An optimized skin texture model using gray-level co-occurrence matrix

Ultrasonic detection of wrinkles in composites with gradual phase shift migration

Fiber reinforced polymer composites (FRPs) are essential for various industrial fields, but wrinkles inside will greatly reduce their mechanical properties. Full-matrix capture (FMC) is a popular data structure for ultrasonic phased array imaging in composites. However, such structure may lead to data redundancy and noise interference. In this paper, a gradual phase shift migration (GPSM) is proposed to characterize wrinkles accurately. The gradual matrix is formed from the expansion along principal diagonal of FMC data with equal transmitter-receiver spacing. The dilemma between lateral resolution and sidelobe interference intensity is resolved to obtain the best imaging resolution by selecting an appropriate data structure. Moreover, to address the inconsistency of ultrasound velocities at different propagation directions caused by anisotropy of composites, the angle-dependant velocity is corrected by backwall reflection method (BRM). Based on gradual matrix data, the velocity-corrected phase shift factor is applied in the GPSM algorithm to obtain the wavefield at different depths through a layer-by-layer wavefield extrapolation. The experimental results indicate that four wrinkles can be detected in thick hybrid carbon-glass FRPs based on GPSM, with angle detection errors less than 6%. Furthermore, the GPSM method combining partial diagonal data takes only 0.5 s, achieving 60% improvement in computational efficiency compared to that with all gradual matrix data. The proposed method can be applied for high-resolution imaging of various multilayered medium in real-time.

Quantitative Evaluation of the Effectiveness of Chemical Peelings in Reducing Acne Lesions Based on Gray-Level Co-Occurrence Matrix (GLCM)

Purpose

Acne vulgaris is a chronic, inflammatory disease accompanied by lesions affecting the structure of the skin. Chemical peels are one of the methods of reducing acne vulgaris. There is still a lack of quantitative methods of assessing impact of cosmetic procedure on the skin. Skin condition depends on skin texture characterization; therefore, the analysis that provides data about the textures can be helpful in assessing the effectiveness of cosmetic treatments.

Patients and Methods

The study involved 24 volunteers with acne lesions. Each participant underwent 4 treatments using chemical peels at two-week intervals. Before, during and after procedure clinical photography were made. To assess effectiveness of chemical peeling in acne lesion reduction, we were used gray-level co-occurrence matrix (GLCM) analysis. Qualitative assessment of acne severity was made by 12 experts in dermatology.

Results

After a series of treatments, the GLCM contrast value decreased in each area of the face, and the GLCM homogeneity value increased, which means that the number of acne lesions was reduced. Expert assessment according to the IGA scale confirms the effectiveness of therapy with both salicylic and glycolic acid and pyruvic acid.

Conclusion

The results of this study prove that GLCM analysis is a useful tool for assessing the effectiveness of chemical peel treatments. It can also be used for quantitative assessment of skin texture.

Quantitative Response of Gray-Level Co-Occurrence Matrix Texture Features to the Salinity of Cracked Soda Saline-Alkali Soil

Desiccation cracking during water evaporation is a common phenomenon in soda saline–alkali soils and is mainly determined by soil salinity. Therefore, quantitative measurement of the surface cracking status of soda saline–alkali soils is highly significant in different applications. Texture features can help to determine the mechanical properties of soda saline–alkali soils, thus improving the understanding of the mechanism of desiccation cracking in saline–alkali soils. This study aims to provide a new standard describing the surface cracking conditions of soda saline–alkali soil on the basis of gray-level co-occurrence matrix (GLCM) texture analysis and to quantitatively study the responses of GLCM texture features to soil salinity. To achieve this, images of 200 field soil samples with different surface cracks were processed and calculated for GLCMs under different parameters, including directions, gray levels, and step sizes. Subsequently, correlation analysis was then conducted between texture features and electrical conductivity (EC) values. The results indicated that direction had little effect on the GLCM texture features, and that four selected texture features, contrast (CON), angular second moment (ASM), entropy (ENT), and homogeneity (HOM), were the most correlated with EC under a gray level of 2 and step size of 1 pixel. The results also showed that logarithmic models can be used to accurately describe the relationships between EC values and GLCM texture features of soda saline–alkali soils in the Songnen Plain of China, with calibration R² ranging from 0.88 to 0.92, and RMSE from 2.12 × 10⁻⁴ to 9.68 × 10⁻³, respectively. This study can therefore enhance the understanding of desiccation cracking of salt-affected soil to a certain extent and can also help to improve the detection accuracy of soil salinity.

Fairness-Oriented Semichaotic Genetic Algorithm-Based Channel Assignment Technique for Node Starvation Problem in Wireless Mesh Networks

Wireless mesh networks (WMNs) have emerged as a scalable, reliable, and agile wireless network that supports many types of innovative technologies such as the Internet of Things (IoT), Wireless Sensor Networks (WSN), and Internet of Vehicles (IoV). Due to the limited number of orthogonal channels, interference between channels adversely affects the fair distribution of bandwidth among mesh clients, causing node starvation in terms of insufficient bandwidth distribution, which impedes the adoption of WMN as an efficient access technology. Therefore, a fair channel assignment is crucial for the mesh clients to utilize the available resources. However, the node starvation problem due to unfair channel distribution has been vastly overlooked during channel assignment by the extant research. Instead, existing channel assignment algorithms equally distribute the interference reduction on the links to achieve fairness which neither guarantees a fair distribution of the network bandwidth nor eliminates node starvation. In addition, the metaheuristic-based solutions such as genetic algorithm, which is commonly used for WMN, use randomness in creating initial population and selecting the new generation usually leading the search to local minima. To this end, this study proposes a Fairness-Oriented Semichaotic Genetic Algorithm-Based Channel Assignment Technique (FA-SCGA-CAA) to solve node starvation problem in wireless mesh networks. FA-SCGA-CAA maximizes link fairness while minimizing link interference using a genetic algorithm (GA) with a novel nonlinear fairness-oriented fitness function. The primary chromosome with powerful genes is created based on multicriterion links ranking channel assignment algorithm. Such a chromosome was used with a proposed semichaotic technique to create a strong population that directs the search towards the global minima effectively and efficiently. The proposed semichaotic technique was also used during the mutation and parent selection of the new genes. Extensive experiments were conducted to evaluate the proposed algorithm. A comparison with related work shows that the proposed FA-SCGA-CAA reduced the potential node starvation by 22% and improved network capacity utilization by 23%. It can be concluded that the proposed FA-SCGA-CAA is reliable to maintain high node-level fairness while maximizing the utilization of the network resources, which is the ultimate goal of many wireless networks.