No abstract

No abstract

Human gait phase recognition is a significant technology for rehabilitation training robot, human disease diagnosis, artificial prosthesis, and so on. The efficient design of the recognition method for gait information is the key issue in the current gait phase division and eigenvalues extraction research. In this paper, a novel voting-weighted integrated neural network (VWI-DNN) is proposed to detect different gait phases from multidimensional acceleration signals. More specifically, it first employs a gait information acquisition system to collect different IMU sensors data fixed on the human lower limb. Then, with dimensionality reduction and four-phase division preprocessing, key features are selected and merged as unified vectors to learn common and domain knowledge in time domain. Next, multiple refined DNNs are transferred to design a multistream integrated neural network, which utilizes the mixture-granularity information to exploit high-dimensional feature representative. Finally, a voting-weighted function is developed to fuse different submodels as a unified representation for distinguishing small discrepancy among different gait phases. The end-to-end implementation of the VWI-DNN model is fine-tuned by the loss optimization of gradient back-propagation. Experimental results demonstrate the outperforming performance of the proposed method with higher classification accuracy compared with the other methods, of which classification accuracy and macro-F1 is up to 99.5%. More discussions are provided to indicate the potential applications in combination with other works.

Distributed computing systems like MapReduce in data centers transfer massive amount of data across successive processing stages. Such shuffle transfers contribute most of the network traffic and make the network bandwidth become a bottleneck. In many commonly used workloads, data flows in such a transfer are highly correlated and aggregated at the receiver side. To lower down the network traffic and efficiently use the available network bandwidth, we propose to push the aggregation computation into the network and parallelize the shuffle and reduce phases. In this paper, we first examine the gain and feasibility of the in-network aggregation with BCube, a novel server-centric networking structure for future data centers. To exploit such a gain, we model the in-network aggregation problem that is NP-hard in BCube. We propose two approximate methods for building the efficient IRS-based incast aggregation tree and SRS-based shuffle aggregation subgraph, solely based on the labels of their members and the data center topology. We further design scalable forwarding schemes based on Bloom filters to implement in-network aggregation over massive concurrent shuffle transfers. Based on a prototype and large-scale simulations, we demonstrate that our approaches can significantly decrease the amount of network traffic and save the data center resources. Our approaches for BCube can be adapted to other servercentric network structures for future data centers after minimal modifications.

a b s t r a c tA fundamental goal of datacenter networking is to efficiently interconnect a large number of servers in a cost-effective way. Inspired by the commodity servers in today's data centers that come with dual-port, we consider how to design low-cost, robust, and symmetrical network structures for containerized data centers with dual-port servers and low-end switches. In this paper, we propose a family of such network structure called a DCube, including H-DCube and M-DCube. The DCube consists of one or multiple interconnected sub-networks, each of which is a compound graph made by interconnecting a certain number of basic building blocks by means of a hypercube-like graph. More precisely, the H-DCube and M-DCube utilize the hypercube and 1-möbius cube, respectively, while the M-DCube achieves a considerably higher aggregate bottleneck throughput compared to H-DCube. Mathematical analysis and simulation results show that the DCube exhibits graceful performance degradation as the failure rate of server or switch increases. Moreover, the DCube significantly reduces the required wires and switches compared to the BCube and fat-tree. In addition, the DCube achieves a higher speedup than the BCube does for the one-to-several traffic patterns. The proposed methodologies in this paper can be applied to the compound graph of the basic building block and other hypercube-like graphs, such as Twisted cube, Flip MCube, and fastcube.

No abstract

The rapid development of mobile computing has prompted indoor navigation to be one of the most attractive and promising applications. Conventional designs of indoor navigation systems depend on either infrastructures or indoor floor maps. This article presents CloudNavi, a ubiquitous indoor navigation solution, which relies on the point clouds acquired by the 3D camera embedded in a mobile device. Particularly, CloudNavi first efficiently infers the walking trace of each user from captured point clouds and inertial data. Many shared walking traces and associated point clouds are combined to generate the point cloud traces, which are then used to generate a 3D path-map. Accordingly, CloudNavi can accurately estimate the location of a user by fusing point clouds and inertial data using a particle filter algorithm and then guiding the user to its destination from its current location. Extensive experiments are conducted on office building and shopping mall datasets. Experimental results indicate that CloudNavi exhibits outstanding navigation performance in both office buildings and shopping malls and obtains around 34% improvement compared with the state-of-the-art method.

This study discusses how to maintain discovered sequential patterns when some information is deleted from a sequence database. A new algorithm, called MA_D (Maintenance Algorithm when Deleting some information), is presented in order to deal with the maintenance of sequential patterns mining resulted from the updating of database and the algorithm makes full use of the information obtained from previous mining results to cut down the cost of finding new sequential patterns in an updated database. Our experimental analysis shows that the new algorithm is more efficient.