A reconfigurable antenna with a compact size and quad-polarization reconfigurability is presented. By employing a switchable feed scheme, the antenna can operate at single-fed and dual-fed modes. At single-fed mode, the feed structure allows output ports switching to produce the excitations for either slant +45° or −45° linear polarization (LP). At dual-fed mode, it serves as a switchable phase-shifting power splitter for generating left-hand circular polarization (LHCP) or right-hand circular polarization (RHCP). To realize a simple and space-saving configuration, the antenna makes efficient use of the proposed feed structure, which is highly shared among the four polarization states. Measured results show that the antenna exhibits operating bandwidths of 230 and 260 MHz at circular polarization CP and LP states, respectively. Moreover, stable broadside radiation patterns as well as moderate gains could also be obtained.

In this letter, a frequency- and polarization-reconfigurable bent monopole antenna using liquid metal is proposed. The proposed antenna is designed to operate at continuously tunable frequencies and switch between left-handed circular polarization (LHCP) and right-handed circular polarization (RHCP) states. The frequency reconfigurability is achieved by controlling the length of the liquid metal to change the electrical length of the antenna. The circularly polarization can be achieved by injecting from the inlet, and excited by orthogonal horizontal radiation as well as vertical radiation. 3 dB axial ratio bandwidth of 37.5%(3.12–4.63 GHz) in LHCP state and 48.8% (3.07–5.05 GHz) in RHCP state can be achieved. The whole antenna is characterized by continuous tunability, reconfigurable frequency and polarization, and wide tunable bandwidth. The measured results of constructed prototypes are in close agreement with the simulation results, and its feasibility is fully proved.

A circular polarization (CP)-agile and continuous beam-steerable array antenna is presented using a hybrid design approach. It is accomplished by combining digital reconfigurable antenna elements with analog continuously tunable phase shifters. To realize both polarization reconfiguration and initial phase generation for beam scanning simultaneously, the designed antenna element enables eight states that correspond to two CP modes with four quantized phases. The phase shifters further provide additional variable phases of −90° to 0° for antenna element excitation. Superposition of the initial and additional phases helps to satisfy full and continuous 360° phase coverage for a beam-steerable array. Meanwhile, phase decomposition also facilitates the design of phase shifters without complex or cascaded structures. By altering the bias states of the electronic components loaded in the array antenna, CP switching and beam scanning can be independently controlled. For verification, prototypes of the antenna element, phase shifter, and a four-element array are all fabricated and tested. Reasonable agreement is obtained between simulation and measurement. The resultant array could scan up to nearly ±40° off the boresight, along with stable radiation patterns and good CP performance.

A liquid metal Filtering Power Divider (FPD) with frequency tuning and controllable number of passbands is proposed in this brief. According to the resonance characteristics of uniform impedance resonator (UIR) and stub loaded resonator (SLR), the structure parameters of the FPD are given. The proposed FPD consists of 4 UIRs, 4 SLRs and the liquid metal adjustable modules. By changing the position and length of liquid metal, different center frequency and passband number can be obtained. Two-passband and three-passband are realized on the same FPD. In the two-passband mode, the center frequency of both passbands can be tuned. In the three-passband mode, there are two tuning modes, which can tune the first two pass bands and the third pass band respectively. The designed FPD innovatively applies liquid metal to the structure and has flexible adjustability. In order to verify the proposed design, a reconfigurable FPD based on liquid metal is fabricated and measured. The measured results are in good agreement with the simulated results.

This paper presents the design and implementation of Fusang, a low-barrier system that brings accurate and robust 3D object recognition to Commercial-Off-The-Shelf mmWave devices. The basic idea of Fusang is leveraging the large bandwidth of mmWave Radars to capture a unique set of fine-grained reflected responses generated by object shapes. Moreover, Fusang constructs two novel graph-structured features to robustly represent the reflected responses of the signal in the frequency domain and IQ domain, and carefully designs a neural network to accurately recognize objects even in different multipath scenarios. We have implemented a prototype of Fusang on a commodity mmWave Radar device. Our experiments with 24 different objects show that Fusang achieves a mean accuracy of 97% in different multipath environments. The code, dataset, and trained models of Fusang can be obtained at https://github.com/OpenNISLab/Pro-Fusang.

Utilizing gestures for human-machine interaction offers numerous benefits for both products and users. Hand Gesture Recognition (HGR) systems, particularly those using radar sensors, are gaining attention due to their potential for real-time applications. However, mainstream methods often rely on unrefined 2D or 3D radar signals, which are typically large and redundant, requiring the use of large neural network classifiers. This high computational demand limits their deployment on low-cost, resource-constrained edge platforms. In this paper, we propose a low-cost, close-range HGR system based on a 24 GHz Frequency Modulated Continuous Wave (FMCW) Multi-Input Multi-Output (MIMO) radar designed for resource-constrained environments. By applying beamforming, Constant False Alarm Rate (CFAR) target detection, and feature extraction, our experiments show that targeted signal processing on dimensions like range, velocity, and angle can effectively classify gestures. This approach achieves a recognition accuracy of 99.60% for four gesture types using a lightweight Conventional Neural Network (CNN). By focusing on efficient radar signal processing rather than complex neural networks, we successfully deployed the system on an embedded platform. The system achieved accuracy rates of 99.36%, 95.00%, and 95.64% for a well-trained user and two non-trained users, respectively.