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Given the ever-expanding scale of WiFi deployments in metropolitan areas, we have reached the point where accurate GPS-free outdoor localization becomes possible by relying solely on the WiFi infrastructure. Nevertheless, the existing industrial practices do not seem to have the right implementation to achieve an adequate accuracy, while the academic researches that are mostly attracted by indoor...
GPS localization drains battery aggressively if it is done continuously on a smartphone. This high battery drainage could be reduced by lowering the sampling rate of GPS. However, to fill the reception gaps introduced by low sampling rate, other methods need to be explored. The sensors available in the smartphones allow incremental positioning based on dead reckoning. Even though dead reckoning is...
This paper proposes a robotic eye based on spherical parallel mechanism with three degrees of freedom. It aims to imitate the human eye with active vision for robots in various unstructured environments. Its kinematic analysis and performance estimation indexes are presented. For the pursuit of best velocity and acceleration accuracies of the eyeball, an optimal design method using evolutionary strategy...
Localization is crucial for wireless ad hoc and sensor networks. As the distance-measurement ranges are often less than the communication ranges for many ranging systems, most communication-dense wireless networks are localization-sparse. Consequently, existing algorithms fail to provide accurate localization supports. In order to address this issue, by introducing the concept of component, we group...
Accurate self-localization is a key enabling technique for many pervasive applications. Existing approaches, most of which are multilateration based, often suffer ambiguities, resulting in unbounded positioning errors. To address this problem, previous approaches discard those positioning results with possible flip ambiguities, trading the performance for robustness. However, the high false positive...
Dirichlet process mixture (DPM) model, which is the state-of-the-art Bayesian nonparametric model, was introduced here to signal processing research field. In present Bayesian statistics it is used to model and inference random nongaussian distributions. We explored its ability to model and estimate nongaussian unknown stationary noise and our work will help dealing with problems in many fields of...
Eigenspace estimation via principal component analysis (PCA) has been used in many applications, e.g., in eigenvoice modeling for speaker adaptation. Here the data of interest are the speaker supervectors, where each supervector is a concatenation of all the mean vectors in the speakerpsilas speaker-dependent (SD) model. One problem is that we often do not have enough speaker-specific data to establish...
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