The study aims to determine the spatial and temporal variation of a longevous region and explore the correlation between longevity and socioeconomic development. Population data at the township level were obtained from the last four population censuses (1982–2010). Five main lifespan indicators and the Human Development Index (HDI) were calculated. Getis-Ord G*, Gravity modeling, and Pearson’s r between lifespan indicators and HDI were applied. In this study, a stable longevous gathering area was discovered in Hechi during different periods. Under the influence of social and economic development, more longevous areas appeared. However, the effects of genetic and natural environmental factors on longevity were always dominant in this remote and mountainous city. Furthermore, longevity indicators lacked any significant correlation with life expectancy. No significant positive correlation was detected between lifespan indicators and HDI. Thus, we conclude that lifespan indicators can determine the spatial distribution and variation pattern of longevity from multiple dimensions. The geographical scope of longevity in Hechi City is gradually expanding, and significant spatial clustering was detected in southwestern, southern, and eastern parts of Hechi. This study also found that social economic development is likely to have a certain impact on new longevous areas, but their role on extreme longevity is not significant. Full article

This study aims to determine the association between the apolipoprotein C-I polymorphism and the longevity and genetic variants in ApoC-I that can influence the serum lipid levels in Bama. ApoC-I genotypes were determined by Taqman single nucleotide polymorphism (SNP) genotyping assays in 178 long-lived inhabitants (longevity group aged from 90 to 110 years), 147 healthy controls (Control 1 group aged from 40 to 79 years old) from Bama County, and 190 healthy controls (Control 2 group aged from 40 to 79 years old) from Nandan County without a family history of longevity. Statistical analysis was conducted using SPSS 16.0. All genotype distributions of rs584007 and rs4420638 were consistent with the Hardy–Weinberg equilibrium (p > 0.05). Significant differences were observed in the frequencies of the three genotypes (GG, AG, and AA) among the longevity and the two control groups (χ² = 11.238, p = 0.024) for rs584007. No significant differences were observed in the frequencies of the three genotypes (GG, AG, and AA) among the longevity and the two control groups (χ² = 4.587, p = 0.318) for rs4420638. The levels of total cholesterol (TC), triglycerides (TG), high-density lipoprotein-cholesterol (HDL-c), and low-density lipoprotein-cholesterol (LDL-c) were not different among the three genotypes of rs584007 in the three groups. The levels of HDL-c for GG, AG, and AA were significantly different (the highest being in the longevity group), while the levels of TG for AA and AG genotypes (the lowest being in the longevity group) and the levels of LDL-c for AG were significantly different (p < 0.05) among the three groups for rs584007. The levels of TG and HDL-c were significantly different among the three rs4420638 genotypes in the longevity group. The levels of TC for GG, AG, and AA were significantly different in the Control 2 group, while the levels of TG and HDL-c for AA and AG genotypes were significantly different (p < 0.05) among the three groups for rs4420638. The level of HDL-c was highest in the longevity group for AA and AG genotypes, and the level of TG was highest in the Control 2 group for rs4420638. Serum lipid parameters were related to environmental factors, including age, gender, BMI, DBP, SBP, rs4420638, and rs584007. The ApoC-I polymorphism might be one of the genetic factors of longevity in Bama. The ApoC-I rs4420638 and rs584007 SNPs are associated with serum TG and HDL-c levels in the longevous population. Full article

Plane segmentation is a basic task in the automatic reconstruction of indoor and urban environments from unorganized point clouds acquired by laser scanners. As one of the most common plane-segmentation methods, standard Random Sample Consensus (RANSAC) is often used to continually detect planes one after another. However, it suffers from the spurious-plane problem when noise and outliers exist due to the uncertainty of randomly sampling the minimum subset with 3 points. An improved RANSAC method based on Normal Distribution Transformation (NDT) cells is proposed in this study to avoid spurious planes for 3D point-cloud plane segmentation. A planar NDT cell is selected as a minimal sample in each iteration to ensure the correctness of sampling on the same plane surface. The 3D NDT represents the point cloud with a set of NDT cells and models the observed points with a normal distribution within each cell. The geometric appearances of NDT cells are used to classify the NDT cells into planar and non-planar cells. The proposed method is verified on three indoor scenes. The experimental results show that the correctness exceeds 88.5% and the completeness exceeds 85.0%, which indicates that the proposed method identifies more reliable and accurate planes than standard RANSAC. It also executes faster. These results validate the suitability of the method. Full article

The segmentation of urban scene mobile laser scanning (MLS) data into meaningful street objects is a great challenge due to the scene complexity of street environments, especially in the vicinity of street objects such as poles and trees. This paper proposes a three-stage method for the segmentation of urban MLS data at the object level. The original unorganized point cloud is first voxelized, and all information needed is stored in the voxels. These voxels are then classified as ground and non-ground voxels. In the second stage, the whole scene is segmented into clusters by applying a density-based clustering method based on two key parameters: local density and minimum distance. In the third stage, a merging step and a re-assignment processing step are applied to address the over-segmentation problem and noise points, respectively. We tested the effectiveness of the proposed methods on two urban MLS datasets. The overall accuracies of the segmentation results for the two test sites are 98.3% and 97%, thereby validating the effectiveness of the proposed method. Full article

Zebra crossings provide guidance and warning to pedestrians and drivers, thereby playing an important role in traffic safety management. Most previous studies have focused on detecting zebra stripes but have not provided full information about the areas, which is critical to both driver assistance systems and guide systems for blind individuals. This paper presents a stepwise procedure for recognizing and reconstructing zebra crossings using mobile laser scanning data. First, we propose adaptive thresholding based on road surface partitioning to reduce the impact of intensity unevenness and improve the accuracy of road marking extraction. Then, dispersion degree filtering is used to reduce the noise. Finally, zebra stripes are recognized according to the rectangular feature and fixed size, which is followed by area reconstruction according to arrangement patterns. We test our method on three datasets captured by an Optech Lynx mobile mapping system. The total recognition rate of 90.91% demonstrates the effectiveness of the method. Full article

As various inertial measurement units (IMUs) from different manufacturers appear every year, it is not affordable to evaluate every IMU through tests. Therefore, this paper presents an IMU evaluation method by grafting data from the tested IMU to the reference data from a higher-grade IMU. The signal grafting (SG) method has several benefits: (a) only one set of field tests with a higher-grade IMU is needed, and can be used to evaluate numerous IMUs. Thus, SG is effective and economic because all data from the tested IMU is collected in the lab; (b) it is a general approach to compare navigation performances of various IMUs by using the same reference data; and, finally, (c) through SG, one can first evaluate an IMU in the lab, and then decide whether to further test it. Moreover, this paper verified the validity of SG to both medium- and low-grade IMUs, and presents and compared two SG strategies, i.e., the basic-error strategy and the full-error strategy. SG provided results similar to field tests, with a difference of under 5% and 19.4%–26.7% for tested tactical-grade and MEMS IMUs. Meanwhile, it was found that dynamic IMU errors were essential to guarantee the effect of the SG method. Full article

We aimed to assess the correlation between docosahexaenoic acid (DHA) dietary intake and the plasma, erythrocyte and breast milk DHA concentrations in lactating women residing in the coastland, lakeland and inland areas of China. A total of 408 healthy lactating women (42 ± 7 days postpartum) were recruited from four hospitals located in Weihai (coastland), Yueyang (lakeland) and Baotou (inland) city. The categories of food containing DHA, the average amount consumed per time and the frequency of consumption in the past month were assessed by a tailored DHA food frequency questionnaire, the DHA Intake Evaluation Tool (DIET). DHA dietary intake (mg/day) was calculated according to the Chinese Food Composition Table (Version 2009). In addition, fasting venous blood (5 mL) and breast milk (10 mL) were collected from lactating women. DHA concentrations in plasma, erythrocyte and breast milk were measured using capillary gas chromatography, and were reported as absolute concentration (μg/mL) and relative concentration (weight percent of total fatty acids, wt. %). Spearman correlation coefficients were used to assess the correlation between intakes of DHA and its concentrations in biological specimens. The study showed that the breast milk, plasma and erythrocyte DHA concentrations were positively correlated with DHA dietary intake; corresponding correlation coefficients were 0.36, 0.36 and 0.24 for relative concentration and 0.33, 0.32, and 0.18 for absolute concentration (p < 0.05). The median DHA dietary intake varied significantly across areas (p < 0.05), which was highest in the coastland (24.32 mg/day), followed by lakeland (13.69 mg/day), and lowest in the inland (8.84 mg/day). The overall relative and absolute DHA concentrations in breast milk were 0.36% ± 0.23% and 141.49 ± 107.41 μg/mL; the concentrations were significantly lower in inland women than those from coastland and lakeland. We conclude that DHA dietary intake is positively correlated with DHA concentrations in blood and breast milk in Chinese lactating women, suggesting that the tailored DHA food frequency questionnaire, DIET, is a valid tool for the assessment of DHA dietary intake. Full article

Indoor wireless localization using Bluetooth Low Energy (BLE) beacons has attracted considerable attention after the release of the BLE protocol. In this paper, we propose an algorithm that uses the combination of channel-separate polynomial regression model (PRM), channel-separate fingerprinting (FP), outlier detection and extended Kalman filtering (EKF) for smartphone-based indoor localization with BLE beacons. The proposed algorithm uses FP and PRM to estimate the target’s location and the distances between the target and BLE beacons respectively. We compare the performance of distance estimation that uses separate PRM for three advertisement channels (i.e., the separate strategy) with that use an aggregate PRM generated through the combination of information from all channels (i.e., the aggregate strategy). The performance of FP-based location estimation results of the separate strategy and the aggregate strategy are also compared. It was found that the separate strategy can provide higher accuracy; thus, it is preferred to adopt PRM and FP for each BLE advertisement channel separately. Furthermore, to enhance the robustness of the algorithm, a two-level outlier detection mechanism is designed. Distance and location estimates obtained from PRM and FP are passed to the first outlier detection to generate improved distance estimates for the EKF. After the EKF process, the second outlier detection algorithm based on statistical testing is further performed to remove the outliers. The proposed algorithm was evaluated by various field experiments. Results show that the proposed algorithm achieved the accuracy of <2.56 m at 90% of the time with dense deployment of BLE beacons (1 beacon per 9 m), which performs 35.82% better than <3.99 m from the Propagation Model (PM) + EKF algorithm and 15.77% more accurate than <3.04 m from the FP + EKF algorithm. With sparse deployment (1 beacon per 18 m), the proposed algorithm achieves the accuracies of <3.88 m at 90% of the time, which performs 49.58% more accurate than <8.00 m from the PM + EKF algorithm and 21.41% better than <4.94 m from the FP + EKF algorithm. Therefore, the proposed algorithm is especially useful to improve the localization accuracy in environments with sparse beacon deployment. Full article

Background: To investigate the risk factors of child lead poisoning in China. Methods: A document retrieval was performed using MeSH (Medical subject heading terms) and key words. The Newcastle-Ottawa Scale (NOS) was used to assess the quality of the studies, and the pooled odd ratios with a 95% confidence interval were used to identify the risk factors. We employed Review Manager 5.2 and Stata 10.0 to analyze the data. Heterogeneity was assessed by both the Chi-square and I² tests, and publication bias was evaluated using a funnel plot and Egger’s test. Results: Thirty-four articles reporting 13,587 lead-poisoned children met the inclusion criteria. Unhealthy lifestyle and behaviors, environmental pollution around the home and potential for parents’ occupational exposure to lead were risk factors of child lead poisoning in the pooled analyses. Our assessments yielded no severe publication biases. Conclusions: Seventeen risk factors are associated with child lead poisoning, which can be used to identify high-risk children. Health education and promotion campaigns should be designed in order to minimize or prevent child lead poisoning in China. Full article

Few studies have examined docosahexaenoic acid (DHA) in pregnant and lactating women in developing countries like China, where DHA-enriched supplements are increasingly popular. We aimed to assess the DHA status among Chinese pregnant and lactating women residing areas differing in the availability of aquatic products. In total, 1211 women in mid-pregnancy (17 ± 2 weeks), late pregnancy (39 ± 2 weeks), or lactation (42 ± 7 days) were enrolled from Weihai (coastland), Yueyang (lakeland), and Baotou (inland) city, with approximately 135 women in each participant group by region. DHA concentrations were measured using capillary gas chromatography, and are reported as weight percent of total fatty acids. Mean plasma DHA concentrations were higher in coastland (mid-pregnancy 3.19%, late pregnancy 2.54%, lactation 2.24%) and lakeland women (2.45%, 1.95%, 2.26%) than inland women (2.25%, 1.67%, 1.68%) (p values < 0.001). Similar differences were observed for erythrocyte DHA. We conclude that DHA concentrations of Chinese pregnant and lactating women are higher in coastland and lakeland regions than in inland areas. DHA status in the study population appears to be stronger than populations from other countries studied to date. Full article

Vegetation is one of the most important components of the terrestrial ecosystem and, thus, monitoring the spatial and temporal dynamics of vegetation has become the key to exploring the basic process of the terrestrial ecosystem. Vegetation change studies have focused on the relationship between climatic factors and vegetation dynamics. However, correlations among the climatic factors always disturb the results. In addition, the impact of anthropogenic activities on vegetation dynamics was indeterminate. Here, vegetation dynamics in 14 provinces in Eastern China over a 10-year period was quantified to determine the driving mechanisms relating to climate and anthropogenic factors using partial correlation analysis. The results showed that from 1999 to 2008, the vegetation density increased in the whole, with spatial variations. The vegetation improvement was concentrated in the Yangtze River Delta, with the vegetation degradation concentrated in the other developed areas, such as Beijing-Tianjin-Hebei Region and the Pearl River Delta. The annual NDVI changes were mainly driven by temperature in Northeast China and the Pearl River Delta, and by precipitation in the Bohai Rim; while in the Yangtze River Delta, the driving forces of temperature and precipitation almost equaled each other. Furthermore, the impact of anthropogenic activities on vegetation dynamics had accumulative effects in the time series, and had a phase effect on the vegetation change trend. Full article

The main aim of this paper is to develop a low-cost GNSS/MEMS-IMU tightly-coupled integration system with aiding information that can provide reliable position solutions when the GNSS signal is challenged such that less than four satellites are visible in a harsh environment. To achieve this goal, we introduce an adaptive tightly-coupled integration system with height and heading aiding (ATCA). This approach adopts a novel redundant measurement noise estimation method for an adaptive Kalman filter application and also augments external measurements in the filter to aid the position solutions, as well as uses different filters to deal with various situations. On the one hand, the adaptive Kalman filter makes use of the redundant measurement system’s difference sequence to estimate and tune noise variance instead of employing a traditional innovation sequence to avoid coupling with the state vector error. On the other hand, this method uses the external height and heading angle as auxiliary references and establishes a model for the measurement equation in the filter. In the meantime, it also changes the effective filter online based on the number of tracked satellites. These measures have increasingly enhanced the position constraints and the system observability, improved the computational efficiency and have led to a good result. Both simulated and practical experiments have been carried out, and the results demonstrate that the proposed method is effective at limiting the system errors when there are less than four visible satellites, providing a satisfactory navigation solution. Full article

This paper presents a method that trains the WiFi fingerprint database using sensor-based navigation solutions. Since micro-electromechanical systems (MEMS) sensors provide only a short-term accuracy but suffer from the accuracy degradation with time, we restrict the time length of available indoor navigation trajectories, and conduct post-processing to improve the sensor-based navigation solution. Different middle-term navigation trajectories that move in and out of an indoor area are combined to make up the database. Furthermore, we evaluate the effect of WiFi database shifts on WiFi fingerprinting using the database generated by the proposed method. Results show that the fingerprinting errors will not increase linearly according to database (DB) errors in smartphone-based WiFi fingerprinting applications. Full article

Numerous solutions/methods to solve the existing problems of pedestrian navigation/localization have been proposed in the last decade by both industrial and academic researchers. However, to date there are still major challenges for a single pedestrian navigation system (PNS) to operate continuously, robustly, and seamlessly in all indoor and outdoor environments. In this paper, a novel method for pedestrian navigation approach to fuse the information from two separate PNSs is proposed. When both systems are used at the same time by a specific user, a nonlinear inequality constraint between the two systems’ navigation estimates always exists. Through exploring this constraint information, a novel filtering technique named Kalman filter with state constraint is used to diminish the positioning errors of both systems. The proposed method was tested by fusing the navigation information from two different PNSs, one is the foot-mounted inertial navigation system (INS) mechanization-based system, the other PNS is a navigation device that is mounted on the user’s upper body, and adopting the pedestrian dead reckoning (PDR) mechanization for navigation update. Monte Carlo simulations and real field experiments show that the proposed method for the integration of multiple PNSs could improve each PNS’ navigation performance. Full article

Providing an accurate and practical navigation solution anywhere with portable devices, such as smartphones, is still a challenge, especially in environments where global navigation satellite systems (GNSS) signals are not available or are degraded. This paper proposes a new algorithm that integrates inertial navigation system (INS) and pedestrian dead reckoning (PDR) to combine the advantages of both mechanizations for micro-electro-mechanical systems (MEMS) sensors in pedestrian navigation applications. In this PDR/INS integration algorithm, a pseudo-velocity-vector, which is composed of the PDR-derived forward speed and zero lateral and vertical speeds from non-holonomic constraints (NHC), works as an update for the INS to limit the velocity errors. To further limit the drift of MEMS inertial sensors, trilateration-based WiFi positions with small variances are also selected as updates for the PDR/INS integrated system. The experiments illustrate that positioning error is decreased by 60%–75% by using the proposed PDR/INS integrated MEMS solution when compared with PDR. The positioning error is further decreased by 15%–55% if the proposed PDR/INS/WiFi integrated solution is implemented. The average accuracy of the proposed PDR/INS/WiFi integration algorithm achieves 4.5 m in indoor environments. Full article

This paper presents a WiFi-aided magnetic matching (MM) algorithm for indoor pedestrian navigation with consumer portable devices. This algorithm reduces both the mismatching rate (i.e., the rate of matching to an incorrect point that is more than 20 m away from the true value) and computational load of MM by using WiFi positioning solutions to limit the MM search space. Walking tests with Samsung Galaxy S3 and S4 smartphones in two different indoor environments (i.e., Environment #1 with abundant WiFi APs and significant magnetic features, and Environment #2 with less WiFi and magnetic information) were conducted to evaluate the proposed algorithm. It was found that WiFi fingerprinting accuracy is related to the signal distributions. MM provided results with small fluctuations but had a significant mismatch rate; when aided by WiFi, MM’s robustness was significantly improved. The outcome of this research indicates that WiFi and MM have complementary characteristics as the former is a point-by-point matching approach and the latter is based on profile-matching. Furthermore, performance improvement through integrating WiFi and MM depends on the environment (e.g., the signal distributions of magnetic intensity and WiFi RSS): In Environment #1 tests, WiFi-aided MM and WiFi provided similar results; in Environment #2 tests, the former was approximately 41.6% better. Our results supported that the WiFi-aided MM algorithm provided more reliable solutions than both WiFi and MM in the areas that have poor WiFi signal distribution or indistinctive magnetic-gradient features. Full article

Inertial technology has been used in a wide range of applications such as guidance, navigation, and motion tracking. However, there are few undergraduate courses that focus on the inertial technology. Traditional inertial navigation systems (INS) and relevant testing facilities are expensive and complicated in operation, which makes it inconvenient and risky to perform teaching experiments with such systems. To solve this issue, this paper proposes the idea of using smartphones, which are ubiquitous and commonly contain off-the-shelf inertial sensors, as the experimental devices. A series of curriculum experiments are designed, including the Allan variance test, the calibration test, the initial leveling test and the drift feature test. These experiments are well-selected and can be implemented simply with the smartphones and without any other specialized tools. The curriculum syllabus was designed and tentatively carried out on 14 undergraduate students with a science and engineering background. Feedback from the students show that the curriculum can help them gain a comprehensive understanding of the inertial technology such as calibration and modeling of the sensor errors, determination of the device attitude and accumulation of the sensor errors in the navigation algorithm. The use of inertial sensors in smartphones provides the students the first-hand experiences and intuitive feelings about the function of inertial sensors. Moreover, it can motivate students to utilize ubiquitous low-cost sensors in their future research. Full article

Occupancy grid map is a popular tool for representing the surrounding environments of mobile robots/intelligent vehicles. Its applications can be dated back to the 1980s, when researchers utilized sonar or LiDAR to illustrate environments by occupancy grids. However, in the literature, research on vision-based occupancy grid mapping is scant. Furthermore, when moving in a real dynamic world, traditional occupancy grid mapping is required not only with the ability to detect occupied areas, but also with the capability to understand dynamic environments. The paper addresses this issue by presenting a stereo-vision-based framework to create a dynamic occupancy grid map, which is applied in an intelligent vehicle driving in an urban scenario. Besides representing the surroundings as occupancy grids, dynamic occupancy grid mapping could provide the motion information of the grids. The proposed framework consists of two components. The first is motion estimation for the moving vehicle itself and independent moving objects. The second is dynamic occupancy grid mapping, which is based on the estimated motion information and the dense disparity map. The main benefit of the proposed framework is the ability of mapping occupied areas and moving objects at the same time. This is very practical in real applications. The proposed method is evaluated using real data acquired by our intelligent vehicle platform “SeTCar” in urban environments. Full article

The errors of low-cost inertial sensors, especially Micro-Electro Mechanical Systems (MEMS) ones, are highly dependent on environmental conditions such as the temperature. Thus, there is a need for the development of accurate and reliable thermal compensation models to reduce the impact of such thermal drift of the sensors. Since the conventional thermal calibration methods are typically time-consuming and costly, an efficient thermal calibration method to investigate the thermal drift of a full set of gyroscope and accelerometer errors (i.e., biases, scale factor errors and non-orthogonalities) over the entire temperature range in a few hours is proposed. The proposed method uses the idea of the Ramp method, which removes the time-consuming process of stabilizing the sensor temperature, and addresses its inherent problems with several improvements. We change the temperature linearly for a complete cycle and take a balanced strategy by making comprehensive use of the sensor measurements during both heating and cooling processes. Besides, an efficient 8-step rotate-and-static scheme is designed to further improve the calibration accuracy and efficiency. Real calibration tests showed that the proposed method is suitable for low-grade IMUs and for both lab and factory calibration due to its efficiency and sufficient accuracy. Full article