Center for Embedded Network Sensing
Parent: UCLA
eScholarship stats: Breakdown by Item for September through December, 2024
| Item | Title | Total requests | Download | View-only | %Dnld |
|---|---|---|---|---|---|
| 19h777qd | Participatory sensing | 160 | 27 | 133 | 16.9% |
| 4zw2f3s6 | Colibration: A Collaborative Approach to In-Place Sensor Calibration | 133 | 23 | 110 | 17.3% |
| 8mb6468v | Staggered Sampling for Efficient Data Collection | 127 | 0 | 127 | 0.0% |
| 4xx221vv | Know Thy Sensor: Trust, Data Quality, and Data Integrity in Scientific Digital Libraries | 99 | 6 | 93 | 6.1% |
| 62p28371 | Directed Diffusion for Wireless Sensor Networking | 89 | 17 | 72 | 19.1% |
| 3ks9198m | Nanorobots, NEMS, and Nanoassembly | 81 | 36 | 45 | 44.4% |
| 85f6w6sv | Forest understory soil temperatures and heat flux calculated using a Fourier model and scaled using a digital camera | 73 | 5 | 68 | 6.8% |
| 8wb4118r | EDU 0: Education Overview | 70 | 3 | 67 | 4.3% |
| 2tp2w3g0 | Time Synchronization in Wireless Sensor Networks | 68 | 45 | 23 | 66.2% |
| 76x92441 | Design Considerations for Solar Energy Harvesting Wireless Embedded Systems | 68 | 48 | 20 | 70.6% |
| 7q60k5d3 | Em View: The Em* Visualizer | 67 | 0 | 67 | 0.0% |
| 4r48w3bb | Efficient Planning of Informative Paths for Multiple Robots | 63 | 6 | 57 | 9.5% |
| 2t29v9wj | Nonmyopic Adaptive Informative Path Planning for Multiple Robots | 60 | 12 | 48 | 20.0% |
| 4c0876vh | Networking Issues in Wireless Sensor Networks | 60 | 37 | 23 | 61.7% |
| 95t603tj | Participatory Sensing for Community Data Campaigns: A case study | 57 | 5 | 52 | 8.8% |
| 44v6b3sj | Use of a Networked Digital Camera to Estimate Net CO2 Uptake of a Desiccation-Tolerant Moss | 56 | 8 | 48 | 14.3% |
| 8v26b5qh | Rapid Deployment with Confidence:Calibration and Fault Detection in Environmental Sensor Networks | 55 | 13 | 42 | 23.6% |
| 6fs4559s | Little Science Confronts the Data Deluge: Habitat Ecology, Embedded Sensor Networks, and Digital Libraries | 54 | 23 | 31 | 42.6% |
| 6xs0j41x | Dynamic Fine-Grained Localization in Ad-Hoc Wireless Sensor Networks | 52 | 7 | 45 | 13.5% |
| 13r0q4fc | Virgil: Objects on the Head of a Pin | 51 | 27 | 24 | 52.9% |
| 0465g4pc | The Design and Implementation of a Self -Calibrating Distributed Acoustic Sensing Platform | 46 | 10 | 36 | 21.7% |
| 5dk8r03w | Subduction Zone Seismic Experiment in Peru: Results From a Wireless Seismic Network | 46 | 4 | 42 | 8.7% |
| 7694j52g | A Wireless Sensor Network for Structural Monitoring | 46 | 9 | 37 | 19.6% |
| 1rb4285n | Sensor Network Data Fault Types | 45 | 27 | 18 | 60.0% |
| 7qd6q8qm | Smart Screen Management on Mobile Phones | 45 | 2 | 43 | 4.4% |
| 6zg2n1rh | Lightweight Temporal Compression of Microclimate Datasets | 42 | 11 | 31 | 26.2% |
| 80c967sz | Geography-informed Energy Conservation for Ad Hoc Routing | 42 | 6 | 36 | 14.3% |
| 5mh7m01j | Timing-sync Protocol for Sensor Networks | 41 | 4 | 37 | 9.8% |
| 81s2s0t2 | Accurate Energy Attribution and Accounting for Multi-core Systems | 41 | 4 | 37 | 9.8% |
| 74h0v84v | Parameter Identification of Framed Structures Using an Improved Finite Element Model Updating Method—Part I: Formulation and Validation | 39 | 6 | 33 | 15.4% |
| 8wb43238 | Ambulation: a tool for monitoring mobility patterns over time using mobile phones | 39 | 2 | 37 | 5.1% |
| 92w6g3md | Deriving State Machines from TinyOS programs using Symbolic Execution | 39 | 2 | 37 | 5.1% |
| 9bp57793 | The Tenet Architecture for Tiered Sensor Networks | 39 | 11 | 28 | 28.2% |
| 7bx0g78h | Participatory Design of Sensing Networks: Strengths and Challenges | 36 | 10 | 26 | 27.8% |
| 2wx27188 | EmTOS: A Development Tool for Heterogeneous Sensor Networks | 35 | 6 | 29 | 17.1% |
| 5h22d6xv | EmStar: An Environment for Developing Wireless Embedded Systems Software | 35 | 23 | 12 | 65.7% |
| 5hp8j5sk | Experiments with Underwater Robot Localization and Tracking | 35 | 3 | 32 | 8.6% |
| 0qt608kr | Pervasive Computing: Embedding the Public Sphere | 34 | 9 | 25 | 26.5% |
| 4sn741ns | Designing the Personal Data Stream: Enabling Participatory Privacy in Mobile Personal Sensing | 34 | 3 | 31 | 8.8% |
| 88b146bk | The Atom LEAP Platform For Energy-Efficient Embedded Computing | 34 | 1 | 33 | 2.9% |
| 9wm343pn | Sharing Sensor Network Data | 34 | 1 | 33 | 2.9% |
| 8pq5g7rm | Budburst and leaf area expansion measured with a ground-based, mobile camera system and simple color thresholding | 33 | 6 | 27 | 18.2% |
| 12v1c6v7 | Sympathy for the Sensor Network Debugger | 32 | 8 | 24 | 25.0% |
| 90j149pp | Participatory Privacy in Urban Sensing | 32 | 5 | 27 | 15.6% |
| 01g148vr | Entropy-based Sensor Selection Heuristic for Target Localization | 31 | 5 | 26 | 16.1% |
| 2xr2r802 | Four Billion Little Brothers? Privacy, mobile phones, and ubiquitous data collection | 31 | 4 | 27 | 12.9% |
| 3s80t0pj | AndWellness: An Open Mobile System for Activity and Experience Sampling | 31 | 8 | 23 | 25.8% |
| 9xc0f566 | Particle Filtering Approach to Localization and Tracking of a Moving Acoustic Source in a Reverberant Room | 31 | 5 | 26 | 16.1% |
| 8ct2h4pk | Understanding Packet Delivery Performance In Dense Wireless Sensor Networks | 30 | 12 | 18 | 40.0% |
| 6w6295sp | Engaging women in computer science and engineering: Insights from a national study of undergraduate research experiences | 29 | 6 | 23 | 20.7% |
Note: Due to the evolving nature of web traffic, the data presented here should be considered approximate and subject to revision. Learn more.