A mixed pseudo-orthogonal frequency coding (Mixed-POFC) structure is proposed as a new spreadspectrum technique in this paper, which employs frequency and time diversity to enhance tag properties and balances the spectrum utilization and code diversity. The coding method of SAW RFID tags in this paper uses Mixed-POFC with multi-track chip arrangements. The cross-correlation and auto correlation of Mixed-POFC and POFC are calculated to demonstrate the reduced overlap between the adjacent center frequencies with the Mixed-POFC method. The center frequency of the IDT and Bragg reflectors is calculated by a coupling of modes (COM) module. The combination of the calculation results of the Bragg reflectors shows that compared with a 7-chip POFC, the coding number of a 7-chip Mixed-POFC is increased from 120 to 144 with the same fractional bandwidth of 12%. To demonstrate the validity of Mixed-POFC, finite element analysis (FEA) technology is used to analyze the frequency characteristics of Mixed-POFC chips. The maximum error between designed frequencies and simulation frequencies is only 1.7%, which verifies that the Mixed-POFC method is feasible.
The three-dimensional (3D) coordinate measurement of radio frequency identification (RFID) multi-tag networks is one of the important issues in the field of RFID, which affects the reading performance of RFID multi-tag networks. In this paper, a novel method for 3D coordinate measurement of RFID multitag networks is proposed. A dual-CCD system (vertical and horizontal cameras) is used to obtain images of RFID multi-tag networks from different angles. The iterative threshold segmentation and the morphological filtering method are used to process the images. The template matching method is respectively used to determine the two-dimensional (2D) coordinate and the vertical coordinate of each tag. After that, the 3D coordinate of each tag is obtained. Finally, a back-propagation (BP) neural network is used to model the nonlinear relationship between the RFID multi-tag network and the corresponding reading distance. The BP neural network can predict the reading distances of unknown tag groups and find out the optimal distribution structure of the tag groups corresponding to the maximum reading distance. In the future work, the corresponding in-depth research on the neural network to adjust the distribution of tags will be done.
The paper discusses possible applications of wireless technologies in support of lean manufacturing tools. The typology of lean tools is provided. It distinguishes three main categories, which are identiﬁcation and analysis of waste, improvement implementation, and process monitoring. The set of lean tools was analyzed in terms of information requirements. On the other hand, the typology of wireless technologies was discussed including RFID and Wi-Fi. The literature review of wireless technology applications for support of lean tools was conducted. The literature was systematically reviewed from the point of view of speciﬁc technologies and speciﬁc tools which were the subjects of the analyzed publications. Both typologies were synthesized to establish a framework for wireless technologies applications in the context of lean manufacturing implementation. It also could serve as a guideline for lean practitioners and implies future research directions. This paper is an extended version of paper published by .