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) introduced on 18 Sep 2018. The release contains significant enhancements for deep learning, along with new capabilities and bug fixes across the product families. The new Deep Learning Toolbox, which replaces Neural Network Toolbox, provides engineers and scientists with a framework for designing and implementing deep neural networks. Now, image processing, computer vision, signal processing, and systems engineers can use MATLAB to more easily design complex network architectures and improve the performance of their deep learning models.Matlab 2018a Key
What's new in Matlab 2018b
Desktop
- Live Editor: Organize live scripts using additional subheading styles
- Live Editor: Navigate within a live script using internal hyperlinks
- Live Editor: Filter table output interactively, and then add the generated code to the live script
- Live Editor: Create new and open existing live scripts faster
- Comparison Tool: Merge two versions of a live script or function
- Add-On Manager: Install and manage multiple versions of a custom toolbox
Language and Programming
- string Arrays: Use string arrays in MATLAB, Simulink, and Stateflow
- Enumerations: Improved performance of set operations with enumerations
Mathematics
- boundaryshape Function: Create a polyshape object from a 2-D triangulation
- polyshape Objects: Specify when to keep collinear points when creating a polyshape
- RandStream Objects: Generate random numbers using Threefry and Philox algorithms
- GraphPlot Object: Customize node and edge labels with font properties
Graphics
- Axes Interactions: Explore data with panning, zooming, data tips, and 3-D rotation enabled by default
- Axes Toolbar: Access and customize a data exploration toolbar for each Axes object
- Geographic Plots: Create line, scatter, and point density plots on interactive maps and control properties of a geographic axes
- stackedplot Function: Plot variables of a table or timetable for comparison using a common x-axis
- scatterhistogram Function: Visualize grouped data as a scatter plot with marginal histograms
- sgtitle Function: Create a title for a grid of subplots
- xline and yline Functions: Add vertical or horizontal lines to a plot
Data Import and Export
- Import Tool: Generate improved code when importing from spreadsheets
- Web-Based Data: Read from web-based data sources like Amazon Web Services and Azure Blob Storage using
- readtable, detectImportOptions, spreadsheetDatastore, imread, and imfinfo
- write Function: Write tall arrays in a variety of formats to local or remote locations
- stlread and stlwrite Functions: Read from and write to STL (Stereolithography) files for triangulations
- TabularTextDatastore Object: Import data containing dates and times from non-English locales
Data Analysis
- grouptransform Function: Transform table or timetable data by groups
- groupsummary Function: Perform group summary computations on matrices
- tall Arrays: Write custom algorithms to operate on tall arrays
- tall Arrays: Operate on tall arrays with more functions, including conv2, wordcloud, and groupsummary
- rmoutliers Function: Remove outliers in an array, table, or timetable
- islocalmin and islocalmax Functions: Specify a range of data for prominence computation
- Table and Timetable Metadata: Store custom metadata for each variable
- timetable Data Type: Save memory when storing row times with regular time steps
Download Matlab 2018a
App Building
- App Designer: Add and configure date selection components on the App Designer canvas
- uigridlayout Function: Configure app layouts using a grid layout manager
- Scrolling Containers: Enable scrolling for figure, panel, tab, and button group containers
- Figure Interactions: Create apps with custom mouse and keyboard interactions using figures created with the
- uifigure function
- Tooltips: Create custom tooltips for UI components in apps
- Deployed Web Apps: Access files in deployed web apps using the uigetfile and uiputfile functions
Performance
- Startup: Increased speed of MATLAB startup
- Execution Engine: Index into large arrays with improved performance when using the colon operator
- Execution Engine: Faster calls to built-in functions
- Live Editor: Create new and open existing live scripts faster
- Enumerations: Improved set function performance with enumerations
Matlab 2018a Update 2
Hardware Support
- MATLAB Online: Communicate with Raspberry Pi hardware board from MATLAB Online
- iOS and Android Sensors: Acquire sensor data when your device does not have network access
- iOS and Android Sensors: Upload sensor logs from the device to MATLAB Drive
- Advanced Software Development
- Python Interface: Pass multidimensional numeric or logical arrays between MATLAB and Python
- C++ MEX API: Call MATLAB asynchronously from within a MEX file using the C++ API
- Unit Testing Framework: Run tests in parallel with more plugins and more intelligent scheduling
- Performance Testing Framework: Measure execution time of fast code more accurately with the
- TestCase.keepMeasuring method
System Requirements - Release 2018b
Windows
Note:- Windows 8.1 is not supported as of R2018b.
- Support for windows Server 2012 and Windows Server 2012 R2 will be will be discontinued in a future release
| 64-Bit MATLAB, Simulink and Polyspace Product Families | ||||
| Operating Systems | Processors | Disk Space | RAM | Graphics |
|---|---|---|---|---|
| Windows 10 Windows 7 Service Pack 1 Windows Server 2016 Windows Server 2012 R2 Windows Server 2012 | Minimum Any Intel or AMD x86-64 processor Recommended Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support | Minimum 2.6 GB of HDD space for MATLAB only, 4–6 GB for a typical installation Recommended An SSD is recommended A full installation of all MathWorks products may take up to 23 GB of disk space | Minimum 4 GB Recommended 8 GB With Polyspace, 4 GB per core is recommended | No specific graphics card is required. Hardware accelerated graphics card supporting OpenGL 3.3 with 1GB GPU memory is recommended. GPU acceleration using the Parallel Computing Toolbox requires a CUDA GPU. See GPU Computing Support for details. |
Linux
Note:- SUSE Linux Enterprise Desktop 15 & SUSE Linux Enterprise Server 15 are supported as of R2018b.
- Ubuntu 18.04 LTS is supported as of R2018b.
- Support for Debian 8 will be discontinued in a future release.
| 64-Bit MATLAB, Simulink and Polyspace Product Families | ||||
| Operating Systems | Processors | Disk Space | RAM | Graphics |
|---|---|---|---|---|
| Ubuntu 18.04 LTS Ubuntu 16.04 LTS Ubuntu 14.04 LTS Debian 9 Debian 8 Red Hat Enterprise Linux 7 (minimum 7.3) Red Hat Enterprise Linux 6 (minimum 6.7) SUSE Linux Enterprise Desktop 15 SUSE Linux Enterprise Desktop 12 (minimum SP2) SUSE Linux Enterprise Server 15 SUSE Linux Enterprise Server 12 (minimum SP2) | Minimum Any Intel or AMD x86-64 processor Recommended Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support | Minimum 2.6 GB of HDD space for MATLAB only, 4-6 GB for a typical installation Recommended An SSD is recommended A full installation of all MathWorks products may take up to 22 GB of disk space | Minimum 4 GB Recommended 8 GB With Polyspace, 4 GB per core is recommended | No specific graphics card is required. Hardware accelerated graphics card supporting OpenGL 3.3 with 1GB GPU memory is recommended. GPU acceleration using the Parallel Computing Toolbox requires a CUDA GPU. See GPU Computing Support for details. |
Mac
Note: macOS El Capitan (10.11) is not supported as of R2018b.| 64-Bit MATLAB, Simulink and Polyspace Product Families | ||||
| Operating Systems | Processors | Disk Space | RAM | Graphics |
|---|---|---|---|---|
| macOS Mojave (10.14) macOS High Sierra (10.13) macOS Sierra (10.12) | Minimum Any Intel or AMD x86-64 processor Recommended Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support | Minimum 2.7 GB of HDD space for MATLAB only, 4–6 GB for a typical installation Recommended An SSD is recommended A full installation of all MathWorks products may take up to 22 GB of disk space | Minimum 4 GB Recommended 8 GB With Polyspace, 4 GB per core is recommended | No specific graphics card is required. Hardware accelerated graphics card supporting OpenGL 3.3 with 1GB GPU memory is recommended. GPU acceleration with Parallel Computing Toolbox is not available on macOS Mojave. |
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