Using smartphones as mobile implementation platforms for applied digital signal processing courses

Due to the pandemic, the vast majority of courses taught in the second half of 2020 proceeded online. We discuss the conversion of a Real-time Digital Signal Processing (DSP) course with traditional in-person hands-on labs to a hybrid (in-person and online) setting. This senior-level undergraduate course is built around the development and testing of DSP software. In the online version of the course, students use a small microcontroller kit and a USB oscilloscope, and they work in online teams. We describe the lab projects and we highlight the mechanisms that enable the students to collaborate on labs. We provide early feedback from students from the first offering of the modified course, and conclude with a list of planned improvements.

Digital Filter Design in electronics/electrical engineering involves complicated mathematical equations and dynamic waveform variations. It is a consensus among educators that using simulation tools helps improve students' learning experience. However, very few studies provided scientific evidence to support this notion. In a Digital Signal Processing (DSP) course, students received three different types of instructional messages (online text only, text plus static illustrations, and text plus simulations). An experimental study was designed to investigate potential main effects of the independent variables: instructional message on students' post-test scores. Multivariate Analysis of Variance (MANOVA) was performed to analyze collected data. Results from 132 students showed significant differences among students in a post-test learning achievement (F(2,130)=13.590, p<.05). A follow-up test indicated that students who used a simulation tool performed significant better scores on their learning achievement test than those who only received online text materials and than those who received text plus static illustrations (p<.05).

Applied or real-time digital signal processing courses offered at many universities have greatly enhanced students’ learning of signal processing concepts by covering practical aspects of implementing signal processing algorithms. DSP processor boards are often deployed in these courses. To a lesser extent, ARM-based boards such as Raspberry Pi [1] are utilized. A number of textbooks are available discussing how to implement signal processing algorithms on DSP boards, e.g., [1—6]. This book is written to provide an alternative hardware platform which students can use in an anywhere-anytime manner and at no cost as it is already in their possession, that being their own smartphones.

:In the course of digital signal processing, MATLAB software can be used in combination to change the traditional teaching form, to show students rich teaching content, to stimulate students’ interest in learning and to improve teaching quality.This paper first introduces the characteristics of digital signal processing course, and then analyzes the advantages of MATLAB software and digital signal processing course. The application of MATLAB software in digital signal processing course teaching is explored in detail in order to provide reference for teaching practice.

NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Main Menu Session 3220 Animating Theoretical Concepts for Signal Processing Courses James H. McClellan, Jordan Rosenthal Georgia Institute of Technology / MIT Lincoln Laboratory Abstract: Although most topics in Digital Signal Processing (DSP) are highly mathematical, most experts possess knowledge of these concepts that is primarily graphical. Therefore, we have developed a variety of multimedia adjuncts for use in an introductory signal processing course at Georgia Tech to teach abstract concepts. These same multimedia tools are also valuable in senior-level and graduate DSP courses. Among the available resources are animations, demonstrations MATLAB GUIs that address nearly every major conceptual issue in a basic DSP course. 1. INTRODUCTION Since 1994 we have developed and used a variety of multimedia animations and demonstrations as an integral part of the sophomore-level signal processing course at Georgia Tech. We have found it most convenient to present these tools to beginning students either in the form of graphical user interfaces (GUIs) programmed in MATLAB, or animations and movies produced in MATLAB. The GUIs and movies can serve a variety of purposes. First of all, they address nearly every major conceptual issue in the course, so we hope that students will use them to gain visual insights that are not possible from a printed page. In addition, we have found that instructors benefit as much as the students, because they can employ the GUIs and movies as lecture aids that supplement traditional methods of instruction. The GUI tools are convenient because they allow the instructor to quickly pose alternate cases or respond to student questions. Animations and movies serve a similar purpose, and even though they are less versatile than a GUI, they can be created more quickly. Ideally, the GUIs would engage students in active learning through interactive exercises, but this is difficult to accomplish in all cases. We have had some success with GUIs that generate drill problems. However, one interesting finding is that students are reluctant to use these new tools when studying alone. Therefore, we have started to create new homework and lab assignments that require active use of the GUIs to compare computer-generated results to analytical derivations. In this paper, we will describe the features of a few GUIs and animations and the scenarios in which they have successfully been deployed. Proceedings of the 2002 American Society for Engineering Education Annual Conference & Exposition Copyright  2002, American Society for Engineering Education Main Menu

Digital signal processing is one important fundamental course for the students in the department of electrical engineering. The paper analyzes its teaching status based on the characteristics and problems in the teaching process of digital signal processing course. Combining the analysis and the personal teaching practice, the paper introduces how to reform the teaching methods and improve the quality in the course of digital signal processing, such as choosing a suitable textbook, inspiring students’ learning interest, organizing the teaching content reasonably, and combining multimedia and board writing, etc. Accordingly, students can better comprehend the basics of digital signal processing, and lay the foundation for further studies.

This paper proposes the design and development of virtual experimental projects in the Digital Signal Processing course, using MATLAB, Proteus, and CCS platforms to develop a library of typical experimental cases for biomedical engineering majors and discusses the design process. Based on these typical cases, this paper explores the secondary design for innovative engineering practice case teaching, which can promote students’ understanding and mastery of digital signal processing theories, algorithms, and technologies in an intuitive, flexible, and efficient way; quickly build new innovative engineering case models and further cultivate students’ engineering application ability as well as innovative thinking.

A senior project course on applications in digital signal processing (DSP) with the TMS320 digital signal processor is described. All experiments and projects are performed using Texas Instruments' TMS320 digital signal processor. The laboratory emphasis in the senior project course reinforces the concepts of DSP covered in the lecture course. Applications in DSP are in areas such as communications, controls, speech, graphics, etc. The DSP projects presented and demonstrated by the students in the spring of 1987 are discussed. These projects can provide a guide to individuals who may incorporate a hands-on approach to DSP in courses such as senior project, senior lab, or DSP lab using the versatile TMS 320 digital signal processor.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The paper is dedicated to the implementation of the artificial neural networks in the laboratories on digital signal processing. The method described is based on artificial neural network implementation. Different architectures and types of artificial neural networks are compared. The training and testing sequences generation problem is discussed. The aim of the irregular mesh coverage of the two-dimensional signal (frame) is to decrease computational cost for the further motion detection between frames. The benefits of the artificial neural network usage are made clear and understandable for the students. Generalized results obtained may be used for pattern recognition, data compression, multidimensional look-up table interpolation. The educational impact of the method proposed is also discussed.

Bilingual teaching is generally used in higher education. English is used as the second language to learn and acquire the interdisciplinary knowledge except the mother tongue. Some high education philosophies are introduced in this paper. The teaching quality control and assessment are introduced. The teaching methods used in Digital Signal Processing course are discussed. Through the bilingual teaching methods, students could broaden their international view.

This six-part series is a minicourse, focused on system concepts, that is aimed at the gap between Signals and Systems and the usual first digital signal processing course. Part I discussed motivation, philosophy, and notation. This second article in the series focuses on oversampling in D/A conversion and the basics of decimation, complex signals, and Nyquist signaling.

The rapid increase in digital signal processing (DSP) applications has generated a strong demand for electrical engineers with DSP backgrounds; however, the gap between industry needs and university curricula still exists. To answer this challenge, a sequence of innovative DSP courses that emphasize hands-on experiments and practical applications were developed for continuing education in electrical and computer engineering. These courses are taught in the evening for night-school students having at least three years of work experience. These courses enable students to experiment with sophisticated DSP applications to augment the theoretical, conceptual, and analytical materials provided in traditional DSP courses. The inclusion of both software and hardware developments allows students to undertake a wide range of DSP projects for real-world applications. Assessment data concludes that the digital signal processor fundamentals course can increase learning interest and overcome the prerequisite problem of DSP laboratory experiments. This paper also briefly introduces representative examples of some challenging DSP applications.

Most Digital Signal Processing (DSP) courses rely heavily on MATLAB and/or C, representing the state of the art in textual programming, for their standard computer tools. Many textbooks are published containing examples, if not sections, devoted to these textual languages. We have argued, in a previous paper, that whereas this environment may be efficient in manipulating equations, textual implementation of processes best described by block diagrams loses its intuitive substance and gave examples in LabVIEW of implementations that are better left graphical. However, the standard DSP toolkit of LabVIEW is aimed at the practicing engineer/scientist who needs to process acquired data to reach other ends in contrast to a student whose aim is to learn about signal processing. LabVIEW's DSP toolkit is rich with high level algorithms but needs to be enhanced in order to serve the pedagogical needs of students of DSP. Having decided to teach DSP with LabVIEW a year ago, I have found myself writing many routines to complement the standard DSP toolkit as I have tried to demonstrate basic concepts. This paper will describe this additional toolkit that has been growing to make LabVIEW a better teaching tool in a DSP class.

Students often face difficulties in grasping and un derstanding fundamental digital signal processing (DSP) concepts in the classroom environment. Most DSP courses do not have labs associated with them, and the ones that do typicall y depend on the usage of text-based programming languages (e.g., MATLAB) that do not allow real-time external hardware interaction. Although the availability of external DSP platforms (e.g. National Instruments Speedy-33, Texas Instruments DSP Student Kits, etc.) for educational purposes has increased because of recent advances in embedded processors and sensor technology, they are rarely used in early DSP courses because of the programming sophistication needed for real-time processing. In this paper we discuss the development of low-co st experimental DSP learning modules for classes that do not usually have a lab component. T hese modules use National Instruments' LabVIEW for their programming and development platform with the Speedy-33 DSP board and LEGO Mindstorms NXT Brick as the hardware platforms. Many of the modules can also be run completely on the host computer's sound card. Modules have been developed for examining different aspects of topics such as sampling, alias ing, and filtering, while working with data that has been captured and processed in real-time. Stud ents are able to interact with the hardware and data through GUIs, thus obviating the need to first develop real-time programming skills. This paper will describe these modules and how they are designed to be used both in lectures and as part of homework assignments.

This paper presents the Tiered Learning Framework (TLF) for course and assessment design in Digital Signal Processing (DSP) education. TLF divides the learning curve within a course into five levels, encourages students to reflect on which level they are currently at in their learning, and guides them on what they need to do to progress to the next level. Within this framework, students have more control and choice over how much they want to learn and how deeply they engage with the material. A key driver of student adherence to the framework and a mechanism for appropriate feedback is the use of suitable tiered assessments. The TLF was specifically trialled in class settings of various sizes over the past three years, for DSP courses and design projects. Feedback from students indicated that the TLF-based assessment inspired and motivated them to self-assess their understanding of the topic or course they studied and guided their improvement to the next level.