Search Results (71)

Recent advancements in wearable technology have allowed researchers to collect high-resolution data on athletes’ workloads and performance, paving the way for more accurate mathematical models in sports science. In this paper, inspired by the modeling of heart rate during exercise, we introduce a novel linear, time-varying, two-state discrete-time dynamical model for predicting athletic performance in elite-level soccer players. Model parameters are estimated via the Differential Evolution optimization algorithm, and GPS-derived metrics such as metabolic power and equivalent distance index are incorporated. The model originally accounts for complex interactions between a performance-related state variable and a second lumped variable, whose dynamics are intertwined. This model was compared to the most effective deterministic (though uncertain) one in the literature, namely the (nonlinear) Busso model. Results, concerning two professional soccer players over a half-season period, show that the proposed model outperforms the traditional approach in estimation and predictive accuracy, with significantly higher correlation coefficients and lower estimation and prediction errors across all players. These findings suggest that integrating two-state dynamics and fine-grained GPS metrics provides a more biologically realistic framework for load monitoring in team sports. The proposed model thus represents a powerful tool for training optimization and athlete readiness assessment, with potential applications in real-time decision support systems for coaching staff. By predicting the effects of training load on future performance, it might also contribute to injury risk reduction and the prevention of maladaptive responses to excessive workload. Full article

This paper applies a Convolutional Neural Network (CNN)-based vehicle security enhancement model, with a specific focus on the South African context. While conventional security systems, including immobilizers, alarms, steering locks, and GPS trackers, provide a baseline level of protection, they are increasingly being circumvented by technologically adept adversaries. These limitations have spurred the development of advanced security solutions leveraging artificial intelligence (AI), with a particular emphasis on computer vision and deep learning techniques. This paper presents a CNN-based Vehicle Security Enhancement Model (CNN-based VSEM) that integrates facial recognition with GSM and GPS technologies to provide a robust, real-time security solution in South Africa. This study contributes a novel integration of CNN-based authentication with GSM and GPS tracking in the South African context, validated on a functional prototype.The prototype, developed on a Raspberry Pi 4 platform, was validated through practical demonstrations and user evaluations. The system achieved an average recognition accuracy of 85.9%, with some identities reaching 100% classification accuracy. While misclassifications led to an estimated False Acceptance Rate (FAR) of ~5% and False Rejection Rate (FRR) of ~12%, the model consistently enabled secure authentication. Preliminary latency tests indicated a decision time of approximately 1.8 s from image capture to ignition authorization. These results, together with positive user feedback, confirm the model’s feasibility and reliability. This integrated approach presents a promising advancement in intelligent vehicle security for regions with high rates of vehicle theft. Future enhancements will explore the incorporation of 3D sensing, infrared imaging, and facial recognition capable of handling variations in facial appearance. Additionally, the model is designed to detect authorized users, identify suspicious behaviour in the vicinity of the vehicle, and provide an added layer of protection against unauthorized access. Full article

With the recent 5G communication technology deployment, Cellular Vehicle-to-Everything (C-V2X) significantly enhances road safety by enabling real-time exchange of critical traffic information among vehicles, pedestrians, infrastructure, and networks. However, further research is required to address real-time application latency and communication reliability challenges. This paper explores integrating cutting-edge C-V2X technology with environmental perception systems to enhance safety at intersections and crosswalks. We propose a multi-module architecture combining C-V2X with state-of-the-art perception technologies, GPS mapping methods, and the client–server module to develop a co-operative perception system for collision avoidance. The proposed system includes the following: (1) a hardware setup for C-V2X communication; (2) an advanced object detection module leveraging Deep Neural Networks (DNNs); (3) a client–server-based co-operative object detection framework to overcome computational limitations of edge computing devices; and (4) a module for mapping GPS coordinates of detected objects, enabling accurate and actionable GPS data for collision avoidance—even for detected objects not equipped with C-V2X devices. The proposed system was evaluated through real-time experiments at the GMMRC testing track at Kettering University. Results demonstrate that the proposed system enhances safety by broadcasting critical obstacle information with an average latency of 9.24 milliseconds, allowing for rapid situational awareness. Furthermore, the proposed system accurately provides GPS coordinates for detected obstacles, which is essential for effective collision avoidance. The technology integration in the proposed system offers high data rates, low latency, and reliable communication, which are key features that make it highly suitable for C-V2X-based applications. Full article

Background: The increasing deployment of unmanned aerial vehicles (UAVs) for logistics in smart cities presents pressing challenges related to identity spoofing, unauthorized payload transport, and airspace security. Existing drone defense systems (DDSs) struggle to verify both drone identity and payload authenticity in real time, while blockchain-assisted solutions are often hindered by high latency and limited scalability. Methods: To address these challenges, we propose iBANDA, a blockchain- and AI-assisted DDS framework. The system integrates a lightweight You Only Look Once 5 small (YOLOv5s) object detection model with a Snowball-based Proof-of-Stake consensus mechanism to enable dual-layer authentication of drones and their attached payloads. Authentication processes are coordinated through an edge-deployable decentralized application (DApp). Results: The experimental evaluation demonstrates that iBANDA achieves a mean average precision of 99.5%, recall of 100%, and an F1-score of 99.8% at an inference time of 0.021 s, validating its suitability for edge devices. Blockchain integration achieved an average network latency of 97.7 ms and an end-to-end transaction latency of 1.6 s, outperforming Goerli, Sepolia, and Polygon Mumbai testnets in scalability and throughput. Adversarial testing further confirmed resilience to Sybil attacks and GPS spoofing, maintaining a false acceptance rate below 2.5% and continuity above 96%. Conclusions: iBANDA demonstrates that combining AI-based visual detection with blockchain consensus provides a secure, low-latency, and scalable authentication mechanism for UAV-based logistics. Future work will explore large-scale deployment in heterogeneous UAV networks and formal verification of smart contracts to strengthen resilience in safety-critical environments. Full article

Unbalanced accelerations occurring during tram travel have a significant impact on passenger comfort and safety, as well as on the rate of wear and tear on infrastructure and rolling stock. Ideally, these dynamic forces should be monitored continuously in real-time; however, traditional systems require high-precision accelerometers and proprietary software—investments often beyond the reach of municipally funded tram operators. To this end, as part of the research project “Accelerometer Measurements in Rail Passenger Transport Vehicles”, pilot measurement campaigns were conducted in Poland on tram lines in Gdańsk, Toruń, Bydgoszcz, and Olsztyn. Off-the-shelf smartphones equipped with MEMS accelerometers and GPS modules, running the Physics Toolbox Sensor Suite Pro app, were used. Although the research employs widely known methods, this paper addresses part of the gap in affordable real-time monitoring by demonstrating that, in the future, equipment equipped solely with consumer-grade MEMS accelerometers can deliver sufficiently accurate data in applications where high precision is not critical. This paper presents an analysis of a subset of results from the Gdańsk tram network. Lateral (x) and vertical (z) accelerations were recorded at three fixed points inside two tram models (Pesa 128NG Jazz Duo and Düwag N8C), while longitudinal accelerations were deliberately omitted at this stage due to their strong dependence on driver behavior. Raw data were exported as CSV files, processed and analyzed in R version 4.2.2, and then mapped spatially using ArcGIS cartograms. Vehicle speed was calculated both via the haversine formula—accounting for Earth’s curvature—and via a Cartesian approximation. Over the ~7 km route, both methods yielded virtually identical results, validating the simpler approach for short distances. Acceleration histograms approximated Gaussian distributions, with most values between 0.05 and 0.15 m/s², and extreme values approaching 1 m/s². The results demonstrate that low-cost mobile devices, after future calibration against certified accelerometers, can provide sufficiently rich data for ride-comfort assessment and show promise for cost-effective condition monitoring of both track and rolling stock. Future work will focus on optimizing the app’s data collection pipeline, refining standard-based analysis algorithms, and validating smartphone measurements against benchmark sensors. Full article

The adoption of AI vision models in specialized industries is often hindered by the substantial requirement for extensive, manually annotated image datasets. Even when employing transfer learning, robust model development typically necessitates tens of thousands of such images, a process that is time-consuming, costly, and demands consistent expert annotation. This technical note introduces a semi-automated method to significantly reduce this annotation burden. The proposed approach utilizes two general-purpose vision-transformer-to-caption (GP-ViTC) models to generate descriptive text from images. These captions are then processed by a custom-developed semantic classifier (SC), which requires only minimal training to predict the correct image class. This GP-ViTC + SC system demonstrated exemplary classification rates in test cases and can subsequently be used to automatically annotate large image datasets. While the inference speed of the GP-ViTC models is not suited for real-time applications (approximately 10 s per image), this method substantially lessens the labor and expertise required for dataset creation, thereby facilitating the development of new, high-speed, custom AI vision models for niche applications. This work details the approach and its successful application, offering a cost-effective pathway for generating tailored image training sets. Full article

Real-time Synthetic Aperture Radar (SAR) imaging for small Unmanned Aerial Vehicles (UAVs) has become a significant research focus. However, limitations in Size, Weight, and Power (SwaP) restrict the imaging quality and timeliness of small UAV-borne SAR, limiting its practical application. This paper presents a non-iterative real-time Feature Sub-image Based Stripmap Phase Gradient Autofocus (FSI-SPGA) algorithm. The FSI-SPGA algorithm combines 2D Constant False Alarm Rate (CFAR) for coarse point selection and spatial decorrelation for refined point selection. This approach enables the accurate extraction of high-quality scattering points. Using these points, the algorithm constructs a feature sub-image containing comprehensive phase error information and performs a non-iterative phase error estimation based on this sub-image. To address the multifunctional, low-power, and real-time requirements of small UAV SAR, we designed a highly efficient hybrid architecture. This architecture integrates dataflow reconfigurability and dynamic partial reconfiguration and is based on an ARM + FPGA platform. It is specifically tailored to the computational characteristics of the FSI-SPGA algorithm. The proposed scheme was assessed using data from a 6 kg small SAR system equipped with centimeter-level INS/GPS. For SAR images of size 4096 × 12,288, the FSI-SPGA algorithm demonstrated a 6 times improvement in processing efficiency compared to traditional methods while maintaining the same level of precision. The high-efficiency reconfigurable ARM + FPGA architecture processed the algorithm in 6.02 s, achieving 12 times the processing speed and three times the energy efficiency of a single low-power ARM platform. These results confirm the effectiveness of the proposed solution for enabling high-quality real-time SAR imaging under stringent SwaP constraints. Full article

Rapid and accurate detection of primary waves (P-waves) using high-rate Global Navigation Satellite System (GNSS) data is essential for earthquake monitoring and tsunami early warning systems, where traditional seismic methods are less effective in noisy environments. We applied a wavelet-based method using a Mexican hat wavelet and dynamic threshold to thoroughly analyze the three-component displacement waveforms of the 2009 Padang, 2012 Simeulue, and 2018 Palu Indonesian earthquakes. Data from the Sumatran GPS Array and Indonesian Continuously Operating Reference Stations were analyzed to determine accurate displacements and P-waves. Validation with Indonesian geophysical agency seismic records indicated reliable detection of the horizontal component, with a time delay of less than 90 s, whereas the vertical component detection was inconsistent, owing to noise. Spectrogram analysis revealed P-wave energy in the pseudo-frequency range of 0.02–0.5 Hz and confirmed the method’s sensitivity to low-frequency signals. This approach illustrates the utility of GNSS data as a complement to seismic networks for the rapid characterization of earthquakes in complex tectonic regions. Improving the vertical component noise suppression might further help secure their utility in real-time early warning systems. Full article

Enhanced real-time onboard orbit determination for low-Earth-orbit satellites is essential for autonomous spacecraft operations. However, the accuracy of such systems is often limited by signal propagation delays between GPS satellites and the user spacecraft. These delays, primarily due to Earth’s rotation and ionospheric effects become particularly significant in high-dynamic LEO environments, leading to considerable errors in range and range rate measurements, and consequently, in position and velocity estimation. To mitigate these issues, this paper proposes a real-time orbit determination algorithm that applies Earth rotation correction and dual-frequency (L1 and L2) ionospheric compensation to raw GPS measurements. The enhanced orbit determination method is processed directly in the Earth-centered Earth-fixed frame, eliminating repeated coordinate transformations and improving integration with ground-based systems. The proposed method employs a reduced-dynamic orbit determination strategy to balance model fidelity and computational efficiency. A predictive correction model is also incorporated to compensate for GPS signal delays under dynamic motion, thereby enhancing positional accuracy. The overall algorithm is embedded within an extended Kalman filter framework, which assimilates the corrected GPS observations with a stochastic process noise model to account for dynamic modeling uncertainties. Simulation results using synthetic GPS measurements, including pseudoranges and pseudorange rates from a dual-frequency spaceborne receiver, demonstrate that the proposed method provides a significant improvement in orbit determination accuracy compared to conventional techniques that neglect signal propagation effects. These findings highlight the importance of performing orbit estimation directly in the Earth-centered, Earth-fixed reference frame, utilizing pseudoranges that are corrected for ionospheric errors, applying reduced-dynamic filtering methods, and compensating for signal delays. Together, these enhancements contribute to more reliable and precise satellite orbit determination for missions operating in low Earth orbit. Full article

In complex indoor and outdoor scenarios, traditional GPS-based ranging technology faces limitations in availability due to signal occlusion and user privacy issues. Wireless signal ranging technology based on 5G base stations has emerged as a potential alternative. However, existing methods are limited by low efficiency in constructing static signal databases, poor environmental adaptability, and high resource overhead, restricting their practical application. This paper proposes a 5G wireless signal ranging framework that integrates mobile edge computing (MEC) and crowdsourced intelligence to systematically address the aforementioned issues. This study designs a progressive solution by (1) building a crowdsourced data collection network, using mobile terminals equipped with GPS technology to automatically collect device signal features, replacing inefficient manual drive tests; (2) developing a progressive signal update algorithm that integrates real-time crowdsourced data and historical signals to optimize the signal fingerprint database in dynamic environments; (3) establishing an edge service architecture to offload signal matching and trajectory estimation tasks to MEC nodes, using lightweight computing engines to reduce the load on the core network. Experimental results demonstrate a mean positioning error of 5 m, with 95% of devices achieving errors within 10 m, as well as building and floor prediction error rates of 0.5% and 1%, respectively. The proposed framework outperforms traditional static methods by 3× in ranging accuracy while maintaining computational efficiency, achieving significant improvements in environmental adaptability and service scalability. Full article

High-precision clock offset products directly affect the performance and reliability of precise point positioning (PPP) applications. Currently, real-time clock offset products offered by institutions such as the Centre national d’études spatiales (CNES) rely on ultra-rapid predicted orbits. However, these orbits have limited accuracy and exhibit jumps during updates, constraining the accuracy of real-time clock estimation. To address this issue, we propose an undifferenced ambiguity resolution (UD AR) technique for clock offset estimation based on epoch-wise updated orbits. Clock estimation experiments were performed using both predicted and epoch-wise updated orbits, with square root information filtering (SRIF) applied in three schemes: double-differenced (DD), UD, and float solutions. Compared with predicted orbits, epoch-wise updated orbits provided smoother sequences with higher accuracy, significantly improving clock offset estimation accuracy in all schemes. Moreover, the UD AR solution significantly enhanced clock offset estimation accuracy, and the high-precision epoch-wise updated orbit products increased the narrow-lane fixing rate of the UD solutions. The clock accuracies of BDS-3, Galileo, and GPS reached 0.032 ns, 0.023 ns, and 0.026 ns, respectively, representing improvements of 36%, 34%, and 41% compared with the float solutions and 41%, 30%, 26% compared with the UD solution based on 1 h predicted orbits. Finally, the positioning performance of the proposed method was validated via PPP using 25 stations, showing improvements of 50%, 48%, and 41% in the north, east, and up directions compared with CNES products. Therefore, by combining epoch-wise updated orbit products with the UD AR to improve clock accuracy, this method provides a new approach to generating high-precision clock products, significantly contributing to enhancing PPP services. Full article

This paper presents a complete system for autonomous navigation in GPS-denied environments using a minimal sensor suite that operates onboard a robotic vehicle. Our system utilizes a single camera and, given a target destination without prior knowledge of the environment, replans in real time to generate a collision-free trajectory that avoids static and dynamic obstacles. To achieve this, we introduce, for the first time, a local Euclidean Signed Distance Field (ESDF) map with variable size and resolution, which scales as a function of the vehicle’s velocity. The map is updated at a high rate, requiring minimal computational power. Additionally, a short-term vicinity-based memory is maintained for previously observed areas to facilitate smooth trajectory generation, addressing the limited field-of-view provided by the RGB-D camera. System validation is carried out by deploying our algorithm on a differential drive vehicle in both simulation and real-world experiments involving static and dynamic obstacles. We benchmark our robotic system against state-of-the-art autonomous navigation frameworks, successfully navigating to designated target locations while avoiding obstacles in both static and dynamic scenarios, all without introducing additional computational overhead. Our approach consistently achieves the target goals even in complex settings where current state-of-the-art methods may fall short. Full article

Soil environmental monitoring is crucial for ensuring the sustainability and productivity of agriculture. This study aims to develop a wireless soil monitoring system that utilizes Narrowband Internet of Things (NB-IoT), solar energy, and Global Positioning System (GPS) technologies to address the issues of high labor demand, high costs, and delayed feedback in traditional soil monitoring methods. This system can collect soil temperature, humidity, and meteorological data in real time, transmit them to a cloud platform for analysis and visualization, and predict future soil data. It employs multiple learning algorithms to build models and uses the Tree-structured Parzen Estimator (TPE) algorithm for hyperparameter optimization. Field stability experiments were conducted on the system, and the performance of the soil moisture prediction model was evaluated. During the 84-day stability experiment, the system operated stably for 80 days, with a data collection success rate of 95.87%. In the performance evaluation of the soil moisture model, the GBDT model achieved a coefficient of determination (R²) of 0.9838 on the validation set and a root-mean-square error (RMSE) of 0.0013, with an RMSE of 0.0013 on the test set as well. The experimental results demonstrate that the system is stable and reliable, featuring low power consumption, wide coverage, and high accuracy. It can effectively predict soil moisture, providing timely and accurate support for irrigation and farming decisions. Full article

In GNSS, a double-difference carrier phase observation model is typically employed, and high-accuracy position coordinates can be obtained by resolving the integer ambiguity within the model through algorithmic processing. To address the challenge of a double-difference integer ambiguity resolution, an enhanced Simulated Annealing Ant Lion Optimizer (SAALO) is proposed. This algorithm is designed to efficiently resolve integer ambiguities. First, the performance of the SAALO algorithm was evaluated by comparing its solving speed and success rate with those of the Ant Lion Optimization Algorithm (ALO), the LAMBDA algorithm and the MLAMBDA algorithm. The results demonstrate that the SAALO algorithm achieved a solution success rate that was 0.0496 s and 0.01 s faster than the LAMBDA and M-LAMBDA algorithms, respectively. Second, to further validate the high-dimensional ambiguity resolution capability of the SAALO algorithm, integer ambiguity resolution tests were conducted in both 6-dimensional and 12-dimensional scenarios. The results indicate that the SAALO algorithm achieves a success rate exceeding 98%, confirming its robust performance in high-dimensional problem-solving. Finally, the practical application of the SAALO algorithm was tested in short- and medium-baseline scenarios using a single-frequency GPS system. With a baseline length of 42.7 km, the SAALO algorithm exhibited a slightly faster average solution time compared to the LAMBDA algorithm, while its solution success rate was 5.2% higher. These findings underscore the effectiveness and reliability of the SAALO algorithm in real-world GNSS applications. Full article

In response to the current key issues in the field of smart irrigation for farmland, such as the lack of data sources and insufficient integration, a low degree of automation in drive execution and control, and over-reliance on cloud platforms for analyzing and calculating decision making processes, we have developed nodes and gateways for smart irrigation. These developments are based on the EC-IOT edge computing IoT architecture and long range radio (LoRa) communication technology, utilizing STM32 MCU, WH-101-L low-power LoRa modules, 4G modules, high-precision GPS, and other devices. An edge computing analysis and decision model for smart irrigation in farmland has been established by collecting the soil moisture and real-time meteorological information in farmland in a distributed manner, as well as integrating crop growth period and soil properties of field plots. Additionally, a mobile mini-program has been developed using WeChat Developer Tools that interacts with the cloud via the message queuing telemetry transport (MQTT) protocol to realize data visualization on the mobile and web sides and remote precise irrigation control of solenoid valves. The results of the system wireless communication tests indicate that the LoRa-based sensor network has stable data transmission with a maximum communication distance of up to 4 km. At lower communication rates, the signal-to-noise ratio (SNR) and received signal strength indication (RSSI) values measured at long distances are relatively higher, indicating better communication signal quality, but they take longer to transmit. It takes 6 s to transmit 100 bytes at the lowest rate of 0.268 kbps to a distance of 4 km, whereas, at 10.937 kbps, it only takes 0.9 s. The results of field irrigation trials during the wheat grain filling stage have demonstrated that the irrigation amount determined based on the irrigation algorithm can maintain the soil moisture content after irrigation within the suitable range for wheat growth and above 90% of the upper limit of the suitable range, thereby achieving a satisfactory irrigation effect. Notably, the water content in the 40 cm soil layer has the strongest correlation with changes in crop evapotranspiration, and the highest temperature is the most critical factor influencing the water requirements of wheat during the grain-filling period in the test area. Full article