We report the first unambiguous demonstration of near-field imaging of optical diffraction radiation (ODR). The source of the ODR was an aluminum metal reflective surface with a 7-GeV electron beam passing nearby its single edge. Because of the high Lorentz factor involved, appreciable ODR is emitted at visible wavelengths even for impact parameters of 1 to 2 mm, so standard imaging techniques were employed. The experimental results are compared to a simple near-field model. We show that the ODR signals are sensitive to both beam size and position. Applications to multi-GeV beams in transport lines in the major synchrotron radiation facilities, x-ray free-electron lasers, energy recovering linacs, and the International Linear Collider are possible.
Phase advance and function are basic lattice functions characterizing the linear properties of an accelerator lattice. Accurate and efficient measurements of these quantities are important for commissioning and operating a machine. For rings with little coupling, we report a new method to measure these lattice functions based on the model-independent analysis technique, which uses beam histories of excited betatron oscillations measured simultaneously at a large number of beam position monitors. It is simple, fast, accurate, and robust. Measurements done at the storage ring of the Advanced Photon Source are reported. Comparisons among various methods are made.
Depth has been a valuable piece of information for perception tasks such as robot grasping, obstacle avoidance, and navigation, which are essential tasks for developing smart homes and smart cities. However, not all applications have the luxury of using depth sensors or multiple cameras to obtain depth information. In this paper, we tackle the problem of estimating the per-pixel depths from a single image. Inspired by the recent works on generative neural network models, we formulate the task of depth estimation as a generative task where we synthesize an image of the depth map from a single Red, Green, and Blue (RGB) input image. We propose a novel generative adversarial network that has an encoder-decoder type generator with residual transposed convolution blocks trained with an adversarial loss. Quantitative and qualitative experimental results demonstrate the effectiveness of our approach over several depth estimation works.
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