Fahim Niaz

Wireless sensing offers a non-destructive method to estimate fruit moisture content, which is essential for assessing internal fruit quality. While fruit quality is often judged by external appearance, this can be misleading, as fruits may look perfect externally but be spoiled internally. Therefore, accurate measurement methods are needed to assess the internal conditions of fruits. Current techniques for measuring fruit moisture, crucial for evaluating quality, are often destructive or expensive, limiting their practicality for everyday use. This paper presents FSense, a novel, non-destructive, and cost-effective system for estimating fruit moisture utilizing millimeter-wave (mmWave) technology. To preprocess radar antenna signals, we introduce a twin-domain model based on the discrete wavelet transform to remove fruit-structure dependencies. Additionally, we propose a Knowledge-Based Transfer Learning Model (KB-TLM) to address variability among fruits with thin and thick pericarps and to leverage transfer learning to apply source-domain knowledge to target domains. This model effectively predicts moisture content across various fruits by employing customized convolutional and generative adversarial networks, which extract stable, moisture-dependent features independent of fruit type. Over one month, extensive experiments were conducted on 240 fruits across four categories, namely apple, pear, peach, and watermelon, in two representative environments, including an empty laboratory and a busy library. Under the original random split setting, FSense achieves an RMSE as low as 0.219. More importantly, under the stricter leave-one-fruit-out evaluation designed to avoid leakage from repeated longitudinal measurements of the same fruit, FSense maintains robust moisture estimation, with RMSE values of 0.276, 0.319, 0.348, and 0.392 for apple, pear, peach, and watermelon, respectively. Additionally, leaving one batch out and leaving one environment out in evaluations further confirm the generalization capability of FSense under fruit identity, batch, and environmental shifts. These results show that commodity 76–81 GHz mmWave radar provides a repeatable moisture-sensitive effective response that can be calibrated for non-destructive fruit moisture estimation, supporting potential applications in fruit preservation, quality monitoring, and post-harvest management.

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.

Land Surface Temperature (LST) is a vital geophysical parameter influencing environmental and biophysical processes. However, acquiring high spatiotemporal resolution LST data (≤100 m, ∼daily) is challenging due to the trade-off between spatial and temporal resolutions of satellite thermal infrared data. Spatiotemporal fusion of cross-sensor LSTs suffers from overpass time mismatches, varying viewing geometries, and cloud contamination, limiting the availability of clear images for statistical models. To address these limitations, we propose a deep learning super-resolution (DL-SR) approach that requires no additional data once trained, to enhance the resolution of Moderate Resolution Imaging Spectroradiometer (MODIS) LST, referred hereafter as the self-sufficient DL-SR. We developed a novel transfer-learning-based super-resolution model, TNetSR, which leverages a pre-trained Very Deep Super-Resolution (VDSR) network and is refined via a Multi-Residual U-Net. Furthermore, we integrated a kernel-based Random Forest (RF) regression method with DL-SR, which generated high-resolution training data using surface reflectance, spectral indices, albedo, digital elevation model, and land cover. These outputs and low-resolution MODIS LST were used to train DL-SR models, enabling the self-sufficient DL-SR of MODIS inputs. By evaluating across 9 Australian OzFlux sites, TNetSR outperforms comparative models, achieving a mean squared error of 1.027 K (test sites) and 1.229 K (external test site), a peak signal-to-noise ratio of 28.196 dB (test sites) and 27.216 dB (external test site), and a structural similarity index of 0.842 (test sites) and 0.838 (external test site) against the high-resolution dataset. Comparisons with in-situ LST yield a Pearson correlation coefficient (R) ≥ 0.94, a bias of 2.97 K (test sites) and 3.0 K (external site), and an unbiased root mean squared error of 3.67 K (test sites) and 3.71 K (external site). The results demonstrated that TNetSR consistently improved LST accuracy across landscapes, climatic zones, and seasons.