MultiScale Sensing: A new paradigm for actuated sensing of dynamic phenomena Diane Budzik 9-8-06 Electrical Engineering Department Center for Embedded.

Slides:

Advertisements

Similar presentations

Bayesian Belief Propagation

Advertisements

Design Guidelines for Maximizing Lifetime and Avoiding Energy Holes in Sensor Networks with Uniform Distribution and Uniform Reporting Stephan Olariu Department.

Adapting Ocean Surveys to the Observed Fields Characteristics Maria-João Rendas I3S, CNRS-UNSA.

Dynamical Downscaling of surface wind circulations in the Northeast of the Iberian Peninsula Pedro A. Jiménez (UCM-CIEMAT) J. Fidel González-Rouco (UCM)

Scaling Laws, Scale Invariance, and Climate Prediction

The Global Digital Elevation Model (GTOPO30) of Great Basin Location: latitude 38  15’ to 42  N, longitude 118  30’ to 115  30’ W Grid size: 925 m.

Characteristics of Instruments P M V Subbarao Professor Mechanical Engineering Department A Step Towards Design of Instruments….

Radiometric and Geometric Errors

Multiple Criteria for Evaluating Land Cover Classification Algorithms Summary of a paper by R.S. DeFries and Jonathan Cheung-Wai Chan April, 2000 Remote.

Modeling Pixel Process with Scale Invariant Local Patterns for Background Subtraction in Complex Scenes (CVPR’10) Shengcai Liao, Guoying Zhao, Vili Kellokumpu,

Problem Description: Networked Aquatic Microbial Observing System (NAMOS) Problem Description: Networked Aquatic Microbial Observing System (NAMOS) Proposed.

1 Energy-Efficient localization for networks of underwater drifters Diba Mirza Curt Schurgers Department of Electrical and Computer Engineering.

1 Efficient planning of informative paths for multiple robots Amarjeet Singh *, Andreas Krause +, Carlos Guestrin +, William J. Kaiser *, Maxim Batalin.

Dynamic Medial Axis Based Motion Planning in Sensor Networks Lan Lin and Hyunyoung Lee Department of Computer Science University of Denver

Those Interfering Signals Modes and Dispersion in Fibers.

Prediction-based Monitoring in Sensor Networks: Taking Lessons from MPEG Samir Goel and Tomasz Imielinski Department of Computer Science Rutgers, The State.

Problem Description: To develop an autonomous network for monitoring aquatic environment Problem Description: To develop an autonomous network for monitoring.

The Global Digital Elevation Model (GTOPO30) of Great Basin Location: latitude 38  15’ to 42  N, longitude 118  30’ to 115  30’ W Grid size: 925 m.

1 Efficient Management of Data Center Resources for Massively Multiplayer Online Games V. Nae, A. Iosup, S. Podlipnig, R. Prodan, D. Epema, T. Fahringer,

More Realistic Power Grid Verification Based on Hierarchical Current and Power constraints 2 Chung-Kuan Cheng, 2 Peng Du, 2 Andrew B. Kahng, 1 Grantham.

ALBERT PARK EEL 6788: ADVANCED TOPICS IN COMPUTER NETWORKS Energy-Accuracy Trade-off for Continuous Mobile Device Location, In Proc. of the 8th International.

End-to-End Delay Analysis for Fixed Priority Scheduling in WirelessHART Networks Abusayeed Saifullah, You Xu, Chenyang Lu, Yixin Chen.

Imagers as sensors: Correlating plant CO 2 uptake with digital visible-light imagery Josh Hyman, Eric Graham Mark Hansen, Deborah Estrin Center for Embedded.

World Renewable Energy Forum May 15-17, 2012 Dr. James Hall.

Embedded sensor network design for spatial snowcover Robert Rice 1, Noah Molotch 2, Roger C. Bales 1 1 Sierra Nevada Research Institute, University of.

Multi-Scale Sampling Outline Introduction Information technology challenges Example: light patterns in forest Greg Pottie

Eric GrahamNathan Yau Staff Ecologist, CENSGraduate Student, Department of Statistics Use CasesSensorBase Coupled Human-Observational Systems Technology.

Quantitative Estimates of Biomass and Forest Structure in Coastal Temperate Rainforests Derived from Multi-return Airborne Lidar Marc G. Kramer 1 and Michael.

Tracking with Unreliable Node Sequences Ziguo Zhong, Ting Zhu, Dan Wang and Tian He Computer Science and Engineering, University of Minnesota Infocom 2009.

Introduction To describe the dynamics of the global carbon cycle requires an accurate determination of the spatial and temporal distribution of photosynthetic.

Department of Computer Science City University of Hong Kong Department of Computer Science City University of Hong Kong 1 A Statistics-Based Sensor Selection.

Performance evaluation of adaptive sub-carrier allocation scheme for OFDMA Thesis presentation16th Jan 2007 Author:Li Xiao Supervisor: Professor Riku Jäntti.

Multi-scale Integration Introduction to the Panel - Michael Hamilton Multi-Scale Sampling - Greg Pottie Scaling Challenges in Ecology - Michael Hamilton.

Adaptive Data Aggregation for Wireless Sensor Networks S. Jagannathan Rutledge-Emerson Distinguished Professor Department of Electrical and Computer Engineering.

Prognosis of Gear Health Using Gaussian Process Model Department of Adaptive systems, Institute of Information Theory and Automation, May 2011, Prague.

Natural Disturbance and Environmental Assessments in the Oil Sands Region Linda Halsey April 2012.

1 Factors influencing the dynamics of excessive algal blooms Richard F. Ambrose Environmental Science and Engineering Program Department of Environmental.

National Ecological Observatory Network

A Node and Load Allocation Algorithm for Resilient CPSs under Energy-Exhaustion Attack Tam Chantem and Ryan M. Gerdes Electrical and Computer Engineering.

Selection and Navigation of Mobile sensor Nodes Using a Sensor Network Atul Verma, Hemjit Sawant and Jindong Tan Department of Electrical and Computer.

BIOPHYS: A Physically-based Algorithm for Inferring Continuous Fields of Vegetative Biophysical and Structural Parameters Forrest Hall 1, Fred Huemmrich.

Using Polynomial Approximation as Compression and Aggregation Technique in Wireless Sensor Networks Bouabdellah KECHAR Oran University.

1 Research Question  Can a vision-based mobile robot  with limited computation and memory,  and rapidly varying camera positions,  operate autonomously.

Modeling Mobile-Agent-based Collaborative Processing in Sensor Networks Using Generalized Stochastic Petri Nets Hongtao Du, Hairong Qi, Gregory Peterson.

1 Iterative Integer Programming Formulation for Robust Resource Allocation in Dynamic Real-Time Systems Sethavidh Gertphol and Viktor K. Prasanna University.

Eric A. Graham UCLA Department of Ecology and Evolutionary Biology Ecological Applications of CENS Technologies at the James Reserve Summer interns: Caitlin.

Segmentation of Vehicles in Traffic Video Tun-Yu Chiang Wilson Lau.

Murat Demirbas Onur Soysal SUNY Buffalo Ali Saman Tosun U. San Antonio Data Salmon: A greedy mobile basestation protocol for efficient data collection.

Chapter 9 Capacity and Level of Service for Highway Segments

A Protocol for Tracking Mobile Targets using Sensor Networks H. Yang and B. Sikdar Department of Electrical, Computer and Systems Engineering Rensselaer.

Problem Description: One line explanation of the problem to be solved Problem Description: One line explanation of the problem to be solved Proposed Solution:

1 Dynamic Speed and Sensor Rate Adjustment for Mobile Robotic Systems Ala’ Qadi, Steve Goddard University of Nebraska-Lincoln Computer Science and Engineering.

Communications Range Analysis Simulation Set Up –Single Biological Threat placed in Soldier Field –Communication range varied from meters –Sensor.

Euro-Par, HASTE: An Adaptive Middleware for Supporting Time-Critical Event Handling in Distributed Environments ICAC 2008 Conference June 2 nd,

Matte-Based Restoration of Vintage Video 指導老師 : 張元翔主講人員 : 鄭功運.

1 Hardware Reliability Margining for the Dark Silicon Era Liangzhen Lai and Puneet Gupta Department of Electrical Engineering University of California,

Energy minimization Another global approach to improve quality of correspondences Assumption: disparities vary (mostly) smoothly Minimize energy function:

ParkNet: Drive-by Sensing of Road-Side Parking Statistics Irfan Ullah Department of Information and Communication Engineering Myongji university, Yongin,

Wireless sensor and actor networks: research challenges Ian. F. Akyildiz, Ismail H. Kasimoglu

Integrating LiDAR Intensity and Elevation Data for Terrain Characterization in a Forested Area Cheng Wang and Nancy F. Glenn IEEE GEOSCIENCE AND REMOTE.

Semiconductor Detectors and Applications on X-ray imaging Natalie Diekmann Particle Physics 1 NIKHEF.

March Outline: Introduction What is the Heat Wave? Objectives Identifying and comparing the current and future status of heat wave events over.

Jan Geletič, Petr Dobrovolný

Inna Khomenko, Oleksandr Dereviaha

PCB 3043L - General Ecology Data Analysis.

ECE 382. Feedback Systems Analysis and Design

Spatially Varying Frequency Compounding of Ultrasound Images

A Framework for Automatic Resource and Accuracy Management in A Cloud Environment Smita Vijayakumar.

788.11J Presentation “Robot Navigation using a Sensor Network ”

Smita Vijayakumar Qian Zhu Gagan Agrawal

Presentation transcript:

MultiScale Sensing: A new paradigm for actuated sensing of dynamic phenomena Diane Budzik Electrical Engineering Department Center for Embedded Networked Sensing University of California Los Angeles

Outline Environmental Sensing and Applications Environmental Phenomena Classification MultiScale Sampling Methodology Results: Simulation and using NIMS 3D Conclusion Future Directions

Environmental Sensing Environmental phenomena are dynamic –Solar light radiation Varying light patterns on forest floor –C0 2 flux –Humidity Dynamic phenomena –High spatial and temporal variation –Requires high spatial sampling rate to achieve desired fidelity Spatial sampling rate of 10 samples/m 2 over a transect exceeding 1000 m 2 –Requires actuated sensing (mobile robot)

Bracken Ferns: James Reserve World-wide distribution, occurring in multiple habitats from cold temperate to tropical forests Fern fronds are carcinogenic and young fronds may release hydrogen cyanide when they are damaged –Threat to livestock if eat bracken ferns –Threat to humans when toxins are passed via milk from affected cows Study growth patterns of bracken ferns Photosynthesis Light distribution

Spatial Frequency Temporal Frequency quasi-static, smooth fields quasi-static, smooth fields Environmental Phenomena Classification Qualitative classification –Spatiotemporal frequency distribution

Environmental Phenomena Classification Spatial Frequency Temporal Frequency terrain, plant distribution terrain, plant distribution

Environmental Phenomena Classification Spatial Frequency Temporal Frequency raster scan sampling

Environmental Phenomena Classification Spatial Frequency Temporal Frequency quasi-static, smooth fields dynamic amplitude variation of smooth field

Environmental Phenomena Classification Spatial Frequency Temporal Frequency quasi-static, smooth fields dynamic amplitude variation of smooth field

Environmental Phenomena Classification Spatial Frequency Temporal Frequency quasi-static, smooth fields dynamic amplitude variation of smooth field

Environmental Phenomena Classification Spatial Frequency Temporal Frequency quasi-static, smooth fields dynamic amplitude variation of smooth field

Environmental Phenomena Classification Spatial Frequency Temporal Frequency terrain, plant distribution terrain, plant distribution contaminant distribution

Environmental Phenomena Classification Spatial Frequency Temporal Frequency static network sampling raster scan sampling

Environmental Phenomena Classification Spatial Frequency Temporal Frequency quasi-static, smooth fields dynamic amplitude variation of smooth field quasi-static, “rough”, complex fields

Environmental Phenomena Classification Spatial Frequency Temporal Frequency quasi-static water flow distribution terrain, plant distribution terrain, plant distribution contaminant distribution

Environmental Phenomena Classification Spatial Frequency Temporal Frequency adaptive sampling static network sampling raster scan sampling

Environmental Phenomena Classification Spatial Frequency Temporal Frequency quasi-static, smooth fields dynamic amplitude variation of smooth field quasi-static, “rough”, complex fields dynamic phenomena

Environmental Phenomena Classification Spatial Frequency Temporal Frequency quasi-static, smooth fields dynamic amplitude variation of smooth field quasi-static, “rough”, complex fields dynamic phenomena

Environmental Phenomena Classification Spatial Frequency Temporal Frequency quasi-static, smooth fields dynamic amplitude variation of smooth field quasi-static, “rough”, complex fields dynamic phenomena

Environmental Phenomena Classification Spatial Frequency Temporal Frequency quasi-static, smooth fields dynamic amplitude variation of smooth field quasi-static, “rough”, complex fields dynamic phenomena

Environmental Phenomena Classification Spatial Frequency Temporal Frequency general example, numerous applications quasi-static water flow distribution terrain, plant distribution terrain, plant distribution contaminant distribution

Environmental Phenomena Classification Spatial Frequency Temporal Frequency adaptive sampling static network sampling raster scan sampling multiscale sampling

Environmental Phenomena Classification Spatial Frequency Temporal Frequency adaptive sampling static network sampling raster scan sampling multiscale sampling

Outline Environmental Sensing and Applications Environmental Phenomena Classification MultiScale Sampling Methodology Results: Simulation and using NIMS 3D Conclusion Future Directions

Sensing Light Distribution Under a Forest Canopy August 12, 2005 James San Jacinto Mountains Reserve

Sensing Light Distribution Under a Forest Canopy James San Jacinto Mountains Reserve August 12, 2005

Sensing Light Distribution Under a Forest Canopy James San Jacinto Mountains Reserve August 12, 2005

Sensing Light Distribution Under a Forest Canopy James San Jacinto Mountains Reserve August 12, 2005

Sensing Light Distribution Under a Forest Canopy James San Jacinto Mountains Reserve August 12, 2005

MultiScale is a method for phenomena sampling that uses a hierarchy of resources of varying sensing and mobility modalities A two-tier MultiScale approach: –High resolution, low fidelity sensor (imager) provides a global view of the environment –Low resolution, high fidelity sensor (PAR sensor) used for actuated sampling MultiScale Sampling (MSS) Methodology

Image Acquisition and Processing Binary Segmented Image Actual Image from JR

Task Prioritization Multi Robot Task Allocation is the problem of assigning robots to tasks Suppose at a given period of time the system maintains a set of tasks and a set of robots Tasks are prioritized based on one of two heuristics: –Largest area first Service fewer tasks, tasks are larger in area –Least service time first Service many tasks, tasks are smaller in area Highest priority task is assigned to the closest robot Commitment policy

Simulation Snapshot Largest area first –Selects one large task to sample –Several minutes to complete task –Use when no prior information about phenomena Lowest service time first –Selects many small tasks sample –Several seconds to complete all tasks –Use when model of phenomena exists Phenomena Heuristic: largest area first Heuristic: lowest service time first

Outline Environmental Sensing and Applications Environmental Phenomena Classification MultiScale Sampling Methodology Results: Simulation and using NIMS 3D Conclusion Future Directions

Simulation Results for Area and Time Heuristics Sampling density (s) Normalized sampled area Sampling time: 0.1 sec Lower sampling density decreases sampling time and intra-task travel (during sampling) time Higher speed decreases intra-task travel time and decreases inter-task travel time Normalized sampled area Total information available during 1 hour of images Took the difference between consecutive images and counted number of white pixels in the differenced images

Simulation Results: Area and Time Heuristics Normalized number of tasks sampled Sampling density (s) Normalized number of tasks sampled Total number of tasks during 1 hour of images For consecutive images, counted number new tasks

Simulation Results: Reconstruction Error Sampling density = 4Sampling density = 10 Sampling density (s) Normalized reconstruction error Count number of pixels in sampled image that are different from input image Normalized with total amount of information available in sampled region

Simulation Results: Multiple Robots Sampling density = 6 Speed = 60 cm/s Maximum possible normalized sampled area = 0.59 –Total maximum amount of information possible = 1.0 –Simulation artifact: centralized task allocation implementation results in some skipped tasks Normalized sampled area Number of mobile robots

NIMS 3D Results 1.Pulley Attachment 2.Node Platform 3.Motor Control Box Normalized sampled area Sampling density (s) PAR Sensor

Conclusion Environmental phenomena are dynamic –Solar light radiation (varying sunlight patterns on forest floor) Static sensor sampling, raster scan sampling, and adaptive sampling are not adequate for sampling dynamic phenomena MultiScale is a method for phenomena sampling that uses a hierarchy of resources of varying sensing and mobility modalities –High resolution, low fidelity sensor (imager) provides a global view of the environment –Low resolution, high fidelity sensor (PAR sensor) used for actuated sampling Task Prioritization –Largest area first –Lowest service time first Simulation results and using NIMS 3D –MultiScale is adequate for sampling dynamic phenomena

Future Directions Online learning for phenomena modeling and sampling optimization One approach –Learn spatiotemporal statistics of tasks –Task is a way to cluster phenomena into entities that are interesting to a scientist

Thank you! ?

Image Processing: Task Extraction Actual Image from JR Segmented Image Binary Segmented Image

MultiScale Sampling vs Adaptive Sampling System Characteristics: –Speed = 50 cm/s –Sampling time = 0.1 sec –Area of the environment: Length = 768 cm Width = 480 cm –Sampling density: Equivalent number of sampling locations for AS and MSS –Phenomena speed = 12 pixels/min (12 cm/min) –Linear Interpolation used in reconstruction –Varying phenomena size

Performance Comparison: AS and MSS Large size phenomenon Dynamic Scene Static Scene

Conclusions RSS and SNS are efficient when the phenomena is static and spatial distribution of the phenomena is known AS is efficient when no prior information is available and when the phenomena has high spatial variability –Sampling latency inherent in AS renders it inadequate for achieving high fidelity sampling of dynamic (high spatiotemporal variation) phenomena MSS is efficient for dynamic phenomena –Sampling latency is reduced because of real-time information provided by a high resolution, low fidelity sensor (imager or network of static sensors)