Ten antenna array using a small footprint capacitive-coupled-shorted loop antenna for 3.5 GHz 5G smartphone applications

ABSTRACT: A self-isolated 10-element antenna array operating in the long-term evolution 42 (LTE42) frequency band is proposed for 5G massive MIMO smartphone applications. The proposed antenna elements are placed in a 2D array configuration; they are placed symmetrically along the two long edges of the mobile chassis. The proposed antenna structure is a shorted loop antenna resonating at half-wavelength mode, which is rarely deployed by researchers due to its large size compared to other quarter wavelength antenna structures. It is a printed, shorted, and compact loop antenna of a total footprint area of 6 × 6.5 mm2 (λ/14.3 ×λ/13.2, where λ is the free space wavelength at 3.5 GHz). A small capacitive coupling flag-shaped strip is used to excite the proposed loop antenna. The compactness is achieved using an inward meandering that forms an internal loop in the element. The position and the dimensions of this loop are used to tune the resonant frequency and matching level at 3.5 GHz. The results (theoretical, simulated, and measured) show that the 3.5 GHz band (3.4-3.6 GHz) is achieved with impedance matching better than −10 dB, and total efficiency higher than 65%. A 10 × 10 MIMO system is formed and it has an excellent MIMO and diversity performance in-terms of the envelope correlation coefficient (below 0.055), and apparently it has the highest channel capacity (about 54.3 bps/Hz) among other MIMO systems of the same order. Simulation results of the specific absorption rate (SAR) demonstrates that the proposed antenna solution satisfied SAR criterion. Thus, the proposed ten-element MIMO antenna represent an excellent candidate for sub-6 GHz 5G smartphone applications

DeepKnuckle: Deep Learning for Finger Knuckle Print Recognition

Biometric technology has received a lot of attention in recent years. One of the most prevalent biometric traits is the finger-knuckle print (FKP). Because the dorsal region of the finger is not exposed to surfaces, FKP would be a dependable and trustworthy biometric. We provide an FKP framework that uses the VGG-19 deep learning model to extract deep features from FKP images in this paper. The deep features are collected from the VGG-19 model’s fully connected layer 6 (F6) and fully connected layer 7 (F7). After applying multiple preprocessing steps, such as combining features from different layers and performing dimensionality reduction using principal component analysis (PCA), the extracted deep features are put to the test. The proposed system’s performance is assessed using experiments on the Delhi Finger Knuckle Dataset employing a variety of common classifiers. The best identification result was obtained when the Artificial neural network (ANN) classifier was applied to the principal components of the averaged feature vector of F6 and F7 deep features, with 95% of the data variance preserved. The findings also demonstrate the feasibility of employing these deep features in an FKP recognition system

DeepKnuckle: Deep Learning for Finger Knuckle Print Recognition

Biometric technology has received a lot of attention in recent years. One of the most prevalent biometric traits is the finger-knuckle print (FKP). Because the dorsal region of the finger is not exposed to surfaces, FKP would be a dependable and trustworthy biometric. We provide an FKP framework that uses the VGG-19 deep learning model to extract deep features from FKP images in this paper. The deep features are collected from the VGG-19 model’s fully connected layer 6 (F6) and fully connected layer 7 (F7). After applying multiple preprocessing steps, such as combining features from different layers and performing dimensionality reduction using principal component analysis (PCA), the extracted deep features are put to the test. The proposed system’s performance is assessed using experiments on the Delhi Finger Knuckle Dataset employing a variety of common classifiers. The best identification result was obtained when the Artificial neural network (ANN) classifier was applied to the principal components of the averaged feature vector of F6 and F7 deep features, with 95% of the data variance preserved. The findings also demonstrate the feasibility of employing these deep features in an FKP recognition system