Search Results (48)

This paper proposes a composite control method that integrates an active fault-tolerant predictive control scheme and an event-triggered mechanism for networked multi-agent systems. The approach considers random communication constraints in the forward and feedback channels as well as actuator faults. At each time instant, the event trigger determines whether to send system outputs based on the current system state. A Kalman filter is then utilized to estimate both the system state and potential faults by incorporating system output information transmitted through the feedback channel. Concurrently, iterative predictions are performed according to the established system model. Furthermore, a predictive sequence of control inputs is generated through the designed control protocol. Leveraging timestamping technology, the system precisely applies the appropriate control commands to the actuator at designated moments. As a result, the proposed control method compensates for both random communication constraints and actuator faults while effectively reducing data transmission over the communication network. Finally, the proposed method is validated through numerical simulations. Full article

When analyzing real-world event data, it is often the case that bin-count processes are observed instead of precise event time-stamps along a continuous timeline, owing to practical limitations in measurement accuracy. In this work, we propose a modeling framework for aggregated event data generated by multivariate Hawkes processes. The introduced model, termed the coarse-grained Hawkes process, effectively captures the second-order statistical characteristics of the bin-count representation of the Hawkes process, particularly when the bin size is large relative to the typical support of the excitation kernel. Building upon this model, we develop a method for inferring the underlying Hawkes process from bin-count observations, and demonstrate through simulation studies that the proposed approach performs comparably to, or even surpasses, existing techniques, while maintaining computational efficiency in parameter estimation. Full article

In distributed energy monitoring systems, precise time synchronization is paramount for efficient data acquisition and energy management. With the high penetration of new energy sources, the distributed energy monitoring system will evolve into a complex heterogeneous network utilizing various short-range wireless communication technologies. Therefore, wireless communication-based time synchronization technologies will be widely applied, and it is important for the distributed energy monitoring system to be aware of the current time synchronization errors as this is crucial for accurate data processing. Our findings propose the first real-time convergence estimation method using an adaptive real-time convergence estimation (ARCE) algorithm, which can accurately estimate the current network time synchronization error and convergence status in real time. This ARCE algorithm is based on synchronization error and distribution of both synchronous and asynchronous time synchronization algorithm. The actual distributions of synchronization error are analyzed and evaluated using a significant amount of experimental results. According to the experimental results and simulations, ARCE can effectively detect the convergence state for all the comparisons. Therefore, ARCE can be used to evaluate the reliability of timestamps in data acquisition. Moreover, it can be utilized to enhance the adaptive capability of time synchronization algorithms, which could be robust and scalable for a large-scale and randomly deployed wireless networks. Full article

Ultra-Wide Band (UWB) real-time localization systems usually require either precise and robust synchronization of the anchor transceivers or sufficiently stable clocks for methods referred to as “synchronization-free”. Typically, reasonably priced crystals or TCXOs are utilized as frequency references for the UWB transceivers. Clock characterization and simulation are necessary to evaluate and tune the synchronization or positioning algorithms without the need of hardware-pulling of the UWB reference oscillators. In this paper, the method of transceiver clock stability measurement is presented, and several modules with various clock sources are evaluated. As a reference, a UWB module with a clock derived from a Caesium standard is utilized. A method for simulating typical timestamp-series errors attributed to UWB transceiver clocks is provided as well. Full article

The latest developments in Advanced Driver Assistance Systems (ADAS) have greatly enhanced the comfort and safety of drivers. These technologies can identify driver abnormalities like fatigue, inattention, and impairment, which are essential for averting collisions. One of the important aspects of this technology is automated traffic accident detection and prediction, which may help in saving precious human lives. This study aims to explore critical features related to traffic accident detection and prevention. A public US traffic accident dataset was used for the aforementioned task, where various machine learning (ML) models were applied to predict traffic accidents. These ML models included Random Forest, AdaBoost, KNN, and SVM. The models were compared for their accuracies, where Random Forest was found to be the best-performing model, providing the most accurate and reliable classification of accident-related data. Owing to the black box nature of ML models, this best-fit ML model was executed with explainable AI (XAI) methods such as LIME and permutation importance to understand its decision-making for the given classification task. The unique aspect of this study is the introduction of explainable artificial intelligence which enables us to have human-interpretable awareness of how ML models operate. It provides information about the inner workings of the model and directs the improvement of feature engineering for traffic accident detection, which is more accurate and dependable. The analysis identified critical features, including sources, descriptions of weather conditions, time of day (weather timestamp, start time, end time), distance, crossing, and traffic signals, as significant predictors of the probability of an accident occurring. Future ADAS technology development is anticipated to be greatly impacted by the study’s conclusions. A model can be adjusted for different driving scenarios by identifying the most important features and comprehending their dynamics to make sure that ADAS systems are precise, reliable, and suitable for real-world circumstances. Full article

We present a system that integrates diverse technologies to achieve real-time, distributed audio surveillance. The system employs a network of microphones mounted on ESP32 platforms, which transmit compressed audio chunks via an MQTT protocol to Raspberry Pi5 devices for acoustic classification. These devices host an audio transformer model trained on the AudioSet dataset, enabling the real-time classification and timestamping of audio events with high accuracy. The output of the transformer is kept in a database of events and is subsequently converted into JSON format. The latter is further parsed into a graph structure that encapsulates the annotated soundscape, providing a rich and dynamic representation of audio environments. These graphs are subsequently traversed and analyzed using dedicated Python code and large language models (LLMs), enabling the system to answer complex queries about the nature, relationships, and context of detected audio events. We introduce a novel graph parsing method that achieves low false-alarm rates. In the task of analyzing the audio from a 1 h and 40 min long movie featuring hazardous driving practices, our approach achieved an accuracy of 0.882, precision of 0.8, recall of 1.0, and an F1 score of 0.89. By combining the robustness of distributed sensing and the precision of transformer-based audio classification, our approach that treats audio as text paves the way for advanced applications in acoustic surveillance, environmental monitoring, and beyond. Full article

With the global aging population on the rise, the health and safety of elderly individuals living alone have become increasingly critical. This study introduces a novel AIoT-based habit mining algorithm designed to enhance activity monitoring in smart home environments. The proposed method integrates a one-dimensional U-Net neural network for accurate behavioral classification and an FP-Growth-based temporal association rule analysis for uncovering meaningful living patterns. By leveraging environmental sensor data, the algorithm first classifies daily activities and then uses timestamps to detect time-sensitive dependencies in behavior sequences, identifying the long-term habits of the elderly. Experimental validation on CASAS datasets (ARUBA and MILAN) demonstrates superior performance, achieving a precision of 84.77%. Compared to traditional techniques, this approach excels in behavior recognition and habit mining, offering a precise and adaptive framework for AIoT-driven smart home safety and health monitoring systems. The results highlight its potential to improve the quality of life and safety for elderly individuals living alone. Full article

Accurately counting chickens in densely packed cages is a major challenge in large-scale poultry farms. Traditional manual counting methods are labor-intensive, costly, and prone to errors due to worker fatigue. Furthermore, current deep learning models often struggle with accuracy in caged environments because they are not well-equipped to handle occlusions. In response, we propose the You Only Look Once-Chicken Counting Algorithm (YOLO-CCA). YOLO-CCA improves the YOLOv8-small model by integrating the CoordAttention mechanism and the Reversible Column Networks backbone. This enhancement improved the YOLOv8-small model’s F1 score to 96.7% (+3%) and average precision_50:95 to 80.6% (+2.8%). Additionally, we developed a threshold-based continuous frame inspection method that records the maximum number of chickens per cage with corresponding timestamps. The data are stored in a cloud database for reliable tracking during robotic inspections. The experiments were conducted in an actual poultry farming environment, involving 80 cages with a total of 493 chickens, and showed that YOLO-CCA raised the chicken recognition rate to 90.9% (+13.2%). When deployed on a Jetson AGX Orin industrial computer using TensorRT, the detection speed increased to 90.9 FPS (+57.6 FPS), although the recognition rate slightly decreased to 93.2% (−2.9%). In summary, YOLO-CCA reduces labor costs, improves counting efficiency, and supports intelligent poultry farming transformation. Full article

In datacenter networks, it is necessary to determine whether the path is congested according to the one-way delay of packets. The accurate measurement of one-way delay depends on the high-precision time synchronization of the source device and destination device. We have proposed a time synchronization method based on timestamp mapping, combined with in-band network telemetry technology to obtain the packet send timestamp and receive timestamp on devices. The results show that the maximum synchronization error is 19 ns, and the standard deviation is 7.8 ns with a 100 ms time synchronization period and offset adjustment strategy. The proposed time synchronization method achieves outstanding synchronization accuracy and stability. Full article

The rapid growth of Earth observation technologies has resulted in over 2000 operational remote sensing satellites, collectively generating an exabyte-scale volume of data. However, despite the availability of large data-sharing platforms, global remote sensing imagery still faces challenges in seamless access, precise querying, and efficient retrieval. To address these limitations, this study introduces the concept of the “Digital Imagery Object” (DIO) and develops a unified identification framework for satellite remote sensing imagery. The proposed approach establishes a structured identification and parsing system based on core metadata, including data acquisition platforms and imaging timestamps. This enhances the consistency and standardization of multisource imagery encoding, enabling unified identification and interpretation under a common set of rules. The system’s feasibility and effectiveness were demonstrated through the integration and management of diverse global datasets, highlighting its ability to streamline multisource data workflows. By supporting standardized management and one-click parsing, this framework facilitates efficient imagery sharing and lays the foundation for its use as a tradable digital resource on the internet. The study offers a practical solution for addressing current challenges in remote sensing imagery management, paving the way for improved accessibility and interoperability of Earth observation data. Full article

Time series data are prevalent in diverse fields such as manufacturing and sensor-based human activity recognition. In real-world applications, these data are often collected with variable sample lengths, which can pose challenges for classification models that typically require fixed-length inputs. Existing approaches either employ models designed to handle variable input sizes or standardize sample lengths before applying models; however, we contend that these approaches may compromise data integrity and ultimately reduce model performance. To address this issue, we propose Time series Into Pixels (TIP), an intuitive yet strong method that maps each time series data point into a pixel in 2D representation, where the vertical axis represents time steps and the horizontal axis captures the value at each timestamp. To evaluate our representation without relying on a powerful vision model as a backbone, we employ a straightforward LeNet-like 2D CNN model. Through extensive evaluations against 10 baseline models across 11 real-world benchmarks, TIP achieves 2–5% higher accuracy and 10–25% higher macro average precision. We also demonstrate that TIP performs comparably on complex multivariate data, with ablation studies underscoring the potential hazard of length normalization techniques in variable-length scenarios. We believe this method provides a significant advancement for handling variable-length time series data in real-world applications. The code is publicly available. Full article

The classification of urban functional zones is crucial for improving land use efficiency and promoting balanced development across urban areas. Existing methods for classifying urban functional zones using mobile signaling data face challenges primarily due to the limitations of single data sources, insufficient utilization of multidimensional data, and inherent inaccuracies in mobile signaling data. To address these issues, this study proposes an innovative classification method that employs advanced multimodal data fusion techniques to enhance the accuracy and reliability of functional zone classification. Mobile signaling data are mapped into image data using timestamp and geographic location information and combined with point of interest (POI) data to construct a comprehensive multimodal dataset. Deep learning techniques are then applied to fuse the multimodal data features, enabling precise and reliable classification of functional zones. The experimental results demonstrate that this method achieves an accuracy of 95.128% in classifying urban functional zones, significantly outperforming methods that use single-modal data. Full article

Achieving precise synchronization is critical for multi-camera systems in various applications. Traditional methods rely on hardware-triggered synchronization, necessitating significant manual effort to connect and adjust synchronization cables, especially with multiple cameras involved. This not only increases labor costs but also restricts scene layout and incurs high setup expenses. To address these challenges, we propose a novel subframe synchronization technique for multi-camera systems that operates without the need for additional hardware triggers. Our approach leverages a time-calibrated video featuring specific markers and a uniformly moving ball to accurately extract the temporal relationship between local and global time systems across cameras. This allows for the calculation of new timestamps and precise frame-level alignment. By employing interpolation algorithms, we further refine synchronization to the subframe level. Experimental results validate the robustness and high temporal precision of our method, demonstrating its adaptability and potential for use in demanding multi-camera setups. Full article

To address the issue that sparse point cloud maps constructed by SLAM cannot provide detailed information about measured objects, and image sequence-based measurement methods have problems with large data volume and cumulative errors, this paper proposes a scene measurement method that integrates image sequences with an improved LiDAR SLAM. By introducing plane features, the positioning accuracy of LiDAR SLAM is enhanced, and real-time odometry poses are generated. Simultaneously, the system captures image sequences of the measured object using synchronized cameras, and NeRF is used for 3D reconstruction. Time synchronization and data registration between the LiDAR and camera data frames with identical timestamps are achieved. Finally, the least squares method and ICP algorithm are employed to compute the scale factor s and transformation matrices R and t between different point clouds from LiDAR and NeRF reconstruction. Then, the precise measurement of the objects could be implemented. Experimental results demonstrate that this method significantly improves measurement accuracy, with an average error within 10 mm and 1°, providing a robust and reliable solution for scene measurement. Full article

This paper explores a periodic watermark-based replay attack detection method for Unmanned Marine Vehicles modeled in the framework of the Takagi–Sugeno fuzzy system. The precise detection of replay attacks is crucial for ensuring the security of Unmanned Marine Vehicles; however, traditional timestamp-based or encoded measurement-dependent detection approaches often sacrifice system performance to achieve higher detection rates. To reduce the potential performance degradation, a periodic watermark-based detection scheme is developed, in which a compensation signal together with a periodic Gaussian watermark signal is integrated into the actuator. By compensation calculations conducted with all compensatory signals in each period, the position corresponding to a minimum value of the detection function can be derived. Then, the time that the attacks occurred can be ensured with the aid of the comparison between this position with the watermark signal in the same period. An application on a UMV is shown to demonstrate the effectiveness of the presented scheme in detecting replay attacks while minimizing control costs. Full article