摘要：
Inspired by that bird sound has various frequency distributions and continuous time-varying properties, a novel method is proposed for the classification of bird sound based on continuous frame sequence and spectrogram-frame linear network (SFLN). In order to form a continuous frame sequence as the standard input for SFLN, a sliding window algorithm of short frame length is suitable for differentiate the Mel-spectrogram of bird sound. The vertical 3D filter in the linear layer moves linearly along the continuous frame and cover its full frequency band. The weight is initialized to a Gaussian distribution to attenuate the high-and low-frequency noise, thereby extracting the long-and short-term features of the continuous frame of the bird sound. Finally, the GRU network is connected and used as a classifier to directly output the prediction results. Four kinds of bird sound from the xeno-canto website are tested to evaluate the influences of different parameters of sliding window on the effect of SFLN-based classification. In the comparison experiment, the mean average precision (MAP) achieves the highest value of 0.97.
摘要：
In this paper, a method for detecting rapid rice disease based on FCM-KM and Faster R-CNN fusion is proposed to address various problems with the rice disease images, such as noise, blurred image edge, large background interference and low detection accuracy. Firstly, the method uses a two-dimensional filtering mask combined with a weighted multilevel median filter (2DFM-AMMF) for noise reduction, and uses a faster two-dimensional Otsu threshold segmentation algorithm (Faster 2D-Otsu) to reduce the interference of complex background with the detection of target blade in the image. Then the dynamic population firefly algorithm based on the chaos theory as well as the maximum and minimum distance algorithm is applied for optimization of the K-Means clustering algorithm (FCM-KM) to determine the optimal clustering class k value while addressing the tendency of the algorithm to fall into the local optimum problem. Combined with the R-CNN algorithm for the identification of rice diseases, FCM-KM analysis is conducted to determine the different sizes of the Faster R-CNN target frame. As revealed by the application results of 3010 images, the accuracy and time required for detection of rice blast, bacterial blight and blight were 96.71%/0.65s, 97.53%/0.82s and 98.26%/0.53s, respectively, indicating clearly that the method is more capable of detecting rice diseases and improving the identification accuracy of Faster R-CNN algorithm, while reducing the time required for identification.
通讯机构：
[Xu, Piao-Rong] C;Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Changsha 410004, Hunan, Peoples R China.
关键词：
The European Physical Journal Applied Physics;journal;EPJ;EDP Sciences
摘要：
A numerical model of carrier saturation velocity and drain current for the monolayer graphene field effect transistors (GFETs) is proposed by considering the exponential distribution of potential fluctuations in disordered graphene system. The carrier saturation velocity of GFET is investigated by the two-region model, and it is found to be affected not only by the carrier density, but also by the graphene disorder. The numerical solutions of the carrier density and carrier saturation velocity in the disordered GFETs yield clear and physical-based results. The simulated results of the drain current model show good consistency with the reported experimental data.
摘要：
Object tracking is a fundamental skill of mobile robotics. For a multi-robot system the object tracking accuracy of each robot is usually different because of the dynamic environment and the different sensing ability. The focus of cooperative object tracking is how to fuse different robots' observation to obtain more robust and accurate estimation of the object. This paper investigates this problem using an adaptive consensus based distributed particle filter algorithm. In this algorithm, each robot runs a particle filter to achieve local object tracking. Then a consensus algorithm which can establish an agreement between all robots is utilized to generalize a global posterior about the object state. To fully consider the difference between each robot's object tracking accuracy, Renyi entropy is used to adaptively adjust the weight of the consensus algorithm. The concept of Renyi entropy is utilized to measure the distance between a robot's local posterior and the global posterior and then a weight will be assigned to the robot according to the Renyi entropy. This weight will be used continuously in the next consensus step. By the proposed algorithm in this paper the accuracy and the robust of the cooperative can be improved. Finally the effectiveness of the proposed algorithm is verified by simulation results.
作者机构：
[吴淇; 周国雄; 陈爱斌] School of Computer and Information Engineering, Central South University of Forestry &, Technology, Changsha, 410004, China;[吴淇] School of Information Science and Engineering, Hunan University, Changsha, 410082, China
通讯机构：
School of Computer and Information Engineering, Central South University of Forestry & Technology, Changsha, China
摘要：
针对延迟容忍网络中存在的数据传输时延较高、摆渡节点间协作性不高,以及如何最优分配摆渡节点等问题,提出一种基于位置信息的摆渡节点延迟容忍网络路由算法（ferries routing mechanism based on location information for delay tolerant network,FRLI）。基于节点位置信息,定义基于位置信息的数据传输机制,通过划分摆渡节点隶属的区域,根据摆渡节点在网络中的初始分布状况,合理分配网络中摆渡节点分布,通过交换彼此区域内缓存的网络节点信息,获取有利于当前区域内数据传输的有效信息,提高区域内数据传输效率;基于节点区域信息,确认目的节点是否属于当前区域后,直接将数据投递至网关节点,渐次转发至目的节点所在区域,有效提高数据传输效率。仿真结果表明,与当前MURA算法、SIRA算法相比,该算法具有更低的数据传输时延与更高的传输效率。
