In order to balance relatively the energy consumption of network node in wireless sensor networks (WSNs), this paper presents Sink mobility based and energy balancing unequal clustering protocol (SEBUCP). By adopting the improved SFLA (shuffled frog leaping algorithm), SEBUCP chooses the nodes having more residual energy and powerful communication capabilities as cluster heads and divides all nodes into clusters of different size. And competition mechanism between cluster heads is introduced, which is helpful to form a more rational cluster topology. In order to reduce the cluster head replacement frequency, cluster head serves continuously and by comparing nodes weight to determine the cluster head exchange time. The greedy algorithm is introduced to select an optimum relay node between cluster head and Sink. To further reduce the energy consumption of nodes, mobile Sink routing algorithm is also introduced to avoid the “hot-spots” problem. Simulation results demonstrate that SEBUCP has good performance on the network lifetime, energy balance and so on
Object detection is a core problem in computer vision. With the development of deep ConvNets, the performance of object detectors has been dramatically improved. The deep ConvNets based object detectors mainly focus on regressing the coordinates of bounding box, e.g., Faster-R-CNN, YOLO and SSD. Different from these methods that considering bounding box as a whole, we propose a novel object bounding box representation using points and links and implemented using deep ConvNets, termed as Point Linking Network (PLN). Specifically, we regress the corner/center points of bounding-box and their links using a fully convolutional network; then we map the corner points and their links back to multiple bounding boxes; finally an object detection result is obtained by fusing the multiple bounding boxes. PLN is naturally robust to object occlusion and flexible to object scale variation and aspect ratio variation. In the experiments, PLN with the Inception-v2 model achieves state-of-the-art single-model and singlescale results on the PASCAL VOC 2007, the PASCAL VOC 2012 and the COCO detection benchmarks without bells and whistles. The source code will be released.
We present a retrieval based system for landmark retrieval and recognition challenge.There are five parts in retrieval competition system, including feature extraction and matching to get candidates queue; database augmentation and query extension searching; reranking from recognition results and local feature matching. In recognition challenge including: landmark and non-landmark recognition, multiple recognition results voting and reranking using combination of recognition and retrieval results. All of models trained and predicted by PaddlePaddle framework 1 . Using our method, we achieved 2nd place in the Google Landmark Recognition 2019 and 2nd place in the Google Landmark Retrieval 2019 on kaggle. The source code is available at here 2 . * The authors contributed equally and they are ordered family alphabetically.
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