Search Results (650)

Background: Tissue expanders with metallic ports are commonly used in post-mastectomy breast reconstruction but can produce significant computed tomography (CT) artifacts, which impair accurate delineation of target volumes during radiotherapy planning. The Motiva Flora^® expander incorporates an integrated radiofrequency identification (RFID) valve, designed to be magnet-free and magnetic resonance imaging (MRI)-conditional, potentially minimizing image distortion and improving the precision of treatment planning. This pilot study aims to quantitatively compare the extent of CT image distortion observed in radiotherapy simulation scans between conventional metallic-valve expanders and RFID-valve expanders, evaluating their impact on radiotherapy planning quality. Methods: Between January 2024 and September 2025, fourteen consecutive patients who underwent post-mastectomy two-stage breast reconstruction followed by adjuvant RT at Hospital Santa Maria della Misericordia (Udine, Italy) were included. Seven patients received Motiva Flora^® tissue expanders with a non-metallic RFID port, and seven received Mentor CPX4^® expanders with a conventional metallic port. The volume of areas with a significant level of artifacts (artifact volume) was quantitatively evaluated by delineating the CT image area of distortion caused by the valve. Moreover, a comparison of the ratio between artifact volume and clinical target volume (artifact volume/CTV volume) between expander types to assess potential imaging-related distortions has been made. Group comparisons of volume ratio were performed using Welch’s t-test. Results: Patients reconstructed with Motiva Flora^® showed a mean artifact volume of 24.5 ± 10.3 cc, whereas those with Mentor CPX4^® expanders presented a mean artifact volume of 64.2 ± 38.1 cc. The ratio between artifact volume and clinical target volume (CTV) was lower in patients reconstructed with Motiva expanders compared to those reconstructed with Mentor expanders and this difference was significant with Welch’s t-test (p = 0.046). Conclusions: The reduced CT distortion observed with the RFID valve-equipped Motiva Flora suggests a superior radiological compatibility compared to conventional metallic-port expanders, with potential to enhance the accuracy of radiotherapy planning. Full article

Radio frequency identification (RFID) technology has been widely adopted in a variety of practical applications. Usually, the size of a passive tag antenna largely determines the read performance of tag. However, excessively large tag antennas can hinder their practical application and a tag that is too small has poor performance. In this paper, a compact, flexible and corrosion-resistant folding dipole tag antenna is proposed, which has a geometrical dimension of 24 mm × 13 mm (0.074${\mathsf{\lambda }}_0\times 0.040{\mathsf{\lambda }}_0$). It is designed on only one surface of a flexible polyethylene terephthalate (PET) substrate, which can be folded. The paper proposes a single-sided laser-patterned GAF/PET flexible RFID tag that is mechanically folded to form a backside dipole arm without vias, targeting compact and corrosion-resistant UHF RFID operation. Changing the size of the folding arm can effectively adjust the resonant frequency and impedance of the tag antenna. A stepped radiation arm is used to extend the current path and lower the resonance frequency. The capacitance and inductance effects introduced by loading a T match for reducing the resonant frequency of the tag to the useful UHF RFID band. Finally, it can achieve a power transfer coefficient of 99.9% and exhibit high impedance matching between the tag antenna and the chip. The proposed tag antenna uses graphene-assembled film (GAF) as its conductor material. Thanks to the physicochemical properties of GAF, the proposed tag antenna maintains stable radiation performance even after prolonged exposure to acidic (5 wt%), alkaline (5 wt%), and salt (5 wt%) corrosion, as well as more than 1000 mechanical bending cycles. When the EIRP of the reader is 2.2 W, the maximum read range of the tag in the 800–1000 MHz is 1.38 m. Full article

Radio Frequency Identification (RFID) is an emerging technology in Industry 4.0 for low-cost logistics, yet direction and orientation estimation typically requires multiple antennas, and robustness under industrial multipath fading, operator variability, and signal fragmentation has not been evaluated. To address this gap, this study proposes a single-antenna RFID system that evaluated thirteen architectures spanning unsupervised methods (clustering algorithms) and supervised methods (classical machine learning, deep learning, and hybrid architectures) on Received Signal Strength Indicator (RSSI) and phase time-series reconstructed through a pipeline of Savitzky–Golay smoothing, phase unwrapping, and cubic spline resampling to $N$ = 50–300 samples, preserving signal morphology across variable-length RFID passes. The system further incorporates a physics-informed augmentation strategy that encodes multipath fading, distance variation, and fragmentation into synthetic training samples for cross-domain generalization without hardware modification. In controlled laboratory experiments, both direction and orientation tasks achieved $>$99.5% accuracy, while direction tracking was additionally validated on an industrial shop floor under varying distances, Non-Line-of-Sight (NLoS) occlusions, and signal fragmentation. Zero-shot transfer caused accuracy to degrade to near-chance levels for several configurations, confirming a pronounced domain gap. Domain adaptation with XGBoost recovered direction accuracy to $>$97% under severe fragmentation under NLoS conditions, with an inference latency of ≈150 $\mu$s. Under domain-adapted shop floor conditions, direction accuracy exceeded the 75–92% reported in prior single-antenna laboratory studies, suggesting that physics-informed domain adaptation is a promising approach for single-antenna RFID tracking in Industrial Internet of Things (IIoT) logistics environments. Full article

Medical device manufacturing requires strict quality control, reliable traceability, and compliance with regulatory requirements. In many cases, inspection activities are still carried out manually and production information is recorded separately, which can result in inconsistent defect detection and limited visibility of manufacturing performance. This paper presents the development and industrial implementation of an integrated closed-loop quality control architecture for a sterile single-use medical device assembly line, addressing the lack of integration between inspection, traceability, and control systems in existing manufacturing approaches. In the proposed approach, we combine radio-frequency identification, machine vision inspection, programmable logic control, and centralized production monitoring. RFID tags store the status of each unit at individual stations so that defective products cannot proceed to downstream operations. Machine vision systems verify component presence, detect missing parts, and confirm color-specific assembly requirements during production. The architecture was tested through implementation on an assembly line and evaluated with comparative pilot studies against a traditional manual inspection process. The upgraded line achieved scrap cost reductions of 52.77% and 53.23% while also improving inspection consistency and production traceability. The results demonstrate that integrating machine vision inspection with RFID traceability can significantly improve quality control and manufacturing efficiency in regulated medical device production. Full article

Background/Objectives: Accurate intraoperative tumour localisation remains challenging in minimally invasive colorectal surgery, where conventional tattooing methods suffer from marker migration, tissue diffusion, and potential allergic reactions. Radio frequency identification (RFID) technology offers a promising alternative through implantable passive transponders detectable via electromagnetic coupling, eliminating ionising radiation exposure. Methods: This preclinical feasibility study evaluated three RFID frequency bands for surgical tumour marking: 134 kHz (low frequency, LF), 13.56 MHz (high frequency, HF), and 868 MHz (ultra-high frequency, UHF). Finite element electromagnetic simulations characterised antenna field distributions, while experimental validation employed glass-encapsulated transponders in air and tissue-simulating saline (0.9% NaCl, σ ≈ 1.5 S/m). Detection ranges were measured across 28 angular configurations with expanded measurement uncertainty (k = 2) ranging from ±0.9 to ±3.2 mm. Results: Maximum detection distances in air were 25.0 ± 0.9 mm (LF), 23.0 ± 1.1 mm (HF), and 68.0 ± 2.3 mm (UHF). In saline, ranges decreased to 22.5 ± 1.0 mm, 20.7 ± 1.2 mm, and 18.0 ± 1.4 mm, respectively, demonstrating tissue attenuation of 10% at LF/HF vs. 74% at UHF. Angular characterisation revealed 64–70% range reduction at orthogonal orientation for LF/HF systems. Computational–experimental correlation yielded r² = 0.975 across 154 paired observations. Conclusions: The 13.56 MHz HF band emerges as the optimal candidate for clinical translation, offering adequate tissue penetration (20.7 mm), superior antenna miniaturisation potential (5 mm diameter), established biocompatibility pathways, and mature near-field communication ecosystem support. Future development should address angular sensitivity through multi-axis antenna configurations and validation in anatomically realistic tissue phantoms. This study establishes the electromagnetic evidence base for clinical system development; translation to clinical practice requires sequential preclinical and clinical evaluation. Full article

Laboratory asset maintenance in higher education institutions often suffers from inefficiencies due to incomplete data and reactive maintenance practices. We designed a radio frequency identification (RFID)-based information system that supports preventive maintenance and decision-making for laboratory asset management. Utilizing the rapid application development method, the system was developed through iterative prototyping and stakeholder engagement. The system integrates RFID-based asset identification with a web-based interface for real-time monitoring and log management. A decision-support module was also implemented, allowing stakeholders to prioritize maintenance tasks based on asset age, repair frequency, and usage patterns. Evaluation results of user acceptance testing showed an average score of 82%, indicating strong usability and relevance. The results demonstrate that integrating RFID with decision-support features significantly improve maintenance planning, reduce operational risk, and optimize resource allocation in academic laboratory environments. Full article

In this paper, inspired by the structural characteristics of the human eyeball, a bionically designed circular resonant structure is proposed, and a flexible chipless radio frequency identification (RFID) tag based on this concept is developed. By selectively adding or removing branch structures, the proposed tag achieves controllable resonant frequency shifts and distinguishable geometric pattern variations. Fabricated on a polyimide substrate with a compact size of 20 × 26 × 0.2 mm³, the tag achieves a coding capacity exceeding 45 bits while operating within an effective frequency bandwidth in 4–12 GHz, realizing a synergistic improvement in coding capacity and structural compactness under limited spectrum constraints. Simulation analyses are performed to investigate the encoding stability of the tag under various bending and rotational conditions relevant to flexible applications. Experimental results obtained under the unbent condition are consistent with the simulations, demonstrating the feasibility of the proposed chipless RFID tag. Full article

Supply Chain Management (SCM) has received considerable attention from the industrial community in recent decades. SCM continues to be an interesting and relevant research topic in many business areas such as revealing supply chain integration benefits, uncertainty and risk mitigation methods, decision-making and optimization methodologies, etc. In current supply chain management, huge volumes of data are being developed each second, and emerging technologies such as Radio Frequency Identification (RFID) have amplified the availability of online data. Using Artificial Intelligence (AI) methods that go beyond simply using the huge volume of online data enables Supply Chain (SC) managers to monitor everything in a timely fashion. There are several aspects of an SC that AI—and specifically Artificial Neural Networks (ANNs)—can be applied to better help them manage and optimize. This study aims to review state-of-the-art ANNs and Deep Neural Networks (DNNs) in the field of supply chain management. One hundred high-quality research studies that applied ANNs in supply chain management are reviewed and categorized into four classes: performance optimization, supplier selection, forecasting, and inventory management studies. Our study shows that there is a significant possibility that we could use ANNs and DNNs to better manage supply chains. Across the reviewed studies, neural networks are frequently reported to improve predictive performance and support monitoring/control in complex, nonlinear supply chain settings, often complementing traditional operations research approaches. Finally, the limitations of ANN models and the possibilities for future studies are presented at the end of this study. Full article

Automated production lines are increasingly being expanded with Industrial Internet of Things (IIoT) devices, creating complex Cyber-Physical Systems (CPSs) that connect physical production with control and information infrastructure. However, the convergence of Information Technology (IT) and Operational Technology (OT) layers creates new entry points for attacks targeting communication availability. Most existing studies analyze Distributed Denial of Service (DDoS) attacks primarily in simulation or testbed environments, with limited experimental verification of their impact on real-world production systems. This article presents an experimental evaluation of the impact of DDoS and Distributed Reflection Denial of Service (DRDoS) attacks carried out directly on a physical automated production line with integrated IIoT infrastructure during real operation. Three attack scenarios (TCP SYN flood, TCP ACK flood, and ICMP reflected attack) were implemented, targeting Programmable Logic Controllers (PLCs), Radio-Frequency Identification (RFID) subsystems, and selected IIoT devices. The results showed rapid degradation of deterministic PROFINET communication, disruption of the link between the OT and IT layers, loss of digital product representation, and physical interruption of the production process. Based on the findings, a minimally invasive security solution based on perimeter protection was designed and experimentally verified. The results emphasize the need to design IIoT-based manufacturing systems with an emphasis on network segmentation and architectural separation of the IT and OT layers. Full article

The integration of wearable inertial measurement units (IMU) in animal welfare Internet of Things (IoT) systems has become crucial for monitoring animal behaviors and enhancing welfare management. However, the vulnerability of IoT devices to network and hardware attacks poses significant risks, potentially compromising data integrity and misleading caregivers, negatively impacting animal welfare. Additionally, current animal monitoring solutions often rely on intrusive tagging methods, such as Radio Frequency Identification (RFID) or ear tagging, which may cause unnecessary stress and discomfort to animals. In this study, we propose a lightweight integrity and provenance-oriented security stack that complements standard transport security, specifically tailored to IMU-based animal motion IoT systems. Our system utilizes a 1D-convolutional neural network (CNN) model, achieving 88% accuracy for precise motion recognition, alongside a lightweight behavioral fingerprinting CNN model attaining 83% accuracy, serving as an auxiliary consistency signal to support collar–animal association and reduce mis-attribution risks. We introduce a dynamically generated pre-shared key (PSK) mechanism based on SHA-256 hashes derived from motion features and timestamps, further securing communication channels via application-layer Hash-based Message Authentication Code (HMAC) combined with Message Queuing Telemetry Transport (MQTT)/Transport Layer Security (TLS) protocols. In our design, MQTT/TLS provides primary device authentication and channel protection, while behavioral fingerprinting and per-window dynamic–HMAC provide auxiliary provenance cues and tamper-evident integrity at the application layer. Experimental validation is conducted primarily via offline, dataset-driven experiments on a public canine IMU dataset; system-level overhead and sensor-to-edge latency are measured on a Raspberry Pi-based testbed by replaying windows through the MQTT/TLS pipeline. Overall, this work integrates motion recognition, behavioral fingerprinting, and dynamic key management into a cohesive, lightweight telemetry integrity/provenance stack and provides a foundation for future extensions to multi-species adaptive scenarios and federated learning applications. Full article

In grain storage and transportation, biological activity, including respiration and metabolism, generates heat, creating temperature gradients that can induce moisture migration and form high-humidity areas. This accelerates fungal and insect activity, leading to quality degradation. Long-term, distributed temperature monitoring inside grain piles is essential for ensuring safe storage and early risk warning. Radio Frequency Identification (RFID) technology has become widely adopted in storage temperature monitoring due to its low cost, maintenance-free operation, and high security. However, traditional RFID systems have limited communication ranges, and the large size of storage facilities necessitates the deployment of multiple readers, which increases costs. Additionally, the high density and fluctuating moisture content of bulk grain lead to significant RF signal absorption and scattering, weakening the accessibility of purely wireless systems to deeper parts of the grain pile. To address these issues, a passive distributed temperature monitoring system based on RFID technology is proposed. The system utilizes RFID readers to harvest RF energy for passive power supply, with an external antenna ensuring stable energy harvesting and data transmission. Single-bus multi-point temperature sensor modules are integrated into the system, enabling distributed temperature measurements across grain piles or warehouses. Experimental results show that the system achieves a temperature collection success rate of 98%, with an accuracy of ±1 °C and a polling cycle of less than 30 s, providing a low-cost, battery-free, and scalable solution for grain storage monitoring, significantly improving storage quality. Full article

As a non-contact identification technology, RFID (Radio Frequency Identification) is widely used in various Internet of Things applications. However, RFID systems are highly vulnerable to diverse attacks due to the openness of communication links between readers and tags, leading to serious security and privacy concerns. Numerous RFID authentication protocols have been designed that employ hash functions and symmetric cryptography to secure communications. Despite these efforts, such schemes generally exhibit limitations in key management flexibility and scalability, which significantly restricts their applicability in large-scale RFID deployments. Confronted with this challenge, public key cryptography offers an effective solution. Taking into account factors such as parameter size, computational complexity, and resistance to quantum attacks, the NTRU algorithm emerges as one of the most promising choices. Since the NTRU signature algorithm is highly complex and requires large parameters, there are currently only a few NTRU encryption-based RFID authentication protocols available, all of which exhibit significant security flaws—such as supporting only one-way authentication, failing to address public key distribution, and so on. Moreover, performance evaluations of the algorithm in these contexts are often incomplete. This paper proposes a mutual authentication protocol for RFID based on the NTRU encryption algorithm to address security and privacy issues. The security of the protocol is analyzed using the BAN-logic tools and some non-formalized methods, and it is further validated through simulation with the AVISPA tool. With the parameter set (N, p, q) = (443, 3, 2048), the NTRU algorithm can provide 128 bits of post-quantum security strength. This configuration not only demonstrates greater foresight at the theoretical security level but also offers significant advantages in practical energy consumption and computation time when compared to traditional algorithms such as ECC, making it a highly competitive candidate in the field of post-quantum cryptography. Full article

Passive radio frequency identification (RFID) sensing systems integrate wireless energy transfer with information identification. However, conventional passive RFID systems still face three key challenges in practical applications: low RF energy harvesting efficiency, high power consumption of sensor loads, and high complexity of tag anti-collision algorithms. To address these issues, this paper proposes a hardware–software co-optimized RFID sensor system. For hardware, low threshold RF Schottky diodes are selected, and an input inductor is introduced into the voltage multiplier rectifier to boost the signal amplitude, thereby enhancing the radio frequency to direct current (RF-DC) energy conversion efficiency. In terms of loading, a low-power management strategy is implemented for the power supply and control logic of the sensor node to minimize the overall system energy consumption. For algorithmic implementation, a Dual-Threshold Stepped Dynamic Frame Slotted ALOHA (DTS-DFSA) anti-collision algorithm is proposed, which adaptively adjusts the frame length based on the observed collision ratio, eliminating the need for complex tag population estimation. The algorithm features low computational complexity and is well suited for resource constrained embedded platforms. Through simulation validation, we compare the conversion efficiency of the RF energy harvesting circuit before and after improvement, the current of the sensor load in active and idle states, and the performance of the proposed algorithm against the low-complexity DFSA (LC-DFSA). The results show that the maximum conversion efficiency of the improved RF energy harvesting circuit has increased from 60.56% to 68.69%; specifically, the sensor load current drastically drops from approximately 2.0 $\mathrm{m}\mathrm{A}$ in the active state to around 74 $\mathsf{\mu }\mathrm{A}$ in the idle state, validating the efficacy of the proposed power gating strategy, and the proposed DTS-DFSA algorithm outperforms existing low-complexity schemes in both identification efficiency and computational complexity. Full article

Background/Objectives: Safe delivery and correct identification of newborns are critical aspects of healthcare systems globally. The accreditation of healthcare and standards regulation significantly promotes the adoption of modern technologies to address risks related to infant abduction and misidentification. The effectiveness and extent of these mandates vary across settings and countries. Therefore, this study aims to map and explore modern technologies used for safe newborn delivery and correct identification aligned with healthcare accreditation and regulatory frameworks. Methods: This review adheres to the Preferred Reporting Items for Systematic Review and Meta-Analysis extension for scoping reviews (PRISMA-ScR) guidelines. The Problem, Intervention, Comparison, and Outcome (PICO) framework was employed to facilitate the development of the research question. This study examined studies reporting technologies such as radio frequency identification (RFID), biometric identification, and real-time monitoring across healthcare settings for infant protection through the Normalization Process Theory (NPT). Among three databases and search engines (PubMed, Google Scholar, and Web of Science). The risk of bias for each study was assessed using the AACODS Checklist, SQUIRE 2.0 Checklist, TIDieR Checklist, and JBI tools. Results: Out of 8753 records, only 27 reports were eligible to be included in this review. The most frequently reported technologies were RFID systems (11 studies, 37.9%) and biometric systems such as footprint and facial recognition (6 studies, 20.7%). Despite strong technological potential, many healthcare institutions struggled with the adoption of infant protection technologies. Accreditation systems among the high-resource settings actively mandate advanced technologies and support the integration of staff training and simulation drills. Comparably, middle- and low-income regions usually face challenges related to regulatory enforcement, infrastructure, staff readiness, and limited adoption of modern technologies. Conclusions: Accreditation and standards development are critical catalysts for the adoption of modern infant protection technology. Standards must be comprehensible, adaptable, and supported by investment in human resources and infrastructure. Future regulation must focus on strengthening enforcement, continuous quality improvement, and capacity building to achieve sustainable protection across the world. Full article

Background: Integrating Radio Frequency Identification (RFID) technology into storage areas within the wiring harness manufacturing industry enables real-time component traceability and supports the implementation of fully automated inventory processes. While RFID systems provide continuous data regarding component type, quantity, and location, periodic inventory validation is still required to verify and correct records in the warehouse management system. Methods: This study examines the feasibility of passive ultra-high-frequency (UHF) RFID technology for automatic inventory management in a components warehouse. It also reviews relevant scientific literature on autonomous RFID signal measurement and Synthetic Aperture Radar (SAR)-based localization methods, which are subsequently adapted for inventory applications. An experimental setup is developed to characterize the reading field, hysteresis effects, and the influence of distance and tag orientation on detection performance. Results: The findings indicate that RFID-based automatic inventory is achievable with high accuracy and stability, especially when tag trajectories correspond to areas of high detection probability and antenna polarization is optimally configured. Conclusions: The proposed RFID-based system can be implemented with minimal hardware changes and low investment, thereby improving stock accuracy, traceability, and operational efficiency in automotive component logistics. Full article