Displaying 1 to 13 from 13 results

Slic3r - Open Source toolpath generator for 3D printers

  •    C++

Slic3r is mainly a toolpath generator for 3D printers: it reads 3D models (STL, OBJ, AMF, 3MF) and it converts them into G-code instructions for 3D printers. But it does much more than that, see the features list below. Slic3r was born in 2011 within the RepRap community and thanks to its high configurability became the swiss-army knife for 3D printing. It served as a platform for experimenting several new ideas that later became technology standards, such as multiple extruders, brim, variable-height layers, per-object settings, modifiers, post-processing scripts, G-code macros and more. Despite being based on volunteer efforts, Slic3r is still pushing the boundaries of 3D printing.

PYLEECAN - Electrical engineering open-source software providing a user-friendly, unified, flexible simulation framework for the multiphysic design and optimization of electrical machines and drives

  •    Python

PYLEECAN project provides a user-friendly, unified, flexible simulation framework for the multiphysic design and optimization of electrical machines and drives. The main objective of PYLEECAN is to boost reproducible research and open-science in electrical engineering. Thus, it is intended for researchers, R&D engineers and teachers in electrical engineering, both on standard and novel topologies of electrical machines.

node-synology-surveillance - Node module to interact with the Surveillance Station provided by Synology

  •    Javascript

Node module to interact with the Surveillance Station by Synology. The params are optional (doumented in the original WebApi document with samples) and are in the form of an object.

SparkFun_Eagle_Settings - The Eagle settings SparkFun uses for board designs


These are the commonly used keyboard shortcuts, CAM and DRU configuration files for SparkFun board design. We use these files at SparkFun for all our design work. Be sure to checkout SparkFun's footprints for Eagle.

camelot - A node wrapper for webcam controller providing configurable async frame grabbing

  •    Javascript

Once grabbing starts (grab()), frames are periodically grabbed off the web cam (/dev/videoX) depending on the provided frequency option. Within Camelot, once a frame is grabbed, an event is emitted ( on (‘frame’, …) ) containing the binary frame. Also, instead of relying on event emission, a callback can also be passed to the grab() function, which will apply the callback upon frame grabbing. As per convensional wisdom, an error event will also be emitted upon Error ( on (‘error’, …) ). This module assumes you have a working binary of fswebcam along with font management library and associated font path definition (e.g. GDFONTPATH). This module has been solely tested on Ubuntu with a Rocketfish 8MP USB HD RF-HDWEB webcam but should work with any webcam supported by the v4l library.

remotecamera - Capture an image and post it to an url

  •    Javascript

Takes a picture and posts it to a remote server. So far only for Debian/Ubuntu (tested) and Mac (untested). The function it takes a picture and attempts to upload it. Run it with node app.js. You should see on the terminal the returned output from httpbin.org.

retrieval-2017-cam - Class-Weighted Convolutional Features for Image Retrieval

  •    Python

Image retrieval in realistic scenarios targets large dynamic datasets of unlabeled images. In these cases, training or fine-tuning a model every time new images are added to the database is neither efficient nor scalable. Convolutional neural networks trained for image classification over large datasets have been proven effective feature extractors for image retrieval. The most successful approaches are based on encoding the activations of convolutional layers, as they convey the image spatial information. In this paper, we go beyond this spatial information and propose a local-aware encoding of convolutional features based on semantic information predicted in the target image. To this end, we obtain the most discriminative regions of an image using Class Activation Maps (CAMs). CAMs are based on the knowledge contained in the network and therefore, our approach, has the additional advantage of not requiring external information. In addition, we use CAMs to generate object proposals during an unsupervised re-ranking stage after a first fast search. Our experiments on two public available datasets for instance retrieval, Oxford5k and Paris6k, demonstrate the competitiveness of our approach outperforming the current state-of-the-art when using off-the-shelf models trained on ImageNet. A preprint of this paper is available on arXiv and in the BMVC 2017 proceedings.

gerber-types-rs - Gerber X2 (compatible with Gerber RS-274X) code-generating library for Rust

  •    Rust

This crate implements the basic building blocks of Gerber X2 (compatible with Gerber RS-274X) code. It focusses on the low level types (to be used like an AST) and code generation and does not do any semantic checking. For example, you can use an aperture without defining it. This will generate syntactically valid but semantially invalid Gerber code, but this module won't complain.

CAM - macOS camera recording using ffmpeg

  •    Javascript

The CAM app was made for recording short tasks with research participants for later scoring or analysis. The CAM app's design is as minimalist as possible. To use, just launch the app and you will immediately see a preview of the camera image. The app will not record until you click the red record button. Before recording though, it's a good idea to use the text input fields to give your recording a name. As you type in the fields you will see an automatically generated preview of what your video will be named.

camdenm - CAM and DENM messages, part of ETSI ITS G5 stack

  •    Java

Basic Java classes to represent Cooperative Awareness Message (CAM, ETSI EN 302 637-2) and Decentralized Environment Notification Message (DENM, ETSI EN 302 637-3). CAM and DENM are built on top of Common Data Dictionary (CDD, ETSI TS 102 894-2). Classes are annoted with asn1-datatypes, so that asn1-uper can pack the objects. CAM and DENM are complete (except for the getter methods). Usage examples for CAM and DENM are in uppertester.

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