Dynamic Sensor Research Articles

Real-time localization with probabilistic maps and unscented kalman filtering: a dynamic sensor fusion approach

This paper presents a robust position estimation technique for autonomous vehicles navigating urban and highway environments. It employs a probabilistic framework, integrating Monte Carlo simulation and Bayesian filtering via particle filters to represent position estimates beyond Gaussian distributions. To improve accuracy, Unscented Kalman Filtering (UKF) is used to fuse radar and lidar data, facilitating the detection of static, pole-like objects. These objects are matched with landmarks from a detailed reference map generated through 3D lidar scans. The proposed method addresses both lateral and longitudinal localisation challenges, ensuring precise vehicle positioning. Comprehensive simulation tests validate the system's effectiveness, demonstrating reliable performance across various driving conditions.

Wireless Dynamic Sensor Network for Water Quality Monitoring Based on the IoT

Water is a critical resource for human survival worldwide, and its availability and quality in natural reservoirs such as lakes and rivers must be monitored. In that way, wireless dynamic sensor networks can help monitor water quality. These networks have significantly advanced across various sectors, including industrial automation and environmental monitoring. Moreover, the Internet of Things has emerged as a global technological marvel, garnering interest for its ability to facilitate information visualization and ease of deployment—the combination of wireless dynamic sensor networks and the Internet of Things improves water monitoring and helps to care for this vital resource. This article presents the design and deployment of a wireless dynamic sensor network comprising a mobile node outfitted with multiple sensors for remote aquatic navigation and a stationary node similarly equipped and linked to a server via the IoT. Both nodes can measure parameters like pH, temperature, and total dissolved solids (TDS), enabling real-time data monitoring through a user interface and generating a database for future reference. The integrated control system within the developed interface enhances the mobile node’s ability to survey various points of interest. The developed project enabled real-time monitoring of the aforementioned parameters, with the recorded data being stored in a database for subsequent graphing and analysis using the IoT. The system facilitated data collection at various points of interest, allowing for a graphical representation of parameter evolution. This included consistent temperature trends, neutral and alkaline zone data for pH levels, and variations in total dissolved solids (TDS) recorded by the mobile node, reaching up to 100 ppm.

Development of 3D UDTs for Traffic Monitoring in Unreal 5 Game Engine

Abstract. Digital Twins recently, are a much regarded research topic in geospatial engineering, offering great potential for optimization of a variety of interrelated processes in urban environments. A major component of a Digital Twin use case is real-time sensor data input and analysis, which is used as foundation for simulations that lead to decision-support for authorities or even automatic response into the physical environment. The use of game engines as platform for urban Digital Twin use cases is promising due to superior visualization and animation capabilities, that facilitate diverse monitoring use cases for highly dynamic sensor data inputs. This contribution evaluates the fit for purpose of the Unreal Engine 5 game engine for digital twin use cases, by conducting a case study on urban traffic monitoring in the city of Liverpool Australia. The case study proved, that the necessary connections of static data provided via a geodatabase and dynamic real-time data from public portals can be integrated into the engine to establish an interactive Digital Twin use cases for evaluating traffic user interactions.

Efficient Multi-floor Indoor Mapping with A Versatile Rotating LiDAR System

Abstract. Light Detection And Ranging (LiDAR) technology has provided an impactful way to capture 3D environmental map. However, consistent mapping in sensing-degenerated and perceptually-limited scenes (e.g. multi-story buildings) or under high dynamic sensor motion (e.g. rotating platform) remains a significant challenge. To this end, an efficient multi-floor indoor mapping system is proposed in this paper utilizing a versatile rotating LiDAR platform. In the front end, measurements from motor, IMU and LiDAR are tightly integrated to track the fast motion state of system, based on iterative Error State Kalman Filter (ESKF). Then linear and planar features are extracted from the point cloud map voxelized with adaptive resolutions. A sliding-window-based batch optimization is performed to simultaneously optimize the states of local frames with reference to the map consistency. In the experiments, we investigated the influence of rotating speed on the mapping performance as well as the superiority of rotating mechanism when compared to standard LiDAR setup. Moreover, comparative studies with one of the SOTA work, FAST-LIO2, have shown the competitive mapping results in multi-floor indoor environments.

Target detection and obstacle avoidance for UAV based on dynamic vision sensor

Aiming at the problem of UAV sensing dynamic targets and avoiding high-speed dynamic obstacles in dynamic environments, a target detection and obstacle avoidance algorithm based on dynamic vision sensor is designed. A filtering method and motion compensation algorithm are designed to filter out background noise and hot spot noise in the event stream as well as redundant events caused by the camera's own motion. A dynamic target fusion detection algorithm that fuses event images and RGB images is designed to ensure the reliability of detection. The target motion trajectory is estimated based on the detection results, and the speed obstacle method is improved to avoid dynamic obstacles by combining the obstacle motion characteristics and UAV dynamic constraints. A large number of simulation tests, handheld tests and flight tests verify the feasibility of the proposed algorithm.

Collaborative Autoethnography of Cancer Patients' Dynamic Sense of Agency.

Through collaborative autoethnography, we studied shifts in cancer patients' sense of agency and the meaning of cancer during the diagnostic and treatment phases. This article contributes to the illness management literature by adopting sense of agency perspective that provides new understanding of retrospective interpretation of cancer patients' agency. The authors' experiences of receiving cancer diagnoses and a related, collectively written story illustrate how relational and contextual elements facilitate rapid shifts in cancer patients' sense of agency and illness management. The findings illustrate shifts in the sense of agency as a collaborative and reflexive process between cognitive, emotional, and bodily constraints and adjustments. We demonstrate how shifts in patients' sense of agency and respective changes in meanings attached to cancer were shaped by near ones, healthcare actors, and other cancer patients, as well as the COVID-19 pandemic and the fear of military conflict due to Finland neighbor Russia's war on Ukraine. Furthermore, the study illustrates how shifts in sense of agency shape and are shaped by changes in the understanding of cancer as either a secondary issue, ambiguous stranger, travel companion, or enemy.

Quantum Sensing of Spin Dynamics Using Boron-Vacancy Centers in Hexagonal Boron Nitride

Quantum Sensing of Spin Dynamics Using Boron-Vacancy Centers in Hexagonal Boron Nitride

Perspective on the Development and Integration of Hydrogen Sensors for Fuel Cell Control

The measurement of hydrogen concentration in fuel cell systems is an important prerequisite for the development of a control strategy to enhance system performance, reduce purge losses and minimize fuel cell aging effects. In this perspective paper, the working principles of hydrogen sensors are analyzed and their requirements for hydrogen control in fuel cell systems are critically discussed. The wide measurement range, absence of oxygen, high humidity and limited space turn out to be most limiting. A perspective on the development of hydrogen sensors based on palladium as a gas-sensitive metal and based on the organic magnetic field effect in organic light-emitting devices is presented. The design of a test chamber, where the sensor response can easily be analyzed under fuel cell-like conditions is proposed. This allows the generation of practical knowledge for further sensor development. The presented sensors could be integrated into the end plate to measure the hydrogen concentration at the anode in- and outlet. Further miniaturization is necessary to integrate them into the flow field of the fuel cell to avoid fuel starvation in each single cell. Compressed sensing methods are used for more efficient data analysis. By using a dynamical sensor model, control algorithms are applied with high frequency to control the hydrogen concentration, the purge process, and the recirculation pump.

OR-LIM: Observability-aware robust LiDAR-inertial-mapping under high dynamic sensor motion

Light Detection And Ranging (LiDAR) technology has provided an impactful way to capture 3D data. However, consistent mapping in sensing-degenerated and perceptually-limited scenes (e.g. multi-story buildings) or under high dynamic sensor motion (e.g. rotating platform) remains a significant challenge. In this paper, we present OR-LIM, a novel observability-aware LiDAR-inertial-mapping system. Essentially, it combines a robust real-time LiDAR-inertial-odometry (LIO) module with an efficient surfel-map-smoothing (SMS) module that seamlessly optimizes the sensor poses and scene geometry at the same time. To improve robustness, the planar surfels are hierarchically generated and grown from point cloud maps to provide reliable correspondences for fixed-lag optimization. Moreover, the normals of surfels are analyzed for the observability evaluation of each frame. To maintain global consistency, a factor graph is utilized integrating the information from IMU propagation, LIO as well as the SMS. The system is extensively tested on the datasets collected by a low-cost multi-beam LiDAR (MBL) mounted on a rotating platform. The experiments with various settings of sensor motion, conducted on complex multi-story buildings and large-scale outdoor scenes, demonstrate the superior performance of our system over multiple state-of-the-art methods. The improvement of point accuracy reaches 3.39–13.6 % with an average 8.71 % outdoor and correspondingly 1.89–15.88 % with 9.09 % indoor, with reference to the collected Terrestrial Laser Scanning (TLS) map.

Influence of tip air injection on the unsteady blade force under circumferential pressure distortion

In order to further understand the impact of tip air injection on compressor stability under circumferential pressure distortion, a series of experimental studies on tip air injection under uniform and circumferential distortion were conducted on a low-speed single rotor axial flow compressor. The unsteady measurements were carried out by use of microsensors and strain gauges bonded on the rotor blade surfaces, and a collection of dynamic pressure sensors installed on the casing wall. Results indicate that with the increase in the injected momentum ratio, the load at the leading edge near the rotor blade tip obviously decreases, thereby delaying the stall. By comparing the pressure distribution on the rotor blade surface under different injected momentum ratios, tip air injection can effectively suppress flow separation at the blade tip to enhance flow capability. In addition, it can also weaken the fluctuation of tip leakage flow and reduce the dynamic stress similar to natural vibration near the blade root both in stationary and rotating coordinate systems. Under the condition of inlet circumferential distortion, tip air injection can significantly reduce the load at the distorted region and suppress the low-frequency disturbances measured on the rotor blade surface caused by inlet distortion. In addition, the dynamic stress near the blade root can also be effectively reduced by tip air injection. It implies that tip air injection can both extend the stall margin and reduce blade vibration when suffering from circumferential distortion; thus, the compressor can safely operate.

Tactile Sensing and Rendering Patch with Dynamic and Static Sensing and Haptic Feedback for Immersive Communication.

Wearable human-machine interface (HMI) with bidirectional and multimodal tactile information exchange is of paramount importance in teleoperation by providing more intuitive data interpretation and delivery of tactilely related signals. However, the current sensing and feedback devices still lack enough integration and modalities. Here, we present a Tactile Sensing and Rendering Patch (TSRP) that is made of a customized expandable array which consists of a piezoelectric sensing and feedback unit fused with an elastomeric triboelectric multidimensional sensor and its inner pneumatic feedback structure. The primary functional unit of TSRP is mainly featured with a soft silicone substrate with compact multilayer structure integrating static and dynamic multidimensional tactile sensing capabilities, which synergistically leverage both triboelectric and piezoelectric effects. Additionally, based on the air chamber created by the triboelectric sensor and the converse piezoelectric effect, it provides pneumatic and vibrational haptic feedback simultaneously for both static and dynamic perception regeneration. With the aid of the other variants of this unit, the array shaped TSRP is capable of simulating different terrains, geometries, sliding, collisions, and other critical interactive events during teleoperation via skin perception. Moreover, immediate manipulation can be done on TSRP through the tactile sensors. The preliminary demonstration of TSRP interface with a completed control module in robotic teleoperation is provided, which shows the feasibility of assisting certain tasks in a complex environment by direct tactile communication. The proposed device offers a potential method of enabling bidirectional tactile communication with enriched key information for improving interaction efficiency in the fields of robot teleoperation and training.

Data Reconstruction Using Smart Sensor Placement.

This paper deals with spatio-temporal field estimation with efficient sensor placement based on the QR decomposition. The proposed method also identifies the optimal number of sensors needed for field estimation that captures the most relevant features of the field of interest. To address the uncertainties inherent in spatio-temporal field estimation, a robust data-driven control method is utilized, providing resilience against unpredictable environmental and model changes. In particular, the approach uses the Kriged Kalman Filter (KKF) for uncertainty-aware field reconstruction. Unlike other reconstruction methods, the positional uncertainty originating from the data acquisition platform is integrated into the KKF estimator. Numerical results are presented to show the efficacy of the proposed dynamic sensor placement strategy together with the KKF field estimator.

MSI-DTrans: A multi-focus image fusion using multilayer semantic interaction and dynamic transformer

Multi-focus image fusion (MFIF) aims to utilize multiple images with different focal lengths to fuse into a single full-focus image. This process enhances the realism and clarity of the resulting image. In this paper, a MFIF method called MSI-DTrans was proposed. On the one hand, in order to fully utilize all the effective information that the source image carries, the proposed method adopts a multilayer semantic interaction strategy to enhance the interaction of high-frequency and low-frequency information. This approach gradually mines more abstract semantic information, guiding the generation of feature maps from coarse to fine. On the other hand, a parallel multi-scale joint self-attention computation model is designed. The model adopts dynamic sense field and dynamic token embedding to overcome the performance degradation problem when dealing with multi-scale objects. This enables self-attention to integrate long-range dependencies between objects of different scales and reduces computational overhead. Numerous experimental results show that the proposed method effectively avoids image distortion, achieves better visualization results, and demonstrates good competitiveness with many state-of-the-art methods in terms of qualitative and quantitative analysis, as well as efficiency comparison. The source code is available at https://github.com/ouyangbaicai/MSI-DTrans.

Understanding Unsteady Vortex Structure and Shedding Frequency of Cylindrical Hole Film Cooling: Insights From Experimental and Numerical Approaches

Abstract To enhance film cooling effectiveness and reduce mixing loss, it is imperative to understand the dynamics of the unsteady vortex system and its interaction with the mainstream flow. In this study, a comprehensive experimental investigation was conducted to assess the film cooling effectiveness of a flat-plate cylindrical hole, along with the structure of the vortex system and the frequency of vortex shedding. This was achieved using a variety of techniques including pressure-sensitive paint (PSP), particle image velocimetry (PIV), hot-wire anemometry, and dynamic pressure sensors. To complement the experimental findings, a detailed analysis of the evolution mechanisms of unsteady coherent vortices was performed using the detached eddy simulation (DES) numerical method. The findings of the study revealed that the Kelvin–Helmholtz (K–H) shear vortex patterns on the windward side undergo a transition from clockwise to counterclockwise rotation with increasing blowing ratios. Large-scale vortex sheds from the K–H shear vortices, ultimately evolving into the hairpin vortex in the downstream region. Additionally, the study proposed two evolution models for the vortex systems in the film cooling flow field at different blowing ratios and elucidated the evolution mechanisms of counter-rotating vortex pair (CRVP). The results of the spectral analysis revealed a notable discrepancy in the slopes of the eigenfrequencies, which could be attributed to variations in the evolution patterns of the vortex systems.

High-Q resonances based on BIC in graphene-dielectric hybrid double-T metasurfaces for terahertz modulation and sensing

In recent years, there has been a growing interest in bound states in the continuum (BIC) in metasurfaces. One particular area of focus is achieving high-quality (Q) factor resonance, as this is crucial for enhancing the performance of refractive index sensors. In this study, a graphene-dielectric hybrid metasurface that supports the bound states in the continuum is proposed. By varying the width of the dielectric rectangle, quasi-BIC resonances with a high Q factor can be excited, and the Q factor can reach 752724.95 and 272004.759 respectively. The analysis of multipole decomposition reveals that the two quasi-BIC resonances are predominantly influenced by the electric quadrupole and magnetic dipole, respectively. Moreover, the transmittance of the resonance point can be changed rapidly with the change of the chemical potential of graphene, so the function of modulation can be realized by changing the chemical potential of graphene. Based on these findings, we have designed a terahertz wave modulator, which exhibits modulation depths of 98.1% and 99.9% at the two resonance peaks, respectively. The corresponding chemical potential shifts are 50 meV and 0.5 eV. Additionally, we have investigated the sensing performance of the metasurface. By analyzing the magnitude of the frequency shifts of the quasi-BIC resonance peaks at different gas refractive indexes, we have determined sensitivities of 740 GHz RIU−1 and 630 GHz RIU−1 at the two resonance peaks. The maximum figure of merit (FOM) values are 132911.39 RIU−1 and 45000 RIU−1, respectively. This research serves as a valuable reference for the design of dynamic optical modulators and sensors operating in the terahertz band.

Combining Machine Learning, Embedded Sensor Networks and Additive Burner Design for Combustor Structural Health Monitoring

Abstract The crystAIr project is about sensitising burners for flame monitoring based on a discrete number of dynamic pressure sensors and a machine learning approach. The context is the development of better aircraft engines and the prospect of technical solutions for hydrogen-fuelled gas turbines. The combination of sensors and appropriate signal processing is designed to mimic a ‘feel’ for the state of the flame. The idea is to monitor the gas turbine's correct operation in real-time and anticipate impending problems such as flame blowout, combustion instability or flashback. Compared to conventional flame monitoring based on signal sampling, thresholding, band-pass analysis and time-lag measurement, this method should be less computationally intensive, more sensitive and more robust to artefacts. Not only should it be more responsive, but it should also anticipate problems based on a lessons-learned approach and outperform the conventional precursors. An unassisted machine learning method is used to achieve this. A custom-built AM burner designed for this experiment provides multiple instrumentation options. A powerful data acquisition system is set up to collect data on multiple channels, over a long time duration and at high speed to collect the learning signal chunks. The focus is on the detection of flames and, more specifically, the moment of their ignition and extinction, the operating conditions that are prone to flashback and the stability of the combustion. Additional measurement techniques are used to refine the learning method. The paper covers all these points and presents the first results.

Star point positioning for large dynamic star sensors in near space based on capsule network

In order to solve the problem that the star point positioning accuracy of the star sensor in near space is decreased due to atmospheric background stray light and rapid maneuvering of platform, this paper proposes a star point positioning algorithm based on the capsule network whose input and output are both vectors. First, a PCTL (Probability-Coordinate Transformation Layer) is designed to represent the mapping relationship between the probability output of the capsule network and the star point sub-pixel coordinates. Then, Coordconv Layer is introduced to implement explicit encoding of space information and the probability is used as the centroid weight to achieve the conversion between probability and star point sub-pixel coordinates, which improves the network’s ability to perceive star point positions. Finally, based on the dynamic imaging principle of star sensors and the characteristics of near-space environment, a star map dataset for algorithm training and testing is constructed. The simulation results show that the proposed algorithm reduces the MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) of the star point positioning by 36.1% and 41.7% respectively compared with the traditional algorithm. The research results can provide important theory and technical support for the scheme design, index demonstration, test and evaluation of large dynamic star sensors in near space.

Identification of Acoustic Sources on Antares Launch Pad Using Microphone Phased Array

A microphone phased array (MPA) was mounted in the vicinity of pad 0A of Mid-Atlantic Regional Spaceport located on NASA’s Wallops Flight Facility to identify the evolution of acoustic sources from hold-down to an elevation of ∼200 ft during the launch of an Antares rocket (Northrop Grumman [NG]-19). The MPA was equipped with 70 piezo-resistive dynamic pressure sensors, a visible-band and an infrared-band camera, and a two-axis accelerometer. The noise map demonstrated an effective use of the water injection system to attenuate noise from plume impingement on the pad. They also pointed out the outlet of the exhaust duct, through which the steam and plume mixture ejected out, to be the primary noise source during a significant interval of time. A comparison with prior such measurements during the maiden launch of the Antares vehicle in 2013 showed significant attenuation of noise sources during the NG-19 launch. A strong source caused by the impingement of plume on the pad surface during the 2013 launch was found to be attenuated in 2023. The results showed the utility of an MPA in quantifying the impact of changes made to a launch pad.

Dynamic Soft Sensor Model for Endpoint Carbon Content and Temperature in BOF Steelmaking Based on Adaptive Feature Matching Variational Autoencoder

The key to endpoint control in basic oxygen furnace (BOF) steelmaking lies in accurately predicting the endpoint carbon content and temperature. However, BOF steelmaking data are complex and change distribution due to variations in raw material batches, process adjustments, and equipment conditions, leading to concept drift and affecting model performance. In order to resolve these problems, this paper proposes a dynamic soft sensor model based on an adaptive feature matching variational autoencoder (VAE-AFM). Firstly, this paper innovatively proposes an adaptive feature matching (AFM) method. This method utilizes the maximum mean discrepancy to calculate the values of the marginal and conditional distributions. Based on the discrepancy between these two values, a dynamic adjustment algorithm is designed to adaptively assign different weights to the two distributions. This approach dynamically and quantitatively evaluates and adjusts the relative importance of different distributions in the domain adaptation process, thereby enhancing the effectiveness of cross-domain data alignment. Secondly, a variational autoencoder (VAE) is employed to process the data, as the VAE model can capture the complex data structures and latent features in the steelmaking process. Finally, the features extracted by the VAE are processed with the adaptive feature matching method, thereby constructing the VAE-AFM dynamic soft sensor model. Experimental studies on actual BOF steelmaking data validate the efficacy of the offered approach, offering a reliable solution to the challenges of high complexity and concept drift in BOF steelmaking data.

Dynamic Probabilistic Sensing for Enhanced Target Coverage in WRSNs

Dynamic Probabilistic Sensing for Enhanced Target Coverage in WRSNs