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Unlike many existing methods that are primarily focused on two-dimensional localization, this research paper extended the scope to three-dimensional localization. This enhancement is particularly significant for unmanned aerial vehicle (UAV) applications that demand precise altitude information, such as infrastructure inspection and aerial surveillance, thereby broadening the applicability of UAV-assisted wireless networks.

The paper introduced a novel method that employs recurrent neural networks (RNNs) for node localization in three-dimensional space within UAV-assisted wireless networks. It presented an optimization perspective to the node localization problem, aiming to balance localization accuracy with computational efficiency. By formulating the localization task as an optimization challenge, the study proposed strategies to minimize errors while ensuring manageable computational overhead, which are crucial for real-time deployment in dynamic UAV environments.

Simulation results demonstrated significant improvements, including a channel capacity of 99.95%, energy savings of 89.42%, reduced latency by 99.88% and notable data rates for UAV-based communication with an average localization error of 0.8462. Hence, the proposed model can be used to enhance the capacity of UAVs to work effectively in diverse environmental conditions, offering a reliable solution for maintaining connectivity during critical scenarios such as terrestrial environmental crises when traditional infrastructure is unavailable.

Conventional localization methods in wireless sensor networks (WSNs), such as received signal strength (RSS), often entail manual configuration and are beset by limitations in terms of capacity, scalability and efficiency. It is not considered for 3-D localization. In this paper, machine learning such as multi-layer perceptrons (MLP) and RNN are employed to facilitate the capture of intricate spatial relationships and patterns (3-D), resulting in enhanced localization precision and also improved in channel capacity, energy savings and reduced latency of UAVs for wireless communication.

This study objective is twofold. This study aims to present an institutional analysis of the implications of job localization programs in Gulf Cooperation Council (GCC) countries, such as Saudi Arabia, United Arab Emirates and Qatar. Further, it highlights the impacts of these programs on the accounting profession.

This study is based primarily on the desktop research method, where data is collected from the review of previous studies, published data on Internet Websites and reports released by International organizations such as the United Nations. In addition, the study benefitted from conducting six interviews with government officials from GCC countries. Theoretically, this study draws upon insights from the institutional logics theory to discern higher-order institutions deriving job localization decisions in the GCC region.

This paper explained how job localization policies in the GCC region are informed by three central logics: economic, socio-political and professional. Despite contributing to achieving some socio-political goals for policymakers, these policies could have serious consequences for the practice of the professions and, hence, the local business environment. Besides, this paper highlighted the serious localization policies' impacts on the accounting profession, especially the quality of the workforce (accountants) and their job readiness.

This study highlights the various implications of job localization policies for locals, foreigners, public and private sector entities and governments. Besides, it has recommended some actions to mitigate the negative influences of such policies on the surrounding society.

This study contributes to the literature by following an interpretative approach in explaining the localization of the accounting profession from an institutional perspective by bringing new evidence from GCC emerging markets.

The Thai video game domain has witnessed substantial growth in recent years. However, many games enjoyed by Thai players are in foreign languages, with only a handful of titles translated/localized into the Thai locale. Some Thai video game enthusiasts have taken on the role of unofficial translators/localizers, contributing to a localization domain that accommodates both official and unofficial translation/localization efforts. This general review paper aims to outline the author's experiences in collecting data within the domain of video game translation/localization in Thailand.

Using a descriptive approach, this general review paper employs the netnography method. It sheds light on the complexities of video game translation/localization in Thailand and incorporates semi-structured interviews with a snowball sampling technique for the selection of participants and in-game data collection methods.

The netnography method has proved instrumental in navigating the intricacies of this evolving landscape. Adopting the netnography method for data collection in this research contributes to establishing more robust connections with the research sites. “Inside” professionals and individuals play a significant role in data gathering by recommending additional sources of information for the research.

While netnography is conventionally applied in the market and consumer research, this paper demonstrates its efficacy in unraveling the dynamics of video game translation/localization in Thailand.

In structural health monitoring, localization of multiple slight damage without baseline data is significant and difficult. The purpose of this paper is to discuss these issues.

Damage in the structure causes singularities of displacement modes, which in turn reveals damage. Methods based on the displacement modes may fail to accurately locate the slight damage because the slight damage in engineering structure results in a relatively small variation of the displacement modes. In comparison with the displacement modes, the strain modes are more sensitive to the slight damage because the strain is the derivative of the displacement. As a result, the slight variation in displacement data will be magnified by the derivative, leading to a significant variation of the strain modes. A novel method based on strain modes is proposed for the purpose of accurately locating the multiple slight damage.

In the two bay beam and steel fixed-fixed beams, the numerical simulations and the experimental cases, respectively, illustrate that the proposed method can achieve more accurate localization in comparison with the one based on the displacement modes.

The paper offers a practical approach for more accurate localization of multiple slight damage without baseline data. And the robustness to measurement noise of the proposed method is evaluated for increasing levels of artificially added white Gaussian noise until its limit is reached, defining its range of practical applicability.

Time of flight (ToF) imaging is a promising emerging technology for the purposes of crop identification. This paper aim to presents localization system for identifying and localizing asparagus in the field based on point clouds from ToF imaging. Since the semantics are not included in the point cloud, it contains the geometric information of other objects such as stones and weeds other than asparagus spears. An approach is required for extracting the spear information so that a robotic system can be used for harvesting.

A real-time convolutional neural network (CNN)-based method is used for filtering the point cloud generated by a ToF camera, allowing subsequent processing methods to operate over smaller and more information-dense data sets, resulting in reduced processing time. The segmented point cloud can then be split into clusters of points representing each individual spear. Geometric filters are developed to eliminate the non-asparagus points in each cluster so that each spear can be modelled and localized. The spear information can then be used for harvesting decisions.

The localization system is integrated into a robotic harvesting prototype system. Several field trials have been conducted with satisfactory performance. The identification of a spear from the point cloud is the key to successful localization. Segmentation and clustering points into individual spears are two major failures for future improvements.

Most crop localizations in agricultural robotic applications using ToF imaging technology are implemented in a very controlled environment, such as a greenhouse. The target crop and the robotic system are stationary during the localization process. The novel proposed method for asparagus localization has been tested in outdoor farms and integrated with a robotic harvesting platform. Asparagus detection and localization are achieved in real time on a continuously moving robotic platform in a cluttered and unstructured environment.

This study aims to examine academic staff’s engagement with sustainable development goals (SDGs) in higher education institutions.

The triangulation, convergence model of the mixed methods research design was adopted as the strategy for inquiry. A total of 56 questionnaires and 25 interviews were used to collect the data, and this was buttressed by document review and use of secondary data obtained from Scival.

The results show moderate levels of engagement of academic staff with the SDGs. However, SDGs familiarisation is not correlated with the rate of localisation. The lack of funding deflated political will by university management, demotivated academia and shrinking government support are the leading impediments to SDGs localisation.

The results could be improved by using a larger sample size equally distributed across disciplines. Triangulation of academics’ views with those of students and non-academic staff could have improved the understanding of other dynamics involved in the localisation of SDGs by university teaching staff.

The results point towards the need for a university-based framework that interweaves national, institutional, thematic, structural and personal aspects into the SDGs implementation matrix. The underlying determinants of successful localisation of SDGs by academia need to be addressed through a bottom-up approach.

To the best of the authors’ knowledge, this paper is the first attempt in Zimbabwe to exclusively look at University teaching staff’s engagement with SDGs.

This paper aims to estimate contact location from sparse and high-dimensional soft tactile array sensor data using the tactile image. The authors used three feature extraction methods: handcrafted features, convolutional features and autoencoder features. Subsequently, these features were mapped to contact locations through a contact location regression network. Finally, the network performance was evaluated using spherical fittings of three different radii to further determine the optimal feature extraction method.

This paper aims to estimate contact location from sparse and high-dimensional soft tactile array sensor data using the tactile image.

This research indicates that data collected by probes can be used for contact localization. Introducing a batch normalization layer after the feature extraction stage significantly enhances the model’s generalization performance. Through qualitative and quantitative analyses, the authors conclude that convolutional methods can more accurately estimate contact locations.

The paper provides both qualitative and quantitative analyses of the performance of three contact localization methods across different datasets. To address the challenge of obtaining accurate contact locations in quantitative analysis, an indirect measurement metric is proposed.

Weak repeatability is observed in handcrafted keypoints, leading to tracking failures in visual simultaneous localization and mapping (SLAM) systems under challenging scenarios such as illumination change, rapid rotation and large angle of view variation. In contrast, learning-based keypoints exhibit higher repetition but entail considerable computational costs. This paper proposes an innovative algorithm for keypoint extraction, aiming to strike an equilibrium between precision and efficiency. This paper aims to attain accurate, robust and versatile visual localization in scenes of formidable complexity.

SiLK-SLAM initially refines the cutting-edge learning-based extractor, SiLK, and introduces an innovative postprocessing algorithm for keypoint homogenization and operational efficiency. Furthermore, SiLK-SLAM devises a reliable relocalization strategy called PCPnP, leveraging progressive and consistent sampling, thereby bolstering its robustness.

Empirical evaluations conducted on TUM, KITTI and EuRoC data sets substantiate SiLK-SLAM’s superior localization accuracy compared to ORB-SLAM3 and other methods. Compared to ORB-SLAM3, SiLK-SLAM demonstrates an enhancement in localization accuracy even by 70.99%, 87.20% and 85.27% across the three data sets. The relocalization experiments demonstrate SiLK-SLAM’s capability in producing precise and repeatable keypoints, showcasing its robustness in challenging environments.

The SiLK-SLAM achieves exceedingly elevated localization accuracy and resilience in formidable scenarios, holding paramount importance in enhancing the autonomy of robots navigating intricate environments. Code is available at https://github.com/Pepper-FlavoredChewingGum/SiLK-SLAM.

This study aims to identify the location of cracks in composite plates using a normalized mode shape curve algorithm. Crack in any structure is a destructive occurrence. Detecting these cracks early is pivotal for ensuring safety and preventing potential accidents. To prevent failure of structures, it is crucial to detect these cracks effectively and take the necessary precautions. Hence, crack detection and localization techniques are used to avoid sudden failures of structures while in operation.

An experimental modal analysis is conducted on composite plates with and without cracks to determine the natural frequencies and mode shapes. For this purpose, an impact hammer, uniaxial accelerometer and four-channel vibration analyzer are used to find the natural frequencies and mode shapes. Numerical modal analysis is performed on no crack and cracked composite plates using ANSYS software, and these are validated by the experimental modal analysis results. The normalized mode shapes algorithm is trained using test data of the first three natural frequencies collected from numerical modal analysis on different cracked composite plates for localization of crack.

The natural frequencies derived from both experimental modal analysis and numerical modal analysis exhibit a variance of 9.6%. The estimation of the crack location is achieved with exceptional precision by intersecting the first three normalized mode shapes. The first three normalized mode shape curve intersections provide a solid indication of the crack’s location. As the difference in error between the actual and estimated crack locations is only 0.9%.

This study introduces the first application of experimental modal analysis in conjunction with the normalized mode shape curve algorithm for localizing cracks in composite plates. The normalization process of mode shapes, derived from experimental modal analysis, forms a fundamental component of the mode shape curve algorithm specifically designed for crack localization. Combining experimental modal analysis with a specific algorithm of normalizing mode shapes is used to identify and locate cracks within these composite plates.

Visual simultaneous localization and mapping (SLAM) has limitations such as sensitivity to lighting changes and lower measurement accuracy. The effective fusion of information from multiple modalities to address these limitations has emerged as a key research focus. This study aims to provide a comprehensive review of the development of vision-based SLAM (including visual SLAM) for navigation and pose estimation, with a specific focus on techniques for integrating multiple modalities.

This paper initially introduces the mathematical models and framework development of visual SLAM. Subsequently, this paper presents various methods for improving accuracy in visual SLAM by fusing different spatial and semantic features. This paper also examines the research advancements in vision-based SLAM with respect to multi-sensor fusion in both loosely coupled and tightly coupled approaches. Finally, this paper analyzes the limitations of current vision-based SLAM and provides predictions for future advancements.

The combination of vision-based SLAM and deep learning has significant potential for development. There are advantages and disadvantages to both loosely coupled and tightly coupled approaches in multi-sensor fusion, and the most suitable algorithm should be chosen based on the specific application scenario. In the future, vision-based SLAM is evolving toward better addressing challenges such as resource-limited platforms and long-term mapping.

This review introduces the development of vision-based SLAM and focuses on the advancements in multimodal fusion. It allows readers to quickly understand the progress and current status of research in this field.