Figure selleck chemicals 3.Diagram of the hyperspectral device with two spectrometers. CL stands for Camera Link, OF: optical fiber, SYNC: synchronization signal, ETH: Ethernet pl
Radio Frequency Identification (RFID) is an electronic tagging technology that allows objects, places, or persons to be automatically identified at a distance without a direct line-of-sight, using an electromagnetic challenge/response exchange [1,2]. RFID offers a possible alternative to barcodes, and has emerged as a key technology for a wide-range of applications, including supply chain, retail stores, and asset management [3]. However, the widespread adoption of RFID technology is limited for the unreliability of the data streams produced by RFID readers [4,5].
RFID data cleaning is therefore widely considered as a principal challenge and has been an important research topic in the last few years [6�C8].Despite the improvement of the accuracy of RFID readers, there are still erroneous readings such as missed readings and ghost readings, due to interference, inappropriate placement of tags, temporary or permanent malfunction of some components.The goal of RFID data cleaning is to eliminate the erroneous readings, especially to reduce or eliminate dropped readings. In this paper, we propose an innovative approach of cleaning RFID raw data Behavior-Based Smoothing for unreliable RFID data (BBS). Unlike conventional techniques, BBS relays primarily on the movement behavior of tags to fill the RFID data. Our biggest obstacle is how to obtain movement behavior characteristics of tags.
To address this problem, a movement behavior detection model is proposed so that we can get the results by analyzing existing uncertain data of the corresponding tags. The contributions of this study are as follows:A Drug_discovery movement behavior detection model. By counting the frequency of tags read in each cycle, we can get the read rate of tags and analyze kinematic characteristics of the tags according to changes of the either read rate sequences, and ultimately to assist in RFID data cleaning.Reverse Order Filling Mechanism (ROFM). Based on the detection model, we design and implement a reversible RFID data filter. When we detect the data has not been filled completely, ROFM will be started to fill the data again in reverse order. The mechanism can ensure a more complete access to get the movement behavior characteristics of tags, and thus significantly improve the accuracy of data cleaning without scanning all the data twice.Improve the positioning accuracy of the RFID reader. Traditional RFID positioning system can only provide the Boolean result such as the condition whether the tag is in the read range of the reader at the time.