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In recent years, mobile medical services have gradually become the common focus of the communications and healthcare industries. People can get medical information and services efficiently and conveniently anytime, anywhere. However, due to the limitations of the mobile terminal's own computing and storage capabilities, mobile therapy is greatly challenged. Therefore, we propose a mobile medical health system based on cloud computing. Firstly, principal component analysis was used to obtain representative features. Then, a simplified feature subset was applied to support vector machine (SVM) based on Sigmoid kernel function. The data set was categorized by SVM as cancer patient and normal object experimental results show that the method is improved in accuracy, sensitivity, and specificity. [ABSTRACT FROM AUTHOR]

To address the issue of spectrum scarcity in future communication for smart world, cognitive radio (CR) has emerged as a promising solution. In CR, spectrum sensing is the premise and key technology. To defense against the PUE attack in spectrum sensing, two spectrum sensing methods against PUE attack based on fractal dimension are proposed, which are spectrum sensing defending against PUE attack based on normalized Sevcik fractal dimension in frequency domain (SSMS) and spectrum sensing defending against PUE attack based on normalized Sevcik fractal dimension in frequency domain and normalized Petrosian fractal dimension (SSMSP). They identify the modulation type of the received signal by SVM classifier according to the fractal dimension of the received signal to detect PUE attack. The simulation results show that the two proposed methods can effectively identify the modulation type and detect the PUE attack. When SNR is larger than 10 dB, the PUE detection probabilities of both SSMS and SSMSP can reach 100%. Even when SNR is - 20 dB, the PUE detection probabilities are still larger than 97%. Therefore, SSMS and SSMSP can effectively defend against PUE attack. [ABSTRACT FROM AUTHOR]

To address the problem of spectrum scarcity in future communication for smart world, cognitive radio (CR) is viewed as an effective way and has been widely studied in recent years. Spectrum sensing is the key to deploy Cognitive radio (CR) network. Parallel cooperative spectrum sensing provides an effective solution in multi-channel scenario. In this paper, joint optimal sensing duration selection and sensing task allocation is studied in parallel cooperative spectrum sensing for heterogeneous multi-channel CR systems. Two cross-layer parallel cooperative spectrum sensing methods based on iterative KM algorithm are proposed. Sensing duration selection and sensing task allocation are designed jointly to maximize the available throughput. A two-step method is implemented. Firstly it determines the optimal sensing task allocation for fixed sensing duration by iterative KM algorithm, and then selects the optimal sensing duration by exhaustion method and quartering method, respectively. The simulation results show that the proposed method can optimally select the sensing duration and obtain higher available throughput than other compared methods. [ABSTRACT FROM AUTHOR]