Normalization of data in classification-based problem is a fundamental task where binary or multi classifier systems integrate it as a sub-system. Normalization can be thought as a mapping function that makes a transformation from one space to another space. Different types of normalization methods are proposed depending on the data content. Recently, researches are carried out on whether this process is really necessary. In this paper, the performances of the different normalization methods for Electroencephalogram (EEG) signal based emotion classification are evaluated. Support vector machine based binary classifier is used in emotion classification. Different kernel functions for support vector machine are also considered. Although the experimental findings may not reveal a significant performance difference between different types of normalization, the normalization process increases classification performance of the emotion recognition, in general.

In this paper, efficient optimal post-process/inloop filtering in video coding is presented by applying two separate one-dimensional filters to significantly decrease the computational load of the optimal filtering process at the expense of only a slight quality loss. Furthermore, the compression of filter coefficients is evaluated using a Golomb-Rice (GR) coding approach together with differential encoding. Experimental results show that the proposed one dimensional processing approach reduces the computational complexity by at least 4 times compared to the formerly proposed two-dimensional optimal filtering case and enables real-time implementation. It is furthermore shown that, the filter coefficients can be compressed by up to 40% if GR coding is used together with differential encoding.

Lane departure warning system used in vehicles has recently become very popular and is about to become a vital component in advanced driver assistance systems. The performance of this system is directly related to lane detection accuracy. In this study, a fuzzy inference system‐based filter for robust lane detection is proposed. The proposed filter has three input parameters which are as follows: the difference between a pixel and its left and right neighbours at a certain distance along the horizontal direction and standard deviation of the pixels between the left and right neighbours. The parameters of the proposed fuzzy filter are determined in a learning phase by taking challenging scenarios such as varying lighting conditions, shadows, and road cracks. Experimental results reveal that the proposed method outperforms existing lane detection filters when integrated into a lane detection system. Since the proposed approach is computationally lightweight, it is suitable for real‐time devices and applications.