In this paper, an Unmanned Aerial Vehicle (UAV) assisted cooperative communication system is studied, wherein a source transmits information to the destination through an energy harvesting decode-and-forward UAV. It is assumed that the UAV can freely move in between the source-destination pair to set up line of sight communications with both nodes. Since the battery of the UAV may be limited, it can harvest energy from the received signal by power splitting technique to be able to perfectly transmit data to the destination. Therefore, we study Throughput maximization problem for multiple time slots data transmission through the cooperative energy harvesting UAV.  To maximize the Throughput, optimal power allocation at the source and the UAV and power splitting ratio at the UAV are studied over each time slot in presence of energy-causality constraints at the UAV. Finally, numerical results are presented to analyze the spectral and energy efficiency of the proposed system, and effects of optimal power allocations and power splitting ratio.  The results indicate that by utilizing optimal resource allocations at the source and the UAV, and utilizing Simultaneous Wireless Information and Power Transfer (SWIPT), significant Throughput improvement is achieved compared to those without optimal resource allocation or SWIPT. All of static UAV scenarios (i.e., the maximum Throughput between the source and the destination) increases, while there is no need to increase the battery capacity of the UAV.

Data mining and knowledge discovery are important technologies for business and research. Despite their benefits in various areas such as marketing, business and medical analysis, the use of data mining techniques can also result in new threats to privacy and information security. Therefore, a new class of data mining methods called privacy preserving data mining (PPDM) has been developed. The aim of researches in this field is to develop techniques those could be applied to databases without violating the privacy of individuals. In this work we introduce a new approach to preserve sensitive information in databases with both numerical and categorical attributes using fuzzy logic. We map a database into a new one that conceals private information while preserving mining benefits. In our proposed method, we use fuzzy membership functions (MFs) such as Gaussian, P-shaped, Sigmoid, S-shaped and Z-shaped for private data. Then we cluster modified datasets by Expectation maximization (EM) algorithm. Our experimental results show that using fuzzy logic for preserving data privacy guarantees valid data clustering results while protecting sensitive information. The accuracy of the clustering algorithm using fuzzy data is approximately equivalent to original data and is better than the state of the art methods in this field.