Search for: All records

Abstract Helical blood flow, characterized by its spiral motion, is a crucial physiological phenomenon observed in various circulatory structures, including the heart, aorta, vessel bifurcations, umbilical cord, and respiratory system. Despite its importance, a comprehensive understanding of helical flow dynamics is limited. This study aims to investigate the transition from laminar to turbulent flow in helical tubes under both steady and pulsatile conditions. An experimental setup was developed, featuring a closed-loop system with a pulsatile flow generator and steady pump, producing sinusoidal waveforms with adjustable frequencies and amplitudes. Five helical tube models, varying in curvature radius and torsion, were fabricated with a fixed inner diameter of 15 mm. High-frequency pressure transducers, ultrasonic flow sensors, and Laser Doppler Velocimetry (LDV) were employed to visualize flow fields and measure turbulence kinetic energy (TKE). Results indicate that helical tube geometry significantly impacts turbulence transitions. Specifically, larger curvature radii stabilize the flow and reduce turbulence downstream, while smaller radii lead to earlier transitions to turbulence. Under steady flow conditions, the critical Reynolds number (Re) for turbulence onset was found to be around 2300 upstream, similar to straight pipes, but significantly higher turbulence levels were observed downstream. Pulsatile flow with high pulsatility indices (PI > 3) near transitional Re markedly increases turbulence, particularly downstream, with large bursts of turbulence occurring during the deceleration phase. These findings enhance the understanding of helical flow dynamics and have implications for biomedical device design, diagnostics, and treatments for circulatory disorders. 

The widespread adoption of heartbeat monitoring sensors has increased the demand for secure and trustworthy multimodal cardiac monitoring systems capable of accurate heartbeat pattern recognition. While existing systems offer convenience, they often suffer from critical limitations, such as variability in the number of available modalities and missing or noisy data during multimodal fusion, which may compromise both performance and data security. To address these challenges, we propose MultiHeart, which is a robust and secure multimodal interactive cardiac monitoring system designed to provide reliable heartbeat pattern recognition through the integration of diverse and trustworthy cardiac signals. MultiHeart features a novel multi-task learning architecture that includes a reconstruction module to handle missing or noisy modalities and a classification module dedicated to heartbeat pattern recognition. At its core, the system employs a multimodal autoencoder for feature extraction with shared latent representations used by lightweight decoders in the reconstruction module and by a classifier in the classification module. This design enables resilient multimodal fusion while supporting both data reconstruction and heartbeat pattern classification tasks. We implement MultiHeart and conduct comprehensive experiments to evaluate its performance. The system achieves 99.80% accuracy in heartbeat recognition, surpassing single-modal methods by 10% and outperforming existing multimodal approaches by 4%. Even under conditions of partial data input, MultiHeart maintains 94.64% accuracy, demonstrating strong robustness, high reliability, and its effectiveness as a secure solution for next-generation health-monitoring applications. 

Deep neural networks (DNNs) have achieved remarkable success in various cognitive tasks through training on extensive labeled datasets. However, the heavy reliance on these datasets poses challenges for DNNs in scenarios with energy constraints in particular scenarios, such as on the moon. On the contrary, animals exhibit a self-learning capability by interacting with their surroundings and memorizing concurrent events without annotated data—a process known as associative learning. A classic example of associative learning is when a rat memorizes desired and undesired stimuli while exploring a T-maze. The successful implementation of associative learning aims to replicate the self-learning mechanisms observed in animals, addressing challenges in data-constrained environments. While current implementations of associative learning are predominantly small scale and offline, this work pioneers associative learning in a robot equipped with a neuromorphic chip, specifically for online learning in a T-maze. The system successfully replicates classic associative learning observed in rodents, using neuromorphic robots as substitutes for rodents. The neuromorphic robot autonomously learns the cause-and-effect relationship between audio and visual stimuli.