Search Results (190)

The global coffee supply chain is a complex multi-stakeholder ecosystem plagued by fragmented records, unverifiable origin claims, and limited real-time visibility. These limitations pose risks to ethical sourcing, product quality, and consumer trust. To address these issues, this paper proposes a blockchain and IoT-enabled framework for secure and transparent coffee supply chain management. The system integrates simulated IoT sensor data such as Radio-Frequency Identification (RFID) identity tags, Global Positioning System (GPS) logs, weight measurements, environmental readings, and mobile validations with Ethereum smart contracts to establish traceability and automate supply chain logic. A Solidity-based Ethereum smart contract is developed and deployed on the Sepolia testnet to register users and log batches and to handle ownership transfers. The Internet of Things (IoT) data stream is simulated using structured datasets to mimic real-world device behavior, ensuring that the system is tested under realistic conditions. Our performance evaluation on 1000 transactions shows that the model incurs low transaction costs and demonstrates predictable efficiency behavior of the smart contract in decentralized conditions. Over 95% of the 1000 simulated transactions incurred a gas fee of less than ETH 0.001. The proposed architecture is also scalable and modular, providing a foundation for future deployment with live IoT integrations and off-chain data storage. Overall, the results highlight the system’s ability to improve transparency and auditability, automate enforcement, and enhance consumer confidence in the origin and handling of coffee products. Full article

Hydropower infrastructure has profoundly altered riverine connectivity, posing challenges to the migratory behavior of aquatic species. This study examined the post-passage migration efficiency of Schizothorax wangchiachii in a regulated river system, focusing on upstream and downstream reaches of the Songxin Hydropower Station on the Heishui River, a tributary of the Jinsha River. We used radio-frequency identification (RFID) tagging to track individuals after fishway passage and coupled this with environmental monitoring data. A Cox proportional hazards model was applied to identify key abiotic drivers of migration success and to develop a predictive framework. The upstream success rate was notably low (15.6%), with a mean passage time of 438 h, while downstream success reached 81.1%, with an average of 142 h. Fish exhibited distinct diel migration patterns; upstream movements were largely nocturnal, whereas downstream migration mainly occurred during daylight. Water temperature (HR = 0.535, p = 0.028), discharge (HR = 0.801, p = 0.050), water level (HR = 0.922, p = 0.040), and diel timing (HR = 0.445, p = 0.088) emerged as significant factors shaping the upstream movement. Our findings highlight that fishways alone may not ensure functional connectivity restoration. Instead, coordinated habitat interventions in upstream tributaries, alongside improved passage infrastructure, are crucial. A combined telemetry and modeling approach offers valuable insights for river management in fragmented systems. Full article

Specific emitter identification (SEI), as an emerging physical-layer security authentication method, is crucial for maintaining information security in the Internet of Things. However, existing deep learning-based SEI methods require extensive labeled data for training, which are often unavailable in untrusted scenarios. Furthermore, due to the subtle nature of radio-frequency fingerprints, unsupervised SEI struggles to achieve high accuracy in identification without the guidance of labels. In this paper, we propose an unsupervised SEI method based on group label-driven contrastive learning (GLD-CL). We propose a novel method for constructing the dataset: all input samples derived from the same received signal segment are grouped together and assigned a unique identifier, termed the group label. Based on this, we improve the loss function of self-supervised contrastive learning. With the assistance of group labels, the feature vectors of the same class in the feature space become more closely clustered, enhancing the accuracy of unsupervised SEI. Extensive experimental results based on real-world datasets demonstrate that the normalized mutual information of GLD-CL achieves 96.4% accuracy, representing an improvement of 5.68% or more compared to the baseline algorithms. Furthermore, GLD-CL exhibits robust performance, achieving good identification accuracy across various signal-to-noise ratio scenarios. Full article

This paper proposes a three-point nonlinear calibration scheme for an ultra-low-power, high-precision temperature sensor to address the issue where the temperature error of a 0.8 μW sensor exceeds ±1 °C in RFID (Radio-Frequency Identification) temperature measurement systems. The proposed calibration scheme introduces a temperature-dependent nonlinearity coefficient to the traditional linear calibration, effectively compensating for the sensor’s nonlinear output characteristics. To minimize calibration costs, a scheme embedding the calibration algorithm into the reader is proposed, along with a dichotomy-based approach for efficient temperature calibration. The experimental results demonstrate that, within the temperature range of −30 °C to 90 °C, the temperature error of five sensor samples can be reduced from ±8 °C to between −1 °C and 0.6 °C. This solution has been successfully implemented in mass production. Full article

This article presents a comprehensive analysis of the technical characteristics of advanced versatile materials used in chipless radio-frequency identification (RFID) tags and antennas. The focus is on materials that are used as radiators and substrates. Crucial aspects include flexibility, weight, size, gain, environmental sustainability, efficiency, fabrication time and type, and cost. A comprehensive set of tables are presented that summarize and compare material properties. The materials include flexible high-tech ink substances, graphene, and liquid crystals, as well as metamaterials which possess properties that allow for an increased bandwidth. Printing techniques are discussed for high-performance high-resolution fabricated tags. This paper contributes by systematically comparing emerging materials for chipless RFID tags, highlighting their impact on performance and sustainability. It also provides practical guidance for material selection and fabrication techniques to enable next-generation wireless applications. It presents a broad understanding of various materials and their use. The paper provides direction for the deployment and utilization of inexpensive passive chipless RFID tags in future intelligent wireless networks. The advancement of chipless RFID is largely driven by the development of innovative materials, especially in the realm of advanced materials and smart materials, which enable the creation of more cost-effective, flexible, and scalable RFID systems. Full article

A new Radio-Frequency Identification (RFID) indoor positioning system (IPS) has been developed to operate in environments where the Global Positioning System (GPS) is unavailable. Traditional RFID tracking systems, such as anti-theft systems in clothing stores, typically work within close proximity to exit doors. This paper presents a novel RFID IPS capable of locating and tracking passive RFID tags over a larger area with greater precision. These tags, costing approximately EUR 0.10 each, are in the form of small stickers that can be attached to any item requiring tracking. The proposed system is designed for an autonomous wheelchair, built from scratch, which will be identified and monitored using passive RFID tags. Our new RFID IPS, with a 12 m range, is implemented in this “smart” wheelchair. Full article

The aim of the research is to investigate the impact of laser operation parameters on the LIG (laser-induced graphene) process. It focuses on evaluating the feasibility of using the induced conductive layers to create antenna circuits that are dedicated to radio-frequency identification (RFID) technology. Given the specific design of textile RFIDtex transponders, applying the LIG technique to fabricate antenna modules on a flexible substrate (e.g., Kapton) opens new possibilities for integrating RFID labels with modern materials and products. The paper analyses the efficiency of energy and data transmission in the proposed innovative UHF RFID_LIG tags. The signal strength, read range, and effectiveness are estimated in the experimental setup, providing key insights into the performance of the devices. Based on the obtained results, it can be concluded that changes in laser cutting parameters, the size of the induced graphene layer, and the method of fixing the Kapton substrate significantly affect the quality of the cutting/engraving components and the conductivity of burned paths. However, these changes do not directly affect the correct operation of the RFID_LIG transponders, owing to the fact that these structures are resistant to external impacts. Nevertheless, an increased range of data readout from the RFID_LIG tags can be achieved by using graphene paths with higher conductivity. The obtained results confirm the validity of the proposed concept and provide a foundation for further research on adapting the LIG method to automated logistics, ultimately leading to the development of more versatile and innovative solutions for identification processes. Full article

Background: The increasing detection of non-palpable breast lesions necessitates accurate preoperative localization to ensure complete excision while preserving healthy tissue and optimizing cosmetic outcomes. Traditional wire-guided localization (WL) has been the gold standard; however, it has several drawbacks, including patient discomfort and scheduling challenges. This study evaluates the accuracy and feasibility of radiofrequency identification (RFID) tag localization using the Hologic LOCalizer™ system as an alternative technique. Methods: This retrospective study included 258 consecutive patients who underwent image-guided RFID tag localization from March 2021 to February 2023 from a single-center London breast unit. The primary outcome measured was the accuracy of RFID tag placement, defined as within 10 mm of the target lesion on post-clip mammograms. Secondary outcomes included type and size of lesions, re-excision rates, review of post-operative specimen radiographs, and patient demographics. Results: A total of 297 RFID tags were placed, with 95.6% accurately positioned within the target range. The median target size was 29 mm, with the most common abnormalities being mass lesions (64%). Among the 13 inaccurately placed RFID tags (4.4%), all were identified preoperatively, with two requiring additional wire placements. RFID tags were successfully identified in 92% of specimen radiographs, and 8% of patients required re-excision due to positive or close margins. Notably, patients with multiple RFID tags showed a higher incidence of re-excision. Conclusions: The LOCalizer™ RFID system demonstrated a high accuracy rate for preoperative localization of breast lesions, presenting a viable alternative to WL. This technique improves surgical scheduling flexibility and enhances patient comfort. Comparative studies with other wire-free localization technologies, such as magnetic seeds and radar reflectors, are needed to determine the optimal approach for clinical practice. Full article

Background/Objectives: Today, aesthetic medicine is becoming increasingly common, with the demand for rejuvenating treatments continuously growing. These procedures are gaining more interest from patients who struggle to accept their aesthetic imperfections. There is a need to understand patients’ subjective evaluations of the effects of aesthetic medicine procedures and the factors that motivate them to undergo invasive treatments. Understanding these aspects allows for better tailoring of therapy to patients’ needs, increasing their satisfaction and quality of life. Additionally, it enables the identification of psychological and social factors influencing their decisions, supporting a more conscious and responsible approach to aesthetic medicine. Methods: This study included 82 women who had undergone treatments with platelet-rich plasma and fibrin (50%) or microneedling radiofrequency (50%), with an average age of 44.3 years. The research questionnaire consisted of 12 original questions. This study was conducted from January to March 2024. Over 90% of the women had been undergoing treatments for at least several months. Results: Participants reported a significantly improved assessment of their appearance after the procedure, with an average increase of 2.72 points on a scale from 1 to 10 (M = 2.72; SD = 1.66) (p < 0.001). Women from rural areas significantly more often (29.6%) cited appearance-related complexes as a reason for the procedure compared to women from cities (3.6%) and large cities (3.7%) (p = 0.003). Over 80% reported an increase in self-confidence due to the procedures they underwent. Conclusions: The analysis showed a significant difference in the assessment of appearance before and after the procedure; participants rated their appearance more favorably after the procedure. The motivating factors for women to undergo aesthetic medicine procedures include the desire to look younger and more attractive. The marital status of the respondents did not influence motivating factors. Full article

This paper examines the design of antennas for Hi-pass type turnstiles needed to implement a subway free-pass system targeting transportation-disadvantaged individuals. The subway free-pass system allows individuals who have a free-pass card to approach the turnstile with the card on their person, which opens the gate automatically. This system, like the highway Hi-pass, allows users to pass through the subway gate without needing to scan a ticket. For the system to function, antennas are required at both the entrance and exit gates, with an additional antenna needed for the free-pass card, totaling three antennas. The free-pass card functions as a radio-frequency identification (RFID) tag, while the system uses Bluetooth communication. In this paper, we propose a square patch-type microstrip antenna that can be integrated within the turnstile, performing excellently in the Bluetooth band range of 2.420 to 2.485 GHz. Full article

In today’s business landscape, the volume of transaction data is rapidly increasing. This study explores the integration of Point of Sale (POS) and Radio-Frequency Identification (RFID) technologies to enhance the analysis of customer transactions using big data tools. By leveraging these technologies, businesses can extract valuable insights to improve processes, optimize inventory, and boost customer satisfaction. The research employs an object—subject management approach, which facilitates real-time decision-making by merging retail transactions of the clients with their movement patterns. An experiment involving around 7000 customers demonstrates the effective collection and processing of POS and RFID data, highlighting the benefits of integrating these data streams. Key metrics, such as time spent in different store sections, provide deeper insights into consumer behavior. The findings reveal the potential of these technologies to transform retail services, offering opportunities for demand forecasting, risk management, and personalized customer experiences. The study concludes that merging POS and RFID data opens new avenues for developing management solutions aimed at enhancing customer engagement and the operational efficiency of the retailer. Future research will focus on further elaborating these solutions to maximize the benefits of integrated data analysis. Full article

This study applied modified linear regression in machine learning (ML) to predict the direction of arrival (DoA) in cellular networks using field measurements and radiofrequency parameters. Models were developed from base station data, with preprocessing for pattern identification and formula adjustments to improve the accuracy across angle ranges. Machine learning, tested here as an additional method to traditional techniques, achieved a root mean square error (RMSE) of 3.63 to 17.93, demonstrating enhanced adaptability. While requiring substantial data and computational resources, this approach highlights machine learning’s potential as a valuable tool for DoA estimation in cellular networks. Full article

LoRa networks, widely adopted for low-power, long-range communication in IoT applications, face critical security concerns as radio-frequency transmissions are increasingly vulnerable to tampering. This paper addresses the dual challenges of privacy-preserving detection of tampered transmissions and the identification of unknown attacks in LoRa-based IoT networks. Leveraging Federated Learning (FL), our approach enables the detection of tampered RF transmissions while safeguarding sensitive IoT data, as FL allows model training on distributed devices without sharing raw data. We evaluated the performance of multiple FL-enabled anomaly-detection algorithms, including Convolutional Autoencoder Federated Learning (CAE-FL), Isolation Forest Federated Learning (IF-FL), One-Class Support Vector Machine Federated Learning (OCSVM-FL), Local Outlier Factor Federated Learning (LOF-FL), and K-Means Federated Learning (K-Means-FL). Using metrics such as accuracy, precision, recall, and F1-score, CAE-FL emerged as the top performer, achieving 97.27% accuracy and a balanced precision, recall, and F1-score of 0.97, with IF-FL close behind at 96.84% accuracy. Competitive performance from OCSVM-FL and LOF-FL, along with the comparable results of K-Means-FL, highlighted the robustness of clustering-based detection methods in this context. Visual analyses using confusion matrices and ROC curves provided further insights into each model’s effectiveness in detecting tampered signals. This research underscores the capability of federated learning to enhance privacy and security in anomaly detection for LoRa networks, even against unknown attacks, marking a significant advancement in securing IoT communications in sensitive applications. Full article

Catheter ablation for ventricular tachycardia (VT) in patients with systolic heart failure remains a critical yet challenging area of non-pharmacological therapy. Despite positive outcomes in atrial fibrillation, evidence for the efficacy of VT ablation in reducing cardiac mortality is inconclusive due to the absence of standardized ablation strategies. The primary challenges include difficulties in identifying suitable ablation targets and their deep locations within myocardial tissue. Current techniques, such as voltage mapping, provide valuable insights; however, they are limited by the presence of numerous bystander areas and the occurrence of incomplete transmural scarring. Recent advancements in functional substrate mapping have focused on identifying critical isthmuses without requiring hemodynamic stabilization during VT, thereby shifting the emphasis to the analysis of potentials during baseline rhythm. While methods like isochronal late activation mapping have improved target identification, they primarily address conduction abnormalities without adequately considering repolarization heterogeneity. This review highlights emerging technologies that utilize unipolar potentials to assess repolarization heterogeneities and identify VT isthmuses. Furthermore, novel ablation sources such as pulsed-field ablation, bipolar ablation, and ultra-low temperature cryoablation are being explored to create deeper and more durable lesions, addressing the limitations of traditional radiofrequency ablation. These advancements aim to reduce VT recurrence and improve overall treatment efficacy. Ultimately, understanding these innovative strategies is expected to optimize procedural outcomes and significantly enhance the management of patients with scar-related VT. Full article

Building Information Modelling (BIM) integration with the Internet of Things (IoT) is progressing. The high level of BIM maturity involves using sensor data to manage processes or objects. The article presents the process of creating a telemetry connection between the BIM model and a Radio-Frequency Identification (RFID) sensor in the context of gaining access to various parts of a building. The process of creating a connection using an experimental set based on a microcontroller board for RFID reader support is described. The set was programmed using multiple programming languages and artificial intelligence. The article presents a unique process of connecting an RFID reader with BIM using a simple model that can be replicated in other contexts (e.g., gaining access to different parts of a construction site). Previous research shows that the unidirectional connection of IoT sensors with BIM models is not difficult. Instead, real-time bidirectional and stable connection (telemetry) is problematic. The authors undertook to fill this research gap using a proprietary IoT kit, programming sequence, and lightweight communication protocol. The paper makes a significant contribution to the discussion and understanding of BIM-IoT technology integration. The article also includes the limitations and possibilities for further development of such a connection. Full article