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The high energy density of solar energy gives wireless sensor networks advantages in outdoor monitoring applications. However, long-term stable monitoring is challenging due to frequent weather changes, shading by buildings and trees, etc. The existing research usually uses two technologies to solve the above problems: (1) the energy prediction algorithm, and (2) the energy-aware routing strategy. However, in an actual deployment, frequent weather changes can significantly reduce the accuracy of the existing prediction algorithms. When using the algorithms as the support for energy-aware routing, the network lifetime is less than ideal. The existing routing strategies are in need of further improvement. Because of its lack of environmental adaptability, nodes consume energy quickly and have a high mortality rate. Therefore, aiming at the long-term stability of solar wireless sensor networks, this paper proposes a prediction algorithm based on classification and recurrent neural networks, and integrates the shadow judgement method from our previous research to correct the predicted values. Furthermore, we propose a routing optimization model that can flexibly adjust the target according to the solar intensity. The experimental results show that the prediction and routing scheduling algorithm can significantly improve the energy prediction accuracy (30–50%) and prolong the network lifetime (10–42%) in outdoor small sensor scenarios. [ABSTRACT FROM AUTHOR]

Prostate cancer is one of the most common cancers in men. Early detection of prostate cancer is the key to successful treatment. Ultrasound imaging is one of the most suitable methods for the early detection of prostate cancer. Although ultrasound images can show cancer lesions, subjective interpretation is not accurate. Therefore, this paper proposes a transrectal ultrasound image analysis method, aiming at characterizing prostate tissue through image processing to evaluate the possibility of malignant tumours. Firstly, the input image is preprocessed by optical density conversion. Then, local binarization and Gaussian Markov random fields are used to extract texture features, and the linear combination is performed. Finally, the fused texture features are provided to SVM classifier for classification. The method has been applied to data set of 342 transrectal ultrasound images obtained from hospitals with an accuracy of 70.93%, sensitivity of 70.00%, and specificity of 71.74%. The experimental results show that it is possible to distinguish cancerous tissues from noncancerous tissues to some extent. [ABSTRACT FROM AUTHOR]

With the development of next-generation communication technologies and smart handheld devices, mobile services have multiplied the global network traffic. Due to the subjectivity of user expectations and the instability of wireless environments, the Quality of Experience (QoE) of the mobile service is not always satisfactory. Recently, many optimization schemes have been proposed to evaluate and improve the QoE, but it is still challenging to deliver advanced mobile services without addressing individual preferences and system integrity. To deal with these challenges, this article considers user preferences in wireless communication resource management, including the preference-aware optimization for user side adaptive streaming, coordinated bandwidth allocation, and network side caching content selection. Moreover, the continuous QoE evaluation models are investigated to act as the performance indicator of the optimization system. These proposals can provide a new perspective for future research on preference-aware resource management and can make the optimization system get smarter with what users really want. [ABSTRACT FROM AUTHOR]