Investigation of zero-crossings as information carriers

Analysis of bandlimited signal transmission by zero crossings of optimum signa

The Hyper Suprime-Cam Software Pipeline

In this paper, we describe the optical imaging data processing pipeline developed for the Subaru Telescope's Hyper Suprime-Cam (HSC) instrument. The HSC Pipeline builds on the prototype pipeline being developed by the Large Synoptic Survey Telescope's Data Management system, adding customizations for HSC, large-scale processing capabilities, and novel algorithms that have since been reincorporated into the LSST codebase. While designed primarily to reduce HSC Subaru Strategic Program (SSP) data, it is also the recommended pipeline for reducing general-observer HSC data. The HSC pipeline includes high level processing steps that generate coadded images and science-ready catalogs as well as low-level detrending and image characterizations.Comment: 39 pages, 21 figures, 2 tables. Submitted to Publications of the Astronomical Society of Japa

Prediction of non-reacting and reacting turbulent jets

Imperial Users onl

Evaluating the Variability of Urban Land Surface Temperatures Using Drone Observations

Urbanization and climate change are driving increases in urban land surface temperatures that pose a threat to human and environmental health. To address this challenge, we must be able to observe land surface temperatures within spatially complex urban environments. However, many existing remote sensing studies are based upon satellite or aerial imagery that capture temperature at coarse resolutions that fail to capture the spatial complexities of urban land surfaces that can change at a sub-meter resolution. This study seeks to fill this gap by evaluating the spatial variability of land surface temperatures through drone thermal imagery captured at high-resolutions (13 cm). In this study, flights were conducted using a quadcopter drone and thermal camera at two case study locations in Milwaukee, Wisconsin and El Paso, Texas. Results indicate that land use types exhibit significant variability in their surface temperatures (3.9–15.8 °C) and that this variability is influenced by surface material properties, traffic, weather and urban geometry. Air temperature and solar radiation were statistically significant predictors of land surface temperature (R2 0.37–0.84) but the predictive power of the models was lower for land use types that were heavily impacted by pedestrian or vehicular traffic. The findings from this study ultimately elucidate factors that contribute to land surface temperature variability in the urban environment, which can be applied to develop better temperature mitigation practices to protect human and environmental health

Speckle-visibility spectroscopy: A tool to study time-varying dynamics

We describe a multispeckle dynamic light scattering technique capable of resolving the motion of scattering sites in cases that this motion changes systematically with time. The method is based on the visibility of the speckle pattern formed by the scattered light as detected by a single exposure of a digital camera. Whereas previous multispeckle methods rely on correlations between images, here the connection with scattering site dynamics is made more simply in terms of the variance of intensity among the pixels of the camera for the specified exposure duration. The essence is that the speckle pattern is more visible, i.e. the variance of detected intensity levels is greater, when the dynamics of the scattering site motion is slow compared to the exposure time of the camera. The theory for analyzing the moments of the spatial intensity distribution in terms of the electric field autocorrelation is presented. It is demonstrated for two well-understood samples, a colloidal suspension of Brownian particles and a coarsening foam, where the dynamics can be treated as stationary. However, the method is particularly appropriate for samples in which the dynamics vary with time, either slowly or rapidly, limited only by the exposure time fidelity of the camera. Potential applications range from soft-glassy materials, to granular avalanches, to flowmetry of living tissue.Comment: review - theory and experimen

Testing Methods of Surficial Sinkhole Identification Using Remotely Sensed Data

Nearly a quarter of all people rely on karst aquifers for drinking water. In the United States, the Safe Water Drinking Act requires a complete assessment of public water systems\u27 vulnerabilities to contamination. As part of that assessment, watershed boundaries must be delineated, while recharge and supply locations identified. In the context of karst aquifers, surficial karst features, such as sinkholes, can act as a point source of direct recharge to karst aquifers and create vulnerabilities to critical drinking water sources. Historical methods of locating these features are inefficient and depend on basic field investigations, resulting in a clear need for advanced identification methods. To this end, this study focuses on developing more efficient identification methods that use remotely sensed data to locate and map surficial karst features that may require protection. Satellite and unmanned aerial vehicle (UAV) data were used to explore the resolution needed to identify surficial karst feature signatures and the most promising methods for analyzing these data. This study\u27s data included red, green, blue, and near-infrared reflectance rasters, thermal mosaics, and digital surface and terrain models. Spectral and thermal properties were used to filter data that could include karst features. Additionally, digital elevation models were used to explore multiple smoothing methods, image differencing, edge detection, terrain curvature, sink location, and watershed delineation. Findings from the different methods were compared to known karst feature locations. Data with a resolution between 0.5 and 2.5 meters per pixel were found to be ideal for most methods tested. However, vegetation removal, followed by a simple interpolation to fill these areas, created data analysis problems and highlighted the need for other data products, such as LiDAR, that provide accurate elevations of terrain shrouded by vegetation. In the end, it was found that edge detection, mapping curvature, and locating of low points (or sinks) via DEM analyses are all promising methods. It was concluded that by combining multiple methods, detailed digital terrain models could accurately locate many surficial karst features

Initialization of ReLUs for Dynamical Isometry

Deep learning relies on good initialization schemes and hyperparameter choices prior to training a neural network. Random weight initializations induce random network ensembles, which give rise to the trainability, training speed, and sometimes also generalization ability of an instance. In addition, such ensembles provide theoretical insights into the space of candidate models of which one is selected during training. The results obtained so far rely on mean field approximations that assume infinite layer width and that study average squared signals. We derive the joint signal output distribution exactly, without mean field assumptions, for fully-connected networks with Gaussian weights and biases, and analyze deviations from the mean field results. For rectified linear units, we further discuss limitations of the standard initialization scheme, such as its lack of dynamical isometry, and propose a simple alternative that overcomes these by initial parameter sharing.Comment: NeurIPS 201

Impact of signal quantization on the performance of RFI mitigation algorithms

Radio Frequency Interference (RFI) is currently a major problem in Communications and Earth Observation, but it is even more dramatic in Microwave Radiometry because of the low power levels of the received signals. Its impact has been attested in several Earth Observation missions. On-board mitigation systems are becoming a requirement to detect and remove affected measurements, increasing thus radiometric accuracy and spatial coverage. However, RFI mitigation methods have not been tested yet in the context of some particular radiometer topologies, which rely on the use of coarsely quantized streams of data. In this study, the impact of quantization and sampling in the performance of several known RFI mitigation algorithms is studied under different conditions. It will be demonstrated that in the presence of clipping, quantization changes fundamentally the time-frequency properties of the contaminated signal, strongly impairing the performance of most mitigation methods. Important design considerations are derived from this analysis that must be taken into account when defining the architecture of future instruments. In particular, the use of Automatic Gain Control (AGC) systems is proposed, and its limitations are discussedPeer ReviewedPostprint (published version