Radio-Frequency Identification (RFID) applications: A brief introduction

Domdouzis, Konstantinos; Kumar, Bimal; Anumba, Chimay J.

doi:10.1016/j.aei.2006.09.001

Cited by 322 publications

(148 citation statements)

References 5 publications

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“…In the PSO-FNN, P i is the position of the ith particle, which can be taken as a solution of the stated problem. (x i , y i ) ∈ P i represents the coordinates of the ith tag, and the original particle P old will be replaced by the new one P new with the probability of pro, according to Equation (7).…”

Section: Location Prediction Model Based On Pso-fnnmentioning

confidence: 99%

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An Improved Particle Swarm Optimization-Based Feed-Forward Neural Network Combined with RFID Sensors to Indoor Localization

Wang

Shi

2017

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Location-based services (LBS) have long been recognized as a significant component of the emerging information services. However, the localization cost and the performance of algorithm still need to be optimized. In the study, an improved particle swarm optimization algorithm based on a feed-forward neural network (IMPSO-FNN) combined with RFID sensors is proposed, which can achieve the best indoor positioning location and overcome the problems effectively. In IMPSO-FNN, an improved PSO algorithm (IMPSO) is developed to determine the optimal connecting weights and markedly optimize the network parameters and structural parameters for the FNN, and then an optimal location prediction model is established by the IMPSO-FNN. To avoid the interference of environmental noise for the experimental data, some preprocessing methods are used during the positioning process. The computational results for learning two continuous functions show that the proposed positioning algorithm has a faster convergence rate and higher generalization performance. The model evaluation results also verify that the proposed positioning method really is superior to other algorithms in terms of the learning ability, efficiency, and positioning accuracy.

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Section: Location Prediction Model Based On Pso-fnnmentioning

confidence: 99%

“…Radio frequency identification (RFID) [7] technology may also be applied in the indoor positioning field. RFID is one of the recent wireless communication technologies, which has many advantages, such as contactless, high repeated availability, durability, and multi-readability.…”

Section: Introductionmentioning

confidence: 99%

An Improved Particle Swarm Optimization-Based Feed-Forward Neural Network Combined with RFID Sensors to Indoor Localization

Wang

Shi

2017

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“…The frequency that matches these conditions is 13.56 MHz. The rate of data transmission at this frequency is 25 Kbits per second which is suitable for the text data stored on the tags [12]. The appropriate standard that matches 13.56 MHz frequency is ISO15693 [13].…”

Section: Rfidmentioning

confidence: 99%

The use of radio frequency identification to track samples in bio-repositories

Zarabzadeh

Watson²,

Grimson

2008

2008 1st International Conference on Information Technology

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“…It is used for tagging and identifying the different items in a store. In the construction industry, RFID is used successfully for the tracking of pipe spools, structural steel members and as an on-site support system [5,6].…”

Section: Introductionmentioning

confidence: 99%

A Novel and Cost Effective Approach to Public Vehicle Tracking System

Mishra¹,

Vasal²,

Tandon³

2012

IJU

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There are lots of efforts being made by public transport corporations to improve public vehicle occupancy by requesting the public to use public transport over other modes of transportation. It can be noted that if the passenger knows with high confidence that the bus is going to come, he/she will definitely wait rather than opting for other modes of transport. Efficient information can therefore help the users to choose faster and more easier connections which saves their time. Trends in wireless technology like Global System for Mobile communication (GSM) and Radio Frequency Identification (RFID) have resulted in easier and faster communication. This paper presents a vehicle tracking system by integrating both of the above mentioned technologies. The central server uses geographic information system (GIS) to track vehicles to

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Radio-Frequency Identification (RFID) applications: A brief introduction

Cited by 322 publications

References 5 publications

An Improved Particle Swarm Optimization-Based Feed-Forward Neural Network Combined with RFID Sensors to Indoor Localization

An Improved Particle Swarm Optimization-Based Feed-Forward Neural Network Combined with RFID Sensors to Indoor Localization

The use of radio frequency identification to track samples in bio-repositories

A Novel and Cost Effective Approach to Public Vehicle Tracking System

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