Low-energy/pulse response and high-resolution-CMOS camera for spatiotemporal femtosecond laser pulses characterization @ 1.55 μm

Two-photon absorption spectrometers for near infrared

We present a Silicon-based Charge-Coupled Device (Si-CCD) sensor applied as a cost-effective spectrometer for femtosecond pulse characterization in the Near Infrared region in two different configurations: two-Fourier and Czerny-Turner setups. To test the spectrometer's performance, a femtosecond Optical Parametric Oscillator with a tuning range between 1100 and 1700 nm and a femtosecond Erbium-Doped Fiber Amplifier at 1582 nm were employed. The nonlinear spectrometer operation is based on the Two-Photon Absorption effect generated in the Si-CCD sensor. The achieved spectrometer resolution was 0.6 ± 0.1 nm with a threshold peak intensity of 2×106Wcm2. An analysis of the nonlinear response as a function of the wavelength, the response saturation, and the criteria to prevent it are also presented.

The Raspberry Pi auto-aligner: Machine learning for automated alignment of laser beams

We present a novel solution to automated beam alignment optimization. This device is based on a Raspberry Pi computer, stepper motors, commercial optomechanics and electronic devices, and the open-source machine learning algorithm M-LOOP. We provide schematic drawings for the custom hardware necessary to operate the device and discuss diagnostic techniques to determine the performance. The beam auto-aligning device has been used to improve the alignment of a laser beam into a single-mode optical fiber from manually optimized fiber alignment, with an iteration time of typically 20 minutes. We present example data of one such measurement to illustrate device performance.

Improve three-dimensional point localization accuracy in stereo vision systems using a novel camera calibration method

Computer vision systems have demonstrated to be useful in applications of autonomous navigation, especially with the use of stereo vision systems for the three-dimensional mapping of the environment. This article presents a novel camera calibration method to improve the accuracy of stereo vision systems for three-dimensional point localization. The proposed camera calibration method uses the least square method to model the error caused by the image digitalization and the lens distortion. To obtain particular three-dimensional point coordinates, the stereo vision systems use the information of two images taken by two different cameras. Then, the system locates the two-dimensional pixel coordinates of the three-dimensional point in both images and coverts them into angles. With the obtained angles, the system finds the three-dimensional point coordinates through a triangulation process. The proposed camera calibration method is applied in the stereo vision systems, and a comparative analysis between the real and calibrated three-dimensional data points is performed to validate the improvements. Moreover, the developed method is compared with three classical calibration methods to analyze their advantages in terms of accuracy with respect to tested methods.