Search Results for "spatial computing"

Apache Sedona™ is a cluster computing system for processing large-scale spatial data. Sedona extends existing cluster computing systems, such as Apache Spark and Apache Flink, with a set of out-of-the-box distributed Spatial Datasets and Spatial SQL that efficiently load, process, and analyze large-scale spatial data across machines. According to our benchmark and third-party research papers, Sedona runs 2X - 10X faster than other Spark-based geospatial data systems on computation-intensive query workloads. ...

This course aims to cover state-of-the-art methods in modern parallel Graphical Processing Unit (GPU) computing, supercomputing and code development with applications to natural sciences and engineering.

The Numenta Platform for Intelligent Computing (NuPIC) is a machine intelligence platform that implements the HTM learning algorithms. HTM is a detailed computational theory of the neocortex. At the core of HTM are time-based continuous learning algorithms that store and recall spatial and temporal patterns. NuPIC is suited to a variety of problems, particularly anomaly detection and prediction of streaming data sources.

Talend Spatial Module (aka Spatial Data Integrator or SDI) is an ETL tool for geospatial. Based on Talend Open Studio, input, output and transform geocomponents are available. IO components read/write GIS formats(eg.PostGIS, GeoRSS). Transformers all

VRTK is a collection of useful, reusable solutions to common problems found when building for virtual reality. VRTK aims to help productivity by speeding up the creation process from prototyping ideas to building complete solutions. The passion behind VRTK is to try and enable as many people as possible to build for VR, from seasoned developers to complete beginners who have a wonderful idea in their head but are unsure of how to create it. VRTK aims to be easy to understand so the absolute...

ForestMTIS is a OnCloud computing compiled and editable project and open source code to generated forest simulators. It was developed to forest growth and yield models that are empirical, non-spatial, deterministic, disaggregated, single species even-aged stand models. This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 613762

...It’s especially useful in web UIs, visualizations, interactive maps, and generative-art contexts where you need dynamic tessellations or diagrammatic layouts. The library exposes API functions for computing cells, retrieving neighbors, and drawing results into canvas or SVG. Because it is pure JavaScript and self-contained, it integrates easily with browser or Node.js applications without heavy dependencies. For developers exploring spatial algorithms, generative UI, or interactive diagrams, this codebase is a practical reference and tool to build upon.

Fast Matrix for Java (fm4j) is a general-purpose matrix utility library for computing with dense matrices. fm4j encapsulated different underlying implementations and select the optimal one in run-time depending on the size of the input matrix. Moreover, fm4j employs Java (Tm) Concurrency to take advantage of the computation power of multi-cor processors.

VAST is a P2P network library that supports virtual environment applications such as Massively Multiplayer Online Games (MMOG) or large-scale simulations. It is scalable to millions of nodes, bandwidth-efficient, and simple to use.

The Spatial Modeling Environment (SME) is an integrated environment for high performance spatial modeling which transparently links icon-based modeling tools with advanced computing resources to support dynamic spatial modeling of complex systems

pySPACE is a modular software for processing of large data streams that has been specifically designed to enable distributed execution and empirical evaluation of signal processing chains. Various signal processing algorithms (so called nodes) are available within the software, from finite impulse response filters over data-dependent spatial filters (e.g. CSP, xDAWN) to established classifiers (e.g. SVM, LDA). pySPACE incorporates the concept of node and node chains of the MDP framework. Due to its modular architecture, the software can easily be extended with new processing nodes and more general operations. Large scale empirical investigations can be configured using simple text- configuration files in the YAML format, executed on different (distributed) computing modalities, and evaluated using an interactive graphical user interface. ...