Abstract-A very compact triple band-notched multiple input multiple output antenna (MIMO) for ultra-wideband (UWB) communications is fabricated on an FR4 dielectric substrate having the overall size of 18 × 21 × 0.8 mm 3 . The proposed antenna consists of two rectangular metal monopoles which are excited by 50-Ω microstrip lines on top of a substrate, and a common protrude ground is at the bottom. To achieve low mutual coupling between radiating elements, a T-shaped stub is protruded from the ground plane. By etching two C-shaped slots on the radiating patches, band-notched functions at 5.15-6 GHz and 7.8-8.4 GHz are obtained. The third notch band from 3.3-3.7 GHz is realized by adding Ushaped metal strips to the ground. The measured and simulated results demonstrate that the proposed antenna offers good impedance bandwidth of |S 11 | ≤ −10 dB from 2.8-12.2 GHz covering whole UWB band except at the designed notch bands, while giving less mutual coupling (|S 21 |) of lower than −25 dB in the whole UWB band. The measured envelope correlation coefficient (ECC < 0.013), nearly constant gain and stable radiation patterns show that the proposed MIMO antenna is an appropriate candidate for portable UWB systems.
A novel compact fractal loaded two- and eight-element multiple input multiple output (MIMO) with strong diversity is designed for 5G Sub 6 GHz and WLAN applications. The suggested antenna is designed and manufactured on inexpensive FR4 dielectric material with small size of 72 mm × 72 mm × 1.6 mm (0.792λ × 0.792λ × 0.0176λ, where λ is calculated at a lower operating frequency). The proposed layout features a partially grounded, protruding T-shaped stub on the underside of the substrate and a set of fractally loaded circular patch antenna elements on the top. Four triangular slots on the substrate and a T-shaped stub on the ground are employed to produce good isolation over the intended bands. The proposed antenna has a frequency range of (3.3–6.0) GHz, making it compatible with the 5G sub-6 GHz bands and the WLAN band thanks to its high isolation of above 15 dB and good impedance matching characteristics. Good agreement is observed between the antenna results and the theory of characteristic mode analysis approach. The designed antenna is well suited for 5G sub-6 GHz and WLAN communication applications due to its low ECC (0.005), total active reflection coefficient (TARC) (−10 dB), mean effective gain (MEG) (−3 dB), and diversity gain (DG) (−10 dB), channel capacity losses (CCL) (0.05), peak gain (>2.5 dBi), radiation efficiency (>95%), and stable boresight radiation patterns.
It can occur on many occasions that you or a loved one requires urgent medical assistance, but they are unavailable due to unforeseen circumstances, or that we are unable to locate the appropriate doctor for the care. As a result, we will try to incorporate an online intelligent Smart Healthcare System in this project to solve this issue. It's a web-based programmed that allows patients to get immediate advice about their health problems.
The aim of the smart healthcare system is to create a web application that can take a user's symptoms and predict diseases, as well as serve as an online consultant for various diseases. We created an expert system called Smart Health Care System, which is used to make doctors' jobs easier. A machine examines a patient at a basic level and recommends diseases that may be present. It begins by inquiring about the patient's symptoms; if the device is able to determine the relevant condition, it then recommends a doctor in the patient's immediate vicinity. The system will show the result based on the available accumulated data. We're going to use some clever data mining techniques here. We use several intelligent data mining techniques to guess the most accurate illness that could be associated with a patient's symptoms, and we use an algorithm (Naive Bayes) to map the symptoms with potential diseases based on a database of many patients' medical records. This system not only makes doctors' jobs easier, but it also benefits patients by getting them the care they need as soon as possible.
Keywords: Disease Prediction, Naïve Bayes, Machine Learning Algorithm, Smart Healthcare System.
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