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Mobile cloud computing (MCC) provides a platform for resource-constrained mobile devices to offload their tasks. MCC has the characteristics of cloud computing and its own features such as mobility and wireless data transmission, which bring new challenges to offloading decision for MCC. However, most existing works on offloading decision assume that mobile cloud environments are stable and only focus on optimizing the consumption of offloaded applications but ignore the consumption caused by offloading decision algorithms themselves. This paper focuses on runtime offloading decision in dynamic mobile cloud environments with the consideration of reducing the offloading decision algorithm’s consumption. A cooperative runtime offloading decision algorithm, which takes advantage of the cooperation of online machine learning and genetic algorithm to make offloading decisions, is proposed to address this problem. Simulations show that the proposed algorithm helps offloaded applications save more energy and time while consuming fewer computing resources.

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Terrain classifications is an important topic in polarimetric synthetic aperture radar (PolSAR) image processing and interpretation. A novel PolSAR classification method based on three-channel convolutional neural network (Tc-CNN) is proposed and this method can effectively take the advantage of unlabeled samples to improve the performance of classification with a small number of labeled samples. Several strategies are included in the proposed method. (1) In order to take the advantage of unlabeled samples, a data enhancement method based on neighborhood nearest neighbor propagation (N3P) method is proposed to enlarge the number of labeled samples. (2) To increase the role of central pixel in CNN classification based on pixel, a spatial weighted method is proposed to increase the weight of central pixel features and weak the weight of other types of pixel features. (3) A specific deep model for PolSAR image classification (named as Tc-CNN) is proposed, which can obtain more scale and deep polarization information to improve the classification results. Experimental results show that the proposed method achieves a much better performance than existing classification methods when the number of labeled samples is few. Index Terms-convolutional neural network (CNN); polarimetric SAR; terrain classification; three-channel convolutional neural network (Tc-CNN)