Soil moisture is pivotal for precision irrigation, hydrologic prediction, and climate-resilient agriculture, yet a persistent gap remains between sparse in-situ probes and predominantly near-surface satellite retrievals. We present Polarimetric Wave Coherence Tomography (PWCT), a methodology that does not require soil-specific calibration and explores dual-polarized 77-81 GHz FMCW radar to recover root-zone moisture profiles. PWCT departs from amplitude-centric approaches by exploiting water-induced depolarization through: (i) a Polarimetric Coherence Index (PCI) that quantifies cross-polar phase decorrelation from subsurface reflections; (ii) frequency-hopped, orthogonal dual-pol chirps to better separate surface and volume interactions; and (iii) a Spatial Moisture Metric (SMM) derived fromkz-domain energy decay via wave-interference tomography. Implemented on a TI AWR1443 with a compact dual-pol MIMO setup, PWCT reconstructs depth profiles over 0-30 cm in 5 cm bins, requiring no soil-specific calibration but utilizing a one-time instrument-level calibration to ensure system consistency. We further introduce HydraNet, a physics-constrained network that ingests PCI-SMM features to deliver texture-agnostic VWC estimates. Across sand, loam, and clay, HydraNet achieves an RMSE of 0.82-2.15% VWC with TDR as the reference and demonstrates real-time edge inference. Results indicate that PWCT + HydraNet provides an accurate, low-maintenance framework for periodic field monitoring and irrigation decision support.

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 SwinLSTM-EmoRec (Shifted Window Transformer + Long Short-Term Memory Emotion Recognition). This non-contact dual-modal framework fuses micro-Doppler signatures captured by a TI IWR1443 mmWave radar with 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 down-weights 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, an LSTM models temporal radar dynamics, and a lightweight cross-attention layer aligns the two streams, boosting F1 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 (F1 ≈ 0.98). It maintains 33.9 ms end-to-end latency on a 15 W Jetson Xavier NX edge device. Performance remains > 92% at 2 m, demonstrating robust, privacy-preserving FER robust, privacy-aware emotion sensing suitable for smart-home, tele-health, and e-sports IoT applications.

Reliable, privacy-preserving emotion sensing is essential for next-generation IoT applications; however, vision or audio-only pipelines often break down when faces are masked, environments are noisy, or multiple speakers coexist. We present AuraVox, a contactless system that couples millimetre-wave lip micro-Doppler with speech acoustics and fuses them through AuraNet, a bespoke cross-modal transformer. The radar first localizes each talker via MUSIC-based direction-of-arrival estimation and Bartlett beamforming, then captures high-resolution Doppler signatures of lip motion; in parallel, prosodic and spectral speech cues are extracted from a lapel microphone. AuraNet holistically attends to these heterogeneous streams and produces a unified representation for emotion classification. Evaluated on a 30-subject bilingual (English/Mandarin) corpus that includes mask-wearing, multi-speaker overlap, and 30–90 cm ranges, AuraVox attains 96% macro-F1, outperforming radar-only and audio-only baselines by up to eight percentage points. End-to-end latency is 12.8 ms per frame on a 10 W Jetson Xavier NX, meeting real-time constraints for edge deployment. By unifying beamformed lip kinematics with speech cues through AuraNet, AuraVox delivers the first multi-speaker, cross-language, mask-resilient emotion recognizer that runs on commodity hardware. Representative use cases include stress-aware in-cabin driver assistance, hospital check-in triage, and mood-adaptive smart-home interfaces.

We propose an active dynamic redundancy-based fault-handling approach exploiting the partial dynamic reconfiguration capability of static random-access memory-based field-programmable gate arrays. Fault detection is accomplished in a uniplex hardware arrangement while an autonomous fault isolation scheme is employed, which neither requires test vectors nor suspends the computational throughput. The deterministic flow of the fault-handling scheme achieves an improved recovery in a bounded number of reconfigurations. This approach extends existing signal processing properties to accommodate fault handling, and is validated by implementing an H.263 video encoder discrete cosine transform (DCT) block. The peak signal-to-noise ratio measure of the video sequences indicates fault tolerance in the DCT block with only limited quality degradation, during the isolation and recovery phases spanning a few frames.

We propose a method of mining most informative features for the event recognition from photo collections. Our goal is to classify different event categories based on the visual content of a group of photos that constitute the event. Such photo groups are typical in a personal photo collection of different events. Visual features are extracted from the images, yet the features from individual images are often noisy and not all of them represent the distinguishing characteristics of an event. We employ the PageRank technique to mine the most informative features from the images that belong to the same event. Subsequently, we classify different event categories using the multiple images of the same event because we argue that they are more informative about the content of an event rather than any single image. We compare our proposed approach with the standard bag of features method (BOF) and observe considerable improvements in recognition accuracy.