Edge Deep Learning and Computer Vision-Based Physical Distance and Face Mask Detection System Using Jetson Xavior NX

This paper proposes a fully automated vision-based system for real-time COVID-19 personal protective equipment detection and monitoring. Through this paper, we aim to enhance the capability of on-edge real-time face mask detection as well as improve social distancing monitoring from real-live digital videos. Using deep neural networks, researchers have developed a state-of-the-art object detector called "You Only Look Once Version Five" (YOLO5). On real images of people wearing COVID19 masks collected from Google Dataset Search, YOLOv5s, the smallest variant of the object detection model, is trained and implemented. It was found that the Yolov5s model is capable of extracting rich features from images and detecting the face mask with a high precision of better than 0.88 mAP_0.5. This model is combined with the Density-Based Spatial Clustering of Applications with Noise method in order to detect patterns in the data to monitor social distances between people. The system is programmed in Python and implemented on the NVIDIA Jetson Xavier board. It achieved a speed of more than 12 frames per second. Doi: 10.28991/ESJ-2023-SPER-05 Full Text: PD

A low-profile holographic antenna with dual-metasurface and printed Yagi feed

A low-profile microwave holographic antenna comprised of a dual-metasurface and an integrated printed Yagi feed is presented. The proposed design utilizes two metasurfaces with an aperture size of 0.0134 m2, with no ground plane, to produce a single pencil beam normal to the plane of the Yagi feed. The holographic antenna is designed at a centre frequency of 20 GHz and shown to have an excellent performance in the frequency range 19.75–21.25 GHz. The aperture efficiency and 1 dB gain bandwidth achieved are 28% and 7.5%, respectively. This is an improved aperture efficiency/bandwidth combination over that of comparable low-profile holographic antennas. Simulated and measured results demonstrate the advantages of the proposed design

Hybrid Isolator for Mutual-Coupling Reduction in Millimeter-Wave MIMO Antenna Systems

A novel millimeter-wave (MMW) hybrid isolator is presented to reduce the mutual-coupling (MC) between two closely-spaced dielectric resonators (DR) antennas at 60 GHz. The proposed hybrid isolator consists of a combination of a new uni-planar compact electromagnetic band-gap (EBG) structure and an MMW choke absorber. The design of the proposed EBG unit-cell is based on the stepped-impedance resonator (SIR) technique. The results show that the proposed EBG structure provides a wide frequency bandgap in the 60 GHz band with miniaturization factors of 0.79 and 0.66 compared to conventional uni-planar EBG and uni-planar compact (UC-EBG) structures, respectively. The proposed EBG is then placed between two Multiple-Input Multiple-Output (MIMO) DR antennas to reduce the MC level. As a result, an average of 7 dB level reduction is obtained. To further reduce the MC level, a thin MMW choke absorber wall is mounted vertically between the two DR antennas and above the EBG structure. An average of 22 dB MC reduction is achieved over the suggested bandwidth while maintaining good radiation characteristics. The measured isolation of the prototype antenna varies from -29 to -49 dB in the frequency range from 59.3 to 64.8 GHz. In fact, the proposed hybrid isolator outperforms other hybrid isolation techniques reported in the literature

Robust deep learning-based detection and classification system for chipless Arabic RFID letters

This work demonstrates a novel approach for reliable and robust identification and detection of realized chipless RFID Arabic alphabets using deep learning (DL) method. The undertaken classification problem of Arabic RFID tags of various fonts and sizes requires a classification technique that can learn long-term dependencies. Hence, a Bi-Long Short-Term Memory (BiLSTM) model is developed to classify 28 chipless Arabic RFID letters of different font types and sizes using their back scattered dual-polarized radar cross section (RCS) characteristics. The RCS frequency response of each Arabic letter tag reflects its signature electromagnetic characteristics that vary with the change in its shape (variations in font type and size). Firstly, an RCS dataset of 28 Arabic alphabet tags with three different font types (Arial, Calibri, and Times New Roman) and 13 different font sizes (16 mm–28 mm with a step size of 1 mm) are generated using Finite-Difference Time-Domain (FDTD) method in the frequency range of 1–12 GHz (1001 steps). The dimensions of the resulting dataset are [28 (letters) × 13 (font sizes) × 1001 (frequency steps) × 2 (polarizations)] × 3 (font types). Multi-class classification of the frequency-series data of all realized 28 alphabet tags of various font types and sizes makes the problem challenging and novel. The developed BiLSTM model can accurately classify the particular letter tag with specific font type and size based on the optimized network with employed Leave-One-Out Cross-Validation (LOOCV). The achieved accuracy with only Arial ([(28 × 13 × 1001 × 2)]), Calibri ([(28 × 13 × 1001 × 2)]), Times New Roman ([(28 × 13 × 1001 × 2)]), and combined data set ([(28 × 13 × 1001 × 2)] × 3) is 75%, 74%, 75%, and 89% respectively. The proposed Bi-LSTM model is shown superior when compared to other methods such as SVM, decision trees, and KNN, as it classifies the data with much higher accuracy for the considered multi-class data. The obtained accuracies of the compared models are 6.4% (SVM), 17.30% (tree) and 27.4% (KNN) respectively, while the developed Bi-LSTM model with optimized hyperparameters achieved an accuracy of 96%

Flexible, Fully Printable, and Inexpensive Paper-Based Chipless Arabic Alphabet-Based RFID Tags

This work presents the design and analysis of newly developed reconfigurable, flexible, inexpensive, optically-controlled, and fully printable chipless Arabic alphabet-based radio frequency identification (RFID) tags. The etching of the metallic copper tag strip is performed on a flexible simple thin paper substrate (ϵr = 2.31) backed by a metallic ground plane. The analysis of investigated tags is performed in CST MWS in the frequency range of 1–12 GHz for the determination of the unique signature resonance characteristics of each tag in terms of its back-scattered horizontal and vertical mono-static radar cross section (RCS). The analysis reflects that each tag has its own unique electromagnetic signature (EMS) due to the changing current distribution of metallic resonator. This EMS of each tag could be used for the robust detection and recognition of all realized 28 Arabic alphabet tags. The study also discusses, for the first time, the effect of the change in font type and size of realized tags on their EMS. The robustness and reliability of the obtained EMS of letter tags is confirmed by comparing the RCS results for selective letter tags using FDTD and MoM numerical methods, which shows very good agreement. The proposed tags could be used for smart internet of things (IoT) and product marketing applications