Optimal Fitting Method of Nonlinear Simultaneous Equations Considering Structural Tensor Image Modeling

For the purpose of resolving the phenomenon of network congestion in the process of many-to-one communication in multimedia networks at present, the optimal fitting method of nonlinear simultaneous equations (OFMNSEs) is applied to the multimedia transmission congestion control system in this paper. The rate control and resource scheduling are effectively combined, and a clustering network structure is used to activate the congestion control method in turn based on the cluster header and intracluster-related indexes. Finally, it can be known through the analysis of the simulation results that the OFMNSE algorithm put forward in this paper can improve the congestion issue of the multimedia network transmission process and reduce the packet loss rate of data during the transmission effectively under the condition of different relative cache sizes compared with the conventional algorithm.

Mathematical Modeling for Ceramic Shape 3D Image Based on Deep Learning Algorithm

Ceramic image shape 3D image modeling focuses on of ceramic that was obtained from the camera imaging equipment such as 2D images, by normalization, gray, filtering denoising, wavelet image sharpening edge enhancement, binarization, and shape contour extraction pretreatment processes such as extraction ceramic image shape edge profile, again, according to the image edge extraction and elliptic rotator ceramics phenomenon. The image distortion effect was optimized by self-application, and then the deep learning modeler was used to model the side edge contour. Finally, the 3D ceramic model of the rotating body was restored according to the intersection and central axis of the extracted contour. By studying the existing segmentation methods based on deep learning, the automatic segmentation of target ceramic image and the effect of target edge refinement and optimization are realized. After extracting and separating the target ceramics from the image, we processed the foreground image of the target into a three-dimensional model. In order to reduce the complexity of the model, a 3D contextual sequencing model is adopted to encode the hidden space features along the channel dimensions, to extract the causal correlation between channels. Each module in the compression framework is optimized by a rate-distortion loss function. The experimental results show that the proposed 3D image modeling method has significant advantages in compression performance compared with the optimal 2D 3D image modeling method based on deep learning, and the experimental results show that the performance of the proposed method is superior to JP3D and HEVC methods, especially at low bit rate points.

A Synthesis of Land Use/Land Cover Studies: Definitions, Classification Systems, Meta-Studies, Challenges and Knowledge Gaps on a Global Landscape

Land is a natural resource that humans have utilized for life and various activities. Land use/land cover change (LULCC) has been of great concern to many countries over the years. Some of the main reasons behind LULCC are rapid population growth, migration, and the conversion of rural to urban areas. LULC has a considerable impact on the land-atmosphere/climate interactions. Over the past two decades, numerous studies conducted in LULC have investigated various areas of the field of LULC. However, the assemblage of information is missing for some aspects. Therefore, to provide coherent guidance, a literature review to scrutinize and evaluate many studies in particular topical areas is employed. This research study collected approximately four hundred research articles and investigated five (5) areas of interest, including (1) LULC definitions; (2) classification systems used to classify LULC globally; (3) direct and indirect changes of meta-studies associated with LULC; (4) challenges associated with LULC; and (5) LULC knowledge gaps. The synthesis revealed that LULC definitions carried vital terms, and classification systems for LULC are at the national, regional, and global scales. Most meta-studies for LULC were in the categories of direct and indirect land changes. Additionally, the analysis showed significant areas of LULC challenges were data consistency and quality. The knowledge gaps highlighted a fall in the categories of ecosystem services, forestry, and data/image modeling in LULC. Core findings exhibit common patterns, discrepancies, and relationships from the multiple studies. While literature review as a tool showed similarities among various research studies, our results recommend researchers endeavor to perform further synthesis in the field of LULC to promote our overall understanding, since research investigations will continue in LULC.

A Pixel-Dependent Finite Element Model for Spatial Frequency Domain Imaging Using NIRFAST

Spatial frequency domain imaging (SFDI) utilizes the projection of spatially modulated light patterns upon biological tissues to obtain optical property maps for absorption and reduced scattering. Conventionally, both forward modeling and optical property recovery are performed using pixel-independent models, calculated via analytical solutions or Monte-Carlo-based look-up tables, both assuming a homogenous medium. The resulting recovered maps are limited for samples of high heterogeneity, where the homogenous assumption is not valid. NIRFAST, a FEM-based image modeling and reconstruction tool, simulates complex heterogeneous tissue optical interactions for single and multiwavelength systems. Based on the diffusion equation, NIRFAST has been adapted to perform pixel-dependent forward modeling for SFDI. Validation is performed within the spatially resolved domain, along with homogenous structured illumination simulations, with a recovery error of <2%. Heterogeneity is introduced through cylindrical anomalies, varying size, depth and optical property values, with recovery errors of <10%, as observed across a variety of simulations. This work demonstrates the importance of pixel-dependent light interaction modeling for SFDI and its role in quantitative accuracy. Here, a full raw image SFDI modeling tool is presented for heterogeneous samples, providing a mechanism towards a pixel-dependent SFDI image modeling and parameter recovery system.

Kemampuan Pemodelan Matematis dalam Menyelesaikan Soal Matematika Kontekstual

AbstrakKemampuan guru menghadirkan soal-soal yang memiliki konteks kehidupan sehari-hari sangat dibutuhkan dalam kelas. Penelitian ini bertujuan untuk mendeskripsikan dan menganalisis kemampuan pemodelan matematis siswa dalam menyelesaikan soal matematika kontekstual. Penelitan ini dirasa penting untuk dikaji karena tuntutan pembelajaran yang mengharuskan siswa tidak hanya pandai berhitung tetapi dapat mengaplikasikan matematika dalam kehidupan, sehingga kemampuan pemodelan matematika sebagai jembatan antara masalah matematika dan masalah nyata dirasa penting untuk dimiliki oleh siswa. Subjek penelitian diberikan instrumen berupa soal matematika kontekstual, kemudian peneliti menganalisis hasil kerja subjek serta melakukan wawancara subjek terkait hasil pengerjaan instrumen tersebut. Dari hasil penelitian ini disimpulkan bahwa kemampuan pemodelan matematis siswa beragam, tidak bergantung pada kemampuan matematika siswa tinggi, sedang, rendah; masih ada siswa yang tidak membuat pemodelan matematis karena tidak memahami soal yang diberikan; pemodelan gambar yang dibuat oleh siswa beragam namun sebagian besar masih kurang tepat dalam membuat pemodelan gambar sesuai dengan permasalahan yang diberikan; kemampuan siswa dalam membuat pemodelan matematis sebagian besar masih kurang. Mathematical Modeling Ability in Solving Contextual Mathematical ProblemsAbstractThe teacher's ability to present questions that have the context of everyday life is needed in the classroom. This study aims to describe and analyze students' mathematical modeling abilities in solving contextual math problems. This research is considered important to study because the demands of learning require students not only to be good at arithmetic but also to be able to apply mathematics in life, the ability of mathematical modeling as a bridge between mathematical problems and real problems is considered important for students to have. The research subject was given an instrument in the form of a contextual math problem, then the researcher analyzed the subject's work and conducted subject interviews related to the results of working on the instrument. The results are mathematical modeling abilities of students varied, it did not depend on high, medium, low students' mathematical abilities; There are still students who do not make mathematical modeling because they don't understand the questions given; the forms of image modeling made by students are still diverse but most of them are still inaccurate in making image modeling according to the problems give and making mathematical modeling is still lacking.