16 September 2016, Bangalore – MathWorks today introduced Release 2016b (R2016b) with new capabilities that simplify working with big data in MATLAB. Engineers and scientists can now more easily work with data too big to fit in memory. R2016b also includes additional features in Simulink; a new product, Risk Management Toolbox; and updates and bug fixes to 83 other products.
Tall arrays now provide a way to work naturally with out-of-memory data using familiar MATLAB functions and syntax, removing the need to learn big data programming. Engineers and scientists can use tall arrays with hundreds of math, statistics, and machine learning algorithms. Code can run on Hadoop clusters or be integrated directly into Spark applications.
R2016b also includes a timetable data container for indexing and synchronizing time-stamped tabular data; string arrays to help manipulate, compare, and store text data efficiently; and new functions for preprocessing data.
“Companies are awash in data, but struggle to take advantage of it to build better predictive models and gain deeper insights,” says David Rich, MATLAB marketing director, MathWorks. “With R2016b, we’ve lowered the bar to allow domain experts to work with more data, more easily. This leads to improved system design, performance, and reliability.”
MATLAB Product Family Updates Include:
· MATLAB:
o Tall arrays for manipulating data too big to fit in memory
o Timetable data container for indexing and synchronizing time-stamped tabular data
o Ability to define local functions in scripts for improved code reuse and readability
o Capabilities for running MATLAB code from Java programs with the MATLAB Engine API for Java
· MATLAB Mobile: Data logging from iPhone and Android sensors on the MathWorks Cloud
· Database Toolbox: Graph database interface for retrieving Neo4j data
· MATLAB Compiler: Support for deploying MATLAB applications, including tall arrays, on a Spark cluster
· Parallel Computing Toolbox: Ability to process big data with tall arrays in parallel on your desktop and on servers and Spark clusters with MATLAB Distributed Computing Server
· Statistics and Machine Learning Toolbox: Big data algorithms for processing out-of-memory data including dimension reduction, descriptive statistics, k-means clustering, linear regression, logistic regression, and discriminant analysis
· Statistics and Machine Learning Toolbox: Bayesian optimization for automatically tuning machine learning algorithm parameters, and neighborhood component analysis (NCA) for choosing machine learning model features
· Statistics and Machine Learning Toolbox: Automatic C/C++ code generation support for SVM and logistic regression models with MATLAB Coder
· Image Processing Toolbox: Support for volumetric image data using 3-D superpixels for simple linear iterative clustering (SLIC) and 3-D median filtering
· Computer Vision System Toolbox: Object detection using deep learning region-based convolutional neural networks (R-CNNs)
· Risk Management Toolbox: A new product for developing risk models and performing risk simulation
· ThingSpeak: Ability to collect data from internet-connected sensors and run MATLAB analytics on the cloud using functions from Statistics and Machine Learning Toolbox, Signal Processing Toolbox, Curve Fitting Toolbox, and Mapping Toolbox
Simulink Product Family Updates Include:
· Simulink:
o Improved performance using JIT compiler for simulations running in Accelerator mode
o Ability to initialize, reset, and terminate subsystems to model dynamic start-up and shut-down behavior
o State reader and writer blocks for full control over reset state behavior from anywhere in the model
o Raspberry Pi 3 and Google Nexus hardware support
· Simulink and Stateflow: Property Inspector, Model Data Editor, and Symbol Manager for streamlined editing of parameters and data
· Simscape: Expanded block libraries for modeling perfect, semiperfect, and real gas systems
Signal Processing and Communications Updates Include:
· Signal Processing Toolbox: Signal Analyzer app to perform time- and frequency-domain analysis of multiple time series
· Phased Array System Toolbox: Modeling support for atmospheric and multipath propagation effects on narrowband and wideband signals
· WLAN System Toolbox: IEEE 802.11ah support and multiuser-MIMO receiver capability
· Audio System Toolbox: Audio plugin hosting to run and test VST plugins directly in MATLAB
Code Generation Updates Include:
· Embedded Coder:
o Cross-release code integration for reuse of code generated from earlier releases
o Ability to generate pluggable code for any software environment including dynamic start-up and shut-down behaviors
o Support for simulating AUTOSAR basic software including Diagnostic Event Manager (DEM) and NVRAM Manager (NvM)
· HDL Coder: Adaptive pipelining for specifying target clock frequency to drive automatic pipeline insertion, and a Logic Analyzer for visualizing and analyzing transitions and states (with DSP System Toolbox)
Verification and Validation Updates Include:
· Simulink Verification and Validation: Edit-time checking for detecting and fixing standards compliance issues at design time
· Simulink Test: Custom criteria definition for test evaluation
· HDL Verifier: FPGA data capture for probing internal FPGA signals to analyze in MATLAB or Simulink
· Polyspace Bug Finder: Support for the CERT C coding standard for cyber-security vulnerability detection
R2016b is available immediately worldwide. For more information, see R2016b Highlights.
