With the rise of the robot and cloud computing technology, human-centric healthcare service attracts widely attention in order to meet the great challenges of traditional healthcare, especially the limited medical resources. This paper presents a healthcare system based on cloud computing and robotics, which consists of wireless body area networks, robot, software system and cloud platform. This system is expected to accurately measure user's physiological information for analysis and feedback, which is assisted by the robot integrated with various sensors. In order to improve the practicability of multi-media transmission in the healthcare system, this paper proposes a novel scheme to deliver real-time video contents through an improved UDP-based protocol. Finally, the proposed approach is evaluated with experimental study based on a real testbed.

Robot and cloud-assisted multi-modal healthcare system

The invention relates to a hybrid storage system based on multidimensional data similarity and a data management method thereof. The hybrid storage system is composed of an upper layer memory unit (3), a middle layer cache unit (2), a lower layer storage unit (1) and a control unit (4). The upper layer memory unit (3) is used for storing system data and data index tables which are frequently accessed in all storage layers. The middle layer cache unit (2) is used for storing part of data which are frequently accessed, and the lower layer storage unit (1) is used for storing all data sets. The control unit (4) is used for being connected with the upper layer memory unit (3), the middle layer cache unit (2) and the lower layer storage unit (1) through I/O ports to constitute the hybrid storage system of a multi-layer storage structure. The invention further discloses the data management method of the hybrid storage system. According to the hybrid storage system based on multidimensional data similarity and the data management method thereof, the accessed speed can be improved, corresponding data can be quickly searched and sought in the accessing process, data access delay is reduced, the data processing efficiency can further be improved, and a request throughput capacity is improved.

Hybrid storage system based on multidimensional data similarity and data management method

The invention discloses a smart power grid data decomposition method based on non-negative matrix factorization. On the basis of a non-negative matrix decomposition algorithm, the similarity featuresamong users are introduced, the performance of the algorithm is improved, so that the decomposition result is closer to actual electricity information of the users, and more precise feedback is provided for the users. As is proved by the experiment result, compared with an existing method, by means of the smart power grid data decomposition method, the performance is obviously improved.

Smart power grid data decomposition method based on non-negative matrix factorization

The invention provides a picture writing method and device, and belongs to the technical field of storage machines. The method is applied to a storage node in a cloud storage system, and the storage node is configured with a memory, a disk and an SSD. The method comprises the following steps: a target storage node receives a writing request of a target picture, and determines whether a picture resource pool to which the target storage node belongs supports accelerated writing or not; when the picture resource pool to which the target storage node belongs supports accelerated writing, if the data volume of the target picture is smaller than the target value, a configured cache type is obtained from the configuration information of the storage node; and if the cache type is memory acceleration, the target picture is written into the memory of the target storage node by the target storage node, and if the cache type is SSD acceleration, the target picture is written into the SSD of the target storage node by the target storage node. By adopting the method, the writing efficiency can be improved.

Picture writing method and device

The present invention provides a method for storing partial or all special function data by using the last level of hybrid cache The method comprises the steps of receiving a request made by a user for the special function data; searching, in the last level of hybrid cache or a storage network or a memory, whether the special function data required by the user exists, and if the special function data required by the user exists, reading the special function data by a processor; and otherwise, performing a flow of generating the special function data by a server so as to generate the special function data required by the user, and then reading the special function data by the processor The method for storing the special function data by using the last level of hybrid cache is beneficial for reducing memory capacity, so that refresh power consumption of the memory can be reduced; a process of transferring the special function data to the memory from the storage network and then transferring the special function data to the cache from the memory is reduced, so that large amounts of time and power consumption caused by data transfer are saved and further the cost of a data center is reduced

Method for storing special function data by using last level of hybrid cache

To boost the performance of massive data processing, solid-state drives (SSDs) have been used as a kind of cache in the Hadoop system. However, most of existing SSD cache management algorithms are ignorant of the characteristics of upper-level applications. In this paper, we propose a novel SSD cache management algorithm called DSA, which can exploit the application-level data similarity to improve the SSD cache performance in Hadoop. Our algorithm takes both temporal similarity and user similarity in querying behaviors into account. We evaluate the effectiveness of our proposed DSA algorithm in a small-scale Hadoop cluster. Our experimental results show that our algorithm can achieve much better performance than other well-known algorithms (e.g., LRU, FIFO). We also clearly point out the underlying tradeoff between cache performance and SSD deployment cost, and identify a number of key factors that affect SSD cache performance. Our findings can provide useful guidelines on how to effectively integrate SSDs into Hadoop.

Exploiting application-level similarity to improve SSD cache performance in Hadoop

The utility model relates to a hybrid storage system based on multidimensional data similarity. The system comprises an upper-layer memory unit (3), a middle-layer caching unit (2), a lower-layer storage unit (1) and a control unit (4). The upper-layer memory unit (3) is used for storing system data and data index tables always being accessed in each storage layer. The middle-layer caching unit (2) is used for storing a part of data which are often accessed. The lower-layer storage unit (1) is used for storing all datasets. The control unit (4) is connected with the upper-layer memory unit (3), the middle-layer caching unit (2) and the lower-layer storage unit (1) through I/O ports to form the hybrid storage system of a multi-layer storage structure. Compared with single HDD storage systems, the hybrid storage system has better data processing performance; meanwhile, by means of the multi-layer storage structure, improvement of performance of the storage system can be achieved at low economic cost; the utilization rate of a cache in the hybrid storage system is improved, and data reading and writing performance is improved through the cache.

Zheng Yuanhuan

Papers

Exploiting application-level similarity to improve SSD cache performance in Hadoop

Hybrid storage system based on multidimensional data similarity and data management method

Hybrid storage system based on multidimensional data similarity