Contribution: A model for integrating data visualization into a multidisciplinary course. This innovative practice paper addresses the challenge of the need for data visualization literacy and the scarcity of time to introduce data visualization concepts and workflow in existing courses. Background: Data sensemaking is becoming a prerequisite for all data-driven activities, from engineering to agriculture and humanities. Students are encouraged to generate graphic visuals to support course deliverables (i.e., reports and presentations), interpret knowledge from data, and disseminate research results with little to no introduction to best practices for visually representing data. This article describes an innovative 16-week course introducing data visualization principles and best practices, visualization tools, GIS concepts, and hands-on experience with edge devices (i.e., NVIDIA Jetson, Raspberry Pi, and Arduino). The first eight weeks of the course were dedicated to data visualization principles and practices. The second half of the semester-long course introduced edge devices. Each student received a J2021-Edge AI Device with NVIDIA Jetson Xavier NX 8GB module. A USB WIFI emulator and a 256GB NVMe SSD were also provided with each device to enable wireless network connectivity and expand onboard storage, respectively. The edge device component of the course comprised several lab-based activities to introduce students to the Linux operating system, command line tools, virtual environments, Python programming language, sensor integration, communication protocols, machine learning, deep learning, computer vision, and application development. The rationale for the instructional approach was to allow students to make meaningful connections between data visualization concepts learned in class and their application to visualizing and analyzing data acquired using edge devices. Intended Outcomes: Students will demonstrate their knowledge and ability to apply data visualization principles to real-world data collected with edge devices, and the course serves as a model for developing interdisciplinary courses that benefit from basic knowledge of visualization concepts. Application Design: The course aims to meet the advanced data visualization needs of the College of Agriculture and Technology students. A higher education challenge grant from the United States Department of Agriculture-National Institute of Food and Agriculture has funded this effort. Findings: Students with little to no background in data visualization and the use of edge devices demonstrated the use of data visualization best practices and utilization of edge devices to complete a required semester project that included a research-quality paper formatted for conference submission.

Computation of passive and compact macromodels of distributed interconnects has gained considerable importance during the recent years. Method of characteristics (MoC) is widely used for macromodeling of transmission lines, however, it may not be guaranteed passive. This paper presents a new algorithm for passivity enforcement of MoC-based macromodels of multiconductor transmission lines. The algorithm is based on the first-order perturbation of the related delay differential equations and can handle single as well as coupled interconnects. Necessary theoretical foundations and validating numerical results are presented.

This paper presents a robust method for guaranteed passive macromodeling of high-speed interconnects characterized by multiconductor transmission lines with frequency-dependent per-unit-length (FD-PUL) parameters, using integrated-congruence-transformation-based model-order-reduction techniques. For this purpose, a new formulation is developed to directly incorporate state-space equations corresponding to the positive-real (PR) rational functions representing the frequency dependence of PUL parameters, in Telegrapher's equations. The proposed formulation overcomes the need for explicit lumped-circuit realization with all positive elements for PR rational functions of FD-PUL parameters. Proof of passivity and numerical examples are presented to validate the theory.

With the ever increasing signal speeds, signal integrity issues of high-speed VLSI designs are presenting increasingly difficult challenges for state-of-the-art modeling and simulation tools. Consequently, characterization and passive macromodeling of high-speed modules such as interconnects, vias, and packages based on tabulated data are becoming important. This paper presents a fast algorithm for passivity verification and enforcement of large order macromodels of scattering parameter based multiport subnetworks. Numerous examples tested on this algorithm demonstrate a significant speed-up compared to the existing algorithms in the literature

With the continually increasing operating frequencies, complex high-speed interconnect and package modules require characterization based on measured/simulated data. Several algorithms were recently suggested for macromodeling such types of data to enable unified transient analysis in the presence of external network elements. One of the critical issues involved here is the passivity violations associated with the computed macromodel. To address this issue, a new passivity enforcement algorithm is presented in this paper. The proposed method adopts a global approach for passivity enforcement by ensuring that the passivity correction at a certain region does not introduce new passivity violations at other parts of the frequency spectrum. It also provides an error estimate for the response of the passivity corrected macromodel.