Electroencephalography (EEG) signals can reflect activities of the human brain and represent different emotional states. However, recognizing emotions based on full-channel EEG signals will lead to redundant data and hardware complexity, thus it is not suitable for designing wearable devices for daily-life emotion recognition. This paper proposes a channel selection method to select an optimal subset of EEG channels by using normalized mutual information (NMI). Compared with other methods, the proposed method solves the problem of obtaining a higher recognition rate while reducing EEG channels sharply. First, EEG signals are sliced into fixed-length pieces with a sliding window, and short-time Fourier transform is adopted to capture EEG spectrogram. Then inter-channel connection matrix is calculated based on NMI, and channel reduction is conducted by using thresholding and connection matrix analysis. The experiments are based on the widely-used emotion recognition database DEAP. It can be derived from the experimental results that the proposed method can select optimal EEG channel subsets to a certain number while maintaining high accuracy of 74.41% for valence and 73.64% for arousal with support vector machines. Further analysis also reveals that the distribution of the selected channels is consistent with cortical areas for general emotion tasks.
Optimizing the cellular network's cell locations is one of the most fundamental problems of network design. The general objective is to provide the desired Quality-of-Service (QoS) with the minimum system cost. In order to meet a growing appetite for mobile data services, heterogeneous networks have been proposed as a cost-and energy-efficient method of improving local spectral efficiency. Whilst unarticulated cell deployments can lead to localized improvements, there is a significant risk posed to network-wide performance due to the additional interference.The first part of the paper focuses on state-of-the-art modelling and radio-planning methods based on stochastic geometry and Monte-Carlo simulations, and the emerging automatic deployment prediction technique for low-power nodes (LPNs) in heterogeneous networks. The technique advises a LPN where it should be deployed, given certain knowledge of the network. The second part of the paper focuses on algorithms that utilize interference and physical environment knowledge to assist LPN deployment.The proposed techniques can not only improve network performance, but also reduce radio-planning complexity, capital expenditure, and energy consumption of the cellular network. The theoretical work arXiv:1307.5182v2 [cs.NI]
Spectral clustering is a very popular approach which has been successfully used in unsupervised classification of polarimetric synthetic aperture radar (PolSAR) imagery. However, due to its high computational complexity, spectral clustering can only be applied to small data sets. This article provides a framework for spectral clustering of large-scale PolSAR data. As computing and processing the pairwise-based affinity matrix is the bottleneck of the spectral clustering approach, we first introduce a representative points-based scheme in which a memory-saving and computationally tractable affinity matrix is designed. The subsequent spectral analysis can be solved efficiently. Second, a simple one-parameter superpixel algorithm is introduced to generate representative points. Through these superpixels, spatial constraints are also naturally integrated into the classification framework. We test the proposed approach on both airborne and space-borne PolSAR images. Experimental results demonstrate its effectiveness.
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