Emotion recognition, particularly contactless recognition via wireless sensing, has shown its promise in diverse applications. However, the previous works only focus on emotions rather than the person, i.e., the premise is already knowing who the subject is, without considering the issue of identifying subjects. We envision that user identification and emotion recognition together will bring more adaptive and personalized Internet of Things applications, e.g., a smart home system can react to specific emotions of a specific user, independently. In this work, we move forward to investigate the problem of simultaneous user identification, using only physiological indicators embedded in wireless signals reflected off from targets. Toward the objective, in this article, we first carry out a comprehensive measurement study, which validates the feasibility of simultaneous user identification and emotion recognition. Moreover, the measurement also discovers that the key challenge lies in the limitation of artificial features and the substantial emotion feature deviation across different days, which hinders accurate and robust sensing. To resolve the challenge, we design two multiscale neural networks, incorporated with a custom-built feature attention mechanism, so as to obtain rich feature expression and, thus, enhance the important features for accurate recognition. We prototype mmEMO using a commercial off-the-shelf millimeter-wave radar and experimental evaluation shows that mmEMO can achieve 87.68% user identification accuracy and 80.59% emotion recognition accuracy, respectively.

Millimeter wave (mmWave) radar is an attractive solution for non-contact vital sign monitoring due to its characteristics of robustness against noise, interference, and high-range resolutions. However, due to the attenuation of mmWave signal strength with distance, most previous methods require users to be relatively close to the radar (<2.5m), and the estimation of vital signs is inaccurate due to the influence of surrounding objects. In this paper, we propose a vital sign remote monitoring method based on mmWave sensing, which can estimate the heart rate of multiple users at a long distance. We first propose an object location detection method based on windowing minimum variance distortionless response to enhance the signal SNR from weak human body reflections and obtain fine-grained each user’s location. Then we design a vital sign estimation method based on wavelet packet decomposition to extract precision vital sign from composite reflection signal. We evaluate the system performance and the impact of different factors, including the distance,angle, and multi-users scenario. Experimental results show that our method can achieve the heart rate accurately with a mean absolute error (MAE) of 3.5Bpm and mean absolute percentage error (MAPE) of 4% within 8m.

In recent years, the rapid growth of Internet of Things (IoT) devices has introduced significant security risks, emphasizing the need for effective IoT device management. Traditional device identification methods, such as traffic analysis and machine learning, which rely heavily on static features and prior knowledge, often struggle to maintain accuracy in dynamic IoT environments characterized by frequent changes and large-scale deployments. To address this challenge, we propose the Feature Extraction-Deep IoT (FE-DIoT) method to improve the accuracy and reliability of IoT device identification in large-scale networks by integrating a feature selection algorithm with a classification model. Our approach leverages the DynamicWeight Adjustment-Based Recursive Feature Elimination (DWA-RFE) algorithm to effectively minimize redundancy, thus enhancing the robustness of the model. Additionally, we implement a Deep Cross Network with Feature Extraction (DCN-FE) module to improve device classification precision by extracting the most significant features from network traffic data. The experimental results in our dataset and other public datasets demonstrate that FE-DIoT significantly outperforms existing methods in terms of robustness and classification accuracy. These findings indicate that FE-DIoT provides a practical and effective solution to improve IoT device management and security in complex large-scale networks.

Land surface temperature (LST) plays an important role in many fields including urban heat island analysis, climate change studies, and agricultural disaster monitoring. However, achieving high-resolution and continuous LST data remains challenging due to the limitations of remote sensing observations and the sparse distribution of ground sensors. Recently, spatio-temporal kriging has been widely used to address such challenges, but often relies on fixed neighborhood locations and typically ignores temporal dependencies in LST data. To address these challenges, we propose AIGR-LSTM, an Adaptive Inductive Graph Representation Learning model based on long short-term memory (LSTM) for LST interpolation. To better determine the spatial proximity relationship between nodes, we propose a threshold-based adaptive neighbor selection module, which effectively captures complex spatial dependencies by adaptively selecting relevant neighbors. Besides, we design a geography-based spatio-temporal modeling module that incorporates geographic spatial relationships, combines temporal encoding information with spatial features, and leverages LSTM to achieve accurate spatio-temporal interpolation. The experiment was conducted on LST and three other datasets, and the results showed that AIGR-LSTM outperformed the six state-of-the-art methods in all evaluation metrics. On the LST dataset, the RMSE was 0.185°C and the MAE was 0.133°C, which improved by 6.09% and 8.28% respectively compared to the best baseline model.