Data

Tools

Indexes

Applications

Experiments

Awesome

An open API service indexing awesome lists of open source software.

DSP-Guide

Digital Signal Processing(DSP) Guide
https://github.com/mikeroyal/DSP-Guide

Last synced: 11 days ago
JSON representation

  • LiDAR Learning Resources

  • LiDAR Tools & Frameworks

    • Light Detection and Ranging (lidar) - resolution models of ground elevation with a vertical accuracy of 10 centimeters (4 inches). Lidar equipment, which includes a laser scanner, a Global Positioning System (GPS), and an Inertial Navigation System (INS), is typically mounted on a small aircraft. The laser scanner transmits brief pulses of light to the ground surface. Those pulses are reflected or scattered back and their travel time is used to calculate the distance between the laser scanner and the ground.  Lidar data is initially collected as a “point cloud” of individual points reflected from everything on the surface, including structures and vegetation. To produce a “bare earth” Digital Elevation Model (DEM), structures and vegetation are stripped away.
    • National Map Data Download and Visualization Services
    • Mola
    • MOLA
    • LASer(LAS) - dimensional point cloud data data between data users. Although developed primarily for exchange of lidar point cloud data, this format supports the exchange of any 3-dimensional x,y,z tuplet. This binary file format is an alternative to proprietary systems or a generic ASCII file interchange system used by many companies. The problem with proprietary systems is obvious in that data cannot be easily taken from one system to another. There are two major problems with the ASCII file interchange. The first problem is performance because the reading and interpretation of ASCII elevation data can be very slow and the file size can be extremely large even for small amounts of data. The second problem is that all information specific to the lidar data is lost. The LAS file format is a binary file format that maintains information specific to the lidar nature of the data while not being overly complex.
    • 3D point cloud - dimensional coordinates system.. Point clouds can be produced directly by 3D scanner which records a large number of points returned from the external surfaces of objects or earth surface. These data are exchanged between LiDAR users mainly through LAS format files (.las).
    • ArcGIS Desktop - effective desktop geographic information system (GIS) software. It is the essential software package for GIS professionals. ArcGIS Desktop users can create, analyze, manage, and share geographic information so decision-makers can make intelligent, informed decisions.
    • USGS 3DEP Lidar Point Cloud Now Available as Amazon Public Dataset
    • National Geospatial Program
    • USGS Lidar Base Specification(LBS) online edition
    • Fuzzy logic - tree processing and better integration with rules-based programming.
    • ResearchGate
    • Support Vector Machine (SVM) - group classification problems.
    • OpenClipArt
    • Convolutional Neural Networks (R-CNN)
    • CS231n
    • Slideteam
    • DeepAI
    • wikimedia
    • Decision trees - structured models for classification and regression.
    • CMU
    • Naive Bayes - theorem.html) with strong independence assumptions between the features.
    • mathisfun
    • wikimedia
    • wikimedia
    • wikimedia
    • wikimedia
    • wikimedia
    • wikimedia
    • wikimedia
    • wikimedia
    • wikimedia
    • wikimedia
    • wikimedia
    • wikimedia
    • wikimedia
    • Support Vector Machine (SVM) - group classification problems.
    • Light Detection and Ranging (lidar) - resolution models of ground elevation with a vertical accuracy of 10 centimeters (4 inches). Lidar equipment, which includes a laser scanner, a Global Positioning System (GPS), and an Inertial Navigation System (INS), is typically mounted on a small aircraft. The laser scanner transmits brief pulses of light to the ground surface. Those pulses are reflected or scattered back and their travel time is used to calculate the distance between the laser scanner and the ground.  Lidar data is initially collected as a “point cloud” of individual points reflected from everything on the surface, including structures and vegetation. To produce a “bare earth” Digital Elevation Model (DEM), structures and vegetation are stripped away.
    • USGS
    • USGS 3DEP Lidar Point Cloud Now Available as Amazon Public Dataset
    • National Geospatial Program
    • USGS Lidar Base Specification(LBS) online edition

  • MATLAB Learning Resources

  • MATLAB Tools, Libraries, Frameworks

    • Image Processing Toolbox™ - standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing.
    • MATLAB and Simulink Services & Applications List
    • MATLAB in the Cloud - cloud) including [AWS](https://aws.amazon.com/) and [Azure](https://azure.microsoft.com/).
    • Simulink - Based Design. It supports simulation, automatic code generation, and continuous testing of embedded systems.
    • Simulink Online™
    • MATLAB Drive™
    • SoC Blockset™
    • Wireless HDL Toolbox™ - verified, hardware-ready Simulink® blocks and subsystems for developing 5G, LTE, and custom OFDM-based wireless communication applications. It includes reference applications, IP blocks, and gateways between frame and sample-based processing.
    • ThingSpeak™ - of-concept IoT systems that require analytics.
    • hctsa - series analysis using Matlab.
    • YALMIP
    • hctsa - series analysis using Matlab.
    • MATLAB Schemer
    • SEA-MAT
    • Gramm - level interface to produce publication-quality plots of complex data with varied statistical visualizations. Gramm is inspired by R's ggplot2 library.
    • GNU Octave - level interpreted language, primarily intended for numerical computations. It provides capabilities for the numerical solution of linear and nonlinear problems, and for performing other numerical experiments. It also provides extensive graphics capabilities for data visualization and manipulation.

  • ML Frameworks, Libraries, and Tools

    • PyTorch
    • Amazon SageMaker
    • Azure Databricks - based big data analytics service designed for data science and data engineering. Azure Databricks, sets up your Apache Spark environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.
    • Apple CoreML - tune models, all on the user's device. A model is the result of applying a machine learning algorithm to a set of training data. You use a model to make predictions based on new input data.
    • Apache OpenNLP - source library for a machine learning based toolkit used in the processing of natural language text. It features an API for use cases like [Named Entity Recognition](https://en.wikipedia.org/wiki/Named-entity_recognition), [Sentence Detection](), [POS(Part-Of-Speech) tagging](https://en.wikipedia.org/wiki/Part-of-speech_tagging), [Tokenization](https://en.wikipedia.org/wiki/Tokenization_(data_security)) [Feature extraction](https://en.wikipedia.org/wiki/Feature_extraction), [Chunking](https://en.wikipedia.org/wiki/Chunking_(psychology)), [Parsing](https://en.wikipedia.org/wiki/Parsing), and [Coreference resolution](https://en.wikipedia.org/wiki/Coreference).
    • Open Neural Network Exchange(ONNX) - in operators and standard data types.
    • Apache MXNet
    • AutoGluon - accuracy deep learning models on tabular, image, and text data.
    • Anaconda
    • Jupyter Notebook - source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Jupyter is used widely in industries that do data cleaning and transformation, numerical simulation, statistical modeling, data visualization, data science, and machine learning.
    • Apache Spark - scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
    • Apache PredictionIO
    • BigDL
    • Eclipse Deeplearning4J (DL4J) - based(Scala, Kotlin, Clojure, and Groovy) deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.
    • XGBoost
    • Azure Databricks - based big data analytics service designed for data science and data engineering. Azure Databricks, sets up your Apache Spark environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.
    • Tensorflow_macOS - optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.
    • PlaidML
    • Caffe
    • Theano - dimensional arrays efficiently including tight integration with NumPy.
    • nGraph - of-use to AI developers.
    • Apache Spark Connector for SQL Server and Azure SQL - performance connector that enables you to use transactional data in big data analytics and persists results for ad-hoc queries or reporting. The connector allows you to use any SQL database, on-premises or in the cloud, as an input data source or output data sink for Spark jobs.
    • Cluster Manager for Apache Kafka(CMAK)
    • Tensorman
    • Numba - aware optimizing compiler for Python sponsored by Anaconda, Inc. It uses the LLVM compiler project to generate machine code from Python syntax. Numba can compile a large subset of numerically-focused Python, including many NumPy functions. Additionally, Numba has support for automatic parallelization of loops, generation of GPU-accelerated code, and creation of ufuncs and C callbacks.
    • cuML - learn.
    • AutoGluon - accuracy deep learning models on tabular, image, and text data.
    • TensorFlow - to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.

  • Photogrammetry Learning Resources

  • Photogrammetry Tools, Libraries, and Frameworks

    • Autodesk® ReCap™
    • Autodesk® ReCap™ Photo - connected solution tailored for drone/UAV photo capturing workflows. Using ReCap Photo, you can create textured meshes, point clouds with geolocation, and high-resolution orthographic views with elevation maps.
    • Pix4D
    • PIX4Dmapper
    • Adobe Scantastic - based photogrammetry pipeline), users can easily scan objects in their physical environment and turn them into 3D models which can then be imported into tools like [Adobe Dimension](https://www.adobe.com/products/dimension.html) and [Adobe Aero](https://www.adobe.com/products/aero.html).
    • Adobe Aero - party apps like Cinema 4D, or asset libraries like Adobe Stock and TurboSquid. Aero optimizes a wide array of assets, including OBJ, GLB, and glTF files, for AR, so you can visualize them in real time.
    • Agisoft Metashape - alone software product that performs photogrammetric processing of digital images and generates 3D spatial data to be used in GIS applications, cultural heritage documentation, and visual effects production as well as for indirect measurements of objects of various scales.
    • MicroStation
    • Leica Photogrammetry Suite (LPS) - friendly environment that guarantees results even for photogrammetry novices.
    • Terramodel
    • COLMAP - purpose Structure-from-Motion (SfM) and Multi-View Stereo (MVS) pipeline with a graphical and command-line interface. It offers a wide range of features for reconstruction of ordered and unordered image collections.
    • Multi-View Environment (MVE) - view datasets and to support the development of algorithms based on multiple views. It features Structure from Motion, Multi-View Stereo and Surface Reconstruction. MVE is developed at the TU Darmstadt.
    • PhotoModeler - effective way for accurate 2D or 3D measurement, photo-digitizing, surveying, 3D scanning, and reality capture.
    • ODM
    • WebODM - friendly, commercial grade software for drone image processing. Generate georeferenced maps, point clouds, elevation models and textured 3D models from aerial images. It supports multiple engines for processing, currently [ODM](https://github.com/OpenDroneMap/ODM) and [MicMac](https://github.com/dronemapper-io/NodeMICMAC/).
    • NodeODM
    • FIELDimageR
    • Regard3D - from-motion program. It converts photos of an object, taken from different angles, into a 3D model of this object.
    • Leica Photogrammetry Suite (LPS) - friendly environment that guarantees results even for photogrammetry novices.
    • MicMac - source photogrammetry software tools for 3D reconstruction.
    • AliceVision - of-the-art computer vision algorithms that can be tested, analyzed and reused.
    • Meshroom - source 3D Reconstruction Software based on the AliceVision framework.
    • MicroStation
Programming Languages
C++ 14 MATLAB 4 Python 4 C 2 Scala 2 Nim 1 HTML 1 Rust 1 Shell 1 Java 1
Categories
CUDA Tools Libraries, and Frameworks 42 LiDAR Tools & Frameworks 42 C/C++ Tools and Frameworks 29 C/C++ Learning Resources 29 ML Frameworks, Libraries, and Tools 28 Photogrammetry Tools, Libraries, and Frameworks 23 Computer Vision Tools, Libraries, and Frameworks 22 Differential Equations Learning Resources 21 Computer Vision Learning Resources 17 MATLAB Tools, Libraries, Frameworks 16 Learning Resources for ML 14 MATLAB Learning Resources 14 LiDAR Learning Resources 13 Photogrammetry Learning Resources 10 Laplace transform 9 CUDA Learning Resources 6 License 1
Sub Categories
Keywords
cuda 8 deep-learning 6 cpp 6 gpu 5 machine-learning 5 nvidia 4 cpp11 3 c 3 matlab 3 computer-vision 3 meshroom 2 photogrammetry 2 structure-from-motion 2 linear-algebra 2 algorithms 2 cpp14 2 cpp17 2 cpp20 2 cxx 2 cxx11 2 camera-tracking 2 cxx14 2 cxx17 2 alicevision 2 cxx20 2 3d-reconstruction 2 tensor 2 gpu-computing 2 nvidia-hpc-sdk 2 opencl 2 cplusplus 2 compiler 2 awesome 2 deep-neural-networks 2 awesome-list 2 swig 1 pinvoke 1 parsing 1 parser 1 mono 1 interop 1 glue 1 dotnet 1 cppsharp 1 clang 1 c-sharp 1 paddlepaddle 1 syntax-tree 1 visitors 1 xamarin 1