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Skip the Grid: Solar Installation for Navajo Communities
No full textIn March 2025, a group of 20 students from California Polytechnic State University, San Luis Obispo took part in Skip the Grid, a hands-on service initiative aimed at expanding access to reliable solar energy in the Navajo Nation. In partnership with SOLV Energy, NextTracker, and the non-profit Heart of America, the team traveled to Chinle, Arizona, where they installed off-grid solar systems in 40 homes across the Chinle School District. Each setup included solar panels, a battery with electrical outlets, lighting, and a refrigerator—providing essential power to families whose homes were previously without it. All of the households served kids from elementary to high school, and the installations aimed to support both daily living and educational success. Students from various majors, including Construction Management, Mechanical and Environmental Engineering, Computer Science, and Psychology, collaborated to complete the installations over three days. In addition to the technical work, the team organized STEM-focused outreach activities for local students. Now in its fourth year, Skip the Grid continues to blend interdisciplinary teamwork, hands-on learning, and community outreach into a meaningful and growing effort
OWL Solar-Powered Link: Duck Radio Mesh for Autonomous Monitoring
No full textThe Solar Duck Sensor Node transforms a standard DuckLink into a fully self-sustaining environmental monitor. A compact solar panel / power bank module plugs into the power ports of both the DuckLink and the Raspberry Pi Zero 2 W, keeping them charged through day-night cycles. The Pi Zero 2 W will perform local processing of sensor data. Sensor output will come from the Raspberry Pi AI Camera and its object detection capabilities. This output will be distilled into metadata and advertised over BLE. The DuckLink captures these packets, encapsulates them into LoRa payloads, and forwards them across the ClusterDuck mesh to an internet-connected PapaDuck gateway, where the OWL DMS takes in the data for real-time visualization. The Raspberry Pi / DuckLink sensor system will fit inside a weather-resistant enclosure that is pole-mounted and will weigh less than 5 pounds. Daily energy consumption from this system is expected to be around 86.76 Wh. Expected run time in absence of solar generation is 12 hours. The solar panel will be supported by a mounting bracket that can be attached to either the gimbal of a tripod or a pole. This will enable continuous operation in remote or disaster-prone sites with zero external power or network infrastructure
The effect of Methylobacterium symbioticum on the growth and yield of dent corn and Romaine lettuce
No full textNitrogen (N) is an essential nutrient for plant growth and is often a limiting nutrient in cropping systems. An estimated half of applied N fertilizers are lost from cropping systems by processes that can be environmentally destructive, such as leaching and volatilization, and cause economic loss for growers. Effective N biofertilizers offer a means to mitigate the need for high inputs of synthetic N. The objectives of this study were to determine the effects of leaf inoculation with the N-fixing bacteria, Methylobacterium symbioticum, in combination with different rates of urea-N fertilizer on the physiology and yield of dent corn (Zea mays L.) and Romaine lettuce (Lactuca sativa L.) crops. Plants were grown in 12 L pots using a loamy sand soil and were organized according to a randomized complete block design with eight replicates for corn and 10 replicates for lettuce per treatment combination. The N fertilizer with the biofertilizer treatment significantly increased the corn leaf surface area, shoot and total aboveground dry matter production of the corn plants. The N fertilizer with the biofertilizer treatment significantly increased the total dry matter production of the lettuce plants. The chlorophyll content and fluorescence of the lettuce leaves were significantly greater with the biofertilizer than without the biofertilizer at 0 kg N/ha. Overall, the M. symbioticum may be effective with lower rates of N to increase the sustainability of conventional lettuce and corn crop production systems. Further research and field studies are required to study the effect of this bacteria with lower N application rates, different N sources and different crops
Alpine Plants and Climate Change: Tracking Community Shifts in Yosemite National
No full textAs climate change progresses, alpine plant communities are predicted to shift upslope as they track temperature over time. Relative to lowland environments, alpine areas are experiencing a faster rate of temperature change due to a phenomenon referred to as elevation-dependent warming. A common approach for tracking plant communities shifts over time, is to conduct resurveys of the same area at several time points, as the Global Observational Research Initiative in Alpine Environments (GLORIA) has done on mountain summits around the world since 2001. In 2011-2013, GLORIA developed a supplemental survey method whereby belt transects were established downslope of summits in order to detect elevation-based changes within a mountain slope. Initially, they established belt transects across five alpine slopes in the Great Basin region of California, providing insights into community turnover in water-limited alpine environments. However, they have yet to establish transects in less water-limited alpine areas, such as the west slope of the Sierra Nevada. Here we fill this gap, by establishing 41 transects on four Sierran alpine slopes in Yosemite National Park in 2023-2025. At each site, a series of 100-meter belt transects are situated 25-vertical meters apart on southwest-facing slopes, extending from treeline to ridgeline. Each transect is split into 5-meter segments surveyed by identifying all species and completing point-intercept surveys. Community composition was similar across sites with similar species appearing in high abundance, and 36-60% of variation in species composition explained by elevation at each site. We found that there may be a greater number of undetected species on Mt. Dana compared to the other sites. In addition to these baseline trends, we also explored inter-observer error whereby species are misidentified or missed altogether, leading to differences between surveys due to observers rather than species turnover. To quantify inter-observer error, we surveyed a subset of 115 segments with two observer teams on the same day, and compared their species lists using the Sorensen dissimilarity index. Inter-observer error was higher on Mt. Dana (20%) than Mt. Lewis (10%) and increases with higher vegetation cover at both sites. Aggregation of morphologically similar species did not reduce inter-observer error in our study. The identity of the observer team did not have a significant effect on the relationship between species richness and elevation. The information acquired in this study may help National Park staff to make informed management decisions when conserving alpine communities and informing interpretation of change over time in the future
Safety-Critical Formation Control of Non-Holonomic Multi-Robot Systems in Communication-Limited Environments
No full textThis project explores advanced methodologies for distributed planning, formation control, and coordination in heterogenous multi-agent robotic systems consisting of small mobile robots and nano unmanned aerial vehicles (UAVs). Leveraging decentralized control theories and optimization techniques, the research aims to develop robust algorithms for real-time collaborative task execution, efficient formation maintenance, and adaptive coordination strategies. Python and ROS will be used extensively to simulate, validate, and experimentally deploy distributed robotics algorithms. The student researcher significantly contributes to the theoretical and practical advancement of multi-agent system technologies
AI Agents for Search and Rescue (AI4SAR)
No full textThe goal of this summer research activity is to expand the capabilities of a system developed in the larger AI in Search and Rescue project. This project supports volunteer organizations participating in the search for missing persons by providing a basic framework for the collection and organization of relevant information, augmented with specific components that utilize a variety of AI methods. The focus of the summer research will be on the development of agent components for selected roles and tasks in a search and rescue mission. The agents utilize generative AI methods such as Large Language Models (LLMs) to provide insights for the people involved in the search effort. A Clue Meister, for example, is responsible for collecting, organizing, evaluating, and distributing all the clues that come in during a search mission. This task and many others can be very challenging for humans, and computer support can significantly speed up and improve the process
Stretchable Mechanoluminescent Devices for Energy Harvesting and Real-Time Force Sensing
No full textOur group has been developing a novel manufacturing technique to fabricate flexible sensing devices using electrostatic force. Previously, we focused on utilizing nanomaterials as the sensing materials to measure strains. Building on our experience with the manufacturing technique, we will move forward to incorporate other smart materials in the flexible device to achieve self-powered sensors. Besides, to better understand the fabricated material properties, we are interested in characterizing the microstructural behavior of the material networks via microscopic imaging and finite element simulations. The students will have the opportunity to closely work with Dr. Wang’s interdisciplinary research group
Augmented Reality Application for Real-Time Coastal Data Visualization
No full textThis project aims to develop an Augmented Reality (AR) application that overlays real-time coastal environmental data onto physical landscapes when viewed through AR devices such as smartphones and mixed reality headsets. By integrating data from machine learning models, computer vision-based event detection, coastal sensors, and geospatial mapping technologies, the application will provide users with an immersive and interactive experience, enhancing their understanding of coastal dynamics and environmental changes. The application will focus on observing coastal phenomena such as rip currents, tracking endangered coastal species and marine mammals, monitoring crowd levels on beaches, etc. Data sources will include NOAA\u27s National Data Buoy Center, the Coastal Data Information Program, and live webcam feeds from NOAA’s WebCOOS, incorporating parameters such as tide levels, wave heights, and weather conditions. Geospatial mapping will be achieved through GPS and GIS technologies to accurately align data overlays with the user\u27s physical environment. Potential user groups for this application include lifeguards, coastal scientists, swimmers, and surfers, with the potential to serve as both a research and educational tool. The student researcher will be responsible for developing the AR interface using platforms like Unity3D and WebXR, integrating real-time data APIs, and ensuring accurate geospatial mapping. The real-time event detection component involves training machine learning models for computer vision capable of running on the targeted AR devices. Additionally, the student will design and implement user interaction features that allow for the selection and visualization of specific data layers and timeframes. This interdisciplinary approach combines computer science, environmental science, and user experience design to advance knowledge in AR applications for environmental monitoring. The project will build upon my previously published works such as RipScout (2025), RipFinder (2025), which focused on utilizing technology for coastal hazard detection and monitoring. Future expansions of this project may explore the potential application of AR devices for citizen science data collection