Enhanced wide-activated residual network for efficient and accurate image deblocking

To save bandwidth and storage space as well as speed up data transmission, people usually perform lossy compression on images. Although the JPEG standard is a simple and effective compression method, it usually introduces various visually unpleasing artifacts, especially the notorious blocking artifacts. In recent years, deep convolutional neural networks (CNNs) have seen remarkable development in compression artifacts reduction. Despite the excellent performance, most deep CNNs suffer from heavy computation due to very deep and wide architectures. In this paper, we propose an enhanced wide-activated residual network (EWARN) for efficient and accurate image deblocking. Specifically, we propose an enhanced wide-activated residual block (EWARB) as basic construction module. Our EWARB gives rise to larger activation width, better use of interdependencies among channels, and more informative and discriminative non-linearity activation features without more parameters than residual block (RB) and wide-activated residual block (WARB). Furthermore, we introduce an overlapping patches extraction and combination (OPEC) strategy into our network in a full convolution way, leading to large receptive field, enforced compatibility among adjacent blocks, and efficient deblocking. Extensive experiments demonstrate that our EWARN outperforms several state-of-the-art methods quantitatively and qualitatively with relatively small model size and less running time, achieving a good trade-off between performance and complexity.     

An overlapping domain decomposition dual reciprocity method

We present a domain decomposition boundary integral equation method for two-dimensional partial differential equations. The overlapping Schwarz method is employed to improve the dual reciprocity boundary element method. The resulting algorithm turns out to be more accurate than a non-overlapping approach previously proposed. Some numerical results showing the improved accuracy and efficiency of the methods are given.     

A rule-based approach for robust clump splitting

This paper presents a robust rule-based approach for the splitting of binary clumps that are formed by objects of diverse shapes and sizes. First, the deepest boundary pixels, i.e., the concavity pixels in a clump, are detected using a fast and accurate scheme. Next, concavity-based rules are applied to generate the candidate split lines that join pairs of concavity pixels. A figure of merit is used to determine the best split line from the set of candidate lines. Experimental results show that the proposed approach is robust and accurate.     

MC-MLAS: Multi-channel Minimum Latency Aggregation Scheduling in Wireless Sensor Networks

Data aggregation is a key, yet time-consuming functionality in wireless sensor networks (WSNs). Multi-channel design is a promising technique to alleviate interference as a primary reason for long latency of TDMA aggregation scheduling. Indeed, it provides more potential of parallel transmissions over different frequency channels, thus minimizing time latency. In this paper, we focus on designing a multi-channel minimum latency aggregation scheduling protocol, named MC-MLAS, using a new joint approach for tree construction, channel assignment, and transmission scheduling. To our best knowledge, this is the first work in the literature which combines orthogonal channels and partially overlapping channels to consider the total latency involved in data aggregation. Extensive simulations verify the superiority of MC-MLAS in WSNs.     

粘连细胞分割算法的研究与实现

以粘连细胞分割为例,提出了一种基于分水岭算法的图像分割算法.算法对分水岭分割之前的距离变换和求种子点进行了改进,得到了较好的分割效果.实验证明,该算法较好地完成了粘连细胞的分割任务.     

一种基于局部拓展的并行重叠社区发现算法

处理海量级数据的有效途径之一是将算法分解为一系列互不依赖的任务,然后利用开源工具并行地执行算法。而在重叠社区发现算法中,基于局部拓展的方法在拓展阶段往往仅需要局部社区及其相应的邻居结点的信息,因而具备可并行执行的可能性。提出了一种可并行化执行的局部拓展算法,并借助开源工具Spark将其实现。算法分为4个阶段。首先,挑选出一组不相关的中心结点并使用其对应的局部网络作为种子;其次,通过删除本身连接不是很紧密的局部网络来过滤选出的种子;然后,采用一种批量式的拓展策略来拓展种子,即一次向局部社区中添加一批邻居结点或从社区中删除一批结点;最后,融合相似的社区。在人工生成的网络以及真实世界中的网络上的实验结果显示 ,所提算法既准确又高效。     

边界重叠图象的一种快速拼接算法

针对边界部分有重叠的图象,提出了一种基于网络的快速对准算法,并通过平滑因子对图象实现了无缝拼接。     

重叠积项的分离算法

本文提出了一个分离重叠积项的算法。该算法在逻辑测试，函数最小化等逻辑综合的领域有重要作用。     

Segmentation of neuronal nuclei based on clump splitting and a two-step binarization of images

In this paper we present an algorithm to segment the nuclei of neuronal cells in confocal microscopy images, a key technical problem in many experimental studies in the field of neuroscience. We describe the whole procedure, from the original images to the segmented individual nuclei, paying particular attention to the binarization of the images, which is not straightforward due to the technical difficulties related to the visualization of nuclei as individual objects and incomplete and irregular staining. We have focused on the division of clusters of nuclei that appear frequently in these images. Thus we have developed a clump-splitting algorithm to separate touching or overlapping nuclei allowing us to accurate account for both the number and size of the nuclei. The results presented in the paper show that the proposed algorithm performs well on different sets of images from different layers of the cerebral cortex.