Honeywell Aerospace is a manufacturer of aircraft engines and avionics, as well as a producer of auxiliary power units (APUs) and other aviation products. Headquartered in Phoenix, Arizona, it is a division of the Honeywell International conglomerate. It generates approximately $10 billion in annual revenue from a 50/50 mix of commercial and defense contracts. The company experienced a boom during World War II, when it equipped bomber planes with avionics and invented the auto-pilot. After the war, it transitioned to a heavier focus on peacetime applications. Today, Honeywell produces space equipment, turbine engines, auxiliary power units, brakes, wheels, synthetic vision, runway safety systems, and other avionics. A Honeywell APU was used in the notable emergency landing of US Airways Flight 1549, and a Honeywell blackbox survived under sea for years, thus exceeding by far its specified limits to reveal the details of the crash of Air France Flight 447. The company was also involved in the making of 2001: A Space Odyssey and in 90 percent of U.S. space missions. It's involved in the U.S. NextGen program and Europe's SESAR program for advancing avionics. President Barack Obama awarded a Honeywell employee the National Medal of Technology for his contributions to air flight safety technology. The company owns dozens of patents related to NextGen technology, aircraft windshields, turbochargers, and more. It was also involved in an 11-year-long patent dispute regarding ring laser gyroscope technology.
Ultra-low-noise stabilized lasers are a fundamental tool for precision quantum technologies, optical clocks, microwave and millimeter-wave generation, and fiber sensing. Existing systems rely on table-top bulk-optic components – discrete lasers, reference cavities, isolators, modulators and frequency shifters – limiting portability, scalability, and manufacturability. While these systems offer flexibility in laser design to tailor linewidth, frequency noise, and wavelength to specific applications, fully integrating a stabilized laser onto a chip without sacrificing performance and versatility has remained elusive. Here, we report integration of the precision stabilized laser in the low-loss silicon nitride photonic platform, combining a flexible isolator-free core laser design with a modulation-free stabilization cavity. We demonstrate a stabilized widely tunable self-isolating extended cavity tunable laser monolithically integrated with an on-chip coil-loaded Mach-Zehnder interferometer (CL-MZI). This design yields a fundamental linewidth of 1.7 - 10.5 Hz across a 60 nm tuning range, integral linewidth of 299 - 505 Hz over a 30 nm tuning range, frequency noise reduction of over 5 orders of magnitude, and an Allan-Deviation (ADEV) of 6.5x10-13 at 0.08 ms. We next highlight the versatility of this approach by demonstrating a self-isolating stimulated Brillouin scattering (SBS) laser, that provides nonlinear noise suppression of high frequency noise by multiple orders of magnitude, stabilized to an on-chip CL-MZI. The stabilized SBS laser achieves 4 Hz fundamental linewidth, 74 Hz integral linewidth, and ADEV of 2.8x10-13 at 5 ms. These results bring the performance and versatility of table-top stabilized laser systems to a chip for the first time, providing a path to scalable, low-cost, and manufacturable precision lasers for portable quantum, sensing, and communications applications.
Detecting small objects in very large raster imagery requires slicing into overlapping tiles, running a detector on each tile, and merging results. SAHI (Slicing Aided Hyper Inference) is the de facto open-source solution for this task, yet it loads tiles into random-access memory (RAM) via the Python Imaging Library (PIL) and performs postprocessing-non-maximum suppression (NMS) and merging-on axis-aligned bounding boxes (AABBs), so that oriented bounding boxes (OBBs) produced by the detector are reduced to AABBs before merging. We propose RAHI (Raster-Aware Hyper Inference), a two-variant framework that replaces these steps with memory-mapped tiling via pyvips, native AABB/OBB IoU merging in the global coordinate space, and lossless AABB/OBB preservation throughout slicing and merging. RAHI-G operates in geographic coordinate space for georeferenced rasters; RAHI-P operates in pixel space for arbitrary large images. On 10 RarePlanes satellite scenes, RAHI-G achieves 31x tiling speedup and 2.0x total speedup. On 15 DOTA-v1 aerial images with oriented plane annotations, RAHI-P achieves 27x tiling speedup, improves mean F1 from 0.644 to 0.793, and raises mean IoU from 0.726 to 0.827 by preserving native OBBs end-to-end, whereas SAHI's postprocessing reduces them to AABBs for merging.
