MathWorks introduced Release 2018a (R2018a) with a range of new capabilities in MATLAB and Simulink. R2018a includes two new products: Predictive Maintenance Toolbox, for designing and testing condition monitoring and predictive maintenance algorithms, and Vehicle Dynamics Blockset, for modeling and simulating vehicle dynamics in a virtual 3D environment.
In addition to new features in MATLAB and Simulink and the new products, the release includes updates and bug fixes to 94 other products.
MATLAB Product Family Updates
- MATLAB:
- Live functions, documentation authoring, debugging and interactive controls for embedding sliders and drop-down menus in the Live Editor
- App (UI) testing framework, C++ MEX interface, custom tab completion and function assistants for advanced software development
- MATLAB Online:
- Hardware connectivity for communicating with USB webcams
- Econometrics Toolbox:
- Econometric Modeler app for performing time series analysis, specification testing, modeling and diagnostics
- Image Processing Toolbox:
- 3D image processing and volume visualization
- Partial Differential Equation Toolbox:
- Structural dynamic analysis to find natural frequencies, mode shapes and transient response
- Optimization Toolbox:
- Branching methods for solving mixed-integer linear problems faster
Deep Learning
- Neural Network Toolbox:
- Support package for importing deep learning layers and networks designed in TensorFlow-Keras
- Long short-term memory (LSTM) networks for solving regression problems and doing text classification with Text Analytics Toolbox
- Adam, RMSProp and gradient clipping to improve network training
- Accelerated training for directed acyclic graph (DAG) networks using multiple GPUs and computing intermediate layer activations
- Computer Vision System Toolbox:
- Image Labeler app to automate labeling of individual pixels for semantic segmentation
- GPU Coder:
- CUDA code generation for networks with DAG topology and pretrained networks like GoogLeNet, ResNet and SegNet
- C code generation for deep learning networks on Intel and ARM processors
Data Analytics
- Statistics and Machine Learning Toolbox:
- High-density data visualization with scatter plots in the Classification Learner app
- Big data algorithms for kernel SVM regression, computing confusion matrices and creating nonstratified partitions for cross-validation
- Text Analytics Toolbox:
- Multiword phrase extraction and counting, HTML text extraction and detection of sentences, email addresses and URLs
- Stochastic LDA model training for large datasets
- Predictive Maintenance Toolbox:
- A new product for designing and testing condition monitoring and predictive maintenance algorithms
Simulink Product Family Updates
- Simulink:
- Predictive quick insert to connect a recommended block to an existing block in a model
- Simulation Pacing for running simulations at wall clock speed or other specified pace for improved visualization
- Simulation Data Inspector in the Live Editor for directly adding, viewing and editing plots
- Simulink 3D Animation:
- Collision detection for sensing collisions of virtual world objects using point clouds, raytracing and primitive geometries
- Simscape:
- Moist air domain and block library to model HVAC and environmental control systems
- Partitioning local solver to increase real-time simulation speed
Automotive
- Automated Driving System Toolbox:
- Driving Scenario Designer app for interactively defining actors and driving scenarios to test control and sensor fusion algorithms
- Model Predictive Control Toolbox:
- ADAS blocks for designing, simulating, and implementing adaptive cruise control and lane-keeping algorithms
- Vehicle Network Toolbox:
- CAN FD protocol support in Simulink, and XCP over Ethernet to communicate with ECUs from MATLAB or Simulink
- Model-Based Calibration Toolbox:
- Powertrain Blockset integration for using measured data to calibrate and generate tables for Powertrain Blockset mapped engines
- Vehicle Dynamics Blockset:
- A new product for modeling and simulating vehicle dynamics in a virtual 3D environment
Code Generation
- Embedded Coder:
- Embedded Coder dictionary for defining custom code generation configurations for data and functions
- Code Perspective for customizing Simulink desktop for code generation workflows
- MATLAB Coder:
- Row-major array layout to simplify interfacing generated code with C environments storing arrays in row-major format
- Sparse matrix support to enable more efficient computation using sparse matrices in generated code
- C code generation for machine learning deployment including k-nearest neighbor, nontree ensemble models and distance calculations with Statistics and Machine Learning Toolbox
- Fixed-Point Designer:
- Lookup table optimization for approximating functions and minimizing existing lookup table RAM usage
- HDL Coder:
- Matrix support enabling HDL code generation directly from algorithms with two-dimensional matrix data types and operations
Signal Processing and Communications
- Signal Processing Toolbox:
- Signal Analyzer app for processing multiple signals and extracting regions of interest from signals
- Vibration signal analysis from rotating machinery using RPM tracking and order analysis
- LTE System Toolbox:
- NB-IoT support to model the narrowband IoT transport and physical downlink shared channel
- RF Blockset:
- Power amplifier model for capturing nonlinearity and memory effects based on input/output device characteristics
- Wavelet Toolbox:
- Continuous and discrete wavelet transform filter banks
- Robotics System Toolbox:
- Lidar-based SLAM for localizing robots and map environments using lidar sensors
Verification and Validation
- Simulink Requirements:
- Requirements import with ReqIF for importing requirements from third-party tools such as IBM Rational DOORS Next Generation or Siemens Polarion
- Simulink Test:
- Coverage aggregation to combine coverage results from multiple test runs
- Polyspace Code Prover:
- AUTOSAR support for static analysis of AUTOSAR software components
R2018a is available immediately worldwide. For more information, see R2018a Highlights.