Courses tagged with "How to Succeed" (381)

An accessible but substantial introduction to quantum mechanics for anyone with a reasonable college-level understanding of physical science or engineering.

An accessible but substantial introduction to quantum mechanics for anyone with a reasonable college-level understanding of physical science or engineering.

Starting from a basic knowledge of quantum mechanics, this course shows how to use and understand it in a broad range of modern applications.

Have you ever wondered what it takes to get your train on the right platform at the scheduled time every day?

Understanding the complexity behind today’s sophisticated railway systems will give you a better insight into how this safe and reliable transportation system works. We will show you the many factors which are involved and how multiple people, behind the scenes, have a daily task that enables you to get from home to work. Journey with us into the world of rail - a complex system that connects people, cities and countries.

Railway systems entail much more than a train and a track. They are based on advanced technical and operational solutions, dealing with continuously changing demands for more efficient transport for both passengers and freight every day. Each system consists of many components that must be properly integrated: from trains, tracks, stations, signaling and control systems, through monitoring, maintenance and the impact on cities, landscape and people. This integration is the big challenge and the source of many train delays, inconvenient connections and other issues that impact our society.

This engineering course attempts to tackle those issues by introducing you to a holistic approach to railway systems engineering. You will learn how the system components depend on each other to create a reliable, efficient and state-of-the-art network.

We will address questions such as:

• How do railways work and how did they evolve over time?
• What factors give rise to everyday issues?
• How do different components of the railway system interact?
• What is the effect of train stations and the network from an urban, social and economic point of view?
• What can be done to improve the monitoring and maintenance of tracks?
• How are timetables designed in a way that balances passenger demand with the capacity of the railway and is adaptable to handle unexpected disturbances?
• What can be done to prevent and deal with disturbances caused by external factors and how do they affect the whole rail system?
• How does the design of railways influence their performance over time?

A new serious game has been designed for this course to guide you through the process of decision making while building a rail network and maintaining it. Cities have to be connected in an ever-changing setting, dealing with wear, capacity, developments and disturbances. What choices do you make and how do they affect the performance of the system?

For this MOOC, our very own TU Delft Measurement Train will be used to give you insights of the track and vehicle design, real-life monitoring and pantograph/catenary interaction. Together with the game this will give you the opportunity to see real-life examples and implement the knowledge you learn in a simulated environment.

This first ever MOOC on railway systems engineering is delivered by the renowned experts of TU Delft and leading professionals working in the industry. It combines theoretical knowledge with practical examples, with the main objective to maintain a high degree of reliability under predictable and unknown circumstances.

If you want to learn about the science behind the exciting world of railway systems - whether train, metro or tram - this course will set you on the right track!

This is an Exploratorium Teacher Institute professional development course open to any middle or high school science teacher. This course is designed to help science teachers infuse their curriculum with hands-on STEM activities that support the NGSS engineering practices.

The increased demand by consumers and businesses for more utility, connectivity and smarter and more efficient electronic technology not only creates a need for more embedded systems but also for engineers in the embedded systems field.

In this lab-based computer science course, explore the complexities of embedded systems and learn how to develop your own real-time operating system (RTOS) by building a personal fitness device with Bluetooth connectivity (BLE). An operating system (OS) is a software system that computers use to manage the resources of a computer. The OS decides which tasks are performed when and decides how resources are utilized. Simple embedded systems, which are a combination of electrical, mechanical, chemical, and computer components designed to perform a dedicated function, originally did not need an OS. However, as embedded systems have evolved, so have their complexities. To manage this, an RTOS is now required.

Embedded systems are often deployed in safety-critical situations such as automotive, military, industrial, and medical applications. In applications such as communications and consumer electronics, response time and processing speed are important. A real-time system not only needs to arrive at the correct answer, but must also get the correct answer at the correct time. A RTOS manages a computer's resources so that tasks are performed in a timely mannner.

In this computer science course, students will learn the design fundamentals of an RTOS from the bottom up and use these fundamentals to build practical real-time applications. We will provide a board support package (BSP), so students will be able to focus on the RTOS and Bluetooth network without needing prior experience in circuits and I/O device driver software. This is a hands-on project-based lab course, where you will incrementally build a personal fitness device with Bluetooth connectivity.

This course is intended for students and professional engineers wishing to improve their skills in the fields of embedded systems, product development, computer architecture, operating systems, and Bluetooth networks.

To complete this course, you will need to purchase a lab kit including a microcontroller board, an I/O board, and a Bluetooth module. Instructions about purchasing the kit and installing required software are at http://edx-org-utaustinx.s3.amazonaws.com/UT601x/RTOS.html .

A PhD or master’s level research project is an enormous undertaking, and you might find yourself a bit uncertain about the process or how to achieve the desired outcome. In this research course, you will learn the underlying principles that are needed to conduct research from an engineering perspective.

This course is designed for engineering students conducting postgraduate research work on engineering projects. The objective of the course is to translate current research methods, which are mostly from a social science perspective, into something more relatable and understandable to engineers. Our hope for this course is to go beyond the concepts to understand the actual reasons for doing research in a certain way. While engineers are the main target audience, non-engineers will find this information useful as well.

The methods taught in this course will equip you with the knowledge needed to design, plan and construct your own research process.

There is no doubt that technological innovation is one of the key elements driving human progress.

However, new technologies also raise ethical questions, have serious implications for society and the environment and pose new risks, often unknown and unknowable before the new technologies reach maturity. They may even lead to radical disruptions. Just think about robots, self-driving vehicles, medical engineering and the Internet of Things.

They are strongly dependent on social acceptance and cannot escape public debates of regulation and ethics. If we want to innovate, we have to do that responsibly. We need to reflect on –and include- our societal values in this process. This course will give you a framework to do so.

The first part of the course focuses on ethical questions/framework and concerns with respect to new technologies.

The second part deals with (unknown) risks and safety of new technologies including a number of qualitative and quantitative risk assessment methods.

The last part of the course is about the new, value driven, design process which take into account our societal concerns and conflicting values.

Case studies (ethical concerns, risks) for reflection and discussions during the course include – among others- nanotechnology, self-driving vehicles, robots, AI smart meters for electricity, autonomous weapons, nuclear energy and CO2capture and coolants. Affordable (frugal) innovations for low-income groups and emerging markets are also covered in the course. You can test and discuss your viewpoint.

The course is for all engineering students who are looking for a methodical approach to judge responsible innovations from a broader – societal- perspective.

This course provides a mathematical introduction to the mechanics and control of robots that can be modeled as kinematic chains. Topics covered include the concept of a robot’s configuration space and degrees of freedom, static grasp analysis, the description of rigid body motions, kinematics of open and closed chains, and the basics of robot control. The emphasis is not on the latest research trends and technological innovations in robotics, but on learning the fundamental concepts and core principles that underlie robotics as a scientific discipline. The intent is to help students acquire a unified set of analytical tools for the modeling and control of robots, together with a reliable physical intuition that recognizes the unique and interdisciplinary nature of robotics—in short, content that will serve as a reliable foundation for whatever trends may appear later, and remain relevant to both the practitioner and researcher. This course is the first of two parts of “Robot Mechanics and Control.” Part II will start shortly after completion of Part I.

We think of Robotics as the science of building devices that physically interact with their environment. The most useful robots do it precisely, powerfully, repeatedly, tirelessly, fast, or some combinations of these. The most interesting robots maybe even do it intelligently. This course will cover the fundamentals of robotics, focusing on both the mind and the body.

We will learn about two core robot classes: kinematic chains (robot arms) and mobile bases. For both robot types, we will introduce methods to reason about 3-dimensional space and relationships between coordinate frames. For robot arms, we will use these to model the task of delivering a payload to a specified location. For mobile robots, we will introduce concepts for autonomous navigation in the presence of obstacles.

Class projects will make use of ROS - the open-source Robot Operating System (www.ros.org) widely used in both research and industry. Computer requirements for working on the projects will include a computer set up with Ubuntu Linux and high bandwidth internet access for downloading and installing ROS packages.

Flying drones or robot manipulators accomplish heavy-duty tasks that deal with considerable forces and torques not covered by a purely robot kinematics framework. Learn how to formulate dynamics problems and design appropriate control laws.

In this course, part of the Robotics MicroMasters program, you will learn how to develop dynamic models of robot manipulators, mobile robots, and drones (quadrotors), and how to design intelligent controls for robotic systems that can grasp and manipulate objects.

We will cover robot dynamics, trajectory generation, motion planning, and nonlinear control, and develop real-time planning and control software modules for robotic systems. This course will give you the basic theoretical tools and enable you to design control algorithms.

Using MATLAB, you will apply what you have learned through a series of projects involving real-world robotic systems.

How do you create robots that operate well in the real world? Learn the key math concepts and tools used to design robots that excel in navigating our complex, unstructured world in environments such as aerospace, automotive, manufacturing and healthcare.

In this course, part of the Robotics MicroMasters program, you will learn how to apply concepts from linear algebra, geometry and group theory and the tools to configure and control the motion of manipulators and mobile robots.

You will also learn how to use MATLAB, the standard robotics programming environment and learn step by step how to use this mathematical tool to write functions, calculate vectors and produce visualizations. You will get hands on experience applying your knowledge to projects using various simulations in MATLAB.

How do robots climb stairs, traverse shifting sand and navigate through hilly and rocky terrain?

This course, part of the Robotics MicroMasters program, will teach you how to think about complex mobility challenges that arise when robots are deployed in unstructured human and natural environments.

You will learn  how to design and program the sequence of energetic interactions that must occur between sensors and mechanical actuators in order to ensure stable mobility. We will expose you to underlying and still actively developing concepts, while providing you with practical examples and projects. 

How do robots “see”, respond to and learn from their interactions with the world around them? This is the fascinating field of visual intelligence and machine learning. Visual intelligence allows a robot to “sense” and “recognize” the surrounding environment. It also enables a robot to “learn” from the memory of past experiences by extracting patterns in visual signals.

You will understand how Machine Learning extracts statistically meaningful patterns in data that support classification, regression and clustering. Then by studying Computer Vision and Machine Learning together you will be able to build recognition algorithms that can learn from data and adapt to new environments.

By the end of this course, part of the Robotics MicroMasters program, you will be able to program vision capabilities for a robot such as robot localization as well as object recognition using machine learning.

Projects in this course will utilize MATLAB and OpenCV and will include real examples of video stabilization, recognition of 3D objects, coding a classifier for objects, building a perceptron, and designing a convolutional neural network (CNN) using one of the standard CNN frameworks.

We encounter signals and systems extensively in our day-to-day lives, from making a phone call, listening to a song, editing photos, manipulating audio files, using speech recognition softwares like Siri and Google now, to taking EEGs, ECGs and X-Ray images. Each of these involves gathering, storing, transmitting and processing information from the physical world. This course will equip you to deal with these tasks efficiently by learning the basic mathematical framework of signals and systems.

This course is divided into two parts. In this part (EE210.1x), we will explore the various properties of signals and systems, characterization of Linear Shift Invariant Systems, convolution and Fourier Transform, while the next part (EE210.2x), will deal with the Sampling theorem, Z-Transform, discrete Fourier transform and Laplace transform. Ideas introduced in this course will be useful in understanding further electrical engineering courses which deal with control systems, communication systems, power systems, digital signal processing, statistical signal analysis and digital message transmission. The concepts taught in this course are also useful to students of other disciplines like mechanical, chemical, aerospace and other branches of engineering and science.

We encounter signals and systems extensively in our day-to-day lives, from making a phone call, listening to a song, editing photos, manipulating audio files, using speech recognition softwares like Siri and Google now, to taking EEGs, ECGs and X-Ray images. Each of these involves gathering, storing, transmitting and processing information from the physical world. This course will equip you to deal with these tasks efficiently by learning the basic mathematical framework of signals and systems.

This course is divided into two parts. In the first part (EE210.1x), we explored the various properties of signals and systems, characterization of Linear Shift Invariant Systems, convolution and Fourier Transform. Building on that, in this part (EE210.2x) we will deal with the Sampling theorem, Z-Transform, discrete Fourier transform and Laplace transform. The contents of the first part are prerequisites for doing this part. Ideas introduced in this course will be useful in understanding further electrical engineering courses which deal with control systems, communication systems, power systems, digital signal processing, statistical signal analysis and digital message transmission. The concepts taught in this course are also useful to students of other disciplines like mechanical, chemical, aerospace and other branches of engineering and science.

This short course teaches students and industry professionals how to design integrated optical devices and circuits, using a hands-on approach with commercial tools. We will fabricate your designs using a state-of-the-art ($5M) silicon photonic rapid-prototyping 100 keV electron-beam lithography facility. We will measure your designs using an automated optical probe station and provide you the data. You will then analyze your experimental data.

Why take this course?

• To get hands on design experience with integrated optics
• To learn how to use advanced optical design tools
• To get your design fabricated, and obtain experimental data

The focus of this course is a design project, guided by lectures, tutorials and activities. As a first-time designer, you will design an interferometer, which is a widely used device in many applications such as communications (modulation, switching) and sensing. Specifically, it is Mach-Zehnder Interferometer, consisting of fibre grating couplers, two splitters, and optical waveguides. For advanced designers, this course is an opportunity to design many other devices, such as directional couplers, ring, racetrack and disk resonators, Bragg gratings including grating assisted contra-directional couplers, photonic crystals, multi-mode interference (MMI) couplers, polarization diversity components, mode-division multiplexing (MDM) components and circuits, novel waveguides such as sub-wavelength grating (SWG) and metamaterial waveguides, slot waveguides, etc.

Commercial software tool licenses are provided in this course (Lumerical Solutions, Mentor Graphics, and MATLAB). Open-source alternatives are provided. Mentor Graphics tools are accessed remotely via a cloud service; the others can be run on your own computer.

You will earn a professional certificate from the University of British Columbia and edX upon successful completion of this course. Certificates can be uploaded directly to your LinkedIn profile.

Learn how to analyse data with the Six Sigma methodology using inferential statistical techniques to determine confidence intervals and to test hypotheses based on sample data. You will also review cause and effect techniques for root cause analysis.

You will learn how to perform correlation and regression analyses in order to confirm the root cause and understand how to improve your process and plan designed experiments.

You will learn how to implement statistical process control using control charts and quality management tools, including the 8 Disciplines and Failure Modes and Effects Analysis to reduce risk and manage process deviations.

To complement the lectures, learners are provided with interactive exercises, which allow learners to see the statistics "in action." Learners then master the statistical concepts by completing practice problems. These are then reinforced using interactive case-studies, which illustrate the application of the statistics in quality improvement situations.

Upon successful completion of this program, learners will earn the Technical University of Munich Lean Six Sigma Yellow Belt Certification, confirming mastery of the fundamentals of Lean Six Sigma to a Yellow Belt level, based on the American Society of Quality's Body of Knowledge for the Certified Six Sigma Yellow Belt. 

Understand the background and meaning of Six Sigma and the five steps of the DMAIC process improvement flow: Define, Measure, Analyse, Improve and Control. Discuss what "Quality" means and how to identify the Voice of the Customer.

You will learn how to set an improvement project goal, calculate process yield, and identify Critical to Quality parameters.

You will learn how to map a process and to use the necessary statistical techniques to establish the baseline performance of a process and to calculate the process capability.

To complement the lectures, we provide interactive exercises, which allow learners to see the statistics "in action." Learners then master the statistical concepts by completing practice problems. These are then reinforced using interactive case-studies, which illustrate the application of the statistics in quality improvement situations.

Upon successful completion of this program, learners will earn the Technical University of Munich Lean Six Sigma Yellow Belt Certification, confirming mastery of the fundamentals of Lean Six Sigma to a Yellow Belt level, based on the American Society of Quality's Body of Knowledge for the Certified Six Sigma Yellow Belt.

Ever wondered why you hear the term “smart grid” so often these days, and what it’s all about? This engineering course will explain the essential nature of the smart grid, an electricity network based on digital technology, and the importance of grid modernization.

This course will provide high-level insight into a smart grid’s many aspects such as distributed energy, energy storage, transmission and distribution automation, microgrids, demand response, data analytics, and cyber security.

This course builds an understanding of key smart grid technologies both from a utility and customer perspective. It delivers a business perspective through cost-benefit analysis, market adoption, and industry mega trends.

It concludes by laying out a typical roadmap for the progression of smart grids, along with an implementation methodology for realizing it.

No previous power systems or utility industry knowledge needed. Simply sit back and enjoy your journey through the world of Smart Grid.