Browsing by Subject "Motion compensation"
Now showing items 1-12 of 12
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Efficient processing of all-k-nearest-neighbor queries in the MapReduce programming framework
(2019)Numerous modern applications, from social networking to astronomy, need efficient answering of queries on spatial data. One such query is the All k Nearest-Neighbor Query, or k Nearest-Neighbor Join, that takes as input ... -
Enhancing Sedona (formerly GeoSpark) with Efficient k Nearest Neighbor Join Processing
(2021)Sedona (formerly GeoSpark) is an in-memory cluster computing system for processing large-scale spatial data, which extends the core of Apache Spark to support spatial datatypes, partitioning techniques, indexes, and ... -
GPU-Based Algorithms for Processing the k Nearest-Neighbor Query on Disk-Resident Data
(2021)Algorithms for answering the k Nearest-Neighbor (k-NN) query are widely used for queries in spatial databases and for distance classification of a group of query points against a reference dataset to derive the dominating ... -
HEVC decoder optimization in low power configurable architecture for wireless devices
(2015)High Efficiency Video Coding (HEVC) is the new video compression standard, reducing bitrates nearly at half compared to H.264, offering potentially significant power savings for wireless video transmission at the network ... -
Implementation and performance comparison of the motion compensation kernel of the AVS video decoder on FPGA, GPU and multicore processors
(2011)Next generation video standards have strict and increasing performance demands due to real-time requirements and the trend towards higher frame resolutions and bit rates. Leveraging the advantages of reconfigurable logic ... -
An Improved GPU-based Algorithmfor Processing the k Nearest Neighbor Query
(2020)The k Nearest Neighbor (k-NN) query is a common spatial query that appears in several big data applications. We propose and implement a new GPU-based algorithm for the k-NN query, which improves our previous Symmetric ... -
Improving Distance-Join Query processing with Voronoi-Diagram based partitioning in SpatialHadoop
(2020)SpatialHadoop is an extended MapReduce framework supporting global indexing techniques that partition spatial datasets across several machines and improve spatial query processing performance compared to traditional Hadoop ... -
In-memory k nearest neighbor GPU-based query processing
(2020)The k Nearest Neighbor (k-NN) algorithm is widely used for classification in several application domains (medicine, economy, entertainment, etc.). Let a group of query points, for each of which we need to compute the k-NNs ... -
MRSLICE: Efficient RkNN Query Processing in SpatialHadoop
(2019)Nowadays, with the continuously increasing volume of spatial data, it is difficult to execute spatial queries efficiently in spatial data-intensive applications, because of the limited computational capability and storage ... -
A Partitioning GPU-based Algorithm for Processing the k Nearest-Neighbor Query
(2020)The k Nearest-Neighbor (k-NN) query is a common spatial query that appears in several big data applications. Typically, GPU devices have much larger numbers of processing cores than CPUs and faster device memory than main ... -
RkNN query processing in distributed spatial infrastructures: A performance study
(2017)The Reverse k-Nearest Neighbor (RkNN) problem, i.e. finding all objects in a dataset that have a given query point among their corresponding k-nearest neighbors, has received increasing attention in the past years. RkNN ... -
Voronoi-diagram based partitioning for distance join query processing in spatialhadoop
(2018)SpatialHadoop is an extended MapReduce framework supporting global indexing techniques that partition spatial data across several machines and improve query processing performance compared to traditional Hadoop systems. ...