A New Fuzzy Additive Noise Reduction Method

In this paper we present a new alternative noise reduction method for color images that were corrupted with additive Gaussian noise. We illustrate that color images have to be processed in a different way than most of the state-of-the-art methods. The proposed method consists of two sub-filters. The main concern of the first subfilter is to distinguish between local variations due to noise and local variations due to image structures such as edges. This is realized by using the color component distances instead of component differences as done by most current filters. The second subfilter is used as a complementary filter which especially preserves differences between the color components. This is realized by calculating the local differences in the red, green and blue environment separately. These differences are then combined to calculate the local estimation of the central pixel. Experimental results show the feasibility of the proposed approach

Radio map fusion for indoor positioning in wireless local area networks

This paper addresses the problem of indoor location estimation (LE) in a Wireless Local Area Network (WLAN) using received signal strength (RSS). The difficultly of the problem lies in the complexity of the indoor propagation channel at operating WLAN frequency of 2.4GHz, resulting in non-linear and non-Gaussian spatio-temporal RSS properties. The first contribution of this paper is the introduction of a non-parametric Nadaraya-Watson estimator for LE using location fingerprints to capture the spatial distribution of RSS. Secondly, a novel method is proposed based on fusion of multiple location fingerprints at each survey location to cope with multimodal temporal probability distributions of RSS. Experimental results using real data collected in an office environment indicate that the proposed multiple-map method outperforms the KNN-based LE methods in terms of root mean square error. \ua9 2005 IEEE