SNAPS: Sensor Analytics Point Solutions for Detection and Decision Support Systems

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2019-11-13
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Datta, Shoumen
Cavallaro, Nicholas
Kiker, Greg
Jenkins, Daniel
Rong, Yue
Gomes, Carmen
Claussen, Jonathan
Vanegas, Diana
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Gomes, Carmen
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Claussen, Jonathan
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Mechanical Engineering
The Department of Mechanical Engineering at Iowa State University is where innovation thrives and the impossible is made possible. This is where your passion for problem-solving and hands-on learning can make a real difference in our world. Whether you’re helping improve the environment, creating safer automobiles, or advancing medical technologies, and athletic performance, the Department of Mechanical Engineering gives you the tools and talent to blaze your own trail to an amazing career.
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Ames National LaboratoryMechanical EngineeringFood Science and Human NutritionVirtual Reality Applications CenterAgricultural and Biosystems Engineering
Abstract

In this review, we discuss the role of sensor analytics point solutions (SNAPS), a reduced complexity machine-assisted decision support tool. We summarize the approaches used for mobile phone-based chemical/biological sensors, including general hardware and software requirements for signal transduction and acquisition. We introduce SNAPS, part of a platform approach to converge sensor data and analytics. The platform is designed to consist of a portfolio of modular tools which may lend itself to dynamic composability by enabling context-specific selection of relevant units, resulting in case-based working modules. SNAPS is an element of this platform where data analytics, statistical characterization and algorithms may be delivered to the data either via embedded systems in devices, or sourced, in near real-time, from mist, fog or cloud computing resources. Convergence of the physical systems with the cyber components paves the path for SNAPS to progress to higher levels of artificial reasoning tools (ART) and emerge as data-informed decision support, as a service for general societal needs. Proof of concept examples of SNAPS are demonstrated both for quantitative data and qualitative data, each operated using a mobile device (smartphone or tablet) for data acquisition and analytics. We discuss the challenges and opportunities for SNAPS, centered around the value to users/stakeholders and the key performance indicators users may find helpful, for these types of machine-assisted tools.

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This article is published as McLamore, Eric S., Shoumen Palit Austin Datta, Victoria Morgan, Nicholas Cavallaro, Greg Kiker, Daniel M. Jenkins, Yue Rong, Carmen Gomes, Jonathan Claussen, Diana Vanegas, and Evangelyn C. Alocilja. "SNAPS: Sensor aNAlytics Point Solutions for detection and decision support systems." Sensors 19, no. 22 (2019): 4935. DOI: 10.3390/s19224935. Posted withi permission.

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Tue Jan 01 00:00:00 UTC 2019
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