Local Technology Research Articles

WSN Localization in Smart Cities Using Hybrid (TDOA & RSSI) Localization Techniques with Extended Kalman Filter

In the context of smart cities, localization technologies are essential for tracking objects in both indoor and outdoor environments. The characteristics of urban settings such as building density, signal interference, and multipath propagation significantly impact the accuracy of localization algorithms. This paper reviews existing localization techniques, categorizing them into range-based and range-free methods, and discusses key algorithms including trilateration, multilateration, and triangulation. We propose a hybrid localization framework that combines Time Difference of Arrival (TDOA) and Received Signal Strength Indicator (RSSI) techniques, enhanced by an Extended Kalman Filter (EKF) for improved accuracy and robustness. Additionally, we present a design architecture for a transmitter and receiver system utilizing Long Range (LoRa) technology, facilitating the development of low-cost, low-power tracking devices that operate independently of Global Positioning System (GPS). Our approach aims to enhance the efficacy of localization in smart city applications, ultimately contributing to improved urban management and safety.

Advances and challenges in UAV navigation based on visual SLAM

Abstract. In recent years, unmanned aerial vehicles (UAVs) have seen extensive use in fields such as agriculture, search and rescue, commercial, and military operations, driving the demand for autonomous navigation capabilities. Though GPS is the traditional method used for navigation, it becomes problematic in harsh environments like deserts. The Visual Simultaneous Localization and Mapping technology offers a solution to enhance UAV navigation in complex environments by constructing maps and localizing the UAV simultaneously in real time. This paper presents state-of-the-art visual SLAM technology developed for UAV navigation regarding algorithms like Oriented FAST and Rotated BRIEF SLAM (ORB-SLAM) and Large-Scale Direct Monocular SLAM (LSD-SLAM), whereby their performance is also discussed, with its positives and negatives. In this regard, the latest progress and challenges in applications are reviewed and analyzed through relevant literature from the databases of PubMed, IEEE, and Google Scholar in the past five years. The novelty of this paper lies in the comprehensive evaluation of the application performance of different visual SLAM algorithms in UAV navigation and the proposal of future research directions.

Review on VSLAM based on deep learning

Abstract. Visual simultaneous localization and mapping technology (VSLAM) provides a theoretical basis for the operation of unmanned equipment such as autonomous vehicles and sweeping robots in unfamiliar environments. Although traditional VSLAM systems have achieved great success after long-term development, it is still difficult to maintain good performance in challenging environments. Deep learning, as a newly developed technology in the field of vision in recent years, has shown outstanding advantages in image processing. Combining deep learning with VSLAM is a hot topic. Deep learning can help traditional VSLAM systems improve the lack of scale information in dynamic environments by improving the performance of traditional VSLAM in depth estimation, pose estimation, and closed loop detection. It can not only reduce the scale of the network model but also improve the accuracy of trajectory estimation. Specifically, in terms of the fusion of VSLAM method flow and deep learning, many researchers have proposed deep learning fusion methods based on visual odometry, loop detection and mapping. This work studies the trend and combination of VSLAM with deep learning algorithms, hoping to provide help for the real autonomy of future mobile robots, and finally puts forward prospects for the development of VSLAM.

The Mediation Effect of Academic Self-Efficacy on Academic Procrastination, Performance, and Satisfaction of Chinese Local Technology University Undergraduates

The Mediation Effect of Academic Self-Efficacy on Academic Procrastination, Performance, and Satisfaction of Chinese Local Technology University Undergraduates

Guangdong’s New R&D Institutes

In pursuit of technological development, China has created new organizations to promote innovation. This brief examines New Research and Development Institutes (NRDIs), which are designed to foster knowledge transfer to industry. NRDIs were pioneered in Guangdong province in the 1990s, and have gained prominence in China’s national science, technology, and innovation policies since the 13th Five-Year Plan (2016–2020). NRDIs are defined by their market orientation and extremely flexible organizational form. They work by establishing “innovation platforms” with local governments and private knowledge actors to carry out research and development (R&D), commercialize scientific and technological achievements, incubate local technology industries, and cultivate high-end talent. NRDIs have been instrumental to regional development in Guangdong, and especially Shenzhen, where they have succeeded in attracting talent from outside the region. NRDIs have important policy implications for international competition for talent. Understanding NRDIs is crucial for other countries that want to improve their own inter-regional innovation resources and respond to the challenge of China’s drive to attract global talent and knowledge resources.

A Review of Indoor Localization Methods Leveraging Smartphone Sensors and Spatial Context

As Location-Based Services (LBSs) rapidly develop, indoor localization technology is garnering significant attention as a critical component. Smartphones have become tools for indoor localization due to their highly integrated sensors, fast-evolving computational capabilities, and widespread user adoption. With the rapid advancement of smartphones, methods for smartphone-based indoor localization have increasingly attracted attention. Although there are reviews on indoor localization, there is still a lack of systematic reviews focused on smartphone-based indoor localization methods. In particular, existing reviews have not systematically analyzed smartphone-based indoor localization methods or considered the combination of smartphone sensor data with prior knowledge of the indoor environment to enhance localization performance. In this study, through systematic retrieval and analysis, the existing research was first categorized into three types to dissect the strengths and weaknesses based on the types of data sources integrated, i.e., single sensor data sources, multi-sensor data fusion, and the combination of spatial context with sensor data. Then, four key issues are discussed and the research gaps in this field are summarized. Finally, a comprehensive conclusion is provided. This paper offers a systematic reference for research and technological applications related to smartphone-based indoor localization methods.

Analysis of Spatial Agglomeration Characteristics of Logistics Enterprises in Henan Province based on Poi

As an important industry connecting urban and rural areas and serving production and life, logistics enterprises, as the micro-foundation of the development of logistics industry, have a profound impact on the spatial reconstruction of regional economic and social development. National Development and Reform Commission issued the "Fourteenth Five-Year Plan" modern logistics development and "Fourteenth Five-Year Plan" modern circulation system construction planning "put forward new requirements for the development of logistics industry, emphasizing the optimization of logistics spatial layout and speed up the construction of national logistics hub, especially pointed out that to make up for the shortcomings of the central and western regions. As an important base for the development of China's logistics industry, the spatial layout of logistics enterprises in Henan province not only affects the high-quality development of the logistics industry in the province, but also plays an important role in the improvement of the national logistics network. In this paper, based on POI data, the mean nearest neighbor method, local Moran scatter plot analysis and kernel density visualization technology are used to analyze the spatial agglomeration characteristics of logistics enterprises in 157 counties in Henan Province. It is found that the spatial distribution of logistics enterprises in Henan Province has gradually evolved from multi-point dispersion to a single-core and multi-core collaborative model with Zhengzhou as the core, and presents a significant spatial positive correlation and agglomeration trend. This change has improved the logistics efficiency and promoted the strengthening of regional economic ties. At the same time, the agglomeration trend of logistics enterprises in Henan Province continues to strengthen, from the initial agglomeration in the core area to the diversified diffusion pattern, showing the continuous optimization and reconstruction of logistics network in the province.

Mini-LED Backlight: Advances and Future Perspectives

Miniaturized-light-emitting diode (mini-LED) backlights have emerged as the state-of-the-art technology for liquid crystal display (LCD), facilitating the improvement in a high dynamic range (HDR) and power saving. The local dimming technology divides the backlight into several dimming zones. Employing mini-LEDs, whose size ranges from 100 to 200 μm, as the light sources can enlarge the number of zones in the local dimming backlight, fulfilling the requirement for HDR. However, the halo effect still acts as one of the primary technological bottlenecks for mini-LED backlights. In this review, packaging technology of LEDs, color conversion, and the driving scheme of mini-LED backlights have been discussed. The strategies to reduce optical crosstalk in adjacent areas by various improved optical structures or to suppress the halo effect of LCDs by mini-LED backlights are summarized. The development trends of mini-LED backlights are also discussed.

Research Progress of Intracardiac Electrogram Localization Technology in PICC Tip Localization

Research Progress of Intracardiac Electrogram Localization Technology in PICC Tip Localization

Conceptual Analysis of the High-Touch, High-Tech Trend: The Suitability of Technology for Tourism Transformation in Rural Destinations

This research aims to evaluate the role of technology in rural tourism development, focusing on the match between technology and the capabilities of local communities. The method used is a quantitative approach, namely data collection through questionnaires distributed to three groups of respondents: local communities, technology providers and tourists. Two main research questions were identified: (1) Is the technology applied by the community service capacity in the tourist village? (2) How can technology be adapted to the needs of local communities and the experiences tourists desire? The research results show that appropriate technology can improve tourist experiences and empower local communities. However, reliance on technological trends without a strong foundation can decrease tourist interest. Therefore, this research concludes that the application of technology must be based on a deep understanding of the local context to avoid inappropriate investments and maintain tourism sustainability. These findings provide important insights for stakeholders in designing appropriate technology strategies for tourist villages. Keywords: rural tourism, technological progress, high technology, emerging markets, tourism services

Workforce Optimization for Quick Commerce Dark Stores: Talent Sourcing and Workforce Modelling Strategies

Quick commerce (Q-commerce) has transformed the retail and e-commerce sectors by enabling ultra-fast delivery of goods through hyper-local logistics and dark stores. The operational success of this model hinges on a structured workforce strategy that supports efficient scaling and agility. This paper explores innovative workforce optimization techniques that focus on organizational design, talent sourcing, and workforce modelling, referencing industry leaders like Flipkart, Zepto, Instamart, and Blinkit. The study identifies the vital roles within dark stores and the approaches to talent acquisition, emphasizing localized recruitment, employee retention, and technology integration. It further presents best practices for scalable workforce modelling to accommodate seasonal demands, high attrition rates, and hyper-growth environments, ensuring sustainable development in the Q-commerce industry. Keywords: Quick Commerce, Workforce Strategy, Talent Sourcing, Dark Stores, Organizational Design, Hyper-Local Delivery, Workforce Modelling

Enabling Technologies and Techniques for Floor Identification

Location information has initiated a multitude of applications such as location-based services, health care, emergency response and rescue operations, and assets tracking. A plethora of techniques and technologies have been presented to ensure enhanced location accuracy, both horizontal and vertical. Despite many surveys covering horizontal localization technologies, the literature lacks a comprehensive survey incorporating up-to-date vertical localization approaches. This article provides a detailed survey of different vertical localization techniques such as path loss models, time of arrival, received signal strength, reference signal received power, fingerprinting utilized by WiFi, radio-frequency identification (RFID), global system for mobile communications (GSM), long-term evolution (LTE), barometer, inertial measurement unit (IMU) sensors, and geomagnetic field. The article primarily aims at human localization in indoor environments using smartphones in essence. Besides the localization accuracy, the presented approaches are evaluated in terms of cost, infrastructure dependence, deployment complexity, and sensitivity. We highlight the pros and cons of these approaches and outline future research directions to enhance the accuracy to meet the future needs of floor identification standards set by the Federal Communications Commission.

Quantum data compression under localized features

In this paper, we first introduce the local quantum information processing task by compressing the density operator based on local quantum Bernoulli noises. Then, the local quantum compression theorem is given, that is, the local quantum entropy is the minimum achievable rate of local compression. Finally, we prove the theorem with the proof of the direct coding theorem and the inverse theorem. The direct coding theorem shows that a scheme with such a local compression rate exists, and that this local compression rate converges to the local quantum entropy. The inverse theorem shows that the compression scheme with the rate below the local entropy is unachievable. With the continuous development of quantum technology, local quantum data compression technology will have broad application prospects and development space.

RS-SLAM: real time semantic slam with driverless car using LiDAR-camera-IMU sensing

Abstract Accurate and robust Simultaneous Localization and Mapping (SLAM) technology is a critical component of driverless cars, and semantic information plays a vital role in their analysis and understanding of the scene. In the actual scene, the moving object will produce the shadow phenomenon in the mapping process. The positioning accuracy and mapping effect will be affected. Therefore, we propose a semantic SLAM framework combining LiDAR, IMU, and camera, which includes a semantic fusion front-end odometry module and a closed-loop back-end optimization module based on semantic information. An improved image semantic segmentation algorithm based on Deeplabv3+ is designed to enhance the performance of the image semantic segmentation model by replacing the backbone network and introducing an attention mechanism to ensure the accuracy of point cloud segmentation. Dynamic objects are detected and eliminated by calculating the similarity score of semantic labels. A loop closure detection method based on semantic information is proposed to detect key semantic features and use threshold range detection and point cloud re-matching to establish the correct loop closure detection, and finally reduce the global cumulative error and improve the global trajectory accuracy using graph optimization to ultimately obtain the global motion trajectory and realize the construction of 3D semantic maps. We evaluated it on the KITTI dataset and collected a dataset for evaluation by ourselves, which includes four different sequences. The results show that the proposed framework has good positioning accuracy and mapping effect in large-scale urban road environments.

SWiLoc: Fusing Smartphone Sensors and WiFi CSI for Accurate Indoor Localization.

Dead reckoning is a promising yet often overlooked smartphone-based indoor localization technology that relies on phone-mounted sensors for counting steps and estimating walking directions, without the need for extensive sensor or landmark deployment. However, misalignment between the phone's direction and the user's actual movement direction can lead to unreliable direction estimates and inaccurate location tracking. To address this issue, this paper introduces SWiLoc (Smartphone and WiFi-based Localization), an enhanced direction correction system that integrates passive WiFi sensing with smartphone-based sensing to form Correction Zones. Our two-phase approach accurately measures the user's walking directions when passing through a Correction Zone and further refines successive direction estimates outside the zones, enabling continuous and reliable tracking. In addition to direction correction, SWiLoc extends its capabilities by incorporating a localization technique that leverages corrected directions to achieve precise user localization. This extension significantly enhances the system's applicability for high-accuracy localization tasks. Additionally, our innovative Fresnel zone-based approach, which utilizes unique hardware configurations and a fundamental geometric model, ensures accurate and robust direction estimation, even in scenarios with unreliable walking directions. We evaluate SWiLoc across two real-world environments, assessing its performance under varying conditions such as environmental changes, phone orientations, walking directions, and distances. Our comprehensive experiments demonstrate that SWiLoc achieves an average 75th percentile error of 8.89 degrees in walking direction estimation and an 80th percentile error of 1.12 m in location estimation. These figures represent reductions of 64% and 49%, respectively for direction and location estimation error, over existing state-of-the-art approaches.

Exploring Indigenous Knowledge Systems Integrated in Natural Sciences Practical Work in Selected South African Schools

This study used the nature of indigenous knowledge (NOIK) framework to explore indigenous knowledge systems (IKS) practices that were integrated when practical work in natural sciences was facilitated. In this interpretive explorative case study, data were collected by semistructured interviews, lesson observations and a focus group interview conducted with three purposively selected natural sciences teachers and two natural sciences district officials in the province of KwaZulu-Natal in South Africa. Thematic analysis techniques were used to analyze the data that were collected. Three findings on ways to integrate IKS in science practical work are the following. First, IKS was integrated in practical work through teaching of applied science and including the learners’ lived experiences of local technologies to solve problems. Second, practical work that integrated IKS was conducted as empirical observations of natural phenomena, which included genetic variation of plants and animals, including human beings, animal behavior, using moon cycles and the movement of the sun to guide day-to-day, cultural and agricultural activities. Third, some of the IKS included myths and superstitions that distorted the public understanding of science but were nevertheless used to persuade people to uphold certain cultural practices. The study recommends comparing NOIK and the nature of science when integrating IKS in the classroom to enable learners to distinguish myths from scientific facts.

ASLAM-FD: a multi-sensor adaptive collaborative fusion SLAM framework based on degradation detection and deep reinforcement learning

Abstract Aiming at the difficulty in ensuring accuracy and performance of existing Simultaneous Localization And Mapping (SLAM) technology based on multi-sensor fusion in complex dynamic environments, a multi-sensor adaptive fusion SLAM framework based on degradation detection and deep reinforcement learning (ASLAM-FD) is proposed. This framework can achieve adaptive collaborative precise adjustment of fusion weights (FWs) based on the real-time self-degradation states and relative degradation states quantified by each sensor, and can adapt to different tightly coupled SLAM algorithms based on FWs. In addition, within this framework, the continuous quantification models for the degradation states of internal/external sensors with certain versatility (we refer to them as EX-DM and IN-DM) is proposed. These quantitative models can achieve continuous quantification of degradation states of various mainstream internal/external sensors. Based on the above sensor degradation state quantification models, this paper further proposes a deep reinforcement learning (DRL) network suitable for adaptive collaborative adjustment of FWs. This network focuses more on the temporal nature of sensor observation data and degradation states, making it more suitable for modeling relationships between data with temporal features. In the experimental section, we adapted the proposed ASLAM-FD to different multi-sensor fusion SLAM algorithms on multiple datasets, and compared it with multiple advanced fusion SLAM algorithms. We found that adapting ASLAM-FD can effectively improve the accuracy and performance of fusion SLAM in complex dynamic environments.

INS/UWB multi-sensor integrated localization scheme in indoor LOS/NLOS environment based on MEF-GPDAF

Localization technology is crucial for indoor robot navigation. Wireless sensor network (WSN) serves as a significant part in indoor localization. However, the intricate indoor conditions make signal propagation susceptible to interference from obstructions, resulting in a decrease in positioning accuracy. Inertial Navigation System (INS) operates as a self-governing navigational setup undisturbed by indoor environment, and its location determinations remain unaltered by non-line-of-sight (NLOS) conditions. This research presents a combined positioning method of Ultra-wide band (UWB) and INS, which inherits advantages of the two subsystems and elevates the positioning system performance . On the basis of INS autonomous positioning, the maximum entropy fuzzy generalized probability data association filter (MEF-GPDAF) is used to modify the INS positioning results by combining the position of beacon nodes. When using UWB to locate the mobile node, a NLOS mitigation algorithm is applied to lessen the influence of NLOS propagation. Simulation experiment results demonstrate that, when compared to existing algorithm, MEF-GPDAF exhibits superior positioning precision.Meanwhile, in the real environment, the average error of the algorithm of MEF-GPDAF was measured to be 0.4961 m, which improved its accuracy by 32.88 % compared with the latest algorithm.

Climate risk and energy-saving technology innovation: Evidence from Chinese prefecture-level cities

In the face of the challenges of energy and environmental issues posed by climate risk, actively promoting energy-saving technology innovation is crucial in addressing climate risk. This study focuses on the electricity elements as the research object, and examines the impact of climate risk on energy-saving technology innovation by constructing a Spatial Durbin Model, using 2007–2021 data on temperature and electricity-saving technology patents at the prefecture-level cities in China. The findings reveal that climate risk promotes energy-saving technology innovation. Furthermore, it is discovered that climate risk exhibits significant positive spillover effects on energy-saving technology innovation through the decomposition of spatial effects, implying that the climate risk in neighboring areas has a notable positive influence on local energy-saving technology innovation. Mechanism tests indicate that climate risk directly promotes regional energy-saving technology innovation through increasing energy consumption, research and development (R&D) investment, and strengthening government environmental regulation. Particularly under the influence of climate risk, the increase in energy consumption and R&D investment more effectively drives regional energy-saving technology innovation. Moreover, variances in cities' geographical locations, carbon emission levels, electricity price levels, and electricity resource endowments result in substantial discrepancies in the effect of climate risk on energy-saving technology innovation. Based on these findings, Governments could incorporate climate risk into synergistic regional planning and enhance energy-saving technology innovation to address climate risk.

Surface Reconstruction from SLAM-Based Point Clouds: Results from the Datasets of the 2023 SIFET Benchmark

The rapid technological development that geomatics has been experiencing in recent years is leading to increasing ease, productivity and reliability of three-dimensional surveys, with portable laser scanner systems based on Simultaneous Localization and Mapping (SLAM) technology, gradually replacing traditional techniques in certain applications. Although the performance of such systems in terms of point cloud accuracy and noise level has been deeply investigated in the literature, there are fewer works about the evaluation of their use for surface reconstruction, cartographic production, and as-built Building Information Model (BIM) creation. The objective of this study is to assess the suitability of SLAM devices for surface modeling in an urban/architectural environment. To this end, analyses are carried out on the datasets acquired by three commercial portable laser scanners in the context of a benchmark organized in 2023 by the Italian Society of Photogrammetry and Topography (SIFET). In addition to the conventional point cloud assessment, we propose a comparison between the reconstructed mesh and a ground-truth model, employing a model-to-model methodology. The outcomes are promising, with the average distance between models ranging from 0.2 to 1.4 cm. However, the surfaces modeled from the terrestrial laser scanning point cloud show a level of detail that is still unmatched by SLAM systems.