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The dual band wearable wideband antennas have been fabricated by reinforced epoxy materials, which have been printed on textile woven for breast tumor diagnosis application. These proposed antennas have been implemented by Microwave Imaging Device. These microstrip slots have been constructed on the wearable cotton and polyester materials and tested by (Agilent N99917A) Microwave Analyzer. They have been offered the dual resonant frequencies 5.22 GHz and 2.67 GHz respectively. Therefore, these wearable antennas have not emitted the reflection loss on the woven materials. Hence, these proposed antenna have a low profile, wideband structure and also provides the wide coverage area. Therefore, these proposed prototypes have been authorized their measurement analysis.

In this research, a 2 × 2 array pattern of EF shaped microstrip slot has woven on textile materials. These prototypes also diagnosed the depth of the cancer in the cancerous organ with the breast equivalent component of the composite model. The use of breast cancer has been explored in the Microwave Imaging (MI) process. For these wearable modules, there was no radiation at all on the human body. It assess the depth and the time of travel by the velocity of propagation, the distance between the cancerous organ and the transceiver antenna of cancerous tissues in the cancerous organ. Thus these recipes were derived from the cancerous organ in the Debye test bed. Thus, in the new Debye research beds the number of depth values validated and the depth cover in cancerous organ.

The goal of this analysis is to compute cancer organ tissue depth numerical values. One big goal is to strengthen and use broadband antennas to detect organ cancer. A 2x2 grid antenna in the form of a ΨU was constructed using the mathematical modeling of the grid using geographic coordinates of: simulated by ADS; optimized by Moments of Methods (MoM), designed as a prototype using MMIC (Microwave Monolithically Integrated Circuits); analyzed with Agilent N99917A, a described microwave analyzer. It is compactly formed 18.64x17.92x1 mm3. The 2x2 model comprises four diameter ΨU radiation slots with a dielectric thickness (1 mm) from FR4 of 1 mm and a conductive soil. The frequency of operation is 5.26 GHz, which includes the broadband between 4.5 GHz and 5.5.89 GHz. It also features a broadband antenna with four slots and a low-profile structure. The broadband antennas were used to detect breast cancer, which is a secondary distribution of study. This broadband antenna is available on existing Debye test beds, with a dielectric model identical to that of the human breast. These large-scale antennas have impressed the coupling materials that act as a link. The results were spread better than the air media. These materials are connected by the higher and lower the dielectric strengths. That was the average number of responses distributed. The velocity of propagation was observed in the cancerous organ between the cancerous organ and the transceiver, dielectric strength and time to travel by scattered responses. Those reactions were thus received from the cancer organ in the test bed. Therefore it expands the numerical depth values and the coverage of cancerous organ validated by existing Debye test beds.

The EF shaped microstrip patch antenna has been fabricated by reinforced epoxy materials and printed on textile woven for breast cancer detection application is proposed in this research. The proposed wearable antennas have been examined with composite materials for coverage of cancer in cancerous organ, which results have been implemented for Microwave Imaging System. These proposed microstrip slots have been printed on the cotton and polyester woven materials by hand block printing technology. They have been offered the resonant at 3.32 GHz and 3.33 GHz respectively, which measured by Agilent (N99917A) Microwave Analyzer. These proposed results have been achieved 99.9% accuracy with fabricated prototypes. Since, these wearable modules have not reflected any kind of losses, which mounted on woven materials. Therefore, the proposed module has a low profile, wideband structure and also provide the wideband of coverage in terms of 27.7% and 26.13% respectively. Therefore, these proposed woven antennas have been examined with their measurement analysis.

This article uses cutting-edge deep learning technology to identify structural damage from images for a civil engineering application. The public infrastructures of the country are generally inspected physically by a visual evaluation by qualified inspectors. However, manual inspections are pretty time-consuming and often require too much labor. The number of experts capable of evaluating such structural damage is inadequate. As a result, computer vision-based techniques for automatic damage detection have been developed. This paper’s civil infrastructure damages are classified into four damages of roads common in Indian highways and the concrete deterioration in the bridges. The convolutional neural network has become a standard tool for organizing and recognizing images. In this paper, an ensemble of three CNN models is proposed, and two are transfer learning-based models. The proposed ensemble transfer learning model provided a validation accuracy of 87.1%.