Ákos Lédeczi

Acoustic localization of supersonic rifle fire in urban environments in military and security operations faces challenges from multipath propagation and non-line-of-sight detections. Physics-based methods predominate because the geometric ground-truth data required for machine learning is hard to collect at scale. Building on consistency-function fusion – an approach that identifies shooter hypotheses by counting supporting sensor observations rather than minimizing measurement residuals – this paper compares two sparse-network architectures: a wearable multi-channel-array system and a single-microphone deployment exploiting geometric shockwave constraints. We introduce two algorithmic refinements: a caliber-aware deceleration correction that removes a systematic position bias growing quadratically with shooter range, and a coplanar-geometry recovery for the multi-channel pipeline that handles trajectories wide of the sensor field. Under noiseless evaluation on the Aberdeen 2006 geometry the refinements drive position error from ∼17 m to ∼1 m for the multi-channel pipeline and from ∼19 m to ∼2 m for the single-channel pipeline. A high-fidelity simulator with a noise budget representing the Aberdeen hardware reproduces the published position MAE values to within a few percent for both architectures. Under mobile-deployment noise the multi-channel pipeline retains both better position accuracy (23 vs 26 m allrange MAE) and a 17 percentage-point localization-rate advantage that widens to nearly 29 points with moderate acoustic multipath. Architecture choice thus emerges as an environment-dependent trade-off – single-channel for cost-constrained line-of-sight deployments, multi-channel for cluttered environments – rather than a universal hierarchy.

Animal-borne acoustic sensors provide valuable insights into wildlife behavior and environments but face significant power and storage constraints that limit deployment duration. We present a novel adaptive acoustic monitoring system designed for long-term, real-time observation of wildlife. Our approach combines low-power hardware, configurable firmware, and an unsupervised machine learning algorithm that intelligently filters acoustic data to prioritize novel or rare sounds while reducing redundant storage. The system employs a variational autoencoder to project audio features into a low-dimensional space, followed by adaptive clustering to identify events of interest. Simulation results demonstrate the system’s ability to normalize the collection of acoustic events across varying abundance levels, with rare events retained at rates of 80–85% while frequent sounds are reduced to 3–10% retention. Initial field deployments on caribou, African elephants, and bighorn sheep show promising application across diverse species and ecological contexts. Power consumption analysis indicates the need for additional optimization to achieve multi-month deployments. This technology enables the creation of novel wilderness datasets while addressing the limitations of traditional static acoustic monitoring approaches, offering new possibilities for wildlife research, ecosystem monitoring, and conservation efforts.

Despite its potential for STEM education, educational robotics remains out of reach for many classrooms due to upfront purchase costs, maintenance requirements, storage space, and numerous other barriers to entry. As demonstrated previously, these physical robot limitations can be reduced or eliminated through simulation. This work presents a new version of RoboScape Online, a browser-based networked educational robotics simulation platform that aims to make robotics education more accessible while expanding both the breadth and depth of topics taught. Through cloud-hosted simulations, this platform enables distant students to collaborate and compete in real-time. Integration with NetsBlox, a block-based programming environment, allows students at any level to participate in computer science activities. By incorporating a virtual machine for running NetsBlox code into the server, RoboScape Online enables scenarios to be built using the same syntax and abstractions used to program the robots. This approach enables more creative curriculum activities while proving that block-based programming is a valuable development tool, not just a “toy language”. Classroom case studies demonstrate RoboScape Online’s potential to improve students’ computational thinking skills and foster positive attitudes toward STEM subjects, with especially significant improvements in attitudes toward self-expression and creativity within the realm of computer science.

We rely on a vast network of devices that communicate autonomously to provide many of the services we use every day. However, the enabling technologies behind the Internet of Things (IoT) are often not taught in K-12 classrooms, in part due to the need for hardware. But most teens in the United States have smartphones. Thus, we introduce PhoneIoT, a mobile app that allows students to access their smartphones programmatically over the Internet. PhoneIoT supports access to live sensor data from the device and controlling a customizable display on the phone's screen. PhoneIoT allows students to learn the fundamental concepts of distributed computing and networked sensing using NetsBlox, a simple but powerful extension of the Snap! block-based programming environment. Because both PhoneIoT and NetsBlox are free and open-source, instructors are able to teach these advanced computer science topics even remotely without extra hardware.

Background. Software Engineering (SE) is a new and emerging topic in secondary computer science classrooms. However, a review of the recent literature has identified an overall lack of reporting on the development of SE secondary curriculum. Previous studies also report low student engagement when teaching these concepts. Objectives. In this experience report, we discuss the development of a 9-week, project-based learning (PBL) SE curriculum for secondary students. During this curriculum, students create a socially relevant project in groups of two to three. We discuss displays of participant engagement with CS concepts through the PBL pedagogy and the SE curriculum. Method. We examine participant engagement through group artifact interviews about student experiences during a week-long, virtual summer camp that piloted activities from our curriculum. During this camp, students followed a modified SE life cycle created by the authors of the paper. Findings. Participants showed engagement with the curriculum through various aspects of PBL, such as autonomy, creativity, and personal interest in their project topic. Implications. The lessons learned from this experience report suggest that PBL pedagogy can increase student engagement when teaching CS concepts, and this pedagogy provides detail and structure for future secondary SE curriculum implementations to support educators in the classroom.

Motivation. We have created a modular project-based learning curriculum, Computer Science Frontiers (CSF) [1, 8], for secondary students in attempts to increase the persistence of computer science (CS) students in higher education. The CSF course is divided into four different modules (Distributed Computing, Internet of Things, Artificial Intelligence, and Software Engineering), each centered around a topic typically introduced to students only in higher education. Using the block-based programming environment NetsBlox [4], students are able to access various Application Programming Interfaces related to their interests [2, 3]. The goal of this course is to increase student interest in CS during high school - when first career choices occur [7] - in hopes they will persist in CS during their undergraduate studies. Research question. The research question for this study was: How does the Computer Science Frontiers course affect student attitudes towards computer science? Research Methods. We conducted over 20 interviews with students throughout a CSF pilot course that took place over the 2022-2023 school year. Interviews were conducted with at least five students at the end of every module. Two researchers have conducted thematic analysis with student responses from the first two modules [5]: Distributed Computing (DC) and Internet of Things (IoT). First, the two researchers developed a norm by tagging one interview together [6]. Next, the researchers independently coded the rest of the interviews for each module. After completing a single module’s interviews, the researchers met to rectify any discrepancies. Finally, the tags were grouped together based on common themes. Through this process, we found a total of seven themes. Results. The themes found through thematic analysis include: computer science, attitudes towards course, student wants, student struggles, attitudes towards projects, collaboration, and student progression. As a result of this study, we have identified different needs for secondary students with varying background in CS when studying more advanced CS topics, such as IoT. For example, a need of students who have less prior CS knowledge than others may be to review programming concepts in order to be successful in the course. We have also identified a positive change in student’s attitudes towards computer science after the first two modules. These insights provide the CS education community with ways to engage students with concepts that they have not been exposed to and how to increase their interest in CS. Implications. The CSF curriculum is currently online, and is available to computer science instructors. Each module is separated into eight to nine units which are accompanied by activities and teaching guides. This curriculum provides educators with materials and activities to introduce students to more advanced CS topics, either through individual modules or as an entire course. In future research, we plan to use CSF in an outreach program and implement the course in two secondary classrooms in the 2023-2024 school year.