Techniques, problems and uses of mega-geomorphological mapping

A plea for a program of global geomorphological mapping based on remote sensing data is presented. It is argued that the program is a necessary step in bringing together the rapidly evolving concepts of plate tectonics with the science of geomorphology. Geomorphologists are urged to bring temporal scales into their subject and to abandon their recent isolation from tectonics and geological history. It is suggested that a start be made with a new geomorphological map of Europe, utilizing the latest space technology

Air-ground interface: Surface waves, surface impedance and acoustic-to-seismic coupling coefficient

In atmospheric acoustics, the subject of surface waves has been an area of discussion for many years. The existence of an acoustic surface wave is now well established theoretically. The mathematical solution for spherical wave propagation above an impedance boundary includes the possibility of a contribution that possesses all the standard properties for a surface wave. Surface waves exist when the surface is sufficiently porous, relative to its acoustical resistance, that it can influence the airborne particle velocity near the surface and reduce the phase velocity of sound waves in air at the surface. This traps some of the sound energy in the air to remain near the surface as it propagates. Above porous grounds, the existence of surface waves has eluded direct experimental confirmation (pulse experiments have failed to show a separate arrival expected from the reduced phase speed) and indirect evidence for its existence has appeared contradictory. The experimental evidence for the existence of an acoustical surface wave above porous boundaries is reviewed. Recent measurements including pulse experiments are also described. A few years ago the acoustic impedance of a grass-covered surface was measured in the frequency range 30 to 300 Hz. Here, further measurements on the same site are discussed. These measurements include core samples, a shallow refractive survey to determine the seismic velocities, and measurements of the acoustic-to-seismic coupling coefficient

Atmospheric Propagation

Reviewed here is the current state of knowledge with respect to each basic mechanism of sound propagation in the atmosphere and how each mechanism changes the spectral or temporal characteristics of the sound received at a distance from the source. Some of the basic processes affecting sound wave propagation which are present in any situation are discussed. They are geometrical spreading, molecular absorption, and turbulent scattering. In geometrical spreading, sound levels decrease with increasing distance from the source; there is no frequency dependence. In molecular absorption, sound energy is converted into heat as the sound wave propagates through the air; there is a strong dependence on frequency. In turbulent scattering, local variations in wind velocity and temperature induce fluctuations in phase and amplitude of the sound waves as they propagate through an inhomogeneous medium; there is a moderate dependence on frequency

Abnormal Infant Movements Classification With Deep Learning on Pose-Based Features

The pursuit of early diagnosis of cerebral palsy has been an active research area with some very promising results using tools such as the General Movements Assessment (GMA). In our previous work, we explored the feasibility of extracting pose-based features from video sequences to automatically classify infant body movement into two categories, normal and abnormal. The classification was based upon the GMA, which was carried out on the video data by an independent expert reviewer. In this paper we extend our previous work by extracting the normalised pose-based feature sets, Histograms of Joint Orientation 2D (HOJO2D) and Histograms of Joint Displacement 2D (HOJD2D), for use in new deep learning architectures. We explore the viability of using these pose-based feature sets for automated classification within a deep learning framework by carrying out extensive experiments on five new deep learning architectures. Experimental results show that the proposed fully connected neural network FCNet performed robustly across different feature sets. Furthermore, the proposed convolutional neural network architectures demonstrated excellent performance in handling features in higher dimensionality. We make the code, extracted features and associated GMA labels publicly available