This paper presents our study on a high power 220GHz frequency tripler based on a pair of GaN planar Schottky barrier diode chain chips. In the proposed frequency tripler, the pair of diode chips was directly mounted across the metal diaphragm in parallel with the opposite polarisation inside the rectangular waveguide. The RF field is tuned to its hot-spot, coupling directly onto the diode chains, turning on a diode chain at a time in an alternative manner. Unlike the traditional ones, in order to achieve a better heat dissipation, the diodes are directly connected with the metal block. Benefitting from a better heat dissipation and the power endure capacity of GaN material, the proposed tripler can work well under a watt level input power. The simulated tripler frequency conversion performance agrees with the measurement results very well. In addition, the experiments show that the frequency tripler can endure a maximum input power of 1.1W. And the output power of this frequency tripler is 17.5mW at 219.5GHz driven by 900mW input power with the best efficiency of 1.93%.

The function of passive wide-angle beam scanning is desired by base station antenna to provide “green” cellular services. In this paper, such function is realized by designing a compact phase-controlled pattern-reconfigurable Dielectric Resonator Antenna (DRA). The proposed DRA has a compact structure with side length of 0.38λc, and excites fundamental TE and TM modes by diverse ports. By assigning phase between diverse ports, E-plane tilted patterns are synthesized with ±66° scan angles. Its pattern-reconfigurable capability makes contributions for increasing array beam scanning angle and minimizing gain fluctuation. A passive four-element array is verified to have an enhanced beam scanning angle of ±81° with gain fluctuation of 1.25 dB. The maximum gain of the scanning beam is located on the off-axis angle of ±66°, and achieves a wide 3 dB scan coverage of ±105°. Good agreements have been shown between measured and simulated results. The proposed phase-controlled pattern reconfigurable DRA and DRA array have impedance bandwidth of 3.33% (2.95-3.05 GHz) for Sub-6GHz wireless system. They enable the wide-angle beam scanning for base station antenna, and provide a cost-effective passive system for “green” communications.

This article presents a 220-GHz four-channel, noncontiguous, and manifold-coupled waveguide multiplexer for future terahertz (THz) multichannel communication application. The multiplexer is composed of four Chebyshev bandpass filters based on metal waveguide technology. Through a unique design in which the tuning dimensional variables are reduced to 14 and a co-design of low-order electromagnetic (EM) distributed models and full-wave EM models, the design optimization is achieved with a good computational efficiency and design accuracy. The proposed multiplexer is fabricated by high-precision computer numerical control (CNC) milling technology, in which the fabrication errors are evaluated to be within $\pm 3~\mu \text{m}$ . The measured results exhibit 1.7 dB of in-band insertion loss and better than 15 dB of average common-port return loss for each of the channel filter. The measured results are all in good agreement with the simulated ones, thereby validating the complete design procedure.

This letter presents our design and experimental verification of a wideband dual-circular-polarized antenna operating in the W-band based on an optimized waveguide septum polarizer and a profiled smooth-wall horn. This antenna is capable of transmitting and receiving two orthogonal circular-polarized signals [left-hand circular polarization (LHCP) and right-hand circular polarization (RHCP)] simultaneously to achieve full-duplex, hence, twofold spectrum efficiency for millimeter-wave (mmWave) wireless communications. The fabricated antenna shows 21% relative bandwidth from 76.8 to 94.7 GHz with axial ratio 20 dB for both LHCP and RHCP.

In this communication, a rectangular dielectric resonator antenna (DRA) is integrated with a backed cavity to enlarge the DRA side length by 1.8 times. The backed cavity also reduces the sensitivity of the DRA resonance to the DRA size, resulting in a relaxed fabrication tolerance. Based on the enlarged DRA design, a stacked DRA (sDRA) is proposed to improve the bandwidth from 8.7% to 15% and enhance the realized gain by an average of 1.5 dB. The proposed mechanisms are verified by fabricating a 4x4 sDRA array with an enlarged DRA side length of 1.8 mm (0.40λ), an improved bandwidth of 62.7-73.9 GHz (16.4%) and an enhanced measured gain of up to 17.2 dBi. The enlarged DRA dimension permits a higher DRA operating frequency without reducing the DRA size. The proposed millimeter-wave (mmW) sDRA array provides a wide bandwidth and high-gain solution for 67 GHz unlicensed band communications.

Premature ventricular contractions (PVCs), common in the general and patient population, are irregular heartbeats that indicate potential heart diseases. Clinically, long-term electrocardiograms (ECG) collected from the wearable device is a non-invasive and inexpensive tool widely used to diagnose PVCs by physicians. However, analyzing these long-term ECG is time-consuming and labor-intensive for cardiologists. Therefore, this paper proposed a simplistic but powerful approach to detect PVC from long-term ECG. The suggested method utilized deep metric learning to extract features, with compact intra-product variance and separated inter-product differences, from the heartbeat. Subsequently, the k-nearest neighbors (KNN) classifier calculated the distance between samples based on these features to detect PVC. Unlike previous systems used to detect PVC, the proposed process can intelligently and automatically extract features by supervised deep metric learning, which can avoid the bias caused by manual feature engineering. As a generally available set of standard test material, the MIT-BIH (Massachusetts Institute of Technology-Beth Israel Hospital) Arrhythmia Database is used to evaluate the proposed method, and the experiment takes 99.7% accuracy, 97.45% sensitivity, and 99.87% specificity. The simulation events show that it is reliable to use deep metric learning and KNN for PVC recognition. More importantly, the overall way does not rely on complicated and cumbersome preprocessing.