Chih-Hsien Huang

Due to the development of semiconductor technology, control and driving circuits for large phased arrays have become cheaper and more compact, enabling air-coupled ultrasonic transducer arrays to be applied in areas such as ultrasonic haptic feedback and acoustic levitation. However, current air-coupled ultrasonic transducers are quite bulky, which prevents the commercialization of these applications. Hence, piezoelectric micromachining ultrasonic transducers (pMUT) have been proposed to replace conventional ultrasound transducers. Currently, PZT, or lead zirconate titanate, thin film is the most reliable and, consequently, most popular piezoelectric layer for pMUT, but existing PZT fabrication methods are too expensive or require rare metals. To counter this problem, this study proposed a cost-effective and reliable PZT thin film fabrication method. By depositing a commercial sol-gel solution as a seed layer followed by radio frequency sputtering, a PZT thin film with remnant polarization of 113.35 µC/cm² and a coercive field of 211.6 kV/cm was demonstrated. Moreover, high-throughput air-coupled pMUTs and pMUT arrays were made based on the proposed PZT thin film. Finite element method simulations were conducted to optimize the structure parameters for the 40 kHz pMUT. A standard microphone was used to measure the acoustic pressure at 3 cm above the pMUT and the pMUT array to evaluate their performance. The pMUT generated an acoustic pressure of 0.764 Pa/V with a resonance frequency of 52.2 kHz, and the pMUT array’s maximum acoustic pressure was 87.4 Pa. The device demonstrated the highest acoustic output pressure among existing sub-100 kHz air-coupled pMUT arrays using PZT as the actuation layer, highlighting the potential of using the proposed PZT thin film deposition method for high-pressure mid-air ultrasonic applications.

Purpose: Research has revealed that strength training can reduce the incidence of chronic diseases and physical deterioration at any age. Therefore, having a movement diagnostic system is crucial for training alone. Hence, this study developed an artificial intelligence and computer vision-based barbell squat coaching system with a real-time mode that immediately diagnoses the issue and provides feedback after each squat. In addition, a replay mode allows users to examine their previous squats and check their comments. Initially, four primary characteristics of the barbell squat were identified: body joint angles, dorsiflexion, the ratio of knee-to-hip movement, and barbell stability. Methods: We collect 8,151 squats from 77 participants, categorizing them as good squats and six issues. Then, we trained the diagnosis models with three machine-learning architectures. Furthermore, this research applied the SHapley Additive exPlanations (SHAP) method to enhance the accuracy of issue prediction and reduce the computation time by feature selection. Results: The F1 score of the six issues reached 86.86%, 69.01%, 77.42%, 90.74%, 95.83%, and 100%. Each squat diagnosis took less than 0.5 seconds. Finally, this study examined the efficacy of the proposed system with two groups of participants trained with and without the system. Subsequently, participants trained with the system exhibited substantial improvements in their squat technique, as assessed both by the system itself and by a professional weightlifting coach. Conclusion: This is a comprehensive study that integrates artificial intelligence, computer vision and multivariable processing technologies, aimed at building a real-time, user-friendly barbell squat feedback and training system.

In this study, we use piezoelectric materials to overcome the high-impedance and electromagnetic interference susceptibility of piezoelectric micromachined ultrasonic transducers (pMUTs). We demonstrate the design and simulation of a novel optical pMUT (OpMUT) that integrates a photonic ring resonator onto the traditional pMUT. When the OpMUT receives ultrasound waves, its diaphragm vibrates, and the length of the ring waveguide changes. Consequently, the ring waveguide's coupling wavelength will shift, and the variation of the bus waveguide's output light intensity can represent the acoustic signals. This study uses the finite element method and numerical analysis to optimize the optical ring resonator's width, radius, and placement. We compare the signal-to-noise ratio (SNR) and noise-equivalent pressure (NEP) of the proposed OpMUT with those of the state-of-the-art pMUT, which has an SNR of 37.3 dB @ 1Pa and a NEP of 0.0014 Pa. When the radii of the ring waveguides are 10.63, 21.27, and 35.45 μm, the SNR values of the 150 kHz OpMUTs are 50.8, 67.2, and 72.6 dB @ 1 Pa, whereas the NEPs are 0.00174, 0.00067, and 0.00029 Pa, respectively. Hence, the proposed OpMUT is considerably better than its competitor. Future improvements of the sensing capability of the micromachined ultrasonic transducers are expected.

Acoustic droplet ejection (ADE) has been proven to move liquids in droplet form from a container to mid-air. However, the droplet sizes produced using the traditional ADE setups are proportional to their heights if the physical framework is unchanged. This limitation obstructs further employment of ADE technology since having the exact sizes of droplets at a specific location or different sizes at different locations is required for many applications. To overcome this limitation and enable more possible applications of ADE, this study proposed an innovative ADE configuration that could manipulate the size and height of the ADE droplets with only electrical signals. To achieve this, a low-voltage driving period and a pinhole structure were added to create a water mound before ejection. First, simulations were conducted to validate the proposed method and find the parameters of the novel ADE setup. After that, a driving circuit featuring a high-voltage pulser and a field-programmable gate array was built. However, a 3D spherical resin model was printed to focus the acoustic wave on the water surface, and a cover with a pinhole was added to create water mound. To observe the behavior of the droplets, a recording system and detection algorithm were developed to capture and identify the dimension/height of the droplets, respectively. Finally, the proposed ADE configuration successfully manipulated the droplet size at the same ejecting height under three driving voltages (65, 70, and 75 V) and pinhole diameters (3.7, 4, and 4.4 mm).

Acoustic gas thermometry is a technique that reconstructs the two-dimensional temperature distribution of a gas medium using the acoustic velocities of multiple propagation paths in a region of interest (ROI). In recent years, this technology has gradually replaced traditional temperature measurement devices in combustion industries because acoustic gas thermometers can be installed on side walls and are resistant to dust interference. However, they are too loud for indoor applications such as intelligent buildings. Thus, monitoring indoor temperature distribution with more innovative and easier approaches has become a widely discussed topic. Some researchers have tried to replace the siren sound source with ultrasonic transducers, but such ultrasound gas thermometers can only handle small ROIs or have unknown dynamic hotspot-tracking abilities. To tackle this problem, this research proposes an indoor 2D acoustic gas thermometer that can immediately monitor a large ROI’s 2D gas temperature distribution. By enhancing the transmitting and receiving of ultrasonic signals with a stacked transducer and pre-vibration method, the hardware alone can implement the proposed time-of-flight calculation algorithm. The system drastically reduced the effort of the computation units and was able to acquire the 2D temperature distribution in our experiment in less than 3 seconds. Additionally, a graphical user interface was developed to display and record the information, including the 2D gas temperature distribution of the entire ROI, the temperature profile of the selected cross-section, the temperature values of specific positions, and the velocity of each acoustic path. The relative standard deviations of acoustic velocities measured at room temperature and the heated environment were less than 2%. Finally, experiments involving a moving heat source were conducted to validate the hotspot-tracking capability of the proposed system. The system tracked the heater’s location successfully when it moved 5 cm per 10 seconds and 30 seconds. Thus, the study demonstrated successfully that the proposed 2D gas acoustic thermometer can quickly and accurately measure the temperature distribution and follow the hotspot within a large ROI, such as an intelligent building’s indoor space.

Computer vision has been applied in many fields recently to extract useful information from images. One such application is the monitoring of various aquaculture species to increase production. By monitoring the locations and quantities of the white shrimp, the aquaculture farmers could deliver the feed more precisely. Besides, the information about the length of the white shrimp could make the farmers estimate the delivery time accurately. Usually, to ensure the accuracy of computer vision, a relatively high-quality picture is required. However, the quality of underwater photography is easily influenced by the light, color, and turbidity of the medium. Compared to optical photos, the imaging mechanism of ultrasound is less affected by these effects. This study proposes an innovative white shrimp monitoring system that combines ultrasound imaging technology with YOLOv4. The database of the shrimps’ ultrasound images was built by imaging the different parts of the shrimp from various viewing angles with a linear ultrasonic probe. Four models using training data of different scenarios were successfully trained to recognize the locations of the shrimps from the ultrasound images. Among these models, the best was the one trained with 650 one-shrimp and 1080 two-shrimp practical transverse ultrasound images and 6500 images from data augmentation. Unlike optical pictures, ultrasound images are cross-sectional images that slice across certain parts of the shrimps. Therefore, a bi-directional scanning approach was adopted in this study. The ultrasound probe was carried by a two-axis motor to capture the images from two orthogonal viewing angles within a 12.69 cm × 12.69 cm region of interest. After 260 images were captured, the locations of the shrimps in each image were recognized and the centers of the bounding boxes were marked. To determine the number of shrimps, a voting method was developed based on the spectral clustering algorithm to group the marked coordinates. Finally, the number, lengths, and positions of the shrimps were reported. To evaluate the accuracy of the proposed monitoring system, 100 shrimps with 40 different layouts containing 1 to 4 shrimps were tested. The averaged quantity error, position error, and length error were 97.3%, 0.97 cm, and 8.99%, respectively. The standard deviation of the position error and the length error were 0.55 cm and 5.42%, respectively. The results demonstrated that the proposed system can provide useful information on shrimps and is feasible for shrimp farming applications.