Abstract This paper presents an open-source aerial neuromorphic dataset that captures pedestrians and vehicles moving in an urban environment. The dataset, titled NU-AIR, features over 70 min of event footage acquired with a 640 $$\times $$ × 480 resolution neuromorphic sensor mounted on a quadrotor operating in an urban environment. Crowds of pedestrians, different types of vehicles, and street scenes featuring busy urban environments are captured at different elevations and illumination conditions. Manual bounding box annotations of vehicles and pedestrians contained in the recordings are provided at a frequency of 30 Hz, yielding more than 93,000 labels in total. A baseline evaluation for this dataset was performed using three Spiking Neural Networks (SNNs) and ten Deep Neural Networks (DNNs). All data and Python code to voxelize the data and subsequently train SNNs/DNNs has been open-sourced.

This paper reports on the design, motivations, and preliminary development outcomes of an ongoing NSF-funded study to create an Internet of Things (IoT) based CS curriculum tailored for high school students. The curriculum adopts the 4-layer IoT model and emphasizes an immersive, hands-on pedagogy using single-board devices and the Python programming language. Prior to designing the curriculum, focus groups were conducted using Keller's ARCS model of motivation as a conceptual framework. Qualitative data analysis about student attention, relevance, confidence, and satisfaction revealed actionable insights used to design the curriculum. Preliminary curriculum implementation has underscored IoT's promise in engaging high school CS students and developing modern computing skills applicable to college and career pathways.

The emergence of the Internet of Things (IoT) has had a transformative effect on our society and has inspired educators to develop innovative approaches to educate the next generation of Computer Science (CS) professionals. This paper presents the design and development of an introductory IoT course suitable for grades 9-12 Computer Science classes. Information about the course content, intended outcomes, and evaluation techniques are presented. The course was introduced in 2 high schools in the US. The course includes a capstone project where the students identified a real-world problem and developed an IoT-based solution to address it. Formative and summative technical evaluation results are presented and suggest that the course provided an effective learning experience for students. The information presented here provides guiding principles for developing an IoT-based curriculum geared towards 9-12 education while also exposing the students to CS fundamental.

MATLABArduino.org and its associated YouTube channel were created to provide free-of-cost, high-definition YouTube videos that demonstrate the use of two powerful technologies - MATLAB and Arduino - to perform data-driven, embedded systems tasks for embedded systems education. This initiative aims to broaden the participation of students in engineering through the use of low-cost hardware, real-time visualizations of otherwise abstract concepts, and easy-to-follow instructional videos. This paper describes the video content developed as part of this educational initiative in detail. Viewership trends of the YouTube channel are reported to describe insights about the video engagement and viewer demographics. A qualitative analysis of the YouTube comments left by the viewers is provided, along with highlighted comments that show recurring themes. Finally, challenges encountered in operating this website and YouTube channel in the context of remote online education for embedded systems are described. As physical computing-based online education becomes increasingly pervasive, we anticipate that open educational resources similar to MATLABArduino.org will drive a significant amount of learning for engineering students. The insights presented here may guide current and future open educational initiatives for embedded systems remote learning.

Ethereum blockchain is a powerful, open-source technology for creating decentralized and secure information sharing systems. The main contribution of this article is the experimental validation of an Ethereum blockchain-based software and hardware architecture that enables secure communication for multiple small unmanned aerial vehicles (sUAVs). The experiments involved three DJI M100 quadrotors that shared images captured during flight based on smart contracts created using Ethereum’s Turing complete programming language. The smart contract was designed so that only the intended recipient sUAV could access a specific image. The effect of image size, difficulty level, and consensus algorithms on image transfer times during flight are noted and point to the feasibility of this system in practical missions. The effects of wireless network disruptions on the Ethereum network are documented. The fully documented smart contract code is open sourced to assist readers in quick prototyping. As efforts for decentralization and security of multirobot systems continue to grow, the system architecture and implementation detailed here may serve as a guide for future research.

This paper addresses the problem of decentralized, outdoor formation coordination with multiple quadcopters. The problem is formulated as a receding horizon, mixed-integer non-linear program (RH-MINLP). Each quadcopter solves this RH-MINLP to generate its time-optimal speed profile along a minimum snap spline path while coordinating its position in a desired formation with other quadcopters. Constraints on quadcopter kinematics, dynamics, collision avoidance, wireless communication connectivity, and geometric formations are modeled. Communication connectivity is modeled as a constraint on maximum separation distance based on a minimum viable received signal strength in the presence of path loss attenuation. The resulting RH-MINLP is non-convex, and is solved using an outer-approximation branch and bound solver with a warm-starting scheme. The framework is validated via Hardware-in-the-Loop (HITL) and outdoor flight test with up to 6 quadcopters. Results demonstrate the effect of number of quadcopters and formation type on total transit time. Average radio packet loss statistics during transit indicate robust network performance for a round robin communication scheduling scheme.

This paper describes a scalable and transferable cloud-based virtual reality pedagogical ecosystem that provides students with the basics of wind turbines and associated wind farm design. The module uses mathematical models derived from the experimental WKA-60 turbine to incorporate realistic wind-turbine behavior. Students are able to design a virtual wind-farm for optimal energy generation by modifying individual wind turbine parameters such as the blade length, count, and angle as well as the spacing of the wind turbines in the wind farm. Live graphs showing how the design parameters affect total power output from the virtual wind farm provide instant feedback to the students. An adaptive methodology that assesses the application of students' independent thought processes, along with design and operational skills in creating the wind farms is implemented. We provide a detailed description of technical approach that was adopted to create this system. The ecosystem uses the Amazon Web Services (AWS) cloud technology in the backend and Unity 3D gaming engine for the front-end rendering. The server-side software runs on Node.js and frameworks such as Express. As educational VR systems continue to develop, we anticipate that cloud based implementations will be utilized for an increasing number of educational needs and that the ecosystem and technical approach presented in this paper may serve as a guide for future educational VR research.

Data Analytics is an all pervasive concept and involves procuring and analyzing data, visualizing the results of the analysis, and drawing conclusions and insights from the results. This paper describes the results of using infographies to teach data analytics to 262 high school students in grades 9 through 12 across four schools in Philadelphia. For the purpose of this paper, an infographie is defined as a graphical representation of information gleaned from data-driven analyses. A collaboration between university professors, Ph.D. students, and high-school teachers enabled covering core data analytics topics in multiple science classes. Results from the pre-class and post-class survey results indicate that students were able to gain skills to create infographies using basic descriptive statistics. Students were able to articulate data-driven insights and explain their findings using the infographies in the classroom.

The main focus of this ongoing effort is to compare the learning propensities of 10 through 12 students for data analytics education. Towards this end, a Microsoft Excel based university-level environmental engineering module was taught in a high school classroom with students in grades 10 through 12. The module focused on understanding the current trends and challenges in environmental pollution management and policy. Students were required to procure, analyze, and visualize data in order to propose an environmental policy that was aimed at reducing pollution. Initial data collected from the assessment of the student work alludes to the fact that despite being taught the same material by the same professor and teaching assistant, the success of the students, as measured by their final grades, varies substantially with their academic year. The underclassmen in high school did not display the academic maturity and comprehension that was displayed by the high school seniors. On the other hand, seniors demonstrated a strong propensity to learn and perform well.

Computational problem solving and programming are foundational skills for engineers. The first undergraduate level course that covers these topics is critical to laying these foundations. As instructors strive to incorporate the spirit of inquiry in their courses, an important question that comes forth is whether the teaching methodology should be student-centered or teacher-centered. This paper adds helpful information in the ongoing debate on this question. The paper reports on the student performance results obtained by teaching two sections (cohorts) of an introductory Computation Lab course sequence. This course sequence aims to teach new engineering students MATLAB scripting and programming in the context of technical problem-solving using mathematical models. Cohort A was taught using a traditional teacher-centered approach, while Cohort B employed an open-ended student-centered approach. Our results indicate that the teacher-centered approach has the potential of creating polarized grade distributions with relatively more A grades in the class compared to the student centered approach. On the other hand, the student-centered approach provided a smoother grade distribution, indicating that a higher number of students demonstrate noticeable progress as compared to the teacher-centered approach.

Mixed Integer Nonlinear Programming (MINLP) techniques are increasingly used to address challenging problems in robotics, especially Multi-Vehicle Motion Planning (MVMP). A particular challenge in using this framework is encoding stochastic phenomena such as communication connectivity in the form of MINLP constraints. The main contribution of this paper is an analytical formulation of communication connectivity constraints using stochastic physical layer communication models. These constraints account for the log-normal channel shadowing in noisy communication environments and specify inter-vehicle connectivity in terms of the outage probability of communication. A method is developed to provably accord robustness to communication failure by specifying an upper bound on the outage probability in terms of the inter-vehicle communication range. Finally, we demonstrate the utility of this formulation in the context of a realistic decentralized Multi-Vehicle Path Coordination (MVPC) scenario in which multiple robotic vehicles travel along predetermined fixed paths and are required to maintain communication connectivity during their transit. Conditions that affect the feasibility of the MVPC problem are formalized. Examples that assist in visualizing these conditions are provided.

Real world Multi-Vehicle Motion Planning (MVMP) problems require the optimization of suitable performance measures under an array of complex and challenging constraints involving kinematics, dynamics, communication connectivity, target tracking, and collision avoidance. The general MVMP problem can thus be formulated as a mathematical program (MP). In this paper we present a mathematical programming (MP) framework that captures the salient features of the general MVMP problem. To demonstrate the use of this framework for the formulation and solution of MVMP problems, we examine in detail four representative works and summarize several other related works. As MP solution algorithms and associated numerical solvers continue to develop, we anticipate that MP solution techniques will be applied to an increasing number of MVMP problems and that the framework and formulations presented in this paper may serve as a guide for future MVMP research.

We present a mathematical programming based decentralized framework to generate time optimal velocity profiles for a group of path constrained mobile vehicle robots subject to communication connectivity constraints. Each vehicle robot starts from a fixed start point and moves towards a goal point along a fixed path so as to avoid collisions with other robots, and remain in communication connectivity with other robots. The main contribution of this paper is the discrete time decentralized Receding Horizon Mixed Integer Nonlinear Programming (RH-MINLP) formulation of the multi-vehicle path coordination problem with constraints on kinematics, dynamics, collision avoidance, and communication connectivity, and the application of state-of-the-art MINLP solution techniques. We test scenarios involving up to ten (10) robots to demonstrate (i) the effect of communication connectivity requirements on robot velocity profiles; and (ii) the dependence of the solution computation time on communication connectivity requirements.

This paper presents a framework for generating time-optimal velocity profiles for a group of path-constrained vehicle robots that have fixed and known initial and goal locations and are required to maintain communication connectivity. Each robot must follow a fixed and known path, arrive at its goal as quickly as possible (or at least not increase the time for the last robot to arrive at its goal) and stay in communication with other robots in the arena throughout its journey. The main contribution of this paper is the formulation of the problem as a discrete time nonlinear programming problem (NLP) with constraints on robot kinematics, dynamics, collision avoidance, and communication connectivity. We develop partition elimination constraints that assist in ensuring that the communication network is fully connected (no network partitions). These constraints are enforced only when network partitions would otherwise occur, an approach which significantly reduces the problem size and the required computational effort. In addition, we introduce path-constrained jammer robots with known paths and velocity profiles into the scenario. These jammer robots have an effective jamming range and disrupt all communications within this range. Except for the jammers, all robots must remain outside this jamming range at all times. We investigate the scalability of the proposed approach by testing scenarios involving up to fifty (50) robots. Solutions demonstrate (i) the trade off between the arrival time and the communication connectivity requirements in scenarios with and without jamming; and (ii) the dependence of computation time on the number of robots.