bluuebunny/crossref_metadata_2025 huggingface dataset:
Implementing Blockchain Technology in Supply Chain Management
Authors: Anand Atul , Seetharaman A , Maddulety K
Published: April 2022

This paper is aimed at studying the factors influencing the implementation of blockchain in supply chain management to solve the current issues faced in the supply chain ecosystem. Supply chains are part and parcel of every business and have multiple inefficiencies in the system. Some of these inefficiencies can be managed by usage of blockchain Platform .Technology, intracompany synergies, intercompany collaboration, extrinsic factors, and innovation are critically evaluated for adoption of blockchain in supply chain. A pilot study is conducted in form survey for analysis of these factors. Hypotheses are derived for these factors for quantitative research. Subsequently these hypotheses are examined with the help of ADANCO2.3 for structural equation modelling. As an outcome, it is evident that Innovation and Extrinsic factors are significantly impacting the adoption of blockchain in supply chain management.
An Intelligent Social-based Assistant Application for Study Time Management using Artificial Intelligence and Natural Language Processing
Authors: Li Haoyu , Yan Ryan , Li Ang
Published: April 2022

The question that we aim to solve is “How will elderly people be able to increase their productivity and remember their tasks?” There are many ways to go about answering this question, but we have devised a simple solution to this question that can directly and quickly have a positive impact on these individuals. Our method to solving this is creating a to-do list in Flutter, which will allow elderly people to have easy access to a list of tasks that they have to complete [5]. Some predicted results of this to-do list is that it can raise productivity for its users. Our to-do list features a ChatBot, which talks to the user through a message-like system in order to prompt user input for specific details such as the time, date, and main description of the task [6]. Then, the ChatBot will take in all this information to produce a clean and concise final task description that takes keywords from the user-inputted description. This provides users of our to-do list with an alternative method of adding tasks, which may be greatly appreciated by those who are less able-bodied or struggle to type. By offering the elderly a way of adding tasks that can take less typing, these individuals may rely on this to-do list as a great convenience to their lives.
Image Encryption Algorithm of Chaos System Adding Cosine Excitation Function
Authors: GUO Zhenzhou , LI Xintong
Published: April 2022

In order to increase the chaotic performance of the chaotic system, the chaotic system A-S proposed by Sprott is improved by adding a cosine excitation function to a controller. A series of new chaotic systems are obtained, and the chaotic performance of the improved system is verified. The image is encrypted by the chaotic sequence generated by the improved A chaotic system. In the scrambling part of the image encryption algorithm, the zigzag transformation is improved, and different directions are selected to start the traversal, so that the scrambling process is not easy to be restored. The diffusion part draws on the traditional IDEA algorithm to perform diffusion operations on the image. Finally, the encryption algorithm is analyzed and tested, and the results show that the algorithm has fast encryption and decryption speed, sufficient key space, and can resist statistical analysis attacks well. The algorithm can provide better guarantee for the security of images.
How to Enhance the Sharing of Cyber Incident Information via Fine-Grained Access Control
Authors: Salonen Jarno , Niskanen Tatu , Raitio Pia
Published: April 2022

Industry 4.0 and the ongoing digital transformation along with a large number interconnected machines anddevices increase the role of cybersecurity, cyber incident handling and incident response in the factories of the future (FoF). Cyber incident information sharing plays a major role when we need to formulate situational pictures about FoF operations and environment, and respond to cybersecurity threats related to e.g. the implementation of novel technologies. Sharing of incident information has a major drawback since it may reveal too much about the attack target, e.g. in the case of legacy systems and therefore restrictions may apply. We have developed a proof-of-concept service that combines access control and encryption of data at high granularity and a mechanism for requesting access to restricted cyber incident information. Theobjective was to demonstrate how access to restricted incident data fields could be managed in a fine-grained manner to enhance information sharing.
Hemşirelik Bakımında Önemli Bir Kavram: Karşılanamayan Hemşirelik Bakımı ve Nedenleri
Authors: GÜLEŞEN Gülçin
Published: April 2022

Kaliteli ve nitelikli hizmet sunumunda en önemli kavram olan hemşirelik bakımı çeşitli nedenlerle ihmal edilerek hasta güvenliğini tehdit eden küresel bir sorun olarak bilimsel çalışmalarda yer almaktadır. Hasta güvenliği literatürüne ihmal hatası olarak giren karşılanamayan hemşirelik bakımı konusunda yurt dışında birçok kanıta dayalı bilimsel çalışmalar olmasına karşın, Türkiye’de sınırlı sayıda yayına ulaşılmaktadır. Literatürde karşılanamayan hemşirelik bakımının boyutu, nedenleri ve sonuçları ile ilgili çalışmalar bulunmaktadır. Karşılanamayan hemşirelik bakımı hem hastalar hem de hemşireler açısından olumsuz sonuçlara neden olmaktadır. Bu derlemede, hemşirelerin ve sağlık kurumlarının karşılanamayan hemşirelik bakımı ve gereken önlemlerin alınması konusunda bilgi sahibi olmaları ve gözden kaçırılan bu ihmal hatası konusunda farkındalık sağlanması amaçlanmıştır.
حقوق نسواں اور تعمیر معاشرہ میں ان کا کردار: سیرت نبویہ کی روشنی میں تحقیقی جائزہ
Authors: Farooq Muhammad Umar , Rehman Ahmed Abdul , Quraishi Muhammad Ibrahim
Published: April 2022

There are so many verses in the holy Quran and Hadith, which told us about the status, importance and rights of women in Islam. Islam is the only religion who has given women the status of human beings, even now if the women should be given their rights which Islam has provided them, they can play their role in the development of society in form of perfect mother, daughter, sister and wife.

Generally, nowadays it’s been thought that west has given too many rights to women and Islam has captured them in houses, but in the reality its totally wrong perception. Now this is very important to elaborate true image of women’s rights which Islam has given to them in the better way, infect this is the duty of every Muslim.
مغرب میں بچوں کو جنسی تعلیم دینے کی وجوہات ، اسباب و نتائج۔
Authors: Yasin Muhammad Rizwan , Rehman Ahmed Abdul , Ahmed Ussama
Published: April 2022

This article is enough to open the eyes that how the West's environment has become so disturbing. Their own statistics indicate that they were so concerned about the sexual transmitted diseases (STDs) and the pregnancy of young girls that they had no other way but to teach sex education in their schools and colleges as part of the curriculum. Since the West believe in the separation of the Church and the State, therefore the Western society has separated the religion from the matters of their daily life. As a result, like other problems, sexual intolerant behavior also increased, but they were able to reduce sexual transmitted deseases to some extent through the sex education. In this context, it was necessary to examine and understand the Western point of view about this education programme for their children.  In this article, we will summarize the various long-standing articles on sex education in the west and through this we will elaburate the researches of these articles and causes or motives, consequences and the reasons for the inclusion of this education in the children’s curriculum in the west.
خطیب ہزارہ مولانا شفیق الرحمن قریشی(2018-1933)کی دینی ،سیاسی اور اصلاحی خدمات کا تجزیاتی مطالعہ
Authors: Awan Hafiz Abdullah Haroon , Khan Muhammad Ajmal , Mukhtar Asia
Published: April 2022

Abstract

In the reform and political efforts in the subcontinent, the name of the land of Hazara began to be taken seriously when Syed Ahmad Shaheed and his caravan turned towards it and got involved in a bloody battle at Balakot and martyred. Allama Anwar Shah Kashmiri also knelt before the high-ranking scholars of the Hazara, which is a testament to the scholarly influence of the local scholars here. Maulana Shafiq-ur-Rehman Hazarawi is one of the most respected scholars in the field of scientific and reforming services in Hazara region. He has rendered invaluable services in the field of religion, politics and reform, for a period of half a century, which need to be brought to the fore so that his services can transcend regional constraints and reach out to people from different regions. This article concludes that Qadianism failed to establish itself in the city of Abbottabad due to his efforts though he had to endure the hardships of imprisonment while facing the tribulation of Qadianism. Due to him, religious awareness was awakened in the public of Abbottabad and the people got an unexpected blessing in the form of Darul Iftaa for the Shariah solution of their problems in their city.

Keywords: Reform, religious, services, Qadianism, darul ifta, Abbottabad, Hazara region
Investigation of annual droughts of Boyabat region using different meteorological drought indices
Authors: ZEYBEKOĞLU Utku , ŞAHİN Ahmet
Published: April 2022

Drought, which is a natural disaster that increases its effects day by day, can cause significant damage. Several drought indices were carried out to determine drought. By means of these indices, drought can be determined and necessary precautions can be taken against drought. In this study, an annual drought survey was conducted using the rainfall records of the Boyabat meteorological observation station in the Kizilirmak River Basin in the 1976-2017. In the drought analysis, the meteorological drought indices Z-Score Index, China-Z Index and Modified China-Z Index were used. The dry and wet periods of the station were determined by means of meteorological drought indices, and the driest and wettest years were also determined. In the study, which also examined the compatibility between indices, it was determined that the coefficient of determination values of MCZI-ZSI and MCZI-CZI were higher than the ZSI-CZI. Drought events in Boyabat region should be investigated using different indices.
A IMPORTÂNCIA DAS REGRAS NA EDUCAÇÃO INFANTIL
Authors: Morais Carla dos Santos , Souza Claudia Walkiria Faria de , Siqueira Rossatia Byanca Curado de , Ferreira Rosilaine Martins , Costa Rosivania Martins Ferreira
Published: April 2022

: O objetivo deste artigo é analisar como são dadas restrições e “regras” às crianças na educação infantil. Diante da falta de constrangimentos que as crianças têm umas com as outras e com seus educadores, o interesse pelo assunto é tão observado quanto o autoritarismo exacerbado, gritos e ameaças dos adultos. O fato de educadores que não fornecem rotinas para crianças e pais não as seguirem quando elas existem, ajuda as crianças a não entenderem os horários, comportamentos e regras que devem existir e que existem em todos os ambientes em que vivemos. Sejam teóricos tradicionais ou não, todos concordam que as crianças têm melhor convivência social quando as restrições são apresentadas a elas, elas entendem quais atitudes podem ter para que sejam menos tendenciosas e desenvolvam um pensamento crítico-social mais consistente e positivo. É fácil perceber a dificuldade como educador com esse assunto, pois o número de alunos e as questões sociais e familiares que os permeiam também favorecem algum tipo de abandono das regras. Muitos parecem preferir manter as crianças como já são, mesmo alegando que não é responsabilidade da escola, dizendo que a educação se aprende em casa. No entanto, este artigo tenta confirmar que a contenção de crianças é responsabilidade de cada indivíduo, família, escola e sociedade. Restrições não são disciplinas, restrições são regras que facilitam a vida de todos. As crianças são os adultos de amanhã e, portanto, é necessário investir nelas uma educação completa com conteúdo científico e social.
O CORPO E O MOVIMENTO NA EDUCAÇÃO INFANTIL
Authors: Morais Carla dos Santos , Souza Claudia Walkiria Faria de , Siqueira Rossatia Byanca Curado de , Ferreira Rosilaine Martins , Costa Rosivania Martins Ferreira
Published: April 2022

Entendemos corporeidade como a capacidade do indivíduo de sentir e utilizar o corpo como ferramenta de expressão e interação, e buscamos delinear conceitos de ensino sobre o trabalho envolvendo o corpo e o movimento infantil, bem como as possíveis contribuições do trabalho corporal para o desenvolvimento infantil, e a importância dele em seus procedimentos de ensino no corpo de trabalho. Por outro lado, alguns educadores encontram dificuldades para introduzir a fisicalidade em sua prática, seja por falta de espaço em sala de aula ou por falta de planejamento. Pela relevância da discussão, sugerimos que é necessário aprofundar a instrução da prática docente para determinar, de fato, como os professores trabalham com as crianças nos exercícios físicos, gestuais e de movimento.
JOINT TRACKING GPS AND LEO SIGNALS WITH ADAPTIVE VECTOR TRACKING LOOP IN CHALLENGING ENVIRONMENTS
Authors: Liu G. , Wang L.
Published: April 2022

Abstract. Navigation from LEO satellites own many merits and attracts increasing popularity recently. In addition to increasing the signal availability, the low signal strength loss and fast satellite geometry change from LEO satellite are particularly appealing in challenging environments. Recently, a few researchers attempt to navigate with non-cooperative signals from LEO satellites with pure phase lock loop (PLL) or frequency lock loop (FLL), while a more practical solution to utilizing LEO navigation is joint positioning with the existing GNSS signals which has not been seriously studied. In this study, we proposed a joint GPS and LEO navigation signal tracking strategy that employs a vector tracking loop (VTL) with fully considering the high dynamic characteristics of the LEO signals. In order to solve the high dynamics problem, the second-order deviation parameters were considered in the extended Kalman filter, which is more adaptive to the non-linear variation of the signal acceleration. In addition, a carrier-to-noise ratio (C/N0) based observation noise determination strategy is employed to adapt different observation conditions. The proposed method was verified with different simulation data and the results indicate the adaptive vector tracking loop is capable of tracking GPS and LEO signals simultaneously and robustly. The benefit is particularly in the weak signal scenarios. The experiment results also reveal that the joint vector tracking loop improves positioning accuracy in GNSS challenging environments.
A PRECISE RANGING WITH SUBCARRIER DIVERSITY FOR 5G NR INDOOR POSITIONING
Authors: Liu Z. , Chen L. , Zhou X. , Ruan Y. , Jiao Z. , Chen R.
Published: April 2022

Abstract. Indoor positioning is an important component of location-based service. Moreover, it has extensive applications in areas such as the Internet of Things (IoT) and Artificial Intelligence (AI). As a new generation of the mobile communication signal, the introduction of technologies such as multi-carrier, massive multiple-input multiple-output (MIMO), and ultra-dense network (UDN) has enabled 5G NR signal to demonstrate unique technical advantages in the field of indoor positioning. In this paper, for indoor positioning, a subcarrier diversity precision ranging method based on a commercial 5G NR signal is proposed. According to the results of field experiments in the indoor environment, the proposed method significantly improves the ranging precision compared to the Reference Signal Receiving Power (RSRP)-based ranging method. When the diversity ratio is set to 0.25, the 95% cumulative distribution function (CDF) of the overall ranging error is less than 3 meters.
ASSESSMENT OF BUILDINGS AND ELECTRICAL FACILITIES DAMAGED BY FLOOD AND EARTHQUAKE FROM SATELLITE IMAGERY
Authors: Ma Y. , Zhou F. , Wen G. , Gen H. , Huang R. , Liu G. , Pei L.
Published: April 2022

Abstract. Natural disasters cause considerable losses to people’s lives and property. Satellite images can provide crucial information of the affected areas for the first time, conducive to relieving the people in disaster and reducing the economic loss. However, the traditional satellite image analysis method based on manual processing drains workforce and material resources, which slowed the government’s response to the disaster. Aiming at the natural disasters like floods and earthquakes that often happen in the south of China, we propose a dual-stage damage assessment method based on LEDNet and ResNet. Our method detects the changes between the satellite images captured before and after a disaster of the same area, segments the buildings, and evaluates the damage level of affected buildings. In addition, we calculate influence maps based on the damage scale to the building and estimate the damage situation for electrical facilities. We used images related to earthquakes and floods in the xBD dataset to train the network model. Moreover, qualitative and quantitative evaluations demonstrated that our method has higher accuracy than the xBD baseline.
PARTICLE SWARM OPTIMIZATION FOR UNDERWATER GRAVITY-MATCHING: APPLICATIONS IN NAVIGATION AND SIMULATION
Authors: OuYang M. , Fan H. , Du P.
Published: April 2022

Abstract. This paper developed a two-stage solution for underwater gravity matching navigation based on the particle swarm optimization algorithm and affine transformation. The first stage established a starting point, and the second stage treated the matching track gained through affine transformation as a particle at the same starting point, followed by the application of the particle swarm optimization algorithm to obtain the optimal solution. To avoid falling into a local optimal solution, a convergence factor was incorporated into the optimization process in addition to the linear decreasing weight. This was followed by the addition of a constraint on the velocity and position of the particles, which was then updated in an iterating process. Two simulated navigation tracks were employed for experiments. The results revealed that the algorithm was capable of matching actual tracks in real time. Additionally, the results were found to be consistent with those obtained from the real-world tracks, with all the locations and gravity anomaly deviations falling within a tolerable range. However, when there were too many matching track points, the algorithm efficiency declined in terms of calculation time. This entails improving the algorithm through the segmentation technique.
PERFORMANCE ANALYSIS OF BLE 5.1 NEW FEATURE ANGLE OF ARRIVAL FOR RELATIVE POSITIONING
Authors: Qian M. , Zhao K. , Seneviratne A. , Li B.
Published: April 2022

Abstract. The official release of Bluetooth Core Specification, version 5.1 in 2019, provides a new feature for direction determination. It introduces a fine-grained angle measurement capability including Angle of Arriva (AoA) and Angle of Departure, which is deemed essential to the development of Internet of Things applications for localization or relative positioning within the area where Global Navigation Satellite System signals cannot be received. In this paper, we set up a series of experiments to empirically evaluate the direction of the Bluetooth Low Energy transmitter based on the AoA mechanism using the commercialised equipment. The experimental evaluation is performed, for the first time to date, under four different testing environments and other various conditions to inspect the fidelity of angle measurement against the high accuracy given in the specifications. In details, an ideal environment (i.e., anechoic chamber with no multi-path effect), an underground mine, an open area and a typical office area are all tested comprehensively. The experimental results reveal that the ideal environment has the best performance with the minimum error and conforms with the provided datasheet. The worst case occurs on the data collected from the office area. We also find a regular pattern always showing up repeatedly on the error plots based on the measurement results and scrutinize the truthfulness of this rule by adding more innumerable tests conducted in the office. Our results show that the performance of accuracy does depend on the data channel selection due to the multi-path effect.
REAL-TIME POSITIONING AND TRACKING FOR VISION-BASED UNMANNED UNDERWATER VEHICLES
Authors: Qin J. , Yang K. , Li M. , Zhong J. , Zhang H.
Published: April 2022

Abstract. Unmanned underwater vehicle (UUV) is a key technology for marine resource exploration and ecological monitoring. How to use vision-based active positioning and three-dimensional perception to realize UUV underwater autonomous navigation and positioning is the basis for UUV's underwater operations. The complexity and unstructured characteristics of seawater bring new challenges to vision-based underwater high-precision positioning. Traditional visual localization algorithms mainly include geometric-based visual localization algorithms (such as ORB-SLAM2) and deep learning-based visual localization algorithms (such as DXSLAM). In this paper, based on the typical marine environment (low brightness, dynamic fish interference, underwater light spot, high turbidity), the experimental analysis and comparison of different visual positioning methods of UUV is carried out, which provides a reference for realizing the real-time localization of UUV, and further provides a better solution for UUV underwater measurement and monitoring operations.
INDOOR ATTITUDE ESTIMATION USING EQUIPPED GYROSCOPES AND DEPTH SENSORS
Authors: Shi Q. , Song Z. , Xiao Z. , Chen S. , Wang F.
Published: April 2022

Abstract. Attitude estimation is central to a wide range of applications such as robotics, virtual reality and mobile smart devices. With the development of sensor technologies, these application devices are often equipped with gyroscopes and depth sensors. In this paper, we propose a novel method to fuse gyroscope and depth information for drift-free and robust attitude estimation in structured indoor applications. Our method relies on the depth information and the Manhattan world assumption to estimate the absolute orientation, which is then fused to correct the accumulated error of the gyroscope-determined attitude. We first utilize the mean shift algorithm on the unit sphere to align the surface normals from the depth measurements with the orthogonal planar structures of the Manhattan world. Therefore, the orientation estimates are drift-free and absolute with respect to the Manhattan world. We then fuse the orientation estimates with the gyroscope measurements in an error-state Kalman filter manner to further improve the attitude estimation accuracy and robustness. We validate the performance of our method on public datasets, demonstrating the robustness and accuracy of the method for attitude estimation.
CROWDSOURCED WIFI FINGERPRINT LOCALIZATION IN URBAN CANYON
Authors: Su Y. , Chen L. , Liu X.
Published: April 2022

Abstract. Although Global Navigation Satellite System (GNSS) has achieved success in outdoor localization, it does not often work well in urban canyon, which is due to the weak signals and the loss of satellites. WiFi technology is widely used at present, and the crowdsourced WiFi data has the advantages of rich sources and low cost. Therefore, utilizing the crowdsourced WiFi data for localization may effectively improve the deficiency of GNSS in the urban canyon. In this paper, we propose a novel method of crowdsourced WiFi fingerprint localization in urban canyon. Considering that the crowdsourced data is noisy, discontinuous and unstable, we carry out pre-processes for data refining, and grid-based statistical method for noise smoothing. Then in order to quickly locate the terminals in large-scale area, the AP coverage intersection method is proposed, in which the coverage range, centers and density of all APs are inferred, and the personal hotspots as well as the mobile APs are removed. To further enhance the positioning accuracy, the fine localization is carried out, which is based on the iterative KWNN algorithm. Extensive field tests are carried out in a typical urban canyon, results show that the average positioning error of our method is 16.82 m, which shows the effectiveness of the proposed method for crowdsourced positioning in urban canyon.
TIGHTLY-COUPLED RTK/INS INTEGRATED NAVIGATION USING A LOW-COST GNSS RECEIVER AND A MEMS IMU
Authors: Sun X. , Zhuang Y. , Chen S. , Shao Y. , Chen D.
Published: April 2022

Abstract. The Global Navigation Satellite System (GNSS) real-time kinematics (RTK) is a technology to provide centimeter-level navigation services in outdoor areas. GNSS/INS integration is a typical integrated navigation system, including loosely, tightly, and ultra-tightly coupled integration, which has been studied for decades. Although GNSS aiding INS is a relatively mature system, RTK is rarely used to be integrated with INS. In this work, we present a model with a tightly-coupled (TC) scheme to integrate RTK and INS using an extended Kalman Filter (EKF). Multi-GNSS multi-frequency double-differenced pseudo-ranges and carrier phases are the updates of the measurement model. To make the ambiguity resolution, we used the well-known LAMBDA algorithm to search for the possible ambiguity series, and then use the best integer equivariant (BIE) method to decide on the most likely ambiguities with a proper weighting strategy to select from the ambiguity candidates. To test the performance of the proposed TC integration model, we implemented two field vehicular tests with a low-cost GNSS module equipped with a Micro-Electro-Mechanical System (MEMS) IMU. Using this low-cost platform, our RTK/INS integrated navigation engine can achieve centimeter-level navigation solutions under open sky conditions. In harsh environments, our TC integration system navigates on two scenarios with mean errors of 0.48 and 0.57 m, which is 51% and 28% better than a loosely-coupled system.
THE PERFORMANCE ANALYSIS OF BDS POSITIONING IN NORDIC AREAS BASED ON THE SMARTPHONE
Authors: Chen C. , Chen Y. , Jiang C. , Bo Y. , Jia J. , Sun H. , He Z.
Published: April 2022

Abstract. Nowadays, the Global Navigation Satellite System (GNSS) is widely used in many applications. As a new system, China's BeiDou navigation system (BDS) is emerging in recent years. The last satellite of the third generation global BeiDou navigation system (BDS-3) has been successfully launched on June 23 in 2020, which means that BDS can offer navigation services worldwide. We evaluate the quality of BDS signals and analyze the performance of BDS positioning based on the smartphone in Espoo, Finland. The static and kinematic experiments were implemented in the parking lot of the Finish Geospatial Research Institute (FGI) and a highway route in Espoo, respectively. Experimental results show that BDS has good satellite visibility and geometric distribution. The signal carrier-to-noise density ratio (C/N0) of BDS-2 reaches 34.22 dB-Hz, which is comparable to the Global Positioning System (GPS). However, the signal carrier-to-noise density of BDS-3 is slightly lower than BDS-2, which is due to the significant number of BDS-3 satellites at low elevation angles. The horizontal precision of BDS positioning in the static and kinematic experiment is comparable to GPS in the east direction and slightly inferior to GPS in the north direction. However, the BDS shows poor precision in the up direction. In addition, the integration of BDS with other GNSS systems can significantly improve the positioning precision. This study intends to provide a reference for further research on the BDS global Positioning, Navigation, and Timing (PNT) services, particularly for LBS and smartphone positioning.
A REVIEW OF MAGNETIC SENSOR-BASED POSITIONING TECHNIQUES FOR CAPSULE ENDOSCOPY
Authors: Xu Y. , Chen R. , Zhang P. , Chen L. , Luo B. , Li Y. , Xiao X. , Dong W.
Published: April 2022

Abstract. The capsule endoscope, as one of important equipment in screening the entire spectrum of digestive tract disease diagnosis, has largely compensated for the limited of vision field and poor patient experience of wired endoscopes. In order to better develop the clinical application of capsule endoscope and assist doctors in the diagnosis and treatment of gastrointestinal diseases, there is an urgent need to solve a key scientific problem, that is obtaining the position information of capsule endoscope in the body. This article reviews the research evolution of the capsule endoscope based on magnetic localization through the last 10 years and describes notable problem, as well as technological challenges to overcome. Besides that, the article also presents the future development in capsule endoscopy localization technology based on magnetic sensors, for further developing capsule endoscopy localization and surgical navigation.
XGB ASSISTED SELF-LEARNING KALMAN FILTER FOR UWB LOCALIZATION
Authors: Xu Y. , Wan D. , Feng J. , Shen T. , Sun B.
Published: April 2022

Abstract. Recent years, more and more mobile robot has been used in many fields. In order to improve the service quality of mobile robot, how to improve the accuracy of robot position information has gradually become a research hotspot in this field. In this work, we will focus on the following situation: in an indoor environment, one mobile robot moves along one similar trajectory repeatedly. And the extreme gradient boosting (XGB) assisted self-learning Kalman filter (KF) will be derived in this work. To the method, the XGB is used to build the mapping between the distances from the ultra wide band (UWB) reference nodes (RNs) to the UWB blind node (BN) and the mobile robot’s position. Then, the XGB is used to build the measurement of the Kalman filter by using the off-line and on-line mode, which is able to provide the accurate position information. The real test has bee done, and the results show that the proposed XGB assisted self-learning KF is able to improve the localization accuracy gradually.
A HEADING CONSTRAINT CALIBRATION METHOD FOR LOW-END INERTIAL MEASUREMENT UNITS
Authors: Xue Y. , Guo X. , Zheng Z. , Li Y.
Published: April 2022

Abstract. The calibration result of the IMU has a strong impact on the accuracy of inertial navigation and its integration with other navigation techniques. Thus, how to efficiently obtain high-precision IMU calibration results is an important research problem for localization and motion tracking with consumer devices. To solve this problem, this paper proposes a handheld calibration method. Similar to our previous work, the pseudo observation is used to replace the measurement equation of the Kalman filter in the GNSS/INS loosely-coupled navigation algorithm. Compared to the existing online calibration algorithm, a more convenient data acquisition method is used, and the heading constraint information is added to assist in obtaining the calibration results of the IMU. To verify the proposed algorithm, a simulator is used to generate the heading updates with various precisions. The proposed algorithm shows the potential to estimate the vertical gyro bias, which does not converge in the existing calibration method, within around 0.5s when the accuracy of the heading’s random error is 5 degrees. When the heading random error is 60 degrees, the vertical gyro bias can converge in about 6 seconds after rotation with the standard deviation of 121.7765 deg/h.
AN ADAPTIVE SMARTPHONE HYBRID INDOOR POSITIONING SOLUTION INCORPORATING HETEROGENEOUS SENSORS
Authors: Yang S. , Liu J. , Gong X. , Huang G. , Yin F.
Published: April 2022

Abstract. Indoor positioning algorithms based on Wi-Fi or Bluetooth low energy (BLE) have been widely used for indoor navigation and Internet of Things (IoT) applications, due to their low cost. However, their positioning accuracy is between 2–5 meters, which is insufficient for many emerging applications requiring higher accuracy. New high-precision positioning technologies, such as light, acoustic, and Bluetooth Angle of Arrival (AoA)-based positioning systems, have been developed, and they can achieve decimeter-level positioning accuracy. However, these high-precision positioning technologies suffer from high costs. In this paper, an adaptive smartphone hybrid indoor positioning solution incorporating heterogeneous sensors is proposed to achieve high accuracy and reliable localization. Multiple indoor scenes are identified and the quality of pedestrian movement is detected, thus enabling the adaptive estimation and adjustment of measurement noise and process noise in the filtering procedure. High-precision sensors (i.e. Bluetooth array, acoustic sensors and light sensors) are utilized in the proposed solution as controlling anchors to improve the positioning accuracy. Low-cost sensors (i.e. Wi-Fi, BLE and inertial measurement unit (IMU) sensors), are integrated to achieve wide-area coverage. An autonomous outlier detection method is developed to improve the positioning accuracy and reliability. Experimental results show that the proposed solution can achieve accuracy of 0.98 m in the test scenario.
INDOOR VISIBLE LIGHT LOCALIZATION METHOD BASED ON EMBEDDED ARTIFICIAL INTELLIGENCE
Authors: Zhang F. , Ke W. , Ouyang H. , Qiu S.
Published: April 2022

Abstract. This paper proposes an indoor visible light location method based on embedded platform and optical frequency image recognition technology with artificial intelligence, which can effectively improve the location effect in complex indoor environment. By transplanting the artificial intelligence (AI) based image classification algorithm into the embedded platform, this method uses a forward neural network to analyse the position information coming from the coded optical frequency image received by a camera, and then the positioning results can be obtained. In view of the "motion state" and "occlusion state" that are most likely to fail in traditional visible image localization, we specially supplement the training set of relevant characteristic optical frequency images to enhance the robustness of the method. According to the track and positioning results of the moving platform receiver, the proposed method can provide accurate and reliable navigation and positioning and has stronger anti-interference ability compared with the traditional light intensity or light image positioning methods.
NOVEL APPROACHES TO ENHANCE CORAL REEFS MONITORING WITH UNDERWATER IMAGE SEGMENTATION
Authors: Zhang H. , Li M. , Pan X. , Zhang X. , Zhong J. , Qin J.
Published: April 2022

Abstract. Coral reefs not only inhabiting millions of species that are primarily or completely associated with them, but also produce economic and cultural benefits to coastal societies around the world. In recent years, affected by climate change and human factors, coral reef ecosystem has been experiencing accelerated degradation. Coral reef monitoring activities are therefore required to assess the impact of adverse factors on corals and to track subsequent recovery or decline. The collection of image data has become a common approach in the field of underwater monitoring, but traditional coral image data analysis mainly has high time and labor costs. We need to investigate the spatial distribution of different coral populations in the study area through image segmentation methods to help oceanographers develop effective management and conservation strategies. In fact, deep learning has shown better prediction performance than traditional image processing or traditional machine learning algorithms in coral image segmentation tasks. Starting from classification of random point annotations, segmentation of sparsely labeled data, and segmentation of densely labeled data, this paper summarizes state-of-the-art techniques of segmentation in deep learning applied to underwater images. Then, we discuss the problems and challenges of CNN-based underwater image segmentation of coral reefs, and make corresponding solutions or possible directions for future.
SEAFLOOR POSITIONING MODEL FOR SIMULTANEOUS ESTIMATION OF SOUND VELOCITY
Authors: Zhang S. , Xu T. , Qin X.
Published: April 2022

Abstract. The spatial and temporal change of sound speed has a significant impact on the accuracy of GNSS-A underwater positioning. However, it is hard to collect enough data to cover all the spatial and temporal changes of sound velocity. We built an acoustic ranging model that included the sound velocity component, and then we looked at how the model could be used for three-dimensional seabed location and undersea crust monitoring. Meanwhile, B-spline curves are used to construct a sound velocity model that encompasses temporal variation as well as two-dimensional spatial gradients. The method was utilized to assess the simulated data and the in-situ data collected in July 2019, respectively. Simulation results show that the root mean square of the horizontal coordinates solved by the ranging model is less than 10 cm, and the model may meet the demands of subsea crust monitoring under certain situations. In the real data experiment, the square root of variance of the coordinate was better than 10 cm. The sound velocity model findings indicated that the variance of sound velocity in the experimental marine region was less than 1m/s, with clear daily patterns and short-period variations. The algorithm does not need the sound velocity profile information and can save the measurement time of the sound velocity profile.
MULTIPLE RSS FINGERPRINT BASED INDOOR LOCALIZATION IN RIS-ASSISTED 5G WIRELESS COMMUNICATION SYSTEM
Authors: Zhang Z. , Wu L. , Zhang Z. , Dang J. , Zhu B. , Wang L.
Published: April 2022

Abstract. The received signal strength (RSS) fingerprint based localization is a widely used technique for location estimation in the indoor environment with the fifth generation (5G) wireless communication. However, the RSS feature is easily affected by the noise and other variations of the propagation channel, thus limiting the localization accuracy. In this paper, we propose a multiple RSS fingerprint based localization scheme in the reconfigurable intelligent surface (RIS) assisted system, where the RSS values under different RIS configurations are leveraged as the fingerprints. However, it is challenging to set the favorable RIS configurations. To tackle this challenge, we design an optimization method based on Cramér-Rao Lower Bound (CRLB) to derive the optimal RIS configurations to achieve a robust and accurate location estimation, where the CRLB is minimized, and projected gradient descent (PGD) method is applied to solve this optimization problem. After the fingerprints are collected, deep neural network (DNN) is employed for location estimation. Simulation results reveal that the proposed scheme performs well in terms of localization accuracy and stability.
OVERVIEW OF THE INTEGRATED NAVIGATION SYSTEM RESEARCH STATUS AND ITS DEVELOPMENT INTEGRATED WITH 5G COMMUNICATION
Authors: Chen Y. , Zhou P. , Sheng M. , Liu K.
Published: April 2022

Abstract. GNSS (Global Navigation Satellite Systems)/INS ( Inertial Navigation Systems) has been the research hotspot in the field of navigation and positioning. while the small-area densification of 5G communication technology and the technical means of large-scale array antennas have provided new developments for integrated navigation research. The GNSS/INS integrated navigation mode was introduced in this paper, the research status of integrated navigation at home and abroad was investigated and analyzed, several improved Kalman filter algorithms were described: unscented Kalman filtering algorithm, adaptive improved algorithm, neural network improved algorithm, deep learning improved algorithm. GNSS/INS/5G integrated navigation system was discussed. Finally, the application prospect of integrated navigation system was given in location service.
RAPID ESTABLISHMENT OF INDOOR WIFI POSITIONING DATABASE IN SHOPPING MALLS BASED ON WEARABLE NAVIGATION DEVICE (WEARTRACK)
Authors: Zheng Z. , Liao Z. , Xue Y. , Li Y.
Published: April 2022

Abstract. Position information is an important attribute in Internet-of-things (IoT) applications. WiFi fingerprinting has been frequently used in shopping malls due to its low cost and the capability to provide accurate localization results in indoor environments with significant multipath signal occlusion. However, the popularization of WiFi fingerprinting in shopping malls still faces a challenge, that is, the database needs to be collected and updated regularly. Therefore, establishing and updating the wireless positioning database efficiently and reliably is the key to the promotion of indoor positioning in shopping malls. In this paper, we use the team’s self-developed wearable navigation device (WearTrack) combined with post-processing to construct the database quickly and reliably. The procedure of database generation is organized and tested in a real shopping mall environment. Compared with the traditional database-generation method, the proposed method can improve the efficiency and maintain the robustness of database collection. Therefore, the method in this paper provides a feasible means for updating the positioning database in shopping malls.
REAL-TIME MARINE ANIMAL DETECTION USING YOLO-BASED DEEP LEARNING NETWORKS IN THE CORAL REEF ECOSYSTEM
Authors: Zhong J. , Li M. , Qin J. , Cui Y. , Yang K. , Zhang H.
Published: April 2022

Abstract. In recent years, with the advancement of marine resources and environment research, the ecological functions of reef-building coral reef ecosystems distributed in warm shallow waters of the ocean are being continuously discovered and valued by people. It is important for ecosystem protection to monitor the population of marine animals. Besides, many projects of Autonomous Underwater Vehicle (AUV) also need technology to perceive and understand environment information in real-time for better decision-making. Therefore, marine animal detection has become a challenge for researchers to study nowadays. Deep neural network models have been used to solve fish-related tasks and gained encouraging achievements, but there are still many problems in this field. In this paper, several YOLO-based methods are chosen for comparison. Experiment results indicate that these methods can recognize the marine animals in coral reef quickly and accurately. Finally, several recommendations for model improvement according to assessment results are presented.
ANALYSIS OF THE GRAVITY MODELS IMPACT ON LEO SATELLITE ORBIT PREDICTION
Authors: Zhuang Y. , Wang L.
Published: April 2022

Abstract. Non-spherical gravity plays a crucial role in the LEO satellite orbit determination and prediction. In recent years, several new gravity models have been proposed with more comprehensive ground and space-borne data. The impact of the gravity models has been extensively studied while its impact on the orbit prediction has not attracted enough attention. With the risen of the mega LEO constellation, new applications such as the LEO navigation requires real-time precise orbit, which increases the importance of the precise orbit prediction. In this study, we selected six popular gravity models, namely JGM3, EGM2008, EGM96, EIGEN2, GL04C, and GGM03S, and compared their performance in different LEO orbit predictions. The comparison results indicate that there is no single optimal gravity model for all LEO orbit prediction scenarios. For short-term prediction, JGM3, EGM2008, GL04C models perform better while in long-term prediction JGM3, EGM96, EIGEN2 have more potential. The results also reveal that the optimal model changed with time. In addition, the impact of the gravity order on the orbit prediction is investigated, the results indicate that for satellites with lower orbital heights, the gravitational field order required to achieve a certain truncation error is higher than for satellites with higher orbital heights. The authors also explore the effect of gravitational field-associated permanent tides on orbital prediction. In one day, for satellites with an orbital altitude of about 970km, the effect of permanent tides on 3D RMS is 6.92m; for satellites around 710km, the effect of permanent tides on 3D RMS is 4.20m; for satellites around 970km, the effect of permanent tides on 3D RMS is 2.07m.
A HIGH PRECISION VISUAL LOCALIZATION METHOD OPTIMIZED BY MULTI-FEATURES
Authors: Deng Y. , Tang S. , Wang W. , Li X. , Guo R.
Published: April 2022

Abstract. The demand for indoor localization has increased in fields such as indoor navigation, virtual reality and emergency response. Traditionally, hardware-based indoor positioning methods require a large number of devices to be deployed and require high maintenance costs. Vision-based localization methods offer a low-cost option for this purpose. Visual Localization has two typical pipeline: end-to-end study and traditional pose estimation based on PnP(Perspective-n-point). However, the quality of the retrieved images and 2D-3D correspondences is vital to the precision and recall of the traditional method. In this paper we try to partly overcome the mentioned drawback by eliminate the error retrieval images with multi-features, and we use several retrieved images to collect enough 2D-3D correspondences to improve the robustness against error input. We also filter the outliers during forming the 2D-3D correspondences with RANSAC and Lowe’s ratio test. As a supplement to the various indoor visual localization dataset production, we introduce a pipeline which can generate point clouds and mesh model via our integrated RGB-D cameras.
SONAR IMAGE RECOGNITION BASED ON MACHINE LEARNING FRAMEWORK
Authors: Dong M. , Qiu H. , Wang H. , Zhi P. , Xu Z.
Published: April 2022

Abstract. In order to improve the robustness and generalization ability of model recognition, sonar images are enhanced by preprocessing such as conversion coordinates, interpolation, denoising and enhancement, and the transfer learning method under the Caffe framework of MATLAB as an interface is used respectively (mainly composed of 8 layers of network structure, including 5 convolutional layers and 3 full chain layers) And the transfer learning method under the Python deep learning framework Inception-Resnet-v2 model for sonar image training and recognition. First of all, part of the sonar image dataset (derived from the 2021 National Robot Underwater Competition online competition data), using MATLAB as the interface Caffe framework, the sonar image is trained to obtain a training model, and then through parameter adjustment, the convolutional neural network model of sonar image automatic recognition is obtained, and the transfer learning method can use less sonar image data to solve the problem of insufficient sonar image data, and then make the training achieve a higher recognition rate in a shorter time. When the training data is randomly sampled for testing, the sonar data recognition model based on the Caffe framework is quickly and fully recognized, and the recognition rate can reach 92% when the test sample does not participate in the training of sonar image data; The transfer learning method under the Inception-Resnet-v2 model of python deep learning framework is used to train recognition on sonar images, and the recognition rate reaches about 97%. Using the two models in this paper, it is feasible to identify sonar images with high recognition rate, which is much higher than traditional recognition methods such as SVM classifiers, and the two sonar image data recognition models based on deep learning have better recognition ability and generalization ability.