Multiscale Sensing: A new paradigm for actuated sensing of high frequency dynamic phenomena

Singh, Amarjeet; Budzik, Diane; Chen, W.; Batalin, Maxim; Stealey, Michael; Borgstrom, Henrik; Kaiser, W.J.

doi:10.1109/iros.2006.282517

Cited by 36 publications

(10 citation statements)

References 12 publications

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“…Hierarchical systems that enable the autonomous arrangement of sensors can optimize sensing fidelity and spatial coverage and have resulted in algorithms to increase the fidelity sampling of high-frequency spatiotemporal environmental phenomena (Singh et al, 2006). In this study, we were able to coordinate a higher level sensor, the digital camera, as a top-tier but lower resolution sensor with in situ, higher resolution sensors, creating a multiscale sensing system for soil surface and subsurface temperature measurement.…”

Section: Discussionmentioning

confidence: 99%

Forest understory soil temperatures and heat flux calculated using a Fourier model and scaled using a digital camera

Graham

Lam

Yuen

2010

Agricultural and Forest Meteorology

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a b s t r a c tThe characterization of the solar radiation environment under a forest canopy is important for both understanding temperature-dependent biological processes and validating energy balance models. A modified sinusoidal model of soil heat conductivity was used to estimate subsurface temperature and heat flux from the uneven but periodic solar heating of the soil surface due to sun flecks from a forest canopy. Using a mobile sensor platform with an infrared thermometer along an 11 m transect, a sunfleck model of soil surface temperature was tested using soil surface temperature maxima, air temperatures, and photodiodes placed on the soil surface to measure sunflecks. A pan-tilt-zoom digital camera on a 10 m tower above the site was then used to capture a time series of panoramic images of sunflecks reflected from the soil surface and to scale the sunfleck temperature model to a wide area. Finally, this image-based model of surface temperatures was combined with the modified sinusoidal model for heat conduction to estimate soil subsurface temperatures and heat flux over a wide area due to sunflecks from a forest canopy.

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Section: Discussionmentioning

confidence: 99%

Forest understory soil temperatures and heat flux calculated using a Fourier model and scaled using a digital camera

Graham

Lam

Yuen

2010

Agricultural and Forest Meteorology

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“…If the target has been detected, it configures the camera to provide high-resolution images for analyzing. A Singh et al 7 introduced a two-tier system for sampling. The first tier provides low-fidelity global information about the environment.…”

Section: Related Workmentioning

confidence: 99%

Adaptive sensing scheme using naive Bayes classification for environment monitoring with drone

Ho¹,

Huang

Chu

et al. 2018

International Journal of Distributed Sensor Networks

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Environmental sensors are important for collecting data to understand environmental changes and analyze environmental issues. In order to effectively monitor environmental changes, high-density sensor deployment and evenly distributed spatial distance between sensors become the requirements and desired properties for such applications. In many applications, sensors are deployed in locations that are difficult and dangerous to reach (e.g. mountaintop or skyscraper roof). To collect data from those sensors, unmanned aerial vehicles are used to act as data mules to overcome the problem of collecting data in challenging environments. In this article, we extend the adaptive return-to-home sensing algorithm with a parameter-tuning algorithm that combines naive Bayes classification and binary search to adapt adaptive return-tohome sensing parameters effectively on the fly. The proposed approach is able to (1) optimize number of sensing attempts, (2) reduce oscillation of the distance for consecutive attempts, and (3) reserve enough power for drone to return-to-home. Our results show that the naive Bayes classification-enhanced adaptive return-to-home sensing scheme is able to avoid oscillation in sensing and guarantees return-to-home feature while behaving more cost-effective in parameter tuning than the other machine learning-based approaches.

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“…The topology of this network is described in Fig. 11 and introduces the concept of multi-scale networks, which have been better formalized by [14] and [15]. In these networks, agents operate at different scales (spatial, temporal, computational) and allocate resources (energy, bandwidth, data) in order to achieve a higher level of representation.…”

Section: A Future Researchmentioning

confidence: 99%

Enabling scientific data sharing and re-use

Darby

Lambert

Matthews

et al. 2012

2012 IEEE 8th International Conference on E-Science

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Higher sensor throughput has increased the demand for cyberinfrastructure, requiring those unfamiliar with large database management to acquire new skills or outsource. Some have called this shift from sensor-limited data collection the "data deluge." As an alternative, we propose that the deluge is the result of sensor control software failing to keep pace with hardware capabilities. Rather than exploit the potential of powerful embedded operating systems and construct intelligent sensor networks that harvest higher quality data, the old paradigm (i.e. collect everything) is still dominant. To mitigate the deluge, we present an adaptive sampling algorithm based on the Nyquist-Shannon sampling theorem. We calibrate the algorithm for both data reduction and increased sampling over "hot moments," which we define as periods of elevated signal activity, deviating from previous works which have emphasized adaptive sampling for data compression via minimization of signal reconstruction error. Under the feature extraction concept, samples drawn from userdefined events carry greater importance and effective control requires the researcher to describe the context of events in the form of both an identification heuristic (for calibration) and a real-time sampling model. This event-driven approach is important when observation is focused on intermittent dynamics. In our case study application, we develop a heuristic to identify hot moments from historical data and use it to train and evaluate the adaptive model in an offline analysis using soil moisture data. Results indicate the adaptive model is superior to uniform sampling, capable of extracting 20% to 100% more samples during hot moments at equivalent levels of overall efficiency.

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Multiscale Sensing: A new paradigm for actuated sensing of high frequency dynamic phenomena

Cited by 36 publications

References 12 publications

Forest understory soil temperatures and heat flux calculated using a Fourier model and scaled using a digital camera

Forest understory soil temperatures and heat flux calculated using a Fourier model and scaled using a digital camera

Adaptive sensing scheme using naive Bayes classification for environment monitoring with drone

Enabling scientific data sharing and re-use

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