Search Results for "sensor"

...Users can configure diverse sensor suites including LIDARs, multiple cameras, depth sensors and GPS among others. Users can easily create their own maps following the OpenDrive standard via tools like RoadRunner.

...It supports a wide array of robotics formats (URDF, MJCF, CAD), includes GPU-accelerated physics, and features immersive RTX rendering and multisensory simulation. Realistic physics via GPU-accelerated engines and RTX ray tracing. Multi-sensor simulation (RGB-D cameras, Lidar, Radar, IMU, contact sensors). Extensible via platform APIs and can integrate into custom USD-based simulators.

This project aims at visualizing what a picture of a star will look like, depending on telescope, seeing conditions, settings of your astro camera etc. It simulates taking exposures of stars over a range of magnitudes and stacks them.

...As done by basic SystemC for signals on the bus, SCNSL provides primitives to model packet trasmission, reception, contention on the channel and wireless path loss. The use of SCNSL allows the easy and complete modelling of distributed applications of networked embedded systems such as wireless sensor networks, routers, and distributed plant controllers. SCNSL has migrated: please go to GitLab for the latest version: https://gitlab.com/open-kappa/scnsl

Liowsn (Linux for works with WSN) was created to assist teachers, students and people involved with projects of Wireless Sensor Network (WSN). The operating system is Ubuntu 9.10 remastered and it contains several tools for simulation and testing installed to work with WSN. Discussion Group: http://groups.google.com/group/liowsn-users User manual: https://dl.dropbox.com/u/4912938/LiowsnManual_EN.pdf (English Version) https://dl.dropbox.com/u/4912938/LiowsnManual_PT.pdf (Portuguese Version)

TRMSim-WSN (Trust and Reputation Models Simulator for Wireless Sensor Networks) is a Java-based simulator aimed to test Trust and Reputation models for WSNs. It provides several Trust and Reputation models and new ones can be easily added.

...The 1-Sphere forward solution for the MEG and the 4-Sphere forward solution for the EEG is implemented in CUDA C and an accelerated solution is obtained using the NVIDIA GPU when the solution is calculated for a large number of dipoles (on the order of 15000 and above) and sensor locations. Speedup by a factor of 22 and 32 is obtained for the EEG and MEG solution respectively when compared to the fastest CPU implementation available in the public domain. The complete source code and pre-compiled binaries are also made available via an open source license (GPL Version 3). A CUDA enabled NVIDIA graphics card is required to use the software.

MutanT is an cross platform framework for performing multi-sensor data processing. It is not limited to any specific task but can be extended arbitrarily by users and developers. Data processing is performed by a chain of filters, a use can combine.

Small simulation that can drive a CIGI compliant IG to do EOIR sensor simulation. This is based on the OpenEaagles framework (HOST) and the MultiPurposeViewer (IG).

Sensor training simulator.

Simulation framework for power aware Wireless Sensor and Actor Network nodes to model all layers of the communication system, the application, the power supply and energy management, the digital processing unit and the sensor-actuator interface.

N-sim is a framework for studying and developing wireless sensor networks. It emulates the computation of sensor platforms, simulates the wireless communications, and allows plug-in models for source detection simulation. Intended for cluster computing.

Container for robot/environment simulation. The container simulates an environment, and gives to the robots living in it access to sensor views. The goal of this project is to provide the open source community a tool for robot behavior test and challeng