Navigation Information Research Articles

Multi-function underwater acoustic networks combining communication, navigation, and sonar

Autonomous underwater vehicles often have on-board acoustic modems and navigation systems that work in connection with multiple vehicles. These capabilities may be combined or enhanced with using multifunction transducers that transmit communication and navigation information. The use of multimode transducers to produce acoustic spiral waves for communication, navigation and sonar are presented.

Dimension-Expanded-Based Matching Method With Siamese Convolutional Neural Networks for Gravity-Aided Navigation

Matching algorithm is the key technique of the gravity-aided inertial navigation system. With the development of artificial intelligence, many neural network-based matching methods have been extensively studied. The pattern recognition-based matching methods transforms the matching problem as pattern recognition, which cannot be used directly on datasets where the neural networks have not been trained. To improve the accuracy of navigation and positioning, it is necessary to extract muti-dimensional gravity features from the limited navigation information. In this paper, the sequence of the gravity anomaly value is expanded to two-dimensional feature map containing time series features by Gramian Angular Fields method, which preserves the numerical information of the one-dimensional sequence and extracts the correlation relationship between each element. In addition, to reduce the influence of gravity measurement instrument error on the position precision of gravity matching algorithm, affine transformation is performed on INS trajectory and a Siamese convolutional neural network model is proposed to compare the measured gravity database with the gravity anomaly value in the pre-stored gravity background map and get the matching position. Simulation results and practical tests show that the proposed method can obtain a more precise location result compared with the traditional matching algorithm.

Analysis of GNSS and IMU Sensor Data Fusion Using the Unscented Kalman Filter Method on Medical Drones in Open Air

Accurate and efficient position estimation is an important element in flight missions for autonomous aerial vehicles such as Unmanned Aerial Vehicles (UAVs). UAVs rely on sensors that provide information regarding position, speed, and orientation. GPS and IMU are sensors that can provide navigation information, where each sensor has characteristics that can complement each other’s shortcomings. Research related to the integration of GPS and IMU sensors for positioning with increasingly higher accuracy has been widely applied, one of which is the Kalman Filter method. Kalman filter not only performs digital signal filtering but is also capable of smoothing and predicting recursion to obtain the most accurate estimation results. Along with the development of the modification of the Kalman Filter, the Unscented Kalman Filter (UKF) method is a solution in overcoming nonlinear systems with estimation results approaching the actual value, so that the positioning accuracy is higher and convergent. This study intends to analyze the fusion performance of GPS and IMU sensor data using the UKF method on Medical Drones in an open space in order to obtain a position with high accuracy. UKF simulations on Medical Drones were carried out using a mathematical model on a 6 DOF (Degree of Freedom) UAV. The sensor data fusion performance was tested by comparing the results of the UKF method with the EKF on the Geopointer software. The results showed that the UKF simulation gave a positional accuracy of 0.022 m to the measurement data. The UKF simulation results in a better positioning accuracy when compared to EKF processing on the Geopointer software which only achieves a position accuracy of 8.467 m. The low accuracy of the EKF processing results is due to the mismatch of the IMU parameters obtained with the actual Medical Drone parameters.

Dynamic surface reconstruction in robot-assisted minimally invasive surgery based on neural radiance fields.

The purpose of this study was to improve surgical scene perception by addressing the challenge of reconstructing highly dynamic surgical scenes. We proposed a novel depth estimation network and a reconstruction framework that combines neural radiance fields to provide more accurate scene information for surgical task automation and AR navigation. We added a spatial pyramid pooling module and a Swin-Transformer module to enhance the robustness of stereo depth estimation. We also improved depth accuracy by adding unique matching constraints from optimal transport. To avoid deformation distortion in highly dynamic scenes, we used neural radiance fields to implicitly represent scenes in the time dimension and optimized them with depth and color information in a learning-based manner. Our experiments on the KITTI and SCARED datasets show that the proposed depth estimation network performs close to the state-of-the-art method on natural images and surpasses the SOTA method on medical images with 1.12% in 3 px Error and 0.45 px in EPE. The proposed dynamic reconstruction framework successfully reconstructed the dynamic cardiac surface on a totally endoscopic coronary artery bypass video, achieving SOTA performance with 27.983dB in PSNR, 0.812 in SSIM, and 0.189 in LPIPS. Our proposed depth estimation network and reconstruction framework provide a significant contribution to the field of surgical scene perception. The framework achieves better results than SOTA methods on medical datasets, reducing mismatches on depth maps and resulting in more accurate depth maps with clearer edges. The proposed ER framework is verified on a series of dynamic cardiac surgical images. Future efforts will focus on improving the training speed and solving the problem of limited field of view.

Predicting Self-Reported Depression and Health Among Adolescents: Time Spent Online Mediated by Digital Skills and Digital Activities.

Although young people spend increasing amounts of time online, many gaps remain in the literature regarding the effect of time spent online on young people's development of well-being. We focus on the influence of time spent online on feelings of self-reported (a) depression and (b) health of adolescents. We also consider the mediating role of digital skills and digital activities, each of which is broken down into five dimensions. We collected data through a two-wave longitudinal online survey among 3,942 adolescents aged 12-17 years in six European countries (first wave [W1] = 2021; second wave [W2] = 2022). We specifically want to understand how feelings of self-reported depression and health at W2 are affected by the time spent online at W1, and how digital skills and digital activities at W2 mediate these relationships. Findings indicate a significant increase in feelings of depression and a decrease in self-reported health between W1 and W2. Regarding digital skills, information navigation and communication and interaction were linked to greater well-being (lower depression and greater self-reported health). Regarding digital activities, the development of social relationships online was linked to lower self-reported depression and greater health, while frequently using the Internet to look up information on physical or mental health issues was strongly linked to greater depression and lower health. We discuss the implications of these findings for practice and policy on young people's well-being.

Users’ Navigation Strategy and Information Search with Health Insurance Decision Aids Using Social Network Analysis

Health insurance enrollment decisions are not only medical decisions but also involve other factors such as employment and financial status. Decision aids such as Virtual Benefits Counselors (VBC) and Human Resources (HR) traditional websites may help users make informed decisions. Little research has explicitly focused on how users navigate and search for information using these aids. We propose the use of the Social Network Analysis (SNA) method to characterize navigation and information search behavior. SNA was applied to the navigation data of 16 participants who were asked to make mock health insurance enrollment decisions using a VBC or HR website. VBC users searched for financial information with less back-and-forth navigation and explored a larger percentage of the system, whereas HR website users had more back-and-forth navigation between the same pages but explored fewer available pages. In conclusion, SNA provided insights into differences between how users interact with different decision aids and what contents they were looking for which may prove useful in understanding users’ interaction strategies.

Electronic Warfare Theory and Strategy: Focusing on the Threat of North Korea’s Electronic Warfare

Purpose: The importance of electronic warfare in modern warfare is increasing day by day. In particular, North Korea has a relative weakness of conventional weapons systems, and has focused on developing asymmetric weapons systems and electronic warfare as a way to overcome them. North Korea is currently expected to further expand its application to some aircraft and guided weapons operations under satellite navigation systems such as GPS, GLONASS, COMPASS and Beidou.
 Method: This study studied the prior theory of electronic warfare at home and abroad. Prior research examined the military doctrines of the United States and trends in Europe. In particular, I studied the doctrines of the United States Joint Navigation Center (Navigation Warfare Center, JNWC) and the Education Command. Based on these prior research materials and literature reviews, I wanted to specifically review North Korea's electronic warfare cases and present our response in terms of legal and institutional aspects.
 Results: The South Korean military should recognize the importance of navigation warfare (part of electronic warfare) that protects our troops' location, navigation and square information so that they can be used without interruption, prevent the enemy's use of related information, and ensure the peaceful use of such information outside the light bulb In addition, we need to build an electronic warfare simulator and continue to promote training based on various scenarios.
 Conclusion: Efforts to train and support professionals are urgently needed. In parallel with these efforts, joint efforts to develop electronic technologies between the civilian and military should be preceded. To win a high-tech war in the 21st century, it is essential, among other things, to have precision weapons and original electronic warfare support capabilities. In addition, it is necessary to foster R&D and defense companies to secure domestic technology.

Los cibercafés como instancias de acceso público a la información y coadyuvante en el cierre de la brecha digital en Morelos, México

This article presents the results of a research aimed at diagnosing the digital divide among users of cybercafes in Morelos (Mexico) in terms of access to Internet services (determinants) as well as the identification and management of information. For this purpose, a survey was designed and applied to 116 informants in 14 municipalities of Morelos. Results indicate that cybercafes function as access points to information, technological devices, and Internet navigation, thus reducing the digital divide, particularly in rural areas. It was also found that cybercafes allow users to access several services, users attend to obtain printing services, carry out governmental procedures and information searches, which would not be possible without the existence of cybercafes.

Hybrid noise removal to improve the accuracy of inertial sensors using lifting wavelet transform optimized by genetic algorithm

An Inertial Navigation System (INS) is a standalone system that provides continuous navigation information on the position, velocity, and attitude in various conditions. Gyroscopes and accelerometers are essential components of INS. However, these sensors are prone to errors that accumulate over time and degrade the accuracy of the INS. Therefore, enhancing the performance of INS is a challenging problem. This paper aims to address this problem by proposing a method to reduce errors in real-time processing. The method uses Lifting Wavelet Transform (LWT), a popular denoising technique that belongs to the wavelet family and has an advantage over the classic wavelets in terms of time and computation complexity. Moreover, the method employs an optimization technique based on the Genetic algorithm as an intelligent averaging for combining different multi-level LWTs, which significantly improves the accuracy and efficiency of INSs. Finally, a new and implementable structure is introduced for denoising inertial sensors. The results show that the proposed method achieves an 83% and 59% improvement in gyroscope and accelerometer data for dynamic data, compared with raw data. Besides, for static data, there are 71% and 36% improvements in gyroscope and accelerometer data, respectively.

Distribution and main sources of oil spills in the Persian Gulf based on satellite monitoring with synthetic aperture radar

ABSTRACT The Persian Gulf is characterized by a huge number of offshore oil and gas platforms, large number of many oil terminals, and busy shipping lines for supporting oil transportation. In this regard, developing various methods for assessing the oil pollution of the Gulf, including remote sensing, is extremely important. For the first time, synthetic aperture radar (SAR) images of the European Sentinel-1A and Sentinel-1B satellites were used routinely to monitor the entire Gulf in 2017–2019. To effectively analyse the SAR images and their detected dark patches, a monitoring method based on manual detection and all contextual information available using web-GIS approach, was used. This involved the creation of a dedicated geoportal with oceanographic, physical – geographical, industrial and navigational information on the water basin, including the offshore oil and gas infrastructure. Using this approach, as well as data from automatic ship identification system, most oil spills were detected, recognized and mapped. The summary oil spill distribution maps and main statistical results of the monitoring are presented and discussed. It is shown that the main sources of oil pollution in the Persian Gulf (oil slicks/spills from 0.5 to about 700 km2) are (in decreasing order) oil production, oil transportation, and local shipping.

Temporal effects of sugar intake on fly local search and honey bee dance behaviour.

Honey bees communicate flight navigational information of profitable food to nestmates via their dance, a small-scale walking pattern, inside the nest. Hungry flies and honey bee foragers exhibit a sugar-elicited search involving path integration that bears a resemblance to dance behaviour. This study aimed to investigate the temporal dynamics of the initiation of sugar-elicited search and dance behaviour, using a comparative approach. Passive displacement experiments showed that feeding and the initiation of search could be spatially and temporally dissociated. Sugar intake increased the probability of initiating a search but the actual onset of walking triggers the path integration system to guide the search. When prevented from walking after feeding, flies and bees maintained their motivation for a path integration-based search for a duration of 3min. In flies, turning and associated characters were significantly reduced during this period but remained higher than in flies without sugar stimulus. These results suggest that sugar elicits two independent behavioural responses: path integration and increased turning, with the initiation and duration of path integration system being temporally restricted. Honey bee dance experiments demonstrated that the motivation of foragers to initiate dance persisted for 15min, while the number of circuits declined after 3min following sugar ingestion. Based on these findings, we propose that food intake during foraging increases the probability to initiate locomotor behaviours involving the path integration system in both flies and honey bees, and this ancestral connection might have been co-opted and elaborated during the evolution of dance communication by honey bees.

A New Classification Method for Ship Trajectories Based on AIS Data

Automatic identification systems (AIS) can record a large amount of navigation information about ships, including abnormal or illegal ship movement information, which plays an important role in ship supervision. To distinguish the trajectories of ships and analyze the behavior of ships, this paper adopts the method of supervised learning to classify the trajectories of ships. First, the AIS data for the ships were marked and divided into five types of ship tracks. The Tsfresh module was then used to extract various ship trajectory features, and a new ensemble classifier based on traditional classification using a machine learning algorithm was proposed for modeling and learning. Moreover, ten-fold cross validation was used to compare the ship trajectory classification results. The classification performance of the ensemble classifier was better than that of the other single classifiers. The average F1 score was 0.817. The results show that the newly proposed method and the new ensemble classifier have good classification effects on ship trajectories.

Neuroarchitecture of the central complex in the Madeira cockroach Rhyparobia maderae: Pontine and columnar neuronal cell types.

Insects have evolved remarkable abilities to navigate over short distances and during long-range seasonal migrations. The central complex (CX) is a navigation center in the insect brain that controls spatial orientation and directed locomotion. It is composed of the protocerebral bridge (PB), the upper (CBU) and lower (CBL) division of the central body, and a pair of noduli. While most of its functional organization and involvement in head-direction coding has been obtained from work on flies, bees, and locusts that largely rely on vision for navigation, little contribution has been provided by work on nocturnal species. To close this gap, we have investigated the columnar organization of the CX in the cockroach Rhyparobia maderae. Rhyparobia maderae is a highly agile nocturnal insect that relies largely but not exclusively on antennal information for navigation. A particular feature of the cockroach CX is an organization of the CBU and CBL into interleaved series of eight and nine columns. Single-cell tracer injections combined with imaging and 3D analysis revealed five systems of pontine neurons connecting columns along the vertical and horizontal axis and 18 systems of columnar neurons with topographically organized projection patterns. Among these are six types of neurons with no correspondence in other species. Many neurons send processes into the anterior lip, a brain area highly reduced in bees and unknown in flies. While sharing many features with the CX in other species, the cockroach CX shows some unique attributes that may be related to the ecological niche of this insect.

A Fast Matching Method for the SAR Images with Large Viewing Angles Based on Inertial Navigation Information and Neighborhood Structure Consensus

In the field of multi-view SAR target location, the greater the difference in viewing angles, the higher the target location accuracy. However, this makes it difficult to match the same target between the SAR images with different viewing angles. The performance of traditional SAR image-matching algorithms will deteriorate or even fail to match the images correctly when the viewing angle is gradually increased. To solve this problem, a fast SAR matching method for the SAR images with large viewing angles based on inertial navigation information and neighborhood structure consensus (ININSC) is proposed in this paper. In this algorithm, the key targets are detected in the SAR images by using the maximum connected domain algorithm and the K-means clustering algorithm, and the connected domain centroid of the target is taken as a feature point. The approximate position of the key targets after the viewing angle change is found through inertial navigation information, and then accurate and fast matching is achieved by using the consensus of the neighborhood topological structure of the key targets. The measured data sets demonstrate that compared with traditional SAR image-matching algorithms, the proposed ININSC algorithm solves such a problem that SAR images cannot be accurately matched under the differences at large viewing angles and has better robustness and timeliness.

"Healthcare should be the same for everyone": perceived inequities in therapeutic trajectories of adult patients with lung cancer in Chile, a qualitative study.

Globally, it has been reported that different social determinants of health affect health outcomes in lung cancer (LC). Research on the therapeutic trajectories of patients (TTP) is a novel field for identifying barriers and facilitators in health. The objective of this study was to reveal perceived differences in TTP with LC in Chile according to selected social determinants of health (SDH) and the experiences of patients, health professionals, and civil society leaders. This is a qualitative paradigm, one case-study design. Online semi-structured interviews were conducted with patients with LC, health professionals, and civil society leaders. The strategies for the recruitment process included social networks, civil society organizations, health professionals, and the snowball technique. A thematic analysis was carried out. Selected SDH impact LC's TTP in Chile, particularly concerning health system access, health services, information, and patient navigation experiences. The analysis of the experiences of the participants allowed us to identify barriers related to the selected SDH in three stages of the TTP: initiation, examinations, and diagnosis and treatment. Individuals with limited education, those residing outside the capital, women, and those in the public health system encountered more barriers throughout their TTP. Study findings suggest that being a woman with low education, from the public health system, and not from the capital might represent one of the most powerful intersections for experiencing barriers to effective healthcare in LC in Chile. It is necessary to monitor the TTP from an SDH perspective to guarantee the rights of access, opportunity, quality, and financial protection.

A Tight Coupling Algorithm for Strapdown Inertial Navigation System (SINS)/Global Positioning System (GPS) Adaptive Integrated Navigation Based on Variational Bayesian

Multi-source nonlinear noise exists in the process of multi-source navigation information fusion in long-endurance positioning systems in complex environments. In such engineering applications, the classical Kalman filter (KF) and the extended Kalman filter (EKF) have the phenomena of noise instability and parameter drift, which lead to the divergence of filtering results and reductions in accuracy over long periods of time. Aiming at the above problems, this paper proposes a fusion algorithm of the variational Bayesian (VB) and the cubature Kalman filter (CKF). Firstly, the system is modeled through nonlinear filtering, and the CKF error equation is established by taking the position difference and velocity difference between SINS and GPS as observation variables. Then, to address the problem of poor self-adaptation of the CKF algorithm, the variational Bayesian adaptive estimation method is introduced into the CKF algorithm, and a measurement noise variance estimation model is introduced to the process of time and measurement updates of the CKF algorithm to finally obtain the adaptive VB–CKF algorithm. The simulation results from the experimental platform show that the proposed fusion algorithm improves the combined SINS/GPS system by about 30% in terms of attitude angle accuracy and reduces speed and position estimation errors (RMSE) by about 45%. At the same time, comprehensive experiments on multiple types of sites show that compared with the CKF algorithm, the VB–CKF algorithm improves the positioning accuracy by 10% when the GPS signal is stable and improves the accuracy by about 38% when the GPS measurement noise changes dramatically in complex terrain, which effectively suppresses the accuracy divergence of the CKF algorithm and has high value for engineering applications.

Current use of intraoperative ultrasound in modern liver surgery

Abstract Ultrasound plays an important role not only in preoperative diagnosis but also in intraoperative guidance for liver surgery. Intraoperative ultrasound (IOUS) has become an indispensable tool for modern liver surgeons, especially for minimally invasive surgeries, partially substituting for the surgeon's hands. In fundamental mode, Doppler mode, contrast enhancement, elastography, and real-time virtual sonography, IOUS can provide additional real-time information regarding the intrahepatic anatomy, tumor site and characteristics, macrovascular invasion, resection margin, transection plane, perfusion and outflow of the remnant liver, and local ablation efficacy for both open and minimally invasive liver resections. Identification and localization of intrahepatic lesions and surrounding structures are crucial for performing liver resection, preserving the adjacent vital vascular and bile ducts, and sparing the functional liver parenchyma. Intraoperative ultrasound can provide critical information for intraoperative decision-making and navigation. Therefore, all liver surgeons must master IOUS techniques, and IOUS should be included in the training of modern liver surgeons. Further investigation of the potential benefits and advances in these techniques will increase the use of IOUS in modern liver surgeries worldwide. This study comprehensively reviews the current use of IOUS in modern liver surgeries.

Extended Reality (XR) - Applications in Thoracic Surgery

Extended reality (XR) includes the sub-terms of virtual reality (VR), augmented reality (AR) and mixed reality (MR) and describes interactive and immersive technologies that replace the real world with digital elements or seamlessly extend it with such approaches. XR thus offers a very wide range of possible applications in medicine. In surgery, and thoracic surgery in particular, XR technologies can be harnessed for treatment planning, navigation, training, and patient information. Such applications are increasingly being tested and need to be evaluated. We provide an overview of the status quo of technical development, current surgical applications of XR, and look into the future of the medical XR landscape with integration of artificial intelligence (AI).

Improved Adaptive Finch Clustering Sonar Segmentation Algorithm Based on Data Distribution and Posterior Probability

This study proposes a side-scan sonar target detection technique for CPU or low-performance GPU to meet the requirement of underwater target detection. To rectify the gray distribution of the original side scan sonar data, enhance picture segmentation, and supply the data distribution probability for the clustering algorithm, the methodology uses a classic image processing technique that is GPU-friendly. The modified adaptive Finch clustering technique is used to segment the image and remove image voids after assessing the processed image attributes. The posterior information is then used to apply a classification label to each pixel. The characteristics of the connected region are analyzed in the data playback of the Tuandao experiment in accordance with the imaging principle of side-scan sonar and the original shape and size characteristics of the target. The predicted target results are combined with the AUV navigation information to obtain the predicted target longitude and latitude information, which is then sent to the AUV master control system to guide the next plan. The Jiaozhou Bay sea test results demonstrate that the traditional target detection algorithm put forth in this paper can be integrated into a low-performance GPU to detect targets and locate them. The detection accuracy and speed exhibit strong performance, and real-time autonomous sonar detection is made possible.

Perancangan Aplikasi Naviku untuk Memberikan Informasi Navigasi Kepada Tunanetra Menggunakan Metode Test Driven Development

Visually impaired is a term for someone who has lost or reduced the function of vision. People with visual disabilities often experience difficulties in daily activities, for example when walking or navigating. They need the help of others to help them go to a room or move from one place to another. Currently, existing navigation aids do not fully meet the needs of the blind. They still need the help of others to get detailed information about visual objects such as the layout of rooms in buildings, traffic signs, and vehicles. Based on these problems, an Android-based navigation application needed that utilizes Text-to-Speech (TTS) technology to help blind people navigate independently by utilizing navigation information provided by voice. In order to help the development process, applications are developed by adopting Agile Software Development principles using the Test Driven Development (TDD) method. The features developed begin with a test fail phase where the developer creates unit test code before the production code is implemented, then refactor the code, and tests afterward (test passes). The success status of the features being tested gets success status with a rate of 100% for all features. Then in the TTS test, testing was carried out with 2 TTS engines, namely Google TTS and Samsung TTS, the results showed that the Word Error Rate (WRE) of Google TTS was 2,86, lower than Samsung TTS which got 4,06.