Search Results (2,298)

The Meteorological Optical Range (MOR) is a measurement of atmospheric visibility. Visibility impairment has been linked to increased aerosol levels in the air. This study conducted statistical analyses using meteorological, air pollutant concentration, and MOR data collected in Mexico City from August 2014 to December 2015 to determine the factors contributing to haze occurrence (periods when MOR < 10,000 m), defined using a light scatter sensor (PWS100). The outcomes revealed seasonal patterns in PM_2.5 and relative humidity (RH) for haze occurrence along the year. PM_2.5 levels during hazy periods in the dry season were higher compared to the wet season, aligning with periods of poor air quality (PM_2.5 > 45 μg/m³). Pollutant-to-CO ratios suggested that secondary aerosols’ production, led by SO₂ conversion to sulfate particles, mainly impacts haze occurrence during the dry season. Meanwhile, during the rainy season, the PWS100 registered haze events even with PM_2.5 values close to 15 μg/m³ (considered good air quality). The broadened distribution of extinction efficiency during the wet period and its correlation with RH suggest that aerosol water vapor uptake significantly impacts visibility during this season. Therefore, attributing poor visibility strictly to poor air quality may not be appropriate for all times and locations. Full article

Fall risk assessment is becoming an important concern, with the realization that falls, and more importantly fainting occurrences, in most cases require immediate medical attention and can pose huge health risks, as well as financial and social burdens. The development of an accurate inertial sensor-based fall risk assessment tool combined with machine learning algorithms could significantly advance healthcare. This research aims to investigate the development of a machine learning approach for falling and fainting detection, using wearable sensors with an emphasis on forward falls. In the current paper we address the problem of the lack of inertial time-series data to differentiate the forward fall event from normal activities, which are difficult to obtain from real subjects. To solve this problem, we proposed a forward dynamics method to generate necessary training data using the OpenSim software, version 4.5. To develop a model as close to the real world as possible, anthropometric data taken from the literature was used. The raw X and Y axes acceleration data was generated using OpenSim software, and ML fall prediction methods were trained. The machine learning (ML) accuracy was validated by testing with data acquired from six unique volunteers, considering the forward fall type. Full article

Knock detection is critical for maintaining engine performance and preventing damage in spark-ignition engines. This study explores the use of ion current and knock indicators derived from a vibration sensor ($K{I}_v$) and ion current ($K{I}_i$) to improve knock detection accuracy. Traditional threshold-based methods rely on $K{I}_v$, but they are susceptible to mechanical noise and cylinder variations. In this work, we applied both logistic regression and neural networks, including fully connected (FCNN) and convolutional neural networks (CNN), to classify knock events based on these indicators. The CNN models used ion current as the primary input, with an extended version incorporating both $K{I}_v$ and $K{I}_i$ into the fully connected layers. The models were evaluated using area under the curve (AUC) as the primary performance metric. The results show that the CNN model with additional inputs outperformed the other models, achieving a better and more consistent performance across cylinders. The dual-input logistic regression and CNN models demonstrated reduced cylinder-to-cylinder variation in classification performance, providing a more consistent knock detection accuracy across all cylinders. These findings suggest that combining ion current and knock indicators enhances knock detection reliability, offering a robust solution for real-time applications in engine control systems. Full article

The monitoring of pre-weaned calf behavior is crucial for ensuring health, welfare, and optimal growth. This study aimed to develop and validate a machine learning-based technique for the simultaneous monitoring of multiple behaviors in pre-weaned beef calves within a cow–calf contact (CCC) system using collar-mounted sensors integrating accelerometers and gyroscopes. Three complementary models were developed to classify feeding-related behaviors (natural suckling, feeding, rumination, and others), postural states (lying and standing), and coughing events. Sensor data, including tri-axial acceleration and tri-axial angular velocity, along with video recordings, were collected from 78 beef calves across two farms. The LightGBM algorithm was employed for behavior classification, and model performance was evaluated using a confusion matrix, the area under the receiver operating characteristic curve (AUC-ROC), and Pearson’s correlation coefficient (r). Model 1 achieved a high performance in recognizing natural suckling (accuracy: 99.10%; F1 score: 96.88%; AUC-ROC: 0.999; r: 0.997), rumination (accuracy: 97.36%; F1 score: 95.07%; AUC-ROC: 0.995; r: 0.990), and feeding (accuracy: 95.76%; F1 score: 91.89%; AUC-ROC: 0.990; r: 0.987). Model 2 exhibited an excellent classification of lying (accuracy: 97.98%; F1 score: 98.45%; AUC-ROC: 0.989; r: 0.982) and standing (accuracy: 97.98%; F1 score: 97.11%; AUC-ROC: 0.989; r: 0.983). Model 3 achieved a reasonable performance in recognizing coughing events (accuracy: 88.88%; F1 score: 78.61%; AUC-ROC: 0.942; r: 0.969). This study demonstrates the potential of machine learning and collar-mounted sensors for monitoring multiple behaviors in calves, providing a valuable tool for optimizing production management and early disease detection in the CCC system Full article

Wireless multimedia sensor networks (WMSNs) have stringent constraints and need to deliver data packets to the sink node within a predefined limited time. However, due to congestion, buffer overflow occurs and leads to the degradation of the quality-of-service (QoS) parameters of event information. Congestion in WMSNs results in exhausted node energy, degraded network performance, increased transmission delays, and high packet loss. Congestion occurs when the volume of data trying to pass through a network exceeds its capacity. First, the BOCC protocol uses two congestion indicators to detect congestion. One is the buffer occupancy and other is the buffer occupancy change rate. Second, a rate controller is proposed to protect high-priority I-frame packets during congestion. BOCC sends a congestion notification to the source node to reduce congestion in the network. The source node adjusts its data transmission rate after receiving the congestion notification message. In the proposed algorithm, the rate adjustment is made by discarding low-priority P-frame packets from the source nodes. Third, to further improve the performance of the BOCC protocol, the problem is formulated as a constrained optimization problem and solved using convex optimization and sequential quadratic programming (SQP) methods. Experimental results based on Raspberry Pi sensor nodes show that the BOCC protocol achieves up to 16% reduction in packet loss and up to 23% reduction in average end-to-end delay compared to state-of-the-art congestion control algorithms. Full article

Concomitant improvements in wireless communication and sensor technologies have increased capabilities of wearable biosensors. These improvements have not transferred to wireless prosthesis/orthosis controllers, in part due to strict latency and power consumption requirements. We used a Bluetooth Low Energy 5.3 (BLE) network to study the influence of the connection interval (10–100 ms) and event length (2500–7500 μs), ranges appropriate for real-time myoelectric prosthesis/orthosis control on the maximum network size, power consumption, and latency. The number of connections increased from 4 to 12 as the connection interval increased from 10 to 50 ms (event length of 2500 μs). For connection intervals ≤50 ms, the number of connections reduced by ≥50% with the increasing event length. At a connection interval of 100 ms, little change was observed in the number of connections vs. event length. Across event lengths, increasing the connection interval from 10 to 100 ms decreased the average power consumed by approximately 16%. Latency measurements showed that an average of one connection interval (maximum of just over two) elapses between the application of the signal at the peripheral node ADC input and its detection on the central node. Overall, reducing the latency using shorter connection intervals reduces the maximum number of connections and increases power consumption. Full article

Recently, solar and wind power generation have gained attention as pathways to achieving carbon neutrality, and Renewable Energy Resource Management System (RERMS) technology has been developed to monitor and control small-scale, distributed renewable energy resources. In this work, we present an Event-Triggered Transmission (ETT) algorithm for RERMS, which transmits sensor measurements to the base station only when necessary. The ETT algorithm helps prevent congestion in the communication channel between RERMS and the base station, avoiding time delays or packet loss caused by the excessive transmission of sensor measurements. We design a hybrid state estimation algorithm that combines Kalman and Finite Impulse Response (FIR) filters to enhance the estimation performance, and we propose a new ETT algorithm based on this design. We evaluate the performance of the proposed algorithm through experiments that transmit actual sensor measurements from a photovoltaic power generation system to the base station, demonstrating that it outperforms existing algorithms. Full article

Monitoring soil water content (SWC) can improve the effectiveness of early warning systems (EWSs) designed to mitigate rainfall-induced shallow landslide risk. In extensive areas, like along linear infrastructures, the adoption of cost-effective sensors is critical for the EWS implementation. The present study aims to evaluate the reliability of different low-cost SWC sensors (frequency domain reflectometry and capacitance-based) in capturing soil moisture conditions critical for EWS, without performing soil-specific calibration. The reliability of the low-cost sensors is assessed through a comparative analysis of their measurements against those from high-cost and well-established sensors (time domain reflectometry) over a two-year period in a shallow landslide-prone area of Oltrepò Pavese, Italy. Although no landslides are observed during the monitoring period, meteorological conditions are reconstructed and statistical analysis of sensor’s responses to different rainfall events is conducted. Results indicate that, despite differences in absolute readings, low-cost sensors effectively capture relative SWC variations and demonstrate sensitivity to rainfall events across both cold and warm periods. The presented low-cost sensors can serve as reliable indicators of soil infiltration and saturation levels, highlighting their potential for real-time monitoring within extensive networks for EWS. Full article

Gyros/star sensor integration provides a potential method to obtain high-accuracy spatial orientation for turntable structures. However, it is subjected to the problem of accuracy loss when the measurement noises become non-Gaussian due to the complex spatial environment. This paper presents an event-driven maximum correntropy filter based on Cauchy kernel to handle the above problem. In this method, a direct installation mode of gyros/star sensor integration is established and the associated mathematical model is derived to improve the turntable’s control stability. Based on this, a Cauchy kernel-based maximum correntropy filter is developed to curb the influence of non-Gaussian measurement noise for enhancing the gyros/star sensor integration’s robustness. Subsequently, an event-driven mechanism is constructed based on the filter’s innovation information for further reducing the unnecessary computational cost to optimize the real-time performance. The effectiveness of the proposed method has been validated by simulations of the gyros/star sensor integration for spatial orientation. This shows that the proposed filtering methodology not only has strong robustness to deal with the influence of non-Gaussian measurement noise but can also achieve superior real-time spatial applications with a small computational cost, leading to enhanced performance for the turntable’s spatial orientation using gyros/star sensor integration. Full article

This study examines the evolution of wave shapes as they propagate over a beach of varying morphology, information essential for understanding coastal dynamics and supporting coastal management. Our objective was to analyze the relationship between wave shape parameters and the local slope of the beach. To achieve this, we used data from pressure sensors and topographic measurements to evaluate the shape of waves on a cross-shore profile of a low-tide terrace beach. The analysis of wave conditions revealed a pronounced modulation of the tidal signal, which is augmented during storm events. Our findings demonstrate that the asymmetry and skewness parameters are more pronounced in the reflective zone of the beach. Considering these results, it can be concluded that the non-linearity of waves is significantly affected by the beach slope. The parameterization method employed in this study effectively incorporates this factor, offering improved accuracy in comparison to the existing approaches. Full article

Earthquakes are one of the most life-threatening natural phenomena, and their prediction is of constant concern among scientists. The study proposes that abrupt weather parameter value fluctuations may influence the occurrence of shallow seismic events by focusing on developing an innovative concept that combines historical meteorological and seismic data collection to predict potential earthquakes. A machine learning (ML) model utilizing the ML.NET framework was designed and implemented. An analysis was undertaken to identify which modeling approach, value prediction, or data classification performs better in forecasting seismic events. The model was trained on a dataset of 8766 records corresponding to the period from 1 January 2001 to 5 October 2024. The achieved accuracy of the model was 95.65% for earthquake prediction based on weather conditions in the Vrancea region, Romania. The authors proposed a unique alerting algorithm and conducted a case study that evaluates multiple predictive models, varying parameters, and methods to identify the most effective model for seismic event prediction in specific meteorological conditions. The findings demonstrate the potential of combining Internet of Things (IoT)-based environmental monitoring with AI to improve earthquake prediction accuracy and preparedness. An IoT-based application was developed using C# with ASP.NET framework to enhance earthquake prediction and public warning capabilities, leveraging Azure cloud infrastructure. The authors also created a hardware prototype for real-time earthquake alerting, integrating the M5Stack platform with ESP32 and MPU-6050 sensors for validation. The testing phase and results describe the proposed methodology and various scenarios. Full article

Standard single-mode telecommunication optical fiber is still one of the most popular in distributed acoustic sensing. Understanding the acoustic, mechanical and optical features of various fibers available currently can lead to a better optimization of distributed acoustic sensors, cost reduction and adaptation for specific needs. In this paper, a study of the performances of seven fibers with different coatings and production methods in a distributed acoustic sensor setup is presented. The main results include the amplitude–frequency characteristic for each of the investigated fibers in the range of acoustic frequencies from 100 to 7000 Hz. A single-mode fiber fabricated using the modified chemical vapor deposition technique together with a polyimide coating has shown the best sensitivity to acoustic events in the investigated range of frequencies. All of this allows us to both compare the studied specialty fibers with the standard single-mode fiber and choose the most suitable fiber for a specific application, providing an enhancement for the performance of distributed acoustic sensors and better adaptation for the newly aroused potential applications. Full article

The advancement of AI technologies and the increasing processing power of computers have made high-speed autonomous racing possible. Different leagues, such as the Abu Dhabi Autonomous Racing League (A2RL) and the Indy Autonomous Challenge (IAC), are organizing races in simulation and with real race cars. In this paper we will describe our experience with the inaugural A2RL event and a SIM race organized by IAC. With respect to A2RL, we will give an overview of the physical parameters of the race car, the sensors we worked with, and our software solution including how we created trajectories for different test scenarios. Full article

Driven by the rapid development of modern industrial applications, multi-agent systems (MASs), integrating computational and physical resources, have become increasingly important in recent years. However, the performance of MASs can be easily compromised by malicious false data injection attacks (FDIAs) due to the inherent vulnerability of the cyber layer. This work focuses on an event-triggered framework for secure reconstruction and consensus control in MASs subject to both sensor and actuator attacks. First, we introduce a class of Takagi–Sugeno fuzzy multi-agent systems that relax the traditional Lipschitz condition and incorporate realistic system dynamics by considering parameter variations governed by Markovian jump principles. Second, an adaptive fuzzy estimator is developed for the simultaneous reconstruction of states and attacks in MASs. The derived estimates are utilized to design an attack-resilient consensus control strategy that compensates for the effects of FDIAs on the closed-loop consensus error dynamics. Meanwhile, the sufficient conditions for the convergence of both estimation and consensus errors are presented and rigorously proved. Finally, evaluation results on an experimental platform through multiple truck-trailer systems are provided to demonstrate the effectiveness and performance of the proposed approach. Full article

Wireless sensor networks are composed of many nodes distributed in a region of interest to monitor different environments and physical variables. In many cases, access to nodes is not easy or feasible. As such, the system lifetime is a primary design parameter to consider in the design of these networks. In this regard, for some applications, it is preferable to extend the system lifetime by actively reducing the number of packet transmissions and, thus, the number of reports. The system administrator can be aware of such reporting reduction to distinguish this final phase from a malfunction of the system or even an attack. Given this, we explore different mathematical functions that drastically reduce the number of packet transmissions when the residual energy in the system is low but still allow for an adequate number of transmissions. Indeed, in previous works, where the negative exponential distribution is used, the system reaches the point of zero transmissions extremely fast. Hence, we propose different dampening functions with different decreasing rates that present oscillations to allow for packet transmissions even at the end of the system lifetime. We compare the system performance under these mathematical functions, which, to the best of our knowledge, have never been used before, to find the most adequate transmission scheme for packet transmissions and system lifetime. We develop an analytical model based on a discrete-time Markov chain to show that a moderately decreasing function provides the best results. We also develop a discrete event simulator to validate the analytical results. Full article