基于最小位移可视差的连续Seam Carving算法在图像缩放中的研究

图像缩放技术要求对图像缩放的同时保证重要信息不丢失且物体边缘不发生扭曲。近年来,Seam Carving及其改进算法得到了广泛的关注和研究。由于采用了离散式最小能量线迭代搜索策略,缩放信息无法在迭代过程中传递导致扭曲现象普遍存在。该文针对上述问题提出最小位移可视差(JND)检测算法,能够有效地检测每一次迭代中出现的潜在扭曲信息。能量权重\begin{document}$ {E}_{w} $\end{document}能够将JND信息累加传递给后续的迭代过程,从而抑制缩放过程中的边缘扭曲现象。通过JND算法和能量权重,该文首次将离散的Seam Carving模型转变为连续缩放模型。最后,在公共数据集RetargetMe上与最新的图像缩放算法进行多组对比实验,验证了所提方法的有效性和先进性。     

Constraint Symbolization Method for Analog Layout Retargeting with Geometric Programming

To satisfy the requirements of complex and special analog layout constraints, a constraint symboliza- tion method based on geometric programming for analog layout retargeting is presented in this paper. Our ap- proach is to build symbolic template for layouts, then uses Geometric programming （GP） to achieve new technology design rules, implement device symmetry and matching constraints, and manage parasitics optimization. The GP, a class of non-linear optimization problem, can be trans- ferred or fitted into a convex optimization problem. There- fore, a global optimum solution can be achieved. The sym- bolization method ensures the layout retargeting automati- cally. The efficiency and effectiveness of the proposed algo- rithm, as compared with the other existing methods, are demonstrated by a basic case-study example and a two- stage Miller-compensated operational amplifier.     

Directly performance-constrained template-based layout retargeting and optimization for analog integrated circuits

Due to intrinsic intricacy, layout parasitics exhibit a significant impact on the performance of analog integrated circuits. In this paper a directly performance-constrained parasitic-aware automatic layout retargeting and optimization algorithm is presented. Unlike the conventional sensitivity analysis, a general central-difference based scheme using any simulator for sensitivity computation is deployed. We propose a piecewise sensitivity model to enforce more accurate sensitivity computation during parasitic optimization. Moreover, mixed-integer performance constraints due to parasitics are included in the formulated mixed integer nonlinear programming problem rather than through either indirect parasitic-bound constraints or inaccurate worst-case sensitivities. A graph technique and mixed-integer nonlinear programming are effectively combined to solve the formulated parasitic optimization problem. The automatically generated target layouts can satisfy performance constraints to ensure the desired specifications. The experimental results show that the proposed algorithm can achieve effective retargeting of analog circuits with less layout area and significant reduction in execution time.     

Online targeted ads: Effects of persuasion knowledge,coping self-efficacy,and product involvement on privacy concerns and ad intrusiveness

This study conducted an online experiment to examine how persuasion knowledge of online targeted advertising (OTA), coping self-efficacy, and product involvement influenced the perceptions of privacy concerns and ad intrusiveness. These findings indicated that persuasion knowledge increased coping self-efficacy, which, in turn, influenced privacy concerns and perceived ad intrusiveness. Furthermore, the relations between coping self-efficacy and the evaluations of the OTA varied, depending on levels of product involvement. Specifically, when participants had high product involvement, their perceptions of privacy concerns and ad intrusiveness increased as their coping self-efficacy enhanced. However, those relationships did not appear among participants with low levels of product involvement. Theoretical and managerial implications of these findings are discussed later.