Effective feature extraction and classification methods are of great importance for motor imagery (MI)-based brain-computer interface (BCI) systems. The common spatial pattern (CSP) algorithm is a widely used feature extraction method for MI-based BCIs. In this work, we propose a novel spatial-frequency-temporal optimized feature sparse representation-based classification method. Optimal channels are selected based on relative entropy criteria. Significant CSP features on frequency-temporal domains are selected automatically to generate a column vector for sparse representation-based classification (SRC). We analyzed the performance of the new method on two public EEG datasets, namely BCI competition III dataset IVa which has five subjects and BCI competition IV dataset IIb which has nine subjects. Compared to the performance offered by the existing SRC method, the proposed method achieves average classification accuracy improvements of 21.568 and 14.38% for BCI competition III dataset IVa and BCI competition IV dataset IIb, respectively. Furthermore, our approach also shows better classification performance when compared to other competing methods for both datasets.

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Fuzzy c-means (FCM) clustering with spatial constraints has attracted great attention in the field of image segmentation. However, most of the popular techniques fail to resolve misclassification problems due to the inaccuracy of their spatial models. This paper presents a new unsupervised FCM-based image segmentation method by paying closer attention to the selection of local information. In this method, region-level local information is incorporated into the fuzzy clustering procedure to adaptively control the range and strength of interactive pixels. First, a novel dissimilarity function is established by combining region-based and pixel-based distance functions together, in order to enhance the relationship between pixels which have similar local characteristics. Second, a novel prior probability function is developed by integrating the differences between neighboring regions into the mean template of the fuzzy membership function, which adaptively selects local spatial constraints by a tradeoff weight depending upon whether a pixel belongs to a homogeneous region or not. Through incorporating region-based information into the spatial constraints, the proposed method strengthens the interactions between pixels within the same region and prevents over smoothing across region boundaries. Experimental results over synthetic noise images, natural color images, and synthetic aperture radar images show that the proposed method achieves more accurate segmentation results, compared with five state-of-the-art image segmentation methods.

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Purpose The purpose of this paper is to propose a framework for assessing the vulnerability of projects to crises. The study seeks to clarify the cascade effects of disruptions leading to project crises and to improve project robustness against crises from a systems perspective. Design/methodology/approach A framework for assessing project vulnerability to crises is developed using complex network theory. The framework includes network representation of project systems, analyzing project network topology, simulating the cascade of unexpected disruptions and assessing project vulnerability. Use of the framework is then illustrated by applying it to a case study of a construction project. Findings Project network topology plays a critical role in resisting crises. By increasing the resilience of the critical tasks and adjusting the structure of a project, the complexity and vulnerability of the project can be reduced, which in turn decreases the occurrence of crises. Research limitations/implications The proposed framework is used in a case study. Further studies of its application to projects in diverse industries would be beneficial to enhance the robustness of the results. Practical implications Project crises can threaten the survival of a project and endanger the organization’s security. The proposed framework helps prevent and mitigate project crises by protecting critical tasks and blocking the diffusion path from a systems perspective. Originality/value This paper presents a novel framework based on complex network theory to assess project vulnerability, which provides a systemic understanding of the cascade of disruptions that lead to project crises.