• Journal of information and communication convergence engineering
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• v.8 no.1
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• pp.45-50
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• 2010

EPCglobal Class-1 Gen-2 standard proposed Q-algorithm to select a frame size for the next query round. Q-algorithm calculates the frame size without estimating the number of tags. Therefore, the Q-algorithm has advantage that the reader's algorithm is simpler than other algorithms. However, it is impossible to allocate the optimized frame size. Also. the conventional Q-algorithm does not define an optimized parameter value C for adjusting the frame size. In this paper, we propose a modified Q-algorithm with the tag number estimation scheme, and evaluate the performance with computer simulations.

• Journal of information and communication convergence engineering
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• v.7 no.2
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• pp.164-169
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• 2009

An RFID system consists of radio frequency tags attached to objects that need to be identified and one or more electromagnetic readers. Unlike the traditional bar code system, the great benefit of RFID technology is that it allows information to be read without requiring contact between the tag and the reader. For this contact-less feature, RFID technology in the near future will become an attractive alternative to bar code in many application fields. In almost all the 13.56MHz RFID systems, FSA (Framed Slot ALOHA) algorithm is used for identifying multiple tags in the reader's identification range. In FSA algorithm, the tag identification time and system efficiency depend mainly on the number of tags and frame size. In this paper, we propose a tag number estimation scheme and a dynamic frame size allocation scheme based on the estimated number of tags.

• IE interfaces
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• v.23 no.3
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• pp.239-245
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• 2010

This paper considers a localization problem of a moving tag on RFID(Radio Frequency Identification) systems, where a positioning engine collects TDOA(Time-difference of Arrival) signal from a target tag to estimate the position of the tag. To localize the tag in the RFID system, we develop two heuristic algorithms and evaluate their performance in the estimation error and computational time by using randomly generated numerical examples. Based upon the performance evaluation, we can conclude our algorithms are valuable for localization the moving target.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.1C
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• pp.38-44
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• 2012

In this paper, we first formulate an optimal design problem for RFID tag identification processes and then propose a simplified estimation method for determining optimal frame sizes and termination time under an independence assumption. Through computer simulations we show that the proposed scheme outperforms Vogt's scheme in terms of identification delay.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1569-1580
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• 2016

For the 2.4 GHz active RFID to be successful in the market, one of the requirements is the increased battery life. However, currently we do not have any accurate power consumption estimation method. In this study we develop a simulation model, which can be used to estimate power consumption of tag accurately. Six different simulation models are proposed depending on collision algorithm and query command method. To improve estimation accuracy, we classify tag operating modes as the wake-up receive, UHF receive, sleep timer, tag response, and sleep modes. Power consumption and operating time are identified according to the tag operating mode. Query command for simplifying collection and ack command procedure and newly developed collision control algorithm are used in the simulation. Other performance measures such as throughput, recognition time for multi-tags, tag recognition rate including power consumption are compared with those from the current standard ISO/IEC 18000-7.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1478-1482
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• 2005

Since it is a very issue that figures out a current position of mobile robots, various methods have been proposed until nowadays. This paper proposes the sensor model of RFID(Radio Frequency Identification) and position estimation algorithm for mobile robots. We designed the sensor model of RFID in experimenting repeatedly. The sensor model of RFID in this case is that of stochastics according to sensing rate. Based on this stochastic sensor model, we designed the algorithm which estimates distance and direction of RFID tag. Therefore we made sure that RFID tag is used as landmark.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.3
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• pp.469-474
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• 2009

An RFID system consists of radio frequency tags attached to objects that need to be identified and one or more electromagnetic readers. Unlike the traditional bar code system, the great benefit of RFID technology is that it allows information to be read without requiring contact between the tag and the reader. For this contact-less feature, RFID technology in the near future will become an attractive alternative to bar code in many application fields. In almost all the 13.56MHz RFID systems, FSA algorithm is used for identifying multiple tags in the reader's identification range. In FSA algorithm, the tag identification time and system efficiency depend mainly on the number of tags and frame size. In this paper, we propose a tag number estimation scheme and a dynamic frame size allocation scheme based on the estimated number of tags.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.12
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• pp.1027-1038
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• 2005

Object recognition in the field of robotics generally has depended on a computer vision system. Recently, RFID(Radio Frequency IDentification) has been suggested as technology that supports object recognition. This paper, introduces the advanced RFID-based recognition using a novel tag which is named a 3D tag. The 3D tag was designed to facilitate object recognition. The proposed RFID system not only detects the existence of an object, but also estimates the orientation and position of the object. These characteristics allow the robot to reduce considerably its dependence on other sensors for object recognition. In this paper, we analyze the characteristics of the 3D tag-based RFID system. In addition, the estimation methods of position and orientation using the system are discussed.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.62-67
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• 2005

Object recognition in the field of robotics generally has depended on a computer vision system. Recently, RFID(Radio Frequency IDentification) technology has been suggested to support recognition and has been rapidly and widely applied. This paper introduces the more advanced RFID-based recognition. A novel tag named 3D tag, which facilitates the understanding of the object, was designed. The previous RFID-based system only detects the existence of the object, and therefore, the system should find the object and had to carry out a complex process such as pattern match to identify the object. 3D tag, however, not only detects the existence of the object as well as other tags, but also estimates the orientation and position of the object. These characteristics of 3D tag allows the robot to considerably reduce its dependence on other sensors required for object recognition the object. In this paper, we analyze the 3D tag's detection characteristic and the position and orientation estimation algorithm of the 3D tag-based RFID system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.8A
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• pp.805-814
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• 2008

Both EPCglobal Generation-2 (Gen2) for passive RFID systems and Intelleflex for semi-passive RFID systems use probabilistic slotted ALOHA with Q algorithm, which is a kind of dynamic framed slotted ALOHA (DFSA), as the tag anti-collision algorithm. A better tag anti-collision algorithm can reduce collisions so as to increase the efficiency of tag identification. In this paper, we introduce and analyze the estimation methods of the number of slots and tags for DFSA. To increase the efficiency of tag identification, we propose two new tag anti-collision algorithms, which are Chebyshev's inequality (CHI) algorithm and hybrid Q algorithm, and compare them with the conventional Q algorithm and adaptive adjustable framed Q (AAFQ) algorithm, which is mentioned in Part I. The simulation results show that AAFQ performs the best in Gen2 scenario. However, in Intelleflex scenario the proposed hybrid Q algorithm is the best. That is, hybrid Q provides the minimum identification time, shows the more consistent collision ratio, and maximizes throughput and system efficiency in Intelleflex scenario.