Photoacoustic (PA) microscopy allows imaging of the soft biological tissue based on optical absorption contrast and spatial ultrasound resolution. One of the major applications of PA imaging is its characterization of microvasculature. However, the strong PA signal from skin layer overshadowed the subcutaneous blood vessels leading to indirectly reconstruct the PA images in human study. Addressing the present situation, we examined a deep learning (DL) automatic algorithm to achieve high-resolution and high-contrast segmentation for widening PA imaging applications. In this research, we propose a DL model based on modified U-Net for extracting the relationship features between amplitudes of the generated PA signal from skin and underlying vessels. This study illustrates the broader potential of hybrid complex network as an automatic segmentation tool for the in vivo PA imaging. With DL-infused solution, our result outperforms the previous studies with achieved real-time semantic segmentation on large-size high-resolution PA images.

One of the best ways to monitor the health of the heart is to regularly record its electrical activity by using an electrocardiogram (ECG). Abnormal ECG signals may indicate conditions such as heart attack, arrhythmia, or heart defects. There are many ECG devices available which can detect and amplify this differential biological signal from the heart, allowing a lot of information to be collected quickly. The ECG is often small and easy to use, but its power is supplied from regular batteries, which need to be replaced after a certain period of use. This causes discomfort for elderly users. To overcome this limitation, in this paper, we aim to develop a solar-powered, portable Bluetooth device for ECG measurements. The device can be interfaced with smartphones or other wireless devices via Bluetooth by a distance up to 100 m. The ECG device was designed to use solar energy, which is also the main power source. Following the solar energy harvesting circuit is a solar panel with an output voltage of 2.4 V and a power out of 0.25 W. We optimized the design to have a very low power consumption and in sleep mode the current consumption is only around 40 µA. The device was designed with 24-bit resolution and a sampling frequency of up to 2133 Hz, which can allow high accuracy ECG measurements. The device is not only used for heart rate monitoring, but it can also assist doctors in analyzing ECG signals with a high accuracy via embedded operating software.

Scanning acoustic microscopy (SAM) system (TSAM-400) was designed and developed for evaluating the quality of resistance spot welding (RSW) joints of 1.5 mm-thick SUS 316 stainless steel sheets under different welding parameters. Indentation and nugget information (diameter and shape) were used for evaluating the RSW quality. SAM A-scan signals were used to calculate the indentation. The nugget diameter was determined in the C-scan image. Internal information (nugget shape, splashes, and defects) was visualized in the B- and C-scan images. In addition, the spot weld area was treated to examine the surface topography effect. The nugget diameters of treated and untreated areas were measured and compared to conclude that the surface topography effect can be neglected in the quality evaluation by SAM system. By implementing TSAM-400, some specimens were successfully tested with a scanning area of 55 × 15 mm², and a scanning time of 33.5 s corresponding to a B-scan frame rate of 4.5 Hz, and a resolution of 0.1 × 0.1 mm². Finally, the good input parameters for welding two 1.5 mm-thick SUS 316 were determined by analyzing the results from TSAM-400.

The biological effects of a light-emitting diode (LED) light therapy device are determined by irradiation parameters, mainly wavelength and power density. However, using a battery to provide power causes a problem in the variation of LED power density during battery discharge. As a result, maintaining a stable LED power density, along with extending battery life and operating time, are the primary concerns in designing a LED light therapy device. The present study aims to introduce a LED light therapy device design with different LED color power density control. A Fuzzy logic, based on the relationship between LED power density and operating time, was proposed to control constant power density in this design. The experimental results demonstrate that by using the designed controller, the LED light therapy device's power density (40 mW/cm², 50 mW/cm², 60 mW/cm² for red, blue, and green light, respectively) can be controlled. The newly designed LED light therapy device could be considered an advanced version with energy savings and stabilized LED power emitting property under a broad range voltage variation.

In recent decades, Scanning Acoustic Tomography (SAT) has become a vital technique for characterizing semiconductor devices in non-destructive evaluation. Precise and efficient segmentation of SAT images is crucial for detecting defects and assessing material properties in the semiconductor industry. However, current manual methods are often expensive and susceptible to human error. This study enhances the segmentation process of SAT images using the deep learning model SemiSA, which is fine-tuned from the Segment Anything model. In our experiments, SemiSA was trained and evaluated on a large-scale dataset from the Ohlabs TSAM-400 system, encompassing various semiconductor devices such as Flip Chip, Power Semiconductor, 6-inch and 12-inch Wafer, Transistor, and Multilayer Ceramic Capacitor. The results demonstrate that SemiSA significantly improves segmentation tasks across all types of SAT images of semiconductor devices. On average, there was a 17.89% enhancement in Dice Similarity Coefficient scores and a 24.26% improvement in Intersection over Union scores across all tasks. Additionally, this work also proposes an efficient framework tailored specifically for SAT images. The main objective of developing this segmentation tool is to provide researchers and experts with a valuable tool for advancing the semiconductor evaluation and quality control field. The code is available at https://github.com/ThuHa96/SemiSA.