ABSTRACT
The construction of the four-port MIMO antenna in the form of a sickle is provided in the article. Initially, the single port element is designed and optimized. Next, a structure with two ports is created, and lastly, a design with four ports is completed. This process is repeated until the design is optimized. Three types of parametric analysis are considered, including variations in length, widths of sickle-shaped patches, and varying sizes of DGS. The frequency range of 2-8 GHz is used for structural investigation. The - 18.77 dB of return loss was observed at 3.825 GHz for a single-element structure. The optimized one-port structure provides a return loss of - 19.79 dB at 3.825 GHz. The port design offers a bandwidth of 0.71 GHz (3.515-4.225). The four-port design represents two bands that are observed at 3 GHz and 5.43 GHz. Both bands provide the return loss at respectively - 19.79 dB and - 20.53 dB with bandwidths of 1.375 GHz (2.14-3.515) and 0.25 GHz (5.335-5.585). The healthy isolation among both transmittance and reflectance response is achieved. The low-profile material was used to create the design that was presented. The article includes a comparison of the findings that were measured and those that were simulated. The four-port design that has been shown offers a total gain of 15.93 dB, a peak co-polar value of 5.46 dB, a minimum return loss of - 20.53 dB, a peak field distribution of 46.43 A/m and a maximum bandwidth of 1.375 GHz. The values for all diversity parameters like ECC are near zero, the Negative value of TARC, Near to zero MEG, DG is almost 10 dB, and a zero value of CCL is achieved. All diversity parameter performance is within the allowable range. The design is well suited for 5G and aeronautical mobile communication applications.
ABSTRACT
COVID-19 is one of the dangerous viruses that cause death if the patient doesn't identify it in the early stages. Firstly, this virus is identified in China, Wuhan city. This virus spreads very fast compared with other viruses. Many tests are there for detecting this virus, and also side effects may find while testing this disease. Corona-virus tests are now rare; there are restricted COVID-19 testing units and they can't be made quickly enough, causing alarm. Thus, we want to depend on other determination measures. There are three distinct sorts of COVID-19 testing systems: RTPCR, CT, and CXR. There are certain limitations to RTPCR, which is the most time-consuming technique, and CT-scan results in exposure to radiation which may cause further diseases. So, to overcome these limitations, the CXR technique emits comparatively less radiation, and the patient need not be close to the medical staff. COVID-19 detection from CXR images has been tested using a diversity of pre-trained deep-learning algorithms, with the best methods being fine-tuned to maximize detection accuracy. In this work, the model called GW-CNNDC is presented. The Lung Radiography pictures are portioned utilizing the Enhanced CNN model, deployed with RESNET-50 Architecture with an image size of 255*255 pixels. Afterward, the Gradient Weighted model is applied, which shows the specific separation regardless of whether the individual is impacted by Covid-19 affected area. This framework can perform twofold class assignments with exactness and accuracy, precision, recall, F1-score, and Loss value, and the model turns out proficiently for huge datasets with less measure of time.