Performance of a U-Net-based neural network for predictive adaptive optics.

We apply a U-Net-based convolutional neural network (NN) architecture to the problem of predictive adaptive optics (AO) for tracking and imaging fast-moving targets, such as satellites in low Earth orbit (LEO). We show that the fine-tuned NN is able to achieve an approximately 50% reduction in mean-squared wavefront error over non-predictive approaches while predicting up to eight frames into the future. These results were obtained when the NN, trained mostly on simulated data, tested its performance on 1 kHz Shack-Hartmann wavefront sensor data collected in open-loop at the Advanced Electro-Optical System facility at Haleakala Observatory while the telescope tracked a naturally illuminated piece of LEO space debris. We report, to our knowledge, the first successful test of a NN for the predictive AO application using on-sky data, as well as the first time such a network has been developed for the more stressing space tracking application.

Motion microscopy for visualizing and quantifying small motions.

Although the human visual system is remarkable at perceiving and interpreting motions, it has limited sensitivity, and we cannot see motions that are smaller than some threshold. Although difficult to visualize, tiny motions below this threshold are important and can reveal physical mechanisms, or be precursors to large motions in the case of mechanical failure. Here, we present a "motion microscope," a computational tool that quantifies tiny motions in videos and then visualizes them by producing a new video in which the motions are made large enough to see. Three scientific visualizations are shown, spanning macroscopic to nanoscopic length scales. They are the resonant vibrations of a bridge demonstrating simultaneous spatial and temporal modal analysis, micrometer vibrations of a metamaterial demonstrating wave propagation through an elastic matrix with embedded resonating units, and nanometer motions of an extracellular tissue found in the inner ear demonstrating a mechanism of frequency separation in hearing. In these instances, the motion microscope uncovers hidden dynamics over a variety of length scales, leading to the discovery of previously unknown phenomena.

A Rare Case of Skin Necrosis Following Extravasation of Prothrombin Complex Concentrate (PCC) Infusion During Warfarin Reversal.

Warfarin-induced skin necrosis is a well-documented complication that can occur following commencement of warfarin. However, skin necrosis following extravasation of prothrombin complex concentrate (PCC) infusion is a very rare adverse event that is not commonly documented. This case illustrates the possibility of developing skin necrosis following the administration of an anticoagulation reversal agent rather than from anticoagulation itself. We report a case of a 58-year-old male who developed skin necrosis at the site of PCC infusion in the right upper extremity (RUE) for warfarin reversal of an elevated international normalized ratio (INR). The skin necrosis progressed into a full thickness chemical burn. As a result, the patient underwent allograft followed by split thickness autograft and RECELL placement. This case presentation describes the first reported case of skin necrosis following extravasation of PCC infusion during warfarin reversal.

Laser vibrometry from a moving ground vehicle.

We investigated the fundamental limits to the performance of a laser vibrometer that is mounted on a moving ground vehicle. The noise floor of a moving laser vibrometer consists of speckle noise, shot noise, and platform vibrations. We showed that speckle noise can be reduced by increasing the laser spot size and that the noise floor is dominated by shot noise at high frequencies (typically greater than a few kilohertz for our system). We built a five-channel, vehicle-mounted, 1.55 µm wavelength laser vibrometer to measure its noise floor at 10 m horizontal range while driving on dirt roads. The measured noise floor agreed with our theoretical estimates. We showed that, by subtracting the response of an accelerometer and an optical reference channel, we could reduce the excess noise (in units of micrometers per second per Hz(1/2)) from vehicle vibrations by a factor of up to 33, to obtain nearly speckle-and-shot-noise-limited performance from 0.3 to 47 kHz.

Visual Vibrometry: Estimating Material Properties from Small Motions in Video.

The estimation of material properties is important for scene understanding, with many applications in vision, robotics, and structural engineering. This paper connects fundamentals of vibration mechanics with computer vision techniques in order to infer material properties from small, often imperceptible motions in video. Objects tend to vibrate in a set of preferred modes. The frequencies of these modes depend on the structure and material properties of an object. We show that by extracting these frequencies from video of a vibrating object, we can often make inferences about that object's material properties. We demonstrate our approach by estimating material properties for a variety of objects by observing their motion in high-speed and regular frame rate video.