首页|New Pattern Recognition and Artificial Intelligence Findings from Guangdong University of Technology Published (An Efficient and Scalable RFID Anti-Collision Algorithm on Optimal Partition and Collided Block Bit-Mapping)
New Pattern Recognition and Artificial Intelligence Findings from Guangdong University of Technology Published (An Efficient and Scalable RFID Anti-Collision Algorithm on Optimal Partition and Collided Block Bit-Mapping)
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NETL
NSTL
World Scientific
New study results on pattern recognition and artificial intelligence have been published. According to news reporting from Guangdong, People’s Republic of China, by NewsRx journalists, research stated, “In RFID systems, many anti-collision algorithms, driven by the concept of rescheduling the response sequence between the reader and unidentified tags, have been put forward to solve tag collision problem, including ALOHA-based, tree-based and hybrid algorithms.” Funders for this research include National Key R&D Program of China; National Natural Science Foundation of China; Guangdong Province Foundation. Our news reporters obtained a quote from the research from Guangdong University of Technology: “In this paper, we propose a novel RFID anti-collision algorithm called EAQ-CBB, which adopts three main approaches: tag population estimation based on collided bit detection method, optimal partitions and trimmed query tree based on the strategy of collided block bit-mapping (QTCBB). The relatively accurate estimation of tag backlog and optimal partition ensure a great reduction of collisions in the initial phase. For each collided partition, a QTCBB process is introduced immediately, which eliminates all the empty slots and significantly reduces the collided slots.”
Guangdong University of TechnologyGuangdongPeople’s Republic of ChinaAsiaAlgorithmsMachine LearningPattern Recognition and Artificial Intelligence
NETL
NSTL
World Scientific
