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Metamaterial antennas are some sort of antennas which utilize metamaterials to improve the efficiency of small (small electrical) antenna systems and also enhance the antenna's radiation pattern. The unusual properties of metamaterials such as coefficients of permittivity and permeability, as well as the negative refractive index, have led to increase the use of these artificial materials in variant fields including microwave engineering and medical applications. This paper explores a metamaterial coating and impact of it on a microstrip patch antenna from two different perspectives on satellite and biomedical applications. For this purpose, a microstrip antenna is modeled using CST software and then a particular metamaterial cover is configured upon the patch antenna to enhance its efficiency such as radiation pattern and directivity. The method is arranged in several stages and the output of the antenna is then analyzed in each stage after applying some adjustments to the design process. On the other hand, because the type and position of the applicator on the tissue is a consequential variable, in order to improve the effectiveness of hyperthermia treatment in medical applications, in the next step the metamaterial antenna is applied to hyperthermia treatment on the body tissue. Eventually the results of metamaterial antenna are compared with the usual antenna.

License plates typically have unique color, size, and shape characteristics in each country. This paper presents a general method for character extraction and pattern matching in license plate recognition systems. The proposed method is based on a combination of morphological operations and edge detection techniques, along with the bounding box method for identifying and revealing license plate characters while removing unwanted artifacts such as dust and fog. The mathematical model of foggy images is presented and the sum of gradients of the image, which represents the visibility of the image, is improved. Previous works on license plate recognition have utilized non-intelligent pattern matching techniques. The proposed technique can be applied in a variety of settings, including traffic monitoring, parking management, and law enforcement, among others. The applied algorithm, unlike SOTA-based methods, does not need a huge set of training data and is implemented only by applying standard templates. The main advantages of the proposed algorithm are the lack of a need for a training set, the high speed of the training process, the ability to respond to different standards, the high response speed, and higher accuracy compared to similar tasks.