#fuzzy logic in matlab

[servotechinc78]

Embedded Control Software Systems: Powering Intelligent Automation

Embedded control software systems are the unseen brains behind many of today’s most advanced technologies. From industrial machines and automotive systems to consumer electronics and medical devices, these systems ensure that everything operates efficiently, safely, and reliably. Embedded control software is a combination of software and hardware engineered to perform dedicated control functions within larger systems, often with real-time constraints and minimal user interaction.

This article explores what embedded control software systems are, how they work, their architecture, applications, benefits, and challenges.

What is an Embedded Control Software System?

An embedded control software system consists of three main components:

Embedded Hardware (Microcontroller or Microprocessor) – This is the processing unit that executes the control logic.

Control Algorithms (Software) – Written in programming languages like C or C++, these define the behavior of the system based on inputs and desired outputs.

Sensors and Actuators – Sensors provide feedback from the environment (e.g., temperature, speed), while actuators carry out control actions (e.g., motor movement).

These systems are “embedded” because they are integrated into larger products, and they typically perform one or more specific control functions rather than general-purpose computing tasks.

Key Features of Embedded Control Software

Real-Time Operation: Embedded control software often operates in real time, meaning it must respond to inputs within a strict time frame.

Determinism: The system must behave in a predictable manner, particularly in safety-critical applications like automotive or aerospace.

Low Power and Resource Constraints: Many embedded systems are deployed on hardware with limited processing power, memory, and energy.

Reliability and Robustness: Since embedded systems are often deployed in harsh environments, they must be stable and resistant to failure.

Minimal User Interaction: Most embedded control systems are designed to operate with limited or no user interface.

Architecture of Embedded Control Systems

Input Interface: Collects data from sensors or communication channels.

Control Software: Applies algorithms (e.g., PID, fuzzy logic, model predictive control) to decide what action should be taken based on inputs.

Output Interface: Sends control signals to actuators (motors, valves, lights, etc.).

Feedback Loop: Ensures the system continuously adjusts itself based on output performance.

For example, in an automotive cruise control system:

Input: Speed sensor measures vehicle speed.

Control Software: Compares current speed with desired speed.

Output: Sends signals to the throttle actuator to maintain or change speed.

Feedback: Adjusts continuously as road conditions or driver input change.

Applications of Embedded Control Software Systems

1. Automotive Industry

Engine control units (ECUs)

Anti-lock braking systems (ABS)

Adaptive cruise control

Lane-keeping assistance

Airbag deployment systems

2. Industrial Automation

Programmable logic controllers (PLCs)

Robotic arms

Process control in manufacturing lines

Condition monitoring systems

3. Consumer Electronics

Smart thermostats

Washing machines

Microwave ovens

Smart TVs and remote controllers

4. Aerospace and Defense

Flight control systems

Missile guidance

Radar systems

UAV navigation

5. Healthcare

Medical infusion pumps

MRI and imaging equipment

Wearable health monitors

Automated ventilators

Development Process for Embedded Control Software

Requirement Analysis: Define functional and non-functional requirements, such as timing constraints and energy consumption.

System Design: Select hardware platform and define software architecture.

Algorithm Development: Create control logic using mathematical models or empirical rules.

Implementation: Code the algorithms using languages like C, C++, or model-based tools such as MATLAB/Simulink.

Simulation and Testing: Validate performance through simulation, hardware-in-the-loop (HIL) testing, and real-world trials.

Deployment and Maintenance: Deploy code to the embedded hardware and ensure updates or bug fixes are managed safely.

Model-Based Design in Embedded Control

A growing trend in embedded control software development is model-based design (MBD). Engineers use graphical models to design, simulate, and automatically generate code for control algorithms. Tools like Simulink allow for rapid prototyping, reduced coding errors, and quicker time to market. This is especially useful for systems that require frequent iterations or are highly complex.

Benefits of Embedded Control Software Systems

Efficiency: Optimizes system performance, energy use, and functionality.

Precision: Ensures accurate control in critical applications like medical devices and aerospace.

Cost-Effectiveness: Minimizes the need for manual intervention and large hardware setups.

Scalability: Can be tailored for use in simple household gadgets or complex industrial machinery.

Safety and Reliability: Especially in automotive and aviation, embedded control systems contribute to increased safety through reliable automation.

Challenges in Embedded Control Software Development

Real-Time Constraints: Meeting strict timing requirements can be difficult.

Resource Limitations: Must operate on hardware with limited memory and processing capabilities.

Debugging Complexity: Difficult to test systems in real-time without disrupting performance.

Cybersecurity Risks: Increased connectivity (IoT integration) exposes systems to potential attacks.

Maintenance and Upgrades: Embedded systems often need to be updated securely, especially in remote or critical applications.

Future Trends in Embedded Control Systems

AI and Machine Learning Integration: Adaptive systems that learn from data for more intelligent control.

Edge Computing: Processing control logic closer to the source (sensors/actuators) to reduce latency.

Wireless and IoT Connectivity: Embedded systems communicating via 5G or other wireless protocols.

Open-Source Platforms: Tools like Arduino and Raspberry Pi are making embedded control development more accessible.

Conclusion

Embedded control software systems by Servotechinc are the cornerstone of modern intelligent automation. By integrating sensors, control algorithms, and actuators into compact, efficient packages, these systems bring intelligence to machines across countless industries. Whether improving the safety of vehicles, the precision of medical devices, or the efficiency of industrial processes, embedded control systems play a pivotal role in shaping a smarter, more connected world.

[smartaddictions-blog]

This is a small clip of a robotics & mechatronics project I worked on recently. Using Lego vehicles we were able to program path tracking and navigation in a simulated city along with many other features, I'll save for later posts. It uses a Fuzzy Logic controller for line following and a lot of code to navigate with cameras position system.

[daxstudies]

17 people, 17 questions

Got tagged a little while ago by the lovely @moonxstudies!!

Nickname: Don’t really have one (idk how you’d shorten Dax haha)  

Sign: Aquarius  ♒ 

Height: 5’4″

Hogwarts house: Ravenclaw

Last thing I googled: pdf Documentation for MATLAB fuzzy logic toolbox   

Song stuck in my head: Wow. by Post Malone

No. Of followers: 28?! After only 4 posts? Ah I love you guys 💕

Amount of sleep: usually 7-8 hours but I’m in a crunch time so its like, 6    

Lucky number: 625

Dream job: CEO of a cutting edge medical equipment/software company (or maybe a surgeon?? idkkk)   

Wearing: Pajamas, the Ultimate Power Suit™ 

Favorite song: Yoko Ono by Moby Rich

Favorite instrument: Bass in any song. . .I just like making floors vibrate

Aesthetic: Urbanized cottagecore. Y’know, like, tiny apartment with lots of plants, thrifted overalls drying out on the fire escape, carrying a bunch of wildflowers on the subway.  

Favorite authors: J.K. Rowling and Becky Chambers 100% 

Favorite animal noise: Rabbits make a very soft whirring sound when they’re enjoying being pet and it elevates my soul   

Random: I went on a walk with my a friend of mine (socially distanced! I promise) and we’ve both been working on CompSci stuff recently so we traded info and it was super peaceful. Gotta love nerdy friends.

Tagging: @redlitmusbluelitmus, @daisiestea, @stellar-kinematics, @flynnstudies, @chazza-studies-alevels

Made by @katiestudies

[cyberrcafe]

Top 10 Must try tools for data scientists

A Data Scientist extracts, manipulates, pre-processes, and predicts data. He needs many statistical tools and programming languages to accomplish this.

One of the most well-liked fields of the 21st century is data science. Companies use data scientists to understand their markets better and develop better products. Decision-makers and Data Scientists are primarily responsible for handling, evaluating vast amounts of unstructured and organized data, and generating insights or solutions from the same.

In order to do so, they use a variety of tools and programming languages to help companies use Data Science for efficiency, productivity and problem-solving.

What are these tools and technologies? Let’s take a quick look.

Most Commonly Used Tools by Data Scientists

SAS

Made especially for statistical processes. Statistical modeling in SAS is done using the standard SAS programming language.

You as a data scientist can model and organize your data using a variety of statistical libraries and tools from SAS.

Apache Spark

The most well-known data science tool and most potent analytics engine is called Spark, also known as Apache Spark or just Spark. Batch and stream processing are the main reasons why Spark was developed.

It has tons of APIs that makes it simpler for data scientists to often access data for things like machine learning and SQL storage.

It is an upgrade over Hadoop.

BigML

Another popular data science tool is BigML. It provides a completely interactive, cloud-based GUI environment for processing machine learning algorithms.

Through it, firms may implement machine learning algorithms across the board. For instance, it can utilize this program for risk analysis, product invention, and sales forecasting.

BigML has a specialty in forecasting. It employs a wide range of machine learning algorithms, including clustering, classification, forecasting of time series, etc.

MATLAB

A multi-paradigm numerical computing environment for processing mathematical data. This closed-source tool facilitates matrix functions, algorithmic implementation, and statistical data modeling. MATLAB is used in the vast majority of scientific disciplines.

Used to simulate neural networks and fuzzy logic. The MATLAB graphics library allows you to build robust visualizations. Signal and image processing also use MATLAB.

This makes it a very adaptable tool for data scientists since they can take on all the challenges, from powerful Deep Learning algorithms to data cleaning and analysis.

Excel

Excel is an effective data science analysis tool. Excel, although the industry standard, is still a strong tool for data analysis.

There are numerous varieties of formulas, tables, filters, and slicers in Excel. Excel also lets you make your unique formulas and functions. Even if Excel is not suitable for calculations involving large amounts of data, it is still the best option for making effective spreadsheets and data visualizations.

SQL can be used, to edit and analyze data, by connecting it to Excel. Excel is a popular tool among data scientists for cleaning data since it offers a user-friendly GUI environment for pre-processing data.

ggplot2

Advanced data visualization software for the R programming language is called ggplot2. This program was developed to take the place of the default R graphics package and make advantage of strong commands to produce impressive visuals.

It is the most popular library that Data Scientists use to produce visualizations using the data they have examined.

The tidyverse R package for data science includes Ggplot2.

The visual appeal of ggplot2 distinguishes it from the rest of the data visualization software. Data scientists can produce specialized visuals with ggplot2 to engage in improved storytelling.

You may annotate data in visualizations using ggplot2, provide data points text labels, and improve the readability of your graphs.

Tableau

Tableau is a tool for creating interactive displays of data that has powerful visuals.

The most important aspect of Tableau is its ability to connect to databases, spreadsheets, OLAP (Online Analytical Processing) cubes, and other systems.

 Along with these capabilities, Tableau also allows for the visualization of geographic data and the mapping of longitudes and latitudes.

Jupyter

To assist developers in creating interactive computing experiences and open-source software, Project Jupyter is an open-source application built on IPython.

It is a web-based tool.

Data scientists can carry out all of their duties within this interactive environment. Due to its numerous presenting features, it is also a potent instrument for narrative.

Data cleansing, statistical calculation, visualization, and the creation of predictive machine learning models may all be done with Jupyter Notebooks. Since it is entirely open-source, it is also cost-free.

SciKit Learn

Scikit-learn is a Python module that is used to implement machine learning algorithms. Because it is simple to use, it is a widely utilized tool for analysis and data science.

Data preprocessing, classification, regression, clustering, dimensionality reduction, and other machine-learning characteristics are supported.

Scikit-learn makes it easy to use complex machine-learning techniques. Therefore, it is an excellent platform for research needing fundamental Machine Learning and is also used in scenarios that call for rapid prototyping. It makes use of a number of the core Python libraries, such as SciPy, Numpy, Matplotlib, etc.

TensorFlow

Deep Learning and other sophisticated machine learning techniques are often used. The name TensorFlow was inspired by multidimensional arrays known as Tensors.

It is a dynamic, open-source toolkit famous for its efficacy and potent computing power. CPUs, GPUs, and more recently, more powerful TPU platforms, are all compatible with TensorFlow.

It therefore has a processing edge over other systems that is unprecedented.

Due to its enormous processing capacity, Tensorflow offers a wide range of applications, including speech recognition, image classification, drug discovery, image and language synthesis, etc. All data scientists with a focus on machine learning should be familiar with the Tensorflow tool.

Conclusion

Data Science is becoming the apple of the tech industry’s eye nowadays. This article discusses the tools used by Data Scientists. Most data science platforms offer integrated delivery of complicated data science tasks. As a result, users can more easily incorporate data science functions without having to start from scratch with their code. Data Scientists are most sought after by many product-based companies. To land a dream job as a Data Scientist, one must be fluent in algorithms and concepts relating to Data Science. Where can a candidate learn these? There are many Data Science training institutes in our county. But at Skillslash, candidates are provided a 1:1 mentorship program and live classes, where doubts can be cleared. Skillslash also has in store, exclusive courses like Data Science Course In Bangalore, System design course online  and Web Development Course  to ensure aspirants of each domain have a great learning journey and a secure future in these fields. To find out how you can make a career in the IT and tech field with Skillslash, contact the student support team to know more about the course and institute.

[rishiaca]

Top Ten Must Try Tools For Data Scientists

Most Commonly Used Tools by Data Scientists

A Data Scientist extracts, manipulates, pre-processes, and predicts data. He needs many statistical tools and programming languages to accomplish this.

One of the most well-liked fields of the 21st century is data science. Companies use data scientists to understand their markets better and develop better products. Decision-makers and Data Scientists are primarily responsible for handling, evaluating vast amounts of unstructured and organized data, and generating insights or solutions from the same.

In order to do so, they use a variety of tools and programming languages to help companies use Data Science for efficiency, productivity and problem-solving.

What are these tools and technologies? Let’s take a quick look.

Most Commonly Used Tools by Data Scientists

SAS

Made especially for statistical processes. Statistical modeling in SAS is done using the standard SAS programming language.

You as a data scientist can model and organize your data using a variety of statistical libraries and tools from SAS.

Apache Spark

The most well-known data science tool and most potent analytics engine is called Spark, also known as Apache Spark or just Spark. Batch and stream processing are the main reasons why Spark was developed.

It has tons of APIs that makes it simpler for data scientists to often access data for things like machine learning and SQL storage.

It is an upgrade over Hadoop.

BigML

Another popular data science tool is BigML. It provides a completely interactive, cloud-based GUI environment for processing machine learning algorithms.

Through it, firms may implement machine learning algorithms across the board. For instance, it can utilize this program for risk analysis, product invention, and sales forecasting.

BigML has a specialty in forecasting. It employs a wide range of machine learning algorithms, including clustering, classification, forecasting of time series, etc.

MATLAB

A multi-paradigm numerical computing environment for processing mathematical data. This closed-source tool facilitates matrix functions, algorithmic implementation, and statistical data modeling. MATLAB is used in the vast majority of scientific disciplines.

Used to simulate neural networks and fuzzy logic. The MATLAB graphics library allows you to build robust visualizations. Signal and image processing also use MATLAB.

This makes it a very adaptable tool for data scientists since they can take on all the challenges, from powerful Deep Learning algorithms to data cleaning and analysis.

Excel

Excel is an effective data science analysis tool. Excel, although the industry standard, is still a strong tool for data analysis.

There are numerous varieties of formulas, tables, filters, and slicers in Excel. Excel also lets you make your unique formulas and functions. Even if Excel is not suitable for calculations involving large amounts of data, it is still the best option for making effective spreadsheets and data visualizations.

SQL can be used, to edit and analyze data, by connecting it to Excel. Excel is a popular tool among data scientists for cleaning data since it offers a user-friendly GUI environment for pre-processing data.

ggplot2

Advanced data visualization software for the R programming language is called ggplot2. This program was developed to take the place of the default R graphics package and make advantage of strong commands to produce impressive visuals.

It is the most popular library that Data Scientists use to produce visualizations using the data they have examined.

The tidyverse R package for data science includes Ggplot2.

The visual appeal of ggplot2 distinguishes it from the rest of the data visualization software. Data scientists can produce specialized visuals with ggplot2 to engage in improved storytelling.

You may annotate data in visualizations using ggplot2, provide data points text labels, and improve the readability of your graphs.

Tableau

Tableau is a tool for creating interactive displays of data that has powerful visuals.

The most important aspect of Tableau is its ability to connect to databases, spreadsheets, OLAP (Online Analytical Processing) cubes, and other systems.

 Along with these capabilities, Tableau also allows for the visualization of geographic data and the mapping of longitudes and latitudes.

Jupyter

To assist developers in creating interactive computing experiences and open-source software, Project Jupyter is an open-source application built on IPython.

It is a web-based tool.

Data scientists can carry out all of their duties within this interactive environment. Due to its numerous presenting features, it is also a potent instrument for narrative.

Data cleansing, statistical calculation, visualization, and the creation of predictive machine learning models may all be done with Jupyter Notebooks. Since it is entirely open-source, it is also cost-free.

SciKit Learn

Scikit-learn is a Python module that is used to implement machine learning algorithms. Because it is simple to use, it is a widely utilized tool for analysis and data science.

Data preprocessing, classification, regression, clustering, dimensionality reduction, and other machine-learning characteristics are supported.

Scikit-learn makes it easy to use complex machine-learning techniques. Therefore, it is an excellent platform for research needing fundamental Machine Learning and is also used in scenarios that call for rapid prototyping. It makes use of a number of the core Python libraries, such as SciPy, Numpy, Matplotlib, etc.

TensorFlow

Deep Learning and other sophisticated machine learning techniques are often used. The name TensorFlow was inspired by multidimensional arrays known as Tensors.

It is a dynamic, open-source toolkit famous for its efficacy and potent computing power. CPUs, GPUs, and more recently, more powerful TPU platforms, are all compatible with TensorFlow.

It therefore has a processing edge over other systems that is unprecedented.

Due to its enormous processing capacity, Tensorflow offers a wide range of applications, including speech recognition, image classification, drug discovery, image and language synthesis, etc. All data scientists with a focus on machine learning should be familiar with the Tensorflow tool.

Conclusion

Data Science is becoming the apple of the tech industry’s eye nowadays. This article discusses the tools used by Data Scientists. Most data science platforms offer integrated delivery of complicated data science tasks. As a result, users can more easily incorporate data science functions without having to start from scratch with their code. Data Scientists are most sought after by many product-based companies. To land a dream job as a Data Scientist, one must be fluent in algorithms and concepts relating to Data Science. Where can a candidate learn these? There are many Data Science training institutes in our county. But at Skillslash, candidates are provided a 1:1 mentorship program and live classes, where doubts can be cleared. Skillslash also has in store, exclusive courses like Data Structures and algorithms system design, Full Stack Developer Course in Hyderabad and Web Development Course  to ensure aspirants of each domain have a great learning journey and a secure future in these fields. To find out how you can make a career in the IT and tech field with Skillslash, contact the student support team to know more about the course and institute.

[loginautocad360]

Matlab Applications

Applications of Matlab:

The current advancements in MATLAB have made it a very competitive tool for Artificial Intelligence, Robotics, Image Processing, Wireless Communication, Machine Learning, Data Analytics, and other fields. MATLAB may be used as a tool for simulating diverse electrical networks. Although it has a wide range of applications, circuit branches and mechanical engineering employ it primarily to tackle a fundamental set of problems. It is a tool that makes it possible to compute, programme, and visually represent the outcomes.

The matrix or array is the fundamental data type in MATLAB, as the name would imply. Professionally constructed MATLAB toolboxes let you materialise your fantasies. Programming in MATLAB is very similar to programming in C, so you just need to brush up on your fundamental programming knowledge.

MATLAB is used in the applications listed below.

Statistics and artificial intelligence (ML)

For programmers, MATLAB's toolbox can be quite useful. It is simple to implement statistical techniques like descriptive or inferential statistics. The same is true for machine learning. Modern challenges can be solved using a variety of models. Big data applications can also utilise the employed methods.

Fitting curves

The toolbox for curve fitting aids in the analysis of data distribution patterns. It is possible to anticipate future trends once a specific trend, which can be a curve or surface, has been obtained. Additional graphing, integral and derivative calculations, interpolation, and other tasks are possible.

Control systems of a certain sort are attainable. Bode plots, Nyquist plots, open-loop and closed-loop dynamics, controllability and observability, and other factors can be acquired One can visualise various controlling methods like PD, PI, and PID. Either time or frequency domain analysis is possible.

Processing of Signals

Various engineering disciplines teach signals, systems, and digital signal processing. But MATLAB offers the chance to properly visualise this. On any given signal, different transforms, including Laplace, Z, and others, can be applied. A theorem may be verified. Either time or frequency domain analysis is possible. There are numerous available built-in functions.

Mapping

There are numerous uses for mapping across numerous industries. For instance, the MapReduce tool, which has numerous real-world applications, is quite important in the field of big data. Analysis of theft or the detection of financial fraud, regression models, and contingency.Data mapping can be used for analysis, social media prediction techniques, data monitoring, etc.

In-depth learning

It's a type of machine learning that can be used for picture analysis in medicine, speech recognition, and financial fraud detection. You can use a variety of technologies, including time-series analysis, artificial neural networks (ANN), and fuzzy logic.

Analyzing finances

Before beginning any project, an entrepreneur must conduct a thorough investigation and a financial analysis to determine the best course of action. All of the necessary tools are contained in MATLAB. It is possible to identify components like profitability, solvency, liquidity, and stability. It is possible to evaluate business valuation, capital budgeting, cost of capital, etc.

Processing images

The most common application that we observe almost every day are bar.When delivering data from distant satellites and receiving and decoding it in the same manner, digital image processing also plays a significant role. All of these applications can be supported by existing algorithms.

Analysis of text

Sentiment analysis can be performed on the text. Within a few milliseconds of entering any text, Google returns millions of search results. Text analysis makes all of this possible. In forensics, handwriting comparison is possible. One piece of software that can do everything, with no restrictions on applications.

Designing electric vehicles

Used for modeling electric vehicles and analyze their performance with a change in system inputs. Speed torque comparison, designing and simulating of a vehicle, whatnot.

Aerospace

This toolbox in MATLAB is used for analyzing offers resources for speech analysis, acoustic measurement, and audio processing. It also offers methods for extracting audio and speech features and transforming audio signals.

Conclusion:

It offers a sizable library of mathematical operations for solving ordinary differential equations and performing linear algebra, statistics, Fourier analysis, filtering, optimization, and numerical integration. It offers tools for creating personalised plots as well as built-in graphics for visualising data.

[bookpiofficial]

Improvement of Power System Performance Using Fuzzy Logic Based Interline Power Flow Controller [IPFC]: A Brief Study | Chapter  10 | Novel Perspectives of Engineering Research Vol. 8

This research created a nonlinear dynamic model of a multi machine power system with an Interline Power Flow Controller [IPFC] to increase damping of power system oscillations and transient stability. The IPFC performance of PI controllers is compared to that of a fuzzy logic-based controller under normal and abnormal operation situations. Many-valued logic is a subset of fuzzy logic. The IPFC fuzzy controller is designed to coordinate two control inputs: voltage change and capacitor voltage change, in order to increase the multimachine system's transient stability. The Interline Power Flow Controller [IPFC] with fuzzy logic controller is built with simple fuzzy rules to coordinate the additional damping signal. The proposed IPFC controller can increase the power system's designed performance. Digital simulations are carried out in the MATLAB environment. Author(S) Details S. N. Dhurvey Department of Electrical Engg, G.H. Raisoni College of Engineering, Nagpur, India. V. K. Chandrakar Department of Electrical Engg, G.H. Raisoni College of Engineering, Nagpur, India. View Book:- https://stm.bookpi.org/NPER-V8/article/view/6116

[breezestudy]

Download Introduction to Fuzzy Logic using MATLAB PDF #9783642627552 #Computers #Software;Systems;scientificcomputing...

[denebola42-blog]

https://youtu.be/fn3KWM1kuAw I saw on YouTube and lost the bookmark that in the days of black and white television there's already someone doing pretty much what Boston Dynamics does now. What we see now in tech is old. As a child at a tech expo in California i saw AI the blew my mind away and the programmer was a racist jerk. I was maybe a tween. I knew about fuzzy logic etc as a youth. I had a book in at least the early 80s on it. There's other AI types I've forgotten about. https://en.m.wikipedia.org/wiki/List_of_programming_languages_for_artificial_intelligence I've heard of lisp, prolog and everyone knows about python such as the hackers i knew. I've heard of IPL and small talk, strips and planner sounds familiar and i did eprom programming as a child but it's boring and my father forced me to. Wolfram, Matlab, perl, and Julia and C++ and haskell I've heard of. I've heard of logic and constraint. https://en.m.wikipedia.org/wiki/History_of_artificial_intelligence I watched Jason and the Argonauts and loved Bubo the Owl in that one movie. Hmm And Muffet in BSG and the robot in Lost in space, black hole and Buck rogers and forbidden Planet etc. And there's Red Dwarf and hitchhikers guide to the galaxy. That's why gangs at the Ogden ATC hated me reading it. Ecoterrorists which all anarchists are. No exceptions. Antifa, black bloc, bash back etc. Anarcho-communism is a thing too and there's Christian and atheist and LDS anarchists etc. And I'm into transhumanism since seeing the Bionic Man and Knight Rider not just others and kids went on, in California, about Terminator and i laughed saying haven't you seen these other movies? So there's gonna be good AI and bad, and those that block it also like blocking guns, they'll have guns and won't. Listen to F the police by NWA. They will make you think fair fight and pull a fast one. Or equality and pull a fast one. The bad guys that is. There's good and bad in every group. (at North Ogden, Utah) https://www.instagram.com/p/CJnDcZCh780/?igshid=18n8vjpcddzav

[pantechelearning]

IETE Masterclass on MATLAB - December 2020 We take immense pleasure in announcing a IETE - Masterclass on MATLAB Why MATLAB ? MATLAB includes applications across image processing, computer vision, signal and audio processing, robotics, and control systems. With Companies and markets moving more towards MATLAB AI , its the need of the hour to understand the concepts. Domain & Schedules : Week 1 : MATLAB - Basic Tools & Image Processing Week 2: Computer Vision & Fuzzy Logic using MATLAB Week 3 : ML & DL Implementation using MATLAB Week 4 : Advanced Feature Extraction & Video Processing Reg Link: https://ift.tt/3lqg77V REGISTRATION FEE- Rs. 950 ( All 4 Programs) Program Organized & Certified by: Institution of Electronics and Telecommunication Engineers Mumbai Chapter #pantechelearning #matlabmasterclass https://instagr.am/p/CISLim0oc24/

[ijtsrd]

Power Factor Improved using CSC Converter in PMBLDC Motor

by Radhika S | Nisha P "Power Factor Improved using CSC Converter in PMBLDC Motor"

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020,

URL: https://www.ijtsrd.com/papers/ijtsrd30863.pdf

Paper Url :https://www.ijtsrd.com/engineering/electrical-engineering/30863/power-factor-improved-using-csc-converter-in-pmbldc-motor/radhika-s

ugcjournallist, listofugcapprovedjournals, researchpublication

This Project deals with a highly reliable electrical drive utilizing the Brushless DC Motor. The motor is fed by Voltage source Inverter VSI with a dc dc converter power factor correction circuit PFC as the VSI's predecessor. The Performance of dc dc converters is analyzed and the results are discussed to arrive at the best suited converter. Neuro fuzzy Logic Controller is used as the Intelligent Controller for the BLDC motor. Reliable, low cost arrangement is thus provided to achieve unity power factor and speed regulation with accuracy. The Bridgeless PFC Modified SEPIC Rectifier performs power factor correction and DC voltage control in single stage using only one controller. The designed PFC converter results in an improved power quality at AC mains in a wide range of speed control and input AC voltage. Power factor is improved by using CSC converter. Proposed method works with better power factor. The proposed method is analyzed in MATLAB simulation and hardware is implemented. 

[clickmyproject]

You can get the most effective final year projects according to your requirements. At the same time, efficient Matlab projects to help you score the most in your final semesters only at http://clickmyproject.com/ .

Latest Matlab Projects:

1)      Image Fusion based on Fuzzy logic

2)      Video magnification for heart rate measurement

3)      Modem Design for GFDM

[pakuniinfo]

Best 2019 Fuzzy Logic PDF Books, Notes, Course Data and Tutorials

Fuzzy logic is a form of many-valued logic in which the truth values of variables may be any real number between 0 and 1. It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely false. By contrast, in Boolean logic, the truth values of variables may only be the integer values 0 or 1. The term fuzzy logic was introduced with the 1965 proposal of fuzzy set theory by Lotfi Zadeh. Fuzzy logic had however been studied since the 1920s, as infinite-valued logic notably by Łukasiewicz and Tarski

This Outline Will be similar with your University Course Outline for Fuzzy Logic Course.

Mathematical introduction of fuzzy sets and fuzzy logic, A study of the fundamentals of fuzzy sets, operations on these sets, and their geometrical interpretations. Methodologies to design fuzzy models and feedback controllers for dynamical systems, fundamental concepts of dynamical systems, multi-input multi-output dynamical systems, stability, feedback- control design, and MATLAB®  Control System Toolbox. Fuzzy systems and properties Fuzzifier and Defuzzifier design, Design of fuzzy systems Fuzzy controllers, Hardware and Software based design of fuzzy logic control system.

Best Recommended Fuzzy Logic PDF Notes, Tutorials and Books in Universities:

Here is detailed list of best Fuzzy Logic books for Universities: Fuzzy Thinking the New Science of Fuzzy by Bart Kosko Fuzzy Sets and Fuzzy Logic: Theory and Applications by George Klir Fuzzy Logic with Engineering Applications  by Timothy J. Ross An Introduction to Fuzzy Logic for Practical Applications by Kazuo Tanaka Fuzzy Logic with Engineering Applications Book by Timothy J. Ross PDF Fuzzy Sets and Fuzzy Logic: Theory and Applications Novel by George Klir PDF Fuzzy Thinking the New Science of Fuzzy Bart Kosko PDF Fuzzy Logic Tutorials by Simple Snippets Fuzzy logic by Subhro Mukherjee NEURAL NETWORKS AND FUZZY LOGIC by Muhammad Shumail

Get Access to  Paid Fuzzy Logic Courses and Books exclusive on  Amazon,  Khan Academy, Scribd,   Coursea, Bightthink, EDX and  BrightStorm

Check out on Amazon Store Check out on  Khan Academy Check out on COURSEA Check out on Bright Storm Check out on Edx.com Read and Learn More about on BIGTHINK Get more Details about  Bachelor's Degree Courses Here. These Course contents belong to HEC outline for this specific Subject. If you have any further inquiries, Please Contact US for details via mail. All the data is extracted from HEC official website. The basic purpose for this to find all course subjects data on one page. Read the full article