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In this paper, we investigate an energy-harvesting cooperative communication network, which comprises of a source, a destination, and multiple decode-and-forward (DF) relays in the presence of multiple passive eavesdropper (Es). Es attempt to intercept confidential information transmissions from the source to destination via DF relays. In this network, all the DF relays harvest energy from radio-frequency (RF) signals of a source through time-switching receivers. In order to improve the physical layer security of energyharvesting cooperative communication networks, we propose a best relay selection (BRS) scheme, where the ''best'' relay is chosen to assist the source-destination transmission. For the purpose of comparison, we consider the classic direct transmission (DT) and equal relay selection (ERS) as benchmark schemes. We derive the exact closed-form expressions of outage probability (OP) and intercept probability (IP) for the ERS and BRS schemes over Rayleigh fading channels. Besides, the security-reliability tradeoff (SRT) is analyzed as a metric to evaluate the tradeoff performance of the proposed BRS scheme. Numerical results show that the SRT of the BRS scheme consistently outperforms that of the ERS scheme, which demonstrates the advantage of our proposed scheme against eavesdroppers. Besides, it is verified that total error rate (TER) defined as the sum of OP and IP can be minimized for both the ERS and BRS schemes through changing the time allocation factor between information transmission and energy harvesting phases. Moreover, there is a best energy conversation efficiency to obtain a maximal SRT value of the ERS and BRS schemes. In addition, as the number of DF relays increases, the SRT of BRS scheme improves notably, while that of ERS scheme remains unchanged. And as the number of Es increases, the SRT of both the ERS and BRS schemes become worse. INDEX TERMS Cooperative communication, physical layer security, best relay selection (BRS), outage probability (OP), intercept probability (IP), security-reliability tradeoff (SRT).

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This paper mainly studies how to use the stereo vision system that combines the monocular vision with parallel path search to locate the target. When the unmanned aerial vehicle (UAV) searches in the mission area according to the parallel path, the SSD image detection algorithm based on deep learning is adopted to detect and identify the target in the area. The image coordinate information is inversely calculated by using the pixel coordinate information fed back by machine vision. The auxiliary coordinate system is established according to the relationship of angle position between the track line and the basic coordinate system in the parallel path. Combining the position relation and the attitude direction information of UAV, the target position conversion relation between the imaging coordinate system and the auxiliary coordinate system is solved by using the direction cosine matrix. Combined with the coordinate information of UAV, the coordinate position of the target point in the basic coordinate system is finally solved through three coordinate conversion operations. In order to avoid the single calculating error of the target coordinates, the weighted average operation is carried out. On the basis of not changing the search trip of the parallel path, the target location function is preliminarily realized through the reverse solution and the weighted average operation of the target coordinates.