Sensor Locations Research Articles

Observation impact explanation in atmospheric state estimation using hierarchical message-passing graph neural networks**This paper is significantly revised from an earlier version [] presented at the Explainable Machine Learning for Sciences Workshop (XAI4Sci) in conjunction with the 38th AAAI Conference on Artificial Intelligence (AAAI 2024) held in Vancouver, Canada, in February 2024.

Abstract The impact of meteorological observations on weather forecasting varies with the sensor type, location, time, and other environmental factors. Thus, the quantitative analysis of observation impacts is crucial for the effective and efficient development of weather forecasting systems. However, existing impact analysis methods are dependent on specific forecast systems, because system-specific adjoint models are used and the sensitivity of the observation to the forecast is measured. This study investigates the impact of observations on atmospheric state estimation in weather forecasting systems by developing a novel graph neural network (GNN) model specialized for analyzing the heterogeneous relations between observations and atmospheric states. The observation impact can then be assessed by applying explainable methods to the proposed GNN model, which is independent of forecasting systems. Further, we develop a novel application called ‘CloudNine,’ a system that provides impact analysis for individual observations with visualization. Our GNN model comprises hierarchical message-passing modules that separately analyze spatial correlations between observations at close locations and atmospheric states at close locations and then examine correlations between observations and atmospheric states. To consider the different factors influencing these correlations, we utilized geo-coordinates and types of observations in the attention mechanism of the modules with their feature vectors. We then applied gradient-based explainability methods to quantify the significance of the different observations in the estimation. Evaluated using data from 11 satellites and land-based observations, the results highlight the effectiveness of the proposed model and the visualization of observation impacts, enhancing the understanding and optimization of observational data in weather forecasting.

Monitoring mangroves with multi-sensor Earth Observation data sets: from one-shot historical cartography to online and near-real-time monitoring tools

Abstract. This study outlines advancements in mangrove monitoring using Earth Observation (EO) technologies, highlighting a transition from historical cartography to modern, near-real-time monitoring systems. Mangroves, critical for biodiversity, climate regulation, and coastal protection, face threats from climate change and human activities. The need for accurate and recurrent mapping tools is emphasized, addressing the limitations of current global datasets like the Global Mangrove Watch (GMW) by incorporating high-resolution data and field measurements. The French National Research Institute for Sustainable Development (IRD) has pioneered mangrove monitoring since the 1990s, with key methodologies including the use of Sentinel-2 and Pleiades satellite imagery for high-resolution mapping, texture-based analysis, and the incorporation of LiDAR data for accurate biomass estimation. Historical cartography is contrasted with contemporary monitoring efforts, focusing on the integration of multi-source data and the importance of localized ground-truthing. This approach enhances the capabilities of earth observation for carbon stock assessments and supports informed decision-making in conservation and climate policy. The work also highlights the need for further integration of local knowledge and advanced EO sensor data to refine carbon sequestration models and improve mangrove management strategies.

Mark3D - A semi-automated open-source toolbox for 3D head- surface reconstruction and electrode position registration using a smartphone camera video.

Source localization in EEG necessitates co-registering the EEG sensor locations with the subject's MRI, where EEG sensor locations are typically captured using electromagnetic tracking or 3D scanning of the subject's head with EEG cap, using commercially available 3D scanners. Both methods have drawbacks, where, electromagnetic tracking is slow and immobile, while 3D scanners are expensive. Photogrammetry offers a cost-effective alternative but requires multiple photos to sample the head, with good spatial sampling to adequately reconstruct the head surface. Post-reconstruction, the existing tools for electrode position labelling on the 3D head-surface have limited visual feedback and do not easily accommodate customized montages, which are typical in multi-modal measurements. We introduce Mark3D, an open-source, integrated tool for 3D head-surface reconstruction from phone camera video. It eliminates the need for keeping track of spatial sampling during image capture for video-based photogrammetry reconstruction. It also includes blur detection algorithms, a user-friendly interface for electrode and tracking, and integrates with popular toolboxes such as FieldTrip and MNE Python. The accuracy of the proposed method was benchmarked with the head-surface derived from a commercially available handheld 3D scanner Einscan-Pro + (Shining 3D Inc.,) which we treat as the "ground truth". We used reconstructed head-surfaces of ground truth (G1) and phone camera video (M1080) to mark the EEG electrode locations in 3D space using a dedicated UI provided in the tool. The electrode locations were then used to form pseudo-specific MRI templates for individual subjects to reconstruct source information. Somatosensory source activations in response to vibrotactile stimuli were estimated and compared between G1 and M1080. The mean positional errors of the EEG electrodes between G1 and M1080 in 3D space were found to be 0.09 ± 0.01mm across different cortical areas, with temporal and occipital areas registering a relatively higher error than other regions such as frontal, central or parietal areas. The error in source reconstruction was found to be 0.033 ± 0.016mm and 0.037 ± 0.017mm in the left and right cortical hemispheres respectively.

A study on sleep posture analysis using fibre bragg grating arrays based mattress

Prolonged sleeping postures or unusual postures can lead to the development of various ailments such as subacromial impingement syndrome, sleep paralysis in the elderly, nocturnal gastroesophageal reflux, sore development, etc Fibre Bragg Gratings (a variety of optical sensors) have gained huge popularity due to their small size, higher sensitivity and responsivity, and encapsulation flexibilities. However, in the present study, FBG Arrays (two FBGs with 10 mm space between them) are employed as they are advantageous in terms of data collection, mitigating sensor location effects, and multiplexing features. In this work, Liquid silicone encapsulated FBG arrays are placed in the head (E), shoulder (C, D), and lower half body (A, B) region for analyzing the strain patterns generated by different sleeping postures namely, Supine (P1), Left Fetus (P2), Right Fetus (P3), and Over stomach (P4). These strain patterns were analyzed in two ways, combined (averaging the data from each FBG of the array) and Individual (data from each FBG was analyzed separately). Both analyses suggested that the FBGs in the arrays responded swiftly to the strain changes that occurred due to changes in sleeping postures. 3D histograms were utilized to track the strain changes and analyze different sleeping postures. A discussion regarding closely related postures and long hour monitoring has also been included. Arrays in the lower half (A, B) and shoulder (C, D) regions proved to be pivotal in discriminating body postures. The average standard deviation of strain for the different arrays was in the range of 0.1 to 0.19 suggesting the reliable and appreciable strain-handling capabilities of the Liquid silicone encapsulated arrays.

Of goats and heat, the differential impact of summer temperature on habitat selection and activity patterns in mountain goats of different ecotypes.

Climate change disproportionately affects northern and alpine environments, with faster rates of warming than the global average. Because alpine and northern species are particularly well adapted to cool temperatures, most species must modify their behavior when temperatures exceed a critical threshold. Evaluating how temperature increases affect species inhabiting northern and alpine environments is therefore essential to understand the effects of projected climate change on these ecosystems. We analyzed the influence of temperature on the activity patterns and habitat selection of four populations of a cold-adapted, mountain specialist, the mountain goat (Oreamnos americanus). We collected GPS location and activity sensor data during 2010-2019 from 223 mountain goats from two distinct ecotypes: coastal and continental. Using a resource selection modeling approach, we determined that mountain goats of both ecotypes decreased selection for alpine meadows when temperatures increased. Reduced selection for open, forage rich habitat was associated with increased selection for habitat dominated by snow/ice patches in coastal areas, and by forests in continental sites. Mountain goats in continental environments selected higher elevation habitats only when temperature increased, whereas goats in coastal environments selected higher elevation habitat at all temperatures. Mountain goats of both ecotypes reduced the proportion of time spent active when temperatures increased during the middle of the day. Our study reveals that mountain goats use diverse tactics to mitigate thermal stress, and that these tactics vary between ecotypes, highlighting the need for considering adaptation to specific environments within a species when assessing climate change impacts on populations.

A fast-aware multi-target response prediction approach and its application in aeronautical engineering

Response prediction is a fundamental yet challenging task in aeronautical engineering, requiring an accurate selection of sensor positions correlated with the target responses to achieve precise predictions. Unfortunately, in large-scale structures, the rigorous selection of reliable sensor candidates for multi-target responses remains largely unexplored. In this paper, we propose a flexible and generalized framework for selecting the most relevant sensors to the multi-target response and predicting the target response, referred to as the Fast-aware Multi-Target Response Prediction (FMTRP) approach in the spirit of divide-and-conquer. Specifically, first, a multi-task learning module is designed to predict multi-point response tasks at the same time. Simultaneously, we meticulously devise adaptive mechanisms to facilitate loss-term reweighting and encourage prioritization of challenging tasks in multiple prediction tasks. Second, to ensure ease of interpretation, we introduce a hybrid penalty to select sensors at the group-sparsity, individual-sparsity and element-sparsity levels. Finally, due to the substantial number of candidate sensors posing a significant computational burden, we develop a more efficient search strategy and support computation to make the proposed approach applicable in practice, leading to substantial runtime improvements. Extensive experiments on aircraft standard model response datasets and large airliner test flight datasets validate the effectiveness of the proposed approach in identifying sensor locations and simultaneously predicting responses at multiple points. Compared to state-of-the-art methods, the proposed approach achieves an accuracy of over 99% in sinusoidal excitation and exhibits the shortest runtime (3.514 s).

An Optimized Method for Wireless Sensor Localization Using Heuristic-Based Adaptive Particle Swarm Fusion

An Optimized Method for Wireless Sensor Localization Using Heuristic-Based Adaptive Particle Swarm Fusion

5G/GPON: A CONVERGENT ARCHITECTURE FOR WIRELESS AND OPTICAL SENSOR NETWORKS IN BOGOTÁ CITY

This paper presents the design of a convergent optical transport architecture for wireless and photonic sensor networks based on the optical access network deployed in Bogotá city. The proposed architecture is evaluated through the transport of services in subcarrier division multiplexing to enable the seamless transport of different standards such as WiFi, Bluetooth, Zigbee, SigFox, among others. The proposed design demonstrates high scalability given the distribution of the previously deployed network, enabling the incorporation of both optical sensors based on FBG and wireless sensors in locations with greater historical, economic, cultural and social relevance. The proposal presents an optimal response in terms of bit error rate (BER) and signal power in the multi-coverage zone for various interference parameters such as SS-RSRP, SS-RSRQ, and Throughput.

Smart Compression Sock for Early Detection of Diabetic Foot Ulcers.

The prevention of diabetic foot ulcers remains a critical challenge. This study evaluates a smart compression sock designed to address this issue by integrating temperature, plantar pressure, and blood oxygen sensors and monitoring data recorded by these sensors. The smart sock, developed with input from a certified Pedorthist, was tested on 20 healthy adult participants aged 16 to 53. It includes four temperature sensors and pressure sensors at common ulcer sites (first and fifth metatarsal heads, calcaneus, and hallux), and a blood oxygen sensor on the hallux. The sensors are monitored, and their transduced data are collected and stored through an app installed on a personal cell phone. The mobile app interface is user-friendly, providing intuitive navigation and easy access to the sensors' data. Using repeated measures ANOVA and post hoc tests, we analyzed the impact of various physical activities on physiological changes in the foot. The device effectively detected significant variations in blood oxygen, temperature, and pressure across six activities. Statistical analyses revealed significant differences based on activity type and sensor location. These results highlight the smart sock's sensitivity and accuracy, suggesting its potential to prevent diabetic foot ulcers. Further clinical trials are needed to evaluate its efficacy in a larger, more diverse population.

Underwater Line Monitoring Using Optimally Placed Inclinometers

Underwater monitoring presents challenges related to maintaining a continuous power supply and communication, necessitating the use of a smaller number of sensors to effectively cover the entire line. An underwater line tracking method is proposed to evaluate global behaviors and stresses in real time. The method employs angles at several points on the line, as well as displacements and curvatures at both ends. In this method, any line displacement, angle, and curvature are expressed as Fourier series, and Fourier coefficients are obtained by utilizing sensor data. Then, the behavior of any line location is assessed. In addition, to reduce the number of sensors and improve accuracy, optimal inclinometer locations are determined by a genetic algorithm. The proposed line tracking algorithm was validated through two numerical examples; one with an inclined tunnel and one with a marine steel catenary riser attached to a Floating Production Storage and Offloading (FPSO) vessel. Through these examples, the proposed algorithm was proven to capture global behaviors accurately when optimally located sensors are used. In the riser monitoring case, the optimized sensor placement with eight intermediate sensors achieved an average mean distance error of 1.91 m, which is lower than the 2.65 m error obtained with ten intermediate sensors without optimization.

A comparative study of vehicle powertrain efficiency: Data-driven analyzing energy consumption and environmental impact

This study investigates the energy consumption of electric vehicles (EVs) in real-world driving conditions, using in-vehicle sensor data and location tracking. The energy consumption of EVs is compared to that of an internal combustion engine vehicle (ICEV) under similar driving tests. Furthermore, an energy-centric life cycle assessment was conducted to assess the environmental impact of the transition to electrified transportation by reporting the carbon emissions of the EVs and ICEV. Since a large volume of driving data was acquired in the experiment, machine learning (ML) techniques are employed to interpret the obtained data and identify a key feature. ML algorithms can help develop predictive models that estimate energy consumption and explain the major factors that impact such consumption. The findings also offer valuable insights into energy consumption, emission, and the underlying factors that can inform policy discussion related to transport electrification and enhance energy efficiency. This research is significant in comprehending the potential of current EVs and their superiority over traditional vehicles. It is vital to facilitate the shift toward electric transportation, which can be driven by the results.

Research on the role of multi-sensor system information fusion in improving hardware control accuracy of intelligent system

Abstract Multi-sensor management and control technology generally constructs a reasonable objective function to solve the optimal control command set to control a limited number of sensors to obtain higher quality measurement information, thus obtaining better target tracking performance. In the process of multi-sensor information fusion, there is not only the problem of information redundancy but also obvious time delay. A sensor fusion algorithm combined with global optimization algorithm is innovatively proposed. According to the key frames saved in the previous steps, feature points in local maps, sensor information, and loop information, a global optimization algorithm based on graph optimization model is constructed to optimize the position and pose of intelligent hardware system and the position of spatial feature points. Moreover, this work studies and experiments on multi-sensor fusion simultaneous localization and mapping (SLAM) comprehensively and systematically, and the experimental results show that the algorithm proposed in this work is superior to common open-source SLAM algorithm in positioning accuracy and mapping effect under special circumstances. Therefore, the method proposed in this work can be applied to intelligent driving of vehicles, vision-assisted movement of robots and intelligent control of unmanned aerial vehicles, thus effectively improving the hardware control accuracy of intelligent systems.

How Connected Cars Could Capture Cloud Dynamics for Solar Forecasting—Evidence from Two Simulation Scenarios

The rapidly increasing share of fluctuating electricity from photovoltaics calls for accurate approaches to estimate cloud motion, the primary source for the varying power supply. While local sensor networks are prominent in targeting forecast horizons too short for image‐based methods, they have minimal spatial coverage. This work presents the first step towards expanding those approaches to spatially scalable sensor networks: With the motivation of using automotive light sensors as a sensor network, two excerpts from a microscopic traffic simulation serve as simulative sensor networks. A fractal‐based cloud shadow pattern passes the sensor network areas with defined velocities and directions, which shall be estimated using the cumulative mean absolute error method. The evaluation results indicate that the more extensive observation areas compensate for the dynamics in the sensor network when compared to a reference work with a static sensor grid. Furthermore, this work shows how the estimates deteriorate with lower vehicle penetration rates (PR) and longer building shadows due to a lower solar elevation angle. At a penetration rate of 40%, the root mean square errors for both sensor networks are still below 5 ms−1. In conclusion, the spatiotemporal characteristics of a vehicle network offer a potential for estimating cloud movements.

Smart composite structural insulated panels (CSIPs) with embedded piezoelectric sensors for damage localization

Coastal communities face increasing vulnerability to natural disasters, necessitating resilient construction solutions. This study presents the development of smart composite structural insulated panels (CSIPs) embedded with piezoelectric sensors for damage localization in hurricane and tornado-prone areas. Experimental studies, such as four-point bending and in-panel compression tests, were conducted to analyse the shock response and failure modes of these smart CSIPs. Material characterization informs the development of finite element models for simulation, enhancing structural design. By integrating structural health monitoring capabilities, these smart CSIPs enable continuous tracking of their response to environmental factors, improving longevity and durability. Shock identification and localization in smart CSIPs are successfully demonstrated through numerical simulations and experimental validation, showcasing the potential step towards self-resilient structures in coastal regions. This novel approach provides real-time information on structural integrity, paving the way for improved safety and predictive maintenance of buildings and infrastructure utilizing CSIPs.

Data-Driven Sparse Sensor Placement Optimization on Wings for Flight-By-Feel: Bioinspired Approach and Application.

Flight-by-feel (FBF) is an approach to flight control that uses dispersed sensors on the wings of aircraft to detect flight state. While biological FBF systems, such as the wings of insects, often contain hundreds of strain and flow sensors, artificial systems are highly constrained by size, weight, and power (SWaP) considerations, especially for small aircraft. An optimization approach is needed to determine how many sensors are required and where they should be placed on the wing. Airflow fields can be highly nonlinear, and many local minima exist for sensor placement, meaning conventional optimization techniques are unreliable for this application. The Sparse Sensor Placement Optimization for Prediction (SSPOP) algorithm extracts information from a dense array of flow data using singular value decomposition and linear discriminant analysis, thereby identifying the most information-rich sparse subset of sensor locations. In this research, the SSPOP algorithm is evaluated for the placement of artificial hair sensors on a 3D delta wing model with a 45° sweep angle and a blunt leading edge. The sensor placement solution, or design point (DP), is shown to rank within the top one percent of all possible solutions by root mean square error in angle of attack prediction. This research is the first to evaluate SSPOP on a 3D model and the first to include variable length hairs for variable velocity sensitivity. A comparison of SSPOP against conventional greedy search and gradient-based optimization shows that SSPOP DP ranks nearest to optimal in over 90 percent of models and is far more robust to model variation. The successful application of SSPOP in complex 3D flows paves the way for experimental sensor placement optimization for artificial hair-cell airflow sensors and is a major step toward biomimetic flight-by-feel.

High-resolution outdoor videography of insects using Fast Lock-On tracking.

Insects have important roles globally in ecology, economy, and health, yet our understanding of their behavior remains limited. Bees, for example, use vision and a tiny brain to find flowers and return home, but understanding how they perform these impressive tasks has been hampered by limitations in recording technology. Here, we present Fast Lock-On (FLO) tracking. This method moves an image sensor to remain focused on a retroreflective marker affixed to an insect. Using paraxial infrared illumination, simple image processing can localize the sensor location of the insect in a few milliseconds. When coupled with a feedback system to steer a high-magnification optical system to remain focused on the insect, a high-spatiotemporal resolution trajectory can be gathered over a large region. As the basis for several robotic systems, we show that FLO is a versatile idea that can be used in combination with other components. We demonstrate that the optical path can be split and used for recording high-speed video. Furthermore, by flying an FLO system on a quadcopter drone, we track a flying honey bee and anticipate tracking insects in the wild over kilometer scales. Such systems have the capability to provide higher-resolution information about insects behaving in natural environments and as such will be helpful in revealing the biomechanical and neuroethological mechanisms used by insects in natural settings.

Adaptive sensor attack detection and defense framework for autonomous vehicles based on density

The security of autonomous vehicles heavily depends on localization systems that integrate multiple sensors, which are vulnerable to sensor attacks and increase the risk of accidents. Given the diversity of sensor attacks and the dynamic changing of driving scenarios of autonomous vehicles, an adaptive and effective attack detection and defense framework faces a considerable challenge. This paper proposes a novel real-time adaptive attack detection and defense framework based on density, which can detect and identify attacked sensors and effectively recover data. We first develop a reinforcement learning multi-armed Bandit-based Density-Based Spatial Clustering of Applications with Noise (BDBSCAN) algorithm that selects hyperparameters adaptively. The Adaptive Extended Kalman Filter (AEKF) combines with the vehicle dynamic model on the localization system and extracts data features used for the BDBSCAN algorithm to monitor potential sensor attacks. If attack detection indicates possible system compromise, AEKF is further employed on localization sensors with anomalies identified through the BDBSCAN algorithm of the attacked sensors. To ensure precision and reliability, the data recovery incorporates a redundancy mechanism to apply a decision tree to select the optimal state estimation between AEKF and Extended Kalman Filter (EKF) to replace corrupted sensor data. To evaluate the effectiveness and adaptability of the proposed framework, we conducted 15,000 experiments using the real-world KITTI and V2V4Real datasets across various driving and sensor attack scenarios. The results demonstrate that our proposed framework achieves 100% accuracy and 0% false alarm rate in various driving scenarios for attack detection within 0.15 s, with a recovery time of 0.08 s.

Cold Storage Temperature Monitoring with Trilinear Interpolation

The Internet of Things (IoT) is widely applied in agriculture and in-dustry. This article proposes a 3-dimensional temperature monitoring model for cold storage, enhancing the ability to observe and detect abnormal temperature changes within the storage. Using real-time temperature acquisition and applying trilinear interpolation techniques, this model determines temperatures at all loca-tions in the storage. It accepts temperature values and sensor locations, appropri-ately segmenting the cold storage space to apply trilinear interpolation and deter-mine the temperature value at every location in the storage. The model was tested based on different number of sensors and different sensor locations. Based on that, we provide an optimal solution for the number of sensors needed for cold storage.

Magnetic Railway Sleeper Detector

In an ever expanding railway network all around the world, the need for track maintenance grows steadily. Traditionally, one major part of track maintenance is ramming large vibrating steel picks into the gravel between and under railway sleepers to compress the gravel and generate a safe substructure. Even today, maintenance personnel still have to manually locate the sleepers if they cannot be detected by computer vision systems or visually by the operator. Here we developed a first of its kind magnetic sleeper detector, even able to find sleepers, buried in gravel, undetectable by vision based systems. Our approach of magnetic detection is based on a DC magnetic field excitation and a detector moving with respect to the rail system, including the sleepers and fasteners for mounting the rails. Due to railway application constraints a large air gap between the sensor and the sleeper structure is required, which significantly complicates the magnetic sensing task for robust sleeper detection. The design and optimization of the magnetic circuit was based on extensive 3D simulation studies to ensure highest possible variation in magnetic flux density at the sensor locations for absence and presence of a sleeper. Furthermore, a low noise and high sensitivity electronic circuit has been realized to cope with sensor signal offsets from unknown or changing sensor orientations with respect to the earth’s magnetic field, or magnetic interferences from other trains potentially passing by during active measurements. Since we only want to detect sleepers in close vicinity of the moving sensor system, digital signal processing of the acquired signals can easily compensate for disturbing slowly changing or static field components within real world application scenarios. We demonstrate that magnetic detection of even buried sleepers on railway tracks is possible for distances of up to 172 mm between the sensor and the sleeper. This enables an even higher level of railway maintenance automation previously impossible in certain scenarios.

Strain Gauge Location Optimization for Operational Load Monitoring of an Aircraft Wing Using an Improved Correlation Measure

Operational loads of an aircraft are the prerequisite for assessing its safety or fatigue life. Traditionally, numerous strain gauge sensors are installed to monitor the operational loads, which inevitably increase the weight and system complexity of the aircraft. Therefore, in order to decrease the maintenance costs and data redundancy, the number and location of strain sensors should be optimized for accurate and reliable operational load monitoring. In this paper, a novel two-stage strain gauge location optimization method is proposed to reduce the number of strain gauges while maintaining the operational load monitoring accuracy, which is validated by a numerical case study of an aircraft wing. In the first stage, the traditional Pearson correlation measure is harnessed to initially eliminate numerous correlated strain gauge monitoring points, reducing 996 original strain gauge measurement points to 13 for the aircraft wing box. In the second stage, an improved correlation measure method is proposed to further reduce the 13 strain gauge points to 2, which can evaluate the correlation degree of several variables and simultaneously determine the optimal strain monitoring locations for the two load actuators in this study. The relative errors between the predicted loads and the actual loads for both load actuators are less than 4% when only two optimized monitoring points are adopted. In addition, a comparison study with LASSO regression and principal component regression methods is conducted. The results demonstrate that the proposed method has the characteristics of less monitoring points and higher load prediction precision.