The introduction of data-driven inertial navigation provides new opportunities that the pedestrian dead reckoning could not well provide for constraining inertial system error drift on smartphones, and has been considered as another promising approach to meet the requirement of location-based services. However, indoor localization systems based on a single technology still have their limitations, such as the drift of inertial navigation and the received signal strength fluctuation of Bluetooth, making them unable to provide reliable positioning. To exploit the complementary strengths of each technology, this paper proposes a feasible fusion framework by utilizing a particle filter to integrate data-driven inertial navigation with localization based on Bluetooth Low Energy (BLE). For data-driven inertial navigation, under the premise of using the deep neural network with great potential in model-free generalization to regress pedestrian motion characteristics, we effectively combined the method of using gravity to stabilize inertial measurement units data to make the network more robust. Experimental results show that in the test of different smartphone usages, the proposed data-driven inertial navigation and BLE-based localization technology have good results in modeling user’s movement and positioning respectively. And due to this, the proposed fusion algorithm has almost unaffected by the usages of smartphones. Compared with BLE-based localization that achieved a good mean positional error (MPE) of 1.76m, for the four usages of texting, swinging, calling and pocket, the proposed fusion algorithm reduced the MPE by 32.35%, 20.51%, 20.74%, and 45.37%, respectively, and can further improve localization accuracy on the basis of existing fusion method.

In this letter, a compact dual-band rectenna is modeled and developed for low radio frequency (RF) power harvesting. The proposed theoretical model can provide a comprehensive analysis of the rectenna, including the power conversion efficiency (PCE) of the diode, matching efficiency of the rectifier, and total PCE of the rectenna. In the rectenna design, a very compact omnidirectional dielectric resonator antenna (DRA) is proposed with a broad bandwidth exceeding 40%. Meanwhile, a dual-band rectifier is designed based on a single branch in the low-power range with bandwidths covering the two fifth-generation (5G) frequency bands in China (2.515–2.675 and 3.4–3.6 GHz). It was found that the theoretical model can show a high accuracy with the calculation error within 5% in analyzing the dual-band rectifier. The DRA and the dual-band rectifier are integrated to form a compact dual-band rectenna. It shows a higher PCE than those of the existing multiband designs working at similar low-power levels. It is hoped to be implemented in 5G-enabled Internet of Things (IoT) applications for powering wireless sensor nodes.

Concrete is the most commonly used construction material nowadays. With emerging cutting-edge technologies such as nanomaterials (graphene, carbon nanotubes, etc.), advanced sensing (fiber optics, computer tomography, etc.), and artificial intelligence, concrete can now achieve self-sensing, self-healing, and ultrahigh performance. The concept and functions of smart concrete have thus been partially realized. However, due to the wider application location (coastal areas, cold regions, offshore, and deep ocean scenarios) and changing climate (temperature increase, more CO₂ emissions, higher moisture, etc.), durability monitoring (pH, ion penetration, carbonation, corrosion, etc.) becomes an essential component for smart concrete. Fiber optic sensors (FOS) have been widely explored in recent years for concrete durability monitoring due to their advantages of high sensitivity, immunity to harsh environments, small size, and superior sensitivity. The purpose of this review is to summarize FOS development and its application in concrete durability monitoring in recent years. The objectives of this study are to (1) introduce the working principle of FOS, including fiber Bragg grating (FBG), long-period fiber grating (LPFG), surface plasmon resonance (SPR), fluorescence-based sensors, and distributed fiber optic sensors (DFOS); (2) compare the sensitivity, resolution, and application scenarios of each sensor; and (3) discuss the advantages and disadvantages of FOS in concrete durability monitoring. This review is expected to promote technical development and provide potential research paths in the future for FOS in durability monitoring in smart concrete.

Machine learning models have recently demonstrated the ability to mine structural health monitoring data. While existing machine learning models can provide better performance than the previous univariate time-series model, it is still an open challenge of fully mining the spatial and temporal characteristics of the structural response to increase their accuracy. In addition, the heterogeneous correlation is always missed in traditional models. This article proposes a heterogeneous structural response prediction (HSRP) framework based on the deep learning model to improve the performance. The HSRP framework cannot only make full use of spatial and temporal correlations but also mine the correlation between heterogeneous responses. Motivated by recent studies in machine learning, an attention module is introduced to learn the correlation between different responses and initial the weights of sensors and past response. The convolutional neural network is also implemented to extract the spatial features and the long short-term memory network is used to extract the weekly, daily, and hourly patterns of structural response. A real-world data set collected from a bridge is used to evaluate the performance of the proposed model on single-step prediction and multistep prediction. The experimental results show that the proposed model outperforms several widely used benchmark models. Furthermore, additional experiments and evaluations are implemented to investigate the sensitivity and robustness of the proposed model.

High temperatures, toxic gases, and smoke resulting from indoor fires pose evident threats to the lives of both trapped individuals and firefighters. This study aims to predict indoor fire development effectively, facilitating rapid rescue decisions and minimizing casualties and property damage. A comprehensive database has been developed using Computational Fluid Dynamics (CFD) tools, primarily focused on basic fire scenarios. A total of 300 indoor fire scenarios have been simulated for different fire locations and severity levels. Using fire databases developed from simulation tools, artificial intelligence models have been developed to make spatial–temporal inferences on indoor temperature, CO concentration, and visibility. Detailed analysis has been conducted to optimize sensor system layouts while investigating the variations in prediction accuracy according to different prediction horizons. The research results show that, in combination with artificial intelligence models, the optimized sensor system can accurately predict temperature distribution, CO concentration, and visibility, achieving R² values of 91%, 72%, and 83%, respectively, while reducing initial hardware costs. The research results confirm the potential of artificial intelligence in predicting indoor fire scenarios and providing practical guidelines for smart firefighting. However, it is important to note that this study has certain limitations, including the scope of fire scenarios, data availability, and model generalization and interpretability.

In this paper, a broadband and low-profile pattern reconfigurable dielectric resonator antenna (DRA) with four different radiation patterns is proposed. By exciting two feeding ports with different phases, four patterns of a conical pattern, a broadside pattern, and two tilted patterns can be obtained. The excited four different patterns with high gains can help to achieve a wide spatial coverage. For demonstration, the proposed DRA was designed at 3.5 GHz frequency band for potential 5G applications. The prototype of the proposed DRA was fabricated and measured for verification. A measured broad bandwidth of 15% (3.38-3.93 GHz) can be achieved using a low profile of ~0.1λ ₀ . Measured maximum antenna gains of 4.98 dBi, 7.84 dBi, and 7.65 dBi are obtained over the impedance bandwidth for conical, broadside, and tilted patterns, respectively. High efficiencies with maximum values of ~95% can be realized. The proposed DRA presents a low profile, a wide impedance bandwidth, high gains, and various radiation patterns simultaneously. It shows great potentials to be implemented in complex communications.