This course can also be taken for academic credit as ECEA 5340, part of CU Boulder’s Master of Science in Electrical Engineering degree. After taking this course, you will be able to: ● Understand how to specify the proper thermal, flow, or rotary sensor for taking real-time process data. ● Implement thermal sensors into an embedded system in both hardware and software. ● Add the sensor and sensor interface into a microprocessor based development kit. ● Create hardware and firmware to process sensor signals and feed data to a microprocessor for further evaluation. ● Study sensor signal noise and apply proper hardware techniques to reduce it to acceptable levels. You will need to buy the following components to do the two course projects based on the videos in this module. Note that if you have already purchased the PSOC 5LP PROTOTYPING KIT, you do not need to buy it again. These parts may be purchased off the Digikey web site, www. Digikey.com. Or, you may obtain the specs from the site, and purchase them elsewhere. These are the part numbers typed out, so you can copy and paste them into the Digikey web site. You will need one of each part. 428-3390-ND NHD-0216BZ-RN-YBW-ND 570-1229-ND A105970CT-ND Additional equipment needed: • Wire - various gauges and lengths • Breadboard • Oscilloscope – suggested models are: o PICOSCOPE 2204A-D2 available on www.digikey.com or o Digilent 410-324 | OpenScope MZ available on www.newark.com Depending on your budget, you can also investigate these models: o Hantek HT6022BE20MHz - https://www.amazon.com/dp/B009H4AYII o SainSmart DSO212 - https://www.amazon.com/dp/B074QBQNB7 o PoScope Mega50 USB - https://www.robotshop.com/en/poscope-mega50-usb-mso-oscilloscope.html o ADALM2000 - https://www.digikey.com/en/products/detail/analog-devices-inc./ADALM2000/7019661

In this course, you will learn to think like a data scientist and ask questions of your data. You will use interactive features in MATLAB to extract subsets of data and to compute statistics on groups of related data. You will learn to use MATLAB to automatically generate code so you can learn syntax as you explore. You will also use interactive documents, called live scripts, to capture the steps of your analysis, communicate the results, and provide interactive controls allowing others to experiment by selecting groups of data. These skills are valuable for those who have domain knowledge and some exposure to computational tools, but no programming background is required. To be successful in this course, you should have some knowledge of basic statistics (e.g., histograms, averages, standard deviation, curve fitting, interpolation). By the end of this course, you will be able to load data into MATLAB, prepare it for analysis, visualize it, perform basic computations, and communicate your results to others. In your last assignment, you will combine these skills to assess damages following a severe weather event and communicate a polished recommendation based on your analysis of the data. You will be able to visualize the location of these events on a geographic map and create sliding controls allowing you to quickly visualize how a phenomenon changes over time.

This course is all about differential equations. Both basic theory and applications are taught. In the first five weeks we will learn about ordinary differential equations, and in the final week, partial differential equations. The course contains 56 short lecture videos, with a few problems to solve after each lecture. And after each substantial topic, there is a short practice quiz. Solutions to the problems and practice quizzes can be found in instructor-provided lecture notes. There are a total of six weeks in the course, and at the end of each week there is an assessed quiz. Download the lecture notes: http://www.math.ust.hk/~machas/differential-equations-for-engineers.pdf Watch the promotional video: https://youtu.be/eSty7oo09ZI

This course can also be taken for academic credit as ECEA 5701, part of CU Boulder’s Master of Science in Electrical Engineering degree. This course introduces more advanced concepts of switched-mode converter circuits. Realization of the power semiconductors in inverters or in converters having bidirectional power flow is explained. Power diodes, power MOSFETs, and IGBTs are explained, along with the origins of their switching times. Equivalent circuit models are refined to include the effects of switching loss. The discontinuous conduction mode is described and analyzed. A number of well-known converter circuit topologies are explored, including those with transformer isolation. The homework assignments include a boost converter and an H-bridge inverter used in a grid-interfaced solar inverter system, as well as transformer-isolated forward and flyback converters. After completing this course, you will: ● Understand how to implement the power semiconductor devices in a switching converter ● Understand the origins of the discontinuous conduction mode and be able to solve converters operating in DCM ● Understand the basic dc-dc converter and dc-ac inverter circuits ● Understand how to implement transformer isolation in a dc-dc converter, including the popular forward and flyback converter topologies Completion of the first course Introduction to Power Electronics is the assumed prerequisite for this course.

This course will introduce you to all aspects of development of Soft Processors and Intellectual Property (IP) in FPGA design. You will learn the extent of Soft Processor types and capabilities, how to make your own Soft Processor in and FPGA, including how to design the hardware and the software for a Soft Processor. You will learn how to add IP blocks and custom instructions to your Soft Processor. After the Soft Processor is made, you learn how to verify the design using simulation and an internal logic analyzer. Once complete you will know how to create and use Soft Processors and IP, a very useful skill. This course consists of 4 modules, approximately 1 per week for 4 weeks. Each module will include an hour or two of video lectures, reading assignments, discussion prompts, and an end of module assessment.

Take the next step in your career by gaining a greater understanding of 5G technology and how it is changing the way we operate both personally and professionally. Learn how 5G is revolutionizing the business world and begin to understand how 5G can benefit your business by helping you meet your ever-evolving needs. You do not need any prior knowledge of 5G or other wireless technology to take this course. WHY TAKE THIS COURSE? - Understand what 5G technology is, the endless possibilities it offers, and how you can leverage it to enhance business strategy. - Receive 5G training from engineers at Qualcomm Technologies – a world leader in 5G tech and wireless communication. - Receive a Qualcomm-branded certificate of completion that may help you get that next job or promotion. COURSE OUTLINE: - Overview of wireless and cellular communications. - Overview of the 5G NR vision. - Introduction to 5G networks and features. - Discuss 5G spectrum and mmWave. - Learn about Massive MIMO and its impact on wireless technology. - Discuss the evolution of the 5G radio access network (RAN). - Dive into 5G private networks and industrial IoT. - Overview of 5G security.

Arduino senses the environment by receiving inputs from add-on devices such as sensors, and can control the world around it by adjusting lights, motors, and other actuators. In this class you will learn how and when to use the different types of sensors and how to connect them to the Arduino. Since the external world uses continuous or analog signals and the hardware is digital you will learn how these signals are converted back-and-forth and how this must be considered as you program your device. You'll also learn about the use of Arduino-specific shields and the shields software libraries to interface with the real world. Please note that this course does not include discussion forums.

Digital Signal Processing is the branch of engineering that, in the space of just a few decades, has enabled unprecedented levels of interpersonal communication and of on-demand entertainment. By reworking the principles of electronics, telecommunication and computer science into a unifying paradigm, DSP is a the heart of the digital revolution that brought us CDs, DVDs, MP3 players, mobile phones and countless other devices. In this series of four courses, you will learn the fundamentals of Digital Signal Processing from the ground up. Starting from the basic definition of a discrete-time signal, we will work our way through Fourier analysis, filter design, sampling, interpolation and quantization to build a DSP toolset complete enough to analyze a practical communication system in detail. Hands-on examples and demonstration will be routinely used to close the gap between theory and practice. To make the best of this class, it is recommended that you are proficient in basic calculus and linear algebra; several programming examples will be provided in the form of Python notebooks but you can use your favorite programming language to test the algorithms described in the course.

Welcome to Quantitative Formal Modeling and Worst-Case Performance Analysis. In this course, you will learn about modeling and solving performance problems in a fashion popular in theoretical computer science, and generally train your abstract thinking skills. After finishing this course, you have learned to think about the behavior of systems in terms of token production and consumption, and you are able to formalize this thinking mathematically in terms of prefix orders and counting functions. You have learned about Petri-nets, about timing, and about scheduling of token consumption/production systems, and for the special class of Petri-nets known as single-rate dataflow graphs, you will know how to perform a worst-case analysis of basic performance metrics, like throughput, latency and buffering. Disclaimer: As you will notice, there is an abundance of small examples in this course, but at first sight there are not many industrial size systems being discussed. The reason for this is two-fold. Firstly, it is not my intention to teach you performance analysis skills up to the level of what you will need in industry. Rather, I would like to teach you to think about modeling and performance analysis in general and abstract terms, because that is what you will need to do whenever you encounter any performance analysis problem in the future. After all, abstract thinking is the most revered skill required for any academic-level job in any engineering discipline, and if you are able to phrase your problems mathematically, it will become easier for you to spot mistakes, to communicate your ideas with others, and you have already made a big step towards actually solving the problem. Secondly, although dataflow techniques are applicable and being used in industry, the subclass of single-rate dataflow is too restrictive to be of practical use in large modeling examples. The analysis principles of other dataflow techniques, however, are all based on single-rate dataflow. So this course is a good primer for any more advanced course on the topic. This course is part of the university course on Quantitative Evaluation of Embedded Systems (QEES) as given in the Embedded Systems master curriculum of the EIT-Digital university, and of the Dutch 3TU consortium consisting of TU/e (Eindhoven), TUD (Delft) and UT (Twente). The course material is exactly the same as the first three weeks of QEES, but the examination of QEES is at a slightly higher level of difficulty, which cannot (yet) be obtained in an online course.

In this course, you will learn to design the computer architecture of complex modern microprocessors. All the features of this course are available for free. It does not offer a certificate upon completion.