The genetic algorithm is one of the most powerful optimization algorithms in electromagnetism. In this paper, we optimized gain of a microstrip patch antenna using genetic algorithms. The cavity method used for the analysis of this antenna. The simulation results were compared with results obtained in previous work. The resulting gave gain more than the previous one Manuscript profile

Road detection from high-resolution satellite images using deep learning is proposed in this article. The VGG19 architecture, which is one of the deep convolutional neural network architectures, is used in the proposed method. To detect the road, two steps are implemented. To achieve high accuracy, image segmentation is done in the first step. At this stage, based on the semantic division, the objects whose area is small are removed. In the second stage, edge detection of images combines two techniques of segmentation and edge detection to improve road detection. Considering the good accuracy of the VGG19 architecture and the need for few parameters, the obtained results are favorable. To check the performance of the proposed method, the IoU criterion was used. The values obtained for this criterion show an improvement of more than 80%. While this criterion is less than 80% for the compared methods. The obtained results can be used for the purposes of digital mapping, transportation management and many other applications. Manuscript profile

In biosensors, the investigated material is biological components such as cells, proteins, and nucleic acids. All compounds of living organisms have sensors with functions similar to mechanical sensors. Most of these sensors are special cells sensitive to the external environment's physical characteristics such as light, movement, temperature, magnetic field, gravity, humidity, vibration, pressure, electric fields, sound, etc. Biosensors can be made using photonic crystals, an example of which is designed here. In order to check the biomolecule detection ability by the biosensor, the refractive index around the sensing hole is changed and the output transmission spectrum is limited. By changing the refractive index, the transmission spectrum can be measured, which enables the sensor to identify biological molecules. In this study, a sensor was designed that is capable of detecting biological molecules. This sensor has good sensitivity, which makes it capable of measuring in a small size. The sensor can accurately predict the result, so this sensor is very suitable for medical purposes. Manuscript profile

In recent years, smart cars have advanced rapidly and use artificial intelligence technology to predict behavior, make decisions, and control goals. This technology significantly determines the knowledge level of vehicles. In the complex and dynamic environment of road traffic, negligence, and inattention can lead to irreparable damage. Real-time identification and positioning of road lines are key to improving the safety of driving cars. To improve the performance of safe driving assistance, this paper shows how to detect road lines using the YOLOV8 algorithm and then make decisions to continue driving straight, turn right, and turn left. Simulation results and accuracy comparison show that this approach can be used as a reliable source for creating driving assistance scenarios in natural road traffic environments. The use of artificial intelligence and the precise architecture of YOLOV8 promise high speed and accuracy in smart cars. Manuscript profile