Reflectance Spectra Analysis Algorithms for the Characterization of Deposits and Condensed Traces on Surfaces

Identification of particulate matter and liquid spills contaminations is essential for many applications, such as forensics, agriculture, security, and environmental protection. For example, toxic industrial compounds deposition in the form of aerosols, or other residual contaminations, pose a secondary, long-lasting health concern due to resuspension and secondary evaporation. This chapter explores several approaches for employing diffuse reflectance spectroscopy in the mid-IR and SWIR to identify particles and films of materials in field conditions. Since the behavior of thin films and particles is more complex compared to absorption spectroscopy of pure compounds, due to the interactions with background materials, the use of physical models combined with statistically-based algorithms for material classification, provides a reliable and practical solution and will be presented.

2D ELASTICITY TENSOR INVARIANTS, INVARIANTS DEFINITE POSITIVE CRITERIA

In this paper, we constructed relationships with the differents 2D elasticity tensor invariants. Indeed, let ${\bf A}$ be a 2D elasticity tensor. Rotation group action leads to a pair of Lax in linear elasticity. This pair of Lax leads to five independent invariants chosen among six. The definite positive criteria are established with the determined invariants. We believe that this approach finds interesting applications, as in the one of elastic material classification or approaches in orbit space description.

CNN Algorithm for Roof Detection and Material Classification in Satellite Images

This paper suggests an algorithm for extracting the location of a building from satellite imagery and using that information to modify the roof content. The materials are determined by measuring the conditions where the building is located and detecting the position of a building in broad satellite images. Depending on the incomplete roof or material, there is a greater possibility of great damage caused by disaster situations or external shocks. To address these problems, we propose an algorithm to detect roofs and classify materials in satellite images. Satellite imaging locates areas where buildings are likely to exist based on roads. Using images of the detected buildings, we classify the material of the roof using a proposed convolutional neural network (CNN) model algorithm consisting of 43 layers. In this paper, we propose a CNN structure to detect areas with buildings in large images and classify roof materials in the detected areas.

Pengembangan E-Modul Klasifikasi Materi dan Perubahannya Berbasis Pendekatan Saintifik pada Kelas VII SMP/MTs

This research is motivated by the Covid-19 pandemic which requires the learning process that should take place at school to be transferred to at home. The purpose of this study was to develop an e-module for material classification and its changes based on a scientific approach for seventh grade junior high schools. The research method used is Research and Development (RD) or development research with a 4-D development model. To produce E-module, the validity and practicality tests were carried out with the research instrument used, namely the validity questionnaire, the results of which were analyzed using the Kappa Cohen formula. This study produced an e-module of material classification and its changes based on a scientific approach with the average data obtained with the kappa moment of 0.88 with a very high level of validity, while the results of the teacher practicality test and the practicality test of students obtained the average kappa moment ( k) 0,86 and 0,87, respectively, with a very high level of practicality. Penelitian ini dilatarbelakangi oleh pandemi Covid-19 yang mengharuskan proses pembelajaran yang seharusnya berlangsung di sekolah dialihkan menjadi di rumah. Tujuan penelitian ini adalah mengembangkan e-modul klasifikasi materi dan perubahannya berbasis pendekatan saintifik untuk kelas VII SMP/MTs. Metode penelitian yang dilakukan yaitu Research and Development (RD) atau penelitian pengembangan dengan model pengembangan 4-D. Untuk menghasilkan E-modul yang standar dan sesuai kebutuhan, maka dilakukan uji validitas dan uji praktikalitas. Instrumen penelitian yang digunakan yaitu angket validitas yang hasilnya akan dianalisis menggunakan formula Kappa Cohen. Penelitian ini telah menghasilkan e-modul klasifikasi materi dan perubahannya berbasis pendekatan saintifik diperoleh nilai momen kappa sebesar 0,88 dengan kategori sangat tinggi, sedangkan hasil uji praktikalitas guru dan uji praktikalitas peserta didik diperoleh rata-rata momen kappa (k) secara berturut-turut 0,86 dan 0,87 dengan kategori sangat tinggi.