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Multi-instance learning studies problems in which labels areassigned to bags that contain multiple instances. In these settings, the relations between instances and labels are usually ambiguous. Incontrast, multi-task learning focuses on the output space in whichan input sample is associated with multiple labels. In real world, asample may be associated with multiple labels that are derived fromobserving...
Device variability has become one of the fundamental challenges to high-resolution and high-accuracy DACs in nanometer and emerging processes. This paper introduces a 15-bit binary-weighted current-steering DAC in a standard 130nm CMOS technology, which utilizes a new random mismatch compensation theory called ordered element matching to improve the static linearity performance with the presence of...
Random mismatch errors in the resistor networks are one of the dominant nonlinearity sources for high resolution and high accuracy resistor DACs. This paper applies the theory of ordered element matching in a high resolution segmented R-2R DAC. It can achieve high matching accuracy by regrouping the resistors in the MSB array according to their resistance ranks obtained by the INL test. The implementation...
A photo sequence captured with different focal settings is called a focal stack image. By merging the focal stack image we can obtain an all-in-focus image. A merging strategy is introduced in the image fusion process with spatial image transform. A fusion selective function is formed using Laplacian transform and image gradient based on the focal stack photography. Two other methods in spatial domain...
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