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Abstract Unmanned Aerial Vehicles (UAVs) hold immense potential across various fields, including precision agriculture, rescue missions, delivery services, weather monitoring, and many more. Despite this promise, the limited flight duration of the current UAVs stands as a significant obstacle to their broadscale deployment. Attempting to extend flight time by solar panel charging during midflight is not viable due to battery limitations and the eventual need for replacement. This paper details our investigation of a battery-free fixed-wing UAV, built from cost-effective off-the-shelf components, that takes off, remains airborne, and lands safely using only solar energy. In particular, we perform a comprehensive analysis and design space exploration in the contemporary solar harvesting context and provide a detailed accounting of the prototype’s mechanical and electrical capabilities. We also derive the Greedy Energy-Aware Control (GEAC) and Predictive Energy-Aware Control (PEAC) solar control algorithm that overcomes power system brownouts and total-loss-of-thrust events, enabling the prototype to perform maneuvers without a battery. Next, we evaluate the developed prototype in a bench-top setting using artificial light to demonstrate the feasibility of batteryless flight, followed by testing in an outdoor setting using natural light. Finally, we analyze the potential for scaling up the evaluation of batteryless UAVs across multiple locations and report our findings. 

This paper introduces MobiChem, a low-cost, portable, practical, and ubiquitous smartphone-based toolkit for fruit monitoring. The key idea is to leverage the light emitted from a smartphone’s screen and front camera, coupled with a custom-built screen cover, to perform comprehensive hyperspectral analysis on targeted objects. Specifically, we designed a zero-powered screen cover that selectively filters wavelengths essential for hyperspectral sensing. We then incorporate a CNN-based algorithm and a novel ranking-based learning technique that manipulates the latent space to classify maturity stages and characterize their chemical and physical factors. To demonstrate MobiChem’s feasibility, robustness, and practicality, we showcase its application in tomato, banana, and avocado sensing. Our system examines the maturity, chlorophyll, lycopene content, free sugar levels, and firmness, enabling various dietary assessments and food safety applications. Experimental results using 117 tomatoes, 98 bananas, and 73 avocados show MobiChem achieved 95.67% accuracy in chlorophyll concentration measurement, 98.76% for lycopene detection, 93.53% for sugar concentrations analysis, and 91.34% average accuracy in classifying maturity (96.64% for tomato, 86.37% for banana, and 91.03% for avocado). 

Epilepsy is one of the most common neurological diseases globally (around 50M people globally). Fortunately, up to 70% of people with epilepsy could live seizure-free if properly diagnosed and treated, and a reliable technique to monitor the onset of seizures could improve the quality of life of patients who are constantly facing the fear of random seizure attacks. The current gold standard, video-EEG (v-EEG), involves attaching over 20 electrodes to the scalp, is costly, requires hospitalization, trained professionals, and is uncomfortable for patients. To address this gap, we developedEarSD, a lightweight and unobtrusive ear-worn system to detect seizure onsets by measuring physiological signals behind the ears. This system can be integrated into earphones, headphones, or hearing aids, providing a convenient solution for continuous monitoring.EarSDis an integrated custom-builtsensing-computing-communicationear-worn platform to capture seizure signals, remove the noises caused by motion artifacts and environmental impacts, and stream the collected data wirelessly to the computer/mobile phone nearby.EarSD’s ML algorithm, running on a server, identifies seizure-associated signatures and detects onset events. We evaluated the proposed system in both in-lab and in-hospital experiments at the University of Texas Southwestern Medical Center with epileptic seizure patients, confirming its usability and practicality.