Wireless laser power transmission is a promising technique in the far-field wireless power supply with the advantages of high-power density, and small transmitter and receiver sizes. This study proposes a high-efficiency pulse width modulation-based wireless laser power transmission step-down system based on an 808 nm laser diode and gallium arsenide photovoltaic cells. The output voltage of the proposed system is controlled with the use of pulse width modulation of laser diodes in the transmitter without switch tubes and rectifier diodes in the receiver. The model of the proposed system is built, and characteristics of the system is analyzed based on the state-space averaging method. Additionally, the steady and dynamic operations, as well as the efficiency analyses, are investigated and evaluated experimentally. With the use of the propounded system, when the load is 3 ohms and the output voltage is 1.5 V, a converter efficiency of 96.6% is obtained that is ~10% and ~5% higher than those of continuous wireless power transmission constant voltage systems with asynchronous and synchronous buck converters, respectively.

Mini-cameras are one of several emerging types of cat-eye devices using small lenses with insufficient retro-reflection for traditional detection systems to identify. Based on a structural analysis of these devices, this paper provides an accurate theoretical description of the received beam profile. The simulated and experimental results have good similarity in terms of profile shapes and trends with different incident angles and defocus distances. An analysis of the detection conditions and the reflection image features described in this article can thus be applied to hidden camera detection. Mini-cameras can be detected according to these features of the received beam profiles.

A mini-camera is one of several emerging cat-eye devices featuring tiny lenses with diffracted retro-reflections. It is hard for traditional active laser detection systems to identify a mini-camera because of their weak reflection. This paper proposes an anti-camera system with a spectrum-based convolutional neural network algorithm to recognize the profile features of the retro-reflection images captured by the system. The network was trained with the spatial spectra of local datasets and uploaded onto the embedded device. The results of several indoor experiments demonstrate that the system reached high accuracy in real-time detection, even with various types of interference.