The paper reviews the principle and recent developments of photonic time-stretch imaging, an ultrafast imaging technique that maps spatial imaging information into a temporal profile using a dispersive medium. This technique achieves imaging speeds of millions of frames per second, surpassing traditional methods. The review covers improvements in imaging resolution and sensitivity, applications in various fields, and future trends, including integration with artificial intelligence and deep learning, system miniaturization, and bandwidth extension. The authors also discuss the physical limits of current systems and the need for calibration to address wavelength-induced variations.

The study presents a coherent frequency-modulated continuous-wave (FMCW) photonic radar system designed to determine the range information of a stationary target under various atmospheric conditions and solar background noise. The system's performance, analyzed using OptiSystem software and MATLAB, is evaluated in terms of signal-to-noise ratio. The findings indicate that solar noise can significantly degrade the performance of photonic radar systems, which also depends on the Sun's position and sky conditions. The proposed system achieves a range resolution of 5 meters at a target range of 500 meters under clear weather conditions with an acceptable signal-to-noise ratio, making it robust for practical applications such as automotive vehicle systems and navigation.

Researchers developed a candle soot-based fiber optoacoustic emitter (CSFOE) that provides high-precision neural stimulation with high optoacoustic conversion efficiency. The CSFOE was optimized through simulation and experiment to generate over 15 MPa peak-to-peak pressure, enabling successful neuron activation and dual-site stimulation. This advancement opens up potential applications in complex animal behavior studies and offers a new tool for effective neuron stimulation with the ability to control multiple target sites in the brain's circuitry. The study was supported by the Brain Initiative R01 NS109794 and conducted by authors affiliated with Boston University.