An interacting multiple model for multi-node target tracking algorithm was proposed based on a fuzzy neural network (FNN) to solve the multi-node target tracking problem of wireless sensor networks (WSNs). Measured error variance was adaptively adjus...
Studies on fault detection and diagnosis of planetary gearboxes are quite limited compared with those of fixed-axis gearboxes. Different from fixed-axis gearboxes, planetary gearboxes exhibit unique behaviors, which invalidate fault diagnosis methods...
In addition to being sensitive to humidity, humidity sensors with moisture sensitive elements are also sensitive to ambient temperature. The fusion of temperature and humidity data is an effective way to improve the accuracy of humidity sensors. In v...
Accurate and reliable wind speed measurement is essential for applications such as wind power generation and meteorological monitoring. Data fusion from multiple anemometers mounted on wind measurement towers is a key approach to obtaining high-preci...
Indoor occupancy prediction is a prerequisite for the management of energy consumption, security, health, and other systems in smart buildings. Previous studies have shown that buildings that automatize their heating, lighting, air conditioning, and...
Multi-human detection and tracking in indoor surveillance is a challenging task due to various factors such as occlusions, illumination changes, and complex human-human and human-object interactions. In this study, we address these challenges by expl...
The point cloud is one of the measurement results of local measurement and is widely used because of its high measurement accuracy, high data density, and low environmental impact. However, since point cloud data from a single measurement are general...
Using a commodity RGB-D sensor is a popular and cost-effective way to enable interaction at room scale, as such a device supports body tracking functionality at a reasonable price point. Even though the capabilities of such devices might be enough fo...
Long-term climate records of soil moisture are of increased importance to climate researchers. In this study, we aim to evaluate the quality of three different fusion approaches that combine soil moisture retrieval from multiple satellite sensors. Th...
A hybrid particle swarm optimization (PSO), able to overcome the large-scale nonlinearity or heavily correlation in the data fusion model of multiple sensing information, is proposed in this paper. In recent smart convergence technology, multiple sim...
Many advanced driver assistance systems (ADAS) are currently trying to utilise multi-sensor architectures, where the driver assistance algorithm receives data from a multitude of sensors. As mono-sensor systems cannot provide reliable and consistent...
Combined with other signal processing methods, related algorithms are widely used in the diagnosis and identification of rotor faults. In order to solve the problem that the vibration signal of a single sensor is too single, a new multi-source vibrat...
This paper addresses the fusion processing techniques for multi-sensor data perceived through the infrared sensors of military surveillance robots, and proposes their decision-theoretic coordination to effectively monitor multiple targets. To combine...
LiDAR sensors are extensively used in autonomous vehicles and their optimal use is a critical concern. In this paper, we propose an embedded software architecture for multiple LiDAR sensors, i.e., a fusion system that acts as an embedded system for p...
Sensor fusion is a technique that combines information from multiple sensors in order to improve the accuracy and reliability of the data being collected. In the context of teleoperation control of an anthropomorphic robotic arm, sensor fusion techno...
Recent studies showed that the performance of the modulation classification (MC) is considerably improved by using multiple sensors deployed in a cooperative manner. Such cooperative MC solutions are based on the centralized fusion of independent fea...
Robotic systems are becoming increasingly crucial in applications requiring high precision. While a robot can operate using basic sensor feedback under controlled conditions, achieving micro-level accuracy requires more comprehensive data integration...
Multi-sensor fusion object detection is an advanced method that improves object recognition and tracking accuracy by integrating data from different types of sensors. As it can overcome the limitations of a single sensor in complex environments, the...
Aiming at the problems of poor efficiency of the intelligent fault diagnosis method of the main reducer and the poor effectiveness of multichannel data fusion, this paper proposes a multichannel data fusion method based on deep belief networks and ra...
Sensor data fusion technology can be used to best extract useful information from multiple sensor observations. It has been widely applied in various applications such as target tracking, surveillance, robot navigation, signal and image processing. T...
The security of wireless sensor networks (WSN) has become a great challenge due to the transmission of sensor data through an open and wireless network with limited resources. In the paper, we discussed a lightweight security scheme to protect the co...
In this paper, we investigate implicit surface reconstruction methods based on deep learning, enhanced by multi-sensor data fusion, to improve the accuracy of 3D reconstruction in complex scenes. Existing single-sensor approaches often struggle with...
Sensor data fusion analysis plays a pivotal role in a variety of fields by integrating data from multiple sensors to produce more accurate, reliable, and comprehensive information than that achieved by individual sensors alone [...]
The safe navigation of unmanned surface vehicles in the marine environment requires multi-sensor collaborative perception, and multi-sensor data fusion technology is a prerequisite for realizing the collaborative perception of different sensors. To a...
Measuring the mass flow of sugarcane in real-time is essential for harvester automation and crop monitoring. Data integration from multiple sensors should be an alternative to receive more reliable, accurate, and valuable predictions than data delive...
This paper presents a novel method for fusing information from multiple sensor systems for bearing fault diagnosis. In the proposed method, a convolutional neural network is exploited to handle multiple signal sources simultaneously. The most importa...
Sensors are increasingly used in mobile environments with wireless network connections. Multiple sensor types measure distinct aspects of the same event. Their measurements are then combined to produce integrated, reliable results. As the number of s...
Sensor-based sorting techniques offer the potential to improve ore grades and reduce the amount of waste material processed. Previous studies show that sensor-based sorting can reduce energy, water and reagent consumption and fine waste production by...
Sensor data fusion is essential for environmental perception within smart traffic applications. By using multiple sensors cooperatively, the accuracy and probability of the perception are increased, which is crucial for critical traffic scenarios or...
Information from complementary and redundant sensors are often combined within sensor fusion algorithms to obtain a single accurate observation of the system at hand. However, measurements from each sensor are characterized by uncertainties. When mul...
The recognition of warheads in the target cloud of the ballistic midcourse phase remains a challenging issue for missile defense systems. Considering factors such as the differing dimensions of the features between sensors and the different recogniti...
To achieve autonomous navigation in complex marine environments, unmanned surface vehicles are equipped with a variety of sensors for sensing the surrounding environment and their own state. To address the issue of unsatisfactory multi-sensor informa...
Today, clinicians often do diagnosis and classification of diseases based on information collected from several physiological sensor signals. However, sensor signal could easily be vulnerable to uncertain noises or interferences and due to large indi...
In this paper, acoustic emission data fusion based on multiple measurements is presented for damage detection and identification in oxide-based ceramic matrix composites. Multi-AE (acoustic emission) sensor fusion is considered with the aim of a bett...
Information fusion has been a topic of immense interest owing to its applicability in various applications. This brings to the fore the need for a flexible and accurate fusion algorithm that can be versatile. The Brooks–Iyengar algorithm is one...
Nowadays, the use of freeform surfaces in various functional applications has become more widespread. Multi-sensor coordinate measuring machines (CMMs) are becoming popular and are produced by many CMM manufacturers since their measurement ability ca...
Mobile robots must be capable to obtain an accurate map of their surroundings to move within it. To detect different materials that might be undetectable to one sensor but not others it is necessary to construct at least a two-sensor fusion scheme. W...
Accurate fault diagnosis of hydropower units is crucial for ensuring the efficient and complete utilization of hydropower resources. Existing diagnostic methods predominantly consider either single-sensor or single-scale multi-sensor fusion, failing...
To improve the efficiency, accuracy, and intelligence of target detection and recognition, multi-sensor information fusion technology has broad application prospects in many aspects. Compared with single sensor, multi-sensor data contains more target...
In the context of modern cyber-physical systems, the accuracy of underlying sensor data plays an increasingly important role in sensor data fusion and feature extraction. The raw events of multiple sensors have to be aligned in time to enable high qu...
This paper proposes a novel data fusion technique for a wearable multi-sensory patch that integrates an accelerometer and a flexible resistive pressure sensor to accurately capture breathing patterns. It utilizes an accelerometer to detect breathing-...
Only with new sensor concepts in a network, which go far beyond what the current state-of-the-art can offer, can current and future requirements for flexibility, safety, and security be met. The combination of data from many sensors allows a richer r...
Sensor fusion is becoming increasingly popular in condition monitoring. Many studies rely on a fusion-level strategy to enable the most effective decision-making and improve classification accuracy. Most studies rely on feature-level fusion with a cu...
This paper focuses on data fusion, which is fundamental to one of the most important modules in any autonomous system: perception. Over the past decade, there has been a surge in the usage of smart/autonomous mobility systems. Such systems can be use...
As multisensor measurement technology is rapidly applied in industrial production, one key issue is the data fusion procedure by combining several datasets from multiple sensors to obtain the overall geometric measurement. In this paper, a multisenso...
This paper introduces a team of Automated Guided Vehicles (AGVs) equipped with open-source, perception-enhancing rotating devices. Each device has a set of ArUco markers, employed to compute the relative pose of other AGVs. These markers also serve a...
Internet-of-Things systems are increasingly being installed in buildings to transform them into smart ones and to assist in the transition to a greener future. A common feature of smart buildings, whether commercial or residential, is environmental s...
In the current shipping industry, quantitative measures of ship fuel consumption (SFC) have become one of the most important research topics in environmental protection and energy management related to shipping operations. In particular, the rapid de...
A wireless sensor network (WSN) is typically composed of spatially distributed miniature sensors that help collect large amounts of real-time environmental data from buildings. These environmental data (e.g., temperature, humidity, CO2 concentration)...
Accurate forecasting of energy consumption in sensor-rich environments remains challenging due to strong inter-sensor dependencies, temporal variability, and heterogeneous sensor behavior. This paper proposes a lightweight and interpretable multi-sen...
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