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Changing Consumer Behavior through Ambient Displays in Smart Cafeterias and Detecting Anomalous Reporting Behavior in Wireless Sensors
Title
Changing Consumer Behavior through Ambient Displays in Smart Cafeterias and Detecting Anomalous Reporting Behavior in Wireless Sensors.
Creator
Hughes, Shiree, Hallstrom, Jason O., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Food availability and food waste are signi cant global problems which can be mitigated through the use of sensor networks. Current methods of monitoring food waste require manual data collection and are implemented infrequently, providing imprecise information. The use of sensors to automate food waste measurement allows constant monitoring, provides a better dataset for analysis, and enables real- time feedback, which can be used to affect behavioral change in consumers. The data from such...
Show moreFood availability and food waste are signi cant global problems which can be mitigated through the use of sensor networks. Current methods of monitoring food waste require manual data collection and are implemented infrequently, providing imprecise information. The use of sensors to automate food waste measurement allows constant monitoring, provides a better dataset for analysis, and enables real- time feedback, which can be used to affect behavioral change in consumers. The data from such networks can be used to drive ambient displays designed to educate a target audience, and ultimately reduce the amount of waste generated. We present WASTE REDUCE, a system for automating the measurement of food waste and affecting behavioral change. The challenges and results of deploying such a system are presented. To assess the bene ts of using WASTE REDUCE, two case studies are conducted. The rst study evaluates three different displays, and the second reevaluates one of these displays in a separate location. These studies con rm that the combination of automated monitoring and ambient feedback can reduce food waste for targeted groups.
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Date Issued
2017
PURL
http://purl.flvc.org/fau/fd/FA00004896, http://purl.flvc.org/fau/fd/FA00004896
Subject Headings
Consumer behavior., Intelligent sensors., Wireless sensor networks., Wireless communication systems., Environmental economics., Food consumption--Measurement., Food industry and trade--Safety measures., Food supply--Globalization.
Format
Document (PDF)
APIS: A SOFTWARE AND HARDWARE TOOLKIT FOR FEDERATED POWER MANAGEMENT IN ENERGY HARVESTING APPLICATIONS
Title
APIS: A SOFTWARE AND HARDWARE TOOLKIT FOR FEDERATED POWER MANAGEMENT IN ENERGY HARVESTING APPLICATIONS.
Creator
Prey, Adam, Hallstrom, Jason O., Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description
Embedded systems and Internet of Things (IoT) devices have been limited in application by constraints posed by batteries. Batteries add size, weight, and upkeep costs, while also limiting the lifetime of devices that are preferred to be small, lightweight, and long-lasting. We present Apis, a software and hardware toolkit for federated power management in energy harvesting applications. By replacing batteries with rapid charging storage capacitors, circuitry to control federated energy...
Show moreEmbedded systems and Internet of Things (IoT) devices have been limited in application by constraints posed by batteries. Batteries add size, weight, and upkeep costs, while also limiting the lifetime of devices that are preferred to be small, lightweight, and long-lasting. We present Apis, a software and hardware toolkit for federated power management in energy harvesting applications. By replacing batteries with rapid charging storage capacitors, circuitry to control federated energy storage, and software support to make this architecture useful to developers, embedded devices can potentially run inde nitely with limited maintenance. We present the Apis hardware design for controlling federated energy storage, supporting software for controlling this hardware, and the results of experiments performed to validate the Apis model. The system is named after the taxonomy genus for the honey bee, a creature dedicated to the harvesting and federated storage of energy resources.
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Date Issued
2019
PURL
http://purl.flvc.org/fau/fd/FA00013405
Subject Headings
Internet of things, Energy harvesting, Embedded systems (Computer systems), Telecommunication--Power supply, Application program interfaces (Computer software)
Format
Document (PDF)
RSSI-BASED PASSIVE LOCALIZATION IN COMPLEX OUTDOOR ENVIRONMENTS USING WI-FI PROBE REQUESTS
Title
RSSI-BASED PASSIVE LOCALIZATION IN COMPLEX OUTDOOR ENVIRONMENTS USING WI-FI PROBE REQUESTS.
Creator
Bao, Fanchen, Hallstrom, Jason O., Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description
Capturing pedestrian mobility patterns with high fidelity provides a foundation for data-driven decision-making in support of city planning, emergency response, and more. Due to scalability requirements and the sensitive nature of studying pedestrian movements in public spaces, the methods involved must be passive, low-cost, and privacy-centric. Pedestrian localization based on Received Signal Strength Indicator (RSSI) measurements from Wi-Fi probe requests is a promising approach. Probe...
Show moreCapturing pedestrian mobility patterns with high fidelity provides a foundation for data-driven decision-making in support of city planning, emergency response, and more. Due to scalability requirements and the sensitive nature of studying pedestrian movements in public spaces, the methods involved must be passive, low-cost, and privacy-centric. Pedestrian localization based on Received Signal Strength Indicator (RSSI) measurements from Wi-Fi probe requests is a promising approach. Probe requests are spontaneously emitted by Wi-Fi-enabled devices, are readily captured by of-the-shelf components, and offer the potential for anonymous RSSI measurement. Given the ubiquity of Wi-Fi-enabled devices carried by pedestrians (e.g., smartphones), RSSI-based passive localization in outdoor environments holds promise for mobility monitoring at scale. To this end, we developed the Mobility Intelligence System (MobIntel), comprising inexpensive sensor hardware to collect RSSI data, a cloud backend for data collection and storage, and web-based visualization tools. The system is deployed along Clematis Street in the heart of downtown West Palm Beach, FL, and over the past three years, over 50 sensors have been installed. Our research first confirms that RSSI-based passive localization is feasible in a controlled outdoor environment (i.e., no obstructions and little signal interference), achieving ≤ 4 m localization error in more than 90% of the cases. When significant time-varying signal fluctuations are introduced as a result of long-term deployment, performance can be maintained with an overhaul of the problem formulation and an updated localization model. However, when the outdoor environment is fully uncontrolled (e.g., along Clematis Street), the performance decreases to ≤ 4 m error in fewer than 70% of the cases. However, the drop in performance may be addressed through improved sensor maintenance, additional data collection, and appropriate domain knowledge.
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Date Issued
2023
PURL
http://purl.flvc.org/fau/fd/FA00014299
Subject Headings
Pedestrian traffic flow, Information technology, Computer Science
Format
Document (PDF)
NEURALSYNTH - A NEURAL NETWORK TO FPGA COMPILATION FRAMEWORK FOR RUNTIME EVALUATION
Title
NEURALSYNTH - A NEURAL NETWORK TO FPGA COMPILATION FRAMEWORK FOR RUNTIME EVALUATION.
Creator
Lanham, Grant Jr, Hallstrom, Jason O., Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description
Artificial neural networks are increasing in power, with attendant increases in demand for efficient processing. Performance is limited by clock speed and degree of parallelization available through multi-core processors and GPUs. With a design tailored to a specific network, a field-programmable gate array (FPGA) can be used to minimize latency without the need for geographically distributed computing. However, the task of programming an FPGA is outside the realm of most data scientists....
Show moreArtificial neural networks are increasing in power, with attendant increases in demand for efficient processing. Performance is limited by clock speed and degree of parallelization available through multi-core processors and GPUs. With a design tailored to a specific network, a field-programmable gate array (FPGA) can be used to minimize latency without the need for geographically distributed computing. However, the task of programming an FPGA is outside the realm of most data scientists. There are tools to program FPGAs from a high level description of a network, but there is no unified interface for programmers across these tools. In this thesis, I present the design and implementation of NeuralSynth, a prototype Python framework which aims to bridge the gap between data scientists and FPGA programming for neural networks. My method relies on creating an extensible Python framework that is used to automate programming and interaction with an FPGA. The implementation includes a digital design for the FPGA that is completed by a Python framework. Programming and interacting with the FPGA does not require leaving the Python environment. The extensible approach allows multiple implementations, resulting in a similar workflow for each implementation. For evaluation, I compare the results of my implementation with a known neural network framework.
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Date Issued
2020
PURL
http://purl.flvc.org/fau/fd/FA00013533
Subject Headings
Artificial neural networks, Neural networks (Computer science)--Design, Field programmable gate arrays, Python (Computer program language)
Format
Document (PDF)