An optimal measurement method for spatial distribution of radio frequency identification multi-tag based on image analysis and PSO

2019 ◽  
Vol 41 (12) ◽  
pp. 3331-3339
Author(s):  
Xiaolei Yu ◽  
Yujun Zhou ◽  
Zhenlu Liu ◽  
Zhimin Zhao
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Radio Frequency ◽  
Radio Frequency Identification ◽  
Template Matching ◽  
Detection System ◽  
Optimization Method ◽  
Frequency Identification ◽  
Edge Detection Method ◽  
Reading Distance

In this paper, a multi-tag optimization method based on image analysis and particle swarm optimization (PSO) neural network is proposed to verify the effect of radio frequency identification (RFID) multi-tag distribution on the performance of the system. A RFID tag detection system is proposed with two charge coupled device (CCD). This system can automatically focus on the tag according to its position, so it can obtain the image information more accurately by template matching and edge detection method. Therefore, the spatial structure of multi-tag and the corresponding reading distance can be obtained for training. Because of its excellent performance in multi-objective optimization, the PSO neural network is used to train and predict multi-tag distribution at the maximum reading distance. Compared with other neural networks, PSO is more accurate and its uptime is shorter for RFID multi-tag analysis.

Radio Frequency Identification

2011 ◽  
pp. 502-526
Author(s):  
Róbert Schulcz ◽  
Gábor Varga
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Identification ◽  
Common Coding ◽  
Coding Schemes ◽  
Data Capacity ◽  
History Of ◽  
Frequency Identification ◽  
The Common ◽  
System Selection ◽  
Reading Distance

In this chapter, we will start by briefly summarizing the history of radio frequency identification systems. After that, we will introduce the components of such systems and classify them based on programmability, data capacity, frequency, and reading distance, as well as power supplement and reply transfer methods. We will describe the various coupling types used in RFID systems, present the common coding schemes and modulations, and give an overview of the standardization efforts. This chapter will focus on collision detection and resolution algorithms and conclude by practical suggestions on RFID system selection for different tasks.

Managing Uncertain Inventory in Supply Chain with Neural Network and Radio Frequency Identification (RFID)

2012 ◽  
pp. 155-170
Author(s):  
CKM Lee ◽  
Ng Wenwei Benjamin ◽  
Shaligram Pokharel
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Supply Chain ◽  
Radio Frequency ◽  
Real Time ◽  
Radio Frequency Identification ◽  
Bullwhip Effect ◽  
Distribution Centre ◽  
Frequency Identification ◽  
Artificial Neural

Demand uncertainty leads to fluctuations in inventory position at each echelon of a supply chain causing bullwhip effect, which can lead to significant cost and loss of efficiency and waste of resources. One of the aspects that can reduce potential bullwhip effect is the sharing of real time information for which the recently mass produced Radio Frequency Identification (RFID) can be of great value. The use of RFID technology can also help in increasing the visibility of the flow of goods and material, keeping track of the location and quantity at each distribution centre and warehouses. This will also help in the periodic and near real time optimization of inventory level of goods and material. The data collected with RFID can be analysed in artificial Neural Network (NN) to forecast the future demand. In this chapter, a framework is proposed by combining RFID with artificial neural network so that lean logistics can be realized in the supply chain.

scholarly journals Rancang Bangun Pengaman Sepeda Motor Menggunakan RFID Berbasis Arduino

2020 ◽  
Vol 7 (2) ◽  
pp. 164-171
Author(s):  
Muhammad Ridha Fauzi
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Identification ◽  
Vehicle Safety ◽  
Electrical System ◽  
Vehicle Theft ◽  
Motorcycle Safety ◽  
Rfid Reader ◽  
Frequency Identification ◽  
Reading Distance

The safety of a motorcycle is very important for vehicle owners. Until now, most motorbikes still rely only on the key to contact the vehicle itself. In the case of vehicle theft, it is very easy for the perpetrator of the theft to use the letter "T" key. Therefore an additional vehicle safety is required which is difficult to break apart from the ignition key. The purpose of this research is to design and implement motorcycle safety using Arduino-based Radio Frequency Identification (RFID) as additional vehicle safety. The author uses an RFID card / tag to add security to the vehicle starting system with a card / tag reading distance that can be read by an RFID reader. The ID code on the RFID Card must be inputted into the Arduino so that the RFID reader can read the ID Card that has been inputted into the Arduino. Based on the results of the tests that have been done, it is found that the reading distance of the RFID Card / Tag ranges from 1 cm to 3.5 cm. If the correct ID code is inputted, the electrical system is active and vice versa if the ID code entered is wrong, the buzzer will sound and the electrical system in the vehicle is not active / does not turn

scholarly journals A novel reverse design method of tag antenna based on image analysis

2020 ◽  
Vol 14 ◽  
pp. 174830262090696
Author(s):  
Zhenlu Liu ◽  
Zhimin Zhao ◽  
Xiaolei Yu ◽  
Mengjie Liu ◽  
Rendong Ji
Keyword(s):  
Image Analysis ◽  
Radio Frequency ◽  
Radio Frequency Identification ◽  
High Frequency ◽  
Design Method ◽  
Impedance Matching ◽  
Simulation Software ◽  
Ultra High Frequency ◽  
Antenna Performance ◽  
Frequency Identification

Since the working efficiency of the entire radio frequency identification system depends on the performance of the radio frequency identification tag antenna, the research and design of the tag antenna have received attention, especially for radio frequency identification tags applied to special scenes. We mainly studied the effect of antenna bending on the performance of ultra-high frequency radio frequency identification systems, and combined the bent antenna with the impedance matching loop to achieve impedance matching between the chip and the antenna in the ultra-high frequency band. We analyzed the variation of tag antenna performance with antenna parameters by the simulation software High Frequency Structure Simulator (HFSS). At the same time, we propose a novel antenna reverse design method. Through the analysis of the antenna-specific absorption rate image, the antenna performance intensity is visually reflected. According to the image analysis result, the antenna parameters are corrected to improve the antenna matching performance. This design method is simpler and faster than other methods.

Optimization of RFID reading performance based on YOLOv3 and elman neural network

2021 ◽  
pp. 1-14
Author(s):  
Lin Li ◽  
Xiaolei Yu ◽  
Zhenlu Liu ◽  
Zhimin Zhao ◽  
Chao Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Radio Frequency Identification ◽  
Template Matching ◽  
Reading Performance ◽  
Performance Measurement System ◽  
Automatic Identification ◽  
Elman Neural Network ◽  
Small Targets ◽  
Elman Recurrent Neural Network ◽  
Reading Distance

As a non-contact automatic identification technology, Radio Frequency Identification (RFID) is of great significance to improve the simultaneous identification of multi-target. This paper designs a more efficient and accurate multi-tag reading performance measurement system based on the fusion of YOLOv3 and Elman neural network. In the machine vision subsystem, multi-tag images are collected by dual CCD and detected by neural network algorithm. The reading distance of 3D distributed multi-tag is measured by laser ranging to evaluate the reading performance of RFID system. Firstly, the multi-tag are detected by YOLOv3, which realizes the measurement of 3D coordinates, improves the prediction accuracy, enhances the recognition ability of small targets, and improves the accuracy of 3D coordinate detection. Secondly, the relationship between the 3D coordinates and the corresponding reading distance of RFID multi-tag are modelled by Elman recurrent neural network. Finally, the reading performance of RFID multi-tag is optimized. Compared with the state-of-the-arts, the multi-tag detection rate of YOLOv3 is 17.4% higher and the time is 3.27 times higher than that of the previous template matching algorithm. In terms of reading performance, the MAPE of Elman neural network is 1.46 %, which is at least 21.43 % higher than other methods. In running time, Elman only needs 1.69s, which is at least 28.40% higher than others. Thus, the system not only improves the accuracy, but also improves the speed, which provides a new insight for the measurement and optimization of RFID performance.

scholarly journals Indoor localization algorithm based on artificial neural network and radio-frequency identification reference tags

2018 ◽  
Vol 10 (12) ◽  
pp. 168781401880868 ◽  
Author(s):  
Quangang Wen ◽  
Yanchun Liang ◽  
Chunguo Wu ◽  
Adriano Tavares ◽  
Xiaosong Han
Keyword(s):  
Neural Network ◽  
Radio Frequency ◽  
Radio Frequency Identification ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Cumulative Distribution ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Frequency Identification ◽  
Internet Of Things Technology

With the development of Internet of Things technology, radio-frequency identification localization methods have been widely applied due to their low cost and ease of deployment. The indoor radio-frequency identification localization algorithm based on received signal strength indication technology is a currently hot topic. Because the received signal strength is highly dependent on environments, the classic algorithms may result in large errors in localization accuracy. This article proposed a new radio-frequency identification localization algorithm, named BP_LANDMARC, by utilizing the back propagation neural network, which is designed to address nonlinear changes in radio-frequency signals. A strategy for selecting different working parameters in variable environments is presented. The evaluation methods of root mean square error and cumulative distribution function are used to compare the proposed algorithm with some existing algorithms. Experimental results show that the proposed algorithm remarkably improves the localization accuracy of both absolute distance and cumulative probability. Moreover, the proposed algorithm performs effectively and efficiently when it is applied to a logistics warehouse management system.

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