We experimentally verified that anisotropic Hermite-Gaussian modes can be generated from a hemi-cylindrical laser cavity and can be transformed into high-order Laguerre-Gaussian modes using an extra-cavity cylindrical lens. We further combined the Huygens integral and the ABCD law to clearly demonstrate the transformation along the propagation direction. By controlling the pump offset and the pump size in hemi-cylindrical cavities, we experimentally observed the unique laser patterns that displayed the optical waves related to the coherent superposition of Laguerre-Gaussian modes.

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Thermal radiation is the only heat transfer mechanism with vacuum compatibility, and it carries energy at light speed. These advantages are taken in this work to design an oven for smart phone panels. The temperature of panels is acquired from a numerical method based on finite-difference method. The space configuration of the heating lamps as well as the relative distance between lamps and the panel are control factors for optimization. Full-factorial experiments are employed to identify the main effects from each factor. A fitness function Q considering both temperature uniformity of the panel and the heating capability of the ovens is proposed. The best oven among 27 candidates is able to raise panel temperature significantly with high uniformity.

This paper describes the development of an electronic neuron emulator that has both passive and active electrical properties of a neuron as seen from a single electrode. The main utility of this device is for testing electrophysiological equipment such as a single-electrode voltage clamp or a patch-clamp amplifier. Each spike of the action potential is generated by a limited amount of charge stored on a capacitor. This design results in a more realistic simulation of the action potential generation by ionic currents in a live neuron compared to using a current or voltage source. An embedded microprocessor is used to control the firing of the action potential. Preliminary results from a prototype show that the neuron emulator meets the design specifications and is capable of performing rate responsive current clamp.

Purpose: The research aimed to verify the applicability of low computational complexity and high diagnosis accuracy deep convolutional neural network, using MobileNetV2 to identify the presence of chest catheters and tubes on chest X-ray images. Methods: The dataset of chest X-rays collected from a teaching hospital included the endotracheal tube (ETT), the central venous catheter (CVC), and the nasogastric tube (NGT) datasets. A new method of applying dynamic image size training procedures was implemented and compared with fixed image size training. The idea is to learn more features through dynamic image size training. Transfer learning with pre-trained MobileNetV2 on ImageNet was conducted to accelerate the training process and acquire higher accuracy. Class activation mapping (CAM) was also employed to visualize artificial intelligence (AI) predictions, making AI decisions more explainable. Results: The ETT datasets included 10464 X-ray images, while the CVC and NGT datasets contained 10274 and 9610 images, respectively. The accuracies for ETT, CVC, and NGT are 99.0%, 98.4%, and 96.2% in the validation dataset, while in the testing dataset are 98.8%, 98.6%, and 96.8%, respectively. The area under the receiver operating characteristics (AUROCs) were 0.992, 0.988, and 0.980 in the ETT, CVC, and NGT testing datasets. Conclusion: MobileNetV2 with the dynamic image size achieved dedicated performance in the application of chest catheters and tubes classifications. The similarity of the accuracy between the validation and testing data suggests the good generalization capability of the model.