https://github.com/mikeroyal/cyber-physical-systems-guide

Cyber-Physical Systems Guide
https://github.com/mikeroyal/cyber-physical-systems-guide

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Cyber-Physical Systems Guide

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URL: https://github.com/mikeroyal/cyber-physical-systems-guide
Owner: mikeroyal
Created: 2021-07-25T20:00:39.000Z (almost 5 years ago)
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Last Pushed: 2021-07-25T20:38:19.000Z (almost 5 years ago)
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Topics: cyberphysical, cyberphysical-systems, iot
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- Readme: README.md

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README

Cyber-Physical Systems Guide

#### A guide covering the Cyber-Physical Systems(CPS) the applications, libraries and tools that will make you a better and more efficient with Cyber-Physical Systems(CPS) development.

**Note: You can easily convert this markdown file to a PDF in [VSCode](https://code.visualstudio.com/) using this handy extension [Markdown PDF](https://marketplace.visualstudio.com/items?itemName=yzane.markdown-pdf).**

# Table of Contents

1. [Cyber-Physical Systems Learning Resources](https://github.com/mikeroyal/Cyber-Physical-Systems-Guide#cyber-physical-systems-learning-resources)

2. [Cyber-Physical Systems Tools, Libraries, and Frameworks](https://github.com/mikeroyal/Cyber-Physical-Systems-Guide#cyber-physical-systems-tools-libraries-and-frameworks)

3. [Security Tools and Frameworks](https://github.com/mikeroyal/Cyber-Physical-Systems-Guide#security-tools-and-frameworks)

4. [Algorithms](https://github.com/mikeroyal/Cyber-Physical-Systems-Guide#algorithms)

5. [Machine Learning](https://github.com/mikeroyal/Cyber-Physical-Systems-Guide#machine-learning)

6. [Robotics Development](https://github.com/mikeroyal/Cyber-Physical-Systems-Guide#robotics-development)

7. [Networking](https://github.com/mikeroyal/Cyber-Physical-Systems-Guide#networking)

8. [Telco 5G Development](https://github.com/mikeroyal/Cyber-Physical-Systems-Guide#telco-5g-development)

9. [Cloud Native Development](https://github.com/mikeroyal/Cyber-Physical-Systems-Guide#cloud-native)

10. [MATLAB Development](https://github.com/mikeroyal/Cyber-Physical-Systems-Guide#matlab-development)

11. [R Development](https://github.com/mikeroyal/Cyber-Physical-Systems-Guide#r-development)

12. [Python Development](https://github.com/mikeroyal/Cyber-Physical-Systems-Guide#python-development)

13. [C/C++ Development](https://github.com/mikeroyal/Cyber-Physical-Systems-Guide#cc-development)

14. [Java Development](https://github.com/mikeroyal/Cyber-Physical-Systems-Guide#java-development)

# Cyber-Physical Systems Learning Resources
[Back to the Top](https://github.com/mikeroyal/Cyber-Physical-Systems-Guide#table-of-contents)

[Cyber-Physical Systems (CPS)](https://www.nist.gov/el/cyber-physical-systems) is a system comprised of interacting digital, analog, physical, and human components engineered for function through integrated physics and computer-based algorithms.

[Internet of Things Graduate Program | Stanford Online](https://online.stanford.edu/programs/internet-things-graduate-program)

[Cyber Physical Systems: The Next Computing Revolution](https://www.seas.upenn.edu/~lee/09cis480/lec-CPS.pdf)

[Cyber-Physical Systems | NSF (National Science Foundation)](https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=503286)

[Cybersecurity and Physical Security Convergence | CISA](https://www.cisa.gov/cybersecurity-and-physical-security-convergence)

[Cyber-Physical Systems - MATLAB & Simulink](https://www.mathworks.com/discovery/cyber-physical-systems.html)

[Cyber-Physical Systems (IoT) Security](https://appliedtech.iit.edu/sites/sat/files/pdfs/ITM/CYBER-PHYSICAL%20SYSTEMS%20SECURITY.pdf)

[Online Embedded Systems Courses | Harvard University](https://online-learning.harvard.edu/subject/embedded-systems-1)

[Internet of Things Graduate Program | Stanford Online](https://online.stanford.edu/programs/internet-things-graduate-program)

[Embedded Systems Certificate | UCSC Silicon Valley Extension](https://www.ucsc-extension.edu/certificates/embedded-systems/)

[Embedded Systems Technology (EET) | ODU Online](https://online.odu.edu/programs/embedded-systems-engineering-technology-eet)

[Learn Embedded Systems with Online Courses and Classes | edX](https://www.edx.org/learn/embedded-systems)

[Top Embedded Systems Courses Online | Udemy](https://www.udemy.com/topic/embedded-systems/)

[Top Embedded C Courses Online | Coursera](https://www.coursera.org/courses?query=embedded%20c)

[Embedded Systems | Udacity Free Courses](https://www.udacity.com/course/embedded-systems--ud169)

[Embedded Linux Online Course - Arm®](https://www.arm.com/resources/education/online-courses/embedded-linux)

[Software Development Online Courses | Coursera](https://www.coursera.org/browse/computer-science/software-development)

[Top Software Engineering Courses | Coursera](https://www.coursera.org/courses?query=software%20engineering&index=prod_all_products_term_optimization&completemode=undefined&page=1)

[Learn Software Development with Online Courses and Lessons | edX](https://www.edx.org/learn/software-development)

# Cyber-Physical Systems Tools, Libraries, and Frameworks
[Back to the Top](https://github.com/mikeroyal/Cyber-Physical-Systems-Guide#table-of-contents)

[SysML](https://sysml.org/) is an enabling technology for Model-Based Systems Engineering (MBSE).

[OMG Systems Modeling Language™ (OMG SysML®)](https://www.omgsysml.org/what-is-sysml.htm) is a general-purpose graphical modeling language for specifying, analyzing, designing, and verifying complex systems that may include hardware, software, information, personnel, procedures, and facilities.

[Modelica Standard Library](https://doc.modelica.org/) is a free library from the Modelica Association to model mechanical (1D/3D), electrical (analog, digital, machines), magnetic, thermal, fluid, control systems and hierarchical state machines. Also numerical functions and functions for strings, files and streams are included.

[Netdata](https://github.com/netdata/netdata) is high-fidelity infrastructure monitoring and troubleshooting, real-time monitoring Agent collects thousands of metrics from systems, hardware, containers, and applications with zero configuration. It runs permanently on all your physical/virtual servers, containers, cloud deployments, and edge/IoT devices, and is perfectly safe to install on your systems mid-incident without any preparation.

[Authelia](https://www.authelia.com/) is an open-source highly-available authentication server providing single sign-on capability and two-factor authentication to applications running behind [NGINX](https://nginx.org/en/).

[nginx(engine x)](https://nginx.org/en/) is an HTTP and reverse proxy server, a mail proxy server, and a generic TCP/UDP proxy server, originally written by Igor Sysoev.

[Proxmox Virtual Environment(VE)](https://www.proxmox.com/en/) is a complete open-source platform for enterprise virtualization. It inlcudes a built-in web interface that you can easily manage VMs and containers, software-defined storage and networking, high-availability clustering, and multiple out-of-the-box tools on a single solution.

[Landlock LSM(Linux Security Module)](https://www.kernel.org/doc/html/latest/security/landlock.html) is a framework to create scoped access-control (sandboxing). To harden a whole system, this feature should be available to any process, including unprivileged ones. Because such process may be compromised or backdoored (untrusted), Landlock’s features must be safe to use from the kernel and other processes point of view. Landlock’s interface must therefore expose a minimal attack surface. It is designed to be usable by unprivileged processes while following the system security policy enforced by other access control mechanisms (DAC, LSM( Linux Security Module)).

[Virtualization-based Security (VBS)](https://docs.microsoft.com/en-us/windows-hardware/design/device-experiences/oem-vbs) is a hardware virtualization feature to create and isolate a secure region of memory from the normal operating system.

[Hypervisor-Enforced Code Integrity (HVCI)](https://docs.microsoft.com/en-us/windows-hardware/drivers/bringup/device-guard-and-credential-guard) is a mechanism whereby a hypervisor, such as Hyper-V, uses hardware virtualization to protect kernel-mode processes against the injection and execution of malicious or unverified code. Code integrity validation is performed in a secure environment that is resistant to attack from malicious software, and page permissions for kernel mode are set and maintained by the hypervisor.

[eBPF](https://ebpf.io/) is a technology that can run sandboxed programs in the Linux kernel without changing kernel source code or loading kernel modules. By making the Linux kernel programmable, infrastructure software can leverage existing layers, making them more intelligent and feature-rich without continuing to add additional layers of complexity to the system.

[IDA Pro(Interactive DisAssembler Professional)](https://hex-rays.com/IDA-pro/) is a programmable and multi-processor disassembler combined with a local/remote debugger and along with a complete plugin programming environment. It's a great tool for testing and discovering security vulnerabilities.

[Ghidra](https://github.com/NationalSecurityAgency/ghidra) is a software reverse engineering (SRE) framework developed by NSA's Research Directorate for NSA's cybersecurity mission. It helps analyze any malicious code and malware like viruses, and can give cybersecurity professionals a better understanding of potential vulnerabilities in their networks and systems.

[MiniCPS](https://github.com/scy-phy/minicps) is a framework for Cyber-Physical Systems real-time simulation. It includes support for physical process and control devices simulation, and network emulation. It is build on top of [mininet](https://github.com/mininet/mininet).

[ReachabilityAnalysis.jl](https://juliareach.github.io/ReachabilityAnalysis.jl/dev/) is a library for the [Julia programming language]() used for computing rigorous approximations of the set of states reachable by a dynamical system. In the scope of this package are systems modeled by continuous or hybrid dynamical systems, where the dynamics changes with discrete events. Systems are modelled by ordinary differential equations (ODEs) or semi-discrete partial differential equations (PDEs), with uncertain initial states, uncertain parameters or non-deterministic inputs.

[Lidar Toolbox™](https://www.mathworks.com/products/lidar.html) is a MATLAB tool that provides algorithms, functions, and apps for designing, analyzing, and testing lidar processing systems. You can perform object detection and tracking, semantic segmentation, shape fitting, lidar registration, and obstacle detection. Lidar Toolbox supports lidar-camera cross calibration for workflows that combine computer vision and lidar processing.

[Automated Driving Toolbox™](https://www.mathworks.com/products/automated-driving.html) is a MATLAB tool that provides algorithms and tools for designing, simulating, and testing ADAS and autonomous driving systems. You can design and test vision and lidar perception systems, as well as sensor fusion, path planning, and vehicle controllers. Visualization tools include a bird’s-eye-view plot and scope for sensor coverage, detections and tracks, and displays for video, lidar, and maps. The toolbox lets you import and work with HERE HD Live Map data and OpenDRIVE® road networks. It also provides reference application examples for common ADAS and automated driving features, including FCW, AEB, ACC, LKA, and parking valet. The toolbox supports C/C++ code generation for rapid prototyping and HIL testing, with support for sensor fusion, tracking, path planning, and vehicle controller algorithms.

[Microsoft AirSim](https://microsoft.github.io/AirSim/lidar.html) is a simulator for drones, cars and more, built on Unreal Engine (with an experimental Unity release). AirSim is open-source, cross platform, and supports [software-in-the-loop simulation](https://www.mathworks.com/help///ecoder/software-in-the-loop-sil-simulation.html) with popular flight controllers such as PX4 & ArduPilot and [hardware-in-loop](https://www.ni.com/en-us/innovations/white-papers/17/what-is-hardware-in-the-loop-.html) with PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped into any Unreal environment. AirSim is being developed as a platform for AI research to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles.

[VSLR Framework](https://github.com/IBM/vsrl-framework) is a Verifiably Safe Reinforcement Learning Framework developed by IBM. The Safe reinforcement learning algorithms learn how to solve sequential decision making problems while continuously maintaining safety specifications (e.g., collision avoidance).

[Ariadne](https://www.ariadne-cps.org/) is a tool for reachability analysis and model checking of hybrid systems. Additionally, it is a framework for rigorous computation featuring arithmetic, linear algebra, calculus, geometry, algebraic and differential equations, and optimization solvers.

### Sensors

[CCD(charge coupled device)](https://global.canon/en/technology/s_labo/light/003/04.html) is a semiconductor image sensor used in digital cameras to convert light into electrical signals. CCD Sensors are made up of tiny elements known as pixels with expressions such as 6 MP(megapixel) or 12 MP(megapixel) refering to the number of pixels comprising the CCD Sensors of a camera. With each pixel there is a tiny photodiode that is sensitive to light(photon) and becomes electrically charged in accordance with the strength of light it captures.

[CMOS(Complementary Metal Oxide Semiconductor) sensors](https://global.canon/en/technology/s_labo/light/003/05.html) are semiconductor image sensors that convert light into electrical signals. It includes features such as timing logic, exposure control, analog-to-digital conversion, shuttering, white balance, gain adjustment, and initial image processing algorithms. CMOS sensors contain rows of photodiodes coupled with individual amplifiers to amplify the electric signal from the photodiodes. This not only enables CMOS sensors to operate on less electrical power than CCDs, but also enables speedier and easier reading of electrical charges at a relatively low-cost.

**Sensors from Alps Alpine. Source: [Alps Alpine](https://tech.alpsalpine.com/e/category_sensor.html)**

**Different Types of Sensors. Source: [electronicshub](https://www.electronicshub.org/different-types-sensors/)**

### Actuators

[Actuators](https://www.progressiveautomations.com/pages/actuators) are mechanical or electro-mechanical devices that provide controlled and sometimes limited movements or positioning which are operated electrically, manually, or by various fluids such as air, hydraulic, etc. Actuators are present in most machines such as smartphones, household appliances to vehicles, industrial devices, and robots.

[Complete Guide to Actuators (Types, Attributes, Applications and Suppliers)](https://www.thomasnet.com/articles/pumps-valves-accessories/types-of-actuators/)

[Actuators | SMC Corporation of America](https://www.smcusa.com/products/Actuators)

[Actuators | Emerson US](https://www.emerson.com/en-us/automation/valves-actuators-regulators/actuators)

**Types of Actuators**

# Security Tools and Frameworks
[Back to the Top](https://github.com/mikeroyal/Cyber-Physical-Systems-Guide#table-of-contents)

## Security Standards, Frameworks and Benchmarks

[STIGs Benchmarks - Security Technical Implementation Guides](https://public.cyber.mil/stigs/)

[CIS Benchmarks - CIS Center for Internet Security](https://www.cisecurity.org/cis-benchmarks/)

[NIST - Current FIPS](https://www.nist.gov/itl/current-fips)

[ISO Standards Catalogue](https://www.iso.org/standards.html)

[Common Criteria for Information Technology Security Evaluation (CC)](https://www.commoncriteriaportal.org/cc/) is an international standard (ISO / IEC 15408) for computer security. It allows an objective evaluation to validate that a particular product satisfies a defined set of security requirements.

[ISO 22301](https://www.iso.org/en/contents/data/standard/07/51/75106.html) is the international standard that provides a best-practice framework for implementing an optimised BCMS (business continuity management system).

[ISO27001](https://www.iso.org/isoiec-27001-information-security.html) is the international standard that describes the requirements for an ISMS (information security management system). The framework is designed to help organizations manage their security practices in one place, consistently and cost-effectively.

[ISO 27701](https://www.iso.org/en/contents/data/standard/07/16/71670.html) specifies the requirements for a PIMS (privacy information management system) based on the requirements of ISO 27001.
It is extended by a set of privacy-specific requirements, control objectives and controls. Companies that have implemented ISO 27001 will be able to use ISO 27701 to extend their security efforts to cover privacy management.

[EU GDPR (General Data Protection Regulation)](https://gdpr.eu/) is a privacy and data protection law that supersedes existing national data protection laws across the EU, bringing uniformity by introducing just one main data protection law for companies/organizations to comply with.

[CCPA (California Consumer Privacy Act)](https://www.oag.ca.gov/privacy/ccpa) is a data privacy law that took effect on January 1, 2020 in the State of California. It applies to businesses that collect California residents’ personal information, and its privacy requirements are similar to those of the EU’s GDPR (General Data Protection Regulation).

[Payment Card Industry (PCI) Data Security Standards (DSS)](https://docs.microsoft.com/en-us/microsoft-365/compliance/offering-pci-dss) is a global information security standard designed to prevent fraud through increased control of credit card data.

[SOC 2](https://www.aicpa.org/interestareas/frc/assuranceadvisoryservices/aicpasoc2report.html) is an auditing procedure that ensures your service providers securely manage your data to protect the interests of your comapny/organization and the privacy of their clients.

[NIST CSF](https://www.nist.gov/national-security-standards) is a voluntary framework primarily intended for critical infrastructure organizations to manage and mitigate cybersecurity risk based on existing best practice.

## Security Tools

[DataWave](https://github.com/NationalSecurityAgency/datawave) is an ingest/query framework that leverages [Apache Accumulo](https://accumulo.apache.org/) to provide fast, secure data access.

[Emissary](https://github.com/NationalSecurityAgency/emissary) is a P2P based data-driven workflow engine that runs in a heterogeneous possibly widely dispersed, multi-tiered P2P network of compute resources. Workflow itineraries are not pre-planned as in conventional workflow engines, but are discovered as more information is discovered about the data.

[MADCert](https://github.com/NationalSecurityAgency/MADCert) is a cross-platform tool that consists of a certificate generator, a file system certificate manager, and a command line interface for the purposes of testing.

[BLESS(Bastion's Lambda Ephemeral SSH Service)](https://github.com/Netflix/bless) is an SSH Certificate Authority that runs as an AWS Lambda function and is used to sign SSH public keys.

[Zuul](https://github.com/Netflix/zuul) is an [L7 application gateway](https://www.f5.com/services/resources/glossary/application-layer-gateway) that provides capabilities for dynamic routing, monitoring, resiliency, security, and more.

[Chaos Monkey](https://github.com/Netflix/chaosmonkey) is a resiliency tool that helps applications tolerate random instance failures. It is fully integrated with [Spinnaker](https://www.spinnaker.io/), the continuous delivery platform. Chaos Monkey will work with any backend that Spinnaker supports (AWS, Google Compute Engine, Azure, Kubernetes, Cloud Foundry).

[Priam](https://github.com/Netflix/Priam) is a tool/process for backup/recovery, Token Management, and Centralized Configuration management for Cassandra.

[Vector](https://github.com/Netflix/vector) is an on-host performance monitoring framework which exposes hand picked high resolution metrics to every engineer’s browser.

[Control Groups(Cgroups)](https://www.redhat.com/sysadmin/cgroups-part-one) is a Linux kernel feature that allows you to allocate resources such as CPU time, system memory, network bandwidth, or any combination of these resources for user-defined groups of tasks (processes) running on a system.

[Libgcrypt](https://www.gnupg.org/related_software/libgcrypt/) is a general purpose cryptographic library originally based on code from GnuPG.

[Aircrack-ng](https://www.aircrack-ng.org/) is a network software suite consisting of a detector, packet sniffer, WEP and WPA/WPA2-PSK cracker and analysis tool for 802.11 wireless LANs. It works with any wireless network interface controller whose driver supports raw monitoring mode and can sniff 802.11a, 802.11b and 802.11g traffic.

[Burp Suite](https://portswigger.net/burp) is a leading range of cybersecurity tools.

[Cilium](https://cilium.io/) uses eBPF to accelerate getting data in and out of L7 proxies such as Envoy, enabling efficient visibility into API protocols like HTTP, gRPC, and Kafka.

[Hubble](https://github.com/cilium/hubble) is a Network, Service & Security Observability for Kubernetes using eBPF.

[Istio](https://istio.io/) is an open platform to connect, manage, and secure microservices. Istio's control plane provides an abstraction layer over the underlying cluster management platform, such as Kubernetes and Mesos.

[Certgen](https://github.com/cilium/certgen) is a convenience tool to generate and store certificates for Hubble Relay mTLS.

[Scapy](https://scapy.net/) is a python-based interactive packet manipulation program & library.

[syzkaller](https://github.com/google/syzkaller) is an unsupervised, coverage-guided kernel fuzzer.

[SchedViz](https://github.com/google/schedviz) is a tool for gathering and visualizing kernel scheduling traces on Linux machines.

[oss-fuzz](https://google.github.io/oss-fuzz/) aims to make common open source software more secure and stable by combining modern fuzzing techniques with scalable, distributed execution.

[OSSEC](https://www.ossec.net/) is a free, open-source host-based intrusion detection system. It performs log analysis, integrity checking, Windows registry monitoring, rootkit detection, time-based alerting, and active response.

[Metasploit Project](https://www.metasploit.com/) is a computer security project that provides information about security vulnerabilities and aids in penetration testing and IDS signature development.

[Wfuzz](https://github.com/xmendez/wfuzz) was created to facilitate the task in web applications assessments and it is based on a simple concept: it replaces any reference to the FUZZ keyword by the value of a given payload.

[Nmap](https://nmap.org/) is a security scanner used to discover hosts and services on a computer network, thus building a "map" of the network.

[Patchwork](https://github.com/getpatchwork/patchwork) is a web-based patch tracking system designed to facilitate the contribution and management of contributions to an open-source project.

[pfSense](https://www.pfsense.org/) is a free and open source firewall and router that also features unified threat management, load balancing, multi WAN, and more.

[Snort](https://www.snort.org/) is an open-source, free and lightweight network intrusion detection system (NIDS) software for Linux and Windows to detect emerging threats.

[Wireshark](https://www.wireshark.org/) is a free and open-source packet analyzer. It is used for network troubleshooting, analysis, software and communications protocol development, and education.

[OpenSCAP](https://www.open-scap.org/) is U.S. standard maintained by [National Institute of Standards and Technology (NIST)](https://www.nist.gov/). It provides multiple tools to assist administrators and auditors with assessment, measurement, and enforcement of security baselines. OpenSCAP maintains great flexibility and interoperability by reducing the costs of performing security audits. Whether you want to evaluate DISA STIGs, NIST‘s USGCB, or Red Hat’s Security Response Team’s content, all are supported by OpenSCAP.

[Tink](https://github.com/google/tink) is a multi-language, cross-platform, open source library that provides cryptographic APIs that are secure, easy to use correctly, and harder to misuse.

[OWASP](https://www.owasp.org/index.php/Main_Page) is an online community, produces freely-available articles, methodologies, documentation, tools, and technologies in the field of web application security.

[Open Vulnerability and Assessment Language](https://oval.mitre.org/) is a community effort to standardize how to assess and report upon the machine state of computer systems. OVAL includes a language to encode system details, and community repositories of content. Tools and services that use OVAL provide enterprises with accurate, consistent, and actionable information to improve their security.

# Algorithms
[Back to the Top](https://github.com/mikeroyal/Cyber-Physical-Systems-Guide#table-of-contents)

[Fuzzy logic](https://www.investopedia.com/terms/f/fuzzy-logic.asp) is a heuristic approach that allows for more advanced decision-tree processing and better integration with rules-based programming.

**Architecture of a Fuzzy Logic System. Source: [ResearchGate](https://www.researchgate.net/figure/Architecture-of-a-fuzzy-logic-system_fig2_309452475)**

[Support Vector Machine (SVM)](https://web.stanford.edu/~hastie/MOOC-Slides/svm.pdf) is a supervised machine learning model that uses classification algorithms for two-group classification problems.

**Support Vector Machine (SVM). Source:[OpenClipArt](https://openclipart.org/detail/182977/svm-support-vector-machines)**

[Neural networks](https://www.ibm.com/cloud/learn/neural-networks) are a subset of machine learning and are at the heart of deep learning algorithms. The name/structure is inspired by the human brain copying the process that biological neurons/nodes signal to one another.

**Deep neural network. Source: [IBM](https://www.ibm.com/cloud/learn/neural-networks)**

[Convolutional Neural Networks (R-CNN)](https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-convolutional-neural-networks) is an object detection algorithm that first segments the image to find potential relevant bounding boxes and then run the detection algorithm to find most probable objects in those bounding boxes.

**Convolutional Neural Networks. Source:[CS231n](https://cs231n.github.io/convolutional-networks/#conv)**

[Recurrent neural networks (RNNs)](https://www.ibm.com/cloud/learn/recurrent-neural-networks) is a type of artificial neural network which uses sequential data or time series data.

**Recurrent Neural Networks. Source: [Slideteam](https://www.slideteam.net/recurrent-neural-networks-rnns-ppt-powerpoint-presentation-file-templates.html)**

[Multilayer Perceptrons (MLPs)](https://deepai.org/machine-learning-glossary-and-terms/multilayer-perceptron) is multi-layer neural networks composed of multiple layers of [perceptrons](https://en.wikipedia.org/wiki/Perceptron) with a threshold activation.

**Multilayer Perceptrons. Source: [DeepAI](https://deepai.org/machine-learning-glossary-and-terms/multilayer-perceptron)**

[Random forest](https://www.ibm.com/cloud/learn/random-forest) is a commonly-used machine learning algorithm, which combines the output of multiple decision trees to reach a single result. A decision tree in a forest cannot be pruned for sampling and therefore, prediction selection. Its ease of use and flexibility have fueled its adoption, as it handles both classification and regression problems.

**Random forest. Source: [wikimedia](https://community.tibco.com/wiki/random-forest-template-tibco-spotfirer-wiki-page)**

[Decision trees](https://www.cs.cmu.edu/~bhiksha/courses/10-601/decisiontrees/) are tree-structured models for classification and regression.

***Decision Trees. Source: [CMU](http://www.cs.cmu.edu/~bhiksha/courses/10-601/decisiontrees/)*

[Naive Bayes](https://en.wikipedia.org/wiki/Naive_Bayes_classifier) is a machine learning algorithm that is used solved calssification problems. It's based on applying [Bayes' theorem](https://www.mathsisfun.com/data/bayes-theorem.html) with strong independence assumptions between the features.

**Bayes' theorem. Source:[mathisfun](https://www.mathsisfun.com/data/bayes-theorem.html)**

# Machine Learning
[Back to the Top](https://github.com/mikeroyal/Cyber-Physical-Systems-Guide#table-of-contents)

**Machine Learning/Deep Learning Frameworks.**

# Learning Resources for ML

[Machine Learning](https://www.ibm.com/cloud/learn/machine-learning) is a branch of artificial intelligence (AI) focused on building apps using algorithms that learn from data models and improve their accuracy over time without needing to be programmed.

[Machine Learning by Stanford University from Coursera](https://www.coursera.org/learn/machine-learning)

[AWS Training and Certification for Machine Learning (ML) Courses](https://aws.amazon.com/training/learning-paths/machine-learning/)

[Machine Learning Scholarship Program for Microsoft Azure from Udacity](https://www.udacity.com/scholarships/machine-learning-scholarship-microsoft-azure)

[Microsoft Certified: Azure Data Scientist Associate](https://docs.microsoft.com/en-us/learn/certifications/azure-data-scientist)

[Microsoft Certified: Azure AI Engineer Associate](https://docs.microsoft.com/en-us/learn/certifications/azure-ai-engineer)

[Azure Machine Learning training and deployment](https://docs.microsoft.com/en-us/azure/devops/pipelines/targets/azure-machine-learning)

[Learning Machine learning and artificial intelligence from Google Cloud Training](https://cloud.google.com/training/machinelearning-ai)

[Machine Learning Crash Course for Google Cloud](https://developers.google.com/machine-learning/crash-course/)

[JupyterLab](https://jupyterlab.readthedocs.io/)

[Scheduling Jupyter notebooks on Amazon SageMaker ephemeral instances](https://aws.amazon.com/blogs/machine-learning/scheduling-jupyter-notebooks-on-sagemaker-ephemeral-instances/)

[How to run Jupyter Notebooks in your Azure Machine Learning workspace](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-run-jupyter-notebooks)

[Machine Learning Courses Online from Udemy](https://www.udemy.com/topic/machine-learning/)

[Machine Learning Courses Online from Coursera](https://www.coursera.org/courses?query=machine%20learning&)

[Learn Machine Learning with Online Courses and Classes from edX](https://www.edx.org/learn/machine-learning)

# ML Frameworks, Libraries, and Tools

[TensorFlow](https://www.tensorflow.org) is an end-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.

[Keras](https://keras.io) is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.It was developed with a focus on enabling fast experimentation. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML.

[PyTorch](https://pytorch.org) is a library for deep learning on irregular input data such as graphs, point clouds, and manifolds. Primarily developed by Facebook's AI Research lab.

[Amazon SageMaker](https://aws.amazon.com/sagemaker/) is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models.

[Azure Databricks](https://azure.microsoft.com/en-us/services/databricks/) is a fast and collaborative Apache Spark-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.

[Microsoft Cognitive Toolkit (CNTK)](https://docs.microsoft.com/en-us/cognitive-toolkit/) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.

[Apple CoreML](https://developer.apple.com/documentation/coreml) is a framework that helps integrate machine learning models into your app. Core ML provides a unified representation for all models. Your app uses Core ML APIs and user data to make predictions, and to train or fine-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.

[Tensorflow_macOS](https://github.com/apple/tensorflow_macos) is a Mac-optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.

[Apache OpenNLP](https://opennlp.apache.org/) is an open-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).

[Apache Airflow](https://airflow.apache.org) is an open-source workflow management platform created by the community to programmatically author, schedule and monitor workflows. Install. Principles. Scalable. Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.

[Open Neural Network Exchange(ONNX)](https://github.com/onnx) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types.

[Apache MXNet](https://mxnet.apache.org/) is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines. Support for Python, R, Julia, Scala, Go, Javascript and more.

[AutoGluon](https://autogluon.mxnet.io/index.html) is toolkit for Deep learning that automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy deep learning models on tabular, image, and text data.

[Anaconda](https://www.anaconda.com/) is a very popular Data Science platform for machine learning and deep learning that enables users to develop models, train them, and deploy them.

[PlaidML](https://github.com/plaidml/plaidml) is an advanced and portable tensor compiler for enabling deep learning on laptops, embedded devices, or other devices where the available computing hardware is not well supported or the available software stack contains unpalatable license restrictions.

[OpenCV](https://opencv.org) is a highly optimized library with focus on real-time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.

[Scikit-Learn](https://scikit-learn.org/stable/index.html) is a Python module for machine learning built on top of SciPy, NumPy, and matplotlib, making it easier to apply robust and simple implementations of many popular machine learning algorithms.

[Weka](https://www.cs.waikato.ac.nz/ml/weka/) is an open source machine learning software that can be accessed through a graphical user interface, standard terminal applications, or a Java API. It is widely used for teaching, research, and industrial applications, contains a plethora of built-in tools for standard machine learning tasks, and additionally gives transparent access to well-known toolboxes such as scikit-learn, R, and Deeplearning4j.

[Caffe](https://github.com/BVLC/caffe) is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors.

[Theano](https://github.com/Theano/Theano) is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently including tight integration with NumPy.

[nGraph](https://github.com/NervanaSystems/ngraph) is an open source C++ library, compiler and runtime for Deep Learning. The nGraph Compiler aims to accelerate developing AI workloads using any deep learning framework and deploying to a variety of hardware targets.It provides the freedom, performance, and ease-of-use to AI developers.

[NVIDIA cuDNN](https://developer.nvidia.com/cudnn) is a GPU-accelerated library of primitives for [deep neural networks](https://developer.nvidia.com/deep-learning). cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including [Caffe2](https://caffe2.ai/), [Chainer](https://chainer.org/), [Keras](https://keras.io/), [MATLAB](https://www.mathworks.com/solutions/deep-learning.html), [MxNet](https://mxnet.incubator.apache.org/), [PyTorch](https://pytorch.org/), and [TensorFlow](https://www.tensorflow.org/).

[Jupyter Notebook](https://jupyter.org/) is an open-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](https://spark.apache.org/) is a unified analytics engine for large-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 Spark Connector for SQL Server and Azure SQL](https://github.com/microsoft/sql-spark-connector) is a high-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.

[Apache PredictionIO](https://predictionio.apache.org/) is an open source machine learning framework for developers, data scientists, and end users. It supports event collection, deployment of algorithms, evaluation, querying predictive results via REST APIs. It is based on scalable open source services like Hadoop, HBase (and other DBs), Elasticsearch, Spark and implements what is called a Lambda Architecture.

[Cluster Manager for Apache Kafka(CMAK)](https://github.com/yahoo/CMAK) is a tool for managing [Apache Kafka](https://kafka.apache.org/) clusters.

[BigDL](https://bigdl-project.github.io/) is a distributed deep learning library for Apache Spark. With BigDL, users can write their deep learning applications as standard Spark programs, which can directly run on top of existing Spark or Hadoop clusters.

[Eclipse Deeplearning4J (DL4J)](https://deeplearning4j.konduit.ai/) is a set of projects intended to support all the needs of a JVM-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.

[Tensorman](https://github.com/pop-os/tensorman) is a utility for easy management of Tensorflow containers by developed by [System76]( https://system76.com).Tensorman allows Tensorflow to operate in an isolated environment that is contained from the rest of the system. This virtual environment can operate independent of the base system, allowing you to use any version of Tensorflow on any version of a Linux distribution that supports the Docker runtime.

[Numba](https://github.com/numba/numba) is an open source, NumPy-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.

[Chainer](https://chainer.org/) is a Python-based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using [CuPy](https://github.com/cupy/cupy) for high performance training and inference.

[XGBoost](https://xgboost.readthedocs.io/) is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. It supports distributed training on multiple machines, including AWS, GCE, Azure, and Yarn clusters. Also, it can be integrated with Flink, Spark and other cloud dataflow systems.

[cuML](https://github.com/rapidsai/cuml) is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects. cuML enables data scientists, researchers, and software engineers to run traditional tabular ML tasks on GPUs without going into the details of CUDA programming. In most cases, cuML's Python API matches the API from scikit-learn.

# Robotics Development
[Back to the Top](https://github.com/mikeroyal/Cyber-Physical-Systems-Guide#table-of-contents)

## Robotic Arms & Dev Kits

[Eva](https://automata.tech/about-eva/) is a 6-axis of freedom and ease of use, the lab robot arm easily automates the loading and unloading of samples into lab machines, precision balances and more. Eva can handle a range of common lab items including microplates, PCR tube strips, eppendorf tubes, petri dishes and microscope slides to automating repetitive and strenuous sample handling processes. Common manufacturing machine tending applications with Eva include start/end of line loading and unloading of machines such as CNC machines, CMMs, metal presses and chemical etching machines.

**Eva lab robot arm from Automata. Source: [Automata](https://automata.tech/about-eva/)**

**AWS DeepRacer autonomous Car Kit**

[Checkout the AWS DeepRacer autonomous Car Kit](https://www.amazon.com/AWS-DeepRacer-Fully-autonomous-developers/dp/B07JMHRKQG)

**AWS DeepRacer autonomous Car Kit Hardware Specifications**

- CAR: 18th scale 4WD with monster truck chassis
- CPU: Intel Atom™ Processor
- MEMORY: 4GB RAM
- STORAGE: 32GB (expandable)
- WI-FI: 802.11ac
- CAMERA: 4 MP cameras with MJPEG
- SOFTWARE: Ubuntu OS 16.04 LTS, Intel® OpenVINO™ toolkit, ROS Kinetic
- DRIVE BATTERY: 7.4V/1100mAh lithium polymer
- COMPUTE BATTERY: 13600mAh USB-C PD
- PORTS: 4x USB-A, 1x USB-C, 1x Micro-USB, 1x HDMI
- SENSORS: Integrated accelerometer and gyroscope

[Checkout the SunFounder PiCar-V Kit V2.0 for Raspberry Pi](https://www.sunfounder.com/products/smart-video-car)

## Robotics Learning Resources

[Robotics courses from Coursera](https://www.edx.org/learn/robotics)

[Learn Robotics with Online Courses and Classes from edX](https://www.edx.org/learn/robotics)

[Top Robotics Courses Online from Udemy](https://www.udemy.com/topic/robotics/)

[Free Online AI & Robotics Courses](https://www.futurelearn.com/subjects/it-and-computer-science-courses/ai-and-robotics)

[REC Foundation Robotics Industry Certification](https://www.roboticseducation.org/industry-certifications/)

[Carnegie Mellon Robotics Academy](https://www.cmu.edu/roboticsacademy/Training/Certifications.html)

[RIA Robotic Integrator Certification Program](https://www.robotics.org/robotics/integrator-certification)

[AWS RoboMaker – Develop, Test, Deploy, and Manage Intelligent Robotics Apps](https://aws.amazon.com/blogs/aws/aws-robomaker-develop-test-deploy-and-manage-intelligent-robotics-apps/)

[Microsoft AI School](https://aischool.microsoft.com/en-us/home)

[Language Understanding (LUIS) for Azure Cognitive Services](https://docs.microsoft.com/en-us/azure/cognitive-services/luis/what-is-luis)

[ROS on Windows 10](https://ms-iot.github.io/ROSOnWindows/)

[Windows ML ROS Node](https://ms-iot.github.io/ROSOnWindows/ROSAtMS/WinML.html)

[Azure VM templates to bootstrap ROS and ROS 2 environments](https://ms-iot.github.io/ROSOnWindows/ROSAtMS/AzureVM.html)

[Google Robotics Research](https://research.google/teams/brain/robotics/)

## Robotics Tools and Frameworks

[Robot Framework](https://robotframework.org/) is a generic open source automation framework. It can be used for test automation and robotic process automation. It has easy syntax, utilizing human-readable keywords. Its capabilities can be extended by libraries implemented with Python or Java.

[The Robotics Library (RL)](https://github.com/roboticslibrary/rl) is a self-contained C++ library for robot kinematics, motion planning and control. It covers mathematics, kinematics and dynamics, hardware abstraction, motion planning, collision detection, and visualization.RL runs on many different systems, including Linux, macOS, and Windows. It uses CMake as a build system and can be compiled with Clang, GCC, and Visual Studio.

[Robot Structural Analysis Professional](https://www.autodesk.com/products/robot-structural-analysis/overview?term=1-YEAR) is structural load analysis software developed by Autodesk that verifies code compliance and uses BIM-integrated workflows to exchange data with Revit. It can help you to create more resilient, constructible designs that are accurate, coordinated, and connected to BIM.

[PowerMill](https://www.autodesk.com/products/powermill/overview) is a software developed by Autodesk that provides powerful, flexible, easy-to-use tools for offline programming of robots. Get tools to help you optimize robotic paths and simulate virtual mock-ups of manufacturing cells and systems.

[ROS](https://www.ros.org/) is robotics middleware. Although ROS is not an operating system, it provides services designed for a heterogeneous computer cluster such as hardware abstraction, low-level device control, implementation of commonly used functionality, message-passing between processes, and package management.

[ROS2](https://index.ros.org/doc/ros2/) is a set of [software libraries and tools](https://github.com/ros2) that help you build robot applications. From drivers to state-of-the-art algorithms, and with powerful developer tools, ROS has what you need for your next robotics project. And it’s all open source.

[MoveIt](https://moveit.ros.org/) is the most widely used software for manipulation and has been used on over 100 robots. It provides an easy-to-use robotics platform for developing advanced applications, evaluating new designs and building integrated products for industrial, commercial, R&D, and other domains.

[AutoGluon](https://autogluon.mxnet.io/index.html) is toolkit for [Deep learning](https://gitlab.com/maos20008/intro-to-machine-learning) that automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy deep learning models on tabular, image, and text data.

[Gazebo](http://gazebosim.org/) accurately and efficiently simulates indoor and outdoor robots. You get a robust physics engine, high-quality graphics, and programmatic and graphical interfaces.

[Robotics System Toolbox](https://www.mathworks.com/products/robotics.html) provides tools and algorithms for designing, simulating, and testing manipulators, mobile robots, and humanoid robots. For manipulators and humanoid robots, the toolbox includes algorithms for collision checking, trajectory generation, forward and inverse kinematics, and dynamics using a rigid body tree representation.
For mobile robots, it includes algorithms for mapping, localization, path planning, path following, and motion control. The toolbox provides reference examples of common industrial robot applications. It also includes a library of
commercially available industrial robot models that you can import, visualize, and simulate.

[Intel Robot DevKit](https://github.com/intel/robot_devkit) is the tool to generate Robotics Software Development Kit (RDK) designed for autonomous devices, including the ROS2 core and capacibilities packages like perception, planning, control driver etc. It provides flexible build/runtime configurations to meet different autonomous requirement on top of diversity hardware choices, for example use different hareware engine CPU/GPU/VPU to accelerate AI related features.

[Arduino](https://www.arduino.cc/) is an open-source platform used for building electronics projects. Arduino consists of both a physical programmable circuit board (often referred to as a microcontroller) and a piece of software, or IDE (Integrated Development Environment) that runs on your computer, used to write and upload computer code to the physical board.

[ArduPilot](https://ardupilot.org/ardupilot/index.html) enables the creation and use of trusted, autonomous, unmanned vehicle systems for the peaceful benefit of all. ArduPilot provides a comprehensive suite of tools suitable for almost any vehicle and application.

[AirSim](https://github.com/Microsoft/AirSim) is a simulator for drones, cars and more, built on Unreal Engine (we now also have an experimental Unity release). It is open-source, cross platform, and supports hardware-in-loop with popular flight controllers such as PX4 for physically and visually realistic simulations.

[The JPL Open Source Rover](https://github.com/nasa-jpl/open-source-rover) is an open source, build it yourself, scaled down version of the 6 wheel rover design that JPL uses to explore the surface of Mars. The Open Source Rover is designed almost entirely out of consumer off the shelf (COTS) parts. This project is intended to be a teaching and learning experience for those who want to get involved in mechanical engineering, software, electronics, or robotics.

[Light Detection and Ranging(LiDAR)](https://en.wikipedia.org/wiki/Lidar) is a remote sensing method that uses light in the form of a pulsed laser at an object, and uses the time and wavelength of the reflected beam of light to estimate the distance and in some applications ([Laser Imaging](https://en.wikipedia.org/wiki/Laser_scanning)), to create a 3D representation of the object and its surface characteristics. This technology is commonly used in aircraft and self-driving vehicles.

[AliceVision](https://github.com/alicevision/AliceVision) is a Photogrammetric Computer Vision Framework which provides a 3D Reconstruction and Camera Tracking algorithms. AliceVision aims to provide strong software basis with state-of-the-art computer vision algorithms that can be tested, analyzed and reused. The project is a result of collaboration between academia and industry to provide cutting-edge algorithms with the robustness and the quality required for production usage.

[CARLA](https://github.com/carla-simulator/carla) is an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites and environmental conditions.

[ROS bridge](https://github.com/carla-simulator/ros-bridge) is a package to bridge ROS for CARLA Simulator.

[ROS-Industrial](https://rosindustrial.org/) is an open source project that extends the advanced capabilities of ROS software to manufacturing.

[AWS RoboMaker](https://aws.amazon.com/robomaker/) is the most complete cloud solution for robotic developers to simulate, test and securely deploy robotic applications at scale. RoboMaker provides a fully-managed, scalable infrastructure for simulation that customers use for multi-robot simulation and CI/CD integration with regression testing in simulation.

[Microsoft Robotics Developer Studio](https://www.microsoft.com/en-us/download/details.aspx?id=29081) is a free .NET-based programming environment for building robotics applications.

[Visual Studio Code Extension for ROS](https://github.com/ms-iot/vscode-ros) is an extension provides support for Robot Operating System (ROS) development.

[Azure Kinect ROS Driver](https://github.com/microsoft/azure_kinect_ros_driver) is a node which publishes sensor data from the [Azure Kinect Developer Kit](https://azure.microsoft.com/en-us/services/kinect-dk/) to the [Robot Operating System (ROS)](http://www.ros.org/). Developers working with ROS can use this node to connect an Azure Kinect Developer Kit to an existing ROS installation.

[Azure IoT Hub for ROS](https://github.com/microsoft/ros_azure_iothub) is a ROS package works with the Microsoft Azure IoT Hub service to relay telemetry messages from the Robot to Azure IoT Hub or reflect properties from the Digital Twin to the robot using dynamic reconfigure.

[ROS 2 with ONNX Runtime](https://github.com/ms-iot/ros_msft_onnx) is a program that uses ROS 2 to run on different hardware platforms using their respective AI acceleration libraries for optimized execution of the ONNX model.

[Azure Cognitive Services LUIS ROS Node](https://github.com/ms-iot/ros_msft_luis) is a ROS node that bridges between ROS and the Azure Language Understanding Service. it can be configured to process audio directly from a microphone, or can subscribe to a ROS audio topic, then processes speech and generates "intent" ROS messages which can be processed by another ROS node to generate ROS commands.

# MATLAB Development
[Back to the Top](https://github.com/mikeroyal/Cyber-Physical-Systems-Guide#table-of-contents)

## MATLAB Learning Resources

[MATLAB](https://www.mathworks.com/products/matlab.html) is a programming language that does numerical computing such as expressing matrix and array mathematics directly.

[MATLAB Documentation](https://www.mathworks.com/help/matlab/)

[Getting Started with MATLAB ](https://www.mathworks.com/help/matlab/getting-started-with-matlab.html)

[MATLAB and Simulink Training from MATLAB Academy](https://matlabacademy.mathworks.com)

[MathWorks Certification Program](https://www.mathworks.com/services/training/certification.html)

[MATLAB Online Courses from Udemy](https://www.udemy.com/topic/matlab/)

[MATLAB Online Courses from Coursera](https://www.coursera.org/courses?query=matlab)

[MATLAB Online Courses from edX](https://www.edx.org/learn/matlab)

[Building a MATLAB GUI](https://www.mathworks.com/discovery/matlab-gui.html)

[MATLAB Style Guidelines 2.0](https://www.mathworks.com/matlabcentral/fileexchange/46056-matlab-style-guidelines-2-0)

[Setting Up Git Source Control with MATLAB & Simulink](https://www.mathworks.com/help/matlab/matlab_prog/set-up-git-source-control.html)

[Pull, Push and Fetch Files with Git with MATLAB & Simulink](https://www.mathworks.com/help/matlab/matlab_prog/push-and-fetch-with-git.html)

[Create New Repository with MATLAB & Simulink](https://www.mathworks.com/help/matlab/matlab_prog/add-folder-to-source-control.html)

[PRMLT](http://prml.github.io/) is Matlab code for machine learning algorithms in the PRML book.

## MATLAB Tools

[MATLAB Online](https://matlab.mathworks.com) allows to users to uilitize MATLAB and Simulink through a web browser such as Google Chrome.

[Simulink](https://www.mathworks.com/products/simulink.html) is a block diagram environment for Model-Based Design. It supports simulation, automatic code generation, and continuous testing of embedded systems.

[MATLAB Schemer](https://github.com/scottclowe/matlab-schemer) is a MATLAB package makes it easy to change the color scheme (theme) of the MATLAB display and GUI.

[LRSLibrary](https://github.com/andrewssobral/lrslibrary) is a Low-Rank and Sparse Tools for Background Modeling and Subtraction in Videos. The library was designed for moving object detection in videos, but it can be also used for other computer vision and machine learning problems.

[Robotics Toolbox for MATLAB](https://www.mathworks.com/products/robotics.html) provides a toolbox that brings robotics specific functionality(designing, simulating, and testing manipulators, mobile robots, and humanoid robots) to MATLAB, exploiting the native capabilities of MATLAB (linear algebra, portability, graphics). The toolbox also supports mobile robots with functions for robot motion models (bicycle), path planning algorithms (bug, distance transform, D*, PRM), kinodynamic planning (lattice, RRT), localization (EKF, particle filter), map building (EKF) and simultaneous localization and mapping (EKF), and a Simulink model a of non-holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.

[SEA-MAT](https://sea-mat.github.io/sea-mat/) is a collaborative effort to organize and distribute Matlab tools for the Oceanographic Community.

[Gramm](https://github.com/piermorel/gramm) is a complete data visualization toolbox for Matlab. It provides an easy to use and high-level interface to produce publication-quality plots of complex data with varied statistical visualizations. Gramm is inspired by R's ggplot2 library.

[hctsa](https://hctsa-users.gitbook.io/hctsa-manual) is a software package for running highly comparative time-series analysis using Matlab.

[Plotly](https://plot.ly/matlab/) is a Graphing Library for MATLAB.

[YALMIP](https://yalmip.github.io/) is a MATLAB toolbox for optimization modeling.

[GNU Octave](https://www.gnu.org/software/octave/) is a high-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.

# Networking
[Back to the Top](https://github.com/mikeroyal/Cyber-Physical-Systems-Guide#table-of-contents)

## Network Learning Resources

[AWS Certified Security - Specialty Certification](https://aws.amazon.com/certification/certified-security-specialty/)

[Microsoft Certified: Azure Security Engineer Associate](https://docs.microsoft.com/en-us/learn/certifications/azure-security-engineer)

[Google Cloud Certified Professional Cloud Security Engineer](https://cloud.google.com/certification/cloud-security-engineer)

[Cisco Security Certifications](https://www.cisco.com/c/en/us/training-events/training-certifications/certifications/security.html)

[The Red Hat Certified Specialist in Security: Linux](https://www.redhat.com/en/services/training/ex415-red-hat-certified-specialist-security-linux-exam)

[Linux Professional Institute LPIC-3 Enterprise Security Certification](https://www.lpi.org/our-certifications/lpic-3-303-overview)

[Cybersecurity Training and Courses from IBM Skills](https://www.ibm.com/skills/topics/cybersecurity/)

[Cybersecurity Courses and Certifications by Offensive Security](https://www.offensive-security.com/courses-and-certifications/)

[Citrix Certified Associate – Networking(CCA-N)](http://training.citrix.com/cms/index.php/certification/networking/)

[Citrix Certified Professional – Virtualization(CCP-V)](https://www.globalknowledge.com/us-en/training/certification-prep/brands/citrix/section/virtualization/citrix-certified-professional-virtualization-ccp-v/)

[CCNP Routing and Switching](https://learningnetwork.cisco.com/s/ccnp-enterprise)

[Certified Information Security Manager(CISM)](https://www.isaca.org/credentialing/cism)

[Wireshark Certified Network Analyst (WCNA)](https://www.wiresharktraining.com/certification.html)

[Juniper Networks Certification Program Enterprise (JNCP)](https://www.juniper.net/us/en/training/certification/)

[Networking courses and specializations from Coursera](https://www.coursera.org/browse/information-technology/networking)

[Network & Security Courses from Udemy](https://www.udemy.com/courses/it-and-software/network-and-security/)

[Network & Security Courses from edX](https://www.edx.org/learn/cybersecurity)

## Networking Tools & Concepts

[cURL](https://curl.se/) is a computer software project providing a library and command-line tool for transferring data using various network protocols(HTTP, HTTPS, FTP, FTPS, SCP, SFTP, TFTP, DICT, TELNET, LDAP LDAPS, MQTT, POP3, POP3S, RTMP, RTMPS, RTSP, SCP, SFTP, SMB, SMBS, SMTP or SMTPS). cURL is also used in cars, television sets, routers, printers, audio equipment, mobile phones, tablets, settop boxes, media players and is the Internet transfer engine for thousands of software applications in over ten billion installations.

[cURL Fuzzer](https://github.com/curl/curl-fuzzer) is a quality assurance testing for the curl project.

[DoH](https://github.com/curl/doh) is a stand-alone application for DoH (DNS-over-HTTPS) name resolves and lookups.

[HTTPie](https://github.com/httpie/httpie) is a command-line HTTP client. Its goal is to make CLI interaction with web services as human-friendly as possible. HTTPie is designed for testing, debugging, and generally interacting with APIs & HTTP servers.

[HTTPStat](https://github.com/reorx/httpstat) is a tool that visualizes curl statistics in a simple layout.

[Wuzz](https://github.com/asciimoo/wuzz) is an interactive cli tool for HTTP inspection. It can be used to inspect/modify requests copied from the browser's network inspector with the "copy as cURL" feature.

[Websocat](https://github.com/vi/websocat) is a ommand-line client for WebSockets, like netcat (or curl) for ws:// with advanced socat-like functions.

• Connection: In networking, a connection refers to pieces of related information that are transferred through a network. This generally infers that a connection is built before the data transfer (by following the procedures laid out in a protocol) and then is deconstructed at the at the end of the data transfer.

• Packet: A packet is, generally speaking, the most basic unit that is transferred over a network. When communicating over a network, packets are the envelopes that carry your data (in pieces) from one end point to the other.

Packets have a header portion that contains information about the packet including the source and destination, timestamps, network hops. The main portion of a packet contains the actual data being transferred. It is sometimes called the body or the payload.

• Network Interface: A network interface can refer to any kind of software interface to networking hardware. For instance, if you have two network cards in your computer, you can control and configure each network interface associated with them individually.

A network interface may be associated with a physical device, or it may be a representation of a virtual interface. The "loop-back" device, which is a virtual interface to the local machine, is an example of this.

• LAN: LAN stands for "local area network". It refers to a network or a portion of a network that is not publicly accessible to the greater internet. A home or office network is an example of a LAN.

• WAN: WAN stands for "wide area network". It means a network that is much more extensive than a LAN. While WAN is the relevant term to use to describe large, dispersed networks in general, it is usually meant to mean the internet, as a whole.
If an interface is connected to the WAN, it is generally assumed that it is reachable through the internet.

• Protocol: A protocol is a set of rules and standards that basically define a language that devices can use to communicate. There are a great number of protocols in use extensively in networking, and they are often implemented in different layers.

Some low level protocols are TCP, UDP, IP, and ICMP. Some familiar examples of application layer protocols, built on these lower protocols, are HTTP (for accessing web content), SSH, TLS/SSL, and FTP.

• Port: A port is an address on a single machine that can be tied to a specific piece of software. It is not a physical interface or location, but it allows your server to be able to communicate using more than one application.

• Firewall: A firewall is a program that decides whether traffic coming into a server or going out should be allowed. A firewall usually works by creating rules for which type of traffic is acceptable on which ports. Generally, firewalls block ports that are not used by a specific application on a server.

• NAT: Network address translation is a way to translate requests that are incoming into a routing server to the relevant devices or servers that it knows about in the LAN. This is usually implemented in physical LANs as a way to route requests through one IP address to the necessary backend servers.

• VPN: Virtual private network is a means of connecting separate LANs through the internet, while maintaining privacy. This is used as a means of connecting remote systems as if they were on a local network, often for security reasons.

## Network Layers

While networking is often discussed in terms of topology in a horizontal way, between hosts, its implementation is layered in a vertical fashion throughout a computer or network. This means is that there are multiple technologies and protocols that are built on top of each other in order for communication to function more easily. Each successive, higher layer abstracts the raw data a little bit more, and makes it simpler to use for applications and users. It also allows you to leverage lower layers in new ways without having to invest the time and energy to develop the protocols and applications that handle those types of traffic.

As data is sent out of one machine, it begins at the top of the stack and filters downwards. At the lowest level, actual transmission to another machine takes place. At this point, the data travels back up through the layers of the other computer. Each layer has the ability to add its own "wrapper" around the data that it receives from the adjacent layer, which will help the layers that come after decide what to do with the data when it is passed off.

One method of talking about the different layers of network communication is the OSI model. OSI stands for Open Systems Interconnect.This model defines seven separate layers. The layers in this model are:

• Application: The application layer is the layer that the users and user-applications most often interact with. Network communication is discussed in terms of availability of resources, partners to communicate with, and data synchronization.

• Presentation: The presentation layer is responsible for mapping resources and creating context. It is used to translate lower level networking data into data that applications expect to see.

• Session: The session layer is a connection handler. It creates, maintains, and destroys connections between nodes in a persistent way.

• Transport: The transport layer is responsible for handing the layers above it a reliable connection. In this context, reliable refers to the ability to verify that a piece of data was received intact at the other end of the connection. This layer can resend information that has been dropped or corrupted and can acknowledge the receipt of data to remote computers.

• Network: The network layer is used to route data between different nodes on the network. It uses addresses to be able to tell which computer to send information to. This layer can also break apart larger messages into smaller chunks to be reassembled on the opposite end.

• Data Link: This layer is implemented as a method of establishing and maintaining reliable links between different nodes or devices on a network using existing physical connections.

• Physical: The physical layer is responsible for handling the actual physical devices that are used to make a connection. This layer involves the bare software that manages physical connections as well as the hardware itself (like Ethernet).

The TCP/IP model, more commonly known as the Internet protocol suite, is another layering model that is simpler and has been widely adopted.It defines the four separate layers, some of which overlap with the OSI model:

• Application: In this model, the application layer is responsible for creating and transmitting user data between applications. The applications can be on remote systems, and should appear to operate as if locally to the end user.
The communication takes place between peers network.

• Transport: The transport layer is responsible for communication between processes. This level of networking utilizes ports to address different services. It can build up unreliable or reliable connections depending on the type of protocol used.

• Internet: The internet layer is used to transport data from node to node in a network. This layer is aware of the endpoints of the connections, but does not worry about the actual connection needed to get from one place to another. IP addresses are defined in this layer as a way of reaching remote systems in an addressable manner.

• Link: The link layer implements the actual topology of the local network that allows the internet layer to present an addressable interface. It establishes connections between neighboring nodes to send data.

## Interfaces
**Interfaces** are networking communication points for your computer. Each interface is associated with a physical or virtual networking device. Typically, your server will have one configurable network interface for each Ethernet or wireless internet card you have. In addition, it will define a virtual network interface called the "loopback" or localhost interface. This is used as an interface to connect applications and processes on a single computer to other applications and processes. You can see this referenced as the "lo" interface in many tools.

## Network Protocols

Networking works by piggybacks on a number of different protocols on top of each other. In this way, one piece of data can be transmitted using multiple protocols encapsulated within one another.

**Media Access Control(MAC)** is a communications protocol that is used to distinguish specific devices. Each device is supposed to get a unique MAC address during the manufacturing process that differentiates it from every other device on the internet. Addressing hardware by the MAC address allows you to reference a device by a unique value even when the software on top may change the name for that specific device during operation. Media access control is one of the only protocols from the link layer that you are likely to interact with on a regular basis.

**The IP protocol** is one of the fundamental protocols that allow the internet to work. IP addresses are unique on each network and they allow machines to address each other across a network. It is implemented on the internet layer in the IP/TCP model. Networks can be linked together, but traffic must be routed when crossing network boundaries. This protocol assumes an unreliable network and multiple paths to the same destination that it can dynamically change between. There are a number of different implementations of the protocol. The most common implementation today is IPv4, although IPv6 is growing in popularity as an alternative due to the scarcity of IPv4 addresses available and improvements in the protocols capabilities.

**ICMP: internet control message protocol** is used to send messages between devices to indicate the availability or error conditions. These packets are used in a variety of network diagnostic tools, such as ping and traceroute. Usually ICMP packets are transmitted when a packet of a different kind meets some kind of a problem. Basically, they are used as a feedback mechanism for network communications.

**TCP: Transmission control protocol** is implemented in the transport layer of the IP/TCP model and is used to establish reliable connections. TCP is one of the protocols that encapsulates data into packets. It then transfers these to the remote end of the connection using the methods available on the lower layers. On the other end, it can check for errors, request certain pieces to be resent, and reassemble the information into one logical piece to send to the application layer. The protocol builds up a connection prior to data transfer using a system called a three-way handshake. This is a way for the two ends of the communication to acknowledge the request and agree upon a method of ensuring data reliability. After the data has been sent, the connection is torn down using a similar four-way handshake. TCP is the protocol of choice for many of the most popular uses for the internet, including WWW, FTP, SSH, and email. It is safe to say that the internet we know today would not be here without TCP.

**UDP: User datagram protocol** is a popular companion protocol to TCP and is also implemented in the transport layer. The fundamental difference between UDP and TCP is that UDP offers unreliable data transfer. It does not verify that data has been received on the other end of the connection. This might sound like a bad thing, and for many purposes, it is. However, it is also extremely important for some functions. It’s not required to wait for confirmation that the data was received and forced to resend data, UDP is much faster than TCP. It does not establish a connection with the remote host, it simply fires off the data to that host and doesn't care if it is accepted or not. Since UDP is a simple transaction, it is useful for simple communications like querying for network resources. It also doesn't maintain a state, which makes it great for transmitting data from one machine to many real-time clients. This makes it ideal for VOIP, games, and other applications that cannot afford delays.

**HTTP: Hypertext transfer protocol** is a protocol defined in the application layer that forms the basis for communication on the web. HTTP defines a number of functions that tell the remote system what you are requesting. For instance, GET, POST, and DELETE all interact with the requested data in a different way.

**FTP: File transfer protocol** is in the application layer and provides a way of transferring complete files from one host to another. It is inherently insecure, so it is not recommended for any externally facing network unless it is implemented as a public, download-only resource.

**DNS: Domain name system** is an application layer protocol used to provide a human-friendly naming mechanism for internet resources. It is what ties a domain name to an IP address and allows you to access sites by name in your browser.

**SSH: Secure shell** is an encrypted protocol implemented in the application layer that can be used to communicate with a remote server in a secure way. Many additional technologies are built around this protocol because of its end-to-end encryption and ubiquity. There are many other protocols that we haven't covered that are equally important. However, this should give you a good overview of some of the fundamental technologies that make the internet and networking possible.

[JSON Web Token (JWT)](https://jwt.io) is a compact URL-safe means of representing claims to be transferred between two parties. The claims in a JWT are encoded as a JSON object that is digitally signed using JSON Web Signature (JWS).

[OAuth 2.0](https://oauth.net/2/) is an open source authorization framework that enables applications to obtain limited access to user accounts on an HTTP service, such as Amazon, Google, Facebook, Microsoft, Twitter GitHub, and DigitalOcean. It works by delegating user authentication to the service that hosts the user account, and authorizing third-party applications to access the user account.

## Virtualization

[HVM (Hardware Virtual Machine)](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/virtualization_types.html) is a virtualization type that provides the ability to run an operating system directly on top of a virtual machine without any modification, as if it were run on the bare-metal hardware.

[PV(ParaVirtualization)](https://wiki.xenproject.org/wiki/Paravirtualization_(PV)) is an efficient and lightweight virtualization technique introduced by the Xen Project team, later adopted by other virtualization solutions. PV does not require virtualization extensions from the host CPU and thus enables virtualization on hardware architectures that do not support Hardware-assisted virtualization.

[KVM (for Kernel-based Virtual Machine)](https://www.linux-kvm.org/page/Main_Page) is a full virtualization solution for Linux on x86 hardware containing virtualization extensions (Intel VT or AMD-V). It consists of a loadable kernel module, kvm.ko, that provides the core virtualization infrastructure and a processor specific module, kvm-intel.ko or kvm-amd.ko.

[QEMU](https://www.qemu.org) is a fast processor emulator using a portable dynamic translator. QEMU emulates a full system, including a processor and various peripherals. It can be used to launch a different Operating System without rebooting the PC or to debug system code.

[Hyper-V](https://docs.microsoft.com/en-us/virtualization/hyper-v-on-windows/) enables running virtualized computer systems on top of a physical host. These virtualized systems can be used and managed just as if they were physical computer systems, however they exist in virtualized and isolated environment. Special software called a hypervisor manages access between the virtual systems and the physical hardware resources. Virtualization enables quick deployment of computer systems, a way to quickly restore systems to a previously known good state, and the ability to migrate systems between physical hosts.

[VirtManager](https://github.com/virt-manager/virt-manager) is a graphical tool for managing virtual machines via libvirt. Most usage is with QEMU/KVM virtual machines, but Xen and libvirt LXC containers are well supported. Common operations for any libvirt driver should work.

[oVirt](https://www.ovirt.org) is an open-source distributed virtualization solution, designed to manage your entire enterprise infrastructure. oVirt uses the trusted KVM hypervisor and is built upon several other community projects, including libvirt, Gluster, PatternFly, and Ansible.Founded by Red Hat as a community project on which Red Hat Enterprise Virtualization is based allowing for centralized management of virtual machines, compute, storage and networking resources, from an easy-to-use web-based front-end with platform independent access.

[HyperKit](https://github.com/moby/hyperkit) is a toolkit for embedding hypervisor capabilities in your application. It includes a complete hypervisor, based on [xhyve](https://github.com/mist64/xhyve)/[bhyve](https://bhyve.org/), which is optimized for lightweight virtual machines and container deployment. It is designed to be interfaced with higher-level components such as the [VPNKit](https://github.com/moby/vpnkit) and [DataKit](https://github.com/moby/datakit). HyperKit currently only supports macOS using the [Hypervisor.framework](https://developer.apple.com/library/mac/documentation/DriversKernelHardware/Reference/Hypervisor/index.html) making it a core component of Docker Desktop for Mac.

[Intel® Graphics Virtualization Technology (Intel® GVT)](https://github.com/intel/gvt-linux) is a full GPU virtualization solution with mediated pass-through, starting from 4th generation Intel Core (TM) processors with Intel processor graphics(Broadwell and newer). It can be used to virtualize the GPU for multiple guest virtual machines, effectively providing near-native graphics performance in the virtual machine and still letting your host use the virtualized GPU normally.

[Apple Hypervisor](https://developer.apple.com/documentation/hypervisor) is a frameowrk that builds virtualization solutions on top of a lightweight hypervisor, without third-party kernel extensions. Hypervisor provides C APIs so you can interact with virtualization technologies in user space, without writing kernel extensions (KEXTs). As a result, the apps you create using this framework are suitable for distribution on the [Mac App Store](https://www.appstore.com/).

[Apple Virtualization Framework](https://developer.apple.com/documentation/virtualization) is a framework that provides high-level APIs for creating and managing virtual machines on Apple silicon and Intel-based Mac computers. This framework is used to boot and run a Linux-based operating system in a custom environment that you define. It also supports the [Virtio specification](https://www.redhat.com/en/virtio-networking-series), which defines standard interfaces for many device types, including network, socket, serial port, storage, entropy, and memory-balloon devices.

[Apple Paravirtualized Graphics Framework](https://developer.apple.com/documentation/paravirtualizedgraphics) is a framework that implements hardware-accelerated graphics for macOS running in a virtual machine, hereafter known as the guest. The operating system provides a graphics driver that runs inside the guest, communicating with the framework in the host operating system to take advantage of Metal-accelerated graphics.

[Cloud Hypervisor](https://github.com/cloud-hypervisor/cloud-hypervisor) is an open source Virtual Machine Monitor (VMM) that runs on top of [KVM](https://www.kernel.org/doc/Documentation/virtual/kvm/api.txt). The project focuses on exclusively running modern, cloud workloads, on top of a limited set of hardware architectures and platforms. Cloud workloads refers to those that are usually run by customers inside a cloud provider. Cloud Hypervisor is implemented in [Rust](https://www.rust-lang.org/) and is based on the [rust-vmm](https://github.com/rust-vmm) crates.

[VMware vSphere Hypervisor](https://www.vmware.com/products/vsphere-hypervisor.html) is a bare-metal hypervisor that virtualizes servers; allowing you to consolidate your applications while saving time and money managing your IT infrastructure.

[Xen](https://github.com/xen-project/xen) is focused on advancing virtualization in a number of different commercial and open source applications, including server virtualization, Infrastructure as a Services (IaaS), desktop virtualization, security applications, embedded and hardware appliances, and automotive/aviation.

[Ganeti](https://github.com/ganeti/ganeti) is a virtual machine cluster management tool built on top of existing virtualization technologies such as Xen or KVM and other open source software. Once installed, the tool assumes management of the virtual instances (Xen DomU).

[Packer](https://www.packer.io/) is an open source tool for creating identical machine images for multiple platforms from a single source configuration. Packer is lightweight, runs on every major operating system, and is highly performant, creating machine images for multiple platforms in parallel. Packer does not replace configuration management like Chef or Puppet. In fact, when building images, Packer is able to use tools like Chef or Puppet to install software onto the image.

[Vagrant](https://www.vagrantup.com/) is a tool for building and managing virtual machine environments in a single workflow. With an easy-to-use workflow and focus on automation, Vagrant lowers development environment setup time, increases production parity, and makes the "works on my machine" excuse a relic of the past. It provides easy to configure, reproducible, and portable work environments built on top of industry-standard technology and controlled by a single consistent workflow to help maximize the productivity and flexibility of you and your team.

[Parallels Desktop](https://www.parallels.com) is a Desktop Hypervisor that delivers the fastest, easiest and most powerful application for running Windows/Linux on Mac (including the new [Apple M1 chip](https://www.apple.com/newsroom/2020/11/apple-unleashes-m1/)) and ChromeOS.

[VMware Fusion](https://www.vmware.com/products/fusion.html) is a Desktop Hypervisor that deliver desktop and ‘server’ virtual machines, containers and [Kubernetes clusters](https://www.vmware.com/topics/glossary/content/kubernetes-cluster) to developers, and IT professionals on the Mac.

[VMware Workstation](https://www.vmware.com/products/workstation-pro.html) is a hosted hypervisor that runs on x64 versions of Windows and Linux operating systems; it enables users to set up virtual machines on a single physical machine, and use them simultaneously along with the actual machine.

## File systems & Storage

[NAS (Network Attached Storage)](https://www.synology.com/en-us/solution/what_is_nas) is an intelligent storage device connected to your home or office network. You can store all your family and colleagues' files on the NAS, from important documents to precious photos, music and video collections.

[GlusterFS](https://www.gluster.org/) is a free and open source scalable network filesystem. Gluster is a scalable network filesystem. Using common off-the-shelf hardware, you can create large, distributed storage solutions for media streaming, data analysis, and other data- and bandwidth-intensive tasks.

[Ceph](https://ceph.io/) is a software-defined storage solution designed to address the object, block, and file storage needs of data centers adopting open source as the new norm for high-growth block storage, object stores and data lakes. Ceph provides enterprise scalable storage while keeping [CAPEX](https://corporatefinanceinstitute.com/resources/knowledge/modeling/how-to-calculate-capex-formula/) and [OPEX](https://www.investopedia.com/terms/o/operating_expense.asp) costs in line with underlying bulk commodity disk prices.

[ZFS](https://docs.oracle.com/cd/E19253-01/819-5461/zfsover-2/) is an enterprise-ready open source file system and volume manager with unprecedented flexibility and an uncompromising commitment to data integrity.

[OpenZFS](https://openzfs.org/wiki/Main_Page )is an open-source storage platform. It includes the functionality of both traditional file systems and volume manager. It has many advanced features including:

- Protection against data corruption.
- Integrity checking for both data and metadata.
- Continuous integrity verification and automatic "self-healing" repair.

[Btrfs](https://btrfs.wiki.kernel.org/index.php/Main_Page) is a modern copy on write (CoW) filesystem for Linux aimed at implementing advanced features while also focusing on fault tolerance, repair and easy administration. Its main features and benefits are:

- Snapshots which do not make the full copy of files
- RAID - support for software-based RAID 0, RAID 1, RAID 10
- Self-healing - checksums for data and metadata, automatic detection of silent data corruptions

[Apple File System (APFS)](https://support.apple.com/guide/disk-utility/file-system-formats-available-in-disk-utility-dsku19ed921c/mac) is the default file system for Mac computers using macOS 10.13 or later, features strong encryption, space sharing, snapshots, fast directory sizing, and improved file system fundamentals.

[NTFS(New Technology File System)](https://docs.microsoft.com/en-us/windows-server/storage/file-server/ntfs-overview) is the primary file system for recent versions of Windows and Windows Server—provides a full set of features including security descriptors, encryption, disk quotas, and rich metadata, and can be used with Cluster Shared Volumes (CSV) to provide continuously available volumes that can be accessed simultaneously from multiple nodes of a failover cluster.

[exFAT(Extended File Allocation Table )](https://docs.microsoft.com/en-us/windows/win32/fileio/exfat-specification) is the file system that was the successor to FAT32 in the FAT family of file systems. It was optimized for flash memory such as USB flash drives and SD cards.

# Telco 5G Development
[Back to the Top](https://github.com/mikeroyal/Cyber-Physical-Systems-Guide#table-of-contents)

**VMware Cloud First Approach. Source: [VMware](https://www.vmware.com/products/telco-cloud-automation.html).**

**VMware Telco Cloud Automation Components. Source: [VMware](https://www.vmware.com/products/telco-cloud-automation.html).**

## Telco 5G Learning Resources

[HPE(Hewlett Packard Enterprise) Telco Blueprints overview](https://techhub.hpe.com/eginfolib/servers/docs/Telco/Blueprints/infocenter/index.html#GUID-9906A227-C1FB-4FD5-A3C3-F3B72EC81CAB.html)

[Network Functions Virtualization Infrastructure (NFVI) by Cisco](https://www.cisco.com/c/en/us/solutions/service-provider/network-functions-virtualization-nfv-infrastructure/index.html)

[Introduction to vCloud NFV Telco Edge from VMware](https://docs.vmware.com/en/VMware-vCloud-NFV-OpenStack-Edition/3.1/vloud-nfv-edge-reference-arch-31/GUID-744C45F1-A8D5-4523-9E5E-EAF6336EE3A0.html)

[VMware Telco Cloud Automation(TCA) Architecture Overview](https://docs.vmware.com/en/VMware-Telco-Cloud-Platform-5G-Edition/1.0/telco-cloud-platform-5G-edition-reference-architecture/GUID-C19566B3-F42D-4351-BA55-DE70D55FB0DD.html)

[5G Telco Cloud from VMware](https://telco.vmware.com/)

[Maturing OpenStack Together To Solve Telco Needs from Red Hat](https://www.redhat.com/cms/managed-files/4.Nokia%20CloudBand%20&%20Red%20Hat%20-%20Maturing%20Openstack%20together%20to%20solve%20Telco%20needs%20Ehud%20Malik,%20Senior%20PLM,%20Nokia%20CloudBand.pdf)

[Red Hat telco ecosystem program](https://connect.redhat.com/en/programs/telco-ecosystem)

[OpenStack for Telcos by Canonical](https://ubuntu.com/blog/openstack-for-telcos-by-canonical)

[Open source NFV platform for 5G from Ubuntu](https://ubuntu.com/telco)

[Understanding 5G Technology from Verizon](https://www.verizon.com/5g/)

[Verizon and Unity partner to enable 5G & MEC gaming and enterprise applications](https://www.verizon.com/about/news/verizon-unity-partner-5g-mec-gaming-enterprise)

[Understanding 5G Technology from Intel](https://www.intel.com/content/www/us/en/wireless-network/what-is-5g.html)

[Understanding 5G Technology from Qualcomm](https://www.qualcomm.com/invention/5g/what-is-5g)

[Telco Acceleration with Xilinx](https://www.xilinx.com/applications/wired-wireless/telco.html)

[VIMs on OSM Public Wiki](https://osm.etsi.org/wikipub/index.php/VIMs)

[Amazon EC2 Overview and Networking Introduction for Telecom Companies](https://docs.aws.amazon.com/whitepapers/latest/ec2-networking-for-telecom/ec2-networking-for-telecom.pdf)

[Citrix Certified Associate – Networking(CCA-N)](http://training.citrix.com/cms/index.php/certification/networking/)

[CCNP Routing and Switching](https://learningnetwork.cisco.com/s/ccnp-enterprise)

[Certified Information Security Manager(CISM)](https://www.isaca.org/credentialing/cism)

[Wireshark Certified Network Analyst (WCNA)](https://www.wiresharktraining.com/certification.html)

[Juniper Networks Certification Program Enterprise (JNCP)](https://www.juniper.net/us/en/training/certification/)

[Cloud Native Computing Foundation Training and Certification Program](https://www.cncf.io/certification/training/)

## Telco 5G Tools and Frameworks

[Open Stack](https://www.openstack.org/) is an open source cloud platform, deployed as infrastructure-as-a-service (IaaS) to orchestrate data center operations on bare metal, private cloud hardware, public cloud resources, or both (hybrid/multi-cloud architecture). OpenStack includes advance use of virtualization & SDN for network traffic optimization to handle the core cloud-computing services of compute, networking, storage, identity, and image services.

[StarlingX](https://www.starlingx.io/) is a complete cloud infrastructure software stack for the edge used by the most demanding applications in industrial IOT, telecom, video delivery and other ultra-low latency use cases.

[Airship](https://www.airshipit.org/) is a collection of open source tools for automating cloud provisioning and management. Airship provides a declarative framework for defining and managing the life cycle of open infrastructure tools and the underlying hardware.

[Network functions virtualization (NFV)](https://www.vmware.com/topics/glossary/content/network-functions-virtualization-nfv) is the replacement of network appliance hardware with virtual machines. The virtual machines use a hypervisor to run networking software and processes such as routing and load balancing. NFV allows for the separation of communication services from dedicated hardware, such as routers and firewalls. This separation means network operations can provide new services dynamically and without installing new hardware. Deploying network components with network functions virtualization only takes hours compared to months like with traditional networking solutions.

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