A UHF-RFID Gate Control System Based on a Recurrent Neural Network

Álvarez-Narciandi, Guillermo; Motroni, Andrea; Pino, Marcos R.; Buffi, Alice; Nepa, Paolo

doi:10.1109/lawp.2019.2929416

Cited by 11 publications

(4 citation statements)

References 11 publications

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“…A moving object can shadow the tags and create a signature of the motion of the object itself. The same scheme is applicable for RFID gates, as proposed in [ 37 , 38 ] to solve the problem of pallet trucks crossing a key point (e.g., to monitor the charging of goods on a truck). In both solutions, an antenna is placed at the ceiling facing downwards, and a regular grid of 24 tags is placed on the floor.…”

Section: Rfid Gatesmentioning

confidence: 99%

“…When a metallic cart crosses the target area, the tags are shadowed. Such information is given in input to a Long-Short Term Memory (LSTM) [ 39 ] Recurrent Neural Network (RNN) [ 37 ] or a convolutional neural network [ 38 ]. In both cases, a classification accuracy of

is obtained.…”

Section: Rfid Gatesmentioning

confidence: 99%

“…Therefore, they are classified as “Low Cost”, “Low Encumbrance”, and “High Scalability” [ 33 ] as they still rely on a single antenna, but the scalability is considered “Medium” as the proposed solution is based on a neural network classifier, which requires a time-consuming training stage. Following the same reasoning, the two solutions exposed in [ 37 , 38 ], both based on neural networks classifiers, are considered “Medium Scalability” solutions. In this case, however, the presence of the reference RFID tags on the ground makes the encumbrance of the solution higher with respect to solutions that do not require reference tags.…”

Section: Experimental Analysismentioning

confidence: 99%

See 2 more Smart Citations

An Action Classification Method for Forklift Monitoring in Industry 4.0 Scenarios

Motroni

Buffi

Nepa

et al. 2021

Sensors

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The I-READ 4.0 project is aimed at developing an integrated and autonomous Cyber-Physical System for automatic management of very large warehouses with a high-stock rotation index. Thanks to a network of Radio Frequency Identification (RFID) readers operating in the Ultra-High-Frequency (UHF) band, both fixed and mobile, it is possible to implement an efficient management of assets and forklifts operating in an indoor scenario. A key component to accomplish this goal is the UHF-RFID Smart Gate, which consists of a checkpoint infrastructure based on RFID technology to identify forklifts and their direction of transit. This paper presents the implementation of a UHF-RFID Smart Gate with a single reader antenna with asymmetrical deployment, thus allowing the correct action classification with reduced infrastructure complexity and cost. The action classification method exploits the signal phase backscattered by RFID tags placed on the forklifts. The performance and the method capabilities are demonstrated through an on-site demonstrator in a real warehouse.

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Section: Rfid Gatesmentioning

confidence: 99%

is obtained.…”

Section: Rfid Gatesmentioning

confidence: 99%

Section: Experimental Analysismentioning

confidence: 99%

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An Action Classification Method for Forklift Monitoring in Industry 4.0 Scenarios

Motroni

Buffi

Nepa

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…RFID technology belongs to the sensing layer of the Internet of Things (IoT), which is a non-contact, real-time, quick, secure, and accurate automatic identification technology that is used extensively in the monitoring of logistics, unmanned retail, healthcare, and other fields [1][2][3][4][5]. However, as high-power microwave research continues, military and medical fields are flooded with electromagnetic waves, and accordingly, RFID tags in this application need to be able to work safely under long-term exposure to microwave radiation.…”

Section: Introductionmentioning

confidence: 99%

Anti-High-Power Microwave RFID Tag Based on Highly Thermal Conductive Graphene Films

Liu

Song

et al. 2023

Materials

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In this paper, a radio frequency identification (RFID) tag is designed and fabricated based on highly electrical and thermal conductive graphene films. The tag operates in the ultrahigh-frequency (UHF) band, which is suitable for high-power microwave environments of at least 800 W. We designed the protection structure to avoid charge accumulation at the antenna’s critical positions. In the initial state, the read range of the anti-high-power microwave graphene film tag (AMGFT) is 10.43 m at 915 MHz. During the microwave heating experiment, the aluminum tag causes a visible electric spark phenomenon, which ablates the aluminum tag and its attachment, resulting in tag failure and serious safety issues. In contrast, the AMGFT is intact, with its entire read range curve growing and returning to its initial position as its temperature steadily decreases back to room temperature. In addition, the proposed dual-frequency tag further confirms the anti-high-power microwave performance of graphene film tags and provides a multi-scenario interactive application.

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A machine learning approach for package size estimation using UHF RFID interrogation signature

Vales-Alonso,

López-Matencio

2024

Appl Intell

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This paper introduces a new approach for performing package classification and sizing using Radio-Frequency Identification (RFID) systems. This technique is applicable when packages are labeled with or contain multiple RFID-tagged items. During the interrogation of the tags, received signal strength (RSS) statistics and other information, such as the frame count or the reading time, are collected by the reader and used to predict the package type from a set of candidate classes using an Artificial Neural Network (ANN). The primary challenge lies in acquiring sufficient training data for a target scenario to ensure reliable predictions. To address this, a two-phase training process based on transfer learning is adopted. Initially, a base model is developed using synthetic data generated from a detailed RFID simulator, designed to suit diverse scenarios, establish detailed link budgets, and comprehensively simulate the communication protocols. This model is then refined using a small dataset collected experimentally in the actual scenario. This method was validated in a real testbed with four different package types. The base model was trained using 1000 synthetic samples per package type (4000 in total), whereas the refined model was trained with a dataset consisting of only 25 real interrogation traces (samples) per package type (100 in total). The experimental samples were obtained using a software-defined radio unit, the Ettus B210 Universal Software Radio Peripheral (USRP) platform. This experiment achieved an accuracy of over 92%. In summary, this approach introduces a new feature to existing RFID setups, demonstrating potential for advanced package handling and cost optimization in the logistics sector.

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A UHF-RFID Gate Control System Based on a Recurrent Neural Network

Cited by 11 publications

References 11 publications

An Action Classification Method for Forklift Monitoring in Industry 4.0 Scenarios

An Action Classification Method for Forklift Monitoring in Industry 4.0 Scenarios

Anti-High-Power Microwave RFID Tag Based on Highly Thermal Conductive Graphene Films

A machine learning approach for package size estimation using UHF RFID interrogation signature

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