This paper presents a comprehensive design and implementation approach for robust detection of depolarizing chipless RFID (CRFID) tags. Depolarizing tags are advantageous compared to co-polar CRFID tags due to their improved performance on RF-lossy materials. This work introduces the application of deep learning (DL) regression modelling to a specialised dataset of depolarised Radar Cross Section (RCS) measurements of a custom 3-bit CRFID tag, acquired through an extensive robot-based data acquisition method. A dataset of 12,600 depolarised Electromagnetic (EM) RCS signatures were collected using an automated data acquisition system to train and validate a 1-dimensional Convolutional Neural Network (1D CNN) architecture. A novel hybrid 1D CNN with Bi-LSTM and attention mechanism architecture was also implemented to visualize the model attention and improve detection performance. We present, for the first time reported in literature, a comprehensive design and AI implementation approach for reliably detecting identification (ID) information from depolarized signals. Also, we report the first instance of describing the impact of surface permittivity variations, tag deformations, tilt angles, and read ranges, all integrated into model training for enhanced robustness in detecting ID information. The developed models facilitate real-time identification and recording of objects, enhancing IoT applications in varied environments. It was observed that both models were able to generalize well to given data, with Model-1 achieving a low RMSE of 0.040 (0.66%) on an unseen test dataset. However, the hybrid model reduced the error further by 27.5% with a test RMSE of 0.029 (0.48%).

This paper presents the application of a deep learning (DL) regression model to a specialised dataset of depolarised Radar Cross Section (RCS) measurements of a custom 3-bit CRFID tag. A dataset of 12,600 Electromagnetic (EM) RCS signatures were utilized to train and validate a 1-dimensional Convolutional Neural Network (1D CNN) architecture. For the first time, DL implementation approach for reliably detecting identification (ID) information from depolarized signals is presented. In addition, we report the first case of describing the impact of surface permittivity variations, tag deformations, tilt angles, and read ranges, all integrated into model training for enhanced robustness in detecting the encoded tag ID information. It was observed that the developed model was able to generalize well to given data, achieving a low RMSE of 0.040(0.66%) on an unseen test dataset.

In this article, we present a new approach for robust reading of identification (ID) and sensor data from chipless radio frequency ID (CRFID) sensor tags. For the first time, machine-learning (ML) and deep-learning (DL) regression modeling techniques are applied to a dataset of measured radar cross Section (RCS) data that have been derived from large-scale robotic measurements of custom-designed, 3-bit CRFID sensor tags. The robotic system is implemented using the first-of-its-kind automated data acquisition method using an ur16e industry-standard robot. A dataset of 9600 electromagnetic (EM) RCS signatures collected using the automated system is used to train and validate four ML models and four 1-D convolutional neural network (1-D CNN) architectures. For the first time, we report an end-to-end design and implementation methodology for robust detection of ID and sensing data using ML/DL models. Also, we report, for the first time, the effect of varying tag surface shapes, tilt angles, and read ranges that were incorporated into the training of models for robust detection of ID and sensing values. The results show that all the models were able to generalize well on the given data. However, the 1-D CNN models outperformed the conventional ML models in the detection of ID and sensing values. The best 1-D CNN model architectures performed well with a low root-mean-square error (RMSE) of 0.061 (0.87%) for tag ID and 0.0241 (3.44%) error for capacitive sensing.

In this paper, for the first time, we provide a comprehensive review of Machine Learning (ML) approaches in Chipless Radio Frequency Identification (CRFID) technology, which is a fast-developing sector with applications in inventory management, anti-counterfeiting, health monitoring, and environmental monitoring, to name a few. ML techniques are rapidly being integrated to improve CRFID systems' capabilities for robust detection of information. The combination of ML with CRFID technology is presented, examining various ML approaches, applications, challenges, and future perspectives. It is observed that ML has been successfully deployed in CRFID with high accuracy in the detection of information from CRFID tags. Challenges, such as data quality, security, and scalability are identified. Moreover, the literature currently struggles in the application of ML models on high-capacity tags, and lacks standardized data collection and sharing methodologies. We suggest the development of common data collection protocols, data sharing initiatives, and collaboration to establish a cohesive framework for CRFID data-driven research.

This paper introduces an innovative strategy for the development of sensing-ready concentric rings-based chipless radio frequency identification (CRFID) tags. Our approach is marked by the novel use of exponentially increasing spacing, a significant departure from the conventional uniform spacing method. This innovative design results in an impressive 88.2% improvement in tag data encoding capacity compared to traditional designs. Importantly, our design framework not only advances the current state of CRFID tag technology but also methodically lays the foundation for future integration of high-resolution sensing capabilities. This is achieved by strategically utilizing the innermost ring as a prospective sensing site, complemented by the implementation of nulls for data encoding achieved through the addition of an extra ring at the tag’s outermost edge. Notably, all these features represent advancements that have not been demonstrated in previously published concentric rings-based CRFID tags. To empirically validate our methodology, we have developed and tested 18-bit example tags optimized for operation within the ultrawideband (UWB) spectrum, covering a range from 3.1 to 10.6 GHz. The radar cross-section (RCS) response of these tags exhibits well-distributed resonances, culminating in a high encoding capacity of 17.65 bits/λ2/GHz. Preliminary results using capacitors connected to the innermost ring underscore the future sensing potential of our tags, setting the stage for more advanced sensing implementations in subsequent research.

The concept of optical transparency in antennas for epidermal electronics is demonstrated in this work as a means of improving the long-term comfort-of-wear level and possibly opening up a wider range of applications. In contrast to previous attempts, the epidermal antenna transparency is achieved by employing dielectric and conductive materials that are both transparent and flexible (i.e., polydimethylsiloxane transparent conductive textile composite) via a nonclean room procedure that is relatively simpler and less expensive. To demonstrate the concept, a modified rectangular loop epidermal antenna for an arm-worn wireless sensing system operating at 868-MHz ultra high-frequency (UHF) band is designed. Through a systematic numerical investigation, an interesting radiation response of the loop epidermal antenna as the result of two opposing mechanisms of radiation and loss is revealed, which dictates a specific design guideline for the loop when attached to the body compared to that in free space. Two antenna prototypes were fabricated with the developed transparent composite and its nontransparent counterpart. Then, comprehensive characterizations comparing both epidermal antenna prototypes were carried out, including antenna return loss and far-field tests on a human forearm phantom, and indoor wireless connectivity tests using a human test subject. By showing similar performance between the two prototypes, the study provides a convincing demonstration of the applicability of the developed transparent composite for the class of epidermal antenna and the capability of a transparent antenna to enable wireless connectivity in the context of epidermal electronics.

In this paper, for the first time, a one-dimensional convolutional neural network using a U-shaped architecture is evaluated in the context of radar cross section (RCS) based chipless RFID (CRFID) systems. A 3-bit CRFID tag is utilised to create eight discernible RCS signatures representing identification numbers. A dataset of 9,600 measured RCS signatures was utilised for training, validating, and testing the model. The dataset was collected by placing the tag on varying surface shapes, orientations, and read ranges to enable robust detection. The root mean square error (RMSE) metric was used to assess the model’s performance. The achieved RMSE was 0.11 (1.5%). The low RMSE score demonstrates the effectiveness that this type of architecture has in accurately detecting and generalizing the encoded information from the RCS signatures.

This paper presents the performance of a battery-less near field communication (NFC) sensor for museum artifact monitoring. The radio frequency (RF) receiver sensitivity of the NFC sensor was measured using a commercial Tagformance measurement system. To respond to read commands, the NFC sensor requires a minimum magnetic field strength of 67.8 mA/m in free space, and 87.6 mA/m after integration within an archive box typically used for cultural heritage artifact storage. The integrated NFC loop antenna has a -3 dB bandwidth of 1.16 MHz in free space and 1.14 MHz bandwidth when integrated within the archive box. These measured RF sensitivity and -3 dB bandwidth values are in close agreement with the ISO/IEC 15693 standard. This work demonstrates a DC power consumption of 597 µW which is shown to be the lowest value compared to the state-of-the-art literature. In addition, the wireless communication range of approximately 5 cm was achieved which compares favourably with the maximum read ranges reported in literature. The developed NFC sensor has been deployed in European museums for wireless monitoring of valuable artifacts.

In this paper, a novel automated data acquisition methodology is presented for chipless RFID systems. The proposed method utilises a Raspberry Pi to act as an interface between a vector network analyser and a universal arm robot to perform automated measurements. A 98% improvement in data acquisition time is achieved when compared to standard manual data collection methodology. The system is validated by collecting 9,600 radar cross section electromagnetic signatures from a 3-bit chipless RFID capacitive sensor tag for five different cases at four positions. By enabling large, efficient, and accurate data collection, this methodology can support the development of machine learning models that can improve the performance and functionality of chipless RFID technology.

Radar cross section (RCS) is a measure of the reflective strength of a radar target. Chipless RFID tags use this principle to create a tag that can be read at a distance without needing a power-hungry radio transceiver chip and/or battery. A chipless tag consists of a pattern of conductive and dielectric materials that backscatter electromagnetic (EM) waves in a distinctive pattern. A chipless tag can be read and identified by analysing the reflected waves and matching it with a predefined EM signature. In this paper, for the first time, several regression-based machine learning (ML) models are evaluated to detect identification and sensing information for an RCS-based chipless RFID tag. The simulated EM RCS signatures containing an 8-bit identification code and six capacitive sensing values are evaluated. The EM RCS signatures are evaluated within the UWB frequency band from 3.1 to 10.6 GHz. A dataset of 1,530 simulated signatures with relevant features are utilised for model training, validation, and testing. Root mean square error (RMSE) is used as the quantitative metric to evaluate their performance. It is found that Support Vector Regression (SVR) models provide the minimum RMSE for the identification code. At the same time, the Gradient Boosted Trees (GBT) regression model performed better in detecting the sensing information.

This paper presents the design and assessment of a battery-less NFC sensor transponder to measure biomarkers in the tear fluid of cattle eyes. A battery-less NFC temperature sensor prototype with a diameter of 22 mm is developed for the feasibility analysis of cattle health monitoring. With a measured wireless communication range of 44 mm, the developed NFC sensor prototype is shown to be a potential solution for wireless power and data transfer. In addition, the design method to develop a screen-printed, NFC-enabled smart contact lens for next-generation cattle health monitoring is also presented.

This paper presents a 6-bit 25 mm×25 mm×0.4 mm chipless RFID tag based on square ring resonators. The tag is designed and developed using polydimethylsiloxane (PDMS) and conductive fabric composite, resulting in a flexible, semitransparent, and biocompatible chipless RFID tag, which is suitable for the unobtrusive modern wireless identification and monitoring systems. The tag data is decoded using the monostatic Radar Cross Section method. The tag design is validated with free-space measurements showing a good agreement with simulated results.

In this paper, a Chipless RFID tag design based on Radar Cross Section (RCS) operating principle is proposed. The tag is implemented using a series of circular ring resonators to enable tag identification. The resonators are simulated and then fabricated on an FR4 substrate. A calibration technique using a ring resonator to improve the detection of 8-bit data is proposed and demonstrated. The fabricated tag is measured at a distance of 160 mm using the monostatic RCS method. The measurement results verify the simulation results and show accurate detection of the encoded information.

Underwater wireless communications present challenges due to the characteristics of water as a propagation channel medium. Regardless, wireless communications are needed for a range of systems that operate underwater. Commonly used technologies for these use cases (radiofrequency, acoustic and optical communications) are lacking, as they generally suffer from strong attenuation, multipath effects and propagation delays. In this context, we explore the theoretical models for Path Loss of Radio Frequency Identification (RFID) systems underwater in regards to the salinity of the water. We also discuss RFID systems feasibility in such applications as aquaculture and fish stock management. This paper aims to discuss the theoretical transmission models for RFID systems underwater, separating them into near-field systems – which use Magnetic Induction (MI) to communicate – and far-field systems – that transfer data via Radio Frequency (RF). We determine the path loss for each case, the effect of the salinity in the model for the path loss, and present preliminary measurements of magnetic field strength underwater for different salinity values. KeywordsRFID; underwater wireless communications; underwater RFID; near-field communication; magnetic induction; salinity.

This article describes the state-of-the-art and preliminary Electromagnetic (EM) simulations related to the design of a contact lens for health monitoring of cattle or other animals. This project is part of the SFI center Vistamilk as part of the work program associated with the "Cow cluster" which is focused on research in the Dairy industry sector. In this article, we propose to use NFC (Near-Field Communication) technology to be integrated in a contact lens to measure biomarkers in the tear fluid of a cow's eye for health monitoring. The feasibility of such an approach is described and documented. The EM simulations described show that the design of a contact lens for cows, with integrated sensing capability, is feasible and will inform future research in this area in the implementation of such a system.

Science Foundation Ireland (SFI co-funded under the European Regional Development Fund under Grant Numbers 13/RC/2077 (CONNECT), 12/RC/2289-P2 (INSIGHT) and 16/RC/3835 (VISTAMILK))

This research paper deals with the design and development of a circularly polarized S-band rectangular patch antenna providing performance suitable for application in CubeSat. A CubeSat is a type of miniaturized satellite used primarily by university research groups for demonstration of technology. They are low earth orbiting sun-synchronous (LEO-SS) type of satellites. The design protocol specifies maximum outer dimensions equal to 100 mm × 100 mm × 100 mm and weighing a mass between 1.3-6 kg. However, being small in size, they pose some challenges such as low profile antenna, possibility for cross-link communication with other similar satellites and high reliability of communication in a swarm without the prior knowledge of their positions. Additionally CubeSats dictate the space limitation for placing the antenna within it. With all these, it also requires small antenna with high gain and wide directivity. The most suitable antennas that address most of the aforementioned challenges are planar antennas. The design and simulation of the proposed design of electrically small sband antenna for CubeSat achieves gain of 5.01 dBi with a narrow bandwidth of 100 MHz. The analysis is performed using MATLAB and HFSS (High Frequency Structural Simulator).

—Underwater wireless communications pose challenges due to the characteristics of water as a propagation channel medium. Regardless, it is needed for a range of systems that operate underwater. Commonly used technologies for these use cases (radio-frequency, acoustic and optical communications) are lacking, as they usually suffer from strong attenuation, multipath and propagation delays. In this context, we explore Radio Frequency Identification (RFID) systems underwater and the feasibility of their application. This paper aims to discuss the theoretical transmission models for RFID systems underwater, separating them into near-field systems – which use Magnetic Induction (MI) to communicate – and far-field systems – that transfer data via Radio Frequency (RF). We determine the path loss for each case, explore its value for different system configurations and present preliminary measurements of magnetic field strength.