1,723,057 research outputs found
Han Dong 韩东
No full textTraduzione dal cinese di sei poesie di Han Dong 韩东: Ziyou 自由 (Libertà); Gongzuoshi 工作室 (Lo studio); Qiji 奇迹 (Visione); Chang dongxi 长东西 (Oggetto oblungo); Ziguang 紫光 (Raggio viola); Dahuji 大湖记 (Al grande lago
47 A-InDel loci genotyped from Han Dong Yi and Chuanqing in Southwest China.xlsx
No full textWe genotyped the 47 autosomal InDels of 592 Guizhou individuals from four populations, Han, Dong, Yi and Chuanqing, genotyped via the AGCU InDel 50 kit.</p
라디오 맵 구축 방법
No full textAccording to the present invention, a radio map construction method uses a genetic algorithm and comprises the steps of: (a) generating a plurality of chromosomes, each including a set of pairs consisting of a fingerprint labeled with an address and a position selected within a region s of the address; (b) generating a temporary radio map by using the pairs of the chromosomes; (c) arranging collected fingerprint sequences by using the temporary radio map; and (d) evaluating the placement of the fingerprint sequences
A Sensor Fusion Framework for Indoor Navigation With Online Error Correction and Radio Map Update
No full textThe integration of various sensors in smartphones has enabled the use of sensor fusion for indoor positioning. Sensor fusion frameworks, which integrate data from multiple sensors in a smartphone, are being recognized for their potential to improve indoor positioning accuracy. However, real-time errors arising from the dynamic indoor environment can accumulate, posing challenges in detecting and correcting these errors to maintain precise indoor navigation services. In this article, we propose a novel sensor fusion framework that achieves high positioning accuracy by learning real-time errors accumulated during pedestrian navigation. The proposed system identifies sensor measurement errors, accumulates them, adjusts measurement values based on the accumulated error distribution, and employs these refined data for indoor position estimation. Additionally, we introduce a new technique to detect and exclude anomalous errors during the error adjustment process. The analyzed error information is subsequently used to update the radio map through long-term memory learning during the offline stage, ensuring rapid convergence for our proposed system. In experimental scenarios, the proposed framework achieved an average error distance (AED) of approximately 1.68 m, which is a significant improvement after error correction.
2D Particle Filter Accelerator for Mobile Robot Indoor Localization and Pose Estimation
No full textParticle filtering is a reliable Monte Carlo algorithm for estimating the state of a system in modeling non-linear, non-gaussian elements for estimation and tracking applications in various fields, including robotics, navigation, and computer vision. However, particle filtering can be computationally expensive, particularly in high-dimensional state spaces, and can be a bottleneck for real-time applications due to high memory consumption. This paper proposes a particle filter accelerator that employs a cellular automata-based pseudo-random number generator and an improved systematic resampler based on the Vose Alias method. The particles are distributed across several sub-filters, performing concurrent resampling and importance weights computations. The proposed accelerator leveraged the inherent parallelism and pipelining stages of FPGAs to perform the resampling stage in a parallel fashion, significantly enhancing the particle convergence time. The proposed accelerator deployed on the Zedboard (ZC7020) system-on-chip achieves a low execution time of approximately 4.63 , 21.3 % speedup, and 3.1 % area reduction compared to the recent particle filter accelerator. The proposed design also demonstrates modularity, achieved through multiple parallel hardware subfilters that provide high throughput for real-time sensor data processing. Furthermore, the proposed accelerator performs a high sampling frequency of 216kHz, making it suitable for high throughput and real-time applications.
A Framework for Indoor Map Layout Construction in Collaborative Positioning: Optimized Analysis of Reachability and Vertical Transitions
No full textAbstract— Collaborative indoor positioning requires maps with explicitly defined spatial
connectivity. This paper presents a framework for constructing indoor map layouts using spatial
entities and reachability structures. We introduce the Floor-Group-Area (FGA) Matrix Problem to
visualize and optimize layout connectivity through column reordering. The framework exports 1bpp
OGMs and spatial metadata in JSON. In user evaluations, trained participants completed full layouts
of a four-story museum in 20 minutes, with all computationally intensive algorithms running in few
seconds on a standard PC
Voronoi Tessellation Based Interpolation Method for Wi-Fi Radio Map Construction
No full textThe fingerprint-based approach for positioning in WLAN has been drawing great attention these days. However, the approach usually requires tremendous time and efforts to collect location fingerprints for the target area. In this paper, we propose an interpolation method based on Voronoi tessellation to significantly reduce such calibration efforts and to improve accuracy. Unlike other interpolation methods, our method refines the propagation model for each cell of the target area tessellated by a higher-order Voronoi diagram. Consequently, our method can take into account the signal fading caused by walls and obstacles more accurately. The proposed method significantly outperformed other interpolation methods in accuracy
ILoA: Indoor Localization Using Augmented Vector of Geomagnetic Field
Full text linkIn this article, we propose a new geomagnetic localization scheme, named ILoA, to address error accumulation and global localization. Global localization is a fundamental problem that determines the initial pose under global uncertainty. Moreover, error accumulation using inertial navigation systems (INS) impacts robustness and drift error, making it challenging to achieve reliable estimation. The magnetic field in indoor space generates a unique signature/anomaly, which can be used as a local feature. Earth's magnetic field can be easily influenced by ferromagnetic material from the indoor environment due to its weak intensity. The magnetic field vector measured by a magnetometer depends on the orientation of the sensor, which we term a direction variant. We devise a novel approach to identify location and heading through the direction-variant augmented vector. Since a magnetic field vector under varying poses can produce many different vectors, the geomagnetic map is trained with the transformation. We present experiments in two testbeds, covering open space, showing that the proposed method using the magnetic field vector is efficient for global localization and accuracy compared with a state-of-the-art approach.
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