Accurate Location Information Research Articles

Wi-Fi indoor positioning and navigation: a cloudlet-based cloud computing approach

Wi-Fi-based indoor positioning for determining accurate wireless indoor location information has become crucial in meeting increasing demands for location-based services by leveraging the Internet of Things (IoT) and ubiquitous connectivity. Most Wi-Fi-based indoor positioning techniques using wireless received signal strength (RSS)-based methods are affected by the indoor environment and depend on the respective signals from at least three reference access points. In this paper, we propose a cloudlet-based cloud computing system enabling Wi-Fi indoor positioning and navigation through a Wi-Fi located on a one-hop wireless network. Our cloudlet-based cloud computing system provides the reference point data and real-time interactive response for a self-driving indoor cart. The system was tested in a real environment with the following results: (1) our system autonomously performed actions, such as turning right or left or going straight according to a movement decision algorithm and determined the position within a stable range of Wi-Fi coverage; (2) the cloudlet and core cloud can track navigation for an indoor self-driving cart; (3) the global and local positions designed for reference access points and a specific position can navigate the self-driving cart to a particular position accurately; (4) the moving edge clouds play a role in deciding three action movements (go straight, turn left, and turn right), as well as managing the local position of the items; and (5) a core cloud is deployed to store all information for the items, such as their positions and corresponding Wi-Fi locations. A core cloud manages items that have the same position (i.e., a global position) defined as the corresponding Wi-Fi location. Finally, the practical results have significance in designing a cloudlet-based cloud computing system enabling Wi-Fi indoor positioning and navigation.

Concrete defects inspection and 3D mapping using CityFlyer quadrotor robot

The concrete aging problem has gained more attention in recent years as more bridges and tunnels in the United States lack proper maintenance. Though the Federal Highway Administration requires these public concrete structures to be inspected regularly, on-site manual inspection by human operators is time-consuming and labor-intensive. Conventional inspection approaches for concrete inspection, using RGB imagebased thresholding methods, are not able to determine metric information as well as accurate location information for assessed defects for conditions. To address this challenge, we propose a deep neural network (DNN) based concrete inspection system using a quadrotor flying robot (referred to as CityFlyer) mounted with an RGB-D camera. The inspection system introduces several novel modules. Firstly, a visual-inertial fusion approach is introduced to perform camera and robot positioning and structure 3D metric reconstruction. The reconstructed map is used to retrieve the location and metric information of the defects. Secondly, we introduce a DNN model, namely AdaNet, to detect concrete spalling and cracking, with the capability of maintaining robustness under various distances between the camera and concrete surface. In order to train the model, we craft a new dataset, i.e., the concrete structure spalling and cracking (CSSC) dataset, which is released publicly to the research community. Finally, we introduce a 3D semantic mapping method using the annotated framework to reconstruct the concrete structure for visualization. We performed comparative studies and demonstrated that our AdaNet can achieve 8.41% higher detection accuracy than ResNets and VGGs. Moreover, we conducted five field tests, of which three are manual hand-held tests and two are drone-based field tests. These results indicate that our system is capable of performing metric field inspection, and can serve as an effective tool for civil engineers.

A Novel and Efficient Tumor Detection Framework for Pancreatic Cancer via CT Images.

As Deep Convolutional Neural Networks (DCNNs) have shown robust performance and results in medical image analysis, a number of deep-learning-based tumor detection methods were developed in recent years. Nowadays, the automatic detection of pancreatic tumors using contrast-enhanced Computed Tomography (CT) is widely applied for the diagnosis and staging of pancreatic cancer. Traditional hand-crafted methods only extract low-level features. Normal convolutional neural networks, however, fail to make full use of effective context information, which causes inferior detection results. In this paper, a novel and efficient pancreatic tumor detection framework aiming at fully exploiting the context information at multiple scales is designed. More specifically, the contribution of the proposed method mainly consists of three components: Augmented Feature Pyramid networks, Self-adaptive Feature Fusion and a Dependencies Computation (DC) Module. A bottom-up path augmentation to fully extract and propagate low-level accurate localization information is established firstly. Then, the Self-adaptive Feature Fusion can encode much richer context information at multiple scales based on the proposed regions. Finally, the DC Module is specifically designed to capture the interaction information between proposals and surrounding tissues. Experimental results achieve competitive performance in detection with the AUC of 0.9455, which outperforms other state-of-the-art methods to our best of knowledge, demonstrating the proposed framework can detect the tumor of pancreatic cancer efficiently and accurately.

Road Profile Estimation Using a 3D Sensor and Intelligent Vehicle.

Autonomous vehicles can achieve accurate localization and real-time road information perception using sensors such as global navigation satellite systems (GNSSs), light detection and ranging (LiDAR), and inertial measurement units (IMUs). With road information, vehicles can navigate autonomously to a given position without traffic accidents. However, most of the research on autonomous vehicles has paid little attention to road profile information, which is a significant reference for vehicles driving on uneven terrain. Most vehicles experience violent vibrations when driving on uneven terrain, which reduce the accuracy and stability of data obtained by LiDAR and IMUs. Vehicles with an active suspension system, on the other hand, can maintain stability on uneven roads, which further guarantees sensor accuracy. In this paper, we propose a novel method for road profile estimation using LiDAR and vehicles with an active suspension system. In the former, 3D laser scanners, IMU, and GPS were used to obtain accurate pose information and real-time cloud data points, which were added to an elevation map. In the latter, the elevation map was further processed by a Kalman filter algorithm to fuse multiple cloud data points at the same cell of the map. The model predictive control (MPC) method is proposed to control the active suspension system to maintain vehicle stability, thus further reducing drifts of LiDAR and IMU data. The proposed method was carried out in outdoor environments, and the experiment results demonstrated its accuracy and effectiveness.

Weighted Geocoding Method Based on Chinese Word Segmentation and Its Application to Spatial Positioning of COVID-19 Epidemic Prevention and Control

Locating the coronavirus disease 2019 (COVID-19)cases in the accurate place is important in epidemic prevention and control. Geocoding is an effective method to achieve information space positioning with address description. The English based geocoding methodology is not suitable for Chinese address. Composition and positioning methods of Chinese address are discussed. According to the characteristics of high complexity and diversity of Chinese address, a Chinese word segmentation weighted address matching algorithm considering a variety of semantics is designed, including the same pronunciation but different Chinese word address, abbreviation and alias of Chinese address, different description of the same address. And the matching accuracy and efficiency of the algorithm are tested by using the COVID-19 cases' addresses in Wuhan. The result indicates the algorithm is efficient, accurate, and intelligent, which can realize the efficient location of the COVID-19 cases address, and provide accurate spatial location information for epidemic prevention and control by quickly positioning of the COVID-19 cases.

Less Information, Similar Performance: Comparing Machine Learning-Based Time Series of Wind Power Generation to Renewables.ninja

Driven by climatic processes, wind power generation is inherently variable. Long-term simulated wind power time series are therefore an essential component for understanding the temporal availability of wind power and its integration into future renewable energy systems. In the recent past, mainly power curve-based models such as Renewables.ninja (RN) have been used for deriving synthetic time series for wind power generation, despite their need for accurate location information and bias correction, as well as their insufficient replication of extreme events and short-term power ramps. In this paper, we assessed how time series generated by machine learning models (MLMs) compare to RN in terms of their ability to replicate the characteristics of observed nationally aggregated wind power generation for Germany. Hence, we applied neural networks to one wind speed input dataset derived from MERRA2 reanalysis with no location information and two with additional location information. The resulting time series and RN time series were compared with actual generation. All MLM time series feature an equal or even better time series quality than RN, depending on the characteristics considered. We conclude that MLM models show a similar performance to RN, even when information on turbine locations and turbine types is unavailable.

Restore: Retrospective Fault Localization Enhancing Automated Program Repair

Fault localization is a crucial step of automated program repair, because accurately identifying program locations that are most closely implicated with a fault greatly affects the effectiveness of the patching process. An ideal fault localization technique would provide precise information while requiring moderate computational resources—to best support an efficient search for correct fixes. In contrast, most automated program repair tools use standard fault localization techniques—which are not tightly integrated with the overall program repair process, and hence deliver only subpar efficiency. In this paper, we present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">retrospective fault localization</i> : a novel fault localization technique geared to the requirements of automated program repair. A key idea of retrospective fault localization is to reuse the outcome of failed patch validation to support mutation-based dynamic analysis—providing accurate fault localization information without incurring onerous computational costs. We implemented retrospective fault localization in a tool called <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Restore</small> —based on the <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Jaid</small> Java program repair system. Experiments involving faults from the <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Defects4J</small> standard benchmark indicate that retrospective fault localization can boost automated program repair: <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Restore</small> efficiently explores a large fix space, delivering state-of-the-art effectiveness (41 <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Defects4J</small> bugs correctly fixed, 8 of which no other automated repair tool for Java can fix) while simultaneously boosting performance (speedup over 3 compared to <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Jaid</small> ). Retrospective fault localization is applicable to any automated program repair techniques that rely on fault localization and dynamic validation of patches.

MCNP-polimi simulation for the compressed-sensing based reconstruction in a coded-aperture imaging CAI extended to partially-coded field-of-view

This paper deals with accurate image reconstruction of gamma camera using a coded-aperture mask based on pixel-type CsI(Tl) scintillator coupled with silicon photomultipliers (SiPMs) array. Coded-aperture imaging (CAI) system typically has a smaller effective viewing angle than Compton camera. Thus, if the position of the gamma source to be searched is out of the fully-coded field-of-view (FCFOV) region of the CAI system, artifacts can be generated when the image is reconstructed by using the conventional cross-correlation (CC) method. In this work, we propose an effective method for more accurate reconstruction in CAI considering the source distribution of partially-coded field-of-view (PCFOV) in the reconstruction in attempt to overcome this drawback. We employed an iterative algorithm based on compressed-sensing (CS) and compared the reconstruction quality with that of the CC algorithm. Both algorithms were implemented and performed a systematic Monte Carlo simulation to demonstrate the possiblilty of the proposed method. The reconstructed image qualities were quantitatively evaluated in sense of the root mean square error (RMSE) and the peak signal-to-noise ratio (PSNR). Our simulation results indicate that the proposed method provides more accurate location information of the simulated gamma source than the CC-based method.

Lightweight Attention Pyramid Network for Object Detection and Instance Segmentation

Feature pyramids of convolutional neural networks (ConvNets)—from bottom to top—are used by most recent researchers for the improvement of object detection accuracy, but they seldom aim to address the correlation of each feature channel and the fusion of low-level features and high-level features. In this paper, an Attention Pyramid Network (APN) is proposed, which mainly contains the adaptive transformation module and feature attention block. The adaptive transformation module utilizes the multiscale feature fusion, and makes full use of the accurate target location information of low-level features and the semantic information of high-level features. Then, the feature attention block strengthens the features of important channels and weakens the features of unimportant channels through learning. By implementing the APN in a basic Mask R-CNN system, our method achieves state-of-the-art results on the MS COCO dataset and 2018 WAD database without bells and whistles. In addition, the structure of the APN makes the network parameters lighter, and runs at 4 ms on average, which is ignorable when compared to the inference time of the backbone of ConvNet.

RFI Source Localization in Microwave Interferometric Radiometry: A Sparse Signal Reconstruction Perspective

The Microwave Interferometric Radiometer with Aperture Synthesis (MIRAS) is the payload of the Soil Moisture and Ocean Salinity (SMOS) satellite mission led by the European Space Agency. Although the MIRAS operates at the protected L-band, it is perturbed by radio frequency interferences (RFIs) that contaminate the acquired remote sensing data and further deteriorate the total performance of SMOS mission. Accurate location information of these sources is crucial for switching off illegal RFI emitters or mitigating RFI impacts from contaminated data. This article addresses the localization of SMOS RFI sources from a perspective of sparse signal reconstruction (SSR), which exploits the sparsity of RFI sources in the spatial domain. Such an SSR strategy possesses superior (at least comparable) performances over existing RFI localization methods [e.g., discrete Fourier transformation (DFT) inversion and subspace-based direction-of-arrival (DOA) estimation] using only SMOS measurements and even under situations in the presence of data missing due to correlator failures.

A trust and relevance-based Point-Of-Interest recommendations method with inaccessible user location

Point of interest recommender services are widely studied and applied in various areas. In most cases, these services are based on user location data. However, in some cases, the user’s location may not be available. For example, if the user has closed access to this data on a mobile device or in the case of using a web service that does not have accurate location information. Nevertheless, it is necessary to provide the user with the most relevant POIs. In this paper, we propose a modification of the collaborative filtering method based on the trust and relevance of user experience to solve this problem. We compare the quality of ranking using the proposed method with a set of classical interpreted recommender algorithms, such as content algorithms, and matrix decomposition algorithms. Public catering enterprises (bars, cafes, restaurants) in St. Petersburg were used as data example. The feedback data of 90814 customers and 4256 catering places in St. Petersburg were collected from “Tripadvisor” and “Restoclub” services. To estimate the quality of the results, ranking metric [email protected] was used. Experimental results showed that the proposed method is 1.104 times more efficient (from the point of view of the [email protected] metric) than existing approaches.

High-Accuracy Adaptive Low-Cost Location Sensing Subsystems for Autonomous Rover in Precision Agriculture

With the prosperity of artificial intelligence, more and more jobs will be replaced by robots. The future of precision agriculture (PA) will rely on autonomous robots to perform various agricultural operations. Real time kinematic (RTK) assisted global positioning systems (GPS) are able to provide very accurate localization information with a detection error less than $\pm 2$ cm under ideal conditions. Autonomously driving a robotic vehicle within a furrow requires relative localization of the vehicle with respect to the furrow centerline. This relative location acquisition requires both the coordinates of the vehicle as well as all the stalks of the crop rows on both sides of the furrow. This extensive number of coordinate acquisitions of all the crop stalks demand onerous geographical survey of entire fields in advance. Additionally, real-time RTK-GPS localization of moving vehicles may suffer from satellite occlusion. Hence, the above-mentioned $\pm 2$ cm accuracy is often significantly compromised in practice. Against this background, we propose sets of computer vision algorithms to coordinate with a low-cost camera (50 US dollars), and a LiDAR sensor (1500 US dollars) to detect the relative location of the vehicle in the furrow during early, and late growth season respectively. Our solution package is superior than most current computer vision algorithms used for PA, thanks to its improved features, such as a machine-learning enabled dynamic crop recognition threshold, which adaptively adjusts its value according to the environmental changes like ambient light, and crop size. Our in-field tests prove that our proposed algorithms approach the accuracy of an ideal RTK-GPS on cross-track detection, and exceed the ideal RTK-GPS on heading detection. Moreover, our solution package neither relies on satellite communication nor advance geographical surveys. Therefore, our low-complexity, and low-cost solution package is a promising localization strategy as it is able to provide the same level of accuracy as an ideal RTK-GPS, yet more consistently, and more reliably, as it requires no external conditions or hassle of the work demanded by RTK-GPS.

Enhanced Relative Localization Based on Persistent Excitation for Multi-UAVs in GPS-Denied Environments

Intelligent unmanned aerial vehicles (UAVs) have been applied for civil and military uses. Relative localization (RL) is crucial for multi-UAVs to accomplish complex tasks successfully and safely. In global positioning system (GPS) denied environments, where accurate or meaningful location information is hard to obtain, persistent excitation based RL is a promising approach for multi-UAVs to achieve RL without any needs of external infrastructures. However, for many cases, existing persistent excitation based RL method suffers precision loss, error accumulation and divergence. This article tackles these issues, and proposes an enhanced approach to ensure the practical usage of persistent excitation based RL. Synchronized sensor sample prediction is introduced to confine and reduce RL error, and RL estimation is redesigned to avoid RL error accumulation. We evaluated our solution by simulating various scenarios. The results show that the proposed approach can effectively decrease RL error and prevent RL error accumulation.

The Image-Based Positioning Technique Using Inter-Pixel Relation Network

Accurate location information is very important for human beings and agents. However, GPS has very limited positioning accuracy or even can not work in challenging environments such as indoor and urban canyons, and other solutions suffer from different shortcomings. It is very meaningful to use the scene image captured by the high-definition camera on the smart phone to locate indoor through the neural network, but the existing image-based positioning methods do not take into account the interrelationship of diffenrent instances of the same feature space. In this paper we present a novel network architecture, which embeds Scene Enhancement module and Relational Network (RN) into the end-to-end positioning system. The system was tested in three typical scenarios, and the test results show that its performance outerperforms existing image-based schemes in most cases.

Towards Predicting Vine Yield: Conceptualization of 3D Grape Models and Derivation of Reliable Physical and Morphological Parameters

In viticulture, yield prediction plays an important role, helping winegrowers to predict the start of the next growth stage of vines and to improve vineyard management decision-making. To predict a vineyard’s yield, it is necessary to gather accurate local information about the vine’s phenology and morphology, such as the volume of individual grapes. Traditional collection of these data and yield prediction rely on resource- and time-intensive direct visual and manual in-field work by viticulturists. Thus, only limited sampling in the vineyards is possible, carried out by humans. Automated procedures utilizing sensor-based systems reduce the data acquisition time and enable the collection of high-resolution data from the entire vineyard. Large-scale 3D models of vineyards can be generated from these data and used to analyse, for example, the vineyard’s yield or the vegetative stage of individual vines. We propose a concept for a 3D model that uses close-range photogrammetry. In a laboratory experiment, we tested the acquisition of multi-view image datasets from grapes using close-range photogrammetry and derived physical and morphological parameters from 3D grape models. The results could contribute to the design and implementation of a large-scale in-field experiment.

Angle of Arrival Passive Location Algorithm Based on Proximal Policy Optimization

Location technology is playing an increasingly important role in urban life. Various active and passive wireless positioning technologies for mobile terminals have attracted research attention. However, positioning signals experience serious interference in high-density residential areas or in the interior of large buildings. The main type of interference is that caused by non-line-of-sight (NLOS) propagation. In this paper, we present a new method for optimizing the angle of arrival (AOA) measurement to obtain high accuracy location results based on proximal policy optimization (PPO). PPO is a new family of policy gradient methods for reinforcement learning, which can be used to adjust the sampling data under different environments using stochastic gradient ascent. Therefore, PPO can correct the NLOS propagation errors to produce a clear AOA measurement data set without building an offline fingerprinting database. Then, we used the least square method to calculate the location. The simulation result shows that the AOA passive location algorithm based on PPO produced more accurate location information.

Physician Practice Location

Purpose: This study compares and contrasts locational identification (physician practice ZIP code) between several physician demographic databases and a research team-verified ZIP code to determine spatial concordance. Accuracy of physician location data is critical for both successful national physician workforce planning and for assessing the fulfillment of distinct regional medical school missions.&#x0D; Methods: Three physician databases; the American Medical Association’s Physician Masterfile, the National Provider and Plan Enumeration System (NPPES), and the Minnesota Board of Medical Practice’s Licensure File were compared against each other as well as a set of actively verified practice location ZIP codes. A sampling frame of medical school alumni from 2003 to 2014 was selected. The sample included alumni from both regional and main campuses. From this alumni sample, a random sample of four hundred individuals were selected for closer examination. Descriptive frequencies are presented for the concordance of ZIP codes for the sample of four hundred alumni.&#x0D; Findings: From an initial cohort of 2,605 University of Minnesota medical school alumni, a sample of 400 who also possessed a Minnesota medical license were randomly selected to examine concordance rates. The highest rate of concordance was the verified ZIP code and the Minnesota Licensure Board practice ZIP code at 68.8%. Only 42.0% of practice location ZIP codes matched across all databases.&#x0D; Conclusions: The concordance rate across practice ZIP codes in the databases does not inspire confidence in ability to characterize true physician practice location. Some of the difficulties in defining, identifying and maintaining information on physician practice location are discussed. Without accurate and precise practice location information, the development and implementation of comprehensive national health policies for the United States may face difficulties. The lack of reliable physician practice location data also presents a challenge to regional medical school campuses that depend upon accurate physician location data to evaluate progress toward mission-directed workforce goals.

Location-Based Discovery and Vertical Handover in Heterogeneous Low-Power Wide-Area Networks

Low-power wide-area network (LPWAN) multi-radio access technology (RAT) devices promise enabling Internet of Things (IoT) use-cases that simultaneously require high coverage and data rates, and low energy consumption. For such devices, active probing is usually used for discovering if communication between a mobile terminal (MT) and a base station (BS) or a handover of the MT across LPWAN technologies should be initiated. Because of continuous probing, this procedure increases signaling overhead and energy consumption of the MT. Assuming that the location information of the MT is required for enabling an IoT use-case, this information can potentially also be used for enhancing the discovery and vertical handover procedures in heterogeneous LPWANs. Hence, we propose a location-based mechanism for making discovery and handover decisions in outdoor LPWANs. We do that under the assumption that the location of the MT can be estimated with a certain level of localization errors, while the perfectly accurate location information of the BSs are known to the MT. The mechanism grounds the decisions on the expected SNR between the MT and the BS, which removes the need for continuous probing. If the location information of the MT can be estimated with GPS-like accuracy, we demonstrate that the mechanism can achieve more than 90% correct discovery decisions. We also show that the mechanism is highly accurate in determining if a handover between technologies should be initiated. For an order of magnitude less accurate location information (e.g., for SigFox-based fingerprinting), we show that the mechanism can still make reasonable discovery decisions.

Robust and Cooperative Localization for Underwater Sensor Networks in the Existence of Malicious Anchors.

Precise and robust localization in three-dimensional underwater sensor networks is still an important research problem. This problem is particularly challenging if there are some malicious anchors among ordinary anchor nodes that will broadcast their locations falsely and deliberately. In this paper, we study how to self-localize large teams of underwater sensor nodes under the condition that some malicious anchor nodes mixed with ordinary anchors. Due to malicious characteristic of some deliberate anchor nodes, an iterative and cooperative 3D-localization algorithm for underwater sensor networks in the existence of malicious anchors is proposed in this paper. The proposed robust localization algorithm takes advantage of distributed reputation voting method within 1-Hop neighboring reference nodes to detect and eliminate malicious anchor nodes. Moreover, one kind of Minimum Mean Squared Error estimation based iterative localization method is applied to determine accurate location information. Additionally, we analyze and prove that our localization algorithm would have a bounded error when the number of malicious anchors is smaller than a certain threshold. Extensive simulation results are provided to demonstrate performance improvements comparing to traditional Minimum Mean Squared Error and Attack Resistant Minimum Mean Squared Error based localization methods in terms of localization accuracy and coverage ratio.

BDALT: Bilateration and Double Anchor-based Localization Technique for Wireless Sensor Networks using Angle of Arrival (AoA) and Received Signal Strength (RSS) -A Hybrid Technique

Anchor based localization techniques are better and cost free alternate solutions against GPS based localization techniques in Wireless Sensor Networks (WSN) applications. Anchor based localization techniques using Zigbee technology in WSN have attracted highly in the recent years. As the technology is growing, customers can easily buy the Zigbee technology based beacon nodes at reasonable price. Anchor based wireless sensor networks are used in the localization process because of their advantages in location tracking applications. Many localization systems depend only on RSS and that is not highly reliable, and not accurate. However, the accuracy and the reliability of the network is rather important when it is used in wireless environments. In this paper, a hybrid technique is proposed, which uses not only the RSS as distance estimation parameter, but also the AoA as another angular estimation parameter to improve the localization accuracy. The proposed localization technique uses both the dynamic vectors based localization method, and the bilateration based localization methods to get the target node’s coordinates with good accuracy. The main objective of this research work is to get the accurate location information of the target nodes by using double anchor nodes and simple mathematical computations. The proposed method is simulated in MATLAB and its performance is better than the existing localization techniques.