arXiv subjects

Julia Gersey

Publications and source records attributed to Julia Gersey.

7 recordsLinked to original sources

arXiv2025

A Structured Review of Fixed and Multimodal Sensing Techniques for Bat Monitoring

Effective monitoring of mobile animal populations is crucial for ecological research, wildlife management, and agricultural applications. Monitoring of bats specifically can help understand the spread of disease as well as shine light on bat migration patterns, population dynamics, and the impacts of environmental changes on bat colonies. Fixed sensing modalities, such as infrared sensors, cameras, radar, and acoustic detectors, play a pivotal role in tracking and understanding animal behavior. This survey goes over context-informing details about bat biology, and then reviews these fixed sensing modalities, discussing the unique challenges and contributions of each approach. We highlight the coverage, applications, accuracy, and limitations associated with each of these sensing modalities. By synthesizing recent advances, we provide a comprehensive overview to guide future research in this area.

eess.SY↗

arXiv2025

In-field Calibration of Low-Cost Sensors through XGBoost $\&$ Aggregate Sensor Data

Effective large-scale air quality monitoring necessitates distributed sensing due to the pervasive and harmful nature of particulate matter (PM), particularly in urban environments. However, precision comes at a cost: highly accurate sensors are expensive, limiting the spatial deployments and thus their coverage. As a result, low-cost sensors have become popular, though they are prone to drift caused by environmental sensitivity and manufacturing variability. This paper presents a model for in-field sensor calibration using XGBoost ensemble learning to consolidate data from neighboring sensors. This approach reduces dependence on the presumed accuracy of individual sensors and improves generalization across different locations.

cs.LG↗

arXiv2025

Preliminary Characterization of Bio-inspired Dog-Nose Sampler for Aerosol Detection

Before aerosols can be sensed, sampling technologies must capture the particulate matter of interest. To that end, for systems deployed in open environments where the location of the aerosol is unknown, extending the reach of the sampler could lessen the precision required in sensor placement or reduce the number of sensors required for full spatial coverage. Inspired by the sensitivity of the canine olfactory system, this paper presents a rudimentary sampler that mimics the air flow of a dog's nose. The design consists of speed-controlled inhalation jets, as well as exhalation jets that are angled down and to the side. We tested this design on volatile organic compounds (VOC) in a small number of scenarios to validate the concept and understand how the system behaves. We show that in preliminary testing this dog-nose setup provides improvements over passive and solely inhalation sensing.

eess.SY↗

arXiv2025

Privacy-Preserving Bathroom Monitoring for Elderly Emergencies Using PIR and LiDAR Sensors

In-home elderly monitoring requires systems that can detect emergency events - such as falls or prolonged inactivity - while preserving privacy and requiring no user input. These systems must be embedded into the surrounding environment, capable of capturing activity, and responding promptly. This paper presents a low-cost, privacy-preserving solution using Passive Infrared (PIR) and Light Detection and Ranging (LiDAR) sensors to track entries, sitting, exits, and emergency scenarios within a home bathroom setting. We developed and evaluated a rule-based detection system through five real-world experiments simulating elderly behavior. Annotated time-series graphs demonstrate the system's ability to detect dangerous states, such as motionless collapses, while maintaining privacy through non-visual sensing.

cs.CR↗

arXiv2025

ZV-Sim: Probabilistic Simulation Framework for Pre-emergent Novel Zoonose Tracking

ZV-Sim is an open-source, modular Python framework for probabilistic simulation and analysis of pre-emergent novel zoonotic diseases using pervasive sensing data. It incorporates customizable Human and Animal Presence agents that leverage known and simulated location data, contact networks, and illness reports to assess and predict disease origins and spread. The framework supports Monte Carlo experiments to analyze outcomes with various user-defined movement and probability models. Although initial models are basic and illustrative, ZV-Sim's extensible design facilitates the integration of more sophisticated models as richer data become available, enhancing future capabilities in zoonotic disease tracking. The source code is publicly available \href{https://github.com/jmaff/zv-sim}{\underline{\textit{here}}}.

q-bio.QM↗

arXiv2025

Survey of City-Wide Homelessness Detection Through Environmental Sensing

The growing homelessness crisis in the U.S. presents complex social, economic, and public health challenges, straining shelters, healthcare, and social services while limiting effective interventions. Traditional assessment methods struggle to capture its dynamic, dispersed nature, highlighting the need for scalable, data-driven detection. This survey explores computational approaches across four domains: (1) computer vision and deep learning to identify encampments and urban indicators of homelessness, (2) air quality sensing via fixed, mobile, and crowdsourced deployments to assess environmental risks, (3) IoT and edge computing for real-time urban monitoring, and (4) pedestrian behavior analysis to understand mobility patterns and interactions. Despite advancements, challenges persist in computational constraints, data privacy, accurate environmental measurement, and adaptability. This survey synthesizes recent research, identifies key gaps, and highlights opportunities to enhance homelessness detection, optimize resource allocation, and improve urban planning and social support systems for equitable aid distribution and better neighborhood conditions.

cs.CY↗

arXiv2025

WeVibe: Weight Change Estimation Through Audio-Induced Shelf Vibrations In Autonomous Stores

Weight change estimation is crucial in various applications, particularly for detecting pick-up and put-back actions when people interact with the shelf while shopping in autonomous stores. Moreover, accurate weight change estimation allows autonomous stores to automatically identify items being picked up or put back, ensuring precise cost estimation. However, the conventional approach of estimating weight changes requires specialized weight-sensing shelves, which are densely deployed weight scales, incurring intensive sensor consumption and high costs. Prior works explored the vibration-based weight sensing method, but they failed when the location of weight change varies. In response to these limitations, we made the following contributions: (1) We propose WeVibe, a first item weight change estimation system through active shelf vibration sensing. The main intuition of the system is that the weight placed on the shelf influences the dynamic vibration response of the shelf, thus altering the shelf vibration patterns. (2) We model a physics-informed relationship between the shelf vibration response and item weight across multiple locations on the shelf based on structural dynamics theory. This relationship is linear and allows easy training of a weight estimation model at a new location without heavy data collection. (3) We evaluate our system on a gondola shelf organized as the real-store settings. WeVibe achieved a mean absolute error down to 38.07g and a standard deviation of 31.2g with one sensor and 10% samples from three weight classes on estimating weight change from 0g to 450g, which can be leveraged for differentiating items with more than 100g differences.

eess.SP↗