A hidden Markov model for distinguishing between RFID-tagged objects in adjacent areas

Hauser, Matthias; Griebel, Matthias; Thiesse, Frédéric

doi:10.1109/rfid.2017.7945604

Cited by 14 publications

(9 citation statements)

References 19 publications

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“…In practice, such a situation could easily arise when friends are shopping together, which highlights the limitations of the pilot implementation. To solve this issue, various model improvements could be considered to bolster detection reliability: Probabilistic models may be able to improve the accuracy of item paths (Hauser et al 2017). Furthermore, the integration of additional data sources can improve the assignment process.…”

Section: Discussionmentioning

confidence: 99%

Towards Digital Transformation in Fashion Retailing: A Design-Oriented IS Research Study of Automated Checkout Systems

et al. 2018

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Automated checkout systems promise greater sales due to an improved customer experience and cost savings because less store personnel is needed. The present design-oriented IS research study is concerned with an automated checkout solution in fashion retail stores. The implementation of such a cyberphysical system in established retail environments is challenging as architectural constraints, well-established customer processes, and customer expectations regarding privacy and convenience impose limits on system design. To overcome these challenges, the authors design an IT artifact that leverages an RFID sensor infrastructure and software components (data processing and prediction routines) to jointly address the central problems of detecting purchases in a reliable and timely fashion and assigning these purchases to individual shopping baskets. The system is implemented and evaluated in a research laboratory under real-world conditions. The evaluation indicates that shopping baskets can indeed be detected reliably (precision and recall rates greater than 99%) and in an expeditious manner (median detection time of 1.03 s). Moreover, purchase assignment reliability is 100% for most standard scenarios but falls to 42% in the most challenging scenario. Keywords Design-oriented IS research Á Digital innovation Á Internet of things Á Cyberphysical systems Á Retail industry Á Radio frequency identification Á Machine learning Á Automated checkout systems Accepted after two revisions by the editors of the special issue.

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Section: Discussionmentioning

confidence: 99%

Towards Digital Transformation in Fashion Retailing: A Design-Oriented IS Research Study of Automated Checkout Systems

et al. 2018

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“…Teknologi Radio Frequency IDentification (RFID) telah berkontribusi pada pengembangan berbagai bidang antara lain di ritel [1] - [2], kesehatan [3] - [4], manufaktur, rantai pasokan, dan logistik [5]. RFId Merupakan teknologi otomatis identifikasi yang paling murah untuk memudahkan penyebaran informasi menjadi lebih cepat dan akurat [6].…”

Section: Rfidunclassified

Rancang Bangun Automatic Smart Energy Meter Berbasis RFId

Ridzki

Hakim

Kusuma

2021

ELPOSYS

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The development of technology today makes it easier for humans in their daily activities. One of them is in the digital world. In the industrial era 4.0. more and more companies are issuing breakthrough new products based on digital technology that aim to be practical in their use. One of the technologies applied is the use of smart cards. For example in the use of smart keys in homes and car doors. Then in the current transaction process, many have implemented this smart card technology. The purpose of this research is to apply the latest technology in the energy transaction process which will be applied to the KWh meter. The smart card used is RFId and is integrated in the database and website so that its use is more practical and flexible. Then the use of this smart card can be applied to the smart energy meter so that later the prepaid energy transaction process can be applied easily with this smart energy meter.

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“…Radio Frequency IDentification (RFID) technology has contributed to the development of a wide range of applications in retail [1]- [2], healthcare [3]- [4], manufacturing, supply chain and logistics [5]. In particular, in supply chain applications sometimes it could not be necessary to measure the good position in real-time through localization systems [6], but rather to identify specific actions such as entering or exiting This work has been supported by the Gobierno del Principado de Asturias (PCTI)/FEDER under project IDI/2018/000191; the Ministerio de Educación y Formación Profesional of Spain under the FPU grant FPU15/06431 and EST17/00813, and by the Ministerio de Ciencia, Innovación y Universidades under project ARTEINE (TEC2017-86619-R).…”

Section: Introductionmentioning

confidence: 99%

“…Recently, machine learning methods were used in the context of RFID systems for localization purposes [14]- [2] and to discriminate tag actions in UHF-RFID gates [15]. This paper presents an innovative approach to classify transpallet movements at checkpoints by using a Convolutional Neural Network (CNN) [16].…”

Section: Introductionmentioning

confidence: 99%

A UHF-RFID gate control system based on a Convolutional Neural Network

Álvarez-Narciandi

Motroni

Pino

et al. 2019

2019 IEEE International Conference on RFID Technology and Applications (RFID-TA)

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In this paper, a robust, easy-to-deploy UHF-RFID system to classify transpallet actions at gates or checkpoints has been presented. The system is based on deploying a set of reference RFID tags on the floor of the checkpoint arranged in a matrix form. In addition, a single RFID reader antenna, which is over the checkpoint, is used to query both the reference tags of the system and those that are used identify goods and transpallets. When a transpallet crosses the controlled gate or checkpoint, it directly blocks the reference tags under it. Hence, reference RFID tags under the transpallet are progressively shadowed. As a consequence, if the number of readings of each reference tag is observed versus time, the movement direction of the transpallet can be inferred. This information was used to build images which are then fed to a Convolutional Neural Network (CNN) that classifies transpallet movements in incoming, outgoing or passing through the controlled checkpoint. A total of 159 measurements were acquired for different transpallet trajectories using 24 reference tags and a CNN was trained showing promising results.

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A hidden Markov model for distinguishing between RFID-tagged objects in adjacent areas

Cited by 14 publications

References 19 publications

Towards Digital Transformation in Fashion Retailing: A Design-Oriented IS Research Study of Automated Checkout Systems

Towards Digital Transformation in Fashion Retailing: A Design-Oriented IS Research Study of Automated Checkout Systems

Rancang Bangun Automatic Smart Energy Meter Berbasis RFId

A UHF-RFID gate control system based on a Convolutional Neural Network

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