Search Results (2,225)

To improve the current indoor positioning algorithms, which have insufficient positioning accuracy, an ultra-wideband (UWB) positioning algorithm based on the Levenberg–Marquardt algorithm with improved Kalman filtering is proposed. An alternative double-sided two-way ranging (ADS-TWR) algorithm is used to obtain the distance from the UWB tag to each base station and calculate the initial position of the tag by the least squares method. The Levenberg–Marquardt algorithm is used to correct the covariance matrix of the Kalman filter, and the improved Kalman filtering algorithm is used to filter the initial position to obtain the final position of the tag. The feasibility and effectiveness of the algorithm are verified by MATLAB simulation. Finally, the UWB positioning system is constructed, and the improved Kalman filter algorithm is experimentally verified in LOS and NLOS environments. The average X-axis and the Y-axis positioning errors in the LOS environment are 6.9 mm and 5.4 mm, respectively, with a root mean square error of 10.8 mm. The average positioning errors for the X-axis and Y-axis in the NLOS environment are 20. 8 mm and 18.0 mm, respectively, while the root mean square error is 28.9 mm. The experimental results show that the improved algorithm has high accuracy and good stability. At the same time, it can effectively improve the convergence speed of the Kalman filter. Full article

This study proposes a Generative Adversarial Network (GAN)-based method for virtual furniture replacement within indoor scenes. The proposed method addresses the challenge of accurately positioning new furniture in an indoor space by combining image reconstruction with geometric matching through combining spatial transformer networks and GANs. The system leverages deep learning architectures like Mask R-CNN for executing image segmentation and generating masks, and it employs DeepLabv3+, EdgeConnect algorithms, and ST-GAN networks for carrying out virtual furniture replacement. With the proposed system, furniture shoppers can obtain a virtual shopping experience, providing an easier way to understand the aesthetic effects of furniture rearrangement without putting in effort to physically move furniture. The proposed system has practical applications in the furnishing industry and interior design practices, providing a cost-effective and efficient alternative to physical furniture replacement. The results indicate that the proposed method achieves accurate positioning of new furniture in indoor scenes with minimal distortion or displacement. The proposed system is limited to 2D front-view images of furniture and indoor scenes. Future work would involve synthesizing 3D scenes and expanding the system to replace furniture images photographed from different angles. This would enhance the efficiency and practicality of the proposed system for virtual furniture replacement in indoor scenes. Full article

Under the influence of the sedimentation process, the phenomenon of intraformational non-homogeneity is widely observed in low-permeability reservoirs. In the development process of water and gas replacement (WAG), the transport law of water and gas and the distribution of residual oil are seriously affected by the non-homogeneity of reservoir properties. In this paper, a study on two types of reservoirs with certain lengths and thicknesses is carried out, and a reasonable development method is proposed according to the characteristics of each reservoir. Firstly, through indoor physical simulation experiments combined with low-field nuclear magnetic resonance scanning (NMR), this study investigates the influence of injection rate and core length on the double-layer low-permeability inhomogeneous core replacement and pore throat mobilization characteristics. Then, a two-layer inhomogeneous low-permeability microscopic model is designed to investigate the model’s replacement and pore throat mobilization characteristics under the combined influence of rhythmites, gravity, the injection rate, etc. Finally, based on the results of the core replacement and numerical simulation, a more reasonable development method is proposed for each type of reservoir. The results show that for inhomogeneous cores of a certain length, the WAG process can significantly increase the injection pressure and effectively seal the high-permeability layer through the Jamin effect to improve the degree of recovery. Moreover, for positive and reverse rhythm reservoirs of a certain thickness, the injection rate can be reduced according to the physical properties of the reservoir, and the gravity overburden phenomenon of the gas is used to achieve the effective development of the upper layers. The effect of the development of a positive rhythm reservoir therefore improved significantly. These findings provide data support for improving the development effectiveness of CO₂ in low-permeability inhomogeneous reservoirs and emphasize the importance of the influence of multiple factors, such as injection flow rate, gravity, and rhythm, in CO₂ replacement. Full article

Localization of unmanned aircraft systems (UASs) in indoor scenarios and GNSS-denied environments is a difficult problem, particularly in dynamic scenarios where traditional on-board equipment (such as LiDAR, radar, sonar, camera) may fail. In the framework of autonomous UAS missions, precise feedback on real-time aircraft position is very important, and several technologies alternative to GNSS-based approaches for UAS positioning in indoor navigation have been recently explored. In this paper, we propose a low-cost IPS for UAVs, based on Bluetooth low energy (BLE) beacons, which exploits the RSSI (received signal strength indicator) for distance estimation and positioning. Distance information from measured RSSI values can be degraded by multipath, reflection, and fading that cause unpredictable variability of the RSSI and may lead to poor-quality measurements. To enhance the accuracy of the position estimation, this work applies a differential distance correction (DDC) technique, similar to differential GNSS (DGNSS) and real-time kinematic (RTK) positioning. The method uses differential information from a reference station positioned at known coordinates to correct the position of the rover station. A mathematical model was established to analyze the relation between the RSSI and the distance from Bluetooth devices (Eddystone BLE beacons) placed in the indoor operation field. The master reference station was a Raspberry Pi 4 model B, and the rover (unknown target) was an Arduino Nano 33 BLE microcontroller, which was mounted on-board a UAV. Position estimation was achieved by trilateration, and the extended Kalman filter (EKF) was applied, considering the nonlinear propriety of beacon signals to correct data from noise, drift, and bias errors. Experimental results and system performance analysis show the feasibility of this methodology, as well as the reduction of position uncertainty obtained by the DCC technique. Full article

This article presents SmartRoutes, (version 1) a sensory navigation system designed for the localization and guidance of individuals with cognitive disabilities in both indoor and outdoor environments. The platform facilitates route generation in both contexts and provides detailed instructions, enabling effective task execution and seamless integration into daily activities or high-stress situations, such as emergency evacuations. SmartRoutes aims to enhance users’ independence and quality of life by offering comprehensive support for navigation across various settings. The platform is specifically designed to manage routes in both indoor and outdoor environments, targeting individuals with cognitive disabilities that affect orientation and the ability to follow instructions. This solution seeks to improve route learning and navigation, facilitating the completion of routine tasks in work and social contexts. Additionally, in exceptional situations such as emergencies, SmartRoutes ensures that users do not become disoriented or blocked. The application effectively guides users to the most appropriate exit or evacuation point. This combination of route generation and detailed instructions underscores the platform’s commitment to inclusion and accessibility, ultimately contributing to the well-being and autonomy of individuals with cognitive disabilities. Full article

When robots perform localization in indoor low-light environments, factors such as weak and uneven lighting can degrade image quality. This degradation results in a reduced number of feature extractions by the visual odometry front end and may even cause tracking loss, thereby impacting the algorithm’s positioning accuracy. To enhance the localization accuracy of mobile robots in indoor low-light environments, this paper proposes a visual inertial odometry method (L-MSCKF) based on the multi-state constraint Kalman filter. Addressing the challenges of low-light conditions, we integrated Inertial Measurement Unit (IMU) data with stereo vision odometry. The algorithm includes an image enhancement module and a gyroscope zero-bias correction mechanism to facilitate feature matching in stereo vision odometry. We conducted tests on the EuRoC dataset and compared our method with other similar algorithms, thereby validating the effectiveness and accuracy of L-MSCKF. Full article

Over the past decade, there has been a growing recognition of the importance of indoor environmental quality (IEQ) in influencing occupant productivity. Researchers have studied various buildings, including offices, schools, hospitals, and residential settings, to understand the relationship between IEQ and productivity outcomes. Studies have taken a multifactorial approach, considering multiple aspects of IEQ. Evidence from the literature review suggests that the quality of the indoor environment is an essential factor that affects the productivity of building occupants, and it is one of the fundamental issues in the development of societies. This area of research requires the responsible participation of researchers at all levels, as there is significant scope to contribute to knowledge. Therefore, this study aims to conduct a bibliometric analysis of the published literature on indoor environmental quality and its impact on building occupant productivity through the scientific literature available from one of the largest and most famous academic databases, Scopus; the study was determined in 2011 to 2023. The search used differential thresholds for IEQ keywords affecting building occupant productivity. Three discrete queries were performed, resulting in approximately 3861 publications. These were filtered by reducing false positives and excluding publications irrelevant to the research topic. The final results were 72 publications. This study also used Excel and VOS viewer to analyse and create graphs and network visualisation maps to show the growth of publications and their types, active countries and institutions for recovered publications, international collaboration, author keywords, active journals, and citation analysis. This study can significantly advance our understanding of building occupant productivity and enhance quality of life and work. Evaluating the research outputs is essential for highlighting contributions to knowledge and global collaboration in this research area. The potential impact of this study is not just theoretical. It can shape the future of our built environments and the lives of those occupying them. Full article

Soil carbon mineralization plays an important role in the carbon cycle of terrestrial ecosystems. When it comes to the soil carbon cycle, however, research on how carbon mineralization characteristics of fertilized Camellia oleifera forest soil respond to temperature changes remains limited. This study used an indoor constant temperature incubation method to examine the effects of the vermicomposting of cow dung by applying it at three different quantities (A: 0.8 kg earthworm + 62.5 kg cow dung/Camellia oleifera; B: 1.6 kg earthworm + 125 kg cow dung/Camellia oleifera; C: 2.4 kg earthworm + 187.5 kg cow dung/Camellia oleifera) and set a control group with Camellia oleifera forest not being fertilized (CK). This research was conducted with incubators set at 5 °C, 15 °C, 25 °C, and 35 °C, and with continuous monitoring of soil carbon mineralization characteristics and temperature sensitivity of organic carbon mineralization. The results showed significant increases in soil MBC, MBN, DOC, DON, NO₃⁻-N, and NH₄⁺-N in groups with applications of cow-dung vermicomposting compared to CK. Except at 35 °C, soil respiration in the Camellia oleifera of Group A was consistently the strongest. The maximum soil carbon emission (C₀) was determined through a simulation of potential carbon emissions, with all correlation coefficients exceeding 0.95. The contents of TC and TN were positively correlated with MBC and MBN (p <0.001), while the C: N_micro was negatively correlated with TN, AN, MBN, and inorganic nitrogen. Based on temperature sensitivity (Q₁₀), the influence of temperature on soil mineralization rate was observed. The vermicomposting of cow dung had a noticeable effect, as Group B showed significantly stronger enzyme activity compared to other groups. These results indicate that changes in MBC can impact the stability of soil carbon mineralization. The roles of soil moisture and microorganisms should be considered when predicting dynamic changes in the soil carbon pool of Camellia oleifera when applying fertilizers and improving its soil carbon sequestration capacity. Full article

The renovation and revitalization of vernacular architecture are pivotal in sustainable rural development. In regions like Shangri-La, traditional structures not only safeguard cultural heritage but also provide a foundation for enhancing local communities’ living conditions. However, these villages face growing challenges, including infrastructure decay, cultural erosion, and inadequate adaptation to modern living standards. Addressing these issues requires innovative research approaches that combine heritage preservation with the integration of contemporary functionality. This study employs a research-by-design approach, focusing on the Shanpian House as a case study, to explore how pre-planning and post-evaluation methods can revitalize traditional vernacular architecture. The pre-planning phase utilizes field surveys and archival research to assess spatial, cultural, and environmental conditions, framing a design strategy informed by field theory. In doing so, it evaluates how traditional architectural elements can be preserved while introducing modern construction techniques that meet current living standards. The post-evaluation phase, conducted through questionnaires and semi-structured interviews, assesses user satisfaction, focusing on the impact of architectural esthetics, structural stability, and material choices. Key findings from an OLS regression highlight the strong positive correlation between architectural style, structural choices, and cultural relevance with resident satisfaction. The research emphasizes that design elements such as structural details, materials, and infrastructure upgrades are critical in shaping perceptions of both functionality and cultural identity. Interestingly, the model reveals that improving architectural esthetics, alongside modern indoor features such as network connectivity, has a significant impact on enhancing overall resident satisfaction (significance level: 0.181). This study contributes to the broader discourse on sustainable building renovation by demonstrating how traditional architecture can be thoughtfully adapted for contemporary use and also proposes a paradigm shift in the renovation of historic buildings, advocating for a balance between preservation and modernization. The application of sustainable materials, digital modeling, and innovative construction techniques further ensures that these traditional structures meet the demands of modern civilization while maintaining their cultural integrity. Full article

The global increase in the population of Visually Impaired People (VIPs) underscores the rapidly growing demand for a robust navigation system to provide safe navigation in diverse environments. State-of-the-art VIP navigation systems cannot achieve the required performance (accuracy, integrity, availability, and integrity) because of insufficient positioning capabilities and unreliable investigations of transition areas and complex environments (indoor, outdoor, and urban). The primary reason for these challenges lies in the segregation of Visual Impairment (VI) research within medical and engineering disciplines, impeding technology developers’ access to comprehensive user requirements. To bridge this gap, this paper conducts a comprehensive review covering global classifications of VI, international and regional standards for VIP navigation, fundamental VIP requirements, experimentation on VIP behavior, an evaluation of state-of-the-art positioning systems for VIP navigation and wayfinding, and ways to overcome difficulties during exceptional times such as COVID-19. This review identifies current research gaps, offering insights into areas requiring advancements. Future work and recommendations are presented to enhance VIP mobility, enable daily activities, and promote societal integration. This paper addresses the urgent need for high-performance navigation systems for the growing population of VIPs, highlighting the limitations of current technologies in complex environments. Through a comprehensive review of VI classifications, VIPs’ navigation standards, user requirements, and positioning systems, this paper identifies research gaps and offers recommendations to improve VIP mobility and societal integration. Full article

In this paper, a novel adjustment algorithm of the position-predicted period (PP) in an indoor visible light communication (VLC) network is proposed. The algorithm is adaptive to the movement speed. It contains two key parts: speed-adaptive position information (PI) collection and speed-adaptive PP adjustment. By the user’s mobile characteristics, speed-adaptive PI collection is realized to lift prediction accuracy. By distribution characteristics of received power, speed-adaptive PP adjustment is achieved to avoid unnecessary predictions. By the access point (AP) selection, based on position prediction and the PP adjustment algorithm adaptive to movement speed, the user’s transmission quality under different movement speeds can be improved. Finally, by simulation, the effectiveness of this algorithm is demonstrated. Full article

This study addresses the challenges of human–robot interactions in real-time environments with adaptive field-programmable gate array (FPGA)-based accelerators. Predicting human posture in indoor environments in confined areas is a significant challenge for service robots. The proposed approach works on two levels: the estimation of human location and the robot’s intention to serve based on the human’s location at static and adaptive positions. This paper presents three methodologies to address these challenges: binary classification to analyze static and adaptive postures for human localization in indoor environments using the sensor fusion method, adaptive Simultaneous Localization and Mapping (SLAM) for the robot to deliver the task, and human–robot implicit communication. VLSI hardware schemes are developed for the proposed method. Initially, the control unit processes real-time sensor data through PIR sensors and multiple ultrasonic sensors to analyze the human posture. Subsequently, static and adaptive human posture data are communicated to the robot via Wi-Fi. Finally, the robot performs services for humans using an adaptive SLAM-based triangulation navigation method. The experimental validation was conducted in a hospital environment. The proposed algorithms were coded in Verilog HDL, simulated, and synthesized using VIVADO 2017.3. A Zed-board-based FPGA Xilinx board was used for experimental validation. Full article

Designing and improving collection systems for dust and toxic pollutants is crucial for improving the safety and indoor air quality of laboratory buildings. Push–pull ventilation systems with uniformly distributed parallel airflow have been proven to be of great help in this task. Designing exhaust hoods with parallel airflow distribution can effectively enhance the airflow uniformity of push–pull ventilation systems, especially when combining it with the implementation of rectifying orifice plates on the exhaust hoods. Therefore, this study combines a computational fluid dynamics (CFD) method and experimental approach to analyze the influence of key factors that lead to improvements in the airflow uniformity through the use of rectifying orifice plates, namely the aperture and porosity, as well as the number of rectifying orifice plates on the airflow uniformity of exhaust hoods. The study shows the following: (1) The aperture of the rectifying orifice plate considerably affects the airflow uniformity of the exhaust hood. Specifically, near the exhaust hood outlet, the airflow uniformity is negatively correlated with the aperture; conversely, near the exhaust hood inlet, the airflow uniformity is positively correlated with the aperture. (2) A rectifying orifice plate with a porosity of 35.43% can effectively improve the airflow uniformity of the exhaust hood. (3) Exhaust hoods with a double-layer rectifying orifice plate structure can improve airflow uniformity by approximately 40% compared to those with a single-layer structure. The above research results can guide the optimization of exhaust hood design to improve airflow uniformity, thereby effectively enhancing the capture efficiency of the push–pull ventilation system for dust and toxic pollutants and providing a safer environment for experimenters in laboratory buildings. Full article

In the post-pandemic context, data-driven design interventions that can endow architectural spaces with mixed-use and open characteristics that are adaptable and environmentally resilient are increasingly important. Ubiquitous semi-public architecture, such as hotel buildings, plays a crucial role in public health emergencies. Many hotels adopt mixed-use and open room spatial layouts, integrating diverse daily functions into a single tiny space, fostering flexible utilization and micro-scale space sharing; however, these also introduce potential health risks. This study offers a comprehensive evaluation of the indoor environmental quality (IEQ) of a hotel room space and discusses feasible intervention strategies for healthier renovation and rehabilitation. Taking a hotel in Shenzhen as a case, a multi-domain environmental assessment was conducted during the COVID-19 quarantine period in the summer of 2022. The study examines the health risks inherent in the hotel’s guest room and the varying patterns of IEQ factors across the hotel’s domains, including volatile organic compound concentrations, physical environmental parameters, and heat stress indices. The results illustrate diverse change trends in the chemical, physical, and heat stress factors present in the tested quarantined hotel room space throughout a typical summer day. Although most of the examined environmental factors meet local and global standards, some problems draw attention. In particular, the PM2.5 concentration was generally observed to be above the World Health Organization (WHO) air quality guideline (AQG) standards, and the interior lighting did not meet required standards most of the time. Moreover, correlation and multiple regression analyses uncover significant influence by physical environmental conditions on the concentrations of chemical pollutants in the hotel room. The study preliminarily identifies that higher relative humidity could lead to a lower concentration of CO₂ while a higher PM2.5 concentration. Wet bulb globe temperature (WBGT) was observed to positively affect CO₂ concentration. Further, the results suggest that even with relatively rigorous initial adjustment and re-renovation, multi-domain environmental quality in air-conditioned quarantine hotel rooms should be monitored and ameliorated from time to time. Overall, this study offers a scientific foundation for healthier upgrades of existing hotel buildings as well as provides insights into achieving environmental resilience in newly constructed hotel buildings for the post-pandemic era. Full article

Smartphone-based location estimation technology is becoming increasingly important across various fields. Accurate location estimation plays a critical role in life-saving efforts during emergency rescue situations, where rapid response is essential. Traditional methods such as GPS often face limitations in indoors or in densely built environments, where signals may be obstructed or reflected, leading to inaccuracies. Similarly, fingerprinting-based methods rely heavily on existing infrastructure and exhibit signal variability, making them less reliable in dynamic, real-world conditions. In this study, we analyzed the strengths and weaknesses of different types of wireless signal data and proposed a new deep learning-based method for location estimation that comprehensively integrates these data sources. The core of our research is the introduction of a ‘matching-map image’ conversion technique that efficiently integrates LTE, WiFi, and BLE signals. These generated matching-map images were applied to a deep learning model, enabling highly accurate and stable location estimates even in challenging emergency rescue situations. In real-world experiments, our method, utilizing multi-source data, achieved a positioning success rate of 85.27%, which meets the US FCC’s E911 standards for location accuracy and reliability across various conditions and environments. This makes the proposed approach particularly well-suited for emergency applications, where both accuracy and speed are critical. Full article