Kouichi Mutsuura

A new indoor position estimation method of RFID tags for continuous moving navigation systems

The RFID (Radio Frequency Identification) is considered as one of the most preferable ways for the position estimation in indoor environments, since GPS does not work in such situations. In RFID system, an RFID reader enables to estimate the position of RFID tags easily and inexpensively. In applications with the position estimation of RFID tags, indoor robot navigations are very important for human society. The problem is how to obtain the position estimations of RFID tags as accurately as possible. Previously S-CRR (Swift Communication Range Recognition) has been proposed for the appropriate estimation method of this kind of applications. This method is capable of the accurate position estimation of an RFID tag in very short time. The disadvantage of S-CRR is that the mobile robot must stop to search RFID tags accurately at each position. In indoor robot navigations, mobile entities like robots have to move continuously because they need to navigate smoothly and safely. In this paper, we propose a new position estimation method of RFID tags with continuous moving only using RFID technology. We call this Continuous Moving CRR (CM-CRR). CM-CRR uses two communication ranges, long and short ranges and switches them appropriately. The system estimates the position of RFID tags using their approaches and continuous moving. To show the effectiveness of CM-CRR, we evaluate the estimation error of an RFID tag by computer simulations. From the results, CM-CRR can accurately estimate the position of RFID tags with continuously moving of the mobile robot and be applied to indoor robot navigations.

An Adaptive Likelihood Distribution Algorithm for the Localization of Passive RFID Tags

The RFID (Radio Frequency IDentification) tag technology is expected as a tool of localization. By the localization of RFID tags, a mobile robot which installs in RFID readers can recognize surrounding environments. In addition, RFID tags can be applied to a navigation system for walkers. In this paper, we propose an adaptive likelihood distribution scheme for the localization of RFID tags. This method adjusts the likelihood distribution depending on the signal intensity from RFID tags. We carry out the performance evaluation of estimated position error by both computer simulations and implemental experiments. We show that the proposed system is more effective than the conventional system.

Accurate indoor position estimation by Swift-Communication Range Recognition (S-CRR) method in passive RFID systems

RFID (Radio Frequency IDentification) systems have become meaningful as a new identification source that is applicable in ubiquitous environments. Each RFID tag has a unique ID, and is attached to some object. A user reads the unique ID of a RFID tag with RFID readers and obtains the information on the object.

A Novel localization scheme for passive RFID tags; Communication Range Recognition (CRR)

This paper focuses on the localization technology of passive RFID (Radio Frequency IDentification) tags. By this technology, we will be able to detect some target, to get information on it, and to find the position and the distance of it. This paper proposes a novel scheme named Communication Range Recognition (CRR) in order to localize RFID tags effectively. This scheme finds the edges of the communication range of the RFID reader antenna when its position is moving by the robot appropriately. We evaluate the performance of the estimated position error through numerous experiments. We show that our proposed scheme can reduce the moving distance of the RFID reader without degrading the accuracy of localization in comparison with the conventional schemes.

A Novel Method for Position Estimation of Passive RFID Tags; Swift Communication Range Recognition (S-CRR) Method

The RFID (Radio Frequency IDentification) system is paid attention to as a new identification source that achieves a ubiquitous environment. Each RFID tag has the unique ID, and is attached to some object. A user reads the unique ID of a RFID tag with RFID readers and obtains the information on the object. One of the most important technologies that use the RFID system is the position estimation of RFID tags. The position estimation means to estimates the location of the object with the RFID tag. It can be very useful to acquire the location information of RFID tag. If a user can understand the position of the RFID tag, the position estimation can be applied to a navigation system for walkers. In this paper, we propose a new method named as Swift Communication Range Recognition (S-CRR) method as an extended improvement of the previous CRR method on the estimation delay. In this method, the position of RFID tag is estimated by selecting the communication area model which corresponds to those boundary angles. We carry out the performance evaluation by the experiments of RFID system and show the effectiveness of S-CRR for position estimation.

A Novel Scheme for Spatial Localization of Passive RFID Tags; Communication Range Recognition (CRR) Scheme

The RFID (Radio Frequency IDentification) tag is expected to be used as a tool of localization. With the localization of RFID tags, a mobile robot with an installed RFID reader can recognize the surrounding environment. In addition, it can be applied to a navigation system for walkers. In this paper, we propose a new scheme named Communication Range Recognition (CRR) in order to localize RFID tags effectively. In this scheme, the RFID reader finds the edges of the communication range when its position is moved appropriately by the robot. We evaluate the performance of the estimated position error through numerous experiments. We show that our proposed scheme can reduce the moving distance of the RFID reader without degrading the accuracy of localization in comparison with the conventional schemes.

Localization of passive RFID tags with robot using adaptive likelihood distribution algorithm

The RFID (radio frequency identification) tag is expected as a tool of localization. By the localization of RFID tags, a mobile robot which installs in RFID readers can recognize surrounding environments. In addition, it can be applied to a navigation system for walkers. In this paper, we propose an adaptive likelihood distribution scheme for the localization of RFID tags. This method adjusts the likelihood distribution depending on the signal intensity from RFID tags. We carry out the performance evaluation of estimated position error by both computer simulations and implemental experiments.

Causal Analysis of Student's Characteristics of Note-Taking Activities and Learning Performance during a Fully Online Course

A fully online learning environment requires support procedures in order to promote pro-active learning. This paper presents the causal relationships between students characteristics, note-taking skills, learning experience, note assessment and test scores. A structural equation modeling technique is used to trace students learning activity as note-taking occurs. The results of this modeling technique suggest that key factors and their contributions to course performance can be measured. Also, the factors which contribute to note-taking activity were examined.

Barrier Coverage Constructions for Border Security Systems Using Wireless Sensors

In this paper, we propose network construction methods of sensor nodes for Border Security Systems. A Border Security System watches intruders by using sensor nodes with communication function. The detection of some intruders and the use of a long-term operation system are required in this system. In this paper, we propose two methods to reduce the power consumption of the whole network system by effective control of sensor nodes. By computer simulation, we show that the proposed network construction methods are suitable for Border Security Systems.

A Note Taking Evaluation Index Using Term Networks in a Blended Learning Environment

To assess the transformation of a lecturers term-based concepts presented to students, both the lecturers presentation and the students notes in a blended learning course were lexically analyzed and represented in graphs. The contents of these notes were mathematically compared and evaluated. Graphs were then created using the noun co-occurrences in the notes, and the differences in distances between the graph of the lecturers presentation and the graph of the students notes were calculated. In the results, students recorded additional nouns to replace the nouns given by the lecturer, and introduced new nouns of their own as substitutes as well. Also, the terms students wrote in their notes were not recorded separately, but were linked with other terms.