The heteroepitaxial growth of InP/GaAs/Si by hydride vapor phase epitaxy achieves laser diode (LD) device quality. Etch-pit density (EPD) measurements and transmission electron microscopy reveal two kinds of defects, threading dislocations and stacking faults, in the InP layer. Lowering the growth temperature of the direct InP layer on GaAs/Si reduces stacking fault density to the order of 10⁴ cm^-2. Room-temperature continuous-wave operation of an InGaAsP 1.5-µ m-wavelength LD fabricated on InP/GaAs/Si confirms its crystal quality.

Recently, many studies on unmanned aerial vehicle (UAVs) that perform position control using camera images have been conducted. The measurements of the surrounding environment and position of the mobile robot are important in controlling the UAV. The distance and direction of the optical ray to the object can be obtained from the diameter and coordinates in the image. In these studies, various camera systems using plane cameras, fisheye cameras, or omnidirectional cameras are used. Because these camera systems have different geometrical optics, one simple image position measurement method cannot yield the position and posture. Therefore, we propose a new method that measures the position from the size of three-dimensional landmarks using omnidirectional cameras. Three-dimensional measurements are performed by these omnidirectional cameras using the distance and direction to the object. This method can measure three-dimensional positions from the direction and distance of the ray; therefore, if the optical path such as the reflection or refraction is known, it can perform measurements using a different optical system’s camera. In this study, we construct a method to obtain the relative position and relative posture necessary for the self-position estimation based on an object with an omnidirectional camera; further, we verify this method by experiment.

Infrastructure such as roads, tunnels and bridges needs periodical inspection. The conventional structure inspection method, in which a human inspector uses a crack gauge, may lead to problems such as measurement errors and lengthy inspection times. Mobile robots and image processing have been used in the infrastructure inspection field. An image sensor or any sensor is used for measuring the state of the infrastructures. It is necessary that the mobile robot knows its own position and posture during automatic inspection. Generally, the Global Positioning System (GPS) has been used to sense the position of the mobile robot. However, GPS is not usable in places with the ceilings such as under bridges and in tunnels. Therefore, we developed a system to sense the position and posture of mobile robots. This system uses laser projection markers and cameras, and it has a very simple configuration. The camera photographs a target structure and projection laser markers, and the position and posture of the camera and mobile robot were calculated by an image application. The system makes infrastructure inspection more effective and decreases the time needed for inspection. In this paper, we examined the number of necessary laser markers, and we verified our method by experiment.

Background/Objectives: Circulating tumor cells (CTCs) are important biomarkers for predicting prognosis and evaluating treatment efficacy in cancer. We developed the “CTC-Chip” system based on microfluidics, enabling highly sensitive CTC detection and prognostic assessment in lung cancer and malignant pleural mesothelioma. However, the final identification and enumeration of CTCs require manual intervention, which is time-consuming, prone to human error, and necessitates the involvement of experienced medical professionals. Medical image recognition using machine learning can reduce workload and improve automation. However, CTCs are rare in clinical samples, limiting the training data available to construct a robust CTC image recognition system. In this study, we established a highly accurate artificial intelligence-based CTC recognition system by pre-training convolutional neural networks using images from lung cancer cell lines. Methods: We performed transfer learning of convolutional neural networks. Initially, the models were pre-trained using images obtained from lung cancer cell lines. The model’s accuracy was improved by training with a limited number of clinical CTC images. Results: Transfer learning significantly improved the CTC classification accuracy to an average of 99.51%, compared to 96.96% for a model trained solely on pre-trained cell lines (p < 0.05). This approach showed notable efficacy when clinical training images were limited, achieving statistically significant accuracy improvements with as few as 17 clinical CTC images (p < 0.05). Conclusions: Overall, our findings demonstrate that pre-training with cancer cell lines enables rapid and highly accurate automated CTC recognition even with limited clinical data, significantly enhancing clinical applicability and potential utility across diverse cancer diagnostic workflows.