Implementing Data-Driven Approach for Modelling Ultrasonic Wave Propagation Using Spatio-Temporal Deep Learning (SDL)

Seismic Wave-Field Propagation Modelling using the Euler Method

Wave-field extrapolation based on solving the wave equation is an important step in seismic modeling and needs a high level of accuracy. It has been implemented through a various numerical methods such as finite difference method as the most popular and conventional one. Moreover, the main drawbacks of the finite difference method are the low level of accuracy and the numerical dispersion for l...

Modelling spatio-temporal environmental data

A conceptual model for environmental data is presented with special emphasis on the ability to store spatio-temporal references of the data. Other aspects of the model are the ability to handle hierarchical data and semantics of the measurements. Introduction How people perceive things and their relationships is not only the basis of forming theories but also for collection and storage of infor...

Spatio-Temporal Data Modelling for "4D"-Databases

Intra-body Communication Using Ultrasonic Wave Propagation

With advances in technology and people’s increasing emphasis on health issues, there has been a rapid development of wearable and implantable biomedical sensors. However, radio frequency waves dominate wireless communications, despite disadvantages such as high attenuation in the human body, considerable power consumption, and complicated wireless antenna design, which has hampered the developm...

Data-Driven Ghosting using Deep Imitation Learning

Current state-of-the-art sports statistics compare players and teams to league average performance. For example, metrics such as “Wins-above-Replacement” (WAR) in baseball [1], “Expected Point Value” (EPV) in basketball [2] and “Expected Goal Value” (EGV) in soccer [3] and hockey [4] are now commonplace in performance analysis. Such measures allow us to answer the question “how does this player...