Rate splitting (RS) has the potential to significantly enhance both energy efficiency (EE) and spectral efficiency (SE) of wireless networks. In this paper, we propose joint design of the beamforming and rate allocation to maximize both EE and SE of a downlink multicell multiple-input single-output (MISO) system with rate splitting and common beamforming coordination (RSCBC). This design problem is formulated as a non-convex quadratically-constrained multi-objective optimization problem (MOOP). By investigating the quasi-concavity relationship between EE and SE, the formulated MOOP is transformed into a single-objective optimization problem (SOOP) to offer a tradeoff between EE and SE by maximizing EE in any achievable SE region. A series of transformations are then applied to make the SOOP tractable, after which an efficient iterative algorithm based on successive convex approximation (SCA) is proposed to solve the problem. Simulation results demonstrate the effectiveness of the proposed algorithm and unveil interesting tradeoffs between EE and SE under different parameter settings.

Handwritten signature verification has become one of the most important document authentication methods that are widely used in the financial, legal, and administrative sectors. Compared with offline methods based on static signature images, online handwritten signature verification methods are more reliable because of the temporary dynamic information (e.g., signing velocity, writing force, stroke order) that alleviates the risk of being forged. However, most existing online handwritten signature verification solutions are reliant on specific signing devices (e.g., customized pens or writing pads) and require extensive data collection during the registration phase, resulting in poor adaptability and applicability for new users. In this article, we propose mmSign, a millimeter wave (mmWave)–based online handwritten signature verification system, which enables accurate sensing of the user’s hand movements when signing through the superior sensing capability of mmWave. mmSign extracts the time-velocity feature maps from the captured mmWave signals by the carefully designed signal processing algorithms and then exploits a transformer-based verification model for signature verification. In addition, a novel meta-learning strategy with proposed task generation and data augmentation methods is introduced in mmSign to teach the verification model to learn effectively with limited samples, allowing our model to quickly adapt to new users. Extensive experiments show that mmSign is a robust, efficient, and secure handwritten signature verification system, achieving 84.07%, 87.31%, 91.12%, and 96.54% verification accuracy when 1, 3, 5, and 10 labeled signatures are available, respectively, while being resistant to common forgery attacks.

In this work, we consider a covert communication scenario, where a transmitter Alice communicates to a receiver Bob with the aid of a probabilistic and uninformed jammer against an adversary warden’s detection. The transmission status and power of the jammer are random and follow some priori probabilities. We first analyze the warden’s detection performance as a function of the jammer’s transmission probability, transmit power distribution, and Alice’s transmit power. We then maximize the covert throughput from Alice to Bob subject to a covertness constraint, by designing the covert communication strategies from three different perspectives: Alice’s perspective, the jammer’s perspective, and the global perspective. Our analysis reveals that the minimum jamming power should not always be zero in the probabilistic jamming strategy, which is different from that in the continuous jamming strategy presented in the literature. In addition, we prove that the minimum jamming power should be the same as Alice’s covert transmit power, depending on the covertness and average jamming power constraints. Furthermore, our results show that the probabilistic jamming can outperform the continuous jamming in terms of achieving a higher covert throughput under the same covertness and average jamming power constraints.

Although there are lots of studies on scene text recognition, few of them focus on the recognition of the incomplete text. The recognition performance of existing text recognition algorithms on the incomplete text is far from the expected, and the recognition of the incomplete text is still challenging. In this paper, an end-to-end Two-Stage Inpainting Network for Incomplete Text (TSINIT) is proposed to reconstruct the incomplete text into the complete one even when the text is in various styles and with various backgrounds, and the reconstructed text can be recognized by the existing text recognition algorithms correctly. The proposed TSINIT is divided into text extraction module (TEM) and text reconstruction module (TRM) to make the inpainting only focus on the text. TEM separates the incomplete text from the background and character-like regions at the pixel level, which can reduce the ambiguity of text reconstruction caused by the background. TRM reconstructs the incomplete text towards the most possible text with the consideration of the abstract and semantic structures of the text. Furthermore, we build a synthetic incomplete text dataset (SITD), which contains contaminated and abraded text images. SITD is divided into 6 incomplete levels according to the number of pixels in the incomplete regions and the ratio of the incomplete characters to all characters. The experimental results show that the proposed method has better inpainting ability for the incomplete text compared with traditional image inpainting algorithms on the proposed SITD and real images. When using the same text recognition method, the recognition accuracy of the incomplete text on SITD can be improved much more with the help of the proposed TSINIT than with the traditional image inpainting methods.

The outbreak of COVID-19 has greatly changed everyone's lifestyle all over the world. One of the best ways to prevent the spread of infections is by washing hands properly. Although a number of hand hygiene monitoring systems have been proposed, they either cannot achieve high accuracy in practice or work only in limited environments such as hospitals. Therefore, a ubiquitous, energy-efficient and highly accurate hand hygiene monitoring system is still lacking. In this paper, we present WashRing —the first smart ring-based handwashing monitoring system. In WashRing, we design a Partially Observable Markov Decision Process (POMDP) based adaptive sampling approach to achieve high energy efficiency. Then, we design an automatic feature extraction scheme based on wavelet scattering and a CNN-LSTM neural network to achieve fine-grained gesture recognition. Finally, we model the handwashing gesture classification as a few-shot learning problem to mitigate the burden of collecting extensive data from five fingers. We collect data from 25 subjects over 2 months and evaluate the system performance on both commercial OURA ring and customized ring. Evaluation results show that WashRing achieves 97.8% accuracy which is 10.2%–15.9% higher than state-of-the-arts. Our adaptive sampling approach reduces energy consumption by 64.2% compared to fixed duty cycle sampling strategies.

This work analyzes the impact of the transceivers' relative locations on the physical layer security achieved by directional modulation with a random frequency diverse array (DM-RFDA). Based on the adopted path loss model, we first derive the probability of non-zero secrecy capacity, denoted by pn, achieved at a legitimate receiver for an eavesdropper with a fixed location. We then examine how far we need to push the eavesdropper away from the transmitter in order to guarantee pn ≥ δ, where δ is a given value determining a certain level of security. The results reveal that the DM-RFDA system ensures a high level of security (e.g., δ = 0.95) for the legitimate user who is significantly further away from the transmitter than the eavesdropper. Furthermore, the results demonstrate that it is easier to guarantee the required level of security when there are more resource (e.g., higher bandwidth and a larger number of transmit antennas) in the DM-RFDA system.