MathWorks MATLAB R2020a v220.127.116.117392 (Win / macOS / Linux) | 69.7 GB
Includes MathWorks MATLAB R2020a Update 4 only
Includes MathWorks MATLAB R2020a Update 4 only
Millions of engineers and scientists worldwide use MATLAB to analyze and design the systems and products transforming our world. MATLAB is in automobile active safety systems, interplanetary spacecraft, health monitoring devices, smart power grids, and LTE cellular networks. It is used for machine learning, signal processing, image processing, computer vision, communications, computational finance, control design, robotics, and much more.
Math. Graphics. Programming.
The MATLAB platform is optimized for solving engineering and scientific problems. The matrix-based MATLAB language is the world’s most natural way to express computational mathematics. Built-in graphics make it easy to visualize and gain insights from data. A vast library of prebuilt toolboxes lets you get started right away with algorithms essential to your domain. The desktop environment invites experimentation, exploration, and discovery. These MATLAB tools and capabilities are all rigorously tested and designed to work together.
Scale. Integrate. Deploy.
MATLAB helps you take your ideas beyond the desktop. You can run your analyses on larger data sets and scale up to clusters and clouds. MATLAB code can be integrated with other languages, enabling you to deploy algorithms and applications within web, enterprise, and production systems.
- High-level language for scientific and engineering computing
- Desktop environment tuned for iterative exploration, design, and problem-solving
- Graphics for visualizing data and tools for creating custom plots
- Apps for curve fitting, data classification, signal analysis, and many other domain-specific tasks
- Add-on toolboxes for a wide range of engineering and scientific applications
- Tools for building applications with custom user interfaces
- Interfaces to C/C++, Java®, .NET, Python®, SQL, Hadoop®, and Microsoft® Excel®
- Royalty-free deployment options for sharing MATLAB programs with end users
MATLAB is the easiest and most productive software for engineers and scientists. Whether you’re analyzing data, developing algorithms, or creating models, MATLAB provides an environment that invites exploration and discovery. It combines a high-level language with a desktop environment tuned for iterative engineering and scientific workflows.
MATLAB Speaks Math
The matrix-based MATLAB language is the world’s most natural way to express computational mathematics. MATLAB supports both numeric and symbolic calculations. Linear algebra in MATLAB looks like linear algebra in a textbook; symbolic calculations look like the equations you write on paper. This makes it straightforward to capture the mathematics behind your ideas, which means your code is easier to write, easier to read and understand, and easier to maintain.
You can trust the results of your computations. MATLAB, which has strong roots in the numerical analysis research community, is known for its impeccable numerics. A MathWorks team of 350 engineers continuously verifies quality by running millions of tests on the MATLAB code base every day.
MATLAB does the hard work to ensure your code runs quickly. Math operations are distributed across multiple cores on your computer, library calls are heavily optimized, and all code is just-in-time compiled. You can run your algorithms in parallel by changing for-loops into parallel for-loops or by changing standard arrays into GPU or distributed arrays. Run parallel algorithms in infinitely scalable public or private clouds with no code changes.
The MATLAB language also provides features of traditional programming languages, including flow control, error handling, object-oriented programming, unit testing, and source control integration.
MATLAB Is Designed for Engineers and Scientists
MATLAB provides a desktop environment tuned for iterative engineering and scientific workflows. Integrated tools support simultaneous exploration of data and programs, letting you evaluate more ideas in less time.
- You can interactively preview, select, and preprocess the data you want to import.
- An extensive set of built-in math functions supports your engineering and scientific analysis.
- 2D and 3D plotting functions enable you to visualize and understand your data and communicate results.
- MATLAB apps allow you to perform common engineering tasks without having to program. Visualize how different algorithms work with your data, and iterate until you’ve got the results you want.
- The integrated editing and debugging tools let you quickly explore multiple options, refine your analysis, and iterate to an optimal solution.
- You can capture your work as sharable, interactive narratives.
Comprehensive, professional documentation written by engineers and scientists is always at your fingertips to keep you productive. Reliable, real-time technical support staff answers your questions quickly. And you can tap into the knowledge and experience of over 100,000 community members and MathWorks engineers on MATLAB Central, an open exchange for MATLAB and Simulink® users.
MATLAB and add-on toolboxes are integrated with each other and designed to work together. They offer professionally developed, rigorously tested, field-hardened, and fully documented functionality specifically for scientific and engineering applications
MATLAB Integrates Workflows
Major engineering and scientific challenges require broad coordination to take ideas to implementation. Every handoff along the way adds errors and delays.
MATLAB automates the entire path from research through production. You can:
- Build and package custom MATLAB apps and toolboxes to share with other MATLAB users.
- Create standalone executables to share with others who do not have MATLAB.
- Integrate with C/C++, Java, .NET, and Python. Call those languages directly from MATLAB, or package MATLAB algorithms and applications for deployment within web, enterprise, and production systems.
- Convert MATLAB algorithms to C, HDL, and PLC code to run on embedded devices.
- Deploy MATLAB code to run on production Hadoop systems.
MATLAB is also a key part of Model-Based Design, which is used for multidomain simulation, physical and discrete-event simulation, and verification and code generation.
MATLAB Web Apps
MATLAB Web App Server™ lets you host MATLAB® apps and Simulink® simulations as interactive web apps. You can create apps using App Designer, package them using MATLAB Compiler™, and host them using MATLAB Web App Server. Your end-users can access and run the web apps using a browser without installing additional software.
MATLAB Web App Server supports integration with authentication standards such as OpenID Connect and LDAP so that you can control access to your web apps. You can host and share multiple apps developed using different releases of MATLAB and Simulink.
Simulink Compiler™ enables you to share Simulink® simulations as standalone executables. You can build the executables by packaging the compiled Simulink model and the MATLAB® code used to set up, run, and analyze a simulation. Standalone executables can be complete simulation apps that use MATLAB graphics and UIs designed with MATLAB App Designer. To co-simulate with an external simulation environment, you can generate standalone Functional Mockup Unit (FMU) binaries that adhere to the Functional Mockup Interface (FMI) standard.
To provide browser-based access to your deployed simulation, you can create a web app and host it with MATLAB Web App Server™. Simulink simulations can be packaged into software components for integration with other programming languages (with MATLAB Compiler SDK™). Large-scale deployment to enterprise systems is supported through MATLAB Production Server™. To generate C and C++ source code from Simulink, use Simulink Coder™.
Data Preparation and Labeling
- Video Labeler: Label ground-truth data in a video or image sequences
- Audio Labeler: Interactively define and visualize ground-truth labels for audio datasets
- New Signal Labeler: Visualize and label signals interactively
- New Pixel label datastore: Store pixel information for 2D and 3D semantic segmentation data
- New Audio datastore: Manage large collections of audio recordings
- New Image datastore: Support for 3D data
- New Build advanced network architectures like GANs, Siamese networks, attention networks, and variational autoencoders
- Train a “you-only-look-once” (YOLO) v2 deep learning object detector and generate C and CUDA code
- Deep Network Designer: Graphically design and analyze deep networks and generate MATLAB code
- Custom layers support: Define new layers with multiple inputs and outputs, and specify loss functions for classification and regression
- Combine LSTM and convolutional layers for video classification and gesture recognition
Deep Learning Interoperability
- Import and export models with other deep learning frameworks using the ONNX model format and generate CUDA code
- New Ability to work with MobileNet-v2, ResNet-101, Inception-v3, SqueezeNet, NASNet-Large, and Xception
- Import TensorFlow-Keras models and generate C, C++ and CUDA code
- Import DAG networks in Caffe model importer
- Automatically validate network performance, and stop training when the validation metrics stop improving
- New Train deep learning networks on 3D image data
- Perform hyperparameter tuning using Bayesian optimization
- Additional optimizers for training: Adam and RMSProp
- Train DAG networks in parallel and on multiple GPUs
- Train deep learning models on NVIDIA DGX and cloud platforms
Debugging and Visualization
- DAG activations: Visualize intermediate activations for networks like ResNet-50, ResNet-101, GoogLeNet, and Inception-v3
- Monitor training progress with plots for accuracy, loss, and validation metrics
- Network Analyzer: Visualize, analyze, and find problems in network architectures before training
- New Visualize activations of LSTM networks and use Grad-CAM to understand classification decisions
- New Generate code for networks such as YOLO V2 object detector, DeepLab-v3+, MobileNet-v2, Xception, DenseNet-201, and recurrent networks
- New Deploy deep learning networks to ARM Mali GPUs
- New Automated deployment to Jetson AGX Xavier and Jetson Nano platforms
- Apply CUDA optimized transposes using shared memory for improved performance
- New Reinforcement Learning Algorithms: Train deep neural network policies using DQN, DDPG, A2C, PPO, and other algorithms
- Environment Modeling: Create MATLAB and Simulink models to represent environments and provide observation and reward signals for training policies
- Training Acceleration: Parallelize policy training on GPUs and multicore CPUs
- New Reference Examples: Implement policies for automated driving, robotics, and control design applications
5G and LTE Mobile Communications Standards
- New 5G support in Wireless Waveform Generator App: Generate NR-TM, and uplink and downlink FRC waveforms using the Wireless Waveform Generator app
- New Support for PRACH physical channels: Model physical random access channel (PRACH) used in initial system access
- Support for SRS, DM-RS and PT-RS signals: Model 5G signals used for uplink channel sounding, channel estimation and phase tracking
- New Deep learning data synthesis for 5G channel estimation: Generate deep learning training data for convolutional neural networks (CNN) used in 5G channel estimation
- NB-IoT Uplink Shared Channel Modeling: Generate and decode the narrowband Internet of Things (NB-IoT) uplink shared channel
WLAN and Connectivity Standards
- New System-level simulation Examples: Model an 802.11ax downlink orthogonal frequency-division multiple access (OFDMA) scenario, multiple space-time streams, and 802.11a Minstrel rate adaptation
- New Support for IEEE 802.11ax Draft 4.1 (Wi-Fi6): Generate high-efficiency single-user null data packets (NDPs) with preamble puncturing, as defined in IEEE® P802.11ax™ Draft 4.1
- New Link-level simulation of IEEE 802.11ax Trigger-Based Format: Configure, generate, demodulate and decode high-efficiency trigger-based (HE TB) waveforms
- New Blind Signal Recovery and Analysis Example: blindly detect, decode and analyze multiple IEEE 802.11a and IEEE 802.11ax packets in a waveform
- New Bluetooth Low Energy BR/EDR waveform generation and link-level simulation: Generate, demodulate, and decode Bluetooth® basic rate (BR)and extended data rate (EDR) PHY waveforms
- Bluetooth Support in Wireless Waveform Generator App: Generate and export Bluetooth Low Energy waveforms from the Wireless Waveform Generator app
- Bluetooth Low Energy (BLE) Examples: Simulate BLE coexistence with WLAN, and perform BLE RF-PHY blocking, intermodulation, and carrier to interference (C/I) performance receiver tests
Massive MIMO, Multi-User MIMO, and Beamforming
- New Multiuser Block Diagonalization Beamforming: Compute precoding and combining weights for multiuser MIMO systems
- Massive MIMO: Simulate an end-to-end MIMO link using hybrid beamforming
- New Transmit and Receive Signals with Unlimited Antennas: Apply WLAN transmission, multipath channel modeling and receiver operations with arbitrary number of antennas and links
- Wireless LAN: 802.11ad waveform generation with beamforming
Channel and Propagation Modeling
- New Ray Tracing Propagation Model: New propagation model using ray tracing method of images with material reflection loss
- New RF propagation using ray tracing: Predict the total received power and generate coverage maps with ray tracing
- New Rain Attenuation Models: Predict signal attenuation with Global Crane Rain Attenuation and ITU models
- RF Propagation Visualization with Ray Tracing: Configure and visualize transmitter and receiver sites, buildings, links, ray tracing results, and coverage maps using free-space, terrain, and weather-effects propagation models
- SINR Visualization: Visualize transmitter site signal-to-interference-plus-noise ratio (SINR) on a map
RF and Digital Front End
- Power amplifier (PA) modeling: Model wideband and narrowband power amplifiers, capturing non-linearity and memory effects based on input/output device characteristics
- S-parameter block: Model frequency response of RF devices using S-parameter data
- Linearize power amplifiers with DPD: Simulate linearization of RF power amplifiers with memory using digital predistortion (DPD)
- RF budget analyzer: Analytically compute gain, noise figure, and IP3 for cascaded RF components, and visualize using Smith and polar plots
- New Custom Antenna Patterns: Import custom patterns expressed in phi-theta coordinates using Sensor Array Analyzer app
- Antenna designer: Interactively select and analyze antennas with desired characteristics
- PCB stack antenna: Design custom PCB antennas with arbitrary metal-dielectric layers and advanced meshing control
- Gerber file generation: Prototype and implement antennas using a customizable library of RF connectors and PCB manufacturing services
- Standard-compliant LTE and WLAN: Over-the-air waveform generation and capture
- USRP E300 Series software-defined radio: Prototype and test wireless system designs on Ettus Research USRP E-300 SDRs
- ADALM-PLUTO software-defined radio: Prototype and test wireless system designs on Analog Devices PlutoSDR
C/C++ Code Generation
- MATLAB Coder: Generate C++ classes from MATLAB classes
- Embedded Coder: Generate C/C++ Code for Software Compositions with Message-Based Communication
- Fixed-Point Designer: Explore signal ranges and convert Simulink models using data type optimization
Perception System Design
- New Lidar Sensor Model: Generate synthetic point clouds from programmatic driving scenarios
- New Tracking Examples: Fuse radar and lidar tracks, perform track-to-track fusion in Simulink
- Unreal Engine® Compatible Sensor Models: Integrate your Simulink model with a camera, lidar, or radar sensor model simulating in an Unreal Engine scene
- Monocular Camera Parameter Estimation: Configure a monocular camera by estimating its extrinsic parameters
- Radar Sensor Model Enhancements: Model occlusions in radar sensors
- Sensor fusion and tracking examples
- Path Planning: Plan driving paths using an RRT* path planner and costmap
- Lidar Segmentation: Quickly segment 3D point clouds from lidar
Test and Verification
- New MDF Read on Linux: Open and read MDF files on Linux platform
- MDF File Information and Sorting Functions: Quickly access MDF file metadata and sort the contents of an MDF file
- MDF File Import Performance: Open and read MDF files significantly faster than in previous releases
- Kinematics and Compliance Virtual Test Laboratory: Generate mapped suspension calibration parameters from spreadsheet data
- Vector BLF File Format Support: Read and write binary BLF logging files from MATLAB
- Prebuilt Driving Scenarios: Test driving algorithms using Euro NCAP® and other prebuilt scenarios
- OpenDRIVE® File Import Support: Load OpenDRIVE roads into a driving scenario
- Driving Scenario Designer: Interactively define actors and driving scenarios to test controllers and sensor fusion algorithms
- Preassembled maneuvers for common ride and handling tests, including a double-lane change and constant radius test
Ground Truth Labeling
- New Lidar labeling: Label lidar point clouds to train deep learning models
- New Multisignal Ground Truth Labeling: Label multiple lidar and video signals simultaneously
- Ground Truth Labeling: Organize labels by logical groups
- Define multiple custom labels in Ground Truth Labeler connector
- Ground Truth Pixel Labeling: Interactively label individual pixels in video data
- Ground Truth Label Attributes: Organize and classify ground truth labels using attributes and sublabels
- New 3D Simulation Version Upgrade: Run 3D simulations using Unreal Engine, Version 4.23
- New Headless Mode: Run 3D simulations without opening the Unreal Engine 3D visualization display
- 3D Simulation: Develop, test, and verify driving algorithms in a 3D simulation environment rendered using the Unreal Engine by Epic Games®
- Unreal Engine Scenes: Use prebuilt 3D scenes, including a parking lot, highway segment, and Mcity, or create your own custom scene with the Unreal Editor
- HERE HD Live Map Reader: Read and visualize data from high-definition maps designed for automated driving applications
- Unreal Engine 4 Interface: Use support package to customize and install additional 3D scenes
- Maneuver Reference Applications: Use 3D environment ray tracing to determine ground location under tires during vehicle maneuver
- Bird's-Eye Scope for Simulink: Analyze sensor coverages, detections, and tracks in your model
- Virtual Calibration: Use Model-Based Calibration Toolbox™ to calibrate Mapped Motor and Three-Phase Voltage Source Inverter block efficiency maps with measured data
- Getting Started Example: Generate current controller calibration tables for flux-based motor controllers
- Libraries of propulsion, steering, suspension, vehicle body, brake, and tire components
- Flux-Based Motor Parameterization: Generate parameters for Flux-Based PMSM and Flux-Based PM Controller blocks
- Battery Parameterization: Generate parameters for Datasheet Battery and Equivalent Circuit Battery blocks
- Deep Learning Engine Model: Generate a deep learning engine model for algorithm design and performance, fuel economy, and emissions analysis
- ASAM CDFX File Format Support: Import, export, and modify files in calibration data file format (CDFX)
- Timestamp Support for XCP Blocks: Communicate timestamped data between Simulink models and XCP slaves
- Virtual Calibration: Use Model-Based Calibration Toolbox to calibrate SI and CI mapped engine blocks
- CI and SI Engine Dynamometer Reference Applications: Resize engines and recalibrate controllers based on desired engine displacement and number of cylinders
Fuel Economy and Performance Analysis
- New Vehicle and Trailer Blocks: Implement 3DOF trailers and vehicles with three axles
- New Transmission Control Module: Optimize shift schedules for algorithm design and performance, fuel economy, and emissions analysis
- HEV Reference Applications: New or updated reference applications for single-motor HEV architectures P0, P1, P2, P3, and P4. Fully assembled models use a new equivalent consumption minimization strategy (ECMS) for the supervisory hybrid control
- Powertrain Efficiency: Evaluate and report energy and power losses at the component and system level
- HEV Input Power-Split Reference Application: Use fully assembled model for HIL testing, tradeoff analysis, and control parameter optimization of a power-split hybrid like the Toyota Prius
- New AUTOSAR Adaptive Platform Release 19-03: Use the 000047 (R19-03) schema to import and export ARXML files and generate AUTOSAR-compatible C ++ code
- Blocks for Basic Software Services: Use blocks for modeling and simulating Basic Software services, including Diagnostic Event , Function Inhibition and NVRAM Manager blocks
- Create AUTOSAR Architecture Models: Author AUTOSAR compositions, view component/composition dependencies via spotlights and simulate functional behavior with basic software services using Composition Editor (requires System Composer™). Then you can generate and package composition arxml descriptions and component code (requires Embedded Coder®).
- AUTOSAR SW-C App: Easily configure a model as AUTOSAR classic component or adaptive application
Motor Control Blockset
Motor Control Blockset™ provides Simulink® blocks that let you create an accurate motor model. The blockset also provides tools for collecting data directly from hardware and calculating motor parameters.