The Infinity Project is a joint effort between university educators, high school teachers, administrators, and industrial leaders to establish an engineering curriculum that is taught within the regular high school day. The curriculum teaches students about the design of technology-driven systems and motivates them to learn fundamental concepts of mathematics, science, and engineering. The curriculum currently consists of (1) a course text [1], (2) integrated laboratory exercises with real-time signal processing hardware, (3) summer teacher training institutes, and (4) a web community portal for information sharing (www.infinity-project.org). In this paper, we briefly describe three real-time signal processing laboratory exercises—SketchWave, Coin Counter, and Video Steganography—that are used within the course to both teach and excite students about engineering design, fundamental scientific and mathematical concepts, and the power of problem solving. These representative exercises are drawn from over 200 laboratory worksheets developed for the course and available online for participating schools.

The recent trend has been to move DSP education earlier in the curriculum. This paper demonstrates using music to teach sinusoids and MIDI at both the undergraduate (DSP First) and high school (Infinity Project) level.

This paper outlines the goals, structure, and technology elements of the INFINITY Project, a joint effort between university educators, industrial partners, and civic leaders to introduce a signal-processing-based engineering curriculum at the high school educational level. Implementation issues of the program are addressed, including class textbook and laboratory creation, teacher training, and online classroom support. Initial responses to the effort from teachers and students at fourteen different high schools are highly positive, and plans for further expansion of the program are given.

We assert that digital signal processing (DSP) can and should be taught early (sophomore-junior) in the electrical and computer engineering curricula. This paper looks at the impact this has on the rest of the curriculum, both in electrical and computer engineering and in other engineering curricula. While the early introduction of basic DSP makes it possible to offer better senior electives and graduate courses in DSP, the biggest benefit is the ability to build on DSP core concepts just as we have traditionally built on circuits core concepts in the past. Further, motivational examples in DSP lend themselves to multimedia and are often more familiar to today's students than basic circuits.

In this paper we describe experiences gained from teaching an introductory electrical engineering course based on digital signal processing rather than the traditional first course in analog circuit theory. We discuss our motivation for teaching DSP first, before covering analog circuits and systems. We describe the style of the course and point out difficulties, as well as advantages, in the organization of basic material. At Georgia Tech and Rose-Hulman, this beginning course has been required of all computer engineering majors. Finally, we make some comments about extending this approach to encompass a wider range of students from other disciplines.

A new digital signal processing (DSP) course designed to build students intuition about signals and systems is presented. The course makes extensive use of multi-media demonstrations to relate real-world signals and discrete-time systems to their mathematical descriptions. In addition to the numerous in-class demonstrations, weekly laboratory exercises have been developed to further strengthen the ties between DSP theory and real-world signals and systems. Many of the in-class demonstrations have been captured on the World Wide Web (WWW), so the students can explore them outside of class. In the near future, the WWW-based demonstrations will be packaged on a CD-ROM as an integral part of a new text.

A typical path in learning digital signal processing begins at the theoretical end and progresses toward the practical constraints imposed by implementation in hardware or software. On this path, the student would learn how to convert mathematical theory into algorithms and then algorithms into efficient implementations. In this paper, we first summarize the electronic courseware we have already developed in Mathematica, MATLAB, and Ptolemy to teach DSP theory, algorithms, and implementation, respectively. Then, we discuss ways to integrate our efforts to help students discover the connections between these topics.

The use of two parallel architectures, a single-instruction-stream, multiple-data-stream (SIMD) machine and a VLSI processor array, to implement an isolated word recognition system is examined. SIMD and VLSI processor array algorithms are written for each of the components of the recognition system. The component parallel algorithms are simulated along with two complete recognition systems, one composed of SIMD algorithms and other composed of VLSI processor array algorithms. The simulations show that a SIMD machine with 100 processing elements, each based on an 8-MHz MC68000, can perform isolated word recognition over a large vocabulary in real time. The VLSI processor array simulations show that an array using 51 cells, each containing a 12-MHz Intel 8051, can recognize speech in real time over a small vocabulary. The simulation revealed certain architectural features that can enhance the overall performance of the speech system.>

As the complexity of speech recognition systems increases, conventional computers are unable to perform all the operations quickly enough to process the speech input in real time. This paper examines the use of a single instruction stream - multiple data stream (SIMD) parallel architecture to reduce the computation time for a dynamic time warping template matching based isolated word recognition system. Each of the components of the recognition system was written as an SMD parallel algorithm. The component SIMD algorithms and the complete word recognition system were simulated. The simulations showed that with 100 processing elements, each an 8 MHz MC68000, the speech system can perform isolated word recognition over a large vocabulary in real time. This real-time speech system used a 20 KHz sampling rate, 16 bits per sample, 100 samples per frame, 8 LPC coefficients per frame, 16 bits per LPC coefficient, and a 1,000 word vocabulary.

Synchronous, highly parallel algorithms to perform dynamic time warping are presented. Algorithms are developed for two classes of parallel systems: SIMD (single instruction stream-multiple data stream) machines and arrays of VLSI processors. The machine capabilities required for the SIMD and VLSI array approaches are compared, and algorithm complexities are given.