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The document discusses the Minkowski sum, which is an operation that combines two sets in 2D geometry by translating one set along the border of the other. It provides examples of applying the Minkowski sum to polygons and discs. The Minkowski sum has applications in motion planning to determine if a moving object will collide with obstacles. It can be computed for convex polygons by taking every vertex combination, and for general polygons by decomposition or convolution methods.

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An Introduction to Neural Architecture Search

An Introduction to Neural Architecture Search

Number Theory - Lesson 1 - Introduction to Number Theory

Number Theory - Lesson 1 - Introduction to Number Theory

Graphs for Data Science and Machine Learning

Graphs for Data Science and Machine Learning

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An Introduction to Neural Architecture Search

https://learn.xnextcon.com/event/eventdetails/W19071910
video: https://www.youtube.com/channel/UCj09XsAWj-RF9kY4UvBJh_A
photos: flickr.com/photos/nextcon/albums

Number Theory - Lesson 1 - Introduction to Number Theory

This document provides an introduction to number theory, including:
- Number theory is the study of integers and their properties
- It discusses the origins and early developments of number theory in places like Mesopotamia, India, Greece, and Alexandria
- It defines different types of numbers like natural numbers, integers, rational numbers, irrational numbers, and describes properties like prime and composite numbers
- It discusses applications of number theory like public key cryptography and error-correcting codes

Graphs for Data Science and Machine Learning

This document discusses how graphs and graph databases can be used for data science and machine learning. It provides an overview of Neo4j's graph data science capabilities including graph algorithms, machine learning techniques, and real-world use cases.
The key points are:
1) Neo4j provides a graph data science library with over 70 graph algorithms and machine learning methods that can be used for tasks like link prediction, node classification, and graph feature engineering.
2) The library allows for both unsupervised and supervised machine learning on graph data in order to identify patterns, anomalies, and make predictions.
3) Real-world examples are presented where companies have used Neo4j's graph data

Discrete Mathematics Lecture

The document discusses key concepts in discrete mathematics and logic. It defines propositions as basic building blocks represented by letters, and connectives as operators used to combine propositions like conjunction, disjunction, negation, and conditional statements. It provides truth tables showing the truth values of propositions under different connectives and examples of applying these logic rules.

Relation matrix & graphs in relations

This document discusses relation matrices and graphs. It begins by defining a relation matrix as a way to represent a relation between two finite sets A and B using a matrix with 1s and 0s. An example is provided to demonstrate how to construct a relation matrix. The document then discusses how relations can be represented using graphs by connecting elements with edges. Properties of relations like reflexive, symmetric, and anti-symmetric are explained through examples using relation matrices. Finally, the conclusion restates that relation matrices and graphs can be used to represent relations between sets.

Discrete Math Presentation(Rules of Inference)

This document presents an introduction to rules of inference. It defines an argument and valid argument. It then explains several common rules of inference like modus ponens, modus tollens, addition, and simplification. Modus ponens and modus tollens are based on tautologies that make the conclusions logically follow from the premises. It also discusses two common fallacies - affirming the conclusion and denying the hypothesis - which are not valid rules of inference because they are not based on tautologies. Examples are provided to illustrate each rule of inference and fallacy.

Section 10: Lagrange's Theorem

Lagrange's theorem states that for any finite group G and subgroup H of G, the order of H divides the order of G. The document provides the proof of Lagrange's theorem and several examples. It also discusses corollaries, including that every group of prime order is cyclic, every group of order less than 6 is abelian, and the order of an element must divide the group order. However, the converse of Lagrange's theorem is false - there can exist groups where not every divisor of the group order is a possible subgroup order.

KNN Algorithm using Python | How KNN Algorithm works | Python Data Science Tr...

** Python for Data Science: https://www.edureka.co/python **
This Edureka tutorial on KNN Algorithm will help you to build your base by covering the theoretical, mathematical and implementation part of the KNN algorithm in Python. Topics covered under this tutorial includes:
1. What is KNN Algorithm?
2. Industrial Use case of KNN Algorithm
3. How things are predicted using KNN Algorithm
4. How to choose the value of K?
5. KNN Algorithm Using Python
6. Implementation of KNN Algorithm from scratch
Check out our playlist: http://bit.ly/2taym8X

Linear models and multiclass classification

The document discusses various methods for multiclass classification including Gaussian and linear classifiers, multi-class classification models, and multi-class strategies like one-versus-all, one-versus-one, and error-correcting codes. It also provides summaries of naive Bayes, linear/quadratic discriminant analysis, stochastic gradient descent, multilabel vs multiclass classification, and one-versus-all, one-versus-one, and error-correcting output codes classification strategies.

Naive Bayes Classifier

1. The Naive Bayes classifier is a simple probabilistic classifier based on Bayes' theorem that assumes independence between features.
2. It has various applications including email spam detection, language detection, and document categorization.
3. The Naive Bayes approach involves computing the class prior probabilities, feature likelihoods, and applying Bayes' theorem to calculate the posterior probabilities to classify new instances. Laplace smoothing is often used to handle cases with insufficient training data.

Regression and Classification: An Artificial Neural Network Approach

This presentation introduces artificial neural networks (ANN) as a technique for regression and classification problems. It provides historical context on the development of ANN, describes common network structures and activation functions, and the backpropagation algorithm for training networks. Experimental results on 7 datasets show ANN outperformed other methods for both regression and classification across a variety of problem types and data characteristics. Limitations of ANN and areas for further research are also discussed.

Discrete mathematics Ch2 Propositional Logic_Dr.khaled.Bakro د. خالد بكرو

Discrete Mathematics chapter 2 covers propositional logic. A proposition is a statement that is either true or false. Propositional logic uses propositional variables and logical operators like negation, conjunction, disjunction, implication and biconditional. Truth tables are used to determine the truth value of compound propositions formed using these operators. Logical equivalences between compound propositions can be shown using truth tables or by applying equivalence rules.

Lecture5

Here are the key steps in a direct proof of this theorem:
1) Assume r and s are rational numbers
2) By definition, there exist integers p,q and t,u such that r = p/q and s = t/u
3) Then r + s = (pq + tu)/qu
4) pq + tu and qu are integers
5) Therefore, by the definition, r + s is rational
So we have directly proven that if r and s are rational, then their sum is rational.

Relations and functions

The document discusses functions, relations, domains, ranges and using vertical line tests, mappings, tables and graphs to represent and analyze functions. It provides examples of determining if a relation is a function, finding domains and ranges, modeling function rules with tables and graphs using given domains, and solving other function problems.

Naive bayes

This document discusses Naive Bayes classifiers. It begins with an overview of probabilistic classification and the Naive Bayes approach. The Naive Bayes classifier makes a strong independence assumption that features are conditionally independent given the class. It then presents the algorithm for Naive Bayes classification with discrete and continuous features. An example of classifying whether to play tennis is used to illustrate the learning and classification phases. The document concludes with a discussion of some relevant issues and a high-level summary of Naive Bayes.

Nonlinear dimension reduction

The document summarizes Yan Xu's upcoming presentation at the Houston Machine Learning Meetup on dimension reduction techniques. Yan will cover linear methods like PCA and nonlinear methods such as ISOMAP, LLE, and t-SNE. She will explain how these methods work, including preserving variance with PCA, using geodesic distances with ISOMAP, and modeling local neighborhoods with LLE and t-SNE. Yan will also demonstrate these methods on a dataset of handwritten digits. The meetup is part of a broader roadmap of machine learning topics that will be covered in future sessions.

Intro to Discrete Mathematics

This document provides an overview of the topics covered in a discrete structures course, including logic, sets, relations, functions, sequences, recurrence relations, combinatorics, probability, and graphs. It defines discrete mathematics as the study of mathematical structures that have distinct, separated values rather than varying continuously. Some examples given are problems involving a fixed number of islands/bridges or connecting a set number of cities with telephone lines. Logic is introduced as the study of valid vs. invalid arguments, and basic logical concepts like statements, truth values, compound statements, logical connectives, negation, and truth tables are outlined.

Sets and relations

This document defines and explains various concepts related to sets and relations. It discusses the four main set operations of union, intersection, complement, and difference. It then explains eight types of relations: empty, universal, identity, inverse, reflexive, symmetric, transitive, and equivalence relations. Finally, it defines partial ordering as a relation that is reflexive, antisymmetric, and transitive.

Chapter 2: Relations

Mathematics; Product sets; Relations; Inverse Relation; Representing Relations Using Matrices; Composition of Relations; Types of Relations; Reflexive and Irreflexive Relations; Symmetric and Antisymmetric Relations; Transitive Relations
Equivalence Relations
Partial Ordering Relations
Closure Properties

Knowledge representation and reasoning

This document discusses various knowledge representation methods used in expert systems, including rules, semantic networks, frames, and constraints. It provides examples and explanations of each method. Procedural and declarative programming techniques are also covered. Forward and backward chaining for rule-based inference engines are explained through examples. Propositional and predicate logic are discussed as mathematical methods for representing knowledge.

An Introduction to Neural Architecture Search

An Introduction to Neural Architecture Search

Number Theory - Lesson 1 - Introduction to Number Theory

Number Theory - Lesson 1 - Introduction to Number Theory

Graphs for Data Science and Machine Learning

Graphs for Data Science and Machine Learning

Discrete Mathematics Lecture

Discrete Mathematics Lecture

Relation matrix & graphs in relations

Relation matrix & graphs in relations

Discrete Math Presentation(Rules of Inference)

Discrete Math Presentation(Rules of Inference)

Section 10: Lagrange's Theorem

Section 10: Lagrange's Theorem

KNN Algorithm using Python | How KNN Algorithm works | Python Data Science Tr...

KNN Algorithm using Python | How KNN Algorithm works | Python Data Science Tr...

Linear models and multiclass classification

Linear models and multiclass classification

Naive Bayes Classifier

Naive Bayes Classifier

Regression and Classification: An Artificial Neural Network Approach

Regression and Classification: An Artificial Neural Network Approach

Discrete mathematics Ch2 Propositional Logic_Dr.khaled.Bakro د. خالد بكرو

Discrete mathematics Ch2 Propositional Logic_Dr.khaled.Bakro د. خالد بكرو

Lecture5

Lecture5

Relations and functions

Relations and functions

Naive bayes

Naive bayes

Nonlinear dimension reduction

Nonlinear dimension reduction

Intro to Discrete Mathematics

Intro to Discrete Mathematics

Sets and relations

Sets and relations

Chapter 2: Relations

Chapter 2: Relations

Knowledge representation and reasoning

Knowledge representation and reasoning

Translation, Dilation, Rotation, ReflectionTutorials Online

In these slides you will learn the concepts and the basics of Translation, Reflection, Dilation, and Rotation.
http://www.winpossible.com/lessons/Geometry_Translation,_Reflection,_Dilation,_and_Rotation.html

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Translations, rotations, reflections, and dilations

This document discusses different types of geometric transformations including translations, rotations, reflections, and dilations. Translations move a figure across a plane without changing its size. Rotations turn a figure around a point or line. Reflections flip a figure across a line to create a mirror image. Dilation changes the size of a figure by enlarging or reducing it using a scale factor, while keeping the shape intact. The document provides examples and definitions of each transformation type.

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Oliviamath problem

100 dyas

Maths activity

The document provides the 100m race times for two classes and asks five questions about analyzing and comparing the results between the classes. It lists the individual times for each student in each class, and gives the answers to the five questions, including: the average time for each class, which class was slower/faster, the range of times for each class, and the mode time for each class.

3002 a more with parrallel lines and anglesupdated 10 22-13

1. If x = 1 and y = 2008, the value of 1/x + 1/y is 105.85.
2. The document provides instructions for homework to be placed on the corner of a desk. It also contains objectives and a two-column proof regarding parallel lines cut by a transversal.

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Proving quads are parralelograms

The document contains notes from a geometry drill on identifying parallelograms and determining values of x and y in parallelogram figures. It lists homework answers and a classwork assignment to identify parallelograms from figures and state the relevant definition or theorem, as well as an assignment to complete 15 problems showing work.

Linear approximations and_differentials

The document discusses linear approximations and differentials. It explains that a linear approximation uses the tangent line at a point to approximate nearby values of a function. The linearization of a function f at a point a is the linear function L(x) = f(a) + f'(a)(x - a). Several examples are provided of finding the linearization of functions and using it to approximate values. Differentials are also introduced, where dy represents the change along the tangent line and ∆y represents the actual change in the function.

Olivia’s math problem2

100 day of school

2d 3d animation and Digital services from Vinformax and Creantt

This document summarizes the services of a creative company called Aviformax. It has production hubs in Stockholm, the US, UK and India. The company provides creative and visual design, pre-production, production and post-production services. It helps clients with 2D and 3D motion graphics, product visualization, visual branding and digital signage solutions. The document highlights the advantages of digital signage for branding, finance, operations and technical aspects. It also describes the company's content management system and data analytics dashboard tools.

Math project

1) The document provides steps to find the coordinates of the circumcenter of a triangle with vertices A(-4,0), B(2,6), and C(8,-4).
2) It finds the equations of the perpendicular bisectors of each side by calculating the midpoints and slopes to get the equations.
3) The intersections of the three perpendicular bisectors are calculated to find the circumcenter, which is determined to be (2.5,-0.5).

Congruent figures 2013

The document provides information about congruent triangles:
- Two triangles are congruent if their corresponding sides are congruent and they have the same shape and size.
- Examples are provided to demonstrate using properties of congruent triangles to find missing angle measures and prove triangles are congruent by showing corresponding parts are equal.
- One example proves two triangles are congruent by showing bisectors of angles bisect the opposite sides, making corresponding parts congruent.

114333628 irisan-kerucut

The document discusses properties of parabolas, including their definition as the set of points equidistant from a focus point and directrix line. It presents the standard equation for a par

Power series

A power series is an infinite series of the form Σcixi or Σci(x-a)i, where the cis are constants. It represents a "polynomial" with infinitely many terms that can be used to expand functions. Common power series include the Taylor series expansions of exponential, logarithmic, and other important functions. Power series are very useful for certain mathematical calculations.

Deductivereasoning and bicond and algebraic proofs

1. The document discusses biconditional statements, conditional statements, and using deductive reasoning in geometry. It provides examples of identifying conditionals within biconditionals, writing definitions as biconditionals, and solving equations with justification in both algebra and geometry.
2. Key concepts covered include using properties of equality to write algebraic proofs, properties of congruence corresponding to properties of equality, and identifying properties of equality and congruence that justify statements.
3. Examples are provided of solving equations algebraically and geometrically with justification for each step, identifying conditionals within biconditionals, and writing definitions as biconditionals.

Symmetry,rotation, reflection,translation

The document discusses different types of symmetry including lines of symmetry, reflection, rotation, and translation. It provides examples of these symmetries using shapes like hearts, flags, polygons and math symbols. Regular polygons are noted to have multiple lines of symmetry and there is a pattern to how many lines different regular polygons will have.

Local linear approximation

The document discusses local linear approximations, which provide a linear function that closely approximates a given non-linear function near a specific point. It defines the local linear approximation at a point x0 as f(x0) + f'(x0)(x - x0). Graphs and examples are provided to illustrate how the local linear approximation can be used to estimate function values close to x0. The concept of differentials is also introduced to estimate small changes in a function using its derivative. Examples demonstrate using differentials to approximate changes and estimate errors in computations involving measured values.

Graphing inverse functions

Graphing inverse functions

Translation, Dilation, Rotation, ReflectionTutorials Online

Translation, Dilation, Rotation, ReflectionTutorials Online

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Translations, rotations, reflections, and dilations

Translations, rotations, reflections, and dilations

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Oliviamath problem

Oliviamath problem

Maths activity

Maths activity

3002 a more with parrallel lines and anglesupdated 10 22-13

3002 a more with parrallel lines and anglesupdated 10 22-13

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Proving quads are parralelograms

Proving quads are parralelograms

Linear approximations and_differentials

Linear approximations and_differentials

Olivia’s math problem2

Olivia’s math problem2

2d 3d animation and Digital services from Vinformax and Creantt

2d 3d animation and Digital services from Vinformax and Creantt

Math project

Math project

Congruent figures 2013

Congruent figures 2013

114333628 irisan-kerucut

114333628 irisan-kerucut

Power series

Power series

Deductivereasoning and bicond and algebraic proofs

Deductivereasoning and bicond and algebraic proofs

Symmetry,rotation, reflection,translation

Symmetry,rotation, reflection,translation

Local linear approximation

Local linear approximation

Graphing inverse functions

Graphing inverse functions

Password Rotation in 2024 is still Relevant

Password Rotation in 2024 is still Relevant

The Role of Technology in Payroll Statutory Compliance (1).pdf

With the speed at which companies are transforming in today’s world, technology becomes a key factor when it comes to the efficiency of various organizational processes, such as payroll. Another problem that is considered by managers as one of the most critical is the issue of compliance with the numerous statutory requirements. To tackle this, organizational structures should incorporate efficient payroll management and statutory compliance services especially in thriving cities such as Pune and Delhi.

Pigging Unit Lubricant Oil Blending Plant

Utilizing pigged pipeline technology proves advantageous for the transfer of a diverse range of products. Addressing a significant challenge in Lube Oil Blending Plants, pigged manifolds seamlessly interconnect numerous source tanks with various destinations like filling and loading. This innovative approach enhances efficiency and resolves complexities associated with managing multiple product transfers within the blending facility.

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July Patch Tuesday

Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.

Three New Criminal Laws in India 1 July 2024

In Deloitte's latest article, discover the impact of India's
three new criminal laws, effective July 1, 2024. These laws, replacing the IPC,
CrPC, and Indian Evidence Act, promise a more contemporary, concise, and
accessible legal framework, enhancing forensic investigations and aligning with
current societal needs.
Learn how these Three New Criminal Laws will shape the
future of criminal justice in India
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Dublin_mulesoft_meetup_Mulesoft_Salesforce_Integration (1).pptx

Mulesoft Integration with Salesforce

Best Practices for Effectively Running dbt in Airflow.pdf

As a popular open-source library for analytics engineering, dbt is often used in combination with Airflow. Orchestrating and executing dbt models as DAGs ensures an additional layer of control over tasks, observability, and provides a reliable, scalable environment to run dbt models.
This webinar will cover a step-by-step guide to Cosmos, an open source package from Astronomer that helps you easily run your dbt Core projects as Airflow DAGs and Task Groups, all with just a few lines of code. We’ll walk through:
- Standard ways of running dbt (and when to utilize other methods)
- How Cosmos can be used to run and visualize your dbt projects in Airflow
- Common challenges and how to address them, including performance, dependency conflicts, and more
- How running dbt projects in Airflow helps with cost optimization
Webinar given on 9 July 2024

RPA In Healthcare Benefits, Use Case, Trend And Challenges 2024.pptx

Your comprehensive guide to RPA in healthcare for 2024. Explore the benefits, use cases, and emerging trends of robotic process automation. Understand the challenges and prepare for the future of healthcare automation

Amul milk launches in US: Key details of its new products ...

Amul milk launches in US: Key details of its new products ...

Recent Advancements in the NIST-JARVIS Infrastructure

Recent advancements in the NIST-JARVIS infrastructure: JARVIS-Overview, JARVIS-DFT, AtomGPT, ALIGNN, JARVIS-Leaderboard

The Evolution of Remote Server Management

In today’s interconnected world, remote server management has emerged as a vital component of every business’s IT framework. With the widespread adoption of remote work and digital transformation, the ability to efficiently oversee servers from any location has become indispensable. This article delves into the evolution of remote server management, its pivotal role in modern business operations, and the strategies and tools that empower enterprises to harness its full potential.

Evolution of iPaaS - simplify IT workloads to provide a unified view of data...

Evolution of iPaaS
Integration is crucial for digital transformation, and iPaaS simplifies IT workloads, providing a unified view of enterprise data and applications.
🔸 Early Days (2000s)
The rise of cloud computing and SaaS set the stage for iPaaS to address integration needs. Key milestones include:
➤ Early reliance on IBM WebSphere and Oracle middleware.
➤ Informatica Cloud launch in 2006.
➤ Boomi's AtomSphere introduction in 2008.
➤ Gartner's term "iPaaS" in 2011.
🔸 Cloud First Approach (2010-2020)
The shift to cloud-based applications accelerated iPaaS adoption. Developments include:
➤ Low-code/no-code iPaaS platforms like SnapLogic.
➤ Integration of on-premise, cloud, and SaaS applications.
➤ Enhanced capabilities such as API management and data governance.
➤ Emphasis on security and compliance with platforms like Jitterbit.
➤ Leveraging AI/ML technologies for integration tasks.
🔸 Challenges and Costs
MuleSoft's survey highlights costly integration failures. Key issues include:
➤ High labor costs for custom integrations.
➤ Complexities in mapping and managing data.
➤ Integration challenges in industries like airlines and healthcare.
➤ Increased costs due to lack of standardization and security breaches.
🔸 Future of iPaaS
iPaaS will continue to evolve with increased sophistication and adaptability. Future trends include:
➤ Wider adoption across industries.
➤ Hybrid integrations connecting diverse environments.
➤ AI and ML for automating tasks.
➤ IoT integrations for better decision-making.
➤ Event-driven architectures for real-time responses.
iPaaS is essential for addressing integration challenges and supporting business innovation, making strategic investment crucial for competitive resilience and growth.

Data Integration Basics: Merging & Joining Data

Are you tired of dealing with data trapped in silos? Join our upcoming webinar to learn how to efficiently merge and join disparate datasets, transforming your data integration capabilities. This webinar is designed to empower you with the knowledge and skills needed to efficiently integrate data from various sources, allowing you to draw more value from your data.
With FME, merging and joining different types of data—whether it’s spreadsheets, databases, or spatial data—becomes a straightforward process. Our expert presenters will guide you through the essential techniques and best practices.
In this webinar, you will learn:
- Which transformers work best for your specific data types.
- How to merge attributes from multiple datasets into a single output.
- Techniques to automate these processes for greater efficiency.
Don’t miss out on this opportunity to enhance your data integration skills. By the end of this webinar, you’ll have the confidence to break down data silos and integrate your data seamlessly, boosting your productivity and the value of your data.

Acumatica vs. Sage Intacct vs. NetSuite _ NOW CFO.pdf

now cfo slides

How Social Media Hackers Help You to See Your Wife's Message.pdf

In the modern digital era, social media platforms have become integral to our daily lives. These platforms, including Facebook, Instagram, WhatsApp, and Snapchat, offer countless ways to connect, share, and communicate.

Use Cases & Benefits of RPA in Manufacturing in 2024.pptx

SynapseIndia offers top-tier RPA software for the manufacturing industry, designed to automate workflows, enhance precision, and boost productivity. Experience the benefits of advanced robotic process automation in your manufacturing operations.

BT & Neo4j: Knowledge Graphs for Critical Enterprise Systems.pptx.pdf

Presented at Gartner Data & Analytics, London Maty 2024. BT Group has used the Neo4j Graph Database to enable impressive digital transformation programs over the last 6 years. By re-imagining their operational support systems to adopt self-serve and data lead principles they have substantially reduced the number of applications and complexity of their operations. The result has been a substantial reduction in risk and costs while improving time to value, innovation, and process automation. Join this session to hear their story, the lessons they learned along the way and how their future innovation plans include the exploration of uses of EKG + Generative AI.

Implementations of Fused Deposition Modeling in real world

The presentation showcases the diverse real-world applications of Fused Deposition Modeling (FDM) across multiple industries:
1. **Manufacturing**: FDM is utilized in manufacturing for rapid prototyping, creating custom tools and fixtures, and producing functional end-use parts. Companies leverage its cost-effectiveness and flexibility to streamline production processes.
2. **Medical**: In the medical field, FDM is used to create patient-specific anatomical models, surgical guides, and prosthetics. Its ability to produce precise and biocompatible parts supports advancements in personalized healthcare solutions.
3. **Education**: FDM plays a crucial role in education by enabling students to learn about design and engineering through hands-on 3D printing projects. It promotes innovation and practical skill development in STEM disciplines.
4. **Science**: Researchers use FDM to prototype equipment for scientific experiments, build custom laboratory tools, and create models for visualization and testing purposes. It facilitates rapid iteration and customization in scientific endeavors.
5. **Automotive**: Automotive manufacturers employ FDM for prototyping vehicle components, tooling for assembly lines, and customized parts. It speeds up the design validation process and enhances efficiency in automotive engineering.
6. **Consumer Electronics**: FDM is utilized in consumer electronics for designing and prototyping product enclosures, casings, and internal components. It enables rapid iteration and customization to meet evolving consumer demands.
7. **Robotics**: Robotics engineers leverage FDM to prototype robot parts, create lightweight and durable components, and customize robot designs for specific applications. It supports innovation and optimization in robotic systems.
8. **Aerospace**: In aerospace, FDM is used to manufacture lightweight parts, complex geometries, and prototypes of aircraft components. It contributes to cost reduction, faster production cycles, and weight savings in aerospace engineering.
9. **Architecture**: Architects utilize FDM for creating detailed architectural models, prototypes of building components, and intricate designs. It aids in visualizing concepts, testing structural integrity, and communicating design ideas effectively.
Each industry example demonstrates how FDM enhances innovation, accelerates product development, and addresses specific challenges through advanced manufacturing capabilities.

Password Rotation in 2024 is still Relevant

Password Rotation in 2024 is still Relevant

The Role of Technology in Payroll Statutory Compliance (1).pdf

The Role of Technology in Payroll Statutory Compliance (1).pdf

Pigging Unit Lubricant Oil Blending Plant

Pigging Unit Lubricant Oil Blending Plant

High Profile Girls call Service Pune 000XX00000 Provide Best And Top Girl Ser...

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July Patch Tuesday

July Patch Tuesday

Three New Criminal Laws in India 1 July 2024

Three New Criminal Laws in India 1 July 2024

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Dublin_mulesoft_meetup_Mulesoft_Salesforce_Integration (1).pptx

Dublin_mulesoft_meetup_Mulesoft_Salesforce_Integration (1).pptx

Best Practices for Effectively Running dbt in Airflow.pdf

Best Practices for Effectively Running dbt in Airflow.pdf

RPA In Healthcare Benefits, Use Case, Trend And Challenges 2024.pptx

RPA In Healthcare Benefits, Use Case, Trend And Challenges 2024.pptx

Amul milk launches in US: Key details of its new products ...

Amul milk launches in US: Key details of its new products ...

Recent Advancements in the NIST-JARVIS Infrastructure

Recent Advancements in the NIST-JARVIS Infrastructure

The Evolution of Remote Server Management

The Evolution of Remote Server Management

Evolution of iPaaS - simplify IT workloads to provide a unified view of data...

Evolution of iPaaS - simplify IT workloads to provide a unified view of data...

Data Integration Basics: Merging & Joining Data

Data Integration Basics: Merging & Joining Data

Acumatica vs. Sage Intacct vs. NetSuite _ NOW CFO.pdf

Acumatica vs. Sage Intacct vs. NetSuite _ NOW CFO.pdf

How Social Media Hackers Help You to See Your Wife's Message.pdf

How Social Media Hackers Help You to See Your Wife's Message.pdf

Use Cases & Benefits of RPA in Manufacturing in 2024.pptx

Use Cases & Benefits of RPA in Manufacturing in 2024.pptx

BT & Neo4j: Knowledge Graphs for Critical Enterprise Systems.pptx.pdf

BT & Neo4j: Knowledge Graphs for Critical Enterprise Systems.pptx.pdf

Implementations of Fused Deposition Modeling in real world

Implementations of Fused Deposition Modeling in real world

- 1. The Minkowski sum (applied to 2d geometry) cloderic.mars@gmail.com http://www.crowdscontrol.net clodericmars
- 2. Formal deﬁnition A and B are two sets A⊕B is the Minkowski sum of A and B A⊕B = {a+b! a∈A, b∈B}
- 3. What if A and B are 2D shapes ? Hard to visualize ? Let’s see some examples...
- 4. Example 1 A is any polygon B is a convex polygon
- 5. A B x y
- 6. A⊕B x y
- 7. Example 2 A is any polygon B is any disc
- 8. A B x y
- 9. A⊕B x y
- 10. Intuitive deﬁnition What is A⊕B ? Take B Dip it into some paint Put its (0,0) on A border Translate it along the A perimeter The painted area is A⊕B
- 11. What can you do with that ? Notably, motion planning
- 12. Free space A is an obstacle any 2D polygon B is a moving object 2D translation : t shape : a convex polygon or a disc t ∈ A⊕-B collision
- 13. Example 1 A is any polygon B is a convex polygon
- 14. A B x y -B
- 15. A⊕-B x y
- 16. x y t t ∉ A⊕-B no collision
- 17. x y t t ∈ A⊕-B collision
- 18. Example 2 A is any polygon B is any disc
- 19. A B=-B x y
- 20. A⊕-B x y
- 21. A⊕-B x y t t ∉ A⊕-B no collision
- 22. A⊕-B x y t t ∈ A⊕-B collision
- 23. How is it computed ?
- 24. Two convex polygons ConvexPolygon minkowskiSum(ConvexPolygon a, ConvexPolygon b) { Vertex[] computedVertices; foreach(Vertex vA in a) { foreach(Vertex vB in b) { computedVertices.push_back(vA+vB); } } return convexHull(computedVertices); }
- 25. Any polygons Method 1 : decomposition decompose in convex polygons compute the sum of each couple the ﬁnal sum is the union of each sub-sum Method 2 : convolution cf. sources
- 26. Polygon offsetting P is a polygon D is a disc of radius r Computing P⊕D = Offsetting P by a radius r Computation Easy for a convex polygon cf. sources