Navigation Applications Research Articles

A method for phase estimation of X-ray pulsar signals: Combining a transformer network structure and a two-dimensional profile map

The high-precision phase estimation of X-ray pulsar signals is crucial for X-ray pulsar navigation. This paper introduces a novel feature modeling method named Two-Dimensional Profile Map (2D-PM), which enhances the time-domain information of the period axis using the epoch folding algorithm. Compared to traditional profiles, the newly proposed 2D-PM model exhibits richer information content, and greater robustness in terms of folding period precision. Furthermore, a new time-series neural network structure, inspired by groundbreaking advancements in the field of Natural Language Processing (NLP), is developed using the TensorFlow framework. This structure is designed to learn the proposed features and achieve precise phase estimation. The paper also outlines training schemes and debugging strategies for hyperparameters. To test the method, sample sets were created using simulated data and Rossi X-ray Timing Explorer (RXTE) observational data. These samples are specifically designed to enhance feature consistency and the generalization capability of the network. The effectiveness of the proposed method is demonstrated through a comparative analysis with traditional cross-correlation algorithms. The results confirm a high degree of alignment between the network outputs and the feature model, highlighting the significant potential of the proposed method for application in X-ray pulsar navigation.

Surgical Navigation and CAD-CAM-Designed PEEK Prosthesis for the Surgical Treatment of Facial Intraosseous Vascular Anomalies.

Background: Intraosseous vascular anomalies in the facial skeleton present significant diagnostic and therapeutic challenges due to complex anatomy. These anomalies represent about 0.5-1% of bony neoplastic and tumor-like lesions, usually presenting as a firm, painless mass. Most described intraosseous vascular malformations are venous malformations (VMs) and, more rarely, arteriovenous malformations. Objectives: The objectives of this work are to show our experience, protocol and the applications of computer planning, virtual surgery, CAD-CAM design, surgical navigation, and computer-assisted navigated piezoelectric surgery in the treatment of facial intraosseous vascular anomalies and to evaluate the advantages and disadvantages. Methods: Three females and one male with periorbital intraosseous vascular anomalies were treated using en-block resection and immediate reconstruction with a custom-made PEEK prosthesis. One lesion was in the supraorbital rim and orbital roof, one in the frontal bone and orbital roof, and two in the zygomatic region. We accomplished the resection and reconstruction of the lesion using virtual planning, CAD-CAM design, surgical navigation and piezoelectric device navigation. Results: There were no complications related to the surgery assisted with navigation. With an accuracy of less than 1 mm, the procedure may be carried out in accordance with the surgical plan. The surgeon's degree of uncertainty during deep osteotomies and in locations with low visibility was decreased by the use of the navigated piezoelectric device. Conclusions: Resection and reconstruction of facial intraosseous vascular anomalies benefit from this new surgical strategy using CAD-CAM technologies, computer-assisted navigated piezoelectric surgery, and surgical navigation.

Integrating query data for enhanced traffic forecasting: A Spatio-Temporal Graph Attention Convolution Network approach with delay modeling

Accurate prediction of road traffic conditions is essential for the effectiveness of Intelligent Transportation Systems (ITS) and the advancement of smart cities. While existing methodologies mainly focus on the complex structures of road networks and consider factors like weather, holidays, and accidents, the impact of social events is often overlooked. Queries, representing user requests in navigation applications, offer valuable insights into the popularity of social events and their effects on the road network. In this paper, we introduce the Query-aware Spatio-Temporal Graph Attention Convolution Network (QA-STGACN), which utilizes query data to model the impacts of events and forecast traffic conditions. Initially, spatial and temporal blocks are developed to extract spatial dependencies among road segments and capture traffic dynamics. A dynamic query-aware graph constructor then adaptively learns and retains periodic interactions between road segments and queries. Furthermore, three novel query-aware blocks – convolution, attention, and pattern transformation – are designed to uncover hidden dependencies between traffic conditions and query counts during events, as well as to model the delayed impacts of these counts on traffic. Ultimately, a gated fusion mechanism integrates the spatio-temporal correlations in road networks with the representations derived from queries. Comprehensive experiments on six datasets validate the superiority of QA-STGACN over robust baselines.

NavigateMe : An In-Building Navigation Application

For some people to reach their destination and complete their daily tasks, navigation systems serve as an aid, a tool, or an assistance. Currently, International Islamic University Malaysia (IIUM) lacks an in-building navigation system that could assist users, particularly new students, in finding their way to the location of their choice. This paper has two goals. This study first examines the current campus navigation systems and the relevant research article. Second, this work suggests a mobile navigation system that facilitates user navigation within Kuliyyah of Information and Communication Technology (KICT) facilities. Methods used to build the application were according to Software Development Life Cycle (SDLC). The suggested navigation application, NavigateMe, helps users navigate within buildings by providing sequential instructions and images of landmarks along the route. It stands out for being usable even in the event of sporadic internet connectivity. In conclusion, visitors and the IIUM community, particularly the new students of KICT, will benefit from an in-building navigation application like NavigateMe to effortlessly reach their destination.

The XAI system for answer set programming xASP2

Abstract Explainable artificial intelligence (XAI) aims at addressing complex problems by coupling solutions with reasons that justify the provided answer. In the context of Answer Set Programming (ASP) the user may be interested in linking the presence or absence of an atom in an answer set to the logic rules involved in the inference of the atom. Such explanations can be given in terms of directed acyclic graphs (DAGs). This article reports on the advancements in the development of the XAI system xASP by revising the main foundational notions and by introducing new ASP encodings to compute minimal assumption sets, explanation sequences, and explanation DAGs. DAGs are shown to the user in an interactive form via the xASP navigator application, also introduced in this work.

An Efficient Approach for Shortest Path Planning in Automotive Navigation System

Emerging automotive engineering solutions extensively rely on navigation tools. Shortest path evaluation is core competency of optimal path planning, one of the most critical component of any navigation application. Fibonacci heap variant of state-of-the-art Dijkstra’s algorithm solve single source shortest path problems in asymptotic O(E + V log V) worst-case time. In spite Fibonacci heap is theoretically dominant in time efficiency, it might not work much better empirically with real-world data, because although large and intricate, the real-world road network graphs are sparse and the graph topology greatly affects the performance of the algorithms. SGO algorithm, a more efficient approch to address single source shortest path problem, is applied to solve optimal path planning problem in automotive navigation applications. Each road segment of the road graph leads to a particular location thus will hold unique distance values all the time. The SGO calculates the total distance from start location to the end of the each road segment and evaluates shortest path based on these calculations. The SGO algorithm and the Fibonacci and Binary heap variants of Dijkstra’s algorithm were tested on real-world intercity and intra-city road network graphs to evaluate running time efficiency of the algorithms in automotive navigation systems. The experimental results show that the SGO algorithm outperformed both the variants. The empirical superiority of the SGO algorithm suggests its usefulness in automotive navigation systems.

Implant placement using mixed reality-based dynamic navigation: A proof of concept

Objectives: To present the first clinical application of a novel mixed reality-based dynamic navigation (MR-DN) system in the rehabilitation of a single tooth gap.Methods: The protocol consisted of the following: (1) three-dimensional patient data acquisition using intraoral scanning (IOS) and cone-beam computed tomography (CBCT), (2) implant planning using guided surgery software, (3) holography-guided implant placement using the novel MR-DN system (ANNA®, MARS Dental, Haifa, Israel) and (4) placement accuracy verification.Results: The novel MR-DN system was safe and time-efficient, as the surgery took 30 min from anaesthesia to suturing. The accuracy of implant placement was high with minimal deviations recorded in the three planes of space compared to the presurgical planning: the error at the entry point planar distance (XY) was 0.381 mm, and the entry point planar distance (Z) was 0.173 mm, for a 3D entry point distance (En) of 0.417 mm. A 3D apex deviation (An) of 0.193 mm was registered, with an angle difference of 1.852°Conclusions: This proof-of-concept study demonstrated the clinical feasibility of MR-DN for guided implant placement in single tooth gaps. Further clinical studies on a large sample of patients are needed to confirm these positive preliminary results.Statement of clinical relevance: The use of MR-DN can change the perspectives of guided dental implant surgery as a possible alternative to the classic static and dynamic guided surgical techniques for the rehabilitation of single tooth gaps.

Source localization based on field signatures: Laboratory ultrasonic validation

Location awareness in wireless networks is essential for emergency services, navigation, gaming and many other applications. This article presents a method for source localization based on measuring the amplitude-phase distribution of the field at the base station. The existing scatterers in the target area create unique scattered field interference at each source location. The unique field interference at each source location results in a unique field signature at the base station which is used for source localization. In the proposed method, the target area is divided into a grid with a step of less than half the wavelength. Each grid node is characterized by its field signature at the base station. Field signatures corresponding to all nodes are normalized and stored in the base station as fingerprints for source localization. The normalization of the field signatures avoids the need for time synchronization between the base station and the source. When a source transmits signals, the generated field signature at the base station is normalized and then correlated with the stored fingerprints. The maximum correlation value is given by the node to which the source is the closest. Numerical simulations and results of experiments on ultrasonic waves in the air show that the ultrasonic source is correctly localized using broadband field signatures with one base station and without time synchronization. The proposed method is potentially applicable for indoor localization and navigation of mobile robots.

Global ionospheric total electron content short-term forecast based on Light Gradient Boosting Machine, Extreme Gradient Boosting, and Gradient Boost Regression

Total Electron Content (TEC) forecasting using machine learning has been extensively preferred in characterizing the spatio-temporal variability of the ionosphere, to support space communication and navigation applications. Although, a variety of machine learning methods have been evolved, still, there is a greater persistence in the prediction of ionospheric TEC due to adverse space weather impacts and the complex behavior of ionosphere. And hence, more sophisticated short-term ionospheric TEC forecasting models should be developed for better prediction accuracy. The present study investigated and evaluated the performance of three models – Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Gradient Boost Regression (GBR) algorithms using the data set for 25 years (1997–2021) GNSS Earth Observation Network System (GEONET) Global mean TEC time-series data. For better prediction accuracy, the data of geomagnetic storm indices Dst and Mg-II index were also utilized in training the three models. One year of data for both high (2015) and low (2020) solar activity were used to test the three models. The prediction plots are used to visualize the level of prediction error for the three algorithms. The statistical results illustrate that the LightGBM model performed better in predicting TEC with an RMSE value of 2.25 TECU (2015) and 0.52 TECU (2020) for high and low solar activity years respectively for global mean TEC data and with an RMSE of 2.34 TECU at Japan grid point location of (34.95°N and 134.05°E). In turn the XGBoost and GBR models provide an absolute TEC forecasting with an RMSE of 2.76 and 2.59 TECU (2015) and 0.60 and 0.55 TECU (2020) correspondingly for global data and as 2.39 and 2.93 TECU for grid point location.

Large-Scale Indoor Camera Positioning Using Fiducial Markers.

Estimating the pose of a large set of fixed indoor cameras is a requirement for certain applications in augmented reality, autonomous navigation, video surveillance, and logistics. However, accurately mapping the positions of these cameras remains an unsolved problem. While providing partial solutions, existing alternatives are limited by their dependence on distinct environmental features, the requirement for large overlapping camera views, and specific conditions. This paper introduces a novel approach to estimating the pose of a large set of cameras using a small subset of fiducial markers printed on regular pieces of paper. By placing the markers in areas visible to multiple cameras, we can obtain an initial estimation of the pair-wise spatial relationship between them. The markers can be moved throughout the environment to obtain the relationship between all cameras, thus creating a graph connecting all cameras. In the final step, our method performs a full optimization, minimizing the reprojection errors of the observed markers and enforcing physical constraints, such as camera and marker coplanarity and control points. We validated our approach using novel artificial and real datasets with varying levels of complexity. Our experiments demonstrated superior performance over existing state-of-the-art techniques and increased effectiveness in real-world applications. Accompanying this paper, we provide the research community with access to our code, tutorials, and an application framework to support the deployment of our methodology.

Ultrasound propagation characteristics within the bone tissue of miniature ultrasound probes: implications for the spinal navigation of pedicle screw placement.

The accuracy of pedicle screw fixation is crucial for patient safety. Traditional navigation methods based on computed tomography (CT) imaging have several limitations. Therefore, this study aimed to investigate the ultrasonic propagation characteristics of bone tissue and their relationship with CT imaging results, as well as the potential application of ultrasound navigation in pedicle screw fixation. The study used three bovine spine specimens (BSSs) and five human vertebral allograft bones (HABs) to progressively decrease the thickness of the cancellous bone layer, simulating the process of pedicle screw perforation. Five unfocused miniature ultrasound probes with frequencies of 2.2, 2.5, 3, 12, and 30 MHz were employed for investigating the ultrasonic propagation characteristics of cancellous and cortical bone through ultrasound transmission and backscatter experiments. The CT features of the bone tissue was obtained with the Skyscan 1174 micro-CT scanner (Bruker, Billerica, MA, USA). The experimental results demonstrated that low-frequency (2-3 MHz) ultrasound effectively penetrated the cancellous bone layer up to a depth of approximately 5 mm, with an attenuation coefficient below 10 dB/cm. Conversely, high-frequency (12 MHz) ultrasound exhibited significant signal attenuation in cancellous bone, reaching up to 55.8 dB/cm. The amplitude of the backscattered signal at the cancellous bone interface exhibited a negative correlation with the bone sample thickness (average r=-0.84), meaning that as the thickness of the cancellous bone layer on the cortical bone decreases, the backscattered signal amplitude gradually increases (P<0.05). Upon reaching the cortical bone interface, there was a rapid surge in echo signal amplitude, up to 8 times higher. Meanwhile, the statistical results indicated a significant correlation between the amplitude of the echo signal and the micro-CT scanning results of bone trabecular structure. Theoretically, using multiple ultrasonic probes (≥3) and regions of interest (ROIs) (≥5) has the potential to provide surgeons with early warning signals for pedicle perforation based on three or more successive increases in echo signal amplitude or a sudden substantial increase. The statistical results indicate a significant correlation between the amplitude of the echo signal and the micro-CT scanning results of bone trabeculae, suggesting the potential use of ultrasound as opposed to CT for real-time intraoperative bone navigation.

A shipboard integrated navigation algorithm based on smartphone built-in GNSS/IMU/MAG sensors

The rapid development of built-in sensors in smartphones has inspired a variety of ubiquitous public navigation applications. Nevertheless, previous works have focused on vehicle and pedestrian navigation by smartphones, with limited research on shipboard navigation. In this contribution, we designed a multi-sensors fusion navigation algorithm that integrates the global navigation satellite systems (GNSS), inertial measurement unit (IMU), and magnetometer (MAG) to achieve high-precision horizontal positioning on shipboard for smartphones. Considering the changeable motion state of a ship and the setting of non-holonomic constraint (NHC) noise, a magnetometer-based turning detection method is proposed, and a NHC model with adaptive noise is constructed based on the turning detection results. We conducted a lake experiment in Taku Forts Lake, Tianjin, using sensor data collected from a Xiaomi MI8 through our self-developed ’Sensor Logger’ app. The experimental results demonstrated a high success rate of 94.22 % for magnetometer-based turning detection which can effectively detect turning motions. Compared to the traditional algorithm without constraint information, the roll, pitch and yaw accuracy improve by approximately 34.7 %, 17.9 %, and 57.9 %, respectively. Additionally, the position and velocity accuracy in the east and north directions improve to 0.0926 m, 0.1779 m, 0.0548 m/s, and 0.0589 m/s, respectively, representing enhancements of about 10.7 %, 7.4 %, 10.5 %, and 6.4 %. Overall, the proposed algorithm efficiently improves the shipboard navigation accuracy of smartphones, especially in terms of attitude accuracy.

Accumulative cartography: a visual semiotic analysis of online mobile maps

Abstract Dynamic maps featured in navigation applications draw on conventional cartographic practices for representing the urban environment, engaging drivers and users of public transportation in their efforts to reach their destinations. However, since these maps are produced within an economy of data accumulation, their construction of territory and user-friendly interface are designed to generate value. In this article, we explore what we define as accumulative cartography—mapping designed for the accumulation of data and capital—by developing a semiotic walk-through method for analyzing dynamic maps. Our visual semiotic analysis of Waze, Moovit, and Gett reveals that their maps are personalized, centering on the user; they commodify space, aiming to promote their specific business models; and they are preoccupied with time. We suggest that in contrast to printed maps, dynamic maps don’t provide users with the information required for navigation but instead navigate algorithmically for them, while accumulating their information.

Development of a Location-Based Augmented Reality Application for Navigation

Development of a Location-Based Augmented Reality Application for Navigation

Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network

In response to the challenge of single navigation methods failing to meet the high precision requirements for unmanned aerial vehicle (UAV) navigation in complex environments, a novel algorithm that integrates Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) navigation information is proposed to enhance the positioning accuracy and robustness of UAV navigation systems. First, the fundamental principles of Kalman filtering and its application in navigation are introduced. Second, the basic principles of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks and their applications in the navigation domain are elaborated. Subsequently, an algorithm based on a CNN and LSTM-assisted Kalman filtering fusion navigation is proposed. Finally, the feasibility and effectiveness of the proposed algorithm are validated through experiments. Experimental results demonstrate that the Kalman filtering fusion navigation algorithm assisted by a CNN and LSTM significantly improves the positioning accuracy and robustness of UAV navigation systems in highly interfered complex environments.

Improving the stochastic model for code pseudorange observations from Android smartphones

In recent years, there has been increasing attention to positioning, navigation, and timing applications with smartphones. Because of frequently disrupted carrier phase observations, code observations remain critical for smartphone-based positioning. Considering a realistic stochastic model is mandatory to obtain the utmost positioning performance, this study proposes a sound stochastic approach for code observations from Android smartphones. The proposed approach includes a modified version of the SIGMA-ɛ variance model with different coefficients for each GNSS constellation and a robust Kalman filter method. First the noise characteristics of observations from the Xiaomi Mi 8 smartphone are analyzed utilizing code-minus-phase combinations to estimate the coefficients for each GNSS constellation. This includes the determination of a variance model as well as a check of the probability distribution. Finally, the proposed approach is validated in the positioning domain using single-frequency code observation-based real-time standalone positioning. The results show that more than 95% of observations follow the normal distribution when the proposed approach is applied. Compared with the conventional stochastic approach, including a C/N0-dependent model and standard Kalman filter, it improves the positioning accuracy by 45.8% in a static experiment, while its improvement is equal to 26.6% in a kinematic experiment. For the static and kinematic experiments, in 50% of the epochs, the 3D positioning errors are smaller than 3.0 m and 3.4 m for the proposed stochastic approach. The results exhibit that the stochastic properties of code observations from smartphones can be successfully represented by the proposed approach.

Computer-assisted open exposure of palatally impacted canines for orthodontic eruption: A randomized clinical trial

ObjectiveThis randomized clinical trial aimed to assess the feasibility of computer-assisted open exposure of palatally impacted canines. Materials and methodsPatients aged 11–30 years who required orthodontic eruption for the full palatal impaction of their canines were included in this study. Exclusion criteria were psychosocial and dental contraindications of orthodontic treatment, congenital craniofacial disorders, and trauma in the patient's history in the vicinity of the surgical site. Virtual planning software was used to register the intraoral scans and cone-beam computed tomography data and to design a surgical template. In the test group, exposure of the canines was guided by a surgical template, whereas in the control group, the surgeon relied on the surgical plan to localize the impacted canine. The success of the intervention, duration of surgery, and complications, including excessive hemorrhage, damage to the canine or neighboring anatomical landmarks, and postoperative inflammation of the surgical site were assessed. Postoperative pain was reported by the patients using the visual analog pain scale (VAS). ResultsSurgery was deemed successful in all patients in both groups. During healing, no complications were observed. The duration of surgery decreased significantly in the test group (4 min 45.1 s ± 1 min 8.4 s) compared to that in the control group (7 min 22.3 s ± 56.02 s). No statistically significant differences were observed between the VAS scores of the two study groups. ConclusionsThe application of virtual planning and static navigation is a viable approach for the open exposure of palatally impacted canines. Clinical trial registration numberNCT05909254. Clinical significanceComputer-assisted surgery is a feasible method for open exposure of palatally impacted canines, which decreases the duration of surgery compared to the freehand method.

Geometry Optimization of Stratospheric Pseudolite Network for Navigation Applications

A stratospheric pseudolite (SP) is a pseudolite installed on a stratospheric airship. A stratospheric pseudolite network (SPN) is composed of multiple SPs, which shows promising potential in navigation applications because of its station-keeping capability, long service duration, and flexible deployment. Most traditional research about SPN geometry optimization has centered on geometric dilution of precision (GDOP). However, previous research rarely dealt with the topic of how SPN geometry configuration not only affects its GDOP, but also affects its energy balance. To obtain an optimal integrated performance, this paper employs the proportion of energy consumption in energy production as an indicator to assess SPN energy status and designs a composite indicator including GDOP and energy status to assess SPN geometry performance. Then, this paper proposes an SPN geometry optimization algorithm based on gray wolf optimization. Furthermore, this paper implements a series of simulations with an SPN composed of six SPs in a specific service area. Simulations show that the proposed algorithm can obtain SPN geometry solutions with good GDOP and energy balance performance. Also, simulations show that in the supposed scenarios and the specific area, a higher SP altitude can improve both GDOP and energy balance, while a lower SP latitude can improve SPN energy status.

Designing a Dual-Feed Circular Polarization Antenna

This paper describes a novel dual-feed circularly polarized antenna, and the dual feeding mode is realized by grooving on the antenna radiator. The antenna utilizes air dielectric material to meet the requirements of low weight and cost. Test results demonstrate that the antenna exhibits capacitive loading between the metal antenna patch and the ground floor, allowing for adjustment of the working frequency of the dual-feed circularly polarized microstrip antenna. Specifically, the original center frequency of 2.264 GHz was reduced to 1.582 GHz, facilitating antenna miniaturization and broad bandwidth. With a return loss (S11) below -10 dB, a bandwidth of 72 MHz (1.552-1.624 GHz) was obtained. Additionally, the dual-feed microstrip antenna incorporates a 90° bridge, resulting in circular polarization gains of 2.26 dBi at 1.561 GHz and 2.45 dBi at 1.575 GHz. Overall, the antenna design offers a large working bandwidth and excellent circular polarization characteristics, making it suitable for a wide range of applications in satellite navigation and positioning terminals.

Simultaneous localization and mapping (SLAM)-based robot localization and navigation algorithm

This research paper presents a comprehensive study of the simultaneous localization and mapping (SLAM) algorithm for robot localization and navigation in unknown environments. The SLAM algorithm is a widely used approach for building a map of an environment and estimating the robot’s position within it, which is especially useful in dynamic and unstructured environments. The paper discusses various SLAM techniques, including the Kalman filter (KF) and GraphSLAM algorithms, and their use in probabilistic estimation of the robot’s position and orientation. The paper also explores different path-planning techniques that can be used with the map created by the SLAM algorithm to generate collision-free paths for the robot to navigate toward its goal. The paper also discusses recent advances in deep learning-based SLAM algorithms and their applications in indoor navigation with ORB and RGB-D cameras. The research concludes that SLAM-based robot localization and navigation algorithms are a promising approach for robots navigating in unstructured environments and present various opportunities for future research.