Performance and Analysis of Stack Junctionless Tunnel Field Effect Transistor

Silicon - To overcome the fabrication complexity and achieve a better switching ratio is a major grave concern for applications in semiconductor devices. In this regards, a novel stack gate-oxide...     

Early Skin Disease Identification Using eep Neural Network

Skin lesions detection and classification is a prominent issue and difficult even for extremely skilled dermatologists and pathologists. Skin disease is the most common disorder triggered by fungus, viruses, bacteria, allergies, etc. Skin diseases are most dangerous and may be the cause of serious damage. Therefore, it requires to diagnose it at an earlier stage, but the diagnosis therapy itself is complex and needs advanced laser and photonic therapy. This advance therapy involves financial burden and some other ill effects. Therefore, it must use artificial intelligence techniques to detect and diagnose it accurately at an earlier stage. Several techniques have been proposed to detect skin disease at an earlier stage but fail to get accuracy. Therefore, the primary goal of this paper is to classify, detect and provide accurate information about skin diseases. This paper deals with the same issue by proposing a high-performance Convolution neural network (CNN) to classify and detect skin disease at an earlier stage. The complete methodology is explained in different folds: firstly, the skin diseases images are pre-processed with processing techniques, and secondly, the important feature of the skin images are extracted. Thirdly, the pre-processed images are analyzed at different stages using a Deep Convolution Neural Network (DCNN). The approach proposed in this paper is simple, fast, and shows accurate results up to 98% and used to detect six different disease types.     

A miniaturized uniplanar MIMO antenna for n79/n46/millimeter-wave applications

This research suggests a compact uniplanar multiple-input multiple-output (MIMO) with four ports for n79/n46/millimeter-wave (mm-wave) applications. The size of the quad MIMO is only 30 × 30 × 0.8 mm³. MIMO system consists of four identical Z-shaped radiators and common ground on the same plane and no decoupling structures are used for isolation. The system covers the bandwidth of 1.9 GHz (4.4–6.3 GHz) with a mid-frequency of 5.6 GHz and also covers the high-band frequencies ranging from 18 to 30 GHz with a bandwidth of 12 GHz. The suggested quad MIMO is fabricated on an FR-4 board, and the measured outcomes are well in line with the simulated results. An isolation value of −11 dB has been achieved for mid-band frequency and −24 dB has been attained for mm-wave bands. Through the value of DG = 10 dB, ECC < 0.07, TARC < −3 dB, MEG < −5 dB, and the ratio of MEG = 1 dB, uniplanar quad MIMO shows acceptable MIMO diversity performance. The entire system was evaluated for the users' hand specific absorption rate (SAR) impacts and is within the limits. After the complete analysis of the miniature quad MIMO antenna, an 8-port, and a 16-port uniplanar MIMO are simulated for smartphone-sized dielectric substrates and the performances were examined. The suggested MIMO system provides an efficient single-layer MIMO antenna to 5G smartphones with high bandwidth and low SAR. The proposed quad MIMO systems are suitable for both the sub-6 GHz band and the mm-wave band.