Unknown Locations Research Articles

Multiple Leak Detection in Water Distribution Networks Following Seismic Damage

Water pipe leaks due to seismic damage are more difficult to detect than bursts, and such leaks, if not repaired in a timely manner, can eventually reduce supply pressure and generate both pollutant penetration risks and economic losses. Therefore, leaks must be promptly identified, and damaged pipes must be replaced or repaired. Leak-detection using equipment in the field is accurate; however, it is a considerably labor-intensive process that necessitates expensive equipment. Therefore, indirect leak detection methods applicable before fieldwork are necessary. In this study, a computer-based, multiple-leak-detection model is developed. The proposed technique uses observational data, such as the pressure and flow rate, in conjunction with an optimization method and hydraulic analysis simulations, to improve detection efficiency (DE) for multiple leaks in the field. A novel approach is proposed, i.e., use of a cascade and iteration search algorithms to effectively detect multiple leaks (with the unknown locations, quantities, and sizes encountered in real-world situations) due to large-scale disasters, such as earthquakes. This method is verified through application to small block-scale water distribution networks (WDNs), and the DE is analyzed. The proposed detection model can be used for efficient leak detection and the repair of WDNs following earthquakes.

Fast hardware-oriented algorithm for 3D positioning in line-of-sight and single bounced non-line-of-sight environments

The ability to find the location of a mobile object and track it in two-dimensional (2D) and three-dimensional (3D) space is an indispensable feature of wireless communication systems. In particular, the increase in demand for using self-navigation technology in unmanned vehicles is attracting additional attention to this area of research. The methods and techniques developed for these systems compete in terms of simplicity, performance, and accuracy. However, the majority of solutions focus on only one of these performance metrics and are not applicable to the projected systems. In this study, a new location estimation solution that satisfies all three performance metrics (simplicity, accuracy, and efficiency) is achieved. The developed algorithms can be executed on a mobile object to allow it to learn its position in space. Furthermore, the simplicity of the mathematical operations used, namely binary shift and add operations, makes our algorithm hardware-friendly. Accelerated coordinate rotation digital computer (CORDIC) algorithms are used for the first time with line-of-sight (LOS) and single-bounced-scattering non-line-of-sight (SBS NLOS) signal arriving approaches for mobile object self-positioning. This study also introduces a newly developed 'vector-breaking' method (VBM) to estimate the location of a mobile object using the arriving signals from scatters with unknown locations. Test results using the developed algorithm show that the projected level of accuracy has been achieved. Furthermore, the developed algorithm performs better than the solutions currently available in the field.

Smartphone Location Recognition with Unknown Modes in Deep Feature Space.

Smartphone location recognition aims to identify the location of a smartphone on a user in specific actions such as talking or texting. This task is critical for accurate indoor navigation using pedestrian dead reckoning. Usually, for that task, a supervised network is trained on a set of defined user modes (smartphone locations), available during the training process. In such situations, when the user encounters an unknown mode, the classifier will be forced to identify it as one of the original modes it was trained on. Such classification errors will degrade the navigation solution accuracy. A solution to detect unknown modes is based on a probability threshold of existing modes, yet fails to work with the problem setup. Therefore, to identify unknown modes, two end-to-end ML-based approaches are derived utilizing only the smartphone’s accelerometers measurements. Results using six different datasets shows the ability of the proposed approaches to classify unknown smartphone locations with an accuracy of 93.12%. The proposed approaches can be easily applied to any other classification problems containing unknown modes.

Impedance Optimization for Uncertain Contact Interactions Through Risk Sensitive Optimal Control

This letter addresses the problem of computing optimal impedance schedules for legged locomotion tasks involving complex contact interactions. We formulate the problem of impedance regulation as a trade-off between disturbance rejection and measurement uncertainty. We extend a stochastic optimal control algorithm known as Risk Sensitive Control to take into account measurement uncertainty and propose a formal way to include such uncertainty for unknown contact locations. The approach can efficiently generate optimal state and control trajectories along with local feedback control gains, i.e. impedance schedules. Extensive simulations demonstrate the capabilities of the approach in generating meaningful stiffness and damping modulation patterns before and after contact interaction. For example, contact forces are reduced during early contacts, damping increases to anticipate a high impact event and tracking is automatically traded-off for increased stability. In particular, we show a significant improvement in performance during jumping and trotting tasks with a simulated quadruped robot.

The Case for Medical Device Cybersecurity Hygiene Practices for Frontline Personnel

The Case for Medical Device Cybersecurity Hygiene Practices for Frontline Personnel

Geostatistics for Environmental Scientists

There are many theories that are seemly worth linking the environment and statistical methods through a space or space-time sampling framework. A prominent theory of spatial random processes is fully explored in Geostatistics for Environmental Scientists, a book that was first published in 2001, and later revised in a second edition in 2007. The book grasps the definition of ‘Geostatistics’ from a farmer’s imaginary story, to connect it to a soil/environment survey. The soil survey is very important for the authors’ methodology. It is characterized by sampled locations to determine how much is known and how soil/environmental surveys can apply Geostatistics methods to generate estimates and predict values for unknown locations through variogram and covariance.

Cultural Heritage Restoration of a Hemispherical Vault by 3D Modelling and Projection of Video Images with Unknown Parameters and from Unknown Locations

Reverse engineering applied to architectural restoration for the reconstruction of structural surfaces depends on metric precision. Sometimes there are elements on these surfaces whose value is even higher than the building itself. This is the case for many churches whose ceilings have pictorial works of art. Reconstruction requires the existence of some identifiable remainder and/or a surface geometry that enables mathematical development. In our case, the vault has an irregular hemispherical geometry (without possible mathematical development), and there are no significant remains of the painting (which was destroyed by a fire). Through the 3D modelling of the irregular vault and two historic frames with a camera of unknown geometry, an inverse methodology is designed to project the original painting without metric deformations. For this, a new methodology to locate the camera positions is developed. After, a 3D virtual mathematical model of the complete image on the vault is calculated, and from it, partial 3D virtual images are automatically calculated depending on the variable unknown positions of the video cannons (distributed along the upper corridor of the apse) that will project them (visually forming a perfect complete 3D image).

Using GTFS to Calculate Travel Time Savings Potential of Bus Preferential Treatments

Dedicated bus lanes and other transit priority treatments are a cost-effective way to improve transit speed and reliability. However, creating a bus lane can be a contentious process; it requires justification to the public and frequently entails competition for federal grants. In addition, more complex bus networks are likely to have unknown locations where transit priority infrastructure would provide high value to riders. This analysis presents a methodology for estimating the value of bus preferential treatments for all segments of a given bus network. It calculates the passenger-weighted travel time savings potential for each inter-stop segment based on schedule padding. The input data, ridership data, and General Transit Feed Specification (GTFS) trip-stop data are universally accessible to transit agencies. This study examines the 2018 Metropolitan Atlanta Rapid Transit Authority (MARTA) bus network and identifies a portion of route 39 on Buford Highway as an example candidate for a bus lane corridor. The results are used to evaluate the value of time savings to passengers, operating cost savings to the agency, and other benefits that would result from implementing bus lanes on Buford Highway. This study does not extend to estimating the cost of transit priority infrastructure or recommending locations based on traffic flow characteristics. However, it does provide a reproducible methodology to estimate the value of transit priority treatments, and it identifies locations with high value, all using data that are readily available to transit agencies. Conducting this analysis provides a foundation for beginning the planning process for transit priority infrastructure.

Deferral and the Dream

Deferral and the Dream

Physical Layer Security for MISO NOMA VLC System Under Eavesdropper Collusion

In this paper, we investigate the physical layer security (PLS) for a multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) visible light communication (VLC) system, whose information is intercepted by collusive eavesdroppers. For the case of known eavesdroppers' locations, a precoding scheme is proposed to maximize the secrecy capacity of the system by suppressing the rate of collusive eavesdroppers. Then we extend the scheme to the case of unknown eavesdroppers' locations. Specifically, the artificial noise is introduced in the precoding scheme to further suppress the average rate of eavesdroppers. To solve the non-convex optimization problem, we propose an iterative algorithm based on concave-convex procedure. Simulation results illustrate the outperformance of the proposed schemes in guaranteeing the PLS for the MISO NOMA VLC system under the interception of collusive eavesdroppers.

Predictive Exoskeleton Control for Arm-Motion Augmentation Based on Probabilistic Movement Primitives Combined With a Flow Controller

There are many work-related repetitive tasks where the application of exoskeletons could significantly reduce the physical effort by assisting the user in moving the arms towards the desired location in space. To make such control more user acceptable, the controller should be able to predict the motion of the user and act accordingly. This letter presents an exoskeleton control method that utilizes probabilistic movement primitives to generate predictions of user movements in real-time. These predictions are used in a flow controller, which represents a novel velocity-field-based exoskeleton control approach to provide assistance to the user in a predictive way. We evaluated our approach with a haptic robot, where a group of twelve participants had to perform movements towards different target locations in the frontal plane. We tested whether we could generalize the predictions for new and unknown target locations whilst providing assistance to the user without changing their kinematic parameters. The evaluation showed that we could accurately predict user movement intentions while at the same time significantly decrease the overall physical effort exerted by the participants to achieve the task.

Mapping the Groundwater Level and Soil Moisture of a Montane Peat Bog Using UAV Monitoring and Machine Learning

One of the best preconditions for the sufficient monitoring of peat bog ecosystems is the collection, processing, and analysis of unique spatial data to understand peat bog dynamics. Over two seasons, we sampled groundwater level (GWL) and soil moisture (SM) ground truth data at two diverse locations at the Rokytka Peat bog within the Sumava Mountains, Czechia. These data served as reference data and were modeled with a suite of potential variables derived from digital surface models (DSMs) and RGB, multispectral, and thermal orthoimages reflecting topomorphometry, vegetation, and surface temperature information generated from drone mapping. We used 34 predictors to feed the random forest (RF) algorithm. The predictor selection, hyperparameter tuning, and performance assessment were performed with the target-oriented leave-location-out (LLO) spatial cross-validation (CV) strategy combined with forward feature selection (FFS) to avoid overfitting and to predict on unknown locations. The spatial CV performance statistics showed low (R2 = 0.12) to high (R2 = 0.78) model predictions. The predictor importance was used for model interpretation, where temperature had strong impact on GWL and SM, and we found significant contributions of other predictors, such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Index (NDI), Enhanced Red-Green-Blue Vegetation Index (ERGBVE), Shape Index (SHP), Green Leaf Index (GLI), Brightness Index (BI), Coloration Index (CI), Redness Index (RI), Primary Colours Hue Index (HI), Overall Hue Index (HUE), SAGA Wetness Index (TWI), Plan Curvature (PlnCurv), Topographic Position Index (TPI), and Vector Ruggedness Measure (VRM). Additionally, we estimated the area of applicability (AOA) by presenting maps where the prediction model yielded high-quality results and where predictions were highly uncertain because machine learning (ML) models make predictions far beyond sampling locations without sampling data with no knowledge about these environments. The AOA method is well suited and unique for planning and decision-making about the best sampling strategy, most notably with limited data.

Identification of genetic determinants of hemolytic activity of Riemerella anatipestifer using random transposon mutagenesis

Riemerella anatipestifer causes epizootic infectious disease in poultry resulting in serious economic losses especially to the duck industry. In our previous study, R. anatipestifer was found to lyse duck erythrocytes in vitro. In the present study, a random Tn4351 mutagenesis library of hemolytic R. anatipestifer strain SX containing 4000 mutants was constructed to investigate the genetic basis of hemolytic activity. Thirty mutants with reduced hemolytic activity and one with increased hemolytic activity were screened and insertions in 24 genes were identified. Of these genes, four were predicted to encode outer membrane proteins, one encoded a cytoplasmic membrane protein, 11 encoded cytoplasmic proteins, and eight encoded proteins with unknown locations. Based on current annotations of the R. anatipestifer genomes, of the 24 genes, 7 (29.17%) were involved in iron utilization. The hemolytic activities of the complemented strains M2 (pRES-Riean_0790) and M18 (pRES-Riean_0653) were restored, indicating that both Riean_0653 and Riean_0790 are involved in the hemolytic activity of strain SX. However, the recombinant proteins rRiean_0317, rRiean_0790, rRiean_0653, rRiean_1027, rRiean_1143, and rRiean_1561 had no hemolytic activity, suggesting that none were hemolysins.

A method to avoid spatial overfitting in estimation of grassland above-ground biomass on the Tibetan Plateau

Accurate assessments of grassland above-ground biomass (AGB) are crucial for the sustainable utilization and protection of grassland resources and the eco-environment. In this study, a random forest (RF) model combined with the forward feature selection (FFS) and leave-location-out cross-validation (LLO-CV) methods was trained to predict the dry weight (DW) of grassland AGB based on multiple factors. The final model exhibited a performance of R2 = 0.66, root mean square error (RMSE) of 503.86 kg DW/ha and mean absolute error (MAE) of 376.51 kg DW/ha. The spatial distribution of grassland AGB increased from northwest to southeast over the entire Tibetan Plateau (TP) from 2001 to 2018. Grassland AGB increased more than it decreased (70.6% vs 29.4%, respectively) during the study period. Using a combination of FFS and LLO-CV, spatial overfitting was reduced, and the predictive accuracy of the RF was improved, thus enhancing the ability to predict the AGB in unknown locations from training data. This study proposes a robust methodology with which to improve the transferability of machine learning algorithms to predict grassland AGB in unknown locations.

Accounting for theory errors with empirical Bayesian noise models in nonlinear centroid moment tensor estimation

SUMMARYCentroid moment tensor (CMT) parameters can be estimated from seismic waveforms. Since these data indirectly observe the deformation process, CMTs are inferred as solutions to inverse problems which are generally underdetermined and require significant assumptions, including assumptions about data noise. Broadly speaking, we consider noise to include both theory and measurement errors, where theory errors are due to assumptions in the inverse problem and measurement errors are caused by the measurement process. While data errors are routinely included in parameter estimation for full CMTs, less attention has been paid to theory errors related to velocity-model uncertainties and how these affect the resulting moment-tensor (MT) uncertainties. Therefore, rigorous uncertainty quantification for CMTs may require theory-error estimation which becomes a problem of specifying noise models. Various noise models have been proposed, and these rely on several assumptions. All approaches quantify theory errors by estimating the covariance matrix of data residuals. However, this estimation can be based on explicit modelling, empirical estimation and/or ignore or include covariances. We quantitatively compare several approaches by presenting parameter and uncertainty estimates in nonlinear full CMT estimation for several simulated data sets and regional field data of the Ml 4.4, 2015 June 13 Fox Creek, Canada, event. While our main focus is at regional distances, the tested approaches are general and implemented for arbitrary source model choice. These include known or unknown centroid locations, full MTs, deviatoric MTs and double-couple MTs. We demonstrate that velocity-model uncertainties can profoundly affect parameter estimation and that their inclusion leads to more realistic parameter uncertainty quantification. However, not all approaches perform equally well. Including theory errors by estimating non-stationary (non-Toeplitz) error covariance matrices via iterative schemes during Monte Carlo sampling performs best and is computationally most efficient. In general, including velocity-model uncertainties is most important in cases where velocity structure is poorly known.

Determination of soil C:N suitability zones for organic farming using an unsupervised classification in eastern Croatia

Soil carbon-to-nitrogen ratio (C:N) represents an indicator of soil quality and fertility, having a major impact on agricultural land management for organic farming. Determination of soil C:N suitability zones is a necessary procedure in the process, enabling effective land management. A total of 72 soil samples at two soil layers at 0–10 cm and 20–30 cm were used in the study, evenly distributed in the Osijek-Baranja County in eastern Croatia. Ordinary kriging (OK) and Lognormal kriging (LK) using linear, Gaussian and spherical mathematical models, alongside Inverse distance weighted (IDW) were evaluated for the spatial prediction of soil C:N. Inner accuracy representing retention of input sample values in the interpolation results and outer accuracy representing a prediction accuracy at unknown locations were used to determine optimal interpolation parameters. K-means unsupervised classification algorithm was used for the objective determination of soil C:N suitability zones in five classes specified by the Food and Agriculture Organization (FAO). IDW resulted in the highest inner accuracy, while OK with the Gaussian model produced the highest outer accuracy with the average R2 = 0.7908 and NRMSE = 0.0544. Contrary to the previous research, higher mean soil C:N results in a lower soil layer of 20–30 cm with 13.41, compared to 12.03 at 0–10 cm soil layer. The highest soil C:N suitability was determined in only 4.8% of the study area, with the suitability index of 18.44. Meanwhile, the two largest classes were marginally suitable and currently unsuitable class, covering 35.5% and 27.7% of the study area, respectively. These results indicated a necessity for the adjustment of agricultural land management practices to enable sustainable organic farming.

Spatial mapping of COVID-19 for Indian states using Principal Component Analysis

Spatial mapping of COVID-19 for Indian states using Principal Component Analysis

Quantifying the dynamics of migration after Hurricane Maria in Puerto Rico

Population displacement may occur after natural disasters, permanently altering the demographic composition of the affected regions. Measuring this displacement is vital for both optimal postdisaster resource allocation and calculation of measures of public health interest such as mortality estimates. Here, we analyzed data generated by mobile phones and social media to estimate the weekly island-wide population at risk and within-island geographic heterogeneity of migration in Puerto Rico after Hurricane Maria. We compared these two data sources with population estimates derived from air travel records and census data. We observed a loss of population across all data sources throughout the study period; however, the magnitude and dynamics differ by the data source. Census data predict a population loss of just over 129,000 from July 2017 to July 2018, a 4% decrease; air travel data predict a population loss of 168,295 for the same period, a 5% decrease; mobile phone-based estimates predict a loss of 235,375 from July 2017 to May 2018, an 8% decrease; and social media-based estimates predict a loss of 476,779 from August 2017 to August 2018, a 17% decrease. On average, municipalities with a smaller population size lost a bigger proportion of their population. Moreover, we infer that these municipalities experienced greater infrastructure damage as measured by the proportion of unknown locations stemming from these regions. Finally, our analysis measures a general shift of population from rural to urban centers within the island. Passively collected data provide a promising supplement to current at-risk population estimation procedures; however, each data source has its own biases and limitations.

Localisation of wireless nodes with partial connectivity in wireless sensor systems

Owing to their short communication range, wireless nodes in wireless sensor networks (WSNs) can exchange information with devices in their vicinity only. Thus, in sparse networks, the full connectivity of the network is rarely achieved. This renders a centralised approach towards localisation in WSNs useless. Moreover, the exploitation of a centralised algorithm for localisation compromises the scalability in dense networks. Thus, a decentralised, location-aware network with partial connectivity and hybrid (range and direction) measurements obtained between known sensors (reference sensors) and sensors at unknown locations (target sensors) is under focus. The decentralised location estimation is obtained using a linear least squares (LLS) approach and performance enhancements are achieved by introducing a weighing strategy to produce weighted least squares (WLS) estimates. This distributed estimation is made possible by designing a map stitching technique that forms the global map of the network from individual local maps of the wireless nodes without compromising the distributed nature of the network. In the analytical section of the study, theoretical mean squares error expression is derived for LLS estimation, and a Cramer–Rao lower bound is derived to bind the performance of the WLS solution. The algorithm's performance validation is conducted both theoretically and via simulations.

What Martian Meteorites Reveal About the Interior and Surface of Mars

AbstractMartian meteorites are the only direct samples from Mars, thus far. Currently, there are a total of 262 individual samples originating from at least 11 ejection events. Geochemical analyses, through techniques that are also used on terrestrial rocks, provide fundamental insights into the bulk composition, differentiation and evolution, mantle heterogeneity, and role of secondary processes, such as aqueous alteration and shock, on Mars. Martian meteorites display a wide range in mineralogy and chemistry, but are predominantly basaltic in composition. Over the past 6 years, the number of martian meteorites recovered has almost doubled allowing for studies that evaluate these meteorites as suites of igneous rocks. However, the martian meteorites represent a biased sampling of the surface of Mars with unknown ejection locations. The geology of Mars cannot be unraveled solely by analyzing these meteorites. Rocks analyzed by rovers on the surface of Mars are of distinct composition to the meteorites, highlighting the importance of Mars missions, especially sample return. The Mars 2020 Perseverance rover will collect and cache—for eventual return to Earth—over 30 diverse surface samples from Jezero crater. These returned samples will allow for Earth‐based state‐of‐the‐art analyses on diverse martian rocks with known field context. The complementary study of returned samples and meteorites will help to constrain the evolution of the martian interior and surface. Here, we review recent findings and advances in the study of martian meteorites and examine how returned samples would complement and enhance our knowledge of Mars.