In today's global work environment, inclusive and clear communication has become increasingly important. Educators have the responsibility to equip computer science and software engineering undergraduates with communication skills that can help them, as budding professionals, to manage these global challenges. Participants of this workshop will learn about a spectrum of work approaches and communication styles; they will learn to use this information so that they may teach students how to develop more effective teaming skills and more effective technical presentations related to software development. If students are armed with such skills, they will carry keys to successful collaborative software development.

Undergraduate computer science degree programs often provide an opportunity for students to experience real software projects as a part of their programs of study. These experiences frequently reside in a course in which students form software development teams, are assigned to a project offered by a corporate sponsor and devote one or two semesters to the task of making progress on the project. In an ideal model, faculty mentor student teams who, in turn, behave as sub-contractors or consultants to the sponsor. Students work for a grade, not directly for the sponsor as a true sub-contractor would. In the ideal model, students demonstrate what they have learned about software engineering process, as well as their ability to implement programmed solutions. Student teams provide progress reports, both oral and written, and directly experience many of the challenges and successes of true software engineering professionals. This paper reports on one such program after 10 years of operation. The technologies and software development processes of interest to sponsors are summarized and presented as an informal survey. Student response is discussed in terms of software systems they produced and how they went about producing them. The maturation of these students as software engineering professionals is also discussed

Concurrent process structured software can be used as a tool for studying the behavior of parallel digital signal processing systems. By modeling alternative architectures for a given application and characterizing the interactions of concurrent processes, an evaluation of relative efficiencies can be made. Presented here are models of three architectures for time domain harmonic scaling (TDHS) of speech. Their implementations and the software tools required to study their behavior are described.

The educational objectives of two upper level operating systems courses are discussed. The instructional environment in which these courses are taught is also described. The primary goal is to expose students to all of the important issues involved in designing and implementing operating systems. Students should start with the architecture of the target machine and proceed through to an implementation of some functioning operating system. This is a difficult task to achieve in the confines of a single semester. However, a careful choice of software exercises that start very simply and build on each other to illustrate important points, and a high level language facility for writing operating system programs have combined to produce a successful experiment. This technique is applied in a senior level Computer Science course on Operating Systems Principles and a graduate level course on Real Time Systems. The program development system described is not only a good educational tool, but also provides a vehicle for research in comparing concurrency constructs in languages and the performance and architectural implications of concurrent programs.

The Integrated Photogrammetric Instrument Network (IPIN) is being designed and developed by the Defense Mapping Agency Aerospace Center in St. Louis, Mo. to meet database demands for terrain elevation information. IPIN is a network of computers and instrumentation dedicated to digitizing and editing photo-source terrain data [1]. Editing procedures are required because of digitizing errors inherent in the data-collection process. Some of these errors are random; others are systematic and predictable.