# threshold function

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 Noun 1 threshold function - a function that takes the value 1 if a specified function of the arguments exceeds a given threshold and 0 otherwisefunction, mapping, mathematical function, single-valued function, map - (mathematics) a mathematical relation such that each element of a given set (the domain of the function) is associated with an element of another set (the range of the function)
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References in periodicals archive ?
Image decomposition can be done by using 2D wavelet transform to obtain the wavelet coefficients; then, the threshold function is adopted to compress the wavelet coefficients, and the denoised wavelet coefficients can be achieved.
According to (1) and (2), there are smoothness and continuity problems when denoising by the hard threshold function. When using the soft threshold function, it is very likely to lose important information in the original signal.
A threshold function can be implemented in the same way as any logic function, i.e., as a network of logic primitives nFETs.
A constant is used to replace the mean function, which makes the calculation simpler, and the traditional threshold function has better denoising effect in this paper.
Next, we propose the improved threshold function as follows:
Kim, "Image denoising based on improved wavelet threshold function for wireless camera networks and transmissions," International Journal of Distributed Sensor Networks, vol.
The threshold function depends on the energy of adjacent pixels.
Their filtering is achieved based on multicriterion threshold function. The method is evaluated by comparing the output DTM with the reference Light Detection and Ranging data (LiDAR).
In other words, a threshold function is built to every parameter so as to improve the accuracy of pedestrian counting.
The second includes the new ability of systems to be characterized by a system contrast threshold function just like the human eye, and a new target task performance metric that can provide more accurate performance predictions than the previous limiting frequency methods.
What the tables (2) and (3) can be concluded that the model trained with the first input pattern with 14 provinces sigmoid for the input parameters (1-5-14) means 14 input neurons, 5 hidden neurons and Way that the results of the model trained with a threshold function tangent hyperbolic Has created a better and less error, the predicted peak discharge operation is successful.

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