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1. Work in Progress: Interactive Introductory Online Modules on Wireless Communications and Radio-frequency Spectrum Sharing

   Dietrich, C. B. ; Polys, N. F. ; Reid, K. ; García Sheridan, J. A. ; Emenonye, D. ; Gomez, X. ; Tolley, J. ; Makin, C. ; Gaeddert, J. ( July 2021 , ASEE Annual Conference proceedings)

   1. Description of the objectives and motivation for the contribution to ECE education The demand for wireless data transmission capacity is increasing rapidly and this growth is expected to continue due to ongoing prevalence of cellular phones and new and emerging bandwidth-intensive applications that encompass high-definition video, unmanned aerial systems (UAS), intelligent transportation systems (ITS) including autonomous vehicles, and others. Meanwhile, vital military and public safety applications also depend on access to the radio frequency spectrum. To meet these demands, the US federal government is beginning to move from the proven but inefficient model of exclusive frequency assignments to a more-efficient, shared-spectrum approach in some bands of the radio frequency spectrum. A STEM workforce that understands the radio frequency spectrum and applications that use the spectrum is needed to further increase spectrum efficiency and cost-effectiveness of wireless systems over the next several decades to meet anticipated and unanticipated increases in wireless data capacity. 2. Relevant background including literature search examples if appropriate CISCO Systems’ annual survey indicates continued strong growth in demand for wireless data capacity. Meanwhile, undergraduate electrical and computer engineering courses in communication systems, electromagnetics, and networks tend to emphasize mathematical and theoretical fundamentals and higher-layer protocols, withmore »less focus on fundamental concepts that are more specific to radio frequency wireless systems, including the physical and media access control layers of wireless communication systems and networks. An efficient way is needed to introduce basic RF system and spectrum concepts to undergraduate engineering students in courses such as those mentioned above who are unable to, or had not planned to take a full course in radio frequency / microwave engineering or wireless systems and networks. We have developed a series of interactive online modules that introduce concepts fundamental to wireless communications, the radio frequency spectrum, and spectrum sharing, and seek to present these concepts in context. The modules include interactive, JavaScript-based simulation exercises intended to reinforce the concepts that are presented in the modules through narrated slide presentations, text, and external links. Additional modules in development will introduce advanced undergraduate and graduate students and STEM professionals to configuration and programming of adaptive frequency-agile radios and spectrum management systems that can operate efficiently in congested radio frequency environments. Simulation exercises developed for the advanced modules allow both manual and automatic control of simulated radio links in timed, game-like simulations, and some exercises will enable students to select from among multiple pre-coded controller strategies and optionally edit the code before running the timed simulation. Additionally, we have developed infrastructure for running remote laboratory experiments that can also be embedded within the online modules, including a web-based user interface, an experiment management framework, and software defined radio (SDR) application software that runs in a wireless testbed initially developed for research. Although these experiments rely on limited hardware resources and introduce additional logistical considerations, they provide additional realism that may further challenge and motivate students. 3. Description of any assessment methods used to evaluate the effectiveness of the contribution, Each set of modules is preceded and followed by a survey. Each individual module is preceded by a quiz and followed by another quiz, with pre- and post-quiz questions drawn from the same pool. The pre-surveys allow students to opt in or out of having their survey and quiz results used anonymously in research. 4. Statement of results. The initial modules have been and are being used by three groups of students: (1) students in an undergraduate Introduction to Communication Systems course; (2) an interdisciplinary group of engineering students, including computer science students, who are participating in related undergraduate research project; and (3) students in a graduate-level communications course that includes both electrical and computer engineers. Analysis of results from the first group of students showed statistically significant increases from pre-quiz to post-quiz for each of four modules on fundamental wireless communication concepts. Results for the other students have not yet been analyzed, but also appear to show substantial pre-quiz to post-quiz increases in mean scores.« less
2. Virtual Reality Laboratory Experiences for Electricity and Magnetism Courses

   Ilie, R. ( July 2021 , 2021 ASEE Virtual Annual Conference Content Access)

   A solid understanding of electromagnetic (E&M) theory is key to the education of electrical engineering students. However, these concepts are notoriously challenging for students to learn, due to the difficulty in grasping abstract concepts such as the electric force as an invisible force that is acting at a distance, or how electromagnetic radiation is permeating and propagating in space. Building physical intuition to manipulate these abstractions requires means to visualize them in a three-dimensional space. This project involves the development of 3D visualizations of abstract E&M concepts in Virtual Reality (VR), in an immersive, exploratory, and engaging environment. VR provides the means of exploration, to construct visuals and manipulable objects to represent knowledge. This leads to a constructivist way of learning, in the sense that students are allowed to build their own knowledge from meaningful experiences. In addition, the VR labs replace the cost of hands-on labs, by recreating the experiments and experiences on Virtual Reality platforms. The development of the VR labs for E&M courses involves four distinct phases: (I) Lab Design, (II) Experience Design, (III) Software Development, and (IV) User Testing. During phase I, the learning goals and possible outcomes are clearly defined, to provide context for themore »VR laboratory experience, and to identify possible technical constraints pertaining to the specific laboratory exercise. During stage II, the environment (the world) the player (user) will experience is designed, along with the foundational elements, such as ways of navigation, key actions, and immersion elements. During stage III, the software is generated as part of the course projects for the Virtual Reality course taught in the Computer Science Department at the same university, or as part of independent research projects involving engineering students. This reflects the strong educational impact of this project, as it allows students to contribute to the educational experiences of their peers. During phase IV, the VR experiences are played by different types of audiences that fit the player type. The team collects feedback and if needed, implements changes. The pilot VR Lab, introduced as an additional instructional tool for the E&M course during the Fall 2019, engaged over 100 students in the program, where in addition to the regular lectures, students attended one hour per week in the E&M VR lab. Student competencies around conceptual understanding of electromagnetism topics are measured via formative and summative assessments. To evaluate the effectiveness of VR learning, each lab is followed by a 10-minute multiple-choice test, designed to measure conceptual understanding of the various topics, rather than the ability to simply manipulate equations. This paper discusses the implementation and the pedagogy of the Virtual Reality laboratory experiences to visualize concepts in E&M, with examples for specific labs, as well as challenges, and student feedback with the new approach. We will also discuss the integration of the 3D visualizations into lab exercises, and the design of the student assessment tools used to assess the knowledge gain when the VR technology is employed.« less
3. Intersections of Design Thinking and Perceptions of Success for Electrical, Computer, and Software Engineering Students

   https://doi.org/10.18260/1-2--33010  

   Rodriguez, Sarah ; Doran, Erin ; Hengesteg, Paul ( June 2019 , 2019 ASEE Annual Conference)

   Engineering design thinking has become an important part of the educational discussion for both researchers and practitioners. Colleges and universities seek to graduate engineering students who can engage in the complex nature of combining both technical performance with design thinking skills. Prior research has shown that design thinking can be a solution for solving complicated technical and social issues in a holistic, adaptive way. However, little is known about how students make sense of their design thinking experiences and reconcile that into their perceptions of what it means to be a successful engineer. As part of a five-year National Science Foundation REvolutionizing Engineering and Computer Science Departments (NSF-RED) grant, this study highlights the experiences of students engaged in a course which has been redesigned to enhance student development through design thinking pedagogy. This case study sought to understand how electrical, computer, and software engineering students engage with design thinking and how that engagement shapes their perceptions of what success looks like. The case study was informed through observations of lecture and lab classroom contexts, interviews with students, and a review of relevant course documents. Participants met the following criteria: (a) were over the age of 18, (b) majoring in CESmore »engineering, and (c) were currently enrolled in one of two courses currently undergoing redesign: a second-year electrical engineering course called Circuits or a second-year computer engineering course called Embedded Systems. Preliminary findings reveal that students engaged in the design thinking course described a disconnect between design thinking elements of the course and their perceptions of what it meant to be a successful electrical, computer, or software engineer. Although design thinking concepts focused on empathy-building and customer needs, it was often difficult for engineering students to see beyond the technical content of their course and conceptualize elements of design thinking as essential to their successful performance as engineers. This study bears significance to practitioners and researchers interested in (re)designing curriculum to meet the growing needs of innovation for today’s customer’s. Implications for policy and practice will be discussed to enhance the way that engineering programs, curricula, and workforce training are created.« less
4. Implementation of Security Modules with Model-Eliciting Activities in Computer Science Courses

   https://doi.org/10.18260/1-2--34776  

   Yang, Jeong ; Earwood, Brandon ; Kim, Young Rae ; Lodgher, Akhtar ( June 2020 , 2020 ASEE Virtual Annual Conference)

   Security is a critical aspect in the design, development, and testing of software systems. Due to the increasing need for security-related skills within software systems and engineering, there is a growing demand for these skills to be taught at the university level. A series of 41 security modules was developed to assess the impact of these modules on teaching critical cyber security topics to students. This paper presents the implementation and outcomes of the first set of six security modules in a Freshman level course. This set consists of five modules presented in lectures as well as a sixth module emphasizing encryption and decryption used as the semester project for the course. Each module is a collection of concepts related to cyber security. The individual cyber security concepts are presented with a general description of a security issue to avoid, sample code with the security issue written in the Java programming language, and a second version of the code with an effective solution. The set of these modules was implemented in Computer Science I during the Fall 2019 semester. Incorporating each of the concepts in these modules into lectures depends on both the topic covered and the approach to resolvingmore »the related security issue. Students were introduced to computing concepts related to both the security issue and the appropriate solution to fully grasp the overall concept. After presenting the materials to students, continual review with students is also essential. This reviewal process requires exploring use-cases for the programming mechanisms presented as solutions to the security issues discussed. In addition to the security modules presented in lectures, students were given a hands-on approach to understanding the concepts through Model-Eliciting Activities (MEAs). MEAs are open-ended, problem-solving activities in which groups of three to four students work to solve realistic complex problems in a classroom setting. The semester project related to encryption and decryption was implemented into the course as an MEA. To assess the effectiveness of incorporating security modules with the MEA project into the curriculum of Computer Science I, two sections of the course were used as a control group and a treatment group. The treatment group included the security modules in lectures and the MEA project while the control group did not. To measure the overall effectiveness of incorporating security modules with the MEA project, both the instructor’s effectiveness as well as the student’s attitudes and interest were measured. For instructors, the primary question to address was to what extent do instructors change their attitudes towards student learning and their teaching practices because of the implementation of cyber security modules through MEAs. For students, the primary question to address was how the inclusion of security modules with the MEA project improved their understanding of the course materials and their interests in computer science. After implementing security modules with the MEA project, students showed a better understanding of cyber security concepts and a greater interest in broader computer science concepts. The instructor’s beliefs about teaching, learning, and assessment shifted from teacher-centered to student-centered, during his experience with the security modules and MEA.« less
5. The Deep Learning Epilepsy Detection Challenge: Design, Implementation, and Test of a New Crowd-Sourced AI Challenge Ecosystem

   Kiral, Isabell ; Roy, Subhrajit ; Mummert, Todd ; Braz, Alan ; Tsay, Jason ; Tang, Jianbin ; Asif, Umar ; Schaffter, Thomas ; Mehmet, Eren ; Picone, Joseph ; et al ( April 2019 , Challenges in Machine Learning Competitions for All (CiML))

   The DeepLearningEpilepsyDetectionChallenge: design, implementation, andtestofanewcrowd-sourced AIchallengeecosystem Isabell Kiral*, Subhrajit Roy*, Todd Mummert*, Alan Braz*, Jason Tsay, Jianbin Tang, Umar Asif, Thomas Schaffter, Eren Mehmet, The IBM Epilepsy Consortium◊ , Joseph Picone, Iyad Obeid, Bruno De Assis Marques, Stefan Maetschke, Rania Khalaf†, Michal Rosen-Zvi† , Gustavo Stolovitzky† , Mahtab Mirmomeni† , Stefan Harrer† * These authors contributed equally to this work † Corresponding authors: rkhalaf@us.ibm.com, rosen@il.ibm.com, gustavo@us.ibm.com, mahtabm@au1.ibm.com, sharrer@au.ibm.com ◊ Members of the IBM Epilepsy Consortium are listed in the Acknowledgements section J. Picone and I. Obeid are with Temple University, USA. T. Schaffter is with Sage Bionetworks, USA. E. Mehmet is with the University of Illinois at Urbana-Champaign, USA. All other authors are with IBM Research in USA, Israel and Australia. Introduction This decade has seen an ever-growing number of scientific fields benefitting from the advances in machine learning technology and tooling. More recently, this trend reached the medical domain, with applications reaching from cancer diagnosis [1] to the development of brain-machine-interfaces [2]. While Kaggle has pioneered the crowd-sourcing of machine learning challenges to incentivise data scientists from around the world to advance algorithm and model design, the increasing complexity of problem statements demands of participants to be expert datamore »scientists, deeply knowledgeable in at least one other scientific domain, and competent software engineers with access to large compute resources. People who match this description are few and far between, unfortunately leading to a shrinking pool of possible participants and a loss of experts dedicating their time to solving important problems. Participation is even further restricted in the context of any challenge run on confidential use cases or with sensitive data. Recently, we designed and ran a deep learning challenge to crowd-source the development of an automated labelling system for brain recordings, aiming to advance epilepsy research. A focus of this challenge, run internally in IBM, was the development of a platform that lowers the barrier of entry and therefore mitigates the risk of excluding interested parties from participating. The challenge: enabling wide participation With the goal to run a challenge that mobilises the largest possible pool of participants from IBM (global), we designed a use case around previous work in epileptic seizure prediction [3]. In this “Deep Learning Epilepsy Detection Challenge”, participants were asked to develop an automatic labelling system to reduce the time a clinician would need to diagnose patients with epilepsy. Labelled training and blind validation data for the challenge were generously provided by Temple University Hospital (TUH) [4]. TUH also devised a novel scoring metric for the detection of seizures that was used as basis for algorithm evaluation [5]. In order to provide an experience with a low barrier of entry, we designed a generalisable challenge platform under the following principles: 1. No participant should need to have in-depth knowledge of the specific domain. (i.e. no participant should need to be a neuroscientist or epileptologist.) 2. No participant should need to be an expert data scientist. 3. No participant should need more than basic programming knowledge. (i.e. no participant should need to learn how to process fringe data formats and stream data efficiently.) 4. No participant should need to provide their own computing resources. In addition to the above, our platform should further • guide participants through the entire process from sign-up to model submission, • facilitate collaboration, and • provide instant feedback to the participants through data visualisation and intermediate online leaderboards. The platform The architecture of the platform that was designed and developed is shown in Figure 1. The entire system consists of a number of interacting components. (1) A web portal serves as the entry point to challenge participation, providing challenge information, such as timelines and challenge rules, and scientific background. The portal also facilitated the formation of teams and provided participants with an intermediate leaderboard of submitted results and a final leaderboard at the end of the challenge. (2) IBM Watson Studio [6] is the umbrella term for a number of services offered by IBM. Upon creation of a user account through the web portal, an IBM Watson Studio account was automatically created for each participant that allowed users access to IBM's Data Science Experience (DSX), the analytics engine Watson Machine Learning (WML), and IBM's Cloud Object Storage (COS) [7], all of which will be described in more detail in further sections. (3) The user interface and starter kit were hosted on IBM's Data Science Experience platform (DSX) and formed the main component for designing and testing models during the challenge. DSX allows for real-time collaboration on shared notebooks between team members. A starter kit in the form of a Python notebook, supporting the popular deep learning libraries TensorFLow [8] and PyTorch [9], was provided to all teams to guide them through the challenge process. Upon instantiation, the starter kit loaded necessary python libraries and custom functions for the invisible integration with COS and WML. In dedicated spots in the notebook, participants could write custom pre-processing code, machine learning models, and post-processing algorithms. The starter kit provided instant feedback about participants' custom routines through data visualisations. Using the notebook only, teams were able to run the code on WML, making use of a compute cluster of IBM's resources. The starter kit also enabled submission of the final code to a data storage to which only the challenge team had access. (4) Watson Machine Learning provided access to shared compute resources (GPUs). Code was bundled up automatically in the starter kit and deployed to and run on WML. WML in turn had access to shared storage from which it requested recorded data and to which it stored the participant's code and trained models. (5) IBM's Cloud Object Storage held the data for this challenge. Using the starter kit, participants could investigate their results as well as data samples in order to better design custom algorithms. (6) Utility Functions were loaded into the starter kit at instantiation. This set of functions included code to pre-process data into a more common format, to optimise streaming through the use of the NutsFlow and NutsML libraries [10], and to provide seamless access to the all IBM services used. Not captured in the diagram is the final code evaluation, which was conducted in an automated way as soon as code was submitted though the starter kit, minimising the burden on the challenge organising team. Figure 1: High-level architecture of the challenge platform Measuring success The competitive phase of the "Deep Learning Epilepsy Detection Challenge" ran for 6 months. Twenty-five teams, with a total number of 87 scientists and software engineers from 14 global locations participated. All participants made use of the starter kit we provided and ran algorithms on IBM's infrastructure WML. Seven teams persisted until the end of the challenge and submitted final solutions. The best performing solutions reached seizure detection performances which allow to reduce hundred-fold the time eliptologists need to annotate continuous EEG recordings. Thus, we expect the developed algorithms to aid in the diagnosis of epilepsy by significantly shortening manual labelling time. Detailed results are currently in preparation for publication. Equally important to solving the scientific challenge, however, was to understand whether we managed to encourage participation from non-expert data scientists. Figure 2: Primary occupation as reported by challenge participants Out of the 40 participants for whom we have occupational information, 23 reported Data Science or AI as their main job description, 11 reported being a Software Engineer, and 2 people had expertise in Neuroscience. Figure 2 shows that participants had a variety of specialisations, including some that are in no way related to data science, software engineering, or neuroscience. No participant had deep knowledge and experience in data science, software engineering and neuroscience. Conclusion Given the growing complexity of data science problems and increasing dataset sizes, in order to solve these problems, it is imperative to enable collaboration between people with differences in expertise with a focus on inclusiveness and having a low barrier of entry. We designed, implemented, and tested a challenge platform to address exactly this. Using our platform, we ran a deep-learning challenge for epileptic seizure detection. 87 IBM employees from several business units including but not limited to IBM Research with a variety of skills, including sales and design, participated in this highly technical challenge.« less