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.

Wireless sensing offers a promising approach for non-destructive and contactless identification of the moisture content in fruits. Traditional methods assess fruit quality based on external features, such as color, shape, size, and texture. However, fruits often appear perfect externally while being rotten inside. Thus, accurately measuring internal conditions is crucial. This paper introduces mmFruit, a non-destructive and ubiquitous system that employs mmWave signals for precise and robust moisture level sensing in thin and thick pericarp fruits. We propose a novel dual incidence moisture estimation model for regular moisture monitoring to achieve high granularity and eliminate fruit type and size dependency. Additionally, we leverage unique reflection responses across different mmWave frequencies to provide discriminative information about fruit moisture levels. Our comprehensive theoretical model demonstrates how fruits’ refractive index, attenuation factor, and elasticity can be estimated by eliminating fruit type dependency. We developed an electric field distribution model utilizing two receiving antennas to address the challenge of varying fruit sizes through a differential approach, aiming to improve overall robustness. mmFruit integrates a customized Spatial-invariant network (SpI-Net) to eliminate interference from different frequencies and locations, ensuring stable moisture monitoring regardless of target displacement. Extensive experiments were conducted over a month in varied environments on seven types of fruits with thin and thick pericarps (apple, pear, peach, mango, orange, dragon fruit, and watermelon). The results demonstrate that mmFruit achieves a commendable RMSE of 0.276 in moisture estimation. It accurately distinguishes fruits with minor moisture level differences (0% to 7%) with 93.6% accuracy and higher moisture differences (45% to 65%) with over 95.1% accuracy, even in scenarios involving diverse displacements and rotations.

Abstract: Target material sensing in non-invasive and ubiquitous contexts plays an important role in various applications. Recently, a few wireless sensing systems have been proposed for material identification. In this article, we introduce mm-CUR, A Novel Ubiquitous, Contact-free, and Location-aware Counterfeit Currency Detection in Bundles using a Millimeter-Wave Sensor. This system eliminates the need for individual note inspection and pinpoints the location of counterfeit notes within the bundle. We use Frequency Modulated Continuous Wave (FMCW) radar sensors to classify different counterfeit currency bundles on a tabletop setup. To extract informative features for currency detection from FMCW signals, we construct a Radio Frequency Snapshot (RFS) and build signal scalogram representations that capture the distinct patterns of currency received from different currency bundles. We refine the RFS by eliminating multi-path interference, and noise cancellation and apply high pass filters for mitigating the smearing effect with the continuous wavelet transform (CWT). To broaden the usage of mm-CUR, we built a transferable learning model that yields robust detection results in different scenarios. The classification results demonstrated that the proposed counterfeit currency detection system can detect counterfeit notes in 100-note bundles with an accuracy greater than 93%. Compared to the standard CNN and DNN methods, the proposed mm-CUR model showed superior performance in distinguishing each bundle data, even for a limited-size dataset.