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We can utilize the ambient noise data collected by seismometers (or geophones) to create images for the subsurface under the following processes. Since the ambient noise is considered as diffuse wavefield, we can correlate the filtered ambient noise data from a pair of seismic stations (or seismometers) to find the velocities of seismic ...
strategic noise map shall mean a map designed for the global assessment of noise exposure in a given area due to different noise sources or for overall predictions for such an area." EU Member States are required to produce strategic noise maps in their main cities, near the main transport infrastructure and industrial sites. The main goals of ...
Machine learning techniques such as Random sample consensus (RANSAC) and Density-based spatial clustering of applications with noise (DBSCAN) can be applied to identify phase shifts (mapping to azimuths) and amplitudes (mapping to elevations) of each discontinuous sinusoidal waveform in the ICTD signal. [19]
Both of these techniques, when used in water, are known as sonar; passive sonar and active sonar are both widely used. Acoustic mirrors and dishes, when using microphones, are a means of passive acoustic localization, but when using speakers are a means of active localization. Typically, more than one device is used, and the location is then ...
The Sound Around You Project began as a soundscape research project [7] at the University of Salford, UK in 2007.The project allows people to use audio recorders to record clips or sonic postcards of around 30 seconds in length from different sound environments, or ‘soundscapes’ from a family car journey to a busy shopping centre, and to upload them to the virtual map, along with their ...
Military applications of ground-penetrating radar include detection of unexploded ordnance and detecting tunnels. In military applications and other common GPR applications, practitioners often use GPR in conjunction with other available geophysical techniques such as electrical resistivity and electromagnetic induction methods.
2005 DARPA Grand Challenge winner Stanley performed SLAM as part of its autonomous driving system. A map generated by a SLAM Robot. Simultaneous localization and mapping (SLAM) is the computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it.
Initially these functions were based on simple combination of procedural noise functions like Simplex noise or Perlin noise. Currently a vast arsenal of techniques are available, ranging from structured regular texture (like a brick wall), to structured irregular textures (like a stonewall), to purely stochastic textures. [4]
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