A Single-Switch-per-Bit Topology for mmWave and THz Reconfigurable Reflective Surfaces

<div> <div> <div> <p>We present novel multi-bit unit-cell topologies for reconfigurable reflective surfaces –RRSs– (e.g., reflectarray antennas) with compact designs for millimeter-wave and terahertz (mmWave/THz) applications. Typically, mmWave/THz RRSs utilize one or multiple single-pole-single-throw (SPST) switches leading to single- or dual-bit modulated surfaces. These surfaces utilize the switches to manipulate the phase of the imping waves, beamforming the reflected waves to the desired direction. As such, RRSs are leveraged either for imaging or wireless communication applications, which typically require the formation of a single beam (no grating lobes) and high gains. The gain and quantization lobe levels of an RRS is strictly related to the number of phase bits utilized in the unit-cell. Explicitly, more phase bits lead to lower quantization errors and better maximum gain/aperture efficiency. However, increasing the number of phase bits requires more SPST switches integrated within the unit-cell, leading to complex designs with high RF losses. Herein, we present, for the first time, RRSs with up to 4 phase quantization bits (16 states) that maintain one switch-per-bit topology thus retaining a low-complexity design. The proposed RRSs is presented alongside a series of analytical and full-wave simulated results. </p> </div> </div> </div>

A Single-Switch-per-Bit Topology for mmWave and THz Reconfigurable Reflective Surfaces

<div> <div> <div> <p>We present novel multi-bit unit-cell topologies for reconfigurable reflective surfaces –RRSs– (e.g., reflectarray antennas) with compact designs for millimeter-wave and terahertz (mmWave/THz) applications. Typically, mmWave/THz RRSs utilize one or multiple single-pole-single-throw (SPST) switches leading to single- or dual-bit modulated surfaces. These surfaces utilize the switches to manipulate the phase of the imping waves, beamforming the reflected waves to the desired direction. As such, RRSs are leveraged either for imaging or wireless communication applications, which typically require the formation of a single beam (no grating lobes) and high gains. The gain and quantization lobe levels of an RRS is strictly related to the number of phase bits utilized in the unit-cell. Explicitly, more phase bits lead to lower quantization errors and better maximum gain/aperture efficiency. However, increasing the number of phase bits requires more SPST switches integrated within the unit-cell, leading to complex designs with high RF losses. Herein, we present, for the first time, RRSs with up to 4 phase quantization bits (16 states) that maintain one switch-per-bit topology thus retaining a low-complexity design. The proposed RRSs is presented alongside a series of analytical and full-wave simulated results. </p> </div> </div> </div>

A Novel Feature Extraction Based Person Recognition Using Local Phase Quantization and Geometric Features

At present, it is simple for everyone to generate digital pictures of their routine life and use them for different purposes. Similarly, facial recognition is a trending technology that can identify or verify an individual from a video frame or digital image from any source. There are numerous techniques involved in the working principle of facial recognition. But the simplified method is feature extraction by comparing the particular facial features of the images from the collected dataset. Multiple algorithms are existing for feature extraction, but they fail to give high accuracy. The proposed algorithm based on deep learning provides a high recognition rate by using a convolutional neural network for classification. For feature extraction, Local Phase quantization, Geometric-based features, and directional graph-based methods are implemented. Various performance metrics, such as recognition rate, classification accuracy, accuracy, precision, recall, F1-score is evaluated. The proposed method achieves high-performance values when it is compared with other existing methods. It is mainly developed to calculate the casual visit of a person to the mall, and it is also deployed for criminal identification.