In this letter, we propose a semi-blind joint channel estimation and symbol detection scheme for reconfigurable intelligent surface (RIS)-empowered multiuser millimeter wave (mmWave) systems. Combined with the coding scheme at user equipments (UEs) and RIS reflection coefficient design, we prove that the received signals at the base station (BS) follow a PARATUCK2 tensor model, and then a two-stage fitting algorithm is derived by exploiting the low-rank structure of mmWave channel. Without a dedicated training stage, the proposed scheme can jointly detect information symbols of all UEs and estimate the channels of the UEs-RIS and RIS-BS links. In comparison to the existing methods, the proposed system can increase spectrum efficiency and obtain better channel estimation and symbol detection performance. Numerical results are presented to verify the effectiveness of the proposed scheme.

Although miniaturized acoustic localization sensors have significant advantages in many fields, such as video conferencing, smart homes, intelligent scene monitoring, and robot audition, it is challenging to achieve high localization accuracy using them. In this study, a miniaturized acoustic localization sensor called SuperSoundcompass is developed. The system consists of seven identical microelectromechanical systems microphones distributed on a circular substrate with a radius of 4.5 cm. A high localization accuracy can be obtained with a limited number of acoustic sensors with compressive sensing applied. In order to verify the effectiveness of SuperSoundcompass, a series of simulations and experiments are performed under various conditions, such as low signal-to-noise ratio, coherent arrivals, and snapshot-starved data. The average root-mean-square error of SuperSoundcompass at 0 dB is 1.81 ^∘ . The results confirm its excellent performance in terms of accuracy.

This paper mainly describes the transmission reliability and physical layer security of multiantenna decode-and-forward relay networks over the - shadowed fading distribution. We consider the existence of residual hardware impairments (RHIs) and channel estimation errors (CEEs) at the same time. Firstly, we give the network model. On this basis, we analyze the reliability and security by deriving the exact expressions of outage probability (OP) and intercept probability (IP). To further verify and investigate the theoretical derivation, the approximate analysis of OP is obtained under the high signal-to-noise ratio. In addition, we also use an optimized antenna selection technology at the transmitter to improve the security performance. Finally, through the comparative study of theoretical simulation and Monte Carlo simulation results, the factors affecting network transmission and security are acquired. The interesting conclusion is that both RHIs and CEEs can increase OP and decrease IP; that is to say, the existence of RHIs and CEEs can reduce reliability and enhance security.

In the fields of biological and medical research, precise detection of microscale structural changes is crucial. This paper presents a microwave sensor based on a microstrip line-coupled split-ring resonator structure for detecting structural bending changes and force conditions. The sensor is composed of Flame-Retardant 4（FR4） epoxy resin, copper foil, and an organic protective layer, with a resonant frequency of 3.60801 GHz. Detection of bending and microforces is achieved through the shift in resonant frequency. When the bending angle is between 0.1° and 1°, the sensor's resonant frequency shift range is from 3.60771 GHz to 3.60280 GHz; when the bending angle is between 5° and 90°, the resonant frequency shift range is from 3.60801 GHz to 3.60126 GHz, with a sensitivity of 74.916 kHz/°. Additionally, the sensor can measure forces in the range of 0 to 20 N, and for microforces within the range of 0.1 mN to 1 mN, the resonant frequency shift range is from 3.56256 GHz to 3.56219 GHz, with a sensitivity of 372 Hz/μN. The sensor has the advantages of high resolution, high sensitivity, and a broad measurement range. Validated by pulse measurement, it successfully measured the wrist arterial pulse force of approximately 1.5 mN, providing a new perspective for the fields of biological and medical monitoring.

Deep neural networks generally cannot gather more helpful information with limited data in image classification, resulting in poor performance. Self-distillation, as a novel knowledge distillation technique, integrates the roles of teacher and student into a single network to solve this problem. A better understanding of the efficiency of self-distillation is critical to its advancement. In this paper, we provide a new perspective: the effectiveness of self-distillation comes not only from distillation but also from the supervisory information provided by the shallow networks. At the same time, we find a barrier that limits the effectiveness of self-distillation. Based on this, reverse self-distillation is proposed. In contrast to self-distillation, the internal knowledge flow is in the opposite direction. Experimental results show that reverse self-distillation can break the barrier of self-distillation and further improve the accuracy of networks. On average, 2.8% and 3.2% accuracy boosts are observed on CIFAR100 and TinyImageNet.