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The field of interior home design has witnessed a growing utilization of machine learning. However, the subjective nature of aesthetics poses a significant challenge due to its variability among individuals and cultures. This paper proposes an applied machine learning method to enhance manufactured custom doors in a proper and aesthetic home design environment. Since there are millions of possible custom door models based on door types, wood species, dyeing, paint, and glass types, it is impossible to foresee a home design model fitting every custom door. To generate the classification data, a home design expert has to label thousands of door/home design combinations with the different colors and shades utilized in home designs. These data train a random forest classifier in a supervised learning context. The classifier predicts a home design according to a particular custom door. This method is applied in the following context: A web page displays a choice of doors to a customer. The customer selects the desired door properties, which are sent to a server that returns an aesthetic home design model for this door. This door configuration generates a series of images through the Unity 3D engine module, which are returned to the web client. The customer finally visualizes their door in an aesthetic home design context. The results show the random forest classifier's good performance, with an accuracy level of 86.8%, in predicting suitable home design, marking the way for future developments requiring subjective evaluations. The results are also explained using a feature importance graphic, a decision tree, a confusion matrix, and text. [ABSTRACT FROM AUTHOR]

Modern spectrally efficient modulation techniques produce radio-frequency signals with varying envelopes. Transmitting these signals through nonlinear amplifiers results in spectral regrowth and degradation of the bit error rate. Lookup table (LUT) predistorters have been widely used to mitigate the effects of nonlinear amplifiers. This paper theoretically proves that the use of linearly interpolated LUT predistorters results in superior rejection of residual nonlinear distortions, as compared with conventional noninterpolated LUT predistorters. The refinement of LUT performance with linear interpolation results in 12-dB additional rejection for every bit of precision, as opposed to 6 dB for noninterpolated LUTs. It is also shown that linearly interpolated LUT predistorters benefit more from optimal spacing than their noninterpolated counterparts. Besides improved mapping fidelity, potential benefits also include reduced gate count and faster convergence of adaptive LUT training algorithms. [ABSTRACT FROM AUTHOR]