Characterization of a dense aperture array for radio astronomy

EMBRACE@Nancay is a prototype instrument consisting of an array of 4608 densely packed antenna elements creating a fully sampled, unblocked aperture. This technology is proposed for the Square Kilometre Array and has the potential of providing an extremely large field of view making it the ideal survey instrument. We describe the system,calibration procedures, and results from the prototype.Comment: 17 pages, accepted for publication in A&

Cybernetics of the mind:learning individual's perceptions autonomously

In this article, we describe an approach to computational modeling and autonomous learning of the perception of sensory inputs by individuals. A hierarchical process of summarization of heterogeneous raw data is proposed. At the lower level of the hierarchy, the raw data autonomously form semantically meaningful concepts. Instead of clustering based on visual or audio similarity, the concepts are formed at the second level of the hierarchy based on observed physiological variables (PVs) such as heart rate and skin conductance and are mapped to the emotional state of the individual. Wearable sensors were used in the experiments

A Python-enhanced urban land surface model SuPy (SUEWS in Python, v2019.2): development, deployment and demonstration

Accurate and agile modelling of the climate of cities is essential for urban climate services. The Surface Urban Energy and Water balance Scheme (SUEWS) is a state-of-the-art, widely used, urban land surface model (ULSM) which simulates urban-atmospheric interactions by quantifying the energy, water and mass fluxes. Using SUEWS as the computation kernel, SuPy (SUEWS in Python), stands on the Python-based data stack to streamline the pre-processing, computation and post-processing that are involved in the common modelling-centred urban climate studies. This paper documents the development of SuPy, which includes the SUEWS interface modification, F2PY (Fortran to Python) configuration and Python frontend implementation. In addition, the deployment of SuPy via PyPI (Python Package Index) is introduced along with the automated workflow for cross-platform compilation. This makes SuPy available for all mainstream operating systems (Windows, Linux, and macOS). Furthermore, three online tutorials in Jupyter notebooks are provided to users of different levels to become familiar with SuPy urban climate modelling. The SuPy package represents a significant enhancement that supports existing and new model applications, reproducibility, and enhanced functionality

Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions

Cybernetics of the mind:learning individual's perceptions autonomously

In this article, we describe an approach to computational modeling and autonomous learning of the perception of sensory inputs by individuals. A hierarchical process of summarization of heterogeneous raw data is proposed. At the lower level of the hierarchy, the raw data autonomously form semantically meaningful concepts. Instead of clustering based on visual or audio similarity, the concepts are formed at the second level of the hierarchy based on observed physiological variables (PVs) such as heart rate and skin conductance and are mapped to the emotional state of the individual. Wearable sensors were used in the experiments

SKYNET: an efficient and robust neural network training tool for machine learning in astronomy

We present the first public release of our generic neural network training algorithm, called SkyNet. This efficient and robust machine learning tool is able to train large and deep feed-forward neural networks, including autoencoders, for use in a wide range of supervised and unsupervised learning applications, such as regression, classification, density estimation, clustering and dimensionality reduction. SkyNet uses a `pre-training' method to obtain a set of network parameters that has empirically been shown to be close to a good solution, followed by further optimisation using a regularised variant of Newton's method, where the level of regularisation is determined and adjusted automatically; the latter uses second-order derivative information to improve convergence, but without the need to evaluate or store the full Hessian matrix, by using a fast approximate method to calculate Hessian-vector products. This combination of methods allows for the training of complicated networks that are difficult to optimise using standard backpropagation techniques. SkyNet employs convergence criteria that naturally prevent overfitting, and also includes a fast algorithm for estimating the accuracy of network outputs. The utility and flexibility of SkyNet are demonstrated by application to a number of toy problems, and to astronomical problems focusing on the recovery of structure from blurred and noisy images, the identification of gamma-ray bursters, and the compression and denoising of galaxy images. The SkyNet software, which is implemented in standard ANSI C and fully parallelised using MPI, is available at http://www.mrao.cam.ac.uk/software/skynet/.Comment: 19 pages, 21 figures, 7 tables; this version is re-submission to MNRAS in response to referee comments; software available at http://www.mrao.cam.ac.uk/software/skynet

Multidecadal climate variability over northern France during the past 500 years and its relation to large-scale atmospheric circulation

(IF 3.76; Q1)International audienceWe examine secular changes and multidecadal climate variability on a seasonal scale in northern France over the last 500 years and examine the extent to which they are driven by large‐scale atmospheric variability. Multiscale trend analysis and segmentation procedures show statistically significant increases of winter and spring precipitation amounts in Paris since the end of the 19th century. This changes the seasonal precipitation distribution from one with a pronounced summer peak at the end of the Little Ice Age to an almost uniform distribution in the 20th century. This switch is linked to an early warming trend in winter temperature. Changes in spring precipitation are also correlated with winter precipitation for time scales greater than 50 years, which suggests a seasonal persistence. Hydrological modelling results show similar rising trends in river flow for the Seine at Paris. However, such secular trends in the seasonal climatic conditions over northern France are substantially modulated by irregular multidecadal (50–80 years) fluctuations. Furthermore, since the end of the 19th century, we find an increasing variance in multidecadal hydroclimatic winter and spring, and this coincides with an increase in the multidecadal North Atlantic Oscillation (NAO) variability, suggesting a significant influence of large‐scale atmospheric circulation patterns. However, multidecadal NAO variability has decreased in summer. Using Empirical Orthogonal Function analysis, we detect multidecadal North Atlantic sea‐level pressure anomalies, which are significantly linked to the NAO during the Modern period. In particular, a south‐eastward (south‐westward) shift of the Icelandic Low (Azores High) drives substantial multidecadal changes in spring. Wetter springs are likely to be driven by potential changes in moisture advection from the Atlantic, in response to northward shifts of North Atlantic storm tracks over European regions, linked to periods of positive NAO. Similar, but smaller, changes in rainfall are observed in winter

Texture to the Rescue : Practical Paper Fingerprinting based on Texture Patterns

In this article, we propose a novel paper fingerprinting technique based on analyzing the translucent patterns revealed when a light source shines through the paper. These patterns represent the inherent texture of paper, formed by the random interleaving of wooden particles during the manufacturing process. We show that these patterns can be easily captured by a commodity camera and condensed into a compact 2,048-bit fingerprint code. Prominent works in this area (Nature 2005, IEEE S&P 2009, CCS 2011) have all focused on fingerprinting paper based on the paper "surface." We are motivated by the observation that capturing the surface alone misses important distinctive features such as the noneven thickness, random distribution of impurities, and different materials in the paper with varying opacities. Through experiments, we demonstrate that the embedded paper texture provides a more reliable source for fingerprinting than features on the surface. Based on the collected datasets, we achieve 0% false rejection and 0% false acceptance rates. We further report that our extracted fingerprints contain 807 degrees of freedom (DoF), which is much higher than the 249 DoF with iris codes (that have the same size of 2,048 bits). The high amount of DoF for texturebased fingerprints makes our method extremely scalable for recognition among very large databases; it also allows secure usage of the extracted fingerprint in privacy-preserving authentication schemes based on error correction techniques