With the development of intelligent products, products are increasingly able to provide personalised feedback to users. Product designs must meet users’ emotional needs to deliver a positive experience. The current study examined emotional design, focusing on emotional arousal and methods for converting users from negative to positive emotional states. However, products are typically used in an emotional context with a specific usage scenario. Integrating emotional elements into a design can reveal users’ concerns and needs, enabling user-centered emotional design. The current study examined the distribution of users’ attention to product features under different emotional conditions. On the basis of the theory of visual attention, this study used emotional induction methodology and eye-tracking technology to observe users’ attention to different product features after the induction of three different emotional states. We established a labeled product feature database and explored the influence of emotional scenes on user preferences. These research findings can provide theoretical support for emotional design of products under specific emotional scenarios and effectively guide designers to arrange design elements.

AI and GPU technologies have been widely applied to solve big data problems. The total data volume worldwide reaches 200 zettabytes in 2022. How to efficiently index the required content among massive data becomes serious. Recently, a promising learned index has been proposed to address this challenge: It has extremely high efficiency while retaining marginal space overhead. However, we notice that previous learned indexes have mainly focused on CPU architecture, while ignoring the advantages of GPU. Because traditional indexes like B-Tree, LSM, and bitmap have greatly benefited from GPU acceleration, a combination of a learned index and GPU has great potentials to reach tremendous speedups. In this paper, we propose a GPU-based learned index, called G-Learned Index, to significantly improve the performance of learned index structures. The primary challenges in developing G-Learned Index lie in the use of thousands of GPU cores including minimization of synchronization and branch divergence, data structure design for parallel operations, and usage of memory bandwidth including limited memory transactions and multi-memory hierarchy. To overcome these challenges, a series of novel technologies are developed, including efficient thread organization, succinct data structures, and heterogeneous memory hierarchy utilization. Compared to the state-of-the-art learned index, the proposed G-Learned Index achieves an average of 174× speedup (and 107× of its parallel version). Meanwhile, we attain 2× less query time over the state-of-the-art GPU B-Tree. Our further exploration of range queries shows that G-Learned Index is 17× faster than CPU multi-dimensional learned index. We have made G-Learned Index available athttps://anonymous.4open.science/r/G-Learned-Index-8D89.

In fringe projection profilometry (FPP) based on temporal phase unwrapping (TPU), reducing the number of projecting patterns has become one of the most important works in recent years. To remove the $2\pi $ ambiguity independently, this paper proposes a TPU method based on unequal phase-shifting code. Wrapped phase is still calculated from $N$ -step conventional phase-shifting patterns with equal phase-shifting amount to guarantee the measuring accuracy. Particularly, a series of different phase-shifting amounts relative to the first phase-shifting pattern are set as codewords, and encoded to different periods to generate one coded pattern. When decoding, Fringe order with a large number can be determined from the conventional and coded wrapped phases. In addition, we develop a self-correction method to eliminate the deviation between the edge of fringe order and the $2\pi $ discontinuity. Thus, the proposed method can achieve TPU but need to only project one additional coded pattern (e. g. 3+1), which can significantly benefit dynamic 3D shape reconstruction. The theoretical and experimental analysis verify that the proposed method performs high robustness on the reflectivity of the isolated object while ensuring the measuring speed.