Sensor Locations Research Articles

Towards multimodal visualization of esophageal motility: fusion of manometry, impedance, and videofluoroscopic image sequences.

Dysphagia is the inability or difficulty to swallow normally. Standard procedures for diagnosing the exact disease are, among others, X-ray videofluoroscopy, manometry and impedance examinations, usually performed consecutively. In order to gain more insights, ongoing research is aiming to collect these different modalities at the same time, with the goal to present them in a joint visualization. One idea to create a combined view is the projection of the manometry and impedance values onto the right location in the X-ray images. This requires to identify the exact sensor locations in the images. This work gives an overview of the challenges associated with the sensor detection task and proposes a robust approach to detect the sensors in X-ray image sequences, ultimately allowing to project the manometry and impedance values onto the right location in the images. The developed sensor detection approach is evaluated on a total of 14 sequences from different patients, achieving a F1-score of 86.36%. To demonstrate the robustness of the approach, another study is performed by adding different levels of noise to the images, with the performance of our sensor detection method only slightly decreasing in these scenarios. This robust sensor detection provides the basis to accurately project manometry and impedance values onto the images, allowing to create a multimodal visualization of the swallow process. The resulting visualizations are evaluated qualitatively by domain experts, indicating a great benefit of this proposed fused visualization approach. Using our preprocessing and sensor detection method, we show that the sensor detection task can be successfully approached with high accuracy. This allows to create a novel, multimodal visualization of esophageal motility, helping to provide more insights into swallow disorders of patients.

CFD Analysis to Optimize Air Quality in Aeroponic Tower Garden

Aeroponic tower gardens represent an innovative approach to urban agriculture, utilizing vertical structures to cultivate plants without soil. Research highlights the efficiency of this method in optimizing resource usage, such as water and space, compared to traditional farming. Studies emphasize increased crop yields, faster growth rates, and reduced environmental impact. Additionally, aeroponic systems enable precise nutrient delivery to plants, fostering optimal growth conditions. While the technology shows promise, further investigations are needed to address potential challenges and optimize its application in diverse agricultural settings. The study presents the simulation of air temperature and humidity distribution in a closed chamber of the vertical farm for optimization purposes. The environmental conditions inside the chamber are optimized for plant growth, to maintain the temperature at a range of 18-24 °C temperature and the relative humidity at a range of 75-85 %. The paper also discusses the challenges, limitations, and future recommendations for improving the system. Simulation of the system, on the other hand, is crucial not only for prototype fabrication but also for the optimization of sensor locations. The simulation of the tower garden system was performed using ANSYS-FLUENT to portray how the airflow inside the tower garden's enclosed environment was distributed. The system parameters such as humidity and temperature were set to meet the plants' growth requirements, and the system was treated as the steady state with an adiabatic boundary. Based on the simulation results, the best design recommended is to use two humidity and temperature sensors, one at the top and the other at the core of the prototype. Hence, a complete mapping of temperature and humidity distribution that is critical for plant growth would be occurred.

Analyzing Optimal Wearable Motion Sensor Placement for Accurate Classification of Fall Directions.

Falls represent a significant risk factor, necessitating accurate classification methods. This study aims to identify the optimal placement of wearable sensors-specifically accelerometers, gyroscopes, and magnetometers-for effective fall-direction classification. Although previous research identified optimal sensor locations for distinguishing falls from non-falls, limited attention has been given to the classification of fall direction across different body regions. This study assesses inertial measurement unit (IMU) sensors placed at 12 distinct body locations to determine the most effective positions for capturing fall-related data. The research was conducted in three phases: first, comparing classifiers across all sensor locations to identify the most effective; second, evaluating performance differences between sensors placed on the left and right sides of the body; and third, exploring the efficacy of combining sensors from the upper and lower body regions. Statistical analyses of the results for the most effective classifier model demonstrate that the support vector machine (SVM) is more effective than other classifiers across all sensor locations, with statistically significant differences in performance. At the same time, the comparison between the left and right sensor locations shows no significant performance differences within the same anatomical areas. Regarding optimal sensor placement, the findings indicate that sensors positioned on the pelvis and upper legs in the lower body, as well as on the shoulder and head in the upper body, were the most effective results for accurate fall-direction classification. The study concludes that the optimal sensor configuration for fall-direction classification involves strategically combining sensors placed on the pelvis, upper legs, and lower legs.

ATD-300 an AI empowered miniature acoustic device for automatic gunshot attacks detection and image capture

Built on over 30 years of acoustic source recognition expertise in transient impulsives signature, recently highly empowered by neural networks, and wave propagation model driven algorithms, ACOEM has developed the ATD-300, an acoustic sensor for automatic gunshot detection, identification and localization. What sets this sensor apart from the competition is its reliability in terms of detection rate over false alarm and robust high resolution shooter pointing capability. Therefore it can be used directly by police departments without third-party human validation. Resulting from the design effort to distribute intelligence among a collaborative sensor network, processing and artificial intelligence algorithms are fully embedded locally, enabling an alert to be raised in less than three seconds and a camera to be pointed at the shooter with only one sensor. In addition, as the system is already installed in several American cities, we will show how a device-to-server distributed artificial intelligence approach continuously improves the performance of the solution, fed by real field data.

Development of low-cost MEMS microphone array for sound localization in urban environments

Understanding noise pollution and mitigating its impact on the quality of life in urban environment is a major challenge from a societal and health point of view. In order to monitor and ultimately understand these noisy environments a process of long term acoustic measurements and analysis is required. These urban environments are noisy and obtaining a clear recording of a desired acoustic signal is difficult hence an effective approach is the use of beamforming theory coupled with a microphone array. In this paper a novel approach utilizing compact low-cost microphone arrays equipped with MEMS sensors for continuous sound localization in urban areas is proposed. This approach focuses on leveraging the affordability and scalability of MEMS sensors to deploy distributed acoustic sensor networks across diverse urban locations. The aim is to capture real-time acoustic data, enabling comprehensive monitoring of sound sources and their spatial distribution within the urban environment. This sensor unit can be upgraded with a camera, i.e. acoustic camera, which will provide information on the conditions of the environment where the measurements are performed. This low-cost solution facilitates long-term monitoring, providing valuable insights into the dynamic nature of urban soundscapes.

Pertinence of finite element method for impulse localization in urban environments

Although urban combat is nothing new, recent conflicts have shed light on Western armies' difficulties when operating in such environments. Thus, new surveillance and hazard detection challenges in complex environments are addressed. A new decision aid tool for acoustic sensor positioning has been developed to improve reconnaissance performance. The software's sensor location optimization kernel is based on a genetic algorithm developed by our partners from FKIE. It resolves successive localization problems using pre-computed Times Of Arrival (TOA) between an impulsive acoustic source and various surveillance microphones. The present study discusses the pertinence of the Time-Domain Finite-Element Method (TD-FEM) to simulate wave propagation within an urban area to obtain TOA and resolve localization problems. The approach is compared to experimental measurements in an urban area, and the pros and cons of the TD-FEM for this configuration are discussed.

A method for temperature sensor and model selection for machine tool thermal error modelling using ANFIS and ANN

AbstractThermal errors account for a significant part of the dimensional errors of components produced by precision machine tools. These errors are commonly compensated using predictions from temperature-based empirical models. The accuracy and robustness of these predictions are affected by the locations of temperature sensors used to obtain the model’s input data. Methods for sensor selection found in literature are often difficult to replicate and automate because they require tuning of several hyperparameters. This work presents a sensor and model selection approach that uses proper orthogonal decomposition (POD) and QR pivoting to select a subset of sensors that have been preinstalled in the machine tool as possible model inputs. These sensors are then sorted according to their correlation with the thermal error being modelled. The final set of inputs and thermal error model structure is chosen using Bayesian Information Criterion (BIC) to limit model overfitting. The approach was tested by modelling the Y-axis thermal error measured from air-cutting experiments performed under different spindle speeds and feed rates. This enabled determination of the structure and the choice inputs for Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) thermal error models. The accuracy of these models was compared to that of models trained using inputs selected by conventional approaches: the least absolute shrinkage and selection operator (LASSO), fuzzy c-means clustering (FCM), and principal component regression (PCR). The presented approach had better or comparable results to the conventional approaches while using fewer inputs. The presented approach is also well suited for automation compared to conventional approaches that require expert input.

Machine Learning Approach for Spatiotemporal Multivariate Optimization of Environmental Monitoring Sensor Locations

Abstract Long-term environmental monitoring is critical for managing the soil and groundwater at contaminated sites. Recent improvements in state-of-the-art sensor technology, communication networks, and artificial intelligence have created opportunities to modernize this monitoring activity for automated, fast, robust, and predictive monitoring. In such modernization, it is required that sensor locations be optimized to capture the spatiotemporal dynamics of all monitoring variables as well as to make it cost-effective. The legacy monitoring datasets of the target area are important to perform this optimization. In this study, we have developed a machine-learning approach to optimize sensor locations for soil and groundwater monitoring based on ensemble supervised learning and majority voting. For spatial optimization, Gaussian process regression (GPR) is used for spatial interpolation, while the majority voting is applied to accommodate the multivariate temporal dimension. Results show that the algorithms significantly outperform the random selection of the sensor locations for predictive spatiotemporal interpolation. While the method has been applied to a four-dimensional dataset (with two-dimensional space, time, and multiple contaminants), we anticipate that it can be generalizable to higher-dimensional datasets for environmental monitoring sensor location optimization.

Evidence-based strategies for optimizing long-term temperature monitoring in offices

Long-term monitoring of the thermal environment in office buildings has become increasingly relevant with the rise of wireless sensor networks. However, there is a notable absence of explicit guidelines for implementing monitoring strategies in such contexts. This lack of direction can lead to inconsistent deployment of sensor networks, resulting in higher maintenance costs and inaccurate long-term assessments of thermal conditions. Based on data analyses of high-accuracy, high-frequency field measurements conducted over a year or longer across multiple offices in Sydney and Shanghai, this study proposes a strategy for long-term temperature monitoring. The strategy advises practitioners to prioritize considerations such as air-conditioning type, room size, and space function when selecting "representative" sensor locations. Typically, sampling every 30minutes is deemed adequate for shared offices where an error margin of ±0.5°C is acceptable. For environments with stable indoor temperatures, less frequent sampling intervals suffice. A power regression model tailored for offices equipped with central AC and no operable windows was developed to predict the maximum allowable sampling interval based on several days of indoor temperature monitoring in winter. Regarding monitoring duration, the study advocates a preferred sampling period of one year to comprehensively capture seasonal variations. Alternatively, a minimum monitoring period of four to six months commencing in late spring or early summer is identified as potentially sufficient. These findings offer valuable insights for optimizing long-term thermal monitoring practices in offices and may contribute to expanding the scope of thermal comfort standards.

A temperature-sensitive points selection method for machine tool based on rough set and multi-objective adaptive hybrid evolutionary algorithm

Reasonable deployment of temperature sensors is the key to accurately monitoring the temperature field of machine tools and improving the accuracy of thermal error prediction and compensation models. To determine the optimal deployment location of sensors, this paper proposes a temperature-sensitive points selection method tightly coupled with rough set and multi-objective optimization. Firstly, the importance of each temperature measurement point to the thermal error is calculated based on the rough set, and information entropy is introduced to amplify the importance difference among adjacent measurement points at the same heat source. Then, with the temperature measurement points groups as the variables, the number of temperature measurement points in the group, and the information importance of the group as the objectives, a multi-objective attribute reduction model is established, which transforms the temperature-sensitive points selection problem into a discrete multi-objective optimization problem. Finally, a multi-objective adaptive hybrid evolutionary algorithm is proposed, which designs a population initialization method based on mutual information and interval probability, and dynamic adaptive evolutionary parameters to achieve optimal temperature-sensitive points selection. Experiments on the high-speed dry hobbing machine verify the superiority and effectiveness of the proposed method.

Distribution-free prediction intervals with conformal prediction for acoustical estimation.

Acoustical parameter estimation is a routine task in many domains. The performance of existing estimation methods is affected by external uncertainty, yet the methods provide no measure of confidence in the estimates. Hence, it is crucial to quantify estimate uncertainty before real-world deployment. Conformal prediction (CP) generates statistically valid prediction intervals for any estimation model using calibration data; a limitation is that calibration data needed by CP must come from the same distribution as the test-time data. In this work, we propose to use CP to obtain statistically valid uncertainty intervals for acoustical parameter estimation using a data-driven model or an analytical model without training data. We consider direction-of-arrival estimation and localization of sources. The performance is validated on plane wave data with different sources of uncertainty, including ambient noise, interference, and sensor location uncertainty. The application of CP for data-driven and traditional propagation models is demonstrated. Results show that CP can be used for statistically valid uncertainty quantification with proper calibration data.

Fisher Information–Based Optimal Sensor Locations for Structural Identification of Nonclassically Damped Systems

Fisher Information–Based Optimal Sensor Locations for Structural Identification of Nonclassically Damped Systems

Design and test of real-time monitoring system for maize entrainment loss based on piezoelectric signal classification

This study describes a novel entrainment loss monitoring system. The features of the collision signals of different maize materials were extracted in the time and frequency domains, and a kernel loss identification model was constructed using machine learning algorithms. The results show that the random forest model performed optimally, with classification accuracies of 96.83% and 94.85% on the training and test sets, respectively. An entrainment loss monitoring system was developed, and the optimal sensor location was determined. The accuracy of kernel classification was 91.09% and that of cob classification was 86.69% for the kernel/cob mixture. Field validation tests showed that the proposed entrainment loss monitoring system outperformed the traditional monitoring system. The method enables the precise monitoring of entrainment losses during maize harvesting, enabling intelligent maize production.

Research on the fusion positioning system of UWB/LiDAR based on the algorithm of SPF and KF

In order to overcome the disadvantages of single sensor localization in complex indoor environments, this paper proposes a design scheme for the ultra-wide band (UWB) and LiDAR co-location fusion system. To attain the ideal positioning effect of this system, a segmented point fusion approach based on Euclid's theorem is put forward to optimize the conventional extended Kalman filter and unscented Kalman filter algorithms. It can effectively reduce the positioning error of UWB in non-line-of-sight environments and under the multipath effect, and also assist LiDAR in correcting trajectory drift in sparsely textured scenes. Through both simulations and experiments, the feasibility of the proposed algorithm in the UWB/LiDAR positioning system is verified. The results reveal that the positioning error of the enhanced multi-sensor fusion algorithm is reduced by 22% compared with the original algorithm and 25.7% compared with the single sensor positioning method.

Soil Parameters Monitoring System

Soil parameters monitoring is vital for different areas such as climatological and ecological studies, agriculture, etc. Large scale measurement systems are based on satellite technologies however their spatial and temporal resolutions still must be improved for real-time measurement tasks and control of local areas. Such systems also require in-situ measurements for calibration. It rises the need of local sensor networks development. The paper proposes a soil monitoring system which allows measuring temperature and humidity. Wireless communication between sensors and data collection unit allows to form sensor array at required area without additional cables. Usage of thermal noise method allows to perform measurement of both parameters with a single sensor which allows to avoid soil moisture content changes due to absence of local soil heating which could be caused by probing currents in other methods. Proposed method also allows to increase measurement accuracy.

A physics-driven sensor placement optimization methodology for temperature field reconstruction

Perceiving the global field from sparse sensors has been a grand challenge in the monitoring, analysis, and design of physical systems. In this context, sensor placement optimization is a crucial issue. Most existing works require large and sufficient data to construct data-based criteria, which are intractable in data-free scenarios without numerical and experimental data. To this end, we propose a novel physics-driven sensor placement optimization (PSPO) method for temperature field reconstruction using a physics-based criterion to optimize sensor locations. In our methodological framework, we firstly derive the theoretical upper and lower bounds of the reconstruction error under noise scenarios by analyzing the optimal solution, proving that error bounds correlate with the condition number determined by sensor locations. Furthermore, the condition number, as the physics-based criterion, is used to optimize sensor locations by the genetic algorithm. Finally, the best sensors are validated by reconstruction models, including non-invasive end-to-end models, non-invasive reduced-order models, and physics-informed models. Experimental results, both on a numerical and an application case, demonstrate that the PSPO method significantly outperforms random and uniform selection methods, improving the reconstruction accuracy by nearly an order of magnitude. Moreover, the PSPO method can achieve comparable reconstruction accuracy to the existing data-driven placement optimization methods.

Greedy selection of optimal location of sensors for uncertainty reduction in seismic moment tensor inversion

We address an optimal sensor placement problem through Bayesian experimental design for seismic full waveform inversion for the recovery of the associated moment tensor. The objective is that of optimally choosing the location of the sensors (stations) from which to collect the observed data. The Shannon expected information gain is used as the objective function to search for the optimal network of sensors. A closed form for such objective is available due to the linear structure of the forward problem, as well as the Gaussian modeling of the observational errors and prior distribution. The resulting problem being inherently combinatorial, a greedy algorithm is deployed to sequentially select the sensor locations that form the best network for learning the moment tensor. Numerical results are presented to display the optimal network of sensors and how the uncertainty in the inferred seismic moment tensor contracts. The scenario of full three-dimensional velocity models or unknown earthquake source locations is treated as nuisance uncertainty, contributing to the overall uncertainty without being the focus of the inversion. This is addressed using a consensus approach over a set of realizations of the nuisance parameter. We analyzed the resulting network of stations for the moment tensor inversion under model misspecification, which reflects realistic data-generating processes.

Retraction Note: Application of light sensor localization based on deep learning algorithm in tourist path recommendation in cultural scenic spots

Retraction Note: Application of light sensor localization based on deep learning algorithm in tourist path recommendation in cultural scenic spots

Performance characterization of a wireless sensors network system (WSNS) for measurements of hurricane wind effects on structures

This paper presents a new wireless sensors network system (WSNS) designed for high-resolution absolute pressure measurements, wind speed, and direction. The system is tailored to assess the impact of hurricane winds on residential structures, both in laboratory settings and field environments. Importantly WSNS stands out for its unique ability to provide waterproof, surface-mounted external pressure measurements. The system's performance is evaluated during deployment on a full-scale house model at the Wall of Wind facility. The WSNS sensors were installed on different surfaces of a single-story residential building model. The sensor locations mirrored the locations of surface pressure taps connected to a Scanivalve differential pressure measurement system. Due to the size and shape of the WSNS pressure module, a casing effect was observed, which may result in pressure offsets under certain wind speeds and directions, depending on the sensor's location and the conditions, including dry and light rain (50 mm per hour). The comparison between WSNS and Scanivalve indicates that the sensor's casing geometry does not cause significant differences in the time-averaged measurements for low-turbulence regions. Conversely, this is not true for high-turbulence regions, which should be marked for future deployments using smaller surface-mounted pressure taps.

Variation of Seismocardiogram-Derived Cardiac Time Intervals and Heart Rate Variability Metrics Across the Sternum

Abstract Cardiac time intervals (CTIs) are vital indicators of cardiac health and can be noninvasively assessed using a combination of electrocardiography (ECG) and seismocardiography (SCG), a method of capturing cardiac-induced chest vibrations via accelerometers. SCG signals can be measured from different chest locations. However, more investigations are needed to evaluate the impact of sensor placement on SCG-derived cardiac parameters. This study investigates the effect of accelerometer placement along the sternum on SCG-derived CTI estimations and heart rate variability (HRV) parameters. A semi-automated algorithm was developed to detect SCG fiducial points and seven CTIs from thirteen healthy individuals. Comparative analysis with manually selected peaks and gold-standard ECG was conducted to assess fiducial point detection accuracy. Results indicate the highest recall and precision in aortic valve opening (0.84–1.00 and 0.96–1.00, respectively) and mitral valve closure (0.77–1.00 and 0.93–1.00, respectively) detection. Aortic valve closure (0.43–1.00 and 0.61–1.00, respectively) and mitral valve opening (0.64–1.00 and 0.91–1.00, respectively) detection, although slightly less accurate due to signal intensity variations, demonstrated overall effectiveness compared to manually selected peaks. Furthermore, SCG-derived heart rates showed a high correlation coefficient (r > 0.9) with the gold-standard ECG heart rates. Single-factor ANOVA revealed significant differences (p < 0.05) in SCG-derived CTI estimations based on sensor locations on the sternum, highlighting the importance of sensor placement for accurate assessments.