85,285 research outputs found
Optimized mobile thin clients through a MPEG-4 BiFS semantic remote display framework
According to the thin client computing principle, the user interface is physically separated from the application logic. In practice only a viewer component is executed on the client device, rendering the display updates received from the distant application server and capturing the user interaction. Existing remote display frameworks are not optimized to encode the complex scenes of modern applications, which are composed of objects with very diverse graphical characteristics. In order to tackle this challenge, we propose to transfer to the client, in addition to the binary encoded objects, semantic information about the characteristics of each object. Through this semantic knowledge, the client is enabled to react autonomously on user input and does not have to wait for the display update from the server. Resulting in a reduction of the interaction latency and a mitigation of the bursty remote display traffic pattern, the presented framework is of particular interest in a wireless context, where the bandwidth is limited and expensive. In this paper, we describe a generic architecture of a semantic remote display framework. Furthermore, we have developed a prototype using the MPEG-4 Binary Format for Scenes to convey the semantic information to the client. We experimentally compare the bandwidth consumption of MPEG-4 BiFS with existing, non-semantic, remote display frameworks. In a text editing scenario, we realize an average reduction of 23% of the data peaks that are observed in remote display protocol traffic
Scalable RDF Data Compression using X10
The Semantic Web comprises enormous volumes of semi-structured data elements.
For interoperability, these elements are represented by long strings. Such
representations are not efficient for the purposes of Semantic Web applications
that perform computations over large volumes of information. A typical method
for alleviating the impact of this problem is through the use of compression
methods that produce more compact representations of the data. The use of
dictionary encoding for this purpose is particularly prevalent in Semantic Web
database systems. However, centralized implementations present performance
bottlenecks, giving rise to the need for scalable, efficient distributed
encoding schemes. In this paper, we describe an encoding implementation based
on the asynchronous partitioned global address space (APGAS) parallel
programming model. We evaluate performance on a cluster of up to 384 cores and
datasets of up to 11 billion triples (1.9 TB). Compared to the state-of-art
MapReduce algorithm, we demonstrate a speedup of 2.6-7.4x and excellent
scalability. These results illustrate the strong potential of the APGAS model
for efficient implementation of dictionary encoding and contributes to the
engineering of larger scale Semantic Web applications
Semantic 3D Occupancy Mapping through Efficient High Order CRFs
Semantic 3D mapping can be used for many applications such as robot
navigation and virtual interaction. In recent years, there has been great
progress in semantic segmentation and geometric 3D mapping. However, it is
still challenging to combine these two tasks for accurate and large-scale
semantic mapping from images. In the paper, we propose an incremental and
(near) real-time semantic mapping system. A 3D scrolling occupancy grid map is
built to represent the world, which is memory and computationally efficient and
bounded for large scale environments. We utilize the CNN segmentation as prior
prediction and further optimize 3D grid labels through a novel CRF model.
Superpixels are utilized to enforce smoothness and form robust P N high order
potential. An efficient mean field inference is developed for the graph
optimization. We evaluate our system on the KITTI dataset and improve the
segmentation accuracy by 10% over existing systems.Comment: IROS 201
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