Search Results (55)

Android devices employ several methods to calculate their position. This paper’s focus is the Network Location Provider (NLP), which leverages Wi-Fi and cell tower signals via the fingerprinting/database approach. Unlike GNSS-based positioning, the NLP should be able to compute positions even when the device is indoors or experiencing GNSS radio frequency interference (RFI), making it an enticing candidate for ensuring robust PNT solutions. However, the inner workings of NLP are largely undisclosed, remaining as a ‘black-box’ system. Using the Samsung S24 and Xiaomi Redmi K80 Ultra, we explored the NLP’s response to GNSS spoofing and offline operation (no network connection), as well as attempting NLP spoofing. The GNSS spoofing test confirmed that when satellite signals are spoofed, the NLP solution is maintained at the truth location. This reinforces the robustness of the NLP in RFI environments. In offline mode, NLP continued to operate without a Google server connection, indicating that it can compute positions locally using internally stored cache data. This behavior deviates from the conventional understanding of NLP and offers valuable insights into the latest NLP mechanism. These findings build upon previous work to uncover the inner workings of the NLP and ultimately contribute to robust smartphone PNT. Full article

Children with intoeing gait are at increased risk of tripping and consequent injury, reduced mobility, and psychological issues. Quantification of tripping is needed outside the gait lab during daily life for improved clinical assessment and treatment evaluation and to enrich the database for artificial intelligence (AI) learning. This paper presents the development of a low-cost, wearable tripping monitor to log a child’s Tripping Hazard Events (THEs) and steps taken during two weeks of everyday activity. A combination of sensors results in a high probability of THE detection, even during rapid gait, while guarding against false positives and minimizing power and therefore monitor size. A THE is logged when the feet come closer than a predefined threshold during the intoeing foot swing phase. Foot proximity is determined by a Radio Frequency Identification (RFID) reader in “sniffer” mode on the intoeing foot and a target of passive Near-Field Communication (NFC) tags on the contralateral foot. A Force Sensitive Resistor (FSR) in the intoeing shoe sets a time window for sniffing during gait and enables step counting. Data are stored in 15 min epochs. Laboratory testing and an IRB-approved human participant study validated system performance and identified the need for improved mechanical robustness, prompting a redesign of the monitor. A custom Python (version 3.10.13)-based Graphical User Interface (GUI) lets clinicians initiate recording sessions and view time records of THEs and steps. The monitor’s flexible design supports broader applications to real-world activity detection. Full article

Background: Seafarers are highly susceptible to work-related injuries, which can result in serious consequences or permanent disabilities. Understanding the frequency and characteristics of occupational injuries is crucial for developing effective prevention strategies and identifying their underlying patterns and causes. This study aimed to determine the frequency and characteristics of telemedicine-assisted work-related injuries among seafarers on board Italian-flagged vessels. Methods: A retrospective descriptive study was conducted to analyze occupational injuries using medical data recorded in the Centro Internazionale Radio Medico (C.I.R.M.) database from 1 January 2010 to 31 December 2022. Injuries in the database were coded according to the 10th revision of the International Classification of Diseases (ICD-10) by the World Health Organization (WHO). Variables extracted from the database included injury type, seafarers’ age, rank, nationality, worksite, gender, date of injury, affected body region, clinical outcomes, and other demographic and occupational characteristics. Injury frequency and characteristics (e.g., location, type, and cause) were analyzed and stratified by seafarers’ rank and worksite groups. Results: The analysis included 793 seafarers who sustained injuries. Their average age was 39.15 ± 10.49 years (range: 21 to 70 years). Deck ratings and engine officers accounted for 27.9% and 20% of those who claimed injuries, respectively. 39.2% of injured seafarers were aged between 30 and 40 years. In terms of affected body parts, the most reported injuries were to the hand/wrist (33.3%), followed by the knee/lower legs (21%), and the head/eye (19%). Open wounds (38%) and burns/abrasions (14%) were the most common types of injury. Slips/falls (32%), burns/explosions (16.6%), and overexertion while lifting or carrying (14.8%) were the leading causes of injury during the study period. Nearly 35% of injuries affected workers on the deck and were due mainly to slips/falls, 19% in the engine room were due to being caught in machinery or equipment, and 32.5% in the catering department were due to burns/explosions. Conclusions: One-third of seafarers who suffered work-related injuries sustained hand and/or wrist injuries, with slips/falls being a significant cause. The results of this study emphasize the need for preventative measures in the marine sector, particularly to reduce risks associated with slips and falls, overexertion, and other injury-causing factors. Campaigns for the larger use of protective equipment are desirable to reduce occupational accidents at sea and provide better health protection for seafarers. Full article

Rain attenuation of radio waves with frequencies above 10 GHz causes a serious problem in wireless communications. For wireless systems design, highly accurate methods for estimating the magnitude of attenuation have long been studied. ITU-R recommends a calculation method for rain attenuation using R_0.01, the 1 min rainfall rate that is exceeded for 0.01% of an average year. Accordingly, an R_0.01 database suitable for this calculation has been constructed. In recent years, global warming has emerged as an important climatological issue. If the predicted rise in temperatures associated with global warming induces a significant effect on rainfall characteristics, the existing R_0.01 database will need to be revised. However, there is currently no information for quantitatively evaluating the likely long-term change in R_0.01. In our previous study, the long-term trend in annual maximum values for 1-day, 1 h, and 10 min rainfall in Japan was estimated from a large amount of meteorological data and a 95% confidence interval approach was used to identify an increasing trend of more than 10% over approximately 100 years. In this paper, we investigate the long-term trend in greater detail using non-linear approximations for three types of rainfall and adopt the Akaike Information Criterion to determine the optimal order of the non-linear approximation. The future trend of R_0.01 is then estimated based on the long-term change in annual maximum 1 h rainfall, exploiting the strong correlation between long-term average annual maximum 1 h rainfall and R_0.01. Full article

Device identification based on radio frequency fingerprinting is widely used to improve the security of Internet of Things systems. However, noise and acquisition inconsistencies in raw radio frequency signals can affect the effectiveness of classification, identification and authentication algorithms used to distinguish Bluetooth devices. This study investigates how the RF signal preprocessing techniques affect the performance of a support vector machine classifier based on radio frequency fingerprinting. Four options derived from an RF signal preprocessing technique are evaluated, each of which is applied to the raw radio frequency signals in an attempt to improve the consistency between signals emitted by the same Bluetooth device. Experiments conducted on raw Bluetooth signals from twentyfour smartphone radios from two public databases of RF signals show that selecting an appropriate RF signal preprocessing approach can significantly improve the effectiveness of a support vector machine classifier-based algorithm used to discriminate Bluetooth devices. Full article

With an increasing number of illegal radio stations, connected cars, and IoT devices, high-accuracy radio source localization techniques are in demand. Traditional methods such as GPS positioning and triangulation suffer from accuracy degradation in NLOS (non-line-of-sight) environments due to obstructions. In contrast, the fingerprinting method builds a database of pre-collected radio information and estimates the source location via pattern matching, maintaining relatively high accuracy in NLOS environments. This study aims to improve the accuracy of fingerprinting-based localization by optimizing UAV flight paths. Previous research mainly relied on RSSI-based localization, but we introduce an AOA model considering AOA (angle of arrival) and EOA (elevation of arrival), as well as a HYBRID model that integrates multiple radio features with weighting. Using Wireless Insite, we conducted ray-tracing simulations based on the Institute of Science Tokyo’s Ookayama campus and optimized UAV flight paths with PSO (Particle Swarm Optimization). Results show that the HYBRID model achieved the highest accuracy, limiting the maximum error to 20 m. Sequential estimation improved accuracy for high-error sources, particularly when RSSI was used first, followed by AOA or HYBRID. Future work includes estimating unknown frequency sources, refining sequential estimation, and implementing cooperative localization. Full article

The design of transformers, a key component of radio frequency integrated circuits (RFICs), is traditionally carried out through an iterative process involving extensive electromagnetic simulations. While process design kits (PDKs) offer tools based on interpolation or fitting equations to simplify parameter estimation, these tools are restricted to standard geometries, leaving designers to manually simulate and optimize custom designs. This approach is inefficient and resource intensive. This paper proposes an automated process to generate a database containing the physical and electrical parameters of a wide range of transformers. This database is part of a tool designed to efficiently identify the desired transformer. To evaluate the tool’s effectiveness in reducing the time required for design, a millimeter-wave (mm-Wave) 69.4–74.2 GHz differential low-noise amplifier (LNA) is designed using GlobalFoundries 45 nm silicon-on-insulator (SOI) technology. This circuit demonstrates a noise figure (NF) of 4.1 dB, a gain of 10.1 dB, an input third-order intercept point (IIP3) of −10.78 dBm, and a power consumption of 4.7 mW from a 0.406 V DC supply. Moreover, the simulated performance achieves these specifications within a highly compact area of 0.12 mm². The transformer selection process for the circuit takes only a few seconds, whereas the conventional method of manual transformer design and electromagnetic simulation would require a significantly greater amount of time. Full article

Objective: This study provides a comprehensive global overview of surgical instrument traceability systems and accentuates their growing importance in healthcare. Background: Surgical instruments pose risks to patient safety, economic costs, logistical challenges, and environmental impact. The increasing focus on instrument traceability reflects its potential to address these issues. Methods: We performed a systematic review using PRISMA guidelines, analyzing articles from 2000 to 2023 across five digital libraries (PubMed, Web of Science, IEEE, ACM, Google Scholar). Our review concentrated on traceability systems’ lifecycle for reusable and sterile surgical instruments. Results: Out of 7189 articles retrieved, 22 were selected for evaluation, and only 6 were considered relevant after a thorough examination. These studies mainly deployed Radio Frequency Identification (RFID) technology. They enhance patient safety, reduce environmental impact, improve economic efficiency, and optimize logistics. Additionally, these systems encourage more responsible surgical practices. Conclusions: Our study underscores the limited applied research in this field and discusses system architectures and performance metrics. It proposes future research directions, including the development of public databases, integration of automation, and investment in artificial intelligence (AI) and computer vision to improve traceability and risk analysis. Full article

The accurate and continuous location monitoring of athletes helps in meeting health and safety requirements and supporting the infotainment needs of large marathon events with thousands of participants. Currently, the tracking of individuals and groups of athletes at mass sports events is only possible to a limited extent, due to the weight, size, and cost constraints of the necessary devices. At marathon events, the usual infrastructure for timekeeping is Radio Frequency Identification (RFID) technology, which allows only precise tracking at huge intervals, with heuristic and interpolative algorithms to estimate runner positions in between the measuring points. Setting up RFID tracking stations on site is also material- and labor-intensive. We instead propose a continuous, real-time tracking solution, relying on Long-Range Wide-Area Network (LoRaWAN) GPS trackers. Due to the large geographical area and urban space in which marathon events take place, the positioning of static gateways cannot provide complete and continuous coverage. This research article presents an implementation with multiple LoRa trackers and mobile LoRa gateways installed on vehicle escorts to assess coverage quality. The tracking data collected by a receiving LoRaWAN Network Server (LNS) are stored in a database. Three experiments were conducted at three different official running events: a 10 km race, a half marathon, and a marathon. The backdrop for the 42.195 km event was the official Vienna City Marathon 2024 with more than 35,000 participants. The experimental results under these realistic conditions show the reception quality of this approach; e.g., during the marathon, the received packets from LoRa gateways were at an average distance of about 136 m (σ 157 m) from the tracker with a median update rate of 31 s across all trackers, using DR3/SF9. At greater distances, the quality decreased, although some outliers were received up to a distance of two kilometers. A possible prospect is that the low-power wide-area network (LPWAN) may repeat the history of RFID by entering the mass sports market from the industry domain. Full article

This paper presents the application of self-supervised deep contrastive learning in clustering signals detected in the wideband RF spectrum, presented in the form of spectrograms. Radio clustering is a method of searching for similar signals within the analyzed part of the radio spectrum. Typically, it is based on one or several specific parameters processed from the signal in a given channel. The authors propose a slightly different, innovative approach; thanks to the self-supervised learning of neural networks, there is no need to define specific parameters, and the feature vector, enabling comparison of Euclidean distances between signals, is generated by a deep neural network trained using a contrastive loss function on a dataset containing different radio modulations. The authors describe self-supervised solutions based on contrastive learning and the methods of signal segmentation and augmentation. The training process utilizes a custom database and the Resnet-50 network with a contrastive cost function. Radio clustering is used for autonomous spectrum analysis across wide frequency ranges and enables, among other things, the detection of tactical radio stations operating with widely dispersed frequency-hopping or a significant reduction in computational power required for real-time analysis of a large number of radio signals. Full article

The fifth-generation (5G) mobile cellular network is vulnerable to various security threats. Radio frequency fingerprint (RFF) identification is an emerging physical layer authentication technique which can be used to detect spoofing and distributed denial of service attacks. In this paper, the performance of RFF identification is studied for 5G mobile phones. The differential constellation trace figure (DCTF) is extracted from the physical random access channel (PRACH) preamble. When the database of all 64 PRACH preambles is available at the gNodeB (gNB), an index-based DCTF identification scheme is proposed, and the classification accuracy reaches 92.78% with a signal-to-noise ratio of 25 dB. Moreover, due to the randomness in the selection of preamble sequences in the random access procedure, when only a portion of the preamble sequences can be trained, a group-based DCTF identification scheme is proposed. The preamble sequences generated from the same root value are grouped together, and the untrained sequences can be identified based on the trained sequences within the same group. The classification accuracy of the group-based scheme is 89.59%. An experimental system has been set up using six 5G mobile phones of three models. The 5G gNB is implemented on the OpenAirInterface platform. Full article

Ongoing backscatter communications and localisation research have been able to obtain incredibly accurate results in controlled environments. The main issue with these systems is faced in complex RF environments. This paper investigates concurrent localization and ambient radio frequency (RF) energy harvesting using backscatter communication systems for Internet of Things networks. Dynamic real-world environments introduce complexity from multipath reflection and shadowing, as well as interference from movements. A machine learning framework leveraging K-Nearest Neighbors and Random Forest classifiers creates robustness against such variability. Historically, received signal measurements construct a location fingerprint database resilient to perturbations. The Random Forest model demonstrates precise localization across customized benches with programmable shuffling of chairs outfitted with RF identification tags. Average precision accuracy exceeds 99% despite deliberate placement modifications, inducing signal fluctuations emulating mobility and clutter. Significantly, directional antennas can harvest over −3 dBm, while even omnidirectional antennas provide −10 dBm—both suitable for perpetually replenishing low-energy electronics. Consequently, the intelligent backscatter platform localizes unmodified objects to customizable precision while promoting self-sustainability. Full article

This paper discusses the need to accurately determine the population’s exposure to low-intensity radio-frequency electromagnetic fields (RF-EMF) from modern technologies like mobile networks, Wi-Fi, and IoT and proposes a practical solution for this assessment. There is no scientific consensus on the biological effects, mostly due to challenges in conducting accurate biological experiments. Recent research suggests that real-life exposure sources trigger stronger biological responses than laboratory-generated RF-EMF. However, there is a lack of research comparing the effects of these sources. This paper introduces a portable system for assessing and monitoring EMF exposure in urban areas. Employing a Software-Defined Radio (SDR) platform to ensure adaptability, the system incorporates two measurement configurations. The initial version concentrates on determining the average power within a 20 MHz Wi-Fi channel, whereas the subsequent configuration augments its functionality by introducing a frequency sweep. This sweep broadens the scrutinized bandwidth, thereby enriching the captured data content through the storage of spectrum sweeps corresponding to each average power value. These data can be used to create EMF profile maps based on individuals’ geographical coordinates. Compared to current limited-performance commercial exposimeters, the proposed system offers expanded capabilities by broadening the frequency bandwidth, georeferencing measurements, and storing data in an SQL database. Compared to high-performance commercial exposimeters, the major advantage of the system is its ability to detect short-term fluctuations in signal spectra and store the corresponding data for subsequent analysis. Full article

The atmospheric total water vapor content (TWVC) affects climate change, weather patterns, and radio signal propagation. Recent techniques such as global navigation satellite systems (GNSS) are used to measure TWVC but with either compromised accuracy, temporal resolution, or spatial coverage. This study demonstrates the feasibility of predicting, mapping, and measuring TWVC using spread spectrum (SS) radio signals and software-defined radio (SDR) technology on low Earth-orbiting (LEO) satellites. An intersatellite link (ISL) communication network from a constellation of small satellites is proposed to achieve three-dimensional (3D) mapping of TWVC. However, the calculation of TWVC from satellites in LEO contains contribution from the ionospheric total electron content (TEC). The TWVC and TEC contribution are determined based on the signal propagation time delay and the satellites’ positions in orbit. Since TEC is frequency dependent unlike TWVC, frequency reconfiguration algorithms have been implemented to distinguish TWVC. The novel aspects of this research are the implementation of time stamps to deduce time delay, the unique derivation of TWVC from a constellation setup, the use of algorithms to remotely tune frequencies in real time, and ISL demonstration using SDRs. This mission could contribute to atmospheric science, and the measurements could be incorporated into the global atmospheric databases for climate and weather prediction models. Full article

Radio astronomy uses radio telescopes to detect very faint emissions from celestial objects. However, human-made radio frequency interference (RFI) is currently a common problem faced by most terrestrial radio telescopes, and it is getting worse with the development of the economy and technology. Therefore, it is essential to monitor and evaluate interference during the planning, construction, and operation stages of the radio telescope and protect the quiet radio environment around the radio astronomical site. In this paper, we present a software for an RFI analysis of the radio environment around the telescope. In this software, information has been collected, including the location of the site; the technical specifications, such as aperture and the frequency range of the radio telescopes; and the terrain around the site. The software and its modules are composed of telescope, geographic, and meteorological databases, and analysis modules of terrestrial and space-based RFI. Combined with the propagation characteristics of radio waves, we can analyze and evaluate RFI on the ground and in space around the radio telescope. The feasibility of the software has been proved by the experimental implementation of the propagation properties and RFI source estimation. With this software, efficient technical support can be expected for protecting the radio environment around the telescope, as well as improving site selection for planned radio astronomical facilities. Full article