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A sound speed gradient leads to refraction of sound wavefronts in the direction of lower sound speed, causing the sound rays to follow a curved path. The radius of curvature of the sound path is inversely proportional to the gradient. [2] When the sun warms the Earth's surface, there is a negative temperature gradient in atmosphere.
Gradient noise is a type of noise commonly used as a procedural texture primitive in computer graphics. It is conceptually different from [further explanation needed
Perlin noise is a type of gradient noise developed by Ken Perlin in 1983. It has many uses, including but not limited to: procedurally generating terrain , applying pseudo-random changes to a variable, and assisting in the creation of image textures .
Noise experts and some small specialized companies have slowly developed a limited number of calculation tools, which have increased in number and become more user-friendly, covering more application cases, and adding service elements to the noise calculation tools. The noise calculation process is complex in input (gathering data, correctly ...
The principle behind all acoustic networks is the same. Distance = speed x travel time. If the travel time and speed of the sound signal are known, we can calculate the distance between source and receiver. In most networks, the speed of the acoustic signal is assumed at a specific value.
Statistical energy analysis (SEA) is a method for predicting the transmission of sound and vibration through complex structural acoustic systems. The method is particularly well suited for quick system level response predictions at the early design stage of a product, and for predicting responses at higher frequencies.
The Leeson equation is presented in various forms. In the above equation, if f c is set to zero the equation represents a linear analysis of a feedback oscillator in the general case (and flicker noise is not included), it is for this that Leeson is most recognised, showing a −20 dB/decade of offset frequency slope. If used correctly, the ...
Simplex noise. Simplex noise is the result of an n-dimensional noise function comparable to Perlin noise ("classic" noise) but with fewer directional artifacts, in higher dimensions, and a lower computational overhead. Ken Perlin designed the algorithm in 2001 [1] to address the limitations of his classic noise function, especially in higher ...