Jian Zhang

Covalent organic frameworks (COFs) have attracted growing attention as tunable, stable, and highly designable photocatalysts for CO₂ reduction reactions (CO₂RR). Metalloporphyrins, recognized for their superior photophysical properties and well-defined M-N₄ sites, are widely employed in photocatalysis systems. In this work, two imine-linked porphyrin-based COFs, denoted as CoPor-Tz and CoPor-Bz, were constructed by integrating cobalt(II) 5,15-bis(4-aminophenyl)-10,20-diphenylporphyrin (CoPor) with either 2,4,6-tris(4-formylphenyl)-1,3,5-triazine (Tz) or 1,3,5-tris(4-formylphenyl)benzene (Bz). The electron-deficient Tz linker creates a microenvironment in CoPor-Tz COF that promotes more efficient oxidative half-reactions and charge separation compared with CoPor-Bz COF. Periodic Z-scheme molecular junctions are established between the CoPor and Tz units, enabling directional electron transfer from Tz to the Co centers, where CO₂ activation and reduction occur. Under visible-light (λ ≥ 420 nm) irradiation, CoPor-Tz COF exhibits a remarkable CO yield of 12,909 μmol g^-1 h^-1, which is 2.2 times higher than that of CoPor-Bz COF (5638 μmol g^-1 h^-1). Experimental and theoretical studies reveal that the enhanced CO₂RR activity of the CoPor-Tz COF originates from its superior electron transfer efficiency, stronger CO₂ adsorption/activation capability, and accelerated interfacial redox kinetics compared to the CoPor-Bz COF. This study provides mechanistic insight into periodic molecular junction engineering within COFs, highlighting an effective strategy for constructing highly efficient photocatalytic CO₂RR systems.

Accurate authentication of paper-based materials is crucial for secure document verification, counterfeit currency detection, and intelligent packaging across industries such as finance, logistics, and security. Differentiating between subtle variations, such as glossy, recycled, coated, or counterfeit paper, remains a significant challenge due to minimal material-level differences. We introduce PSense, the first millimeter-wave MIMO radar-based sensing framework for paper material analysis, offering a fully contactless solution that relies solely on reflected signals, eliminating the need for transmission-based or dual-sided setups. To characterize intrinsic paper properties, we propose a novel material-sensitive feature called the Energy Reflection Ratio (ERR), which encodes key physical interactions including surface reflectivity, internal attenuation, and dispersion, uniquely capturing the electromagnetic signature of each paper type. We further present Trans-PapNet, a dual-model transfer learning framework designed to enhance classification robustness and generalization. One model extracts spatial and spectral patterns from radar signal scalograms using ResNet-50, while the other processes ERR-based physical features. These complementary representations are fused via a shared attention mechanism, enabling effective domain adaptation across paper types. Our method achieves an overall classification accuracy of 97.85%, with strong performance in three knowledge-based transfer scenarios (94.8%, 93.0%, and 90.0%) and under both seen and unseen material conditions (95.3%). These results underscore PSense's robustness and scalability, paving the way for real-world deployment in mobile, secure, and intelligent paper-based authentication systems.

Maintaining focus is essential for carrying out tasks accurately and efficiently, but it can be difficult to do so in settings that are full of distractions and continuous change in daily life, the workplace, and education. Traditional assessment methods such as self-reporting, eye tracking, or camera-based observation are often intrusive, subjective, or limited by privacy concerns. To address these limitations, this study proposes a novel radar-based framework for continuous estimation of human attention during fine-grained hand–object interaction tasks using frequency-modulated continuous-wave (FMCW) millimeter-wave (mmWave) radar. We capture fine-grained Doppler–time motion patterns. The considered activities included pouring water, stacking cups, and writing, representing different motion types (translational, repetitive, and fine-motor), collected under focused and distracted conditions. A multi-input deep regression network is introduced, which combines handcrafted behavioral descriptors with temporal Doppler–time feature embeddings using a 1-D–convolutional neural network (CNN)–bidirectional long short-term memory (BiLSTM)–attention fusion pipeline. This network simultaneously encodes motion smoothness, energy dynamics, and temporal regularity to derive attention scores on a 0–100 scale. Extensive experiments show the model’s capability to generalize across different subjects and activities, achieving an overall $RMSE$ of $\approx$ 2.21 and a coefficient of determination ( $R^{2}$ ) of 0.9959. Ablation analysis validates the needs of both handcrafted features and multihead-attention fusion for performance. signal-to-noise ratios below 15 dB, noise tests revealed a performance decline of less than 5%. Changes in attention impact behavioral patterns were gained through correlation analysis. Our approach offers a privacy-preserving and contactless alternative to existing methods, making it ideal for use in classroom monitoring, workplace productivity assessment, and cognitive health evaluation.

Sitting posture is closely related to our health. Poor sitting posture can cause various diseases and jeopardize our health. Among the current methods for detecting sitting posture, computer vision solutions suffer from privacy leakage and wearable sensor solutions suffer from inconvenience and cost of wearing. In this study, we introduce 3D-Sitpose, which leverages millimeter-wave radar to detect human sitting posture. 3D-Sitpose utilizes wireless signal transmission for non-contact detection, ensuring privacy protection and cost reduction. Firstly, we analyze the impact of variations in human sitting posture on millimeter-wave radar signals, and design sophisticated signal processing methods to refine the collected radar data, yielding clearer point cloud information for volunteers in various sitting postures. Secondly, we develop a two-channel neural network to extract fine-grained features related to volunteers from the point cloud data. Finally, we obtain coordinates for 25 human skeletal points. 3D-Sitpose can instruct users to maintain correct sitting posture based on a set of six key angles. We recruit 20 volunteers from our institute to conduct comprehensive evaluations of 3D-Sitpose. Experiments are conducted in two indoor environments to estimate sitting posture. The results reveal the mean Euclidean distance error for all skeletal point locations is 6.65 cm. This demonstrates that our method is able to estimate various sitting changes in volunteers.

Camera-based facial emotion recognition (FER) suffers from poor lighting, occlusion, and privacy exposure, whereas mmWave-only solutions lack the spatial detail required for fine-grained affect analysis. To close this gap, we present a shifted-window transformer + long short-term memory emotion recognition (SwinLSTM-EmoRec). This noncontact dual-modal framework fuses micro-Doppler signatures captured by a TI IWR1443 mmWave radar with red–green–blue image modality (RGB) imagery while treating radar as the primary, identity-obscured source and adaptively limiting reliance on RGB. Privacy is preserved because the cross-attention gate downweights or bypasses RGB when illumination is poor or when potential identity exposure is detected, leaving decisions dominated by illumination-invariant radar dynamics. A shifted-window Swin Transformer extracts spatial facial cues, a long short-term memory (LSTM) models temporal radar dynamics, and a lightweight cross-attention layer aligns the two streams, boosting $F_{1}$ by up to 4% over early, late, and self-attention baselines. On a 50-participant interactive-gaming dataset recorded under varied lighting and distances of 0.5–2 m, the system achieves 98.5% accuracy ( $F_{1}\!\approx \!0.98$ ). It maintains 33.9-ms end-to-end latency on a 15-W Jetson Xavier NX edge device. Performance remains $\gt$ 92% at 2 m, demonstrating robust, privacy-preserving FER robust, privacy-aware emotion sensing suitable for smart-home, tele-health, and e-sports internet of things (IoT) applications.

In recent years, more and more people choose to work out at home or in the office to improve their physique and build muscle. However, the lack of professional guidance makes it difficult for many fitness practitioners to achieve optimal results. Consequently, research on noncontact fitness monitoring using wireless signals has gained attention. Existing studies primarily focus on isolated exercises, while compound exercises, which involve multiple isolated exercises, remain underexplored. This combination introduces new challenges for fitness recognition and monitoring. To this end, we propose M-Fitness, a millimeter-wave (mmWave) radar-based fitness assistant system capable of recognizing and monitoring both isolated and compound exercises. First, we capture fine-grained motion features and design image enhancement algorithms to generate intuitive motion images. Next, we design a novel motion segmentation method for fitness actions. We further formulate compound exercise recognition as a sequential task and develop customized deep learning models that allow users to incorporate new compound exercises. Finally, we perform a comprehensive fitness assessment based on the frequency, intensity, time, and type (FITT) principle. Experiments on a dataset of over 5000 movement samples from 18 volunteers demonstrate that M-Fitness achieves 96.5% accuracy for isolated exercise recognition and 92.7% accuracy for compound exercise recognition, exhibiting strong adaptability to diverse environments.

Early detection of myocardial infarction (MI) is essential for alleviating symptoms and improving daily activity performance. Researchers typically employ continuous segments of heartbeat signals (20–30 seconds), such as ECG signals, for MI detection, as MI often induces changes in heartbeat patterns. Current MI detection methods, like wearable sensors, may induce discomfort from prolonged wear, and Radio Frequency (RF) based approaches might fail to extract fine-grained heartbeat signals during vigorous movement. This article presents a reliable and motion-robust MI detection method based on RF signals. By developing a series of advanced signal processing algorithms, MI-Ra can capture fine-grained heartbeat signals during various daily activities. Our design is inspired by the fact that RF reflections caused by heartbeat signals are mixed with other motion-induced reflections in a nonlinear manner. We utilize the Taylor series expansion method to extract the linear component of these mixed non-linear signals and propose a novel Generative Adversarial Networks (GAN) method, named IQ-TransGAN, to separate the heartbeat signal. To enhance MI detection reliability, MI-Ra employs a multi-periodicity modeling method to extract refined signal representations from recovered heartbeat signals. We have recruited 50 volunteers with MI from Zhongnan Hospital of Wuhan, China, and 50 volunteers without MI, for comprehensive evaluations. The results demonstrate that MI-Ra achieves an average MI detection accuracy of 95.2% when user is quasi-stationary. Even during user non-stationary conditions, MI-Ra maintains an average detection accuracy of 90.5%. MI-Ra shows promise in paving way for smart home healthcare.

This study introduces a novel dual-modality emotion recognition system that combines mm-wave radar with camera-based labeling to provide accurate and privacy-preserving emotion detection. The mm-wave radar captures subtle physiological signals through micro-Doppler and time-frequency characteristics, while the camera assists in labeling facial expressions. The radar data is transformed into spectrograms, which are then fused with camera datasets to train deep learning models, employing convolutional layers for feature extraction and recurrent layers for temporal pattern recognition. Performance evaluation, conducted across a wide range of real-world occlusion and interference scenarios, shows that the system achieves 98.5% accuracy, 0.98 F1-score, and 0.98 recall, significantly outperforming traditional systems. Other experiments, including those for multi-person interference, hand-held paper occlusion, and industrial goggles, achieved accuracy rates of 92%, 91%, and 92%, respectively. The system’s latency for real-time processing is 60.5 ms on edge devices like the NVIDIA Jetson, making it suitable for applications requiring low-latency emotion recognition. Additionally, radar parameter optimization, such as adjusting the ADC sample rate and chirp size, has been shown to improve classification accuracy. These findings highlight the system’s robustness and adaptability to varying environmental conditions and its potential use in privacy-sensitive applications, including healthcare, security, and interactive media. Future work will explore radar-only systems, further reducing dependence on visual data, and investigate more advanced deep learning techniques to improve performance, scalability, and real-time deployment.

Label correction methods are popular for their simple architecture in learning with noisy labels. However, they suffer severely from false label correction and achieve subpar performance compared with state-of-the-art methods. In this paper, we revisit the label correction methods through theoretical analysis of gradient scaling and demonstrate that the sample-wise dynamic and class-wise uniformity of interpolation weight prevents memorization of the mislabeled samples. We then propose DULC, a simple yet effective label correction method that uses the normalized Jensen-Shannon divergence (JSD) metric as the interpolation weight to promote sample-wise dynamic and class-wise uniformity. Additionally, we provide theoretical evidence that sharpening predictions in label correction facilitates the memorization of true class, and we achieve it by employing the augmentation strategy along with the sharpening function. Extensive experiments on CIFAR-10, CIFAR-100, TinyImageNet, WebVision and Clothing1M datasets demonstrate substantial improvements over state-of-the-art methods.