Radio Frequency Identification Tag Detection Research Articles

Improving RFID performance through enhanced electromagnetic wave absorption with Fe2O3 and polytetrafluoroethylene-based bilayer polydimethylsiloxane composites

This study presents a bilayer polydimethylsiloxane (PDMS) composite microwave absorber, consisting of Fe2O3 and polytetrafluoroethylene (PTFE) fillers, to enhance the functioning of radio frequency identification (RFID) systems on conductive surfaces. The uniform Fe2O3/PDMS and PTFE/PDMS composites are prepared by mixing PDMS and respective filler through a desktop three-axis roller machine followed by curing at 80 °C for two hours. The composites are then joined to create a bilayer composite mat. The combination of a 70 wt% Fe2O3/PDMS composite with a 50 wt% PTFE/PDMS composite demonstrates the best performance. The Fe2O3/PDMS composite shows the high reflection loss of 47.98 dB and a total shielding of 4.1 dB, while the PTFE/PDMS composite exhibits the reflection loss of 47.0 dB and a total shielding of 0.6 dB. The optimized bilayer composite enables a tag recovery of approximately 87.57 %. These findings demonstrate its practical application in reducing interference and enhancing the RFID tag detection.

A Novel Detection Method Based on Maximum-Likelihood Estimation Decoding of a 6-Bit Chipless Radio Frequency Identification Coded Tag

The detection of chipless radio frequency identification (RFID) tags is mostly realized by vector network analyzers. In actual detection, this method is expensive and not flexible enough. A flexible new detection method implemented on the Universal Software Radio Peripheral (USRP) is proposed in this article. The measurement object is a 6-bit chipless RFID coded tag sensor based on a spiral resonator trap circuit. The power detection function of the frequency band is designed and implemented on USRP's open-source platform GNU Radio. On this basis, for chipless RFID tags with spectrum coding, a decoding identification method based on maximum-likelihood estimation is designed. The frequency-domain curve of the scattered signal of the sample label is detected by the power detection method, and the decoding and identification test of the sample label is performed by the maximum-likelihood estimation decoding identification method. It is shown that the maximum-likelihood estimation decoding algorithm based on the USRP platform proposed can be effectively implemented in passive wireless networks through experimental results, which provides a new idea for the flexible and economical RFID tag detection method.

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

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.

Tumor localization using radio-frequency identification clip marker: experimental results of an ex vivo porcine model.

With the widespread use of minimally invasive surgery, tumor detection is becoming more difficult. We present the experimental results of a radio-frequency identification (RFID) lesion detection system in an ex vivo porcine model. The efficacy and feasibility of a newly developed RFID lesion detection system were examined. It was applied to the stomach and colon of pigs weighing 40kg. The RFID clip was attached to the upper and lower mucosal sides of the stomach. Colon specimens with thin and thick walls were used. The clipped sites were marked on the serosa by a pin. The longest distance from the pin the RFID tag could be detected was measured 25 times in each direction. In the upper gastric wall, the RFID tag detection distance was 4.5 ± 0.9mm, 5.6 ± 0.7mm, 12.5 ± 0.7mm, and 5.3 ± 0.5mm in the four directions, respectively (right, left, upper, and lower). In the antrum, the RFID tag detection distance was 5.8 ± 0.7mm, 6.9 ± 0.5mm, 5.6 ± 0.5mm, and 3.7 ± 0.5mm in the four directions. In the thin colon, the RFID tag detection distance was 6.3 ± 0.5mm, 5.0 ± 0.5mm, 9.7 ± 0.7mm, and 6.4 ± 0.4mm in the four directions. In the thick colon, the RFID tag detection distance was 3.5 ± 0.8mm, 6.6 ± 0.5mm, 8.4 ± 0.6mm, and 9.8 ± 0.5mm in the four directions. The area of detection was smallest for the antrum (83.7mm2) and similar for the other sites (150.6, 154.7 and 157.7mm2 for the upper body, thin colon, and thick colon, respectively). The distance at which the RFID tag was detected was usually within 10mm. These results indicate the feasibility of the clinical application of the add-on clip and RFID tag as a marker for identifying the location of various gastrointestinal tumors.

Clone tag detection in distributed RFID systems

Although Radio Frequency Identification (RFID) is poised to displace barcodes, security vulnerabilities pose serious challenges for global adoption of the RFID technology. Specifically, RFID tags are prone to basic cloning and counterfeiting security attacks. A successful cloning of the RFID tags in many commercial applications can lead to many serious problems such as financial losses, brand damage, safety and health of the public. With many industries such as pharmaceutical and businesses deploying RFID technology with a variety of products, it is important to tackle RFID tag cloning problem and improve the resistance of the RFID systems. To this end, we propose an approach for detecting cloned RFID tags in RFID systems with high detection accuracy and minimal overhead thus overcoming practical challenges in existing approaches. The proposed approach is based on consistency of dual hash collisions and modified count-min sketch vector. We evaluated the proposed approach through extensive experiments and compared it with existing baseline approaches in terms of execution time and detection accuracy under varying RFID tag cloning ratio. The results of the experiments show that the proposed approach outperforms the baseline approaches in cloned RFID tag detection accuracy.

Multiobjective Optimization Design for Electrically Large Coverage: Fragment-Type Near-Field\/Far-Field UHF RFID Reader Antenna Design

The design of an ultrahigh-frequency (UHF) radio-frequency identification (RFID) reader antenna covering an electrically large near-field area is challenging for near-field UHF RFID applications. In such a design, magnetic field distribution on a large coverage area is required to be as uniform as possible. For some specific UHF near-field RFID applications, given radiation pattern characteristics are expected. A fragment-type wire structure is quite suitable for these demands because uniform magnetic field distribution and given patterns could be generated through optimizing the fragmented wires in a designated electrically large area to obtain corresponding current distribution. In this article, the concept of a fragment-type structure as well as some design guidelines are reviewed and summarized. Then, two omnidirectional fragment-type wire antennas with different cell size and two directional fragment-type wire antennas are designed. Both simulation and experiment results show that there is no reading null within an electrically large near-field zone having a perimeter of four operating wavelengths at 915 MHz (i.e., 320 mm x 320 mm). The omnidirectional designs are promising in the applications of UHF near-field RFID tag detection in self-confined volumes, and the directional designs are potential in the systems of UHF near-field/far-field RFID.

Design of an HF-Band RFID System with Multiple Readers and Passive Tags for Indoor Mobile Robot Self-Localization.

Radio frequency identification (RFID) technology has already been explored for efficient self-localization of indoor mobile robots. A mobile robot equipped with RFID readers detects passive RFID tags installed on the floor in order to locate itself. The Monte-Carlo localization (MCL) method enables the localization of a mobile robot equipped with an RFID system with reasonable accuracy, sufficient robustness and low computational cost. The arrangements of RFID readers and tags and the size of antennas are important design parameters for realizing accurate and robust self-localization using a low-cost RFID system. The design of a likelihood model of RFID tag detection is also crucial for the accurate self-localization. This paper presents a novel design and arrangement of RFID readers and tags for indoor mobile robot self-localization. First, by considering small-sized and large-sized antennas of an RFID reader, we show how the design of the likelihood model affects the accuracy of self-localization. We also design a novel likelihood model by taking into consideration the characteristics of the communication range of an RFID system with a large antenna. Second, we propose a novel arrangement of RFID tags with eight RFID readers, which results in the RFID system configuration requiring much fewer readers and tags while retaining reasonable accuracy of self-localization. We verify the performances of MCL-based self-localization realized using the high-frequency (HF)-band RFID system with eight RFID readers and a lower density of RFID tags installed on the floor based on MCL in simulated and real environments. The results of simulations and real environment experiments demonstrate that our proposed low-cost HF-band RFID system realizes accurate and robust self-localization of an indoor mobile robot.

Prospective Descriptive Study of RFID Tag Detection Rates based on Various Exploratory Scenarios Aimed at Identifying Optimal Conditions of Use

AbstractObjectiveThe main objective is to evaluate RFID tags detection rates using various exploratory scenarios in order to identify optimal conditions of use. The secondary objective is to evaluate RFID tags detection rates based on a real-life scenario involving a cardiorespiratory resuscitation drug tray used within our institution in order to identify optimal conditions of use.BackgroundThe traceability of goods has been a subject of interest for more than a century. Traceability makes it possible to locate goods at every step in the chain from production through to disposal. Just as with other Automatic Identification and Data Capture technologies, radio frequency identification (RFID) is used to increase the traceability of objects.ResultsSeven variables that could influence RFID tags detection rates were evaluated in eight exploratory scenarios. Optimal detection parameters allowing to a 100 % detection rate were identified: a 10-second reading time; a reading distance of 10 cm; parallel orientation of reader-antenna and at least two back and forth readings for a total of 6 sec were required for optimal reading. Detection rates decreased after 100 RFID tags and it were not affected by the shape of the RFID tags. Reader-antenna and RFID tag interferences resulted from aluminum paper or RFID tags that touched one another. RFID tag detection rates obtained per operator were similar. Regarding real-life scenarios, detection rates increased with reading times and a plateau effect was observed after 10 sec. Undetected elements varied and non-detection was almost always related to the proximity of two RFID tags rather than the nature of the items read.ConclusionTo our knowledge, this is the first prospective descriptive study that compares RFID tag detection rates based on various exploratory scenarios in order to identify optimal conditions of use. Such results can be used to develop a software application supporting drug replenishing through RFID in the drug use process.

Mobile RFID Tag Detection Influence Factors and Prediction of Tag Detectability

Radio-frequency identification (RFID) readers are powered RF devices that communicate with tags (whether mobile or fixed) and read necessary information to be processed. A mobile RFID tag is detected by an RFID antenna. In a mobile RFID where the RFID tag is attached to a mobile object such as a vehicle, a human, or an animal, information is more difficult to detect than in the case where the tag is attached to a stationary object. Currently, deployment engineers and researchers use trial-and-error approaches to decide on the best conditions of the tag detection influence factors which affect tag detectability (detection rate). As expected, these approaches are time consuming. Even though mobile RFID systems have become widely used in industry and tag detection problems are crucial at deployment, very few researches on them have been conducted so far. Thus, a quick and simple method for finding tag detectability is needed to improve the traditional time consuming trial-and-error method. In this paper, we propose a unique approach ldquothe intelligent prediction method of tag detection rate using support vector machine (SVM).rdquo The intelligent method predicts the mobile RFID tag detectability instead of the trial-and-error experimental procedures. The simulation results of the proposed method are very comparable to the trial-and-error experimental approach. The proposed intelligent method gives a very high accuracy of mobile RFID tag detectability prediction and proves to be superior to the current method in time as well cost savings. The predicted tag detectability results can be used for analyzing mobile RFID tag detection influence factors and their conditions.