Abstract-Recent years have witnessed growing popularity of sensor and sensor-network technologies, supporting important practical applications. One of the fundamental issues is how to accurately locate a user with few labelled data in a wireless sensor network, where a major difficulty arises from the need to label large quantities of user location data, which in turn requires knowledge about the locations of signal transmitters, or access points. To solve this problem, we have developed a novel machine-learning-based approach that combines collaborative filtering with graph-based semi-supervised learning to learn both mobile-users' locations and the locations of access points. Our framework exploits both labelled and unlabelled data from mobile devices and access points. In our two-phase solution, we first build a manifold-based model from a batch of labelled and unlabelled data in an offline training phase and then use a weighted k-nearest-neighbor method to localize a mobile client in an online localization phase. We extend the two-phase co-localization to an online and incremental model that can deal with labelled and unlabelled data that come sequentially and adapt to environmental changes. Finally, we embed an action model to the framework such that additional kinds of sensor signals can be utilized to further boost the performance of mobile tracking. Compared to other state-of-the-art systems, our framework has been shown to be more accurate while requiring less calibration effort in our experiments performed at three different test-beds.
We proposed a design for a LASER pico-projector with a low speckle contrast value and high contrast ratio that maintains system efficiency. The method for speckle contrast reduction utilizes two diffusers and a Voice Coil Motor (VCM) oscillator. The two different diffusers for a high contrast ratio and high system efficiency can be divided into two categories. In Category 1, the speckle contrast value can be decreased to 2.80% by using a circular symmetric diffuser. At the same time, the full-on/full-off (FO:FO) contrast ratio can be maintained within 1200:1-1300:1, but the system efficiency decreases 1.50%. In Category 2, the speckle contrast value can be reduced to 6.50% by using an elliptically scattering diffuser. At the same time, the FO:FO contrast ratio can be maintained within 1300:1-1400:1, and the system efficiency decreases by only 1.00%.
We introduce a hybrid backlight module, which consists of a hybrid light guide plate (HLGP) and a brightness enhancement film (BEF). The HLGP comprises functions of a conventional light guide plate, a reflector, and a BEF. The HLGP allows one-dimensional rays to be collimated. We add a BEF above the HLGP, and let the crossed-dimension rays to be collimated. Comparing with the conventional edge-lit backlight module, the optical efficiency improves to 1.3-times and the on-axis luminance improves to 3.7-times by using the hybrid backlight module.
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