Decoding chemical composition of urinary crystals from ultrasonic echo signals via deep learning
Mar 31, 2026
AI-augmented ultrasound analysis of noninvasive quantification of hydrogels concentration for bioprinting
Mar 26, 2026
Tooth aging monitoring system based on secondary dentin analysis using ultrasound and artificial intelligence
Jan 1, 2026
Decoding chemical composition of urinary crystals from ultrasonic echo signals via deep learning
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Jan 1, 2026
AI-augmented ultrasound analysis of noninvasive quantification of hydrogels concentration for bioprinting
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Jan 1, 2026
Face Spoofing Detection using Deep Learning
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Jan 1, 2025
Machine learning-Assisted Object Monitoring supported by UWOC for IoUT
The research introduces an advanced fish movement tracking system that addresses critical challenges in underwater monitoring. By developing a lightweight sensor node integrated with an Inertial Measurement Unit (IMU) and underwater optical wireless communication (UWOC) modem, the system enables real-time data transmission and movement analysis. The technological innovation centers on sophisticated relay performance enhancement through software-based combining techniques like Majority Logic Combining (MLC), Equal Gain Combining (EGC), and Selection Combining (SC). These methods are intelligently integrated into the microcontroller to optimize communication reliability and signal processing in challenging underwater environments. A key contribution is the adaptive communication algorithm (ACA), which strategically exploits combining techniques to improve underwater wireless optical communication performance. The system's prototypes underwent rigorous testing in a 4-meter water tank, validating its feasibility and practical applicability. The research further advances underwater monitoring by developing machine learning models—including LSTM, Spatial Attention, RNN, Transformer, and GRU—trained on comprehensive IMU sensor data. These models analyze complex fish movement parameters, predicting acceleration states with remarkable precision and offering unprecedented insights into aquatic behavior.
Dec 5, 2024
Image-based VLC Signal Demodulation Using Machine Learning
This letter presents an image-based demodulation technique for OOK-modulated VLC signals from air quality sensors. We optimized system performance by transforming received signals into images using segmentation algorithms, bicubic interpolation, and image thresholding, enhancing demodulation accuracy through data augmentation. Experimental results show that our ML-driven demodulator achieves 97.58% accuracy, an extended communication range of up to 10 m, and improved noise tolerance. These advancements indicate that our proposed system is more efficient than conventional demodulation schemes. In future studies, we aim to improve VLC range, data rate, and noise tolerance by replacing the photodiode with an image sensor for various modulation schemes. We will also explore the effects of varying light conditions and signal interference in real-world VLC environments.
Nov 22, 2024
Aqua-Sense: Relay-Based Underwater Optical Wireless Communication for IoUT Monitoring
Water turbulence, range, and misalignment reduce underwater wireless optical communication (UWOC) performance. These obstacles may hinder large-scale deployment. Compared to acoustic and RF communication, UWOC can connect IoUT devices. “Aqua-sense,” a relay-based UWOC system, was designed and tested to improve communication connection performance and optical receiver reception area. EGC, MLC, and SC are used in the optical relay to increase diversity gain. A channel-aware algorithm powers the optical relay, or “opto-relay,” to increase communication connection performance. At 0.2 Mbps and 7.5 meters, the aqua-sense system had a 68% packet success rate in fairly murky water at 25 NTU. The sensor node, “opto-sense,” transmitted 0.5 Mbps in clear water with 0.01 NTU turbidity. Moderate water waves and air bubbles with a displacement and airflow rate of 5 liters/min within a 2-meter communication link range did not affect our results.
Feb 19, 2024
Multiuser Data Transmission Aided by Simultaneous Transmit and Reflect Reconfigurable Intelligent Surface in Underwater Wireless Optical Communications
The reconfigurable intelligent surface (RIS) can improve energy and spectrum efficiency by directing radio waves. Recent wireless communications research has focused on it. We utilized the simultaneous transmit and reflect (STAR)-RIS to an underwater UWOC system in this work. Despite underwater turbulence, beam attenuation, blockage, and pointing mistakes, a STAR-RIS obtains a UWOC channel to allow multiple users to transfer data in all directions. Exponential and generalized Gamma distributions were used to calculate underwater turbulence-induced fading, and numerical analysis was used to evaluate the outage probability, bit error rate (BER), and channel capacity of direct, conventional, and STAR-RIS assisted UWOC channels. BER performance was also assessed based on reflective element count, modulation method, pointing errors, blockage levels, and transmit-reflection coefficient. Monte-Carlo simulations verified the analytical BER and outage probability conclusions for the average signal-to-noise ratio. Experiments showed the receiving luminous intensity performance compared to the incident beam luminous intensity of the proposed STAR-RIS aided UWOC channel.
Jan 24, 2024