Haiyong Luo

Optimum bit allocation and rate control for H.264/AVC

For the rate control of H. 264/AVC, one of the most important things is to get the statistics of the current frame accurately. To achieve this, a novel adaptive coding characteristics prediction scheme is presented to improve the accuracy of R-D modeling, by exploiting spatio-temporal correlations. With the proposed prediction scheme, we present a novel rate function and a linear distortion model, and then deduce a simple close-form solution to the problem of optimum bit allocation, just in a TMN-8-alike way. Extensive experiments show that improvements with gains up to 0.92dB per frame over JVT-G012, the current standardized rate control scheme, are achieved by the proposed scheme for a variety of test sequences with less demanding bandwidth.

An RSSI gradient-based AP localization algorithm

Recent rapid rise of indoor location based services for smartphones has further increased the importance of precise localization of Wi-Fi Access Point (AP). However, most existing AP localization algorithms either exhibit high errors or need specialized hardware in practical scenarios. In this paper, we propose a novel RSSI gradient-based AP localization algorithm. It consists of the following three major steps: firstly, it uses the local received signal strength variations to estimate the direction (minus gradient) of AP, then employs a direction clustering method to identify and filter measurement outliers, and finally adopts triangulation method to localize AP with the selected gradient directions. Experimental results demonstrate that the average localization error of our proposed algorithm is less than 2 meters, far outperforming that of the weighted centroid approach.

Fusion of Wi-Fi and bluetooth for indoor localization

The existing localization technology with single mode is limited in accuracy and robustness. To obtain higher accuracy, this paper proposes a novel indoor localization algorithm with WI-FI and Bluetooth. The approach is based on the Bayesian filtering and performs data-level fusion to get the final position estimate. In addition, idea of simulated annealing algorithm is learned into it that makes our algorithm can find the global optimal value in probability. Experimental results demonstrate that the proposed localization algorithm outperforms the single mode localization with accuracy and robustness.

A Low-Cost and Accurate Indoor Localization Algorithm Using Label Propagation Based Semi-supervised Learning

We present a novel approach to indoor wireless localization using label propagation based on semi-supervised learning. Our aim is to reduce the effort of collecting labeled data in the offline training phrase, which are expensive to obtain. This learning algorithm combines labeled and unlabeled data in learning process to fully realize a global consistency assumption: similar data should have similar labels, which has intimate connections with random walks to propagate label through the dataset along high density areas defined by unlabeled data. We test our algorithm in 802.11 wireless LAN environments, and demonstrate the advantage of our approach in both accuracy and its ability to utilize a much smaller set of labeled training data.

An indoor self-localization algorithm using the calibration of the online magnetic fingerprints and indoor landmarks

Personal dead reckoning (PDR) localization technology can provide effective and critical assistance for public security, such as emergency rescue or anti-terror training in the indoor or underground environment without the need of deploying additional positioning infrastructure. However, the PDR suffers from the severe position error accumulation with time due to the inaccurate step length and moving direction estimation. To improve the self-positioning accuracy, this paper proposed a novel indoor self-localization algorithm using two kinds of automatic calibration methods, i.e., opportunistic magnetic trajectory matching and indoor landmark identification. Extensive experiments performed in two representative indoor environments, including an office building and a supermarket, demonstrate that the proposed self-localization algorithm can obtain an 80 percentile localization accuracy of 1.4m and 2m in the two representative indoor environments, respectively, which outperforms the art-of-the-state PDR algorithms.

Location Fingerprint Extraction for Magnetic Field Magnitude Based Indoor Positioning

Smartphone based indoor positioning has greatly helped people in finding their positions in complex and unfamiliar buildings. One popular positioning method is by utilizing indoor magnetic field, because this feature is stable and infrastructure-free. In this method, the magnetometer embedded on the smartphone measures indoor magnetic field and queries its position. However, the environments of the magnetometer are rather harsh. This harshness mainly consists of coarse-grained hard/soft-iron calibrations and sensor electronic noise. The two kinds of interferences decrease the position distinguishability of the magnetic field. Therefore, it is important to extract location features from magnetic fields to reduce these interferences. This paper analyzes the main interference sources of the magnetometer embedded on the smartphone. In addition, we present a feature distinguishability measurement technique to evaluate the performance of different feature extraction methods. Experiments revealed that selected fingerprints will improve position distinguishability.

Clustering algorithms research for device-clustering localization

Crowdsourcing-based localization has attracted wide research concern to the metropolitan-scale positioning. However, crowdsourcing-based fingerprints collection with assorted mobile smart devices brings fingerprint confusion, which significantly degrades the localization accuracy. To solve the device diversity problem, many solutions have been raised like the Device-Clustering algorithm. Based on macro Device-Cluster (DC) rather than natural device, DC algorithm maintains less device types and slight calibration overhead. Despite high positioning accuracy, the selection of suitable clustering algorithms in DC system becomes another puzzle. In this paper, we reshape the novel Device-Clustering algorithm to enhance the indoor positioning by comparing the application of different clustering algorithms. The experimental result indicates the reliability of DC strategy in broad clustering scheme as well as the suitable locating process corresponding to distinct environment.

A Pervasive Sensor Node Architecture

A set of sensor nodes is the basic component of a sensor network. Many researchers are currently engaged in developing pervasive sensor nodes due to the great promise and potential with applications shown by various wireless remote sensor networks. This short paper describes the concept of sensor node architecture and current research activities on sensor node development at ICTCAS.

Constructing an Indoor Floor Plan Using Crowdsourcing Based on Magnetic Fingerprinting

A large number of indoor positioning systems have recently been developed to cater for various location-based services. Indoor maps are a prerequisite of such indoor positioning systems; however, indoor maps are currently non-existent for most indoor environments. Construction of an indoor map by external experts excludes quick deployment and prevents widespread utilization of indoor localization systems. Here, we propose an algorithm for the automatic construction of an indoor floor plan, together with a magnetic fingerprint map of unmapped buildings using crowdsourced smartphone data. For floor plan construction, our system combines the use of dead reckoning technology, an observation model with geomagnetic signals, and trajectory fusion based on an affinity propagation algorithm. To obtain the indoor paths, the magnetic trajectory data obtained through crowdsourcing were first clustered using dynamic time warping similarity criteria. The trajectories were inferred from odometry tracing, and those belonging to the same cluster in the magnetic trajectory domain were then fused. Fusing these data effectively eliminates the inherent tracking errors originating from noisy sensors; as a result, we obtained highly accurate indoor paths. One advantage of our system is that no additional hardware such as a laser rangefinder or wheel encoder is required. Experimental results demonstrate that our proposed algorithm successfully constructs indoor floor plans with 0.48 m accuracy, which could benefit location-based services which lack indoor maps.

Device-clustering algorithm in crowdsourcing-based localization

Abstract Device heterogeneity significantly degrades the localization performance of fingerprinting-based localization, especially in the crowdsourcing-based positioning system. Although manual calibration can reduce positional error, the adjustment overhead is extremely heavy and to maintain ever-increasing device types is overly laborious. In this paper, we propose a novel device-clustering algorithm to operate the positioning system based on macro device-cluster (DC) rather than natural device. In this way, the system maintains less device types and the localization accuracy is improved obviously. The experimental result of different combination indicates the optimal operating flow is to combine DC and kernel density estimator when the tracking device is known and add the linear transformation phase when device is unknown.