Pair Of Coordinates Research Articles

End-to-End Implicit Object Pose Estimation.

To accurately estimate the 6D pose of objects, most methods employ a two-stage algorithm. While such two-stage algorithms achieve high accuracy, they are often slow. Additionally, many approaches utilize encoding-decoding to obtain the 6D pose, with many employing bilinear sampling for decoding. However, bilinear sampling tends to sacrifice the accuracy of precise features. In our research, we propose a novel solution that utilizes implicit representation as a bridge between discrete feature maps and continuous feature maps. We represent the feature map as a coordinate field, where each coordinate pair corresponds to a feature value. These feature values are then used to estimate feature maps of arbitrary scales, replacing upsampling for decoding. We apply the proposed implicit module to a bidirectional fusion feature pyramid network. Based on this implicit module, we propose three network branches: a class estimation branch, a bounding box estimation branch, and the final pose estimation branch. For this pose estimation branch, we propose a miniature dual-stream network, which estimates object surface features and complements the relationship between 2D and 3D. We represent the rotation component using the SVD (Singular Value Decomposition) representation method, resulting in a more accurate object pose. We achieved satisfactory experimental results on the widely used 6D pose estimation benchmark dataset Linemod. This innovative approach provides a more convenient solution for 6D object pose estimation.

Heavy Rainfall Events in Selected Geographic Regions of Mexico, Associated with Hail Cannons

In this article, we document the use of hail cannons in Mexico to dispel or suppress heavy rain episodes, a common practice among farmers, without scientific evidence to support its effectiveness. This study uses two rain databases: one compiled from the Global Precipitation Measurement (GPM) mission and the other generated with the implementation of the Weather Research and Forecasting (WRF) model. The aim is to explore the association between heavy rain episodes and hail cannon locations. The analysis includes two geographic features: a pair of coordinates and a 3 km radius area of influence around each hail cannon. This dimension is based on the size and distribution of the heavy rainfall events. This study analyzes four years of half-hourly rain data using the Python ecosystem environment with machine learning libraries. The results show no relationship between the operation of hail cannons and the dissipation or attenuation of heavy rainfall events. However, this study highlights that the significant differences between the GPM and WRF databases in registering heavy rain events may be attributable to their own uncertainty. Despite the unavailability of ground-based observations, the inefficiency of hail cannons in affecting the occurrence of heavy rain events is evident. Overall, this study provides scientific evidence that hail cannons are inefficient in preventing the occurrence of heavy rain episodes.

An Optimal Choice of Characteristic Polynomial Roots for Pole Placement Control Design

The problem of finding the arrangement of closed-loop control system poles that minimizes an objective function is considered. The system optimality criterion is the value of the H∞ norm of the frequency transfer function relative to the disturbance with constraints imposed on the system pole placement and the values of the H∞ norm of the sensitivity function and the transfer function from measurement noise to control. An optimization problem is formulated as follows: the vector of variables consists of the characteristic polynomial roots of the closed loop system with the admissible values restricted to a given pole placement region; in addition to the optimality criterion, the objective function includes penalty elements for other constraints. It is proposed to use a logarithmic scale for the moduli of the characteristic polynomial roots as elements of the vector of variables. The multi-extremality problem of the objective function is solved using the multiple start procedure. A coordinate descent modification with a pair of coordinates varied simultaneously is used for search.

Analysis of the Dynamics of a Cubic Nonlinear Five-Dimensional Memristive Chaotic System and the Study of Reduced-Dimensional Synchronous Masking

To improve the complexity of the chaotic system and achieve the effective transmission of image information, in this paper, a five-dimensional memristive chaotic system with cubic nonlinear terms is constructed, which has four pairs of symmetric coordinates. First, the cubic nonlinear memristive chaotic system is analyzed using the Lyapunov exponential map, bifurcation map, and attractor phase diagram. The experimental results show that under four pairs of symmetric coordinates, the system exists not only parameter-dependent symmetric rotational coexisting attractor and transient chaotic phenomena but also exists super-multistationary with alternating chaotic cycles dependent on the initial value of the memristor. Then, it is proposed to add a constant term to the linear state variable to explore the effect of the offset increment of the linear state variable on the system in four pairs of symmetric coordinates, while circuit simulation of the five-dimensional chaotic system is carried out using Simulink to verify its existence and realisability. Finally, the synchronization of the dimensionality reduction system and the confidential transmission of the image are achieved, using the control voltage of the system to replace the internal state variables of the memristor to achieve the one-dimensional reduction process, and an adaptive synchronization controller is designed to synchronize the system before and after the dimensionality reduction. Based on the above, an image to be transmitted is modulated into a one-dimensional array and then subjected to the fractional and cyclic operations and combined with the linear encryption and decryption functions and the chaotic masking technique, the simple encryption and decryption of the image processes are realized.

Selection of a Neural Network Architecture for Implementation of Cognitive Functions of Network Software for Control of a Group of Interacting Small Space Vehicles

The purpose of the research is to substantiate and select the architecture of a neural network for the possibility of implementing the cognitive functions of network software for controlling a grouping of interacting small spacecraft.Methods are based on the concepts of AI theory for managing the grouping of small spacecraft - the use of adaptive methods and tools that allow making decisions, similar to the mechanisms of human thinking. With regard to space communication systems with a heterogeneous structure, AI methods and technologies are aimed at the processes of predicting the state in communication channels between network nodes and automatic reconfiguration of the network of devices based on the learning processes of a neural network (NN).Results. In the learning and forecasting mode, it is necessary to use time series of parameters and coordinates of specific pairs of small spacecraft with non-zero line of sight. Especially for time series analysis, recurrent neural networks (RNN) are used, in particular, LSTM. The idea of RNN operation is to use as input data for the current forecast not only the state vectors of the SVs and their coordinates, but also the previous value of the communication quality, actual or predictive. The paper shows that the onboard computing power of a separate MSC does not allow performing forecasting and training on board. Therefore, a dedicated ground segment of forecasting and monitoring is required, which will collect a posteriori information, periodically train the cognitive model, use it to predict the quality of communication, and transmit the results to the network nodes to build data transmission routes.Conclusion. The analysis of modern solutions and the choice of neural network architecture for the implementation of the cognitive functions of the network software for controlling the grouping of interacting small spacecraft showed that the neural networks of the Transformer architecture, which are based on the mechanism of internal attention, most fully meet the requirements of the project. The Transformer architecture allows using the entirety of a priori data, has a high learning and forecasting speed.

On the pythagorean structure of the optimal transport for separable cost functions

In this paper, we study the optimal transport problem induced by two measures supported over two polish spaces, namely, X and Y , which are the product of n smaller polish spaces, that is, X={\Large{\times}}_{j=1}^n X_j and Y={\Large{\times}}_{j=1}^n Y_j . In particular, we focus on problems induced by a cost function c:X\times Y\to [0,+\infty) that is separable; i.e., c is such that c=c_1+\dots+c_n , where each c_j depends only on the couple (x_j,y_j) , and thus c_j:X_j\times Y_j\to [0,+\infty) . Noticeably, if X=Y=\mathbb{R}^n , this class of cost functions includes all the l_p^p costs. Our main result proves that the optimal transportation plan with respect to a separable cost function between two given measures can be expressed as the composition of n different lower-dimensional transports, one for each pair of coordinates (x_i,y_i) in X\times Y . This allows us to decompose the entire Wasserstein cost as the sum of n lower-dimensional Wasserstein costs and to prove that there always exists an optimal transportation plan whose random variable enjoys a conditional independence property with respect to its marginals. We then show that our formalism allows us to explicitly compute the optimal transportation plan between two probability measures when each measure has independent marginals. Finally, we focus on two specific frameworks. In the first one, the cost function is a separable distance, i.e., d=d_1+d_2 , where both d_1 and d_2 are distances themselves. In the second one, both measures are supported over \mathbb{R}^n and the cost function is of the form c(x,y)=h(\vert x_1-y_1\vert)+h(\vert x_2-y_2\vert) , where h is a convex function such that h(0)=0 .

Bridging MRI Cross-Modality Synthesis and Multi-Contrast Super-Resolution by Fine-Grained Difference Learning.

In multi-modal magnetic resonance imaging (MRI), the tasks of imputing or reconstructing the target modality share a common obstacle: the accurate modeling of fine-grained inter-modal differences, which has been sparingly addressed in current literature. These differences stem from two sources: 1) spatial misalignment remaining after coarse registration and 2) structural distinction arising from modality-specific signal manifestations. This paper integrates the previously separate research trajectories of cross-modality synthesis (CMS) and multi-contrast super-resolution (MCSR) to address this pervasive challenge within a unified framework. Connected through generalized down-sampling ratios, this unification not only emphasizes their common goal in reducing structural differences, but also identifies the key task distinguishing MCSR from CMS: modeling the structural distinctions using the limited information from the misaligned target input. Specifically, we propose a composite network architecture with several key components: a label correction module to align the coordinates of multi-modal training pairs, a CMS module serving as the base model, an SR branch to handle target inputs, and a difference projection discriminator for structural distinction-centered adversarial training. When training the SR branch as the generator, the adversarial learning is enhanced with distinction-aware incremental modulation to ensure better-controlled generation. Moreover, the SR branch integrates deformable convolutions to address cross-modal spatial misalignment at the feature level. Experiments conducted on three public datasets demonstrate that our approach effectively balances structural accuracy and realism, exhibiting overall superiority in comprehensive evaluations for both tasks over current state-of-the-art approaches. The code is available at https://github.com/papshare/FGDL.

Building geochemical vectors with trace element compositions of sulfides in orogenic gold mineral systems in northern Finland

Exploring and discovering new ore deposits that supply the needed metals for our current societal transition to a green-energy and technologically advanced low-carbon future is becoming increasingly difficult. To fully convert the current system however, a significant increase of mining of minerals is required, particularly since current global mineral reserves are insufficient to cover the growing demand for a fossil fuel-free infrastructure. It is therefore even more important to develop and introduce new time-and cost-efficient exploration tools, which can be utilized in the discovery of new mineral deposits and applied in the early, or through advanced stages of mineral exploration. Given that Finland is one of the major EU countries that covers the supply chain for the EU battery industry, its metallogenic provinces are extensively tested and investigated by new exploration technologies. For this purpose, a comprehensive dataset of trace element contents in single grains of pyrite, pyrrhotite and chalcopyrite was compiled on the basis of results of laser ablation inductively coupled plasma mass spectrometry (LA-ICPMS) analyses. Samples were systematically collected from drill cores from orogenic Au-only and Au-base-metal deposits in the Paleoproterozoic Svecofennian orogenic belts in northern Finland. The objective of this dataset is to serve as a reference tool in heavy indicator minerals-based exploration. By performing correlation analysis with the symmetric pivot coordinates approach for compositional data, as well as principal component analysis, it is demonstrated how all analyzed sulfide species have the capacity to discriminate between Au-only and Au-base-metal deposit types, as well as occurrences of free Au versus refractory Au. Element pairs of symmetric coordinates most suitable for differentiating Au-only systems from Au-base-metal settings are Co/Au and Au/Se in pyrite; Ni/Bi and Se/As in pyrrhotite; and Ni/Ag, Ag/In, Se/Ag, Cr/Ag, Cr/In, Cr/Zn, Zn/Se and Sn/Se in chalcopyrite. Results from principal component analysis reveal that pyrites from Au-base-metal deposits are associated with the positive loadings of Se-As-Co onto PC1 and those from Au-only deposits with negative loadings of Bi-Sb-Te-Au and Ni onto PC1 and PC2, respectively. In the case of pyrrhotite, samples from Au-Co and Au-Cu deposits are clearly distinguished between the positive loadings of Hg-Se-Co onto PC1, and the negative loadings of Sb-Au-Bi-Ni onto PC1. Positive loadings of Ag-Se and Co along PC1 are indicative of chalcopyrite from Au-Co deposits, while negative loadings of Ni and Zn onto PC1 are pointing towards an origin from Au-Cu, as well as Au-only deposits. Additionally, when testing the indicator mineral method with recovered pyrites from till samples from the Peräpohja belt, a potential relationship with bedrock pyrites is successfully demonstrated. This study aims to present the versatility of sulfide trace elements in geochemical exploration, as it could complement the mineral exploration industry with a more efficient and less expensive practice for profitable deposit discovery.

More than time: travel time to the delivery ward and maternal outcomes – onset of labour, postpartum haemorrhage and obstetric anal sphincter injury

BackgroundClosing delivery units increases travel time for some women. Whether increased travel time is associated with maternal outcomes is important for understanding the consequences of such closures. Previous studies are limited in measuring travel time and restricted to the outcome of caesarean section. MethodsOur population-based cohort includes data from the Swedish Pregnancy Register for women giving birth between 2014 and 2017 (N = 364,630). We estimated travel time from home to the delivery ward using coordinate pairs of actual addresses. The association between travel time and onset of labour was modelled using multinomial logistic regression, and logistic regression was used for the outcomes postpartum haemorrhage (PPH) and obstetric anal sphincter injury (OASIS). FindingsOver three-quarters of women had ≤30 min travel time (median 13.9 min). Women who travelled ≥60 min arrived to care sooner and laboured there longer. Women with further to travel had increased adjusted odds ratio (aOR) of having an elective caesarean section (31–59 min aOR 1.11; 95% confidence interval [CI] 1.07–1.16; ≥60 min aOR 1.25; 95% CI 1.16–1.36) than spontaneous onset of labour. Women (at full term with spontaneous onset) living ≥60 min away had reduced odds of having a PPH (aOR 0.84; 95% CI 0.76–0.94) or OASIS (aOR 0.79; 95% CI 0.66–0.94). InterpretationLonger travel time increased the odds of elective caesarean section. Women with furthest to travel arrived sooner and spent more time in care; although they had a lower risk of PPH or OASIS, they also tended to be younger, have a higher body mass index and were Nordic born.

A Novel Approach for Integrated Shortest Path Finding Algorithm (ISPSA) Using Mesh Topologies and Networks-on-Chip (NOC)

A novel data dispatching or communication technique based on circulating networks of any network IP is suggested for multi data transmission in multiprocessor systems using Networks-On-Chip (NoC). In wireless communication network management have some negatives have heavy data losses and traffic of data sending data while packet scheduling and low performance in the varied network due to workloads. To overcome the drawbacks, in this method proposed system is Integrated Shortest Path Search Algorithm (ISPSA) using mesh topologies. The message is sent to IP (Internet Protocol) in the network until the specified bus accepts it. Integrated Shortest Path Search Algorithm for communication between two nodes is possible at any one moment. On-chip wireless communications operating at specific frequencies are the most capable option for overcoming metal interconnects multi-hop delay and excessive power consumption in Network-on-Chip (NoC) devices. Each node can be indicated by a pair of coordinates (level, position), where the level is the tree's vertical level and the view point is its horizontal arrangement in the sequence of left to right. The output gateway node's n nodes are linked to two nodes in the following level, with all resource nodes located at the bottommost vertical level and the constraint of this topology is its narrow bisection area. The software Xilinx 14.5 tool by using that overall performance analysis of mesh topology, each method are reduced data losses with better accuracy although the productivity of the delay is decreased by 21 % was evaluated and calculated..

Assessment of Adjustment of GNSS Railway Measurements with Parameter-Binding Conditions in a Stationary Scenario

The study aims to assess the applicability of the ordinary least squares method, robust estimation, and conditions-binded adjustment in processing the six synchronous coordinate pairs of global navigation satellite system (GNSS) receivers. The research is part of the research project InnoSatTrack, focused on the enhancement of the determination of geometrical parameters of railway tracks using GNSS, inertial, and other sensors. A fixed frame of installation of six receivers is used in the data processing. Due to the limited availability of railway routes for measurements, the stationary campaign was conducted to gather data for the adjustment procedures tests. Six GNSS receivers in the frame-restricted configuration were set up using the tachymetric method for recording 96,729 synchronous static observations. Post-processed coordinates of the receivers were adjusted according to the geometrical conditions of the survey frame. Analysis showed higher applicability of the conditions-binded method than other estimation methods by obtaining superior results in both the precision and the maintenance of the design conditions factors than in the OLS and robust estimators.

Historical Floras: addressing their genesis in order to be viewed from a modern-day perspective. A case study from Northern Italy

Historical Floras are biodiversity-oriented textual sources, which refer to times when scientific methods were far different compared to the present. They are also geographical documents, as the entire work is referred to a focal region explored by certain collectors and following certain principles, since the taxa recorded are usually accompanied by a description of the locality of the observation. Retrospective georeferencing, as well as taxonomic revision, of the records in Historical Floras are, however, very challenging processes, which are usually not taken into account. As a result, very few global overviews of Historical Floras exist to date. In this article we present the analysis of the 7767 floristic records of the late XIX century Flora of the province of Bologna (N-Italy) compiled by Girolamo Cocconi. We processed these records by georeferencing them, whenever possible, to 659 pairs of coordinates and coupling them with the analysis of the collectors involved besides the author, as to provide the spatial and temporal dimension that permits us to further understand the taxonomic information given by the species listed. This allowed us to detect a bias in the exploration of the territory, which depended on accessibility and/or attractivity of the areas for fieldwork and shifted through time as function of the available collaborators who influenced the definition of the floristic pattern of the territory. Finally, we provided a diachronic analysis with the present flora to document the most significant land use changes based on selected floristic target groups.

TrackMatcher – a tool for finding intercepts in tracks of geographical positions

Abstract. Working with measurement data in atmospheric science often necessitates the co-location of observations from instruments or platforms at different locations with different geographical and/or temporal data coverage. The varying complexity and abundance of the different data sets demand a consolidation of the observations. This paper presents a tool for (i) finding temporally and spatially resolved intersections between two- or three-dimensional geographical tracks (trajectories) and (ii) extracting observations and other derived parameters in the vicinity of intersections to achieve the optimal combination of various data sets and measurement techniques. The TrackMatcher tool has been designed specifically for matching height-resolved remote sensing observations along the ground track of a satellite with position data of aircraft (flight tracks) and clouds (cloud tracks) and is intended to be an extension for ships (ship tracks) and air parcels (forward and backward trajectories). The open-source algorithm is written in the Julia programming language. The core of the matching algorithm consist of interpolating tracks of different objects with a piecewise cubic Hermite interpolating polynomial with the subsequent identification of an intercept point by minimising the norm between the different track point coordinate pairs. The functionality wrapped around the two steps allows for the application of the TrackMatcher tool to a wide range of scenarios. Here, we present three examples of matching satellite tracks with the position of individual aircraft and clouds that demonstrate the usefulness of TrackMatcher for application in atmospheric science.

SCADA system dataset exploration and machine learning based forecast for wind turbines

Effective short-term wind power forecast is essential for adequate power system stability, dispatching and cost control. There are various significant renewable energy sources available, including wind power, which is one of the most favorable power source. As a result, wind power is an important green form of electricity generation. Wind power prediction is a crucial topic in reducing the energy system's unpredictability. The act of balancing energy supply and demand is critical. The main aim of this research is to offer a time-series dataset-based prediction tool for estimating wind power. In addition, for visualizing the dataset obtained from the SCADA (Supervisory Control and Data Acquisition) system, dataset exploration approach is also presented. The dataset analysis approach are shown in polar coordinate systems and pair plot diagram for better understanding of the relation between wind and power production relationship. With the help of given input features i.e. theoretical power, produced active power, wind direction, wind speed, month and hour, generated turbine power is forecasted using various machine learning algorithms. The performance of the developed model has been assessed using several statistical criteria. The experimental findings reveal that the XGBoost regression approach has greater prediction accuracy than the other methods with R2 of around 0.969, MSE of 0.003, RMSE of 0.064, MAPE of 0.282, and MAE of 0.026.

Interpreting and Georeferencing the Concept of “Near” in Locality Descriptors in Biodiversity Records

The process of georeferencing is fundamentally a matter of spatial relationships: where is the point of interest in relation to the Prime Meridian, the Equator, the nearest landmark, etc. The process of adding geographic coordinates and uncertainty measurements to data coming from the work of others is often complicated by interpretation and subjectivity. Take for instance the description, “found near the city of Springfield", which might be assigned a coordinate pair based on the centroid of the city’s footprint. If the implication is that the subject was found around Springfield, then how close? Could this description refer to a point within the city? Is this description referring to the city limits, the farthest reaches of city mailing addresses, or the original author’s interpretation of the territory of Springfield? At present, best practices for adding coordinate uncertainty to such data records is to expand arbitrarily the boundaries of the feature (e.g., the city limits of Springfield), and use the distance from this new shape’s center to its farthest point (Zermoglio et al. 2020). In this talk, I propose a way to objectively quantify the area that can be considered “near” a given reference point. Voronoi diagrams are collections of points (or “cells”) that correspond to a single region. Any coordinate location within a given region is closer to that region’s cell than to any other cell in the diagram (Dobrin 2005). I describe possible applications of Voronoi diagrams in georeferencing sites described as “near” named places, which take into account the uneven distribution of named places across a region. I demonstrate the straightforward process of generating these diagrams using readily available open-source GIS (Geographic Information System) data and programs, and discuss both shortcomings and possible deeper-level applications of these approaches.

A New Calibration Method of Stereo Line-Scan Cameras for 2D Coordinate Measurement

Camera calibration plays an important role in various applications including machine vision and optical measurement. In this paper, we propose a new calibration method of stereo line-scan cameras for 2D coordinate measurement using a simple calibration target rather than requiring complex calibration patterns or an auxiliary area-scan camera. Considering the viewing planes associated with the stereo line-scan cameras are coplanar, an imaging model combining perspective projection and lens distortion are established and can depict the relationship between the 2D world coordinate and its corresponding 1D pixel coordinates of the image pairs. A two-step calibration algorithm is proposed to obtain the optimal values of intrinsic, extrinsic and distortion parameters of stereo line-scan cameras. The performance of the proposed camera calibration method is evaluated on 2D coordinate measurement and the experimental results demonstrate that the proposed method is more effective and accurate than the existing method.

Capturing Spatial Relationship Mapping Patterns between GPS Coordinates and Road Network Using Machine Learning and Partitioning Techniques

Map matching is a technique used to identify which path a vehicle is travelling on in a road map. Since it is a crucial fundamental step for a wide range of transportation applications, many map-matching algorithms have been devised ranging from simple geometric calculation methods to more sophisticated methods. However, the study of spatial relationship patterns between GPS coordinates and road segments mapped with map-matching has not received enough attention from researchers. This paper presents a framework, called Proxy Map Matching (PMM), to learn such patterns using machine learning techniques. However, we find that solely employing machine learning techniques on such data is not sufficient to capture the patterns. Solving this problem that way results in an inaccurate proxy model. In PMM, we construct several proxy map matchers and assign them to each group of data based on their spatial proximity, thereby achieving accuracy improvement. An experiment on real-world data shows that the framework achieves above 85% accuracy with integration of machine learning techniques, and outperforms the methods which solely employ machine learning techniques significantly. Moreover, the proposed proxy model can perform very fast matching. For 14,177 GPS coordinate pairs per second, we achieve 88.4% accuracy.

The Application of a Hybrid Method for the Identification of Elastic-Plastic Material Parameters.

The indentation test is a popular method for the investigation of the mechanical properties of materials. The technique, which combines traditional indentation tests with mapping the shape of the imprint, provides more data describing the material parameters. In this paper, such methodology is employed for estimating the selected material parameters described by Ramberg–Osgood’s law, i.e., Young’s modulus, the yield point, and the material hardening exponent. Two combined identification methods were used: the P-A procedure, in which the material parameters are identified on the basis of the coordinates of the indentation curves, and the P-C procedure, which uses the coordinates describing the imprint profile. The inverse problem was solved by neural networks. The results of numerical indentation tests—pairs of coordinates describing the indentation curves and imprint profiles—were used as input data for the networks. In order to reduce the size of the input vector, a simple and effective method of approximating the branches of the curves was proposed. In the Results Section, we show the performance of the approximation as a data reduction mechanism on a synthetic dataset. The sparse model generated by the presented approach is also shown to efficiently reconstruct the data while minimizing error in the prediction of the mentioned material parameters. Our approach appeared to consistently provide better performance on the testing datasets with considerably easier computation than the principal component analysis compression results available in the literature.

ITALLIC: A tool for identifying and correcting errors in location based plant breeding data

Advances in big data technologies are making it possible to analyze large amounts of data in near real time. These technologies offer great promise in the area of data-driven plant breeding. To fully realize this promise, disparate sources of genotype, environment, management, and socioeconomic data need to be integrated. Collectively, this data could be used to inform genetic predictive models for maize, wheat, and other crops. Some of the primary challenges to analyzing these disparate sources collectively are errors in location data, which include flipped latitude and longitude values, missing negative signs, and, in some cases, missing data. To address these challenges, we have developed an Integrated Tool for AgData Lat Long Imputation and Cleaning (ITALLIC), which detects and corrects errors in location data and imputes missing values for location-dependent data, such as region name.Location information is considered valid if a multipolygon bounding its coordinates corresponds to the country label. This validation step easily detects common errors, such as missing negative signs or flipped latitude and longitude data. To suggest corrections, combinations of alternative latitude and longitude values are generated, and a query is used to determine the country for each of these possible coordinate pairs. If one of the coordinate pairs corresponds to a country, those coordinates are suggested as the putatively correct latitude and longitude values for that data entry. If this approach fails to correct the error, an open-source API is used to geocode the location.In addition to identifying and correcting potential errors, ITALLIC includes a visualization tool that makes it easy for users to validate results. Illustratively, when used to analyze data from over 1,400 plant breeding stations around the world, ITALLIC enabled us to validate or correct errors in over 90% of the data. Being able to examine suggested corrections visually made the validation process seamless and convenient. In a few instances, latitude and longitude values were flipped, resulting in a plant breeding station being listed as located in the middle of the ocean. The visualization tool was able to plot both the location in the middle of the ocean and the station’s suggested correct location with a line connecting them. Being able to visualize both the erroneous data point and its suggested location helped us quickly identify and correct errors. ITALLIC is freely available for installation via the publicly accessible Anaconda package management system and the source code has been made available on GitHub. ITALLIC is under active development and has been integrated into the GEMS™ Agroinformatics platform.

Development and Testing of Road Signs Alert System Using a Smart Mobile Phone

Road traffic accident is a major problem worldwide resulting in significant morbidity and mortality. Advanced driver assistance systems are one of the salient features of intelligent systems in transportation. They improve vehicle safety by providing real-time traffic information to the driver. Road signs play an important role in road safety. To be effective, road signs must be visible at a distance that enables drivers to take the necessary actions. However, static road signs are often seen too late for a driver to respond accordingly. In this study, a system for alerting drivers about road signs has been developed and tested using a smart mobile phone. The study was carried out in Tanzania along an 80 km highway stretch from Arusha to Moshi town. The Haversine formula was used to measure and estimate the distance between two pairs of coordinates using the smartphone-based navigation application, Google Map. The application provides a voice alert to a needed action that enhances driver’s attention. We propose an alternative method that identifies and modifies a specific class of energy inefficiencies. According to the experimental results, the proposed methodology has the benefits of high accuracy within a user radius of 10 meters, minimum bandwidth, and low-cost application. Furthermore, the system application was secured by limiting access to the application program interface key to avoid unauthorized access to sensitive information.