In this work, we consider an integrated sensing and covert communication (ISACC) system with a finite blocklengthLaided by a reconfigurable intelligent surface (RIS) withNelements. Specifically, with the aid of RIS, a transmitter Alice is to sense the potential existence of a target, and she is also probabilistically trying to send information to a receiver Bob covertly (i.e., trying to hide her transmissions from a warden Willie). Meanwhile, Willie is to detect whether Alice is sensing the target only or conducting ISACC. We consider two RIS operation modes, i.e., a non-adaptive mode, where RIS employs a common beamforming vector regardless of whether Alice performs sensing-only or ISACC transmissions, and an adaptive mode, where RIS dynamically switches between two beamforming vectors tailored to the transmission type. For each mode, we formulate and solve an optimization problem that jointly determines Alice’s power allocation fractionρfor covert information signals and RIS beamforming vectors to maximize the effective covert communication throughput, while satisfying sensing and covertness constraints. Our examination shows that in the non-adaptive mode, the optimalρdecreases with the blocklengthL, the number of RIS elementsN, or Alice’s transmit powerPa, reflecting a stronger trade-off between covert throughput and sensing reliability. In contrast, in the adaptive mode, the optimal solution isρ∗ = 1, indicating that Alice can fully rely on RIS adaptivity to conceal covert transmissions by switching its beamforming vectors. Numerical results further demonstrate a significant covert communication throughput gain of the adaptive mode over the non-adaptive mode, which increases with bothNandPa, highlighting the importance of RIS adaptivity in the ISACC systems.

Predicting the performance of deep learning (DL) models, such as execution time and resource utilization, is crucial for Neural Architecture Search (NAS), DL cluster schedulers, and other technologies that advance deep learning. The representation of a model is the foundation for its performance prediction. However, existing methods cannot comprehensively represent diverse model configurations, resulting in unsatisfactory accuracy. To address this, we represent a model as a graph that includes the topology, along with node, edge, and global features, all of which are crucial for effectively capturing the performance of the model. Based on this representation, we propose PerfSeer, a novel predictor that uses a Graph Neural Network (GNN)-based performance prediction model, SeerNet. SeerNet fully leverages the topology and various features, while incorporating optimizations such as Synergistic Max-Mean aggregation (SynMM) and Global-Node Perspective Boost (GNPB) to more effectively capture the critical performance information, enabling it to predict the performance of models accurately. Furthermore, SeerNet can be extended to SeerNet-Multi by using Project Conflicting Gradients (PCGrad), enabling efficient simultaneous prediction of multiple performance metrics without significantly affecting accuracy. We constructed a dataset containing performance metrics for 53k+ model configurations, including execution time, memory usage, and Streaming Multiprocessor (SM) utilization during both training and inference. The evaluation results show that PerfSeer outperforms nn-Meter, Brp-NAS, and DIPPM.

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.

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 presentWashRing—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.

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.

We consider a covert communication system from a transmitter Alice to a receiver Bob with the aid of a proba-bilistic and uninformed jammer against an adversary warden's detection, where the jammer's transmission status and power are random with priori probabilities. We first analyze the warden's detection performance as a function of the jammer's transmission probability, transmit power distribution (e.g., minimum and maximum transmit power, average power), and Alice's transmit power, based on which we optimize these parameters to maximize the communication throughput from Alice to Bob subject to a covertness constraint. Our analysis reveals that the jammer's minimum transmit power is not always zero in the probabilistic jamming strategy, which is different from that in the continuous jamming strategy presented in the literature. Instead, our analysis proves that the jammer's minimum jamming power is the same as Alice's covert transmit power, which depends on the required covertness level and the available average jamming power. Furthermore, our results show that the probabilistic jamming can outperform the continuous jamming in terms of achieving a higher covert communication throughput.

This work examines the covert communication performance gain achieved by deploying a reconfigurable intelligent surface (RIS) with discrete phase shifts. To this end, we first analyze the average receive signal power at a legitimate receiver Bob and a warden Willie as a function of the number of RIS reflecting elements$N$and the number of bits$d$for its discrete phase shift levels. Our analysis reveals that Bob's average power is proportional to$N^{2}$and highly depends on$d$, while Willie's average power is proportional to$N$and does not depend on$d$. This leads to the potential of enhancing the system performance via increasing$N$or$d$in the considered covert communications scenario. Specifically, the performance gain is explicitly examined by tackling the transmission outage probability from a transmitter Alice to Bob subject to a covertness constraint based on Willie's detection performance. After analyzing the transmission outage probability and Willie's total detection error rate, we determine Alice's optimal transmit power. Our explicit examination confirms the performance enhancement achieved via increasing$N$or$d$. Furthermore, our analysis shows that the covert communication performance achieved with$d=3$is already sufficiently close to that achieved with$d\rightarrow\infty$. This shows that the major benefits of deploying RIS in covert communications can be achieved by an RIS with low-resolution phase shifts.

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.

From the perspective of privacy protection and convenience, WiFi-based person identification in wireless sensing has attracted extensive attention in recent years. In this paper, we propose a WiFi-based person IDentification (WiID) method, which can capture people’s valid physiological information from Channel State Information (CSI) of different spatial streams even when people are in motion. The key idea is to detect and extract the short-time static states from the collected CSI and achieve person identification based on these short-time signals. By designing a Motion Sensitivity Vector (MSV) conversion algorithm, WiID is able to segment CSI that carries individual physiological information automatically without the individual performing an assigned action or maintaining a specific state. As far as we know, it is the first work that enables precise person identification using people’s physiological information when people do not keep stationary. Experimental results in real-life scenarios show that WiID can achieve 92.65% of average accuracy in three different environments.

In recent years, scene text recognition algorithms have achieved great progress, but they still face some challenges in practical environment, such as the incomplete scene text, which includes structurally broken characters and occluded characters, as shown in Fig. 1. Existing scene text recognition algorithms can not accurately recognize such incomplete scene text. In this paper, we design an end-to-end Inpainting Network for Incomplete Text (INIT), which can reconstruct each incomplete character into complete character. INIT can separate the text from the background and just reconstruct the text regions, which reduces the influence caused by the background. And INIT is supervised by reconstruction loss and semantic loss to reach the most likely text. Furthermore, to compensate for the absence of incomplete scene text dataset, we propose an incomplete text synthesis method and build an incomplete text dataset (SITD), which is more suitable for practical scenarios. On SITD, the recognition accuracy can achieve 82.99% by the existing text recognition method with the help of INIT, while the accuracy is only 61.99% by the same recognition method with the conventional image inpainting method. Experimental results show that the proposed method has better reconstruction ability for incomplete text compared with the existing image inpainting algorithms.

User identification is becoming more and more important for Apps on mobile devices. However, the identity recognition based on eyes, e.g., iris recognition, is rarely used on mobile devices comparing with those based on face and fingerprint due to its extra cost in hardware and complicated operations during recognition. In this article, an eye-based recognition method is designed for identity recognition on mobile devices, which can be implemented just like face recognition. In the proposed method, the eye feature is composed of the static and dynamic features, where the periocular feature extracted by deep neural network from the eye image is used as the static feature, and the motion feature of saccadic velocity is selected as the dynamic feature. The eye images can be captured by the normal camera on mobile devices just like faces, and dynamic features can provide living information to increase the difficulty of forgery. The GazeCapture dataset is used to test the proposed method, because the eye images in this dataset are captured by mobile devices during daily use. The recognition accuracy of the proposed method on the GazeCapture dataset can reach 96.87% only based on the periocular feature and can be enhanced to 97.99% when it is fused with the saccadic feature. The experiment results show that the performance of the proposed method can be comparative to that of iris recognition methods. It demonstrates that the proposed method is a practical reference for the eye-based identity recognition, and the proposed method provides one more biometric choice for mobile devices.

As a green wireless network access framework, cloud radio access network (C-RAN) has the advantages of reducing energy consumption and improving spectral efficiency. In this paper, we propose to maximize the energy efficiency (EE) in the downlink of the C-RAN. We propose a design of beamforming and rate allocation based on generalized nonorthogonal multiple access (G-NOMA) by using the central cooperation and local coordination across the base transceiver stations (BTSs) in the C-RAN downlink. Our aim is to maximize the EE with the constraints of each BTS power consumption and the minimum quality of service (QoS) requirements, which we propose to solve by an efficient and low complexity successive convex approximation (SCA) algorithm. Simulation results demonstrate that the G-NOMA scheme can effectively achieve the best energy efficiency.

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 (RS-CBC). 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.