Posters
Parent: Center for Embedded Network Sensing
eScholarship stats: Breakdown by Item for May through August, 2024
| Item | Title | Total requests | Download | View-only | %Dnld |
|---|---|---|---|---|---|
| 2wx27188 | EmTOS: A Development Tool for Heterogeneous Sensor Networks | 83 | 44 | 39 | 53.0% |
| 8wb4118r | EDU 0: Education Overview | 65 | 3 | 62 | 4.6% |
| 8qn830cq | Supporting Ecological Research With a Flexible Satellite Sensornet Gateway | 48 | 36 | 12 | 75.0% |
| 5dk8r03w | Subduction Zone Seismic Experiment in Peru: Results From a Wireless Seismic Network | 46 | 6 | 40 | 13.0% |
| 8sv2t5xc | An Overview of Multiscale Actuation and Sensing | 45 | 30 | 15 | 66.7% |
| 4rt7r810 | Ecological Sensing in a Southern California Forest: Integrating Environmental Abiotic and Biotic Measurements to Understand Ecosystem Function. | 44 | 31 | 13 | 70.5% |
| 60h3h1h4 | Entropy Based Sensor Selection Heuristic for Localization | 44 | 6 | 38 | 13.6% |
| 7q60k5d3 | Em View: The Em* Visualizer | 44 | 4 | 40 | 9.1% |
| 8kf9p679 | SYS0: Systems Area Research Overview | 43 | 5 | 38 | 11.6% |
| 3f74t94h | Experimental Study of the Effects of Tx Power Control and Blacklisting in Wireless Sensor Networks | 42 | 24 | 18 | 57.1% |
| 5t09j13j | Personal Data Vault: A Privacy Architecture for Mobile Personal Sensing | 42 | 6 | 36 | 14.3% |
| 6gp6f2dm | Mobile Robots and Sensor Network: Working Together | 42 | 6 | 36 | 14.3% |
| 0cn3s5k9 | Imagers as Sensors: Correlating Plant CO2 Uptake with Digital Visible-Light Imagery | 40 | 24 | 16 | 60.0% |
| 6s15h4k8 | Reliable Actuation for Networked Infomechanical Systems | 40 | 30 | 10 | 75.0% |
| 5v80v4t3 | Networked Infomechanical Systems (NIMS) | 39 | 28 | 11 | 71.8% |
| 6bb984md | Urban Sensing or Personal and Participatory Sensing | 38 | 25 | 13 | 65.8% |
| 9mj6b80x | An Overview of CENS Contaminant Transport Observation and Management Research | 38 | 18 | 20 | 47.4% |
| 01w4350s | SNUSE Sensor Networks for Undersea Seismic Experimentation | 37 | 20 | 17 | 54.1% |
| 296532bf | Trajectory Design and Implementation for Multiple Autonomous Underwater Vehicles Based on Ocean Model Predictions | 37 | 2 | 35 | 5.4% |
| 35z15398 | Visualizing microbial pollution in Santa Monica Bay with Geographic Information Systems (GIS) and through field-testing a rapid, robust, field-portable water detection sensing system | 37 | 22 | 15 | 59.5% |
| 0fs253z6 | Statistical Methods for Recovering 3D Models of Trees from Sensor Data | 36 | 23 | 13 | 63.9% |
| 1z41c7s7 | New Wireless Miniature Sensor Technologies for CENS | 36 | 16 | 20 | 44.4% |
| 4919w4vh | Exploiting Social Networks for Sensor Data Sharing with SenseShare | 36 | 18 | 18 | 50.0% |
| 0491d78j | Visual Localization and Mapping with Multiple View Features | 35 | 21 | 14 | 60.0% |
| 3fk811r7 | Avrora Scalable Simulation of Sensor Networks with Precise Timing | 35 | 14 | 21 | 40.0% |
| 4z9444hm | Task Allocation for Event-Aware Spatiotemporal Sampling of Environmental Variables | 35 | 22 | 13 | 62.9% |
| 8jp308rg | Toward Resource Efficient Homes: From Measurements to Sustainable Choices | 35 | 20 | 15 | 57.1% |
| 2sm2502f | Stargate: Energy Management Techniques | 34 | 18 | 16 | 52.9% |
| 7fp998hk | KNO 1: Facilitating the Adoption of Embedded Networked Sensing by Emerging National Environmental Observatories | 34 | 27 | 7 | 79.4% |
| 7g7940x9 | Mote Herding for Tiered Wireless Sensor Networks | 34 | 26 | 8 | 76.5% |
| 7kb1r614 | Progress in Detection and Identification of Marine Microorganisms | 34 | 17 | 17 | 50.0% |
| 2vh5g17p | Networked Aquatic Microbial Observing Systems: An Overview | 33 | 20 | 13 | 60.6% |
| 33t7h0ks | Development and Environmental Applications of a Nitrate Microsensor Based on Doped Polypyrrole Films | 33 | 24 | 9 | 72.7% |
| 38d713q8 | The Sierra Nevada-San Joaquin Hydrologic Observatory (SNSJHO): a WATERS network test bed | 33 | 18 | 15 | 54.5% |
| 7sh4975w | CENSDC: Adding Context to Content | 33 | 16 | 17 | 48.5% |
| 3kq258gc | A Framework for Data Quality and Feedback in Participatory Sensing | 31 | 19 | 12 | 61.3% |
| 4d1002mx | Evaluation of Imagers in a Biological Sensing Deployment | 31 | 16 | 15 | 51.6% |
| 85186154 | SIP2: High integrity in Sensor Networks: Models, Techniques, and System Support | 31 | 20 | 11 | 64.5% |
| 92w6g3md | Deriving State Machines from TinyOS programs using Symbolic Execution | 31 | 20 | 11 | 64.5% |
| 1rb4285n | Sensor Network Data Fault Types | 30 | 14 | 16 | 46.7% |
| 3m2646q0 | Monitoring and Detecting Harmful Algal Blooms in King Harbor, City of Redondo Beach, CA, Using a Wireless Sensor Network | 29 | 14 | 15 | 48.3% |
| 6b38n1jh | Acoustic Sensor Networks for Woodpecker Localization | 29 | 16 | 13 | 55.2% |
| 2vv078qh | Sympathy: A Debugging System for Sensor Networks | 28 | 14 | 14 | 50.0% |
| 2z88h65j | Field Deployment of Potentiometric Nitrate Sensor System | 28 | 17 | 11 | 60.7% |
| 1b99m17f | Challenges in Adaptive Path Sampling with Mobile Sensors | 27 | 15 | 12 | 55.6% |
| 1nn055gf | Recruitment Services for Participatory Sensing Applications | 27 | 13 | 14 | 48.1% |
| 48h986b4 | Sensor Network Application Construction Kit (SNACK) | 27 | 15 | 12 | 55.6% |
| 4kv261jf | SYS4: Estimating Clock Uncertainty for Efficient Duty-Cycling in Sensor Networks | 27 | 14 | 13 | 51.9% |
| 5rb3s6z1 | Imagers as Biological Sensors | 27 | 17 | 10 | 63.0% |
| 9fh6p3ws | Investigations of Fine-scale Diel Migration of Phytoplankton Populations in King Harbor, Redondo Beach | 27 | 16 | 11 | 59.3% |
Disclaimer: due to the evolving nature of the web traffic we receive and the methods we use to collate it, the data presented here should be considered approximate and subject to revision.