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The proposed antenna design is intended to be used in the 470 MHz to 806 MHz frequency band, which is now available for commercial use following the transfer of television signal transmission from the TV to satellite band. The antenna is more compact than existing RFID antennas in India, with four concentric rings sliced from a circular patch, and patch is shorted to ground by shorting pin. The design was created using FR4, a commonly used and durable substrate. The antenna's gain and directivity are 8.11 dBi and 9.9 dB, respectively. It can operate in two bands within the desired frequency range: 479–492 MHz and 643–653 MHz. These bands have individual bandwidths of 13 MHz and 10 MHz, respectively, which are higher than commercially available UHF RFID antennas. The return loss in these bands is − 29 dB and -14 dB, respectively. The impact of changes in the ground plane is also discussed, and the return loss and bandwidth have been increased to − 29 dB and to 13 MHz, respectively. [ABSTRACT FROM AUTHOR]

This research aims to design and develop an Internet of Things (IoT)-based smart door lock system by utilizing Radio Frequency Identification (RFID) technology and the Blynk application. The system is designed to improve the level of home security by providing a variety of flexible access methods, including the use of RFID cards, smartphone apps, and emergency buttons. The prototype method is applied in system development, which involves creating a physical model to evaluate its performance and functionality. The test results showed that the system was operating properly, where RFID only responded to the registered card to unlock, thus increasing access security. Hardware design that is carried out with an economical approach and simple use of components makes this system more affordable compared to commercial smart door lock products on the market. This research shows great potential in increasing efficiency and comfort in managing home security systems and offers ease of implementation in various types of housing. Thus, this research makes a significant contribution to the development of innovative and practical solutions to improve home safety at a lower cost while providing a sense of security for its occupants. [ABSTRACT FROM AUTHOR]

Microstrip antennas are increasingly utilized in space‐borne applications, spanning areas such as RFID, mobile communication, and healthcare. This paper introduces a novel wearable ultra‐wideband (UWB) microstrip patch antenna, distinctively designed in a star shape. The antenna, with a total volume of 1172 mmÂş, is crafted on a semiflexible substrate using RT/duroid 5880. It exhibits a low loss tangent of 0.0004 and a relative permittivity of 2.2. The proposed design achieves a resonant frequency of 3.4 GHz, an impressive reflection coefficient of −39 dB, and an extensive bandwidth of 5.6 GHz, ranging from 2.8 to 8.4 GHz. Although the antenna demonstrates modest gain and directivity, its expansive bandwidth is a significant attribute. This wideband antenna is proposed as an ideal candidate for wearable biomedical applications, leveraging its substantial bandwidth and unique design characteristics. [ABSTRACT FROM AUTHOR]

Visually Impaired Persons (VIPs) have difficulty in recognizing vehicles used for navigation. Additionally, they may not be able to identify the bus to their desired destination. However, the bus bay in which the designated bus stops has not been analyzed in the existing literature. Thus, a guidance system for VIPs that identifies the correct bus for transportation is presented in this paper. Initially, speech data indicating the VIP's destination are pre-processed and converted to text. Next, utilizing the Arctan Gradient-activated Recurrent Neural Network (ArcGRNN) model, the number of bays at the location is detected with the help of a Global Positioning System (GPS), input text, and bay location details. Then, the optimal bay is chosen from the detected bays by utilizing the Experienced Perturbed Bacteria Foraging Triangular Optimization Algorithm (EPBFTOA), and an image of the selected bay is captured and pre-processed. Next, the bus is identified utilizing a You Only Look Once (YOLO) series model. Utilizing the Sub-pixel Shuffling Convoluted Encoder–ArcGRNN Decoder (SSCEAD) framework, the text is detected and segmented for the buses identified in the image. From the segmented output, the text is extracted, based on the destination and route of the bus. Finally, regarding the similarity value with respect to the VIP's destination, a decision is made utilizing the Multi-characteristic Non-linear S-Curve-Fuzzy Rule (MNC-FR). This decision informs the bus conductor about the VIP, such that the bus can be stopped appropriately to pick them up. During testing, the proposed system selected the optimal bay in 247,891 ms, which led to deciding the bus stop for the VIP with a fuzzification time of 34,197 ms. Thus, the proposed model exhibits superior performance over those utilized in prevailing works. [ABSTRACT FROM AUTHOR]

Leaf moisture content (LMC) directly affects the life activities of plants and becomes a key factor to evaluate the growth status of plants. To explore a low-cost, real-time, rapid, and accurate method for LMC detection, this paper employs Ultra-High-Frequency Radio-Frequency Identification (UHF RFID) sensor technology. By reading the tag information attached to the back of leaves, the parameters of the RSSI, phase, and reading distance of the tags are collected. In this paper, we propose an enhanced Multi-Feature Fusion algorithm based on Hyperdimensional Computing (HDC) called MFFHDC. In our proposed method, the real-valued features are encoded into hypervectors and then combined with Multi-Linear Discriminant Analysis (MLDA) for the feature fusion of different features. Finally, a retraining method based on Cosine Annealing with Warm Restarts (CAWR) is proposed to improve the model and further enhance its accuracy. Tests conducted in the experimental forest show that the proposed mechanism can effectively predict the LMC. The model's Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2) reached 0.0195, 0.0255, and 0.9131, respectively. Additionally, comparisons with other methods demonstrate that the presented system performs excellently in most aspects. As a lightweight model, this study shows great practical application value, particularly for the limited data volume and low hardware costs. [ABSTRACT FROM AUTHOR]

This study assessed the functional effectiveness in terms of passage proportion of three fishways, one nature‐like bypass, one partly nature‐like bypass and partly technical pool‐type and one completely technical, on the Pinka Stream and Rába River in Western Hungary. Radio frequency identification with passive integrated transponder tagging was used to collect data on the upstream passage. A total of 2976 tagged individuals were included in the study; 2863 individuals were either barbel (Barbus barbus), nase (Chondrostoma nasus) or chub (Squalius cephalus), which were chosen as model species, and 113 individuals belonged to four other species. There were 540 individuals of the model species and 18 individuals of the other species detected while successfully ascending one of the fishways. The time‐to‐event (survival) analysis of the data of the model species revealed significant species‐specific differences in passage probability between the fishways. The passage probabilities of the barbel were the highest, and those of the nase were the lowest at all three fishways. The findings demonstrate that single‐species evaluations can lead to incorrect conclusions on fishway effectiveness. Technical fishways can be as effective as nature‐like ones, and hybrid solutions (technical fishways combined with nature‐like bypasses) can function as well. The limitations of the study and recommendations for further evaluations are also discussed. [ABSTRACT FROM AUTHOR]

Summary: In an indoor setting, the radio frequency communication is utilized to locate and track mobile objects utilizing Radio Frequency Identification (RFID) technology. Measurements from the Received Signal Strength Indicator (RSSI) are typically the foundation of the localization technique. In an RFID‐based interior setting, lowering tracking mistakes and increasing the precision of tracking remain difficult tasks. In order to address these issues, we developed the VIRALTRACK (Virtual Reference Tag Localization and Tracking) framework that consists of four procedures: deep reinforced learning‐based tracking, quantum‐based localization, optimization‐based virtual reference tag allocation, and signal enhancement. In order to increase the signal's effectiveness, we initially suggested using the Extended Gradient Filter (EGF) technique to eliminate RSSI oscillations. In the second step, we suggested using the Emperor Penguin Colony (EPC) optimization technique to allocate the virtual reference tag while taking the number of tags, SNR, and temperature and humidity of the surroundings into account. In the third phase, we use a quantum neural network (QNN) for localization in order to estimate the position of the moving target. We introduced the SignRank approach to select the best virtual reference tag for localization, which lowers tracking mistakes. In conclusion, we presented the Twin Delayed Deep Deterministic Policy Gradient (TD3) method that boosts the tracking precision by tracking the moving target tag efficiently and taking into account stage, the orientation, distance, and valuable coordinates. The NS3.26 network simulator is used to run the simulation, and tracking precision, tracking error, and accumulated probabilities are used to assess effectiveness. [ABSTRACT FROM AUTHOR]

A dual funtional dual patch wearable antenna integrated into a military beret, is developed for indoor and outdoor positioning system. The composite antenna consists of an inner double-slit circular patch with an outer square ring patch with four conductive thread vias. The inner patch is designed for global positioning systems, operating in the L1 band (1.563–1.587 GHz), for outdoor applications. The slits are made orthogonal to each other, for exciting two orthogonal modes needed for achieving circular polarization by degenerating the fundamental TM11 mode. The outer square ring patch generating monopole-type radiation pattern, is designed for radio frequency identification in the ISM band (902–928 MHz), for indoor applications. It resonates at a higher-order mode of TM41 at 915 MHz, caused by the integration of the fields developed by suitable positioning of four conductive thread vias. They split the surface current distribution into 4 half-cycles, leading to the generation of the TM41 mode. The antenna is realized and fabricated using felt as substrate and conductive metalized nylon fabric for patch and ground plane of the composite antenna. The bending effects of the wearable antenna over the standard human head phantom is also analyzed by comparing simulated and measured results. A comprehensive specific absorption rate analysis over a typical human head (six-layered) phantom is carried out to evaluate the radiation effects. Comparative analysis with pre-published antenna systems operating in similar frequency bands has been studied, indicating that the proposed antenna system outperforms them. [ABSTRACT FROM AUTHOR]

Monitoring the performance of reinforced concrete structures, particularly in terms of strength reduction, presents significant challenges due to the practical limitations of traditional detection methods. This study introduces an innovative framework that incorporates a non-destructive technique using electromagnetic waves (EM-waves) transmitted via Radio Frequency Identification (RFID) technology, combined with two-dimensional (2-D) Fourier transform, fractal dimension analysis, and deep learning techniques to predict reductions in structural strength. Experiments were conducted on three reinforced concrete beam (RCB) specimens exhibiting various levels of reinforcement corrosion. From these, a dataset of 1,800 EMwave images was generated and classified into “normal” and “reduced strength” categories. These categories were used to train and validate a Convolutional Neural Network (CNN), which demonstrated robust performance, achieving a high accuracy of 0.91 and an F1-score of 0.93 in classifying instances of reduced structural strength. This approach offers a promising solution for detecting strength reduction in reinforced concrete infrastructures, enhancing both safety and maintenance efficiency. First published online 5 November 2024

Radio Frequency Identification (RFID) technology plays a crucial role in various Internet of Things (IoT) applications, necessitating the integration of RFID systems into dense networks. However, the presence of numerous readers leads to collisions, degrading communication between readers and tags and compromising system performance. To tackle this challenge, researchers have proposed Medium Access Control (MAC) layer protocols employing different channel access methods. In this paper, we present a novel solution, the Distributed Time Slot Anti-Collision protocol (DTS-AC), which employs a new TDMA notification system to address Reader-to-Reader Interference (RRI), while incorporating FDMA-based frequency resource management to resolve Reader-to-Tag Interference (RTI) collision issues. Simulation results demonstrate that DTS-AC significantly improves performance in dense RFID networks by enhancing read rates, with scalability benefits based on the number of readers, channels, and Time Slots (TSs). Moreover, the cost-effectiveness of DTS-AC facilitates efficient deployment in RFID networks, emphasizing considerations of time delay and data sensitivity.

This study examines the evolution of vending machines, emphasizing their integration with RFID technology for cashless transactions. Vending machines have transformed human-machine interaction, offering convenient access to products and services. Transitioning from coin-operated to RFIDenabled systems has revolutionized the industry, enhancing security, reducing costs, and improving user experience. Through exploration of technical specifications and design objectives, the research highlights RFID vending machines' potential to reshape consumer behavior and optimize operations. Traditional cashbased vending machines face challenges such as limited storage, recognition issues, and security concerns. To address these, the paper proposes an intelligent RFID-based cashless vending machine for drink sales. The system incorporates RFID technology for payment, allowing users to swipe cards and select drinks without cash involvement. Prototype development involved software design, utilizing C-language for multiproduct vending. Comparatively, the RFID-based system outperforms cash-based counterparts in efficiency, security, and sales tracking, presenting a superior solution for drink sales. [ABSTRACT FROM AUTHOR]

Apis mellifera was used as a model species for ecotoxicological testing. In the present study, we tested the effects of acetone (0.1% in feed), a solvent commonly used to dissolve pesticides, on bees exposed at different developmental stages (larval and/or adult). Moreover, we explored the potential effect of in vitro larval rearing, a commonly used technique for accurately monitoring worker exposure at the larval stage, by combining acetone exposure and treatment conditions (in vitro larval rearing vs. in vivo larval rearing). We then analyzed the life‐history traits of the experimental bees using radio frequency identification technology over three sessions (May, June, and August) to assess the potential seasonal dependence of the solvent effects. Our results highlight the substantial influence of in vitro larval rearing on the life cycle of bees, with a 47.7% decrease in life span, a decrease of 0.9 days in the age at first exit, an increase of 57.3% in the loss rate at first exit, and a decrease of 40.6% in foraging tenure. We did not observe any effect of exposure to acetone at the larval stage on the capacities of bees reared in vitro. Conversely, acetone exposure at the adult stage reduced the bee life span by 21.8% to 60%, decreased the age at first exit by 1.12 to 4.34 days, and reduced the foraging tenure by 30% to 37.7%. Interestingly, we found a significant effect of season on acetone exposure, suggesting that interference with the life‐history traits of honey bees is dependent on season. These findings suggest improved integration of long‐term monitoring for assessing sublethal responses in bees following exposure to chemicals during both the larval and adult stages. Environ Toxicol Chem 2024;43:1320–1331. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC. [ABSTRACT FROM AUTHOR]

Radio Frequency Identification (RFID) technology plays a crucial role in various Internet of Things (IoT) applications, necessitating the integration of RFID systems into dense networks. However, the presence of numerous readers leads to collisions, degrading communication between readers and tags and compromising system performance. To tackle this challenge, researchers have proposed Medium Access Control (MAC) layer protocols employing different channel access methods. In this paper, we present a novel solution, the Distributed Time Slot Anti-Collision protocol (DTS-AC), which employs a new TDMA notification system to address Reader-to-Reader Interference (RRI), while incorporating FDMA-based frequency resource management to resolve Reader-to-Tag Interference (RTI) collision issues. Simulation results demonstrate that DTS-AC significantly improves performance in dense RFID networks by enhancing read rates, with scalability benefits based on the number of readers, channels, and Time Slots (TSs). Moreover, the cost-effectiveness of DTS-AC facilitates efficient deployment in RFID networks, emphasizing considerations of time delay and data sensitivity. [ABSTRACT FROM AUTHOR]

Radio frequency identification (RFID) technology is crucial in revolutionizing the food supply chain and combating global food waste. However, this technology faces challenges in full integration due to disruptive effects on tags caused by the dielectric properties of food and beverage ingredients, chemical constituents, and their packaging. This paper aims to demonstrate the effect of packaging and beverage contents on RFID tag performance. Three commercially available ultra-high frequency (UHF) RFID tags with different designs were tested on polyethylene terephthalate (PET) bottles, measuring tag performance through sensitivity, backscatter, and read range in the presence of various water-based solutions and commercially available beverages. The results highlight the substantial impact of the beverage type and tag design on RFID performance. The results of this study showed that tag 3 was the most consistent and readable tag amongst those tested in the presence of different beverage contents. Tag 3 resulted in a sensitivity ranging from −0.49 to −2.01 dBm, backscatter from −38.16 to 43.59 dBm, and read range from 1.58 to 1.88 m, while tag 1 performed the best in the presence of an empty PET bottle resulting in a sensitivity of −20.78 dBm, backscatter of −23.65 dBm, and read range of 16.34 m. The results of this study can be used for further investigations to develop a mathematical model that predicts the RFID tag performance based on the food composition. This model will be helpful for the design of the tags while facilitating the adoption of smart packaging for food traceability. [ABSTRACT FROM AUTHOR]

The problem of radio frequency identification network planning (RNP) is one of the biggest challenges in the field of RFID research. In order to improve the service quality of RFID systems, in addition to finding the optimal number and location of readers, the interference between readers and tag coverage should be considered. In order to solve the problem of RFID network planning, this paper proposes an improved multi-objective brainstorming optimization algorithm (IMBSO) based on decomposition: Employ the golden sine operator to improve the convergence speed of the algorithm, and the Cauchy mutation operator is introduced to enhance the ability of the algorithm to jump out of the local optimum. The improved algorithm achieves a balance between exploration and development. This paper also combines the decomposition-based method with the IMBSO algorithm to establish an optimization model to optimize the reader position: the multi-objective optimization problem is transformed into multiple sub-problems by means of decomposition, all objective functions are optimized at the same time, which improves the search efficiency. The method proposed in this paper was tested on the RFID model and data set. Under the same experimental conditions, it was compared with multi-objective particle swarm optimization algorithm, multi-objective firefly algorithm and multi-objective brainstorming algorithm. The experimental results show that the algorithm proposed in this paper can be successfully applied to the problem of radio frequency identification network planning by achieving smaller interference and fewer readers while ensuring greater coverage. [ABSTRACT FROM AUTHOR]

By the evolution of context-aware application, indoor location positioning gains much more attention worldwide. Radio Frequency Identification (RFID) is one such wireless positioning technology adopted widely. Moreover, enhancing the tracking accuracy and thus reducing the tracking error remains a challenging task in RFID based indoor environment. So as to overcome this aspect, a localization and tracking method depending on virtual reference tag (VRT) is employed in the indoor environment. This research work includes four modules like (i) data collection and Received Signal Strength Indication (RSSI) estimation;(ii) Optimal allocation of VRT using Spider Monkey Optimization (SMO) at which the allocation of VRT is made by considering number of tags, environmental factors (humidity and temperature), and SNR. Based on this data, the RFID reader allocates VRT for each grid so as to increase the tracking accuracy; (iii) Artificial Neural Network (ANN) module-based localization at which the localization of unknown tags is made; and (iv) tracking of moving target tag is carried based on Aquila Swarm Optimization-Long short-term memory (ASO-LSTM) approach. The optimization at this stage is employed to choose optimal position which enhances the accuracy of tracking. Thus, finally the location estimation is carried by this approach. The simulation is carried in NS3.26 network simulator and the performance are estimated in terms of SNR, tracking error, mean error, tracking accuracy, mean time, and standard deviation. The performance value is then compared with traditional models to show the enhancement of proposed mode over the other conventional techniques. [ABSTRACT FROM AUTHOR]

Radio Frequency Identification (RFID) technology, capable of wirelessly processing large amounts of information, is gaining attention with the advancement of IoT technology. RFID systems can be utilized as Wireless Sensor Network (WSN) technology by introducing sensing materials responsive to external environmental stimuli. To achieve effective information communication and sensing capabilities, various types of nanomaterials are being used as various components of RFID sensors. This paper provides an overview of the RFID sensor system and the nanomaterials used in their composition. Polymers that can achieve flexibility are attracting more attention as user-friendly substrates as demand for them increases in the wearable market. Additionally, advancements in inkjet printing technology, allowing cost-effective and simple production of components, introduce inks and manufacturing methods utilizing various nanomaterials such as metal nanoparticles, carbon materials, and composites. Furthermore, nanomaterials utilized as sensing materials enable the detection of various external environments with high sensitivity and a wide detection range. Consequently, RFID sensor systems that achieve wireless detection in a variety of environments are actively utilized in many applications. By analyzing the current research progress and problems faced in RFID sensor technology, this paper suggests future research directions for its development as a next-generation wireless sensor system. [ABSTRACT FROM AUTHOR]

The demand for low-cost healthcare solutions has been increasing and the use of Internet of Things (IoT) devices is becoming more common. IoT devices have the potential to monitor vital health parameters of patients remotely, providing early detection of health problems and reducing medical costs. Chipless RFID sensors, combined with IoT technology, offer cost-effective solutions for healthcare applications. These sensors have significance for remote data collection, object tracking, authentication, and security. In recent years, there has been a growing attention on the design and development of chipless sensors for monitoring health. This paper provides an extensive review of chipless RFID sensors, including their functioning, fabrication techniques, and classifications. The paper further addresses the various smart sensing materials used in chipless RFID sensors, along with challenges and future trends. The IoT-based health monitoring system permits patients to be tracked 24/7 and provides rapid health consultations from doctors in distant places. The technology also allows guardians or family members to remotely monitor patients, which is regarded as one of the most significant benefits of saving human lives. Overall, this review article provides a comprehensive investigation of the potential advantages of chipless RFID sensors and IoT-based systems for healthcare monitoring, paving the way for cost-effective, easily accessible, and reliable healthcare solutions.

In this paper, we propose a novel method for dynamic positioning using a single passive RFID tag to obtain its position through phase. This method addresses the phase integer ambiguity by introducing a robust phase unwrapping algorithm, solves the initial position problem using the maximum probability algorithm, and achieves accurate dynamic positioning through linear intersection. Experimental results demonstrate that our proposed method supports both static and dynamic positioning (speed

A novel methodology for designing Chipless Radio Frequency Identification (RFID) tags realized using the Additive Manufacturing technologies and exhibiting a high number of identification states is proposed and investigated. An encoding scheme based on the resonances and anti-resonances of the input impedance of the resonator is employed for identifying the tag, which is achieved by exploiting the set of zero-crossing points of the tag reactance. A tolerance analysis is also performed to avoid any ambiguity in the reading process and to minimize the redundancy, thus providing unique electromagnetic signatures which are also suitable for anti-counterfeiting applications. Several prototypes have been fabricated and tested to assess the practical implementation of the proposed encoding scheme.