We introduce a Bayesian framework for measuring spatio-temporal couplings (STCs) in ultra-intense lasers that reconceptualizes what constitutes a 'single-shot' measurement. Moving beyond traditional distinctions between single- and multi-shot devices, our approach provides rigorous criteria for determining when measurements can truly resolve individual laser shots rather than statistical averages. This framework shows that single-shot capability is not an intrinsic device property but emerges from the relationship between measurement precision and inherent parameter variability. Implementing this approach with a new measurement device at the ATLAS-3000 petawatt laser, we provide the first quantitative uncertainty bounds on pulse front tilt and curvature. Notably, we observe that our Bayesian method reduces uncertainty by up to 60% compared to traditional approaches. Through this analysis, we reveal how the interplay between measurement precision and intrinsic system variability defines achievable resolution -- insights that have direct implications for applications where precise control of laser-matter interaction is critical.

A machine-learning non-contact method to determine the temperature of a laser gain medium via its laser emission with a trained few-layer neural net model is presented. The training of the feed-forward Neural Network (NN) enables the prediction of the device's properties solely from spectral data, here recorded by visible-/nearinfrared-light compact micro-spectrometers for both a diode pump laser and optically-pumped gain membrane of a semiconductor disk laser. Fiber spectrometers are used for the acquisition of large quantities of labelled intensity data, which can afterwards be used for the prediction process. Such pretrained deep NNs enable a fast, reliable and easy way to infer the temperature of a laser system such as our Membrane External Cavity Laser, at a later monitoring stage without the need of additional optical diagnostics or read-out temperature sensors. With the miniature mobile spectrometer and the remote detection ability, the temperature inference capability can be adapted for various laser diodes using transfer learning methods with pretrained models. Here, mean-square-error values for the temperature inference corresponding to sub-percent accuracy of our sensor scheme are reached, while computational cost can be saved by reducing the network depth at the here displayed cost of accuracy, as appropriate for different application scenarios.

The Defense Ministry is planning to test the use of lasers to counter drones, sources familiar with the matter have said. Under the plan, a laser system will be installed on vehicles of the Ground Self-Defense Force, including high mobility vehicles, and tests on its response capabilities will be conducted, the sources said. In March, the ministry signed a contract to buy lasers to be mounted on GSDF high mobility vehicles from Kawasaki Heavy Industries for about 1.5 billion and a 1.9 billion deal to purchase lasers for trucks from Mitsubishi Heavy Industries.

With the need to feed a growing world population, the efficiency of crop production is of paramount importance. To support breeding and field management, various characteristics of the plant phenotype need to be measured -- a time-consuming process when performed manually. We present a robotic platform equipped with multiple laser and camera sensors for high-throughput, high-resolution in-field plant scanning. We create digital twins of the plants through 3D reconstruction. This allows the estimation of phenotypic traits such as leaf area, leaf angle, and plant height. We validate our system on a real field, where we reconstruct accurate point clouds and meshes of sugar beet, soybean, and maize.

Increasing complexity of modern laser systems, mostly originated from the nonlinear dynamics of radiation, makes control of their operation more and more challenging, calling for development of new approaches in laser engineering. Machine learning methods, providing proven tools for identification, control, and data analytics of various complex systems, have been recently applied to mode-locked fiber lasers with the special focus on three key areas: self-starting, system optimization and characterization. However, the development of the machine learning algorithms for a particular laser system, while being an interesting research problem, is a demanding task requiring arduous efforts and tuning a large number of hyper-parameters in the laboratory arrangements. It is not obvious that this learning can be smoothly transferred to systems that differ from the specific laser used for the algorithm development by design or by varying environmental parameters. Here we demonstrate that a deep reinforcement learning (DRL) approach, based on trials and errors and sequential decisions, can be successfully used for control of the generation of dissipative solitons in mode-locked fiber laser system. We have shown the capability of deep Q-learning algorithm to generalize knowledge about the laser system in order to find conditions for stable pulse generation. Region of stable generation was transformed by changing the pumping power of the laser cavity, while tunable spectral filter was used as a control tool. Deep Q-learning algorithm is suited to learn the trajectory of adjusting spectral filter parameters to stable pulsed regime relying on the state of output radiation. Our results confirm the potential of deep reinforcement learning algorithm to control a nonlinear laser system with a feed-back. We also demonstrate that fiber mode-locked laser systems generating data at high speed present a fruitful photonic test-beds for various machine learning concepts based on large datasets.

We analyze the reservoir computation capability of the Lang-Kobayashi system by comparing the numerically computed recall capabilities and the eigenvalue spectrum. We show that these two quantities are deeply connected, and thus the reservoir computing performance is predictable by analyzing the eigenvalue spectrum. Our results suggest that any dynamical system used as a reservoir can be analyzed in this way as long as the reservoir perturbations are sufficiently small. Optimal performance is found for a system with the eigenvalues having real parts close to zero and off-resonant imaginary parts.

If any automaker has made its name synonymous with safety, it's Volvo. The Swedish outfit's marketing department deserves some credit there, for sure, but they've got good stuff to work with. Over the decades, Volvo has led the industry with three-point seatbelts, rear-facing child seats, blind-spot monitoring systems, and more. Now it's once again in the vanguard, announcing Wednesday that it will be the first automaker to use a lidar laser vision system to enable what it calls "fully autonomous highway driving" in its cars, starting in 2022. That news is the result of a deal with Luminar, the eight-year-old lidar company helmed by 25-year-old Austin Russell.

The Fraunhofer Institute for Laser Technology (Fraunhofer ILT; Aachen, Germany) will be hosting its AI for Laser Technology Conference on November 6-7, 2019, in Aachen. There, Fraunhofer ILT will address the question of how lasers can be used more efficiently with the help of artificial intelligence (AI), and conference topics will range from machine learning in industrial settings to the use of augmented reality and analysis of neural networks. The idea of holding a conference on the use of AI in laser systems originated at the Process Control group at Fraunhofer ILT, which has been researching AI for a number of years. The scientists are interested in using AI methods to draw one-to-one conclusions on, for example, detecting errors in laser welding. The conference agenda has a practical focus that reflects these first applied uses of AI in laser material processing.

This summer, the US Air Force will begin testing a laser mounted on an F-15 warplane, an official said Monday. The Pentagon last year awarded a $26 million contract to Lockheed Martin for a laser program called SHiELD (Self-protect High Energy Laser Demonstrator.) The idea is to put a laser system on aircraft with an output of about 50 kilowatts to test their ability to zap drones or cruise missiles. Air Force scientists hope to have a laser that can defeat drones and missiles ready to put on an F-15 by summer 2019. 'We have got tests starting this summer and the flight tests next summer,' Jeff Stanley, deputy assistant secretary of the Air Force for science, technology and engineering, told reporters. 'There are still some technical challenges that we have to overcome, mainly size, weight, power.'

Lockheed Martin is developing a powerful new pair of cannons that can shoot down drones using high energy laser beams. Under a $150 million contract from the US Navy, the firm plans to develop, manufacture, and test the new weapons by 2020. The goal is to demonstrate one on land, and the second aboard an Arleigh Burke-class destroyer, according to Motherboard. Under the new contract, Lockheed Martin will develop the laser weapons for land and for an Arleigh Burke-class destroyer. Lockheed Martin's newest weapons will come under a contract with the US Navy to build a High Energy Laser and Integrated Optical-dazzler with surveillance system, the Department of Defense says.