This document provides an introduction to MATLAB and Simulink. It discusses what can be gained from learning MATLAB/Simulink, including being able to solve simple problems and explore the software. The contents include an overview of built-in functions, getting started, vectors and matrices, and modeling examples in MATLAB and Simulink. It also covers M-files, script and functions, and provides examples of basic operations in MATLAB like arithmetic on matrices and accessing matrix elements.
This document provides an introduction to MATLAB and Simulink. It explains that MATLAB is a software package used for numerical computation, while Simulink is used for modeling and simulating dynamic systems. The document outlines some key things readers can learn, including basics of MATLAB/Simulink functions, getting started, vectors and matrices, M-files, and modeling examples. It also provides examples of basic MATLAB operations on vectors and matrices.
This document provides an introduction to MATLAB and Simulink. It discusses how to install MATLAB, get started with the software, work with vectors and matrices, use built-in functions, and visualize data through plotting. Key topics covered include assigning values to variables and arrays, performing arithmetic operations on matrices, and creating 2D and 3D plots of functions using commands like plot, mesh, and surf. M-files are also introduced as a way to save and execute collections of MATLAB commands for more complex problems and analyses.
This document provides an introduction to MATLAB and Simulink. It discusses built-in functions, vectors and matrices, modeling examples, M-files for scripts and functions. It also explores the MATLAB desktop, command window, and workspace. Examples are provided on arithmetic operations on matrices, plotting graphs, writing functions, and modeling an RLC circuit in Simulink.
This document provides an introduction to MATLAB and discusses some of its key features. It describes MATLAB as a popular language for technical computing used in engineering and science. The document then outlines topics that will be covered in the workshop, including elementary operations, vectors and matrices, element-by-element operations, graphics, scripts, functions, and flow control. It provides examples of commands in each of these areas and demonstrates how to perform basic computations and visualizations in MATLAB.
This document provides an introduction and overview of MATLAB. It defines MATLAB as an interactive system for technical computing with matrices as the basic data type. It describes how MATLAB is used in mathematics, industry, and research for numeric computation and visualization. The document outlines MATLAB's toolboxes for specialized applications and provides examples of using matrices, vectors, operators, and functions in MATLAB. It demonstrates how to perform operations like matrix addition and inversion, solve systems of linear equations, and analyze arrays with built-in functions.
The document provides an introduction to MATLAB. It discusses that MATLAB is a numerical computing environment and programming language. It can be used for matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages. The document then covers various MATLAB basics like the MATLAB environment, matrix operations, data types, mathematical and logical operators, and plotting functions. It provides examples of creating and manipulating matrices and vectors in MATLAB.
This document provides an introduction to MATLAB programming. It discusses resources for the course including the course web page and slides. It then explains what MATLAB is, how to get started using it on Windows and Linux systems, and how to get help. It also covers the MATLAB desktop environment, performing calculations on the command line, entering numeric arrays, indexing into matrices, basic plotting commands, and logical indexing.
MATLAB is a high-level programming language and computing environment used for numerical computations, visualization, and programming. The document discusses MATLAB's capabilities including its toolboxes, plotting functions, control structures, M-files, and user-defined functions. MATLAB is useful for engineering and scientific calculations due to its matrix-based operations and built-in functions.
This document provides an introduction to MATLAB and Simulink. It explains that MATLAB is a software package used for numerical computation, while Simulink is used for modeling and simulating dynamic systems. The document outlines some key things readers can learn, including basics of MATLAB/Simulink functions, getting started, vectors and matrices, M-files, and modeling examples. It also provides examples of basic MATLAB operations on vectors and matrices.
This document provides an introduction to MATLAB and Simulink. It discusses how to install MATLAB, get started with the software, work with vectors and matrices, use built-in functions, and visualize data through plotting. Key topics covered include assigning values to variables and arrays, performing arithmetic operations on matrices, and creating 2D and 3D plots of functions using commands like plot, mesh, and surf. M-files are also introduced as a way to save and execute collections of MATLAB commands for more complex problems and analyses.
This document provides an introduction to MATLAB and Simulink. It discusses built-in functions, vectors and matrices, modeling examples, M-files for scripts and functions. It also explores the MATLAB desktop, command window, and workspace. Examples are provided on arithmetic operations on matrices, plotting graphs, writing functions, and modeling an RLC circuit in Simulink.
This document provides an introduction to MATLAB and discusses some of its key features. It describes MATLAB as a popular language for technical computing used in engineering and science. The document then outlines topics that will be covered in the workshop, including elementary operations, vectors and matrices, element-by-element operations, graphics, scripts, functions, and flow control. It provides examples of commands in each of these areas and demonstrates how to perform basic computations and visualizations in MATLAB.
This document provides an introduction and overview of MATLAB. It defines MATLAB as an interactive system for technical computing with matrices as the basic data type. It describes how MATLAB is used in mathematics, industry, and research for numeric computation and visualization. The document outlines MATLAB's toolboxes for specialized applications and provides examples of using matrices, vectors, operators, and functions in MATLAB. It demonstrates how to perform operations like matrix addition and inversion, solve systems of linear equations, and analyze arrays with built-in functions.
The document provides an introduction to MATLAB. It discusses that MATLAB is a numerical computing environment and programming language. It can be used for matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages. The document then covers various MATLAB basics like the MATLAB environment, matrix operations, data types, mathematical and logical operators, and plotting functions. It provides examples of creating and manipulating matrices and vectors in MATLAB.
This document provides an introduction to MATLAB programming. It discusses resources for the course including the course web page and slides. It then explains what MATLAB is, how to get started using it on Windows and Linux systems, and how to get help. It also covers the MATLAB desktop environment, performing calculations on the command line, entering numeric arrays, indexing into matrices, basic plotting commands, and logical indexing.
MATLAB is a high-level programming language and computing environment used for numerical computations, visualization, and programming. The document discusses MATLAB's capabilities including its toolboxes, plotting functions, control structures, M-files, and user-defined functions. MATLAB is useful for engineering and scientific calculations due to its matrix-based operations and built-in functions.
This document provides an introduction and overview of MATLAB. It discusses MATLAB basics like the command window and variables. It also covers topics like working with matrices, vectors, plotting, scripts and functions. Specific MATLAB commands covered include plot, mesh, surf, contour and more. Functions like length, dot, cross and special matrices like ones, zeros and eye are also explained.
The document provides an overview of matrix algebra operations in R, including vectors, matrices, and their applications in psychological data analysis. It covers vector operations like addition, multiplication, and combining vectors into matrices. Matrix topics include addition, multiplication, finding the diagonal, identity matrices, and inversion. The document also demonstrates how these operations can be used for data manipulation tasks like calculating statistics, finding test reliability, and multiple correlation analyses.
This document provides an overview of variables, arrays, and other basic programming concepts in MATLAB. It discusses how variables store and retrieve values, how arrays can have multiple dimensions and elements can be accessed using indexing, and how basic operations can be performed on arrays element-wise or across entire arrays using functions. Various functions for creating arrays filled with zeros, ones, or random values are also introduced.
MATLAB/SIMULINK for Engineering Applications day 2:Introduction to simulinkreddyprasad reddyvari
The document provides an introduction to MATLAB and Simulink through a presentation. It discusses what MATLAB and Simulink are, their basic functions and capabilities, and how to get started using them. The presentation covers topics such as vectors, matrices, plotting, control structures, M-files, and writing user-defined functions. The goal is to help attendees gain basic knowledge of MATLAB/Simulink and be able to explore them on their own.
An Introduction to MATLAB for beginnersMurshida ck
This document provides an introduction to MATLAB, including:
- MATLAB is a program for numerical computation, originally designed for matrix operations. It has expanded capabilities for data analysis, signal processing, and other scientific tasks.
- The MATLAB desktop includes tools like the Command Window, Workspace, and Figure Window. Common commands are introduced for arithmetic, variables, arrays, strings and plots.
- Arrays in MATLAB can represent vectors and matrices. Commands are demonstrated for creating, manipulating, and performing operations on arrays.
This document provides an overview of MATLAB, including:
- MATLAB is a software package for numerical computation, originally designed for linear algebra problems using matrices. It has since expanded to include other scientific computations.
- MATLAB treats all variables as matrices and supports various matrix operations like addition, multiplication, element-wise operations, and matrix manipulation functions.
- MATLAB allows plotting of 2D and 3D graphics, importing/exporting of data from files and Excel, and includes flow control statements like if/else, for loops, and while loops to structure code execution.
- Efficient MATLAB programming involves using built-in functions instead of custom functions, preallocating arrays, and avoiding nested loops where possible through matrix operations.
MATLAB is a numerical computing environment and programming language. It allows matrix manipulations, plotting of functions and data, implementation of algorithms, and interfacing with programs in other languages. MATLAB can be used for applications like signal processing, image processing, control systems, and computational finance. It offers advantages like ease of use, platform independence, and predefined functions. However, it can sometimes be slow and is commercial software. The MATLAB interface includes a command window, current directory, workspace, and command history. Arrays are fundamental data types in MATLAB and can be vectors, matrices, or multidimensional. Variables are used to store information in the workspace and can represent different data types. Common operations include arithmetic, functions, and following the
In MATLAB, a vector is created by assigning the elements of the vector to a variable. This can be done in several ways depending on the source of the information.
—Enter an explicit list of elements
—Load matrices from external data files
—Using built-in functions
—Using own functions in M-files
Matlab is a high-level programming language and environment used for numerical computation, visualization, and programming. The document outlines key Matlab concepts including the Matlab screen, variables, arrays, matrices, operators, plotting, flow control, m-files, and user-defined functions. Matlab allows users to analyze data, develop algorithms, and create models and applications.
This document provides an overview of optimization techniques that can be performed using MATLAB. It discusses unconstrained optimization problems where the goal is to minimize or maximize an objective function without any constraints on the variables. Constrained optimization problems are also discussed, where the goal is to optimize the objective function subject to certain equality and inequality constraints. MATLAB functions like fminsearch and fmincon can be used to find the optimal solution for unconstrained and constrained problems respectively. Gradient-based methods for solving constrained optimization problems are also briefly covered.
This document provides an introduction to MATLAB. It begins with an overview of the MATLAB environment and display windows. It then discusses getting help in MATLAB, variables, vectors, matrices, linear algebra, plotting, built-in functions, selection programming using if/else statements, M-files, user-defined functions, and specific topics. Key points covered include the MATLAB interface, basic programming constructs like variables and arrays, and tools for computation, visualization, and programming in MATLAB.
This document provides an overview of using MATLAB to perform various mathematical operations and graphing functions. It begins with basic MATLAB usage like arithmetic, variables, and commands. It then covers topics like algebraic simplification, solving equations, graphing functions, matrices, limits, derivatives, and exponential/logarithmic functions. The document serves as an introduction to MATLAB's computational abilities for tasks like calculus, linear algebra, and plotting functions.
This document provides an overview of MATLAB, including the MATLAB desktop, variables, vectors, matrices, matrix operations, array operations, built-in functions, data visualization, flow control using if and for statements, and user-defined functions. It introduces key MATLAB concepts like the command window, workspace, and editor. It also demonstrates how to create and manipulate variables, vectors, matrices, and plots in MATLAB.
MATLAB is an interactive development environment and programming language used by engineers and scientists for technical computing, data analysis, and algorithm development. It allows users to access data from files, web services, applications, hardware, and databases, and perform data analysis and visualization. MATLAB can be used for applications in areas like control systems, signal processing, communications, and more.
A matrix is a two-dimensional rectangular data structure that can be created in R using a vector as input to the matrix function. The matrix function arranges the vector elements into rows and columns based on the number of rows and columns specified. Basic matrix operations include accessing individual elements and submatrices, computing transposes, products, and inverses. Matrices allow efficient storage and manipulation of multi-dimensional data.
This document provides an overview of matrix methods for solving systems of linear equations. It begins with an example from structural engineering of setting up a system of 6 equations with 6 unknowns to model the forces and reactions in a statically determinant truss. The equations are represented in matrix notation as [A]{x}={c}. The document then reviews key matrix concepts and operations used to solve systems of linear equations, such as matrix addition, multiplication, transposes, inverses, and types of matrices. It aims to help readers understand how to set up and solve systems of linear equations using matrices.
This document provides an overview of MATLAB and the Signal Processing Toolbox. It discusses MATLAB basics like commands, functions, variables and matrices. It also introduces key signal processing concepts like representing signals, basic waveform generation, convolution, and filters. The Signal Processing Toolbox allows analyzing and processing signals and includes tools for digital filter design and implementation, spectral analysis, and filtering signals.
A Powerpoint Presentation designed to provide beginners to MATLAB an introduction to the MATLAB environment and introduce them to the fundamentals of MATLAB including matrix generation and manipulation, Arrays, MATLAB Graphics, Data Import and Export, etc
The name MATLAB stands for MATrix LABoratory.MATLAB is a high-performance language for technical computing.
It integrates computation, visualization, and programming environment. Furthermore, MATLAB is a modern programming language environment: it has sophisticated data structures, contains built-in editing and debugging tools, and supports object-oriented programming.
These factor make MATLAB an excellent tool for teaching and research.
This document provides an introduction to the Python programming language. It discusses that Python was created in 1991, is an interpreted language useful for scripting, and is used by many companies and organizations. It also gives instructions on installing Python on Windows, Mac OS X, and Linux systems. Finally, it demonstrates some basic Python concepts like print statements, comments, functions, and whitespace significance through simple code examples.
This document provides an introduction and overview of MATLAB. It discusses MATLAB basics like the command window and variables. It also covers topics like working with matrices, vectors, plotting, scripts and functions. Specific MATLAB commands covered include plot, mesh, surf, contour and more. Functions like length, dot, cross and special matrices like ones, zeros and eye are also explained.
The document provides an overview of matrix algebra operations in R, including vectors, matrices, and their applications in psychological data analysis. It covers vector operations like addition, multiplication, and combining vectors into matrices. Matrix topics include addition, multiplication, finding the diagonal, identity matrices, and inversion. The document also demonstrates how these operations can be used for data manipulation tasks like calculating statistics, finding test reliability, and multiple correlation analyses.
This document provides an overview of variables, arrays, and other basic programming concepts in MATLAB. It discusses how variables store and retrieve values, how arrays can have multiple dimensions and elements can be accessed using indexing, and how basic operations can be performed on arrays element-wise or across entire arrays using functions. Various functions for creating arrays filled with zeros, ones, or random values are also introduced.
MATLAB/SIMULINK for Engineering Applications day 2:Introduction to simulinkreddyprasad reddyvari
The document provides an introduction to MATLAB and Simulink through a presentation. It discusses what MATLAB and Simulink are, their basic functions and capabilities, and how to get started using them. The presentation covers topics such as vectors, matrices, plotting, control structures, M-files, and writing user-defined functions. The goal is to help attendees gain basic knowledge of MATLAB/Simulink and be able to explore them on their own.
An Introduction to MATLAB for beginnersMurshida ck
This document provides an introduction to MATLAB, including:
- MATLAB is a program for numerical computation, originally designed for matrix operations. It has expanded capabilities for data analysis, signal processing, and other scientific tasks.
- The MATLAB desktop includes tools like the Command Window, Workspace, and Figure Window. Common commands are introduced for arithmetic, variables, arrays, strings and plots.
- Arrays in MATLAB can represent vectors and matrices. Commands are demonstrated for creating, manipulating, and performing operations on arrays.
This document provides an overview of MATLAB, including:
- MATLAB is a software package for numerical computation, originally designed for linear algebra problems using matrices. It has since expanded to include other scientific computations.
- MATLAB treats all variables as matrices and supports various matrix operations like addition, multiplication, element-wise operations, and matrix manipulation functions.
- MATLAB allows plotting of 2D and 3D graphics, importing/exporting of data from files and Excel, and includes flow control statements like if/else, for loops, and while loops to structure code execution.
- Efficient MATLAB programming involves using built-in functions instead of custom functions, preallocating arrays, and avoiding nested loops where possible through matrix operations.
MATLAB is a numerical computing environment and programming language. It allows matrix manipulations, plotting of functions and data, implementation of algorithms, and interfacing with programs in other languages. MATLAB can be used for applications like signal processing, image processing, control systems, and computational finance. It offers advantages like ease of use, platform independence, and predefined functions. However, it can sometimes be slow and is commercial software. The MATLAB interface includes a command window, current directory, workspace, and command history. Arrays are fundamental data types in MATLAB and can be vectors, matrices, or multidimensional. Variables are used to store information in the workspace and can represent different data types. Common operations include arithmetic, functions, and following the
In MATLAB, a vector is created by assigning the elements of the vector to a variable. This can be done in several ways depending on the source of the information.
—Enter an explicit list of elements
—Load matrices from external data files
—Using built-in functions
—Using own functions in M-files
Matlab is a high-level programming language and environment used for numerical computation, visualization, and programming. The document outlines key Matlab concepts including the Matlab screen, variables, arrays, matrices, operators, plotting, flow control, m-files, and user-defined functions. Matlab allows users to analyze data, develop algorithms, and create models and applications.
This document provides an overview of optimization techniques that can be performed using MATLAB. It discusses unconstrained optimization problems where the goal is to minimize or maximize an objective function without any constraints on the variables. Constrained optimization problems are also discussed, where the goal is to optimize the objective function subject to certain equality and inequality constraints. MATLAB functions like fminsearch and fmincon can be used to find the optimal solution for unconstrained and constrained problems respectively. Gradient-based methods for solving constrained optimization problems are also briefly covered.
This document provides an introduction to MATLAB. It begins with an overview of the MATLAB environment and display windows. It then discusses getting help in MATLAB, variables, vectors, matrices, linear algebra, plotting, built-in functions, selection programming using if/else statements, M-files, user-defined functions, and specific topics. Key points covered include the MATLAB interface, basic programming constructs like variables and arrays, and tools for computation, visualization, and programming in MATLAB.
This document provides an overview of using MATLAB to perform various mathematical operations and graphing functions. It begins with basic MATLAB usage like arithmetic, variables, and commands. It then covers topics like algebraic simplification, solving equations, graphing functions, matrices, limits, derivatives, and exponential/logarithmic functions. The document serves as an introduction to MATLAB's computational abilities for tasks like calculus, linear algebra, and plotting functions.
This document provides an overview of MATLAB, including the MATLAB desktop, variables, vectors, matrices, matrix operations, array operations, built-in functions, data visualization, flow control using if and for statements, and user-defined functions. It introduces key MATLAB concepts like the command window, workspace, and editor. It also demonstrates how to create and manipulate variables, vectors, matrices, and plots in MATLAB.
MATLAB is an interactive development environment and programming language used by engineers and scientists for technical computing, data analysis, and algorithm development. It allows users to access data from files, web services, applications, hardware, and databases, and perform data analysis and visualization. MATLAB can be used for applications in areas like control systems, signal processing, communications, and more.
A matrix is a two-dimensional rectangular data structure that can be created in R using a vector as input to the matrix function. The matrix function arranges the vector elements into rows and columns based on the number of rows and columns specified. Basic matrix operations include accessing individual elements and submatrices, computing transposes, products, and inverses. Matrices allow efficient storage and manipulation of multi-dimensional data.
This document provides an overview of matrix methods for solving systems of linear equations. It begins with an example from structural engineering of setting up a system of 6 equations with 6 unknowns to model the forces and reactions in a statically determinant truss. The equations are represented in matrix notation as [A]{x}={c}. The document then reviews key matrix concepts and operations used to solve systems of linear equations, such as matrix addition, multiplication, transposes, inverses, and types of matrices. It aims to help readers understand how to set up and solve systems of linear equations using matrices.
This document provides an overview of MATLAB and the Signal Processing Toolbox. It discusses MATLAB basics like commands, functions, variables and matrices. It also introduces key signal processing concepts like representing signals, basic waveform generation, convolution, and filters. The Signal Processing Toolbox allows analyzing and processing signals and includes tools for digital filter design and implementation, spectral analysis, and filtering signals.
A Powerpoint Presentation designed to provide beginners to MATLAB an introduction to the MATLAB environment and introduce them to the fundamentals of MATLAB including matrix generation and manipulation, Arrays, MATLAB Graphics, Data Import and Export, etc
The name MATLAB stands for MATrix LABoratory.MATLAB is a high-performance language for technical computing.
It integrates computation, visualization, and programming environment. Furthermore, MATLAB is a modern programming language environment: it has sophisticated data structures, contains built-in editing and debugging tools, and supports object-oriented programming.
These factor make MATLAB an excellent tool for teaching and research.
This document provides an introduction to the Python programming language. It discusses that Python was created in 1991, is an interpreted language useful for scripting, and is used by many companies and organizations. It also gives instructions on installing Python on Windows, Mac OS X, and Linux systems. Finally, it demonstrates some basic Python concepts like print statements, comments, functions, and whitespace significance through simple code examples.
This document provides an introduction to analyzing the runtime of algorithms asymptotically. It discusses how machine instructions each take a fixed amount of time, so basic operations have O(1) runtime. Control structures like loops can affect runtime - for example, a for loop over an array of size n takes O(n) time. Different algorithms are analyzed and compared asymptotically, like some sorting algorithms taking O(n log n) time while others take O(n^2) time. Understanding asymptotic runtime allows predicting how algorithms scale to larger inputs.
Informed search algorithms aim to be smarter than blind search about which paths to explore by using an evaluation function to estimate the cost to the goal for each node, with best-first search always expanding the node with the lowest estimated cost; A* search combines the cost so far and estimated remaining cost to provide an optimal search when the heuristic is admissible or consistent; pattern database heuristics derive more accurate estimates of moves required by solving subproblems exactly and combining the solutions.
This document discusses processes and threads. It begins by defining processes and describing their states, creation, termination, and hierarchies. It then defines threads as components within processes that can run concurrently. Various methods for implementing and scheduling threads are described, including in user space, kernel space, and hybrid approaches. Interprocess communication techniques like critical sections, semaphores, mutexes, monitors, and message passing are covered. Classical synchronization problems like the dining philosophers, readers/writers, and sleeping barber are also summarized along with their solutions.
This document provides an outline and overview of key topics related to data structures and algorithms that will be covered in an ECE 250 course, including different types of memory allocation (contiguous, linked, indexed), examples of basic data structures (arrays, linked lists, trees), analysis of algorithm runtimes for different operations (find, insert, erase) on various data structures, and a brief overview of subsequent topics to be addressed in the course like asymptotic analysis, specific linearly ordered and relation-free data structures, sorting algorithms, and algorithm design techniques.
This document discusses different number systems including positional and non-positional. It describes the decimal, binary, octal, and hexadecimal number systems. For each system it provides the base, symbols used, and examples of converting values between the systems. Key points covered include how positional number systems represent values based on the symbol's place, and algorithms for converting between bases for both integral and fractional values.
This document contains information about a Data Structures and Algorithms course taught by Professor Yusuf Sahillioğlu at Middle East Technical University. It provides details about the course objectives, textbook, grading breakdown, course outline covering topics like sorting, lists, trees and graphs, and motivational examples demonstrating how data structures can be used to efficiently store and process data. It also introduces some basic C++ concepts like classes, objects, encapsulation and information hiding that will be used in the course.
This document discusses binary search trees (BSTs). It defines BSTs as binary trees where the value in each node is greater than all values in its left subtree and less than all values in its right subtree. It explains that searches, insertions and deletions in balanced BSTs have O(log n) complexity on average. The document provides pseudocode and examples for searching, inserting, and removing nodes from a BST. It also discusses how the shape and balance of a BST can impact performance of the operations.
This document summarizes a lecture on programming techniques for teams and software processes. The lecture discusses tips for partitioning tasks across team members either functionally or by task. It emphasizes the importance of communication through documentation, source control tools, and continuous integration and testing. The lecture then covers the waterfall software development process, which involves sequential phases from requirements to design to implementation and testing. However, it notes that pure waterfall is rarely used for software due to long timelines and lack of feedback.
This document provides an introduction to the CS351 Software Engineering course taught by Michael Oudshoorn and Ray Babcock. It outlines the course structure, objectives, assessment, expectations and indicative topics. The course will involve lectures, tutorials and a large individual project. Students are expected to spend around 130 hours over the semester gaining experience in software engineering practices and skills.
The document provides an overview of AWS Free Tier and key AWS services. It discusses how AWS provides global infrastructure across multiple regions and availability zones to provide high availability and meet regulatory requirements. Key services summarized include IAM for access control, S3 for object storage, EC2 for virtual servers, EBS for block storage, load balancers, CloudWatch for monitoring, auto scaling, RDS for databases, VPC for virtual networks, and the AWS CLI.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Low power architecture of logic gates using adiabatic techniquesnooriasukmaningtyas
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
2. Introduction to
MATLAB and Simulink
What can you gain from the course ?
Know basics of MATLAB/Simulink
– know how to solve simple problems
Know what MATLAB/Simulink is
Know how to get started with MATLAB/Simulink
Be able to explore MATLAB/Simulink on
your own !
3. Introduction to
MATLAB and Simulink
Contents
Built in functions
Getting Started
Vectors and Matrices
Introduction
Simulink
Modeling examples
MATLAB
SIMULINK
M–files : script and functions
4. Introduction
MATLAB – MATrix LABoratory
– Initially developed by a lecturer in 1970’s to help students
learn linear algebra.
– It was later marketed and further developed under
MathWorks Inc. (founded in 1984) – www.mathworks.com
– Matlab is a software package which can be used to perform
analysis and solve mathematical and engineering problems.
– It has excellent programming features and graphics capability
– easy to learn and flexible.
– Available in many operating systems – Windows, Macintosh,
Unix, DOS
– It has several tooboxes to solve specific problems.
5. Introduction
Simulink
– Used to model, analyze and simulate dynamic
systems using block diagrams.
– Fully integrated with MATLAB , easy and fast to
learn and flexible.
– It has comprehensive block library which can be
used to simulate linear, non–linear or discrete
systems – excellent research tools.
– C codes can be generated from Simulink models for
embedded applications and rapid prototyping of
control systems.
6. Getting Started
Run MATLAB from Start Programs MATLAB
Depending on version used, several windows appear
• For example in Release 13 (Ver 6), there are several
windows – command history, command, workspace, etc
• For Matlab Student – only command window
Command window
• Main window – where commands are entered
8. Variables
– Vectors and Matrices –
ALL variables are matrices
Variables
•They are case–sensitive i.e x X
•Their names can contain up to 31 characters
•Must start with a letter
Variables are stored in workspace
e.g. 1 x 1 4 x 1 1 x 4 2 x 4
4
2
3
9
6
5
1
2
7
1
2
3
3
9
2
3
4
9. Vectors and Matrices
How do we assign a value to a variable?
>>> v1=3
v1 =
3
>>> i1=4
i1 =
4
>>> R=v1/i1
R =
0.7500
>>>
>>> whos
Name Size Bytes Class
R 1x1 8 double array
i1 1x1 8 double array
v1 1x1 8 double array
Grand total is 3 elements using 24 bytes
>>> who
Your variables are:
R i1 v1
>>>
10. Vectors and Matrices
18
16
14
12
10
B
How do we assign values to vectors?
>>> A = [1 2 3 4 5]
A =
1 2 3 4 5
>>>
>>> B = [10;12;14;16;18]
B =
10
12
14
16
18
>>>
A row vector –
values are
separated by
spaces
A column
vector –
values are
separated by
semi–colon
(;)
5
4
3
2
1
A
11. Vectors and Matrices
If we want to construct a vector of, say, 100
elements between 0 and 2 – linspace
>>> c1 = linspace(0,(2*pi),100);
>>> whos
Name Size Bytes Class
c1 1x100 800 double array
Grand total is 100 elements using 800 bytes
>>>
How do we assign values to vectors?
12. Vectors and Matrices
How do we assign values to vectors?
If we want to construct an array of, say, 100
elements between 0 and 2 – colon notation
>>> c2 = (0:0.0201:2)*pi;
>>> whos
Name Size Bytes Class
c1 1x100 800 double array
c2 1x100 800 double array
Grand total is 200 elements using 1600 bytes
>>>
13. Vectors and Matrices
How do we assign values to matrices ?
Columns separated by
space or a comma
Rows separated by
semi-colon
>>> A=[1 2 3;4 5 6;7 8 9]
A =
1 2 3
4 5 6
7 8 9
>>>
9
8
7
6
5
4
3
2
1
14. Vectors and Matrices
How do we access elements in a matrix or a vector?
Try the followings:
>>> A(2,3)
ans =
6
>>> A(:,3)
ans =
3
6
9
>>> A(1,:)
ans =
1 2 3
>>> A(2,:)
ans =
4 5 6
15. Vectors and Matrices
Some special variables
beep
pi ()
inf (e.g. 1/0)
i, j ( )
1
>>> 1/0
Warning: Divide by zero.
ans =
Inf
>>> pi
ans =
3.1416
>>> i
ans =
0+ 1.0000i
16. Vectors and Matrices
Arithmetic operations – Matrices
Performing operations to every entry in a matrix
Add and subtract
>>> A=[1 2 3;4 5 6;7 8
9]
A =
1 2 3
4 5 6
7 8 9
>>>
>>> A+3
ans =
4 5 6
7 8 9
10 11 12
>>> A-2
ans =
-1 0 1
2 3 4
5 6 7
17. Vectors and Matrices
Arithmetic operations – Matrices
Performing operations to every entry in a matrix
Multiply and divide
>>> A=[1 2 3;4 5 6;7 8 9]
A =
1 2 3
4 5 6
7 8 9
>>>
>>> A*2
ans =
2 4 6
8 10 12
14 16 18
>>> A/3
ans =
0.3333 0.6667 1.0000
1.3333 1.6667 2.0000
2.3333 2.6667 3.0000
18. Vectors and Matrices
Arithmetic operations – Matrices
Performing operations to every entry in a matrix
Power
>>> A=[1 2 3;4 5 6;7 8 9]
A =
1 2 3
4 5 6
7 8 9
>>>
A^2 = A * A
To square every element in A, use
the element–wise operator .^
>>> A.^2
ans =
1 4 9
16 25 36
49 64 81
>>> A^2
ans =
30 36 42
66 81 96
102 126 150
24. Example (cont)
Vectors and Matrices
Arithmetic operations – Matrices
>>> A=[(0.1+0.2j) -0.2j;-0.2j 0.1j]
A =
0.1000+ 0.2000i 0- 0.2000i
0- 0.2000i 0+ 0.1000i
>>> y=[-2j;1.5]
y =
0- 2.0000i
1.5000
>>> x=Ay
x =
14.0000+ 8.0000i
28.0000+ 1.0000i
>>>
* AB is the matrix division of A into B,
which is roughly the same as INV(A)*B *
25. Example (cont)
Vectors and Matrices
Arithmetic operations – Matrices
>>> V1= abs(x(1,:))
V1 =
16.1245
>>> V1ang= angle(x(1,:))
V1ang =
0.5191
V1 = 16.1229.7o V
26. Built in functions
(commands)
Scalar functions – used for scalars and operate
element-wise when applied to a matrix or vector
e.g. sin cos tan atan asin log
abs angle sqrt round floor
At any time you can use the command
help to get help
e.g. >>>help sin
27. Built in functions (commands)
>>> a=linspace(0,(2*pi),10)
a =
Columns 1 through 7
0 0.6981 1.3963 2.0944 2.7925 3.4907
4.1888
Columns 8 through 10
4.8869 5.5851 6.2832
>>> b=sin(a)
b =
Columns 1 through 7
0 0.6428 0.9848 0.8660 0.3420 -0.3420
-0.8660
Columns 8 through 10
-0.9848 -0.6428 0.0000
>>>
28. Built in functions (commands)
Vector functions – operate on vectors returning
scalar value
e.g. max min mean prod sum length
>>> max(b)
ans =
0.9848
>>> max(a)
ans =
6.2832
>>> length(a)
ans =
10
>>>
>>> a=linspace(0,(2*pi),10);
>>> b=sin(a);
29. Built in functions (commands)
Matrix functions – perform operations on
matrices
>>> help elmat
>>> help matfun
e.g. eye size inv det eig
At any time you can use the command
help to get help
31. From our previous example,
1
.
0
j
2
.
0
j
2
.
0
j
2
.
0
j
1
.
0
2
1
V
V
=
5
.
1
2
j
A x y
=
Built in functions (commands)
>>> x=inv(A)*y
x =
14.0000+ 8.0000i
28.0000+ 1.0000i
32. Built in functions (commands)
Data visualisation – plotting graphs
>>> help graph2d
>>> help graph3d
e.g. plot polar loglog mesh
semilog plotyy surf
33. Built in functions (commands)
Data visualisation – plotting graphs
Example on plot – 2 dimensional plot
Example on plot – 2 dimensional plot
>>> x=linspace(0,(2*pi),100);
>>> y1=sin(x);
>>> y2=cos(x);
>>> plot(x,y1,'r-')
>>> hold
Current plot held
>>> plot(x,y2,'g--')
>>>
Add title, labels and legend
title xlabel ylabel legend
Use ‘copy’ and ‘paste’ to add to your
window–based document, e.g. MSword
eg1_plt.m
34. Built in functions (commands)
Data visualisation – plotting graphs
0 1 2 3 4 5 6 7
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
angular frequency (rad/s)
y1
and
y2
Example on plot
sin(x)
cos(x)
Example on plot – 2 dimensional plot
eg1_plt.m
35. Built in functions (commands)
Data visualisation – plotting graphs
Example on mesh and surf – 3 dimensional plot
>>> [t,a] = meshgrid(0.1:.01:2, 0.1:0.5:7);
>>> f=2;
>>> Z = 10.*exp(-a.*0.4).*sin(2*pi.*t.*f);
>>> surf(Z);
>>> figure(2);
>>> mesh(Z);
Supposed we want to visualize a function
Z = 10e(–0.4a) sin (2ft) for f = 2
when a and t are varied from 0.1 to 7 and 0.1 to 2, respectively
eg2_srf.m
36. Built in functions (commands)
Data visualisation – plotting graphs
Example on mesh and surf – 3 dimensional plot
eg2_srf.m
37. Built in functions (commands)
Data visualisation – plotting graphs
Example on mesh and surf – 3 dimensional plot
>>> [x,y] = meshgrid(-3:.1:3,-3:.1:3);
>>> z = 3*(1-x).^2.*exp(-(x.^2) - (y+1).^2) ...
- 10*(x/5 - x.^3 - y.^5).*exp(-x.^2-y.^2) ...
- 1/3*exp(-(x+1).^2 - y.^2);
>>> surf(z);
eg3_srf.m
38. Built in functions (commands)
Data visualisation – plotting graphs
Example on mesh and surf – 3 dimensional plot
eg2_srf.m
39. Solution : use M-files
M-files :
Script and function files
When problems become complicated and require re–
evaluation, entering command at MATLAB prompt is
not practical
Collections of commands
Executed in sequence when called
Saved with extension “.m”
Script Function
User defined commands
Normally has input &
output
Saved with extension “.m”
40. M-files : script and function files (script)
At Matlab prompt type in edit to invoke M-file editor
Save this file
as test1.m
eg1_plt.m
41. M-files : script and function files (script)
To run the M-file, type in the name of the file at the
prompt e.g. >>> test1
Type in matlabpath to check the list of directories
listed in the path
Use path editor to add the path: File Set path …
It will be executed provided that the saved file is in the
known path
42. M-files : script and function files (script)
Example – RLC circuit
Exercise 1:
Write an m–file to plot Z, Xc and XLversus
frequency for R =10, C = 100 uF, L = 0.01 H.
+
V
–
R = 10 C
L
eg4.m
eg5_exercise1.m
43. M-files : script and function files (script)
Example – RLC circuit
Total impedance is given by:
L
C X
X
When
LC
1
o
44. M-files : script and function files (script)
Example – RLC circuit
0 200 400 600 800 1000 1200 1400 1600 1800 2000
0
20
40
60
80
100
120
Z
Xc
Xl
eg4.m
eg5_exercise1.m
45. M-files : script and function files (script)
For a given values of C and L, plot the following versus the frequency
a) the total impedance ,
b) Xc and XL
c) phase angle of the total impedance
for 100 < < 2000
Example – RLC circuit
+
V
–
R = 10 C
L
eg6.m
47. Function is a ‘black box’ that communicates with
workspace through input and output variables.
INPUT OUTPUT
FUNCTION
– Commands
– Functions
– Intermediate variables
M-files : script and function files (function)
48. Every function must begin with a header:
M-files : script and function files (function)
function output=function_name(inputs)
Output variable
Must match the
file name
input variable
49. Function – a simple example
function y=react_C(c,f)
%react_C calculates the reactance of a capacitor.
%The inputs are: capacitor value and frequency in hz
%The output is 1/(wC) and angular frequency in rad/s
y(1)=2*pi*f;
w=y(1);
y(2)=1/(w*c);
M-files : script and function files (function)
File must be saved to a known path with filename the same as the
function name and with an extension ‘.m’
Call function by its name and arguments
help react_C will display comments after the header
50. Function – a more realistic example
function x=impedance(r,c,l,w)
%IMPEDANCE calculates Xc,Xl and Z(magnitude) and
%Z(angle) of the RLC connected in series
%IMPEDANCE(R,C,L,W) returns Xc, Xl and Z (mag) and
%Z(angle) at W rad/s
%Used as an example for IEEE student, UTM
%introductory course on MATLAB
if nargin <4
error('not enough input arguments')
end;
x(1) = 1/(w*c);
x(2) = w*l;
Zt = r + (x(2) - x(1))*i;
x(3) = abs(Zt);
x(4)= angle(Zt);
M-files : script and function files (function) impedance.m
51. We can now add our function to a script M-file
R=input('Enter R: ');
C=input('Enter C: ');
L=input('Enter L: ');
w=input('Enter w: ');
y=impedance(R,C,L,w);
fprintf('n The magnitude of the impedance at %.1f
rad/s is %.3f ohmn', w,y(3));
fprintf('n The angle of the impedance at %.1f rad/s is
%.3f degreesnn', w,y(4));
M-files : script and function files (function) eg7_fun.m
52. Simulink
Used to model, analyze and simulate dynamic
systems using block diagrams.
Provides a graphical user interface for constructing
block diagram of a system – therefore is easy to use.
However modeling a system is not necessarily easy !
53. Simulink
Model – simplified representation of a system – e.g. using
mathematical equation
We simulate a model to study the behavior of a system –
need to verify that our model is correct – expect results
Knowing how to use Simulink or MATLAB does not
mean that you know how to model a system
54. Simulink
Problem: We need to simulate the resonant circuit
and display the current waveform as we change the
frequency dynamically.
+
v(t) = 5 sin t
–
i 10 100 uF
0.01 H
Varies
from 0 to
2000 rad/s
Observe the current. What do we expect ?
The amplitude of the current waveform will become
maximum at resonant frequency, i.e. at = 1000 rad/s
55. Simulink
How to model our resonant circuit ?
+
v(t) = 5 sin t
–
i 10 100 uF
0.01 H
idt
C
1
dt
di
L
iR
v
Writing KVL around the loop,
57. Simulink
Thus the current can be obtained from the voltage:
LC
1
s
L
R
s
)
L
/
1
(
s
V
I
2
LC
1
s
L
R
s
)
L
/
1
(
s
2
V I
58. Simulink
Start Simulink by typing simulink at Matlab prompt
Simulink library and untitled windows appear
It is here where we
construct our model.
It is where we
obtain the blocks to
construct our model
59. Simulink
Constructing the model using Simulink:
‘Drag and drop’ block from the Simulink library
window to the untitled window
1
s+1
Transfer Fcn
simout
To Workspace
Sine Wave
60. Simulink
Constructing the model using Simulink:
LC
1
s
L
R
s
)
L
/
1
(
s
2
6
2
10
1
s
1000
s
)
100
(
s
100s
s +1000s+1e6
2
Transfer Fcn
v
To Workspace1
i
To Workspace
Sine Wave
61. Simulink
We need to vary the frequency and observe the current
100s
s +1000s+1e6
2
Transfer Fcn1
v
To Workspace3
w
To Workspace2
i
To Workspace
Ramp
s
1
Integrator
sin
Elementary
Math
Dot Product3
Dot Product2
1000
Constant
5
Amplitude
eg8_sim.mdl
…From initial problem definition, the input is 5sin(ωt).
You should be able to decipher why the input works, but
you do not need to create your own input subsystems of
this form.
63. Simulink
The waveform can be displayed using scope – similar
to the scope in the lab
100s
s +1000s+1e6
2
Transfer Fcn
0.802
Slider
Gain
Scope
s
1
Integrator
sin
Elementary
Math
Dot Product2
5
Constant1
2000
Constant
eg9_sim.mdl
64. Reference
Internet – search engine
Mastering MATLAB 6 (Prentice Hall)
– Duane Hanselman
– Bruce Littlefield