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In this paper, we propose the design to accommodate for the Ku-band (12–18 GHz), K-band (18–27 GHz), and Ka-band (26.5–40 GHz) frequency used cases as high-fidelity broadcasting for military applications, short-range terrestrial communication, and advanced weather forecasting applications by implementing the design structure with horn antenna having bottleneck profile. To have a ubiquitous application embedded with a single antenna, this structure was developed using advanced design analysis to determine the paragon shape responsible for high radiation efficiency. This constraint is evaluated in terms of return loss, radiation pattern, electromagnetic field patterns, and current distribution. The simulated impedance bandwidth of the bottleneck horn antenna is 100% showing that the antenna is operational at the entire intended frequency band range. The accounted gain is of 23.18 dB and directivity of 23.23 dB having 99.78% efficiency and low operating power of 0.5 W making the antenna high-line ideal for the desired satellite communication.

Pneumonia is one of symptom cause for one of the most widespread Contagious diseases emerged as Coronavirus 2019 known as CoVID-19. This diseases is sharply spreading in most of part of the world effecting small aged and elderly aged people which is causing more than Nine Thousand deaths from all parts of the world. Identification of Pneumonia in Covid cases is happened by analysis of Posteroanterior views (P.A.) X-Rays. Thus, developing a smart and efficient system for detecting Pneumonia must-have utilities even in the future. In Recent Researches, Convolutional neural networks were found to be more accurate and efficient. Nowadays, Features are pre-trained in CNN models on a vast scale of datasets that make medical image identification more efficient. However, analyzing the medical image for Pneumonia’s detection is hard to assess because professional expertise is needed to label them. This Paper shows how a convolutional neural network can be used to detect Pneumonia using any generic python notebook with computer vision support.

Cell phones have been known as most usually utilized electronic gadgets in everyday life today. As equipment inserted in cell phones can perform significantly more assignment than customary telephones, the cell phones are not, at this point only a specialized gadget yet additionally considered as a ground-breaking processing gadget which is ready to catch pictures, record recordings, surf the web and so on. With the headway of innovation, it is conceivable to apply a few strategies to perform content discovery and interpretation. Consequently, an application that permits cell phones to catch a picture and concentrate the content from it to extract its language and information values and discourse it out is not, at this point a dream. In this paper, we address an Android application that is created by coordinating firebase machine learning kit and the WaveNet API with an approach of reinforcement learning to vocalize the texts using advanced speech synthesis algorithms. By utilizing this application, travelers who visit a remote nation ready to comprehend messages depicted in various languages as well as for the users who want to grab the information on the go by listening to the article.

Corona virus is formulated from large group of the viruses that causes illness range from common cold to more severe diseases. This virus has transmitted from animals to human beings. Millions of people throughout the world are affected to this deadly virus. So it is vital to detect this threatening virus. The Genomic Signal processing techniques is a constructive tool to detect infection as it deals with the advance research in the genetics. In this article we delineate the Genomic sequences of Corona Virus infected samples and convert the genomic data into digital signal and on applying Discrete Wavelet Transform and Fast Fourier Transform we performed mathematical modeling of the Corona-Virus gene so that it becomes effortless to predict the Corona-Virus Gene statistically. The motive of this research is to provide predicted results to drug designer for better response to this life threating Virus. We have tested these algorithms on most significant genes of the Corona Virus as well as Normal Cells of Homosapiens. The dataset of all the genomes are available on the National Center of Biotechnology Information (NCBI) website.

AIDS is a most dangerous disease that is developed in the person having HIV virus. HIV stands for human immunodeficiency virus. Lakhs of people suffer from these diseases, and ultimately, they died due to AIDS. So, it is necessary to detect these diseases in an early stage. Genomics signal processing deals with advance research in genetics. So, by applying various GSP techniques, it becomes easier to predict one of the most hazardous diseases AIDS. In this paper, we have represented the binary mapping of the raw genomic data to convert into digital data and on applying the fast Fourier algorithm as well as the discrete wavelet transform in our algorithm to predict abnormalities present in the coding region of DNA of the gene of HIV infected cell. The purpose of this research is to provide an accurate prediction of HIV infected cell to the researcher’s so that the life efficiency of any patient suffering from these deadly disease increases. We have implemented the algorithm on MATLAB 2015a which consists of signal processing toolbox. The proposed algorithm is tested for several DNA sequences present in normal genes as well as HIV infected genes of Homo sapiens chromosomes available in the National Center of Biotechnology Information (NCBI) database.

The face identification system is one of the most emerging methods for authentication of user; it is drawing wide attraction to the surveillance system which reflects innovation in a video surveillance system. This system here represents the automated attendance system using real-time computer vision algorithms and adaptive techniques to track the faces during a specific period of time. Our system works on eigenface recognizers and Intel’s Haar cascades which make the attendance-taking process easier and less time-consuming rather than the traditional process. Our system provides the cheapest solution rather than a previous biometric system like fingerprint authentications. The recorded data is being compared with the training dataset, and the attendance is recorded if the match is found with the help of Python libraries. The camera is being installed at the entry location, so that attendance is recorded as soon as the match of the person entering the particular area is found. So, our main aim is to provide an alternative which is very much convenient to process the attendance and also is very much safe and authentic to have faced as a security option.

The virtual instrumentation is in great demand now a day due to its flexibility as well as cost-effectiveness. Lab VIEW is a great platform in which one can design virtual instruments. This article deals with the design of the virtual biosignal analyzer. This analyzer has been mainly designed for academic purpose. It can be used by industries for understanding the biosignals as well as to find out which filters are best suited for filtering the biosignals. This analyzer consists of the various virtual instrument applications which deal with the analysis of biosignals like ECG, EMG, and EEG. So that the students, professionals as well as industrialists can understand which filter or which analysis is best suited for the particular biosignals, which can help them to design the real-time instrument. The purpose of this article is to provide the virtual toolkit to the professional working in this field so that an accurate real-time instrument can be designed.