A new compact four‐port multi‐input‐multi‐output (MIMO) dielectric resonator (DR) antenna is proposed with pattern diversity. The antenna contains four DR elements, the epsilon‐shaped and cylindrical. These four DR elements are placed together such that they resonate at the same frequency. The specific geometry of the DR elements allows to place them at separation which is much smaller than operating wavelength. Hence, the combination of the four DR elements acts as a single DR. This helps in maintaining the compactness of the antenna structure. The antenna operates with the hybrid of modes HEnormalM11δ and TnormalM01δ excited in epsilon‐shaped and cylindrical DR elements, respectively. The excitation of these orthogonal modes helps in obtaining the pattern diversity allowing the antenna for multi‐directional coverage. The antenna provides 15.73% overlapping 10 dB impedance bandwidth with the variation of gain within ranges 4–6 and 6–6.5 dBi at port 1/2 and port 3/4, respectively. In addition, the antenna provides the high radiation efficiency >93% at port 1/2 and 97% at port 3/4. Other parameters required for MIMO applications such as envelope correlation coefficient and diversity gain are within the acceptable limits.
With continuous growth of web applications around the globe, it is a challenge to find the suitable information needed for the user in a limited time.Number of handheld mobile devices is increasing and most of the business revolves around the correct search of the data. Without a proper recommender system it is very difficult to get required information from the web applications. Web applications use recommender systems to provide suitable data to users based on their choices and interests. For different kinds of needs different types of recommender systems have been proposed. Two most basic types of recommender systems are collaborative filtering recommender system and content based recommender system. Sometimes these two recommender systems are combined to increase the efficiency of a recommender system The generated new recommender system is known as hybrid recommender system. The purpose of this paper is to help readers understand the basics of recommender systems. This paper identifies key areas of research openly available for new researchers. After reading this paper new researchers can understand basic problems of recommender systems which need improvement and hence they can make those problems their area of research.
Advances in medical imaging technology continue to create new possibilities for the collection of medical data that are important in timely and accurate diagnosis, in monitoring progress, and in the treatment of various diseases and in medical research. The capabilities of the new skills arise mainly from the technologies depicted in the vivo interior of the human body. Thus the study of the morphology and function of the various organs and the detection of any pathogens is achieved in a very direct way. The "source imaging data" provided by them is important information, but their large number is constantly growing, but their nature also creates the need for further processing with the help of computers. The primary purpose of processing images is to use denoising that includes the elimination of noise due to technical errors and feature preservation. Following noise reduction, the image segment, i.e. the location or areas of interest in an image, is the central objective of the process. In addition, usually, the complexity of the data in large volumes and charts requires a lot of time to study and a lot of experience to do their interpretation correctly. Therefore, in many cases, its automation using machine learning seeks out the partitioning process, but also categorizes images, i.e. classifying an image or parts of an image into specific categories. In most applications, machine learning performance is better than conventional techniques.
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