The secure transmission of an image can be accomplished by encoding the image information, securely communicating this information, and subsequently reconstructing the image. As an alternative, here we show how the image itself can be directly transmitted while ensuring that the presence of any eavesdropper is revealed in a way akin to quantum key distribution. We achieve this transmission using a photon-pair source with the deliberate addition of a thermal light source as background noise. One photon of the pair illuminates the object, which is masked from an eavesdropper by adding indistinguishable thermal photons, the other photon of the pair acts as a time reference from which the intended recipient can preferentially detect the image carrying photons. These reference photons are themselves made sensitive to the presence of an eavesdropper by traditional polarization-based QKD encoding. Interestingly, the security encoding is performed in the two-dimensional polarization-basis, but the image information is encoded in a much higher-dimensional, hence information-rich, pixel basis. In our example implementation, our images have more than 100 independent pixels. Beyond the secure transmission of images, our approach to the distribution of secure high-dimensional information may create new high-bandwidth approaches to traditional QKD.
A limitation of free-space optical communications is the ease with which the information can be intercepted. Overcoming this limitation is possible by hiding the information within the background optical noise that is present in all real-world situations. We demonstrate the limitations of our experimental system for transferring images over free-space using a photon-pair source emitting two correlated beams. The system uses spontaneous parametric down-conversion to create photon-pairs, where one photon contains the spatial information and the other the heralding information.
Gas leaks pose a prevalent issue in industry and can have pressing impacts on individual safety and the environment. There is demand for new technologies that can ease, and reduce the cost of, detection of the source of leaks, both on a large and small scale. We present a device capable of visualizing the gas involved in the leaks allowing for an accessible tool in source location. Our current device can image methane leaks from ranges of up to 10m. By imaging a scene illuminated using a laser diode tuned to an absorption band of methane, followed by imaging at a similar but non-absorbed wavelength one can build a differential image of the scene and identify the presence of methane. This differential signal is then processed and assigned a false colour, in order to be overlaid upon an accompanying visible live feed. This system is adaptable and could be used to detect other gas species with modification to light source and detector. Future candidate gases would be based upon industry interest with acetylene, a common and flammable welding gas, being an example. The system is also robust enough to be drone mounted, we present data from conducted test flights. These flights demonstrate new ways in which the system can be used, such as in monitoring of difficult to access pipe geometries and for preset flight paths along expansive pipelines. This can allow for a more automated gas detection process, that is straightforward to review.
We present a method of using a high-flux entangled photon-pair source to improve the signal-to-noise ratio of a single-pixel imaging system. Sensing with single-photon counting detectors will often suffer from measurement noise due to any background light levels. Using a single detector enables a high efficiency of detection and when paired with a variable transmission mask enables full images to be captured. The heralding photon from the source acts as a temporal reference, allowing the signal photons to be distinguished from background noise. This heralding method is key to understanding how quantum measurements can produce higher contrast images than their classical equivalent.
Using a convolutional neural network to develop an optimal sampling strategy for LIDAR remote sensing. Detecting the distance to object is important for autonomous vehicles, surveying, and other remote sensing applications. LIDAR detects distances using a pulsed laser and a time-of-flight system to measure the position of all objects in a scene, however they are limited in the maximum distance they can measure due to low signal return. A convolutional neural network has been used to develop a sampling basis to effectively sample the scene, and also the reconstruction algorithm to recreate the 3D scene.
We present a prototype light detection and ranging (lidar) system that compressively samples the scene using our deep learning optimised sampling basis and reconstruction algorithms. This approach improves scene reconstruction quality compared to an orthogonal sampling method, with reflectivity and depth accuracy improvements for one frame per second acquisition rates. This method may pave the way for improved scan-free lidar systems for driverless cars and for fully optimised sampling through to decision-making pipelines. The requirement for 3D imaging is a challenge across a range of sectors including gaming, robotics, health-care and automotive industries. Mature technologies such as radar and ultra-sound sensing are effective at long and short ranges respectively. With lidar capable of millimetric depth precision, with good spatial resolution at ranges of around 100 m, it has become a key technology in this area, with depth information typically gained through time-of-flight photon-counting measurements of a scanned laser spot. Single-pixel imaging (SPI) is an alternative imaging modality for recovering spatial information. SPI methods offer an alternative approach to spot-scanning, which allows a choice of sampling basis. Unlike scanning systems, the freedom to choose the sampling basis in SPI provides the opportunity to use compressed sensing techniques, where a high-quality image can be reconstructed from a number of measurements that is fewer than the number of pixels in the image. Compressed sensing has been demonstrated using an optimised imaging basis and reconstruction algorithm derived from a trained convolutional neural network. This deep learning approach achieves a 4% compression ratio, enabling lidar imaging using 25 times less measurements such that faster acquisition times can be used.
The availability of compact, low-cost, and high-speed MEMS-based spatial light modulators has generated widespread interest in alternative sampling strategies for imaging systems utilizing single-pixel detectors. The development of compressed sensing schemes for real-time computational imaging may have promising commercial applications for high-performance detectors, where the availability of focal plane arrays is expensive or otherwise limited. We discuss the research and development of a prototype light detection and ranging (LiDAR) system via direct time of flight, which utilizes a single high-sensitivity photon-counting detector and fast-timing electronics to recover millimeter accuracy three-dimensional images in real time. The development of low-cost real time computational LiDAR systems could have importance for applications in security, defense, and autonomous vehicles.
Recent European atmospheric imaging missions have seen a move towards the use of CMOS sensors for the visible and NIR parts of the spectrum. These applications have particular challenges that are completely different to those that have driven the development of commercial sensors for applications such as cell-phone or SLR cameras. This paper will cover the design and performance of general-purpose image sensors that are to be used in the MTG (Meteosat Third Generation) and MetImage satellites and the technology challenges that they have presented. We will discuss how CMOS imagers have been designed with 4T pixel sizes of up to 250 μm square achieving good charge transfer efficiency, or low lag, with signal levels up to 2M electrons and with high line rates. In both devices a low noise analogue read-out chain is used with correlated double sampling to suppress the readout noise and give a maximum dynamic range that is significantly larger than in standard commercial devices. Radiation hardness is a particular challenge for CMOS detectors and both of these sensors have been designed to be fully radiation hard with high latch-up and single-event-upset tolerances, which is now silicon proven on MTG. We will also cover the impact of ionising radiation on these devices. Because with such large pixels the photodiodes have a large open area, front illumination technology is sufficient to meet the detection efficiency requirements but with thicker than standard epitaxial silicon to give improved IR response (note that this makes latch up protection even more important). However with narrow band illumination reflections from the front and back of the dielectric stack on the top of the sensor produce Fabry-Perot étalon effects, which have been minimised with process modifications. We will also cover the addition of precision narrow band filters inside the MTG package to provide a complete imaging subsystem. Control of reflected light is also critical in obtaining the required optical performance and this has driven the development of a black coating layer that can be applied between the active silicon regions.
The Transneptunian Automated Occultation Survey (TAOS II) is a robotic telescope system using three telescopes in San Pedro Martir Observatory in Mexico. It measures occultation of background stars by small TransNeptunian Objects (TNO) in order to determine their size distribution. Each telescope focal plane uses ten buttable backthinned CMOS sensors. Key performance features of the sensors are: Large array format 4608 x 1920, Pixel size 16μm, Multi ROIs, 8 analogue video channels, Frame rate of 20-40 fps [using ROIs], Low noise <3e-, Cryogenic dark current <0.1e-/pixel/s, backthinned for >90% peak quantum efficiency. The paper describes top level application requirements for the TAOS II detector. The sensor design including the pixel and buttable package are described together with performance at room temperature and cryogenic temperature of backthinned devices. The key performance specifications have been demonstrated and will be presented. The production set of 40 devices are due for completion within 2017.
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