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Register a new userThe Raspberry Pi Auto-aligner: Machine Learning for Automated Alignment of Laser Beams
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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.
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We report an automated characterization of a single-photon detector based on commercial silicon avalanche photodiode (PerkinElmer C30902SH). The photodiode is characterized by I-V curves at different illumination levels (darkness, 10 pW and 10 uW), dark count rate and photon detection efficiency at different bias voltages. The automated characterization routine is implemented in C++ running on a Linux computer.
Hot cavity resonant ionization laser ion sources (RILIS) provide a multitude of radioactive ion beams with high ionization efficiency and element selective ionization. However, in hot cavity RILIS there still remains isobaric contaminations in the extracted beam from surface ionized species. An ion guide-laser ion source (IG-LIS) has been implemented that decouples the hot isotope production region from the laser ionization volume. A number of IG-LIS runs have been conducted to provide isobar free radioactive ion beams for experiments. Isobar suppression of up to 106 has been achieved, however, IG-LIS still suffers from an intensity loss of 50-100X as compared to hot cavity RILIS. Operating parameters for IG-LIS are being optimized and design improvements are being implemented into the prototype for robust and efficient on-line operation. Recent SIMION ion optics simulation results and the ongoing development status of the IG-LIS are presented.
At CEILAP (CITEDEF-CONICET), a multiangle LIDAR is under development to monitor aerosol extinction coefficients in the frame of the CTA (Cherenkov Telescope Array) Project. This is an initiative to build the next generation of ground-based instruments to collect very high energy gamma-ray radiation (>10 GeV). The atmospheric conditions are very important for CTA observations, and LIDARs play an important role in the measurement of the aerosol optical depth at any direction. The LIDAR being developed at CEILAP was conceived to operate in harsh environmental conditions during the shifts, and these working conditions may produce misalignments. To minimize these effects, the telescopes comprising the reception unit are controlled by a self-alignment system. This paper describes the self-alignment method and hardware automation.
In atomic force microscopy (AFM), the exchange and alignment of the AFM cantilever with respect to the optical beam and position-sensitive detector (PSD) are often performed manually. This process is tedious and time-consuming and sometimes damages the cantilever or tip. To increase the throughput of AFM in industrial applications, the ability to automatically exchange and align the cantilever in a very short time with sufficient accuracy is required. In this paper, we present the development of an automated cantilever exchange and optical alignment instrument. We present an experimental proof of principle by exchanging various types of AFM cantilevers in 6 seconds with an accuracy better than 2 um. The exchange and alignment unit is miniaturized to allow for integration in a parallel AFM. The reliability of the demonstrator has also been evaluated. Ten thousand continuous exchange and alignment cycles were performed without failure. The automated exchange and alignment of the AFM cantilever overcome a large hurdle toward bringing AFM into high-volume manufacturing and industrial applications.
Automated machine learning (AutoML) aims to find optimal machine learning solutions automatically given a machine learning problem. It could release the burden of data scientists from the multifarious manual tuning process and enable the access of domain experts to the off-the-shelf machine learning solutions without extensive experience. In this paper, we review the current developments of AutoML in terms of three categories, automated feature engineering (AutoFE), automated model and hyperparameter learning (AutoMHL), and automated deep learning (AutoDL). State-of-the-art techniques adopted in the three categories are presented, including Bayesian optimization, reinforcement learning, evolutionary algorithm, and gradient-based approaches. We summarize popular AutoML frameworks and conclude with current open challenges of AutoML.
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