![]() ![]() ![]() Our prototype system consists of a mode-locked fiber laser, a compressed pulse fiber amplifier, a "pulse cleaner", a chirped fiber Bragg grating, pulse selectors, a transport fiber system and a large mode area fiber amplifier. Our system focuses on optimizing these parameters. What is of high value is pulse energy, high signal to noise ratio (expressed as pre-pulse contrast), good beam quality, consistent output parameters and timing. High-energy lasers often have low repetition rates (as low as one pulse every few hours), and thus high average power and efficiency are of little practical value. The design requirements for this application are very different than those commonly seen in fiber lasers. Fiber lasers are ideal solutions for these systems as they are highly reliable and enable long term stable operation. ![]() more We are developing an all fiber laser system optimized for providing input pulses for short pulse (1-10ps), high energy (~1kJ) glass laser systems. We are developing an all fiber laser system optimized for providing input pulses for short pulse. Using fewer points to satisfy a sizing function improves the efficiency of some. These properties are local, and we can handle spatially-varying sizing functions. We change the density of the packing one disk at a time, maintaining the minimum disk separation distance and the maximum domain coverage distance required of any maximal packing. The tuned samples are conflict-free, retain coverage maximality, and, except in the extremes, retain the blue noise randomness properties of the input. We may achieve a user-defined density, either more dense or more sparse, almost up to the theoretical structured limits. Starting from any maximal random packing such as a Maximal Poisson-disk Sampling (MPS), we iteratively relocate, inject (add), or eject (remove) disks, using a set of three successively more-aggressive local operations. more We introduce an algorithmic framework for tuning the spatial density of disks in a maximal random packing, without changing the sizing function or radii of disks. We introduce an algorithmic framework for tuning the spatial density of disks in a maximal random. ![]()
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