This document provides an introduction to programming fundamentals and algorithms. It defines an algorithm as a well-ordered collection of unambiguous and effectively computable operations that produces a result and halts in a finite amount of time. The document discusses the history and purpose of programming, and defines computer science as the study of algorithms. It explains the three components that make up all algorithms: sequence, selection, and iteration. The document outlines the process for developing an algorithmic solution, including defining the problem, creating a high-level algorithm, defining inputs/outputs, testing the algorithm, and coding it.
The document discusses the concepts of programs and algorithms. It defines a program as a group of sequential statements that are executed line by line to achieve a goal. An algorithm is described as a step-by-step process that precisely defines a sequence of operations to solve a computational problem. The document also outlines the key components of a programming language, characteristics of good programs, stages of the program development process, and provides examples of algorithms.
A gentle introduction to algorithm complexity analysisLewis Lin 🦊
This document introduces algorithm complexity analysis and "Big O" notation. It aims to help programmers and students understand this theoretical computer science topic in a practical way. The document motivates algorithm complexity analysis by explaining how it allows formal comparison of algorithms' speed independently of implementation details. It then provides an example analysis of finding the maximum value in an array to illustrate counting the number of basic instructions an algorithm requires.
The document provides an introduction to algorithms and their analysis. It defines an algorithm and lists its key criteria. It discusses different representations of algorithms including flowcharts and pseudocode. It also outlines the main areas of algorithm analysis: devising algorithms, validating them, analyzing performance, and testing programs. Finally, it provides examples of algorithms and their analysis including calculating time complexity based on counting operations.
The document provides an introduction to algorithms and their analysis. It defines an algorithm as a step-by-step procedure for solving a problem in a finite amount of time. The analysis of algorithms aims to predict the cost of an algorithm in terms of resources and performance. There are two main aspects of analyzing algorithmic performance - time complexity, which measures how fast an algorithm performs based on input size, and space complexity, which measures how much memory an algorithm requires. Common asymptotic notations used to represent time complexity include Big-O, Omega, and Theta notations. The time complexity of common algorithms like searching, sorting, and traversing data structures are discussed.
Design and Analysis of Algorithm ppt for unit onessuserb7c8b8
The document outlines an algorithms course, including course details, objectives, and an introduction. The course code is 10211CS202 and name is Design and Analysis of Algorithms. It has 4 credits and meets for 6 hours per week. The course aims to teach fundamental techniques for effective problem solving, analyzing algorithm performance, and designing efficient algorithms. It covers topics like sorting, searching, and graph algorithms.
This document provides an introduction to algorithms including definitions, characteristics, and the design process. It defines an algorithm as a finite set of unambiguous instructions to solve a problem. Key points:
- Algorithms must have input, output, be definitive, finite, and effective.
- The design process includes understanding the problem, developing a solution algorithm, proving correctness, analyzing efficiency, and coding.
- Examples of algorithm types are approximate, probabilistic, infinite, and heuristic.
- Pseudocode is commonly used to specify algorithms more clearly than natural language alone.
The document summarizes a lecture on algorithms and algorithm design from a course on algorithms. It introduces the concept of algorithms and what will be covered in the course, which includes analyzing algorithms, sorting techniques as a case study, and various algorithmic problems and solutions. It also discusses implementation issues and keeping mathematical analysis independent of specific programming languages or machines. As an example, it analyzes the brute force algorithm for solving the 2D maxima problem in pseudo-code and calculates its running time.
The document discusses the topic of designing and analyzing algorithms. It states that algorithm design and analysis is an important topic that is frequently covered in exams. It defines an algorithm as a set of unambiguous instructions that takes inputs and produces outputs within a finite amount of time. The key aspects of designing an algorithm are understanding the problem, choosing a data structure and strategy, specifying and verifying the algorithm, analyzing its efficiency, and implementing it in a programming language. Algorithm analysis involves measuring the time and space complexity.
The document discusses the concepts of programs and algorithms. It defines a program as a group of sequential statements that are executed line by line to achieve a goal. An algorithm is described as a step-by-step process that precisely defines a sequence of operations to solve a computational problem. The document also outlines the key components of a programming language, characteristics of good programs, stages of the program development process, and provides examples of algorithms.
A gentle introduction to algorithm complexity analysisLewis Lin 🦊
This document introduces algorithm complexity analysis and "Big O" notation. It aims to help programmers and students understand this theoretical computer science topic in a practical way. The document motivates algorithm complexity analysis by explaining how it allows formal comparison of algorithms' speed independently of implementation details. It then provides an example analysis of finding the maximum value in an array to illustrate counting the number of basic instructions an algorithm requires.
The document provides an introduction to algorithms and their analysis. It defines an algorithm and lists its key criteria. It discusses different representations of algorithms including flowcharts and pseudocode. It also outlines the main areas of algorithm analysis: devising algorithms, validating them, analyzing performance, and testing programs. Finally, it provides examples of algorithms and their analysis including calculating time complexity based on counting operations.
The document provides an introduction to algorithms and their analysis. It defines an algorithm as a step-by-step procedure for solving a problem in a finite amount of time. The analysis of algorithms aims to predict the cost of an algorithm in terms of resources and performance. There are two main aspects of analyzing algorithmic performance - time complexity, which measures how fast an algorithm performs based on input size, and space complexity, which measures how much memory an algorithm requires. Common asymptotic notations used to represent time complexity include Big-O, Omega, and Theta notations. The time complexity of common algorithms like searching, sorting, and traversing data structures are discussed.
Design and Analysis of Algorithm ppt for unit onessuserb7c8b8
The document outlines an algorithms course, including course details, objectives, and an introduction. The course code is 10211CS202 and name is Design and Analysis of Algorithms. It has 4 credits and meets for 6 hours per week. The course aims to teach fundamental techniques for effective problem solving, analyzing algorithm performance, and designing efficient algorithms. It covers topics like sorting, searching, and graph algorithms.
This document provides an introduction to algorithms including definitions, characteristics, and the design process. It defines an algorithm as a finite set of unambiguous instructions to solve a problem. Key points:
- Algorithms must have input, output, be definitive, finite, and effective.
- The design process includes understanding the problem, developing a solution algorithm, proving correctness, analyzing efficiency, and coding.
- Examples of algorithm types are approximate, probabilistic, infinite, and heuristic.
- Pseudocode is commonly used to specify algorithms more clearly than natural language alone.
The document summarizes a lecture on algorithms and algorithm design from a course on algorithms. It introduces the concept of algorithms and what will be covered in the course, which includes analyzing algorithms, sorting techniques as a case study, and various algorithmic problems and solutions. It also discusses implementation issues and keeping mathematical analysis independent of specific programming languages or machines. As an example, it analyzes the brute force algorithm for solving the 2D maxima problem in pseudo-code and calculates its running time.
The document discusses the topic of designing and analyzing algorithms. It states that algorithm design and analysis is an important topic that is frequently covered in exams. It defines an algorithm as a set of unambiguous instructions that takes inputs and produces outputs within a finite amount of time. The key aspects of designing an algorithm are understanding the problem, choosing a data structure and strategy, specifying and verifying the algorithm, analyzing its efficiency, and implementing it in a programming language. Algorithm analysis involves measuring the time and space complexity.
1. The document introduces computer programming and discusses its importance in modern society. It touches on how computers have evolved to become indispensable tools that help solve problems across many domains.
2. It outlines the typical steps involved in programming: problem analysis, algorithm development, coding, testing and debugging. Key aspects like problem definition, input/output determination, and logical processing of data are important parts of problem analysis.
3. The document emphasizes that programming requires both logic and creativity to develop well-designed solutions. Proper documentation is also important for program maintenance and future modifications.
This document provides an overview of problem solving and Python programming. It discusses computational thinking and problem solving, including identifying computational problems, algorithms, building blocks of algorithms, and illustrative problems. It also discusses algorithmic problem solving techniques like iteration and recursion. Finally, it briefly introduces the course titled "GE8151-PROBLEM SOLVING AND PYTHON PROGRAMMING".
This document discusses algorithms and flow charts. It begins by defining an algorithm as a set of instructions to perform a task, noting they can be simple or complex. Algorithms must have defined steps, produce a result, and halt in a finite time. Flow charts provide a graphical representation of processes, using standard symbols to show steps, decisions, and flow. They can help analyze and improve processes by identifying elements and relationships. Key symbols in flow charts include rectangles for steps, diamonds for decisions, and lines to show progression between steps.
The document provides an introduction to data structures and algorithms analysis. It discusses that a program consists of organizing data in a structure and a sequence of steps or algorithm to solve a problem. A data structure is how data is organized in memory and an algorithm is the step-by-step process. It describes abstraction as focusing on relevant problem properties to define entities called abstract data types that specify what can be stored and operations performed. Algorithms transform data structures from one state to another and are analyzed based on their time and space complexity.
The document provides an introduction to computer programming. It discusses what a computer is and its basic parts including hardware and software. It describes the internal and external hardware components. It also explains different types of programming languages from low-level to high-level languages. The document then discusses programming paradigms like procedural, structured, and object-oriented programming. It introduces concepts like algorithms, flowcharts, and the system development life cycle which involves phases from feasibility study to implementation and maintenance.
Here are the steps to solve the problems using IPO table, pseudo code and flowchart:
1. Define the problem and understand requirements
2. Make IPO table:
- Input, Process, Output
3. Write pseudo code using proper indentation and comments
4. Draw flowchart using standard symbols
5. Test and debug the program
This systematic approach helps analyze the problem, design the algorithm and implement it properly. The key is breaking down the problem into smaller understandable steps.
A data structure is a storage that is used to store and organize data. It is a way of arranging data on a computer so that it can be accessed and updated efficiently. A data structure is not only used for organizing the data. It is also used for processing, retrieving, and storing data. That entire data can be represented using an object and can be used throughout the program.
This document provides an introduction to algorithms and their design and analysis. It defines an algorithm as a well-defined set of steps to solve a problem and notes they must be unambiguous and produce the same output each time. Examples are given of finding the largest number in a list and sorting algorithms. The use of algorithms in computer systems is discussed as well as why their design and analysis is important for problem solving and programming.
This document discusses algorithms and provides several examples. It defines an algorithm as a series of instructions to solve a problem using a finite number of steps. Examples given include food recipes, which are like algorithms that provide instructions to complete a task. Flowcharts are presented as a way to visualize algorithms and make the thinking process clear. Pseudocode is introduced as a way to develop algorithms without worrying about specific programming syntax. Several algorithms used widely on the internet are described, including PageRank, EdgeRank, and popularity algorithms used by Amazon and YouTube. The role of algorithms in everyday life and the news is also discussed.
There are three main limitations on what problems can be solved by computers: execution time may be too long, some problems are not computable by any algorithm, and some problems we do not currently know how to write algorithms for. Turing machines provide an abstract model of computation that defines what is computable. The halting problem, determining if an arbitrary program will halt for all inputs, is noncomputable. Computational complexity classifies problems as tractable if solvable in polynomial time or intractable if requiring exponential time. Determining if P=NP, whether easy and hard problems require equal computational resources, is a major open question in computer science.
This document provides an introduction to analyzing the performance of algorithms. It discusses time complexity and space complexity as ways to evaluate how efficiently an algorithm uses resources like time and memory. Time complexity is defined as the total amount of time required for an algorithm to complete, while space complexity refers to the total memory required. Common measures of performance like best-case, worst-case, and average-case scenarios are described. Examples of analyzing time complexity are provided. The document outlines key topics like algorithm specification, asymptotic notations, recursive vs non-recursive algorithms, and sorting and searching algorithms.
This document provides an overview of principles of programming, including problem solving techniques, the problem solving process, input/output statements, programming errors, and debugging. It discusses:
1) Problem solving involves understanding the problem, analyzing it, developing a solution design, and coding/implementing it. An algorithm is a set of steps to solve a problem written in plain language.
2) Programming errors include syntax errors, semantic errors, and logical errors. Syntax errors are detected during compilation, while runtime and logical errors occur during program execution.
3) Debugging is the process of detecting and removing errors to fix problems and ensure correct program operation. Debuggers are tools that help developers test programs and pinpoint issues
This document provides an overview of principles of programming, including problem solving techniques, the problem solving process, input/output statements, programming errors, and debugging. It discusses:
1) Problem solving involves understanding the problem, analyzing it, developing a solution design, and coding/implementing it. Key steps are understanding the problem fully before analyzing possible solutions.
2) Programming errors can occur from syntax, semantics, or logic. Syntax errors prevent compilation while runtime errors occur during execution. Debugging is used to detect and remove errors.
3) Input/output statements like cin and cout are used for getting input and displaying output. Escape codes represent special characters.
4) Debuggers help detect errors by
This document discusses the program development life cycle (PDLC) process for developing computer programs. It describes the 7 main steps in the PDLC as: 1) defining the problem, 2) task analysis, 3) designing, 4) testing algorithms, 5) coding, 6) testing and debugging programs, and 7) documentation and implementation. Key problem solving techniques discussed include algorithms, flowcharts, and pseudocode, which are used to logically solve problems and represent solutions before coding.
The document provides an overview of problem solving and C programming at a basic knowledge level. It covers various topics including introduction to problem solving, programming languages, introduction to C programming, selection structures, arrays and strings, pointers, functions, structures and unions, and files. The objective is to understand problem solving concepts, appreciate program design, understand C programming elements, and write effective C programs. It discusses steps in program development, algorithms, modular design, coding, documentation, compilation and more.
This document defines key concepts in programming and computer science. It explains that a program is a set of instructions that processes input, manipulates data, and outputs a result. Programming involves writing code in various languages to command computers. A system is a set of components that work together to accomplish tasks, while software is a set of programs or modules that achieve a common goal. The document also defines algorithms, flowcharts, and program logic formulation, providing examples of each concept.
Rajat Kumar submitted a project report for partial fulfillment of his B.Tech degree in computer science from Lovely Professional University. The project involved developing a snake game where the player moves a snake to eat fruit without touching itself or the border, which ends the game. The report provided background on data structures, algorithms, common data structures like stacks and queues, and algorithm design techniques like recursion, dynamic programming, and backtracking. It concluded that data structures are important tools that enable efficient information storage, data management, and algorithm design.
This document discusses time and space complexity analysis of algorithms. It defines key concepts like computational problems, algorithms, inputs, outputs, and properties of good algorithms. It then explains space complexity and time complexity, and provides examples of typical time functions like constant, logarithmic, linear, quadratic, and exponential. An example C program for matrix multiplication is provided, with its time complexity analyzed as O(n^2) + O(n^3).
The document discusses algorithms and their building blocks. It defines an algorithm as a collection of well-defined, unambiguous and effectively computable instructions that return a proper output. The three building blocks of algorithms are identified as sequence, selection, and iteration. Sequence refers to a series of actions performed in order. Selection allows a program to choose between different actions or conditions. Iteration allows a block of code to be repeated while a condition is true. Pseudocode and flowcharts are introduced as common ways to express algorithms and illustrate the control flow and logic of a program.
This lecture covers data structures and iteration. It discusses arrays as ordered collections of values that allow items to be accessed by index. Python offers two array-like data structures: lists, which are mutable, and tuples, which are immutable. Loops allow code to repeatedly execute and come in two main types - while loops, which repeat while a condition is true, and for loops. Break and continue statements can be used in loops to exit or skip iterations under certain conditions.
This lecture covers data types and selection statements in programming. It discusses common data types like integers, floats, strings and booleans. It also explains weak and strong typing, implicit and explicit type conversion. The lecture then covers selection statements like if-then, if-then-else and else-if statements which allow code to execute conditionally based on boolean expressions. It provides examples of comparison operators and logical operators used in boolean expressions.
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1. The document introduces computer programming and discusses its importance in modern society. It touches on how computers have evolved to become indispensable tools that help solve problems across many domains.
2. It outlines the typical steps involved in programming: problem analysis, algorithm development, coding, testing and debugging. Key aspects like problem definition, input/output determination, and logical processing of data are important parts of problem analysis.
3. The document emphasizes that programming requires both logic and creativity to develop well-designed solutions. Proper documentation is also important for program maintenance and future modifications.
This document provides an overview of problem solving and Python programming. It discusses computational thinking and problem solving, including identifying computational problems, algorithms, building blocks of algorithms, and illustrative problems. It also discusses algorithmic problem solving techniques like iteration and recursion. Finally, it briefly introduces the course titled "GE8151-PROBLEM SOLVING AND PYTHON PROGRAMMING".
This document discusses algorithms and flow charts. It begins by defining an algorithm as a set of instructions to perform a task, noting they can be simple or complex. Algorithms must have defined steps, produce a result, and halt in a finite time. Flow charts provide a graphical representation of processes, using standard symbols to show steps, decisions, and flow. They can help analyze and improve processes by identifying elements and relationships. Key symbols in flow charts include rectangles for steps, diamonds for decisions, and lines to show progression between steps.
The document provides an introduction to data structures and algorithms analysis. It discusses that a program consists of organizing data in a structure and a sequence of steps or algorithm to solve a problem. A data structure is how data is organized in memory and an algorithm is the step-by-step process. It describes abstraction as focusing on relevant problem properties to define entities called abstract data types that specify what can be stored and operations performed. Algorithms transform data structures from one state to another and are analyzed based on their time and space complexity.
The document provides an introduction to computer programming. It discusses what a computer is and its basic parts including hardware and software. It describes the internal and external hardware components. It also explains different types of programming languages from low-level to high-level languages. The document then discusses programming paradigms like procedural, structured, and object-oriented programming. It introduces concepts like algorithms, flowcharts, and the system development life cycle which involves phases from feasibility study to implementation and maintenance.
Here are the steps to solve the problems using IPO table, pseudo code and flowchart:
1. Define the problem and understand requirements
2. Make IPO table:
- Input, Process, Output
3. Write pseudo code using proper indentation and comments
4. Draw flowchart using standard symbols
5. Test and debug the program
This systematic approach helps analyze the problem, design the algorithm and implement it properly. The key is breaking down the problem into smaller understandable steps.
A data structure is a storage that is used to store and organize data. It is a way of arranging data on a computer so that it can be accessed and updated efficiently. A data structure is not only used for organizing the data. It is also used for processing, retrieving, and storing data. That entire data can be represented using an object and can be used throughout the program.
This document provides an introduction to algorithms and their design and analysis. It defines an algorithm as a well-defined set of steps to solve a problem and notes they must be unambiguous and produce the same output each time. Examples are given of finding the largest number in a list and sorting algorithms. The use of algorithms in computer systems is discussed as well as why their design and analysis is important for problem solving and programming.
This document discusses algorithms and provides several examples. It defines an algorithm as a series of instructions to solve a problem using a finite number of steps. Examples given include food recipes, which are like algorithms that provide instructions to complete a task. Flowcharts are presented as a way to visualize algorithms and make the thinking process clear. Pseudocode is introduced as a way to develop algorithms without worrying about specific programming syntax. Several algorithms used widely on the internet are described, including PageRank, EdgeRank, and popularity algorithms used by Amazon and YouTube. The role of algorithms in everyday life and the news is also discussed.
There are three main limitations on what problems can be solved by computers: execution time may be too long, some problems are not computable by any algorithm, and some problems we do not currently know how to write algorithms for. Turing machines provide an abstract model of computation that defines what is computable. The halting problem, determining if an arbitrary program will halt for all inputs, is noncomputable. Computational complexity classifies problems as tractable if solvable in polynomial time or intractable if requiring exponential time. Determining if P=NP, whether easy and hard problems require equal computational resources, is a major open question in computer science.
This document provides an introduction to analyzing the performance of algorithms. It discusses time complexity and space complexity as ways to evaluate how efficiently an algorithm uses resources like time and memory. Time complexity is defined as the total amount of time required for an algorithm to complete, while space complexity refers to the total memory required. Common measures of performance like best-case, worst-case, and average-case scenarios are described. Examples of analyzing time complexity are provided. The document outlines key topics like algorithm specification, asymptotic notations, recursive vs non-recursive algorithms, and sorting and searching algorithms.
This document provides an overview of principles of programming, including problem solving techniques, the problem solving process, input/output statements, programming errors, and debugging. It discusses:
1) Problem solving involves understanding the problem, analyzing it, developing a solution design, and coding/implementing it. An algorithm is a set of steps to solve a problem written in plain language.
2) Programming errors include syntax errors, semantic errors, and logical errors. Syntax errors are detected during compilation, while runtime and logical errors occur during program execution.
3) Debugging is the process of detecting and removing errors to fix problems and ensure correct program operation. Debuggers are tools that help developers test programs and pinpoint issues
This document provides an overview of principles of programming, including problem solving techniques, the problem solving process, input/output statements, programming errors, and debugging. It discusses:
1) Problem solving involves understanding the problem, analyzing it, developing a solution design, and coding/implementing it. Key steps are understanding the problem fully before analyzing possible solutions.
2) Programming errors can occur from syntax, semantics, or logic. Syntax errors prevent compilation while runtime errors occur during execution. Debugging is used to detect and remove errors.
3) Input/output statements like cin and cout are used for getting input and displaying output. Escape codes represent special characters.
4) Debuggers help detect errors by
This document discusses the program development life cycle (PDLC) process for developing computer programs. It describes the 7 main steps in the PDLC as: 1) defining the problem, 2) task analysis, 3) designing, 4) testing algorithms, 5) coding, 6) testing and debugging programs, and 7) documentation and implementation. Key problem solving techniques discussed include algorithms, flowcharts, and pseudocode, which are used to logically solve problems and represent solutions before coding.
The document provides an overview of problem solving and C programming at a basic knowledge level. It covers various topics including introduction to problem solving, programming languages, introduction to C programming, selection structures, arrays and strings, pointers, functions, structures and unions, and files. The objective is to understand problem solving concepts, appreciate program design, understand C programming elements, and write effective C programs. It discusses steps in program development, algorithms, modular design, coding, documentation, compilation and more.
This document defines key concepts in programming and computer science. It explains that a program is a set of instructions that processes input, manipulates data, and outputs a result. Programming involves writing code in various languages to command computers. A system is a set of components that work together to accomplish tasks, while software is a set of programs or modules that achieve a common goal. The document also defines algorithms, flowcharts, and program logic formulation, providing examples of each concept.
Rajat Kumar submitted a project report for partial fulfillment of his B.Tech degree in computer science from Lovely Professional University. The project involved developing a snake game where the player moves a snake to eat fruit without touching itself or the border, which ends the game. The report provided background on data structures, algorithms, common data structures like stacks and queues, and algorithm design techniques like recursion, dynamic programming, and backtracking. It concluded that data structures are important tools that enable efficient information storage, data management, and algorithm design.
This document discusses time and space complexity analysis of algorithms. It defines key concepts like computational problems, algorithms, inputs, outputs, and properties of good algorithms. It then explains space complexity and time complexity, and provides examples of typical time functions like constant, logarithmic, linear, quadratic, and exponential. An example C program for matrix multiplication is provided, with its time complexity analyzed as O(n^2) + O(n^3).
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This lecture covers data types and selection statements in programming. It discusses common data types like integers, floats, strings and booleans. It also explains weak and strong typing, implicit and explicit type conversion. The lecture then covers selection statements like if-then, if-then-else and else-if statements which allow code to execute conditionally based on boolean expressions. It provides examples of comparison operators and logical operators used in boolean expressions.
Programming Fundamentals Lecture 3 covered data structures and iteration. Data structures allow storing multiple pieces of data in an organized way. Arrays are ordered collections of values that can be accessed by index. Python offers lists and tuples, where lists are mutable and tuples are immutable. Loops called iteration allow repeatedly running a block of code. While loops repeat while a condition is true. Break and continue can be used in loops to exit early or skip iterations conditionally.
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The lecture recaps programming concepts covered so far like variables, data types, selection statements, and functions. It then presents a "Test Generator" case study to demonstrate designing, coding, and enhancing a program through multiple versions. The case study involves generating random math tests with addition or subtraction problems for students. Pseudocode is developed for each version to plan the program logic before coding.
This lecture introduces programming fundamentals and Python. It discusses what programming is, the process of translating source code into machine-readable instructions, and program design using pseudocode and flowcharts. Program design tools like pseudocode and flowcharts allow programmers to plan programs' logic and structure before writing the actual code. The lecture also provides an overview of Python syntax and variables.
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.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
VARIABLE FREQUENCY DRIVE. VFDs are widely used in industrial applications for...PIMR BHOPAL
Variable frequency drive .A Variable Frequency Drive (VFD) is an electronic device used to control the speed and torque of an electric motor by varying the frequency and voltage of its power supply. VFDs are widely used in industrial applications for motor control, providing significant energy savings and precise motor operation.
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.
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1. Programming Fundamentals
Algorithms
Table of Contents
Algorithms............................................................................................................1
Introduction................................................................................................................2
Why programming? ................................................................................................2
History of programming.........................................................................................2
A universal information processor........................................................................3
Computer science definition..................................................................................3
Algorithms ..................................................................................................................4
Formal definition of an algorithm .........................................................................4
Following instructions: People vs Computers......................................................6
Algorithms and Programming Languages............................................................6
Algorithms Representation....................................................................................6
Structured English and Pseudocode.....................................................................7
What can algorithms do? .......................................................................................9
Developing an algorithmic solution .......................................................................11
Problem definition................................................................................................11
High-level algorithm .............................................................................................11
Define inputs and outputs ...................................................................................12
How do you know your algorithm works?..........................................................12
Create a test data set............................................................................................13
Create a low-level algorithm................................................................................13
Code the algorithm...............................................................................................14
Apply the test data................................................................................................14
Summary ...............................................................................................................15
2. Programming Fundamentals
2
Introduction
Why programming?
Why should you learn about programming and computer science?
• Programming helps develop problem-solving skills, in particular, the ability to deal with
complexity.
• Computer technology is pervasive. We live in the so-called “Age of Information”.
Moreover, the impact of the computer on society is increasing.
• Programming skills can be applied in other areas.
• Most “programming” is done by non-computer scientists. Customizing of tools is possible
by those who understand programming. This can range from programming complicated
queries on databases to running complex simulations with spreadsheets.
It's a fascinating challenge to teach the computer how to solve hard problems.
History of programming
The original role of computers and programming was to facilitate ‘number crunching’. However,
perhaps the early computing pioneers failed to appreciate was the flexibility of numbers.
The very name computer suggests that they thought the only applications for computers would
be processing numbers for the purpose of science and engineering. They did not recognize or
appreciate the fact that numbers can be used to encode just about any information one might
want to process.
There are many examples where numbers are used to represent information, but the numeric
values of the numbers used have little significance and are not used for actual computation:
• Post codes,
• Tax File numbers,
• Driver license numbers, ...
By associating a numeric value with each letter of the alphabet (e.g., a=1, b=2, etc.) we can
construct a numerical encoding of any textual information. When a computer manipulates
numbers of this sort, it is really being used as a symbol or information processor rather than a
“computer”.
3. Programming Fundamentals
3
A universal information processor
It is the fact that just about any information can be encoded using numbers that gives the
computer the ability to be a universal information processor. As a result, the encoding of
information forms an important part of all of computer science.
Recognizing the power of numbers as symbols isn’t quite enough to explain the amazing impact
computers have had. Simple, hand-held calculators can process numbers too, but they have not
had the impact of computers.
The computer’s other fundamental capability – the ability to follow pre-supplied instruction or
programs – provides the rest of the explanation.
• The ability to change programs makes computers flexible:
o They can be adapted to new tasks without being rebuilt.
• The ability to follow programs makes it possible to exploit the speed of a computer.
o If the instructions could not be pre-supplied, the computer would spend most of
its time waiting for a slow human to press the next control button.
Computer science definition
Computer science provides the theoretical basis for most programming techniques. So, what is
“computer science”?
One simple definition sometimes used:
Computer Science is the study of algorithms.
Specifically, this entails:
1. Their formal and mathematical properties
o Studying the behaviour of algorithms to determine whether they are correct and
efficient.
2. Their hardware realizations
o Designing and building computer systems that are able to execute algorithms.
3. Their linguistic realizations
o Designing programming languages and translating algorithms into these
languages so that they can be executed by the hardware.
4. Their applications
o Identifying important problems and designing correct and efficient software
packages to solve these problems
So, what exactly are algorithms then?
4. Programming Fundamentals
4
Algorithms
Our main focus will be learning how to prepare the instructions needed to ensure that a
computer can perform a particular task.
The instructions presented to a computer are significantly different from the sorts of instructions
we might prepare for another human being. Computers have no common sense or intuition:
• They can only perform a task if the instructions provided are accurate and totally
unambiguous.
• It must be possible to follow the instructions without any sense or understanding of their
actual purpose.
These instructions are called algorithms.
The instructions in an algorithm must specify what to do rather than what can or might be
done. Thus, the “instructions” for a board game or a card game would not be considered an
algorithm.
The instructions found in a cookbook recipe are much more similar to the instructions that must
be included in an algorithm.
Formal definition of an algorithm
Informally, an algorithm is an ordered sequence of instructions that is guaranteed to solve a
specific problem. Algorithms are important because if you can specify a working algorithm for a
problem then you can:
• Solve the problem
• Get a computer to solve any equivalent forms of that problem automatically for you.
A formal definition of an algorithm is:
An Algorithm is a well-ordered collection of unambiguous and effectively
computable operations that, when executed, produces a result and halts in a finite
amount of time.
The key properties of an algorithm according to this definition are highlighted and we will now
go through them.
“...well-ordered...”
• The order of the operations that makes up an algorithm must be correct (i.e., steps
cannot be in an order which produces the wrong result.)
• There is also often an efficiency aspect to the order of the instructions – that is, one
particular order is more efficient than other orders even if they give the same correct
result.
• It is also important that the first operation is clearly indicated so that it is clear where the
algorithm starts.
• Similarly, its end must be clear, otherwise the algorithm might carry on forever.
5. Programming Fundamentals
5
“...unambiguous...”
• So, each instruction must also be clear in what it is that it does
E.g. Do part 1 or part 2
• The following is less ambiguous but is it completely unambiguous?
if you are over 18 years of age do Part 1
else do Part 2
• The following “Shampoo bottle” example is clearly ambiguous:
STEP 1 Wet hair
STEP 2 Lather
STEP 3 Rinse
STEP 4 Repeat
• Repeat what?
o Steps 1 – 4? (This will go on forever.)
o Step 4? (Ditto but will be even less productive!)
o Steps 1 – 3? (This involves inefficiency because the second time, the hair is
already wet!)
o Steps 2 – 3? (This is correct, but it takes human intuition to realise it!)
“...effectively computable operations...”
• The operation must be within the capabilities of the computer it will be executed on.
• For example, a = b + c is effectively computable only if the computer can perform
addition.
• There are also external limits on the sorts of computations that can be performed.
• For example, the expression, a = b / c is only effectively computable when c is not zero.
“...halts in a finite amount of time...”
• The algorithm must be capable of finding a result, that is completing within a finite
amount of time.
o Some algorithms may take a very long while to complete for certain sets of data
(hours, days, years, centuries...) but they must ultimately be capable of producing
a result.
o Repeating Step 4 (“Repeat”) on the shampoo bottle would represent what's called
an “infinite loop” and so the algorithm would not halt in a finite amount of time.
6. Programming Fundamentals
6
Following instructions: People vs Computers
Our concern in this unit will primarily be to learn how to write good algorithms (i.e. how to write
good instructions).
To appreciate why it might take as long as a semester to accomplish this, consider:
• Humans, in general are very good at writing bad instructions.
• Have you ever tried to assemble something complex by following the manufacturer’s
directions?
However, luckily humans are generally very good at following bad instructions.
Example: A recipe whose first step called for pre-heating the oven and whose second step called
for refrigerating a mixture of ingredients overnight. How many people would be silly enough to
leave the oven on overnight?
Computers are essentially perfect at following instructions exactly. A computer would definitely
leave the oven on overnight.
Algorithms and Programming Languages
Learning to write good algorithms is a skill.
While we can provide advice on how to go about it, there are no set rules that will always enable
you to produce a good algorithm for a given task.
Practice is really the only way to develop the needed skills.
At the same time that you are learning this skill, we will also be looking at programming
languages you can use to communicate your algorithms to a computer.
However, ultimately the algorithms you write must potentially be able to be implemented in a
variety of different languages. This applies even to languages you don't yet know!
Algorithmic Representation
Programmers use different methods of writing down the designs for their algorithms before
they translate them into the code for a particular language.
Although the rules for these representations aren't usually strictly defined, they must be
consistent within themselves.
It is important when you write algorithms that you use some of these representations and do so
consistently.
One technique is the use of flow charts which lay out the flow of control through the program
with different shapes to indicate input, calculations, decisions and output. Structured English and
pseudocode are two other representations.
7. Programming Fundamentals
7
Structured English and Pseudocode
The Structured English approach involves rather verbosely describing how a program will be
organised using indented lines that begin to resemble a program.
Here is a rudimentary algorithm for a “menu” program:
display menu
read keyboard input
depending on user selection do one of the following:
output the 'option 1' message (option 1)
output the 'option 2' message (option 2)
output the 'option 3' message (option 3)
There are no real rules with structured English so long as it is consistent and unambiguous.
• One needs to be very careful using structured English, as it is very easy to be ambiguous
with such algorithms.
In some ways structured English is more suited to high level algorithms and you will see this a lot
in the examples for this unit.
Pseudocode
An alternative to structured English is pseudocode which is written very much like a programming
language with minimal syntactic conventions.
Pseudocode tends to be more precise and terse than structured English, which makes it a
preferred technique for writing low-level algorithms.
The rules for pseudocode are rather loose, since it is to be interpreted by humans, not
computers. However, again inconsistency and ambiguity are to be avoided at all costs.
Example:
print "you have 3 choices"
print "enter a for option 1"
print "enter b for option 2"
print "enter c for option 3"
read response
if response == ‘a’
print "You have selected option 1"
if response == ‘b’
print "You have selected option 2"
if response == ‘c’
print "You have selected option 3"
Pseudocode is better than structured English when you first begin to write algorithms because it
makes it harder to write ambiguous algorithms.
The textbook uses several different styles (see the reading schedule on the web site for notes
about this). The style used predominantly in these lecture notes is also fine.
Just make sure that whatever style you pick, you stick with it – particularly within a single
algorithm!
9. Programming Fundamentals
9
What can algorithms do?
There are only three things that an algorithm can do:
• Sequence
• Selection
• Iteration
All algorithms are made up of combinations of these three structures and this is known
as structure theorem.
Sequence
Sequence relates to steps that are executed one after the other with no variation in their order:
• Take keys out of pocket.
• Find correct key.
• Insert key into door lock.
• Turn key 90 degrees anti-clockwise.
• Remove key from lock.
• Return keys to pocket.
• Open unlocked door.
In the sequential algorithms that we write for computers the steps themselves can be of two
different types:
1. Computation steps involve performing some mathematical or computational operation
and then doing something with the result of that computation.
2. Input/Output steps involve either the getting of new data for the program from some
source external source (e.g., the user typing at the keyboard) or the outputting of data
from the program (e.g., printing it to the screen).
Selection
Selection involves making choices depending on some condition. These conditions always relate
to the truth or falsity of some expression.
If the condition is true, then the algorithm will perform a set of sequential statements (as above).
If the condition is false, then it will perform a different set of sequential statements.
if grade is greater than or equal to 50 then
result = pass
else
result = fail
Selection is important because it allows the algorithm to behave in different ways depending on
these conditions.
In the example above the algorithm will give different results depending on whether the grade is
a pass or fail.
Note that the “result = pass” and “result = fail” are sequential steps involving the assignment of a
value or result.
10. Programming Fundamentals
10
Iteration
Iteration involves the repetition of certain steps, again according to the truth or falsity of some
condition. Iterative statements are very important since they take advantage of the speed of
computers and the fact that they don't “get bored”.
Computers can perform a boring and repetitive task over and over again, very quickly. For
example, a trivial searching algorithm might involve iterating through a large collection of data
very quickly.
A person manually searching through that data would get bored, plus they would do it much
more slowly. If the collection of data is very big then it may not even be feasible for a person to
do it manually!
item = get first item
while item does not equal search_item AND items left
item = get next item
if item = search_item
print "Item found!"
else
print "Item couldn't be found."
The above is a simple search algorithm.
It gets the first item of data in some collection and then iterates or “loops” until it finds the item
being searched for. Once the loop has stopped then either the item has been found or the
search simply ran out of items.
Note the use of sequence, selection and iteration required to form the complete algorithm.
11. Programming Fundamentals
11
Developing an algorithmic solution
Problem definition
Often people take a brief look at a problem and immediately work toward a solution.
Unfortunately for most non-trivial problems this approach is not likely to be successful.
The Golden Rule here is to closely examine the problem to make sure you understand what is
being asked of you or what it is that needs solving. If you get this stage wrong, then everything
that follows will also be wrong even though the program itself may run without crashing.
Example: You're asked to write a simple GST calculator.
What questions should you ask?
• Is this to take GST exclusive value and add the GST or is it the other way around: a GST
inclusive value which you need to output the sell price and GST component?
Other lower-level questions:
• Where will the inputs come from? Keyboard? Database? File? Network resource? Another
external device?
• Do the results need to be output in any particular form (i.e., printed on an invoice)?
High-level algorithm
An algorithm is a series of steps by which a computer can undertake the required processing to
produce the required output. However, for many advanced problems it is too difficult to simply
go straight from the problem statement to a complete solution (algorithm). A more gradual
approach is often called for. A good place to start is to write the high-level steps in the
algorithm. There are no methods used at this level – i.e. no arithmetic operations. In other
words, you simply the major steps to be performed without giving details specifying how.
However, your major steps must still “add up” to a complete solution. In other words, you should
be able to take each of the high-level steps you write and expand it into a collection of more
detailed steps ultimately representing a complete solution to the problem. If you have to add in
extra big steps that aren't part of the high-level algorithm then this suggests your high-level
algorithm was incomplete.
It is not always necessary to write a high-level algorithm. However, if you think that writing one
might help you solve the problem then feel free to do so.
GST Example (continued)
• Get cost of product excluding GST.
• Calculate total cost including GST.
• Output the value to the user.
Notice the three steps above
• Input
• Process
• Output
You will often see these three phases in simple algorithms.
12. Programming Fundamentals
12
Define inputs and outputs
What form will the data be entered in?
• Data types (whole numbers, fractional numbers, single characters, “strings” of characters
together).
• Values like dollar and cents amounts could be read as a single fractional number or as a
pair of whole (integer) numbers each representing the dollar and cents amounts.
• Data obtained from the keyboard, from a file, from a database, or across a network?
How should the output be displayed? Does the data type require special formatting to output
correctly?
• Adding in a dollar sign at the front and printing two decimal places for currency values.
• Appending a percent sign for percentages.
Where should the output go? (To the screen, a file, a database, across a network...)
How do you know your algorithm works?
Validate or check the algorithm.
Choose values that test the boundaries.
Check wrong values (e.g. a or b when expecting a number) – not expected to work (assumptions).
It is an iterative process – do it until there are no errors.
13. Programming Fundamentals
13
Create a test data set
Here we need to consider what results our program should be able to provide.
For most problems this involves being able to do some calculations to verify the sorts of results
the program should obtain for certain data. Note that this implicitly requires a basic
understanding of how to solve the problem but for nearly all problems this is not an issue.
GST Example (continued)
We need to multiply the ex-GST price by 1.1 to calculate the new price including GST. Even
though this is a critical part of the solution, it is a simple calculation to make.
This is important because it allows us to do test calculations to see what sort of results the
program should produce.
This process verifies our algorithm as correct and is called desk checking.
The following table represents some possible values that you can test in a preliminary way to
understand the behaviour of the algorithm.
Note that these are not necessarily ideal test values!
Ex-GST
Price
Expected Result
Inclusive GST Price
0 0
10 11
25 27.5
20000 22000
-5 -5.5
21.95 24.145
Also, the ones highlighted are those that reveal potential issues which would need to be dealt
with to provide a complete and reliable algorithm.
Create a low-level algorithm
Here we put the individual steps that a computer will require to process our input to the desired
output.
Here we take our high-level algorithm and break each step down or decompose it.
This method is called top-down design. It allows us to look at the big picture first then break the
problem up into smaller and smaller parts. For complex and difficult problems this makes
producing a complete solution much easier.
14. Programming Fundamentals
14
Note this example illustrates the process but doesn’t reflect how you should represent each
step.
GST Example (continued)
Get cost of product excluding GST.
Print “Enter price of product ex-GST: ”
Read user input into price
Calculate total cost including GST.
if price < 0 then print “Invalid input!” and exit
total = price * 1.1
Output the value to the user.
total = total rounded to 2 decimal places
print “Total price is: $”, total
Code the algorithm
It is at this point where we take a programming language, capable of carrying out our desired
task and use it to implement our algorithm.
The major task here is to convert the algorithm into the correct syntax of the chosen language.
The syntax of a language is the collection of rules for how a particular operation is expressed
and written in that language.
There are often many syntactical similarities between languages, for example C and Java are very
similar in many ways.
We will deal with the basic components of a program in a later topic.
Apply the test data
Now we will take the test data set that we created earlier in this process and input it into our
running program and check that it gives the expected results.
This allows us to confirm that our implemented program matches the algorithm we designed
(and desk-checked) previously.
If it does produce expected results, then you know that your program matches your algorithm
although it may not be completely bug free.
Test data sets should be created with great care: if the test data does not properly and
comprehensively test the correctness of the algorithm then there may be significant bugs that
remain undiscovered.
Selection of test data is therefore critical!
15. Programming Fundamentals
15
Summary
Computers are powerful devices because they cannot just perform computations but also
because of their ability to encode information into numbers and to follow specified instructions.
Computer science may be described as the study of algorithms.
These are ordered sequences of instructions to solve a given problem.
Computer scientists develop algorithms to solve problems and then convert these into programs
able to be run on a computer.
Algorithms can be made up of sequential steps, selective structures and iterative structures.
They can be represented in the form of structured English and pseudocode.
When developing an algorithm, it is often best to start with the general or “high-level” steps then
refine each of these by specifying the lower-level individual steps that make these up.
After coding it is important to apply test data to the program to ensure that it works correctly.