Rong Xie

In recent years, radio frequency identification (RFID) technology has been applied in various fields to efficiently identify objects. Considering that identification is usually performed continuously, the latest RFID approaches use previous identification results for subsequent identifications and can identify two tags per unit time. However, these approaches have not utilized bit-level collision information in ID transmissions, which can facilitate the identification. Moreover, the assumption of reliable communication is not always valid in real-world applications. In this article, we propose a new approach called the multiple resolution bit tracking (MRB) to further improve the identification performance. This approach dynamically computes a tag set that can be unambiguously identified irrespective of any missing subset. These tags are requested to transmit their IDs in the same timeslot. Strategies to handle unreliable communication have also been proposed and optimized for MRB. We performed extensive simulations to validate the performance of our proposed approach. The results show that MRB can achieve 3.7 tags per unit time, which is 1.85 times the number achieved using the existing approaches. Furthermore, MRB handles unreliable communications well and achieves a small given tag missing rate.

It is commonplace for people to perform various kinds of activities in groups. The recognition of human groups is of importance in many applications including crowd evacuation, teamwork coordination, and advertising. Existing group recognition approaches require snapshots of human trajectories, which is often impossible in the reality due to different data collection start time and frequency, and the inherent time deviations of devices. This study proposes an approach to synchronize the data of people for group recognition. All people’s trajectory data are aligned by using data interpolating. The optimal interpolating points are computed based on our proposed error function. Moreover, the time deviations among devices are estimated and eliminated by message passing. A real-life data set is used to validate the effectiveness of the proposed approach. The results show that 97.7% accuracy of group recognition can be achieved. The approach proposed to deal with time deviations was also proven to lead to better performance compared to that of the existing approaches.

We proposed a pixel-based evolution method to automatically generate evolutionary art. Our method can generate diverse artworks, including original artworks and imitating artworks, with different artistic styles and high visual complexity. The generation process is fully automated. In order to adapt to the pixel-based method, a von Neumann neighbor topology-modified particle swarm optimization (PSO) is employed to the proposed method. The fitness functions of PSO are well prepared. Firstly, we come up with a set of aesthetic fitness functions. Next, the imitating fitness function is designed. Finally, the aesthetic fitness functions and the imitating fitness function are weighted into one single object function, which is used in the modified PSO. Both the original outputs and imitating outputs are shown. A questionnaire is designed to investigate the subjective aesthetic feeling of proposed evolutionary art, and the statistics are shown.

Domain terminology recognition and extraction is the primary work for the construction of domain knowledge graph. Traditional method is tedious, and time-consuming, as well as low accuracy. This paper presents an improved Domain Term Extraction-Impro

It is becoming more and more important to make analysis on activity regularity of population in urban commercial districts, which would be beneficial to commercial infrastructure distribution and construction. The paper presents an approach to mining