Heart rate monitoring (HRM) serves as a fundamental component of healthcare systems, providing essential insight into cardiovascular function and overall physiological status. Conventional HRM systems commonly rely on contact-based sensing modalities, such as electrocardiography (ECG) and photoplethysmography (PPG) wearables, which require direct skin contact and complex wiring. This work presents a noncontact ballistocardiogram (NCBCG) bed sensor system integrated with tiny machine learning (TinyML) to provide on-chip processing for noncontact HRM. The proposed system incorporates a polyvinylidene fluoride (PVDF) film sensor placed beneath the mattress, enabling comfortable, nonintrusive, and continuous monitoring. We design a TinyML model that offers on-chip processing on a microcontroller unit (MCU), reducing dependency on cloud-based or external. Experimental findings demonstrate that the system achieves accurate heart rate (HR) estimation, which is compliant with the AAMI standard. These results highlight the feasibility and reliability of accurate HR estimation using embedded artificial intelligence (AI). The work demonstrates potential for real-world applications in long-term remote healthcare and home-based monitoring.

This study presents an innovative approach for golf club path recognition through on-device processing. The proposed Smart Golf Club Sensing (SGCS) system captures inertial measurement unit (IMU) signals and is designed for deployment on MCU using TinyML. The SSL framework learns representations from unlabeled data through a pretext task and then performs the downstream classification task on labeled data directly on the device. All processing stages, including signal processing, and AI inference, are executed on the MCU. We developed a mobile application to operate our device and visualize the results. The data collected from both professional and amateur golfers. Experimental result demonstrated classification accuracy of 93.40% under 5-fold cross-validation (CV), respectively. When deployed on embedded device, the model achieved an accuracy of 88.61% with int8 quantization and 91.06% with unquantized representation. These results demonstrate the effectiveness of the proposed approach for on-device golf club path recognition, highlighting its potential for practical deployment in golf swing analysis.

This study presents the design and implementation of a compact data acquisition system for immersive ultrasonic inspection of small-diameter pipelines, targeting applications where conventional systems are impractical due to size constraints. The system integrates the Eclipse Z7 platform with a customized pulser-receiver module and a rotary pipeline inspection gauge (PIG) equipped with a 5 MHz immersion-type ultrasonic transducer. The PIG module is designed to scan pipelines with an 8.18 mm wall thickness and a 200 mm inner diameter. Before deployment, real-time system calibration is performed via a connected computer interface to ensure optimal performance. Once inside the pipeline, the PIG operates autonomously, with ultrasonic data being acquired and stored locally on a Raspberry Pi. Post-inspection, the recorded data is extracted and analyzed on the computer to assess pipeline integrity. The proposed system offers a compact alternative to commercial solutions, particularly in scenarios involving limited access and small-diameter pipelines.

Ultrasonic anemometers are widely used for wind measurement owing to their fast response, high temporal resolution, and long operational lifetime with minimal recalibration requirements. However, most configurations suffer from transducer shadowing, where cylindrical probes disrupt local airflow and introduce systematic errors in time-of-flight (TOF) measurements. While prior computational fluid dynamics (CFD)-based investigations have characterized this effect, their analyses remain confined to low wind speeds, and no existing study has explicitly proposed a method to mitigate shadowing-induced bias. This paper presents a coupled CFD and acoustic propagation framework to analyze wake-induced velocity deficits and their effects on TOF measurements for a three-transducer ultrasonic anemometer, with simulations spanning 5 to 75 m/s over the full 360° range. The results show that shadowing distortions are strongly direction-dependent, peaked within approximately ±5° angular sectors, with a near-constant velocity deficit of approximately 40% along affected paths. A shadowing-aware acoustic path selection method is then proposed that selectively excludes corrupted acoustic paths, reducing the average velocity root-mean-square error (RMSE) to 0.349 m/s and the directional RMSE to 1.14°, representing improvements of more than an order of magnitude over shadow-unaware methods. These findings provide a physically grounded, simulation-based framework for shadowing-aware wind measurement using ultrasonic anemometers.