Discrete Time Signals and Systems, Part 1: Time Domain

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Discrete Time Signals and Systems, Part 1: Time Domain

Course Details

Cost

FREE

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  • On demand

Course Provider

edX online courses
Harvard University, the Massachusetts Institute of Technology, and the University of California, Berkeley, are just some of the schools that you have at your fingertips with edX. Through massive open online courses (MOOCs) from the world's best universities, you can develop your knowledge in literature, math, history, food and nutrition, and more. These online classes are taught by highly-regarded experts in the field. If you take a class on computer science through Harvard, you may be tau...
Harvard University, the Massachusetts Institute of Technology, and the University of California, Berkeley, are just some of the schools that you have at your fingertips with edX. Through massive open online courses (MOOCs) from the world's best universities, you can develop your knowledge in literature, math, history, food and nutrition, and more. These online classes are taught by highly-regarded experts in the field. If you take a class on computer science through Harvard, you may be taught by David J. Malan, a senior lecturer on computer science at Harvard University for the School of Engineering and Applied Sciences. But there's not just one professor - you have access to the entire teaching staff, allowing you to receive feedback on assignments straight from the experts. Pursue a Verified Certificate to document your achievements and use your coursework for job and school applications, promotions, and more. EdX also works with top universities to conduct research, allowing them to learn more about learning. Using their findings, edX is able to provide students with the best and most effective courses, constantly enhancing the student experience.

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22120 reviews

Course Description

Technological innovations have revolutionized the way we view and interact with the world around us. Editing a photo, re-mixing a song, automatically measuring and adjusting chemical concentrations in a tank: each of these tasks requires real-world data to be captured by a computer and then manipulated digitally to extract the salient information. Ever wonder how signals from the physical world are sampled, stored, and processed without losing the information required to make predictions and extract meaning from the data?

Students will find out in this rigorous mathematical introduction to the engineering field of signal processing: the study of signals and systems that extract information from the world around us. This course will teach students to analyze discrete-time signals and systems in both the time and frequency domains. Students will learn convolution, discrete Fourier transforms, the z-transform, and digital filtering. ...

Technological innovations have revolutionized the way we view and interact with the world around us. Editing a photo, re-mixing a song, automatically measuring and adjusting chemical concentrations in a tank: each of these tasks requires real-world data to be captured by a computer and then manipulated digitally to extract the salient information. Ever wonder how signals from the physical world are sampled, stored, and processed without losing the information required to make predictions and extract meaning from the data?

Students will find out in this rigorous mathematical introduction to the engineering field of signal processing: the study of signals and systems that extract information from the world around us. This course will teach students to analyze discrete-time signals and systems in both the time and frequency domains. Students will learn convolution, discrete Fourier transforms, the z-transform, and digital filtering. Students will apply these concepts in interactive MATLAB programming exercises (all done in browser, no download required).

Part 1 of this course analyzes signals and systems in the time domain. Part 2 covers frequency domain analysis.

Prerequisites include strong problem solving skills, the ability to understand mathematical representations of physical systems, and advanced mathematical background (one-dimensional integration, matrices, vectors, basic linear algebra, imaginary numbers, and sum and series notation). Part 1 is a prerequisite for Part 2. This course is an excerpt from an advanced undergraduate class at Rice University taught to all electrical and computer engineering majors.

Reviews 9/10 stars
3 Reviews for Discrete Time Signals and Systems, Part 1: Time Domain

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8/10 starsCompleted
4 years, 2 months ago
The course was lot of fun. I found it was relative easy to do, even without having any experience of the topic. Hearing only part 1 alone does not make sense, the two parts belong together.
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9/10 starsCompleted
  • 116 reviews
  • 107 completed
4 years, 2 months ago
Discrete Time Signals and Systems, Part 1: Time Domain is a 4-week introduction to discrete time signals offered by Rice University through the edX platform. This course was originally 8 weeks, but edX split it up into two parts, one covering the time domain and one addressing the frequency domain. Major course topics include signal properties, signals as vectors, linear time-invariant systems and convolution. The course requires some linear algebra and calculus (it has a pre-course assessment) as well as some basic programming in MATLAB. You don't need to know any MATLAB going in, but if you do you can skip the tutorial. Grading is based on a combination of comprehension questions, homework quizzes, peer graded free responses and a final exam. All of the course content other than assignments is available immediately so you can work ahead if you want to. Discrete Time Signals and Systems started around the same time as a similar sign... Discrete Time Signals and Systems, Part 1: Time Domain is a 4-week introduction to discrete time signals offered by Rice University through the edX platform. This course was originally 8 weeks, but edX split it up into two parts, one covering the time domain and one addressing the frequency domain. Major course topics include signal properties, signals as vectors, linear time-invariant systems and convolution. The course requires some linear algebra and calculus (it has a pre-course assessment) as well as some basic programming in MATLAB. You don't need to know any MATLAB going in, but if you do you can skip the tutorial. Grading is based on a combination of comprehension questions, homework quizzes, peer graded free responses and a final exam. All of the course content other than assignments is available immediately so you can work ahead if you want to. Discrete Time Signals and Systems started around the same time as a similar signal processing course on Coursera called "Digital Signal Processing." I found Discrete Time Signals to be much more approachable than the Coursera course; it introduces concepts at a steady but manageable pace and doesn't overload you with math right out of the gate. The course isn't easy, but it isn't too difficult. The lecture videos are well-done and the instruction is very good, although some videos could stand to be broken up into multiple parts. Professor Baraniuk tends to stutter, but it didn't really bother me or detract from the quality of the instruction. The MATLAB programming questions are baked right into the edX website and let you get some hands-on experience with the concepts. The final exam is "closed book" which I think is a mistake as it promotes guessing over learning. All in all, Discrete Time Signals and Systems Part 1 is an excellent introduction to signal processing that is likely to be more accessible than other courses on the same subject you may find elsewhere. The stage is set for a deeper dive into signal processing in Part 2.
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10/10 starsCompleted
4 years, 3 months ago
DSP is beautiful. The more you learn, the more you fall in love with it. If you wanna learn DSP in an interesting way, this is the best place and you've got the best professor here. This course is a bit challenging course; needs lot of hand work. I did this course in the first offering and i'm so grateful to my professor, Richard. Thanks :)
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