Hybrid dispersion laser scanner.

Laser scanning technology is one of the most integral parts of today's scientific research, manufacturing, defense, and biomedicine. In many applications, high-speed scanning capability is essential for scanning a large area in a short time and multi-dimensional sensing of moving objects and dynamical processes with fine temporal resolution. Unfortunately, conventional laser scanners are often too slow, resulting in limited precision and utility. Here we present a new type of laser scanner that offers ∼1,000 times higher scan rates than conventional state-of-the-art scanners. This method employs spatial dispersion of temporally stretched broadband optical pulses onto the target, enabling inertia-free laser scans at unprecedented scan rates of nearly 100 MHz at 800 nm. To show our scanner's broad utility, we use it to demonstrate unique and previously difficult-to-achieve capabilities in imaging, surface vibrometry, and flow cytometry at a record 2D raster scan rate of more than 100 kHz with 27,000 resolvable points

Antenna Beam Coverage Concepts

The strawman Personal Access Satellite System (PASS) design calls for the use of a CONUS beam for transmission between the supplier and the satellite and for fixed beams for transmission between the basic personal terminal and the satellite. The satellite uses a 3 m main reflector for transmission at 20 GHz and a 2 m main reflector for reception at 30 GHz. There are several types of spot beams under consideration for the PASS system besides fixed beams. The beam pattern of a CONUS coverage switched beam is shown along with that of a scanning beam. A switched beam refers to one in which the signal from the satellite is connected alternatively to various feed horns. Scanning beams are taken to mean beams whose footprints are moved between contiguous regions in the beam's coverage area. The advantages and disadvantages of switched and/or scanning beams relative to fixed beams. The consequences of using switched/scanning in lieu of fixed beams in the PASS design and attempts are made to evaluate the listed advantages and disadvantages. Two uses of switched/scanning beams are examined. To illustrate the implications of switched beams use on PASS system design, operation at two beam scan rates is explored

Optical Coherence Tomography – Variations on a Theme

This paper was presented at the 4th Micro and Nano Flows Conference (MNF2014), which was held at University College, London, UK. The conference was organised by Brunel University and supported by the Italian Union of Thermofluiddynamics, IPEM, the Process Intensification Network, the Institution of Mechanical Engineers, the Heat Transfer Society, HEXAG - the Heat Exchange Action Group, and the Energy Institute, ASME Press, LCN London Centre for Nanotechnology, UCL University College London, UCL Engineering, the International NanoScience Community, www.nanopaprika.eu.Optical Coherence Tomography (OCT) has developed extensively over the last 23 years. This paper reviews some of the imaging techniques based on OCT with particular reference to the trade-offs between lateral and axial resolution, working distance, imaging depth, acquisition speed (enabling real time observation and 3D imaging), imaged area/volume, contrast enhancement – including velocity measurement, and system complexity – including detectors, light sources and the optical path. The majority of applications of OCT are biomedical, especially ophthalmology, endoscopy and intravascular imaging. However, some industrial applications are emerging particularly for non-destructive testing and quality control, such as in the production of MEMS devices, or the non-destructive detection of sub-surface strain fields in injected moulded polymer parts

Flow-Guided Feature Aggregation for Video Object Detection

Extending state-of-the-art object detectors from image to video is challenging. The accuracy of detection suffers from degenerated object appearances in videos, e.g., motion blur, video defocus, rare poses, etc. Existing work attempts to exploit temporal information on box level, but such methods are not trained end-to-end. We present flow-guided feature aggregation, an accurate and end-to-end learning framework for video object detection. It leverages temporal coherence on feature level instead. It improves the per-frame features by aggregation of nearby features along the motion paths, and thus improves the video recognition accuracy. Our method significantly improves upon strong single-frame baselines in ImageNet VID, especially for more challenging fast moving objects. Our framework is principled, and on par with the best engineered systems winning the ImageNet VID challenges 2016, without additional bells-and-whistles. The proposed method, together with Deep Feature Flow, powered the winning entry of ImageNet VID challenges 2017. The code is available at https://github.com/msracver/Flow-Guided-Feature-Aggregation