In this paper, we discuss the results and key findings of a daylong “Making and Makerspaces in Electrical and Computer Engineering” (ECE) workshop hosted in conjunction with the ECE Department Heads Association Annual Conference in 2022. This workshop brought together thought leaders in making and makerspaces from across ECE, engineering education, and the broader maker community to discuss the current state of (change)making in ECE, making with purpose, and anti-racist engineering spaces in addition to live maker activities at lunch and an unconference to discuss community, partnerships, and ECE in mechanical engineering makerspaces. Broadly, a key finding from the conference was the need to shift the conversation from talking about making spaces (which tend to focus more on the equipment and facilities management) to ecosystems of making change, including culture, identity, community partnerships, shifting from labs to makerspaces, and shifting from spaces to makerspaces. Another key finding was an ecosystem view of possible touchpoints for making in an engineering program, including making in pre-college, making for recruitment, making in undergraduate design classes, making in extracurricular settings, making for fun, and making at home --all leading toward growing a thriving, diverse engineering workforce of the future. The themes that emerged from the workshop discussions were summarized and presented to department heads about how makerspaces connect to broader efforts in the ECE community such as how to become more equitable and inclusive, how to rebrand programs to attract new students, and promote best practices in educating 21st century learners.

Engineers must be adept in finding, evaluating, and using information in order to create quality designs. Previous research has found first year engineering students frequently do not use sufficient sources to support their design decisions. To further understand what strategies engineering students use in seeking, evaluating, and using information, we interviewed 21 engineering students at a large research university. Results show that while there is variation between students in each group, higher level engineering students reported use of broader and more complex search strategies to assist in finding information for completing projects. In addition, higher level students discussed applying information they found to better understand their design problem, whereas lower level students discussed the application of information less frequently. These results provide insight into how students conceive of the role of information in solving problems at different stages of their educational careers, and can be used to inform teaching and learning in engineering classrooms. These research findings will be used to help inform the development of assessment tools to provide more quantitative evidence of actual skill levels and students' perception of their skills.

Nano-Electro-Mechanical Switches (NEMS) are among the most promising emerging devices due to their near-zero subthreshold-leakage currents. This paper reports device fabrication and modeling, as well as novel logic gate design using "laterally-actuated double-electrode NEMS" structures. The new device structure has several advantages over existing NEMS architectures such as being immune to impact bouncing and release vibrations (unlike a vertically-actuated NEMS) and offer higher flexibility to implement compact logic gates (unlike a single-electrode NEMS). A comprehensive analytical framework is developed to model different properties of these devices by solving the Euler-Bernoulli's beam equation. The proposed model is validated using measurement data for the fabricated devices. It is shown that by ignoring the non-uniformity of the electrostatic force distribution, the existing models "underestimate" the actual value of Vpull-in and Vpull-out. Furthermore, novel energy efficient NEMS-based circuit topologies are introduced to implement compact inverter, NAND, NOR and XOR gates. For instance, the proposed XOR gate can be implemented by using only two NEMS devices compared to that of a static CMOS-based XOR gate that requires at least 10 transistors. © Copyright 2010 ACM.

Clustering is ill-defined. Unlike supervised learning where labels lead to crisp performance criteria such as accuracy and squared error, clustering quality depends on how the clusters will be used. Devising clustering criteria that capture what users need is difficult. Most clustering algorithms search for optimal clusterings based on a pre-specified clustering criterion. Our approach differs. We search for many alternate clusterings of the data, and then allow users to select the clustering(s) that best fit their needs. Meta clustering first finds a variety of clusterings and then clusters this diverse set of clusterings so that users must only examine a small number of qualitatively different clusterings. We present methods for automatically generating a diverse set of alternate clusterings, as well as methods for grouping clusterings into meta clusters. We evaluate meta clustering on four test problems and two case studies. Surprisingly, clusterings that would be of most interest to users often are not very compact clusterings.