted Super-Resolution for brain MR images results in enhanced detection of lesions leading to better diagnosis.

Sensible and Latent Heat Fluxes Over A Processing Cassava Crop with The Surface Renewal and Energy Balance Method

There are several methods for determining the sensible heat flux (H) on natural or agricultural surfaces. One such method is the surface renewal (SR) based on ramps of air temperature measured at high frequency by means of an ultra-thin thermocouple. The micrometeorological tower was installed (13°6'39"S, 39°16'46"W, 154 m anm) to assess the suitability of the method in estimating H on industrial cassava cultivation via calibration in relation to the eddy covariance (EC ), this consisted of a 3D anemometer. In both systems, measurements were made at a frequency of 10 Hz and comprised the period from 17/04 to 25/07/2019 (100 days). In addition to high-frequency measurements of air temperature and sonic temperature, measurements of net radiation and ground heat flux were also made, and all data grouped at 30-min intervals for determination of latent heat flux (LE) via balance solution power. It was found that (a) the SR method was adequate to estimate the sensible heat flux (H) over industrial matched with a calibration coefficient equal to 0.96; (b) under conditions of unstable atmospheric stability (daytime) the SR method showed better performance for estimating H compared to stable atmospheric conditions (nighttime); (c) the SR method proved to be adequate for estimating the latent heat flux (LE), in the industrial cassava cultivation with a high degree of correlation (r2 > 0.90), with the EC method as a reference; and (d) in the area cultivated with industrial cassava, it was found that the heat flux in the soil (G) corresponded on average to 6% of the radiation balance.

Ultrasonic Shaking of Glauconite Pellets with Diverse Reagents for a Comparison of Their K–Ar with Already Published Rb–Sr Results

A combined ultrasonic treatment, with de-ionized H2O, dilute HAc or dilute HCl, of three Mid-Miocene glauconite samples was applied to K–Ar date the different separates in order to compare the results with those obtained by the Rb–Sr method using the same three samples and that were analyzed strictly in the same way. Two aliquots yield opposite elemental and K–Ar trends, which suggests different initial mineral compositions for the various pellets. The K–Ar data of two untreated and leached L7 and L8 aliquots are almost within analytical uncertainty from 17.3 ± 0.6 Ma to 19.6 ± 0.7 Ma (2σ), while those of the third L10 sample are slightly higher at 22.1 ± 1.2 Ma (2σ). Comparatively, the earlier published Rb–Sr ages of the three untreated samples and of the leached aliquots gave similar data for the L7 aliquots by an isochron at 18.1 ± 3.1 (2σ) Ma and for the sample L8 by an isochron with an age of 19.6 ± 1.8 (2σ) Ma, while the untreated L10 aliquot yields a very high Rb–Sr date of 42.1 ± 1.6 (2σ) Ma. This untreated L10 glauconite fraction contains blödite, a Sr-rich carbonate that impacted the two isotopic systems differently. Generally, dilute HCl or HAc acids dissolve carbonates, sulfates, sulfites and oxides, while they do not affect the clay-type crystals such as glauconites. These soluble minerals can be identified indirectly, as here, by X-ray diffraction and the amounts of leached Na2O, CaO and Fe2O3 contents. Together with the leaching of some metallic trace elements, those of NaO confirm the leaching of metals and of blödite that are both hosted by the glauconite pellets. The occurrence of this Sr-enriched mineral explains the age differences of the non-treated aliquots and suggests a systematic leaching of any glauconite separate before isotope determination and, possibly, a comparison of the Rb–Sr and K–Ar results. Ultrasonic shaking appears appropriate for physical disaggregation of any contaminating grains that may remain hosted within the pellets, even after a preliminary H2O wash, which may dissolve and remove the soluble minerals but not the H2O-insoluble silicates. The K–Ar study completed here as a complement to a previous Rb–Sr study highlights, again, the importance of the preparation step in isotopic studies of glauconite-type and, by extension, of any clay material, as all occurring minerals can interfere in the final age determinations and, therefore, differently in the mineral assemblages. All those not in isotopic equilibrium need to be removed before analysis, including the soluble Sr or alkali-enriched ones.

Weak Signal Enhancement For Small Drill Condition Monitoring In PCB Drilling Process By Using Adaptive Multistable Stochastic Resonance

The on-line tool condition monitoring is demanded to detect the tool wear and to ensure the hole drilling process of printed circuit boards (PCB) goes on smoothly. However, due to the impact of ambient noise caused by the limited size of small drill and the laminated material of PCB, the tool wear signal features are too weak to extract. The stochastic resonance (SR) method has been proven to be effective in enhancing weak signals among various weak signal extraction. In this paper, an adaptive multistable stochastic resonance is presented to improve performance of the SR method and process the tool wear signals for PCB drilling. The differential evolution (DE) algorithm is applied to adaptively optimize potential parameters and compensation factor, which makes the SR method suitable for high frequency signal. Moreover, tool wear experiments with different drill wear are carried out to verify the effectiveness of the proposed method. The results indicate that the proposed method improves the signal-to-noise ratio and has great potential in enhancing weak signals for small drill condition monitoring in PCB drilling process.

An improved method for analysis of interrupted time series (ITS) data: accounting for patient heterogeneity using weighted analysis

Interrupted time series (ITS) design is commonly used to evaluate the impact of interventions in healthcare settings. Segmented regression (SR) is the most commonly used statistical method and has been shown to be useful in practical applications involving ITS designs. Nevertheless, SR is prone to aggregation bias, which leads to imprecision and loss of power to detect clinically meaningful differences. The objective of this article is to present a weighted SR method, where variability across patients within the healthcare facility and across time points is incorporated through weights. We present the methodological framework, provide optimal weights associated with data at each time point and discuss relevant statistical inference. We conduct extensive simulations to evaluate performance of our method and provide comparative analysis with the traditional SR using established performance criteria such as bias, mean square error and statistical power. Illustrations using real data is also provided. In most simulation scenarios considered, the weighted SR method produced estimators that are uniformly more precise and relatively less biased compared to the traditional SR. The weighted approach also associated with higher statistical power in the scenarios considered. The performance difference is much larger for data with high variability across patients within healthcare facilities. The weighted method proposed here allows us to account for the heterogeneity in the patient population, leading to increased accuracy and power across all scenarios. We recommend researchers to carefully design their studies and determine their sample size by incorporating heterogeneity in the patient population.

A No-Reference CNN-Based Super-Resolution Method for KOMPSAT-3 Using Adaptive Image Quality Modification

In recent years, research on increasing the spatial resolution and enhancing the quality of satellite images using the deep learning-based super-resolution (SR) method has been actively conducted. In a remote sensing field, conventional SR methods required high-quality satellite images as the ground truth. However, in most cases, high-quality satellite images are difficult to acquire because many image distortions occur owing to various imaging conditions. To address this problem, we propose an adaptive image quality modification method to improve SR image quality for the KOrea Multi-Purpose Satellite-3 (KOMPSAT-3). The KOMPSAT-3 is a high performance optical satellite, which provides 0.7-m ground sampling distance (GSD) panchromatic and 2.8-m GSD multi-spectral images for various applications. We proposed an SR method with a scale factor of 2 for the panchromatic and pan-sharpened images of KOMPSAT-3. The proposed SR method presents a degradation model that generates a low-quality image for training, and a method for improving the quality of the raw satellite image. The proposed degradation model for low-resolution input image generation is based on Gaussian noise and blur kernel. In addition, top-hat and bottom-hat transformation is applied to the original satellite image to generate an enhanced satellite image with improved edge sharpness or image clarity. Using this enhanced satellite image as the ground truth, an SR network is then trained. The performance of the proposed method was evaluated by comparing it with other SR methods in multiple ways, such as edge extraction, visual inspection, qualitative analysis, and the performance of object detection. Experimental results show that the proposed SR method achieves improved reconstruction results and perceptual quality compared to conventional SR methods.

An Enhanced Double-Filter Deep Residual Neural Network for Generating Super Resolution DEMs

High-resolution DEMs are important spatial data, and are used in a wide range of analyses and applications. However, the high cost to obtain high-resolution DEM data over a large area through sensors with higher precision poses a challenge for many geographic analysis applications. Inspired by the convolution neural network (CNN) excellent performance in super-resolution (SR) image analysis, this paper investigates the use of deep residual neural networks and low-resolution DEMs to generate high-resolution DEMs. An enhanced double-filter deep residual neural network (EDEM-SR) method is proposed, which uses filters with different receptive field sizes to fuse and extract features and reconstruct a more realistic high-resolution DEM. The results were compared with those generated with the bicubic, bilinear, and EDSR methods. The numerical accuracy and terrain feature preserving effects of the EDEM-SR method can generate reconstructed DEMs that better match the original DEMs, show lower MAE and RMSE, and improve the accuracy of the terrain parameters. MAE is reduced by about 30 to 50% compared with traditional interpolation methods. The results show how the EDEM-SR method can generate high-resolution DEMs using low-resolution DEMs.

U-Pb age and trace elements composition of titanite from granites of Belokurikhinsky massif, Gorny Altai

As a result of isotope-geochemical study, the age data (U-Pb method, ID-TIMS) of titanite from the first phase granites of the Belokurikhinsky granite massif, Gorny Altai, were obtained for the first time. The concordant value of the titanite age of 255 ± 2 Ma coincides within the margin of error with the previously published results of dating micas from granites of the second and third phases of the Belokurikha massif by the Ar-Ar method (250 ± 3 Ma). At the same time, the results of dating differ significantly from the previously published age values for the granites of the Belokurikha massif (232 ± 5 Ma, U-Pb method for the monofraction of zircon grains; 245 ± 8 Ma, Rb-Sr method for the whole rocks). Therefore, there is every reason to narrow the time interval of the formation of the Belokurikha granite massif to 255-250 Ma. The study of the trace element composition of titanite by SIMS demonstrated their zonal structure. The central part of the titanite grain differs from the rim by a noticeably higher content of REE, Cr, Y, and Nb. The content of V, Zr and Ba decreases to a lesser extent towards the rim, the content of Sr and U remains constant. At the same time, the REE distribution spectra in the central and rim parts are conformal to each other, having a convex spectrum for LREE and a concave one for HREE. Titanite is characterized by a negative Eu-anomaly, the depth of which decreases to the rim of the grain. A negative Eu-anomaly indicates the co-crystallization of titanite and plagioclase. The REE distribution spectra in titanite from the Belokurikha massif correspond to the characteristics of a typical magmatic titanite from granitoids and differ significantly from the distribution spectra in metamorphic titanite.