Design of Deep Convolution Neural Networks for categorical signature classification of raw panchromatic satellite images

Multimedia Tools and Applications - Remote Sensing categorical signature classification has gained significant implications on spatial resolution image analysis due to differences in the...     

A Hybrid Analytic Model for the Effective Prediction of Different Stages in Chronic Kidney Ailments

Wireless Personal Communications - Chronic kidney disease (CKD) is a gradual loss of kidney function over the period of time and it is irrevocable once functionality reaches the critical state....     

A comparison of emulsifiers for the formation of oil-in-water emulsions: stability of the emulsions within 9 h after production and MR signal properties

Objective

To provide a basis for the selection of suitable emulsifiers in oil-in-water emulsions used as tissue analogs for MRI experiments. Three different emulsifiers were investigated with regard to their ability to stabilize tissue-like oil-in-water emulsions. Furthermore, MR signal properties of the emulsifiers themselves and influences on relaxation times and ADC values of the aqueous phase were investigated.

Materials and methods

Polysorbate 60, sodium dodecyl sulfate (SDS) and soy lecithin were used as emulsifiers. MR characteristics of emulsifiers were assessed in aqueous solutions and their function as a stabilizer was examined in oil-in-water emulsions of varying fat content (10, 20, 30, 40, 50%). Stability and homogeneity of the oil-in-water emulsions were evaluated with a delay of 3 h and 9 h after preparation using T₁ mapping and visual control. Signal properties of the emulsifiers were investigated by ¹H-MRS in aqueous emulsifier solutions. Relaxometry and diffusion weighted MRI (DWI) were performed to investigate the effect of various emulsifier concentrations on relaxation times (T₁ and T₂) and ADC values of aqueous solutions.

Results

Emulsions stabilized by polysorbate 60 or soy lecithin were stable and homogeneous across all tested fat fractions. In contrast, emulsions with SDS showed a significantly lower stability and homogeneity. Recorded T₁ maps revealed marked creaming of oil droplets in almost all of the emulsions with SDS. The spectral analysis showed several additional signals for polysorbate and SDS. However, lecithin remained invisible in ¹H-MRS. Relaxometry and DWI revealed different influences of the emulsifiers on water: Polysorbate and SDS showed only minor effects on relaxation times and ADC values of aqueous solutions, whereas lecithin showed a strong decrease in both relaxation times (r_1,lecithin = 0.11 wt.%⁻¹ s⁻¹, r_2,lecithin = 0.57 wt.%⁻¹ s⁻¹) and ADC value (Δ(ADC)_lecithin =  − 0.18 × 10^–3 mm²/s⋅wt.%) with increasing concentration.

Conclusion

Lecithin is suggested as the preferred emulsifier of oil-in-water emulsions in MRI as it shows a high stabilizing ability and remains invisible in MRI experiments. In addition, lecithin is suitable as an alternative means of adjusting relaxation times and ADC values of water.

     

Towards an integrated neonatal brain and cardiac examination capability at 7 T: electromagnetic field simulations and early phantom experiments using an 8-channel dipole array

Objective

Neonatal brain and cardiac imaging would benefit from the increased signal-to-noise ratio levels at 7 T compared to lower field. Optimal performance might be achieved using purpose designed RF coil arrays. In this study, we introduce an 8-channel dipole array and investigate, using simulations, its RF performances for neonatal applications at 7 T.

Methods

The 8-channel dipole array was designed and evaluated for neonatal brain/cardiac configurations in terms of SAR efficiency (ratio between transmit-field and maximum specific-absorption-rate level) using adjusted dielectric properties for neonate. A birdcage coil operating in circularly polarized mode was simulated for comparison. Validation of the simulation model was performed on phantom for the coil array.

Results

The 8-channel dipole array demonstrated up to 46% higher SAR efficiency levels compared to the birdcage coil in neonatal configurations, as the specific-absorption-rate levels were alleviated. An averaged normalized root-mean-square-error of 6.7% was found between measured and simulated transmit field maps on phantom.

Conclusion

The 8-channel dipole array design integrated for neonatal brain and cardiac MR was successfully demonstrated, in simulation with coverage of the baby and increased SAR efficiency levels compared to the birdcage. We conclude that the 8Tx-dipole array promises safe operating procedures for MR imaging of neonatal brain and heart at 7 T.

     

Influence of Titanium Excess in a Ti–Si Reaction Mixture on the Synthesis of a Composite in a Combustion Wave

Theoretical Foundations of Chemical Engineering - On the basis of the classic concepts of the theory of solid-phase combustion, for the first time, a model with a detailed scheme of chemical...     

Low light enhancement algorithm for color images using intuitionistic fuzzy sets with histogram equalization

Multimedia Tools and Applications - In this work, a new fuzzy logic-based algorithm is proposed for the enhancement of low light color images. A generalization of a fuzzy set known as an...     

Endometriosis detection and localization in laparoscopic gynecology

Multimedia Tools and Applications - Endometriosis is a common gynecologic condition typically treated via laparoscopic surgery. Its visual versatility makes it hard to identify for non-specialized...     

直通链路技术的发展与展望

直通链路技术己广泛应用于车联网场景。对于直通链路技术的潜在技术方向给出可行的建议，包括传统直通链路技术的增强方向，如载波聚合、使用非授权频谱等；侧行链路对于中继场景的应用扩展，包括终端到终端之间的中继，以及中继的多链接场景；在高精度定位场景使用直通链路技术。并且，给出直通链路技术与各种新技术的融合应用，如智能反射面与区块链技术，从而解决直通链路技术自身的缺陷。     

Designing optical glasses by machine learning coupled with a genetic algorithm

Engineering new glass compositions have experienced a sturdy tendency to move forward from (educated) trial-and-error to data- and simulation-driven strategies. In this work, we developed a computer program that combines data-driven predictive models (in this case, neural networks) with a genetic algorithm to design glass compositions with desired combinations of properties. First, we induced predictive models for the glass transition temperature (T_g) using a dataset of 45,302 compositions with 39 different chemical elements, and for the refractive index (n_d) using a dataset of 41,225 compositions with 38 different chemical elements. Then, we searched for relevant glass compositions using a genetic algorithm informed by a design trend of glasses having high n_d (1.7 or more) and low T_g (500 °C or less). Two candidate compositions suggested by the combined algorithms were selected and produced in the laboratory. These compositions are significantly different from those in the datasets used to induce the predictive models, showing that the used method is indeed capable of exploration. Both glasses met the constraints of the work, which supports the proposed framework. Therefore, this new tool can be immediately used for accelerating the design of new glasses. These results are a stepping stone in the pathway of machine learning-guided design of novel glasses.