A 4Gbps/lane column driver is implemented in 1.8V 0.13-μm high-voltage CMOS process for 8K UHD 120Hz displays. The proposed column driver presents auto-calibrated 4Gbps receiver per lane to cover up to 8K UHD 120Hz 10bit display with two lanes and can drive 8K UHD panel larger than 85-inches. Measured results show that a 4Gbps/lane column driver with auto equalizer calibration technique to adaptively compensate wide variation of channel length, power supply and temperature simultaneously.
Various dimensionality reduction (DR) schemes have been developed for projecting high-dimensional data into low-dimensional representation. The existing schemes usually preserve either only the global structure or local structure of the original data, but not both. To resolve this issue, a scheme called sparse locality for principal component analysis (SLPCA) is proposed. In order to effectively consider the trade-off between the complexity and efficiency, a robust L2,p-norm-based principal component analysis (R2P-PCA) is introduced for global DR, while sparse representation-based locality preserving projection (SR-LPP) is used for local DR. Sparse representation is also employed to construct the weighted matrix of the samples. Being parameter-free, this allows the construction of an intrinsic graph more robust against the noise. In addition, simultaneous learning of projection matrix and sparse similarity matrix is possible. Experimental results demonstrate that the proposed scheme consistently outperforms the existing schemes in terms of clustering accuracy and data reconstruction error.
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