@conference {472, title = {Domain Specific Modeling Language for Test World Creation}, booktitle = {CAT Vehicle Research Experience for Undergraduates}, year = {2019}, month = {08/2019}, publisher = {The University of Arizona}, organization = {The University of Arizona}, address = {Tucson}, abstract = {

It is often necessary to use a 3D physics simulator in order to model and test complex robotic systems. Verifying certain behaviors of systems in the real world can be costly, time-consuming, and even dangerous, while simulations are relatively cheap and fast. However, creating simulated environments to test robotic behaviors can take up quite a lot of time and processing power. In order for behaviors to be tested in a variety of scenarios, multiple environments must be created, causing verification time to increase. This paper presents a domain-specific modeling language that can be used to speed up this process. This modeling language can be used in WebGME to generate multiple world and launch files in Gazebo, a 3D dynamics simulator. These world files can then be used to test various behaviors of complex robots such as the CAT Vehicle (Cognitive and Autonomous Test Vehicle) in a variety of simulated environments. This model language can save valuable testing time by quickly creating usable test files for complex physics based models such as the CAT Vehicle.

}, keywords = {autonomous vehicles, dsml, simulation}, url = {http://csl.arizona.edu/content/domain-specific-modeling-language-test-world-creation}, author = {Jill Alexander and Alex Pyryt and Rahul Bhadani and Matthew Bunting} } @conference {470, title = {Reinforcement Learning for Autonomous Driving using CAT Vehicle Testbed}, booktitle = {CAT Vehicle Research Experience for Undergraduates}, year = {2019}, month = {08/2019}, publisher = {The University of Arizona}, organization = {The University of Arizona}, type = {Technical Report}, address = {Tucson}, abstract = {

We discuss a deep reinforcement learning implementation using the CAT Vehicle Testbed and discuss the merits of simulation-based deep reinforcement learning. After implementing a simple 3 layered neural network which learned using deep Q-learning, we found some challenges associated with ROS and Gazebo. In spite of these challenges, we demonstrate that our reinforcement learning architecture can teach a car to avoid obstacles. With these preliminary results, we discuss what new things we can do and how we can implement more advanced methods.

}, keywords = {Autonomous Systems, Machine Learning}, url = {http://csl.arizona.edu/content/reinforcement-learning-autonomous-driving-using-cat-vehicle-testbed}, author = {John Nguyen and Hoang Huynh and Eric Av and Rahul Bhadani and Tamal Bose} } @conference {475, title = {Cognitive Engine Testbed for Vehicular Communications}, booktitle = {CAT Vehicle Research Experience for Undergraduates}, year = {2018}, publisher = {The University of Arizona}, organization = {The University of Arizona}, address = {Tucson, Arizona}, abstract = {

Ad-hoc networks have the potential to increase the safety and reliability of autonomous vehicles. The amount of radio spectrum available for such networks is limited, however. The use of cognitive radio, especially when integrated with reinforcement learning algorithms, may help to ease the issue of limited spectrum by finding optimal transmission policies and detecting the presence of other users, especially in a scenario where a Primary User and Secondary User are contesting for spectrum. This paper presents a testbed for simulating cognitive engines in these networks using a variety of reinforcement learning algorithms, including -greedy, Softmax Strategy, and Q-Learning. The goal of these cognitive engines is to learn to choose the best modulation and coding rates given various channel conditions and a user-defined optimization goal (i.e. maximize throughput, minimize bit error rate). The cognitive engine then learns the optimal coding rates and modulation schemes for the given environment, and the testbed displays a visual of the performance of the cognitive engine at each time step. In the case of Q-learning in a Primary User vs. Secondary User scenario, the cognitive engine also learns to choose the best channel for transmission.

}, url = {http://csl.arizona.edu/content/cognitive-engine-testbed-vehicular-communications}, author = {William Anderson and Ashley Aponik and Youssef Daoud and Noel Teku and Garrett Vanhoy and Tamal Bose} } @conference {474, title = {Validation of a CRV Model Using TVWS Measurements}, booktitle = { Proceedings of SDR-WinnComm 2017 Wireless Innovation Conference on Wireless Communications Technologies and Software Defined Radio}, year = {2017}, month = {11/2017}, publisher = {The Wireless Innovation Forum}, organization = {The Wireless Innovation Forum}, address = {San Diego, California}, abstract = {

As autonomous vehicles advance, their commercial popularity will rise. This growth will increase the need for wireless transmission of data to these cars not only to drive more efficiently, but to also entertain the driver. As people are freed from the task of driving, the demand for in-car internet applications, such as Netflix or Skype, will grow. Currently, autonomous vehicles are allowed to transmit using the band specified by the IEEE protocol 802.11p. While vehicles can transmit data using the 5.9 GHz band (5.850-5.925 GHz), the band may not support wireless transmission of media to vehicles{\textquoteright} infotainment systems. This requires an alternative. With the switch from analog to digital television, the government has vacated the analog TV bands. These bands provide a possible solution to the limitations of 802.11p transmissions. The vacated space is called TV white space. One proposed use of this white space is to provide Wi-Fi. This idea has been called White-Fi. According to our research, researchers have measured whether the specific frequencies are occupied but do not provide the unprocessed data. With this in mind, we measure the occupancy of the TV white space and we simulate how a network using this band would perform under the multiple scenarios of everyday driving.

}, url = {https://www.wirelessinnovation.org/assets/Proceedings/2017/winncomm\%202017\%20proceedings.pdf}, author = {Khalil Anderson and Lauren Lusk and Marti Hands and Garrett Vanhoy} } @proceedings {sprinkle271, title = {Power Efficient Vehicular Ad Hoc Networks}, year = {2016}, month = {03/2016}, pages = {26-31}, address = {Reston, VA}, abstract = {Inter-vehicular communication is a growing plat- form for improving roadway safety. The highly mobile nature of Vehicle to Vehicle communications causes rapid changes in network topologies and propagation conditions. Since the advent of Vehicular Ad-Hoc Networks (VANETs), over fifty routing protocols with attendant topologies have been proposed. Despite these protocols{\textquoteright} merits, many of them are not optimized for power management and frequency reuse. Our approach utilizes the one dimensional dynamic of divided highways to simplify the routing problem and reduce energy consumption. Since each car is aware of only two types of connections, up-road and down-road, we can form low power, line of sight links between adjacent vehicles. We also utilize a fuzzy logic algorithm that predicts the location of up-road cars to reduce interference from request for link signals. Once these links have been established, up-road vehicles send data down-road for a length of time based on the relative speed of the two vehicles. After this time period has expired the down-road vehicle must request additional information, restarting the timer. Data sent through the network will include information on up-road vehicles, and when required, messages such as accident notifications, alerts, and traffic warnings. Through simulation, we show that our approach to VANETs maintains its update frequency despite bumper to bumper traffic and uses two to five orders of magnitude less power than an IEEE 802.11 network with clustering and 1 mW transmit power. Overall, the network performs well and is a viable improvement to the standard.}, author = {Sterling Holcomb and Audrey Knowlton and Juan Guerra and Hamed Asadi and Haris Volos and Jonathan Sprinkle and Tamal Bose} } @conference {sprinkle274, title = {Intersection Management via the Opportunistic Organization of Platoons by Route}, booktitle = {WinnComm 2016}, year = {2015}, abstract = {Intersections behave as a pathway for traffic redirection and play an important role in the general movement of traffic, because of this, traffic congestion is not desirable and methods to remove congestion or its effects have been implemented. Generally, congestion occurs when a group of vehicles that is large relative to available road space attempts to move through an area that cannot redirect the group effectively. The are of this paper{\textquoteright}s focus is the intersection. Some methods currently implemented to control congestion are roadway expansion, the use of adaptive traffic lights, and the use of public transportation. Good short term solutions, they only postpone the occurrence of congestion and are also costly. One way to deal with congestion is have vehicles cooperate with each other and their intersections through means of wireless communication. The vehicles can then be organized by a management system in such a way that vehicle throughput at an intersection can be maintained even at high levels of traffic congestion. A requirement of the system proposed in this paper is that all vehicles behave autonomously in order to obtain easily predictable behavior. The following describes the results of such a simulation of such a system. The data gathered over four simulations show that a higher platoon depth causes a general decrease in delay. The results between signalized and un-signalized simulations were vastly different with the un-signalized having significantly better results. The delay at a platoon depth of 9, for the unsignalized intersection, was -1.8, this brings the ideal delay to 98.2 seconds down from 100 seconds. This was due to the way intersection merging and gap threading is performed and is valid for several reasons. Because this method of intersection management desires to decrease the delay in traffic as much as possible, it would not make sense to slow down a platoon in order to perform a maneuver. The signalized method performed significantly worse than the un-signalized due to how the signalized method was implemented. Only one traffic light was green at a time, hence, whenever a platoon was stopped at intersection, that platoon would be guaranteed a delay of at most 56 seconds, which is how long a red light lasts for one signal. Though inefficient signal timing was used, we hypothesize that the behavior of a right of way system, such as a traffic light, is fundamentally flawed. By delegating the right of way to only a few lanes at a time, it is guaranteed that some vehicles will be stopped and will therefore experience delay. In order to show that the capacity of signalized intersection management through the use of traffic lights falls short when compared to un-signalized autonomous intersection management, better traffic light control methods will need to be tested in the future.}, author = {Alberto Heras and Lykes Claytor and Haris Volos and Hamed Asadi and Jonathan Sprinkle and Tamal Bose} } @conference { w:whitsitt-neat-2011, title = {Constrained data acquisition for mobile citizen science applications}, booktitle = {Proceedings of the compilation of the co-located workshops on DSM{\textquoteright}11, TMC{\textquoteright}11, AGERE!{\textquoteright}11, AOOPES{\textquoteright}11, NEAT{\textquoteright}11, \& VMIL{\textquoteright}11}, series = {SPLASH {\textquoteright}11 Workshops}, year = {2011}, pages = {267{\textendash}272}, publisher = {ACM}, organization = {ACM}, address = {New York, NY, USA}, abstract = {

The popularity and ubiquity of personal mobile computing devices{\textendash}-coupled with their powerful sensing capabilities{\textendash}-allow their application in the structured collection of data for societal benefit and science applications. Citizen scientists are willing users and active contributors to scientific research and applications, but if they gather data in an unconstrained or ad hoc manner, their efforts may be of little scientific value. In this paper, we present a user interface for a mobile device which is properly constrained to permit the gathering of valid scientific data. This helps to achieve the goal that any individual with a basic familiarity of the device (but not of the science) should be able to obtain useful data with little learning required. As a use case for this concept, we present a mobile application that allows users to collect location-stamped images to supplement satellite data for climate change research.

}, keywords = {citizen scientist, domain-specific modeling, iphone, mobile phone programming}, isbn = {978-1-4503-1183-0}, doi = {10.1145/2095050.2095095}, url = {http://dx.doi.org/10.1145/2095050.2095095}, author = {Sean Whitsitt and Armando Barreto and Maribel Hudson and Hussain Al-Helal and Diyang Chu and Kamel Didan and Jonathan Sprinkle} } @mastersthesis { a:alhelal-ms, title = {Provable Detection Of Moving Targets With Reliable Sensors}, year = {2011}, month = {December}, school = {University of Arizona}, type = {mastersmasters}, author = {Hussain Al-Helal} } @conference {c:whitsitt-prose-2010, title = {Citizen Science in Support of Vegetation Index and Phenology Research}, booktitle = {Phenology Research and Observations of Southwest Ecosystems Symposium (PROSE)}, year = {2010}, month = {October}, abstract = {

Vegetation indices (VIs) are simple transformations of images into proxy measures of greenness and vegetation health and change over time. They are also used to derive information about the land surface phenology status, providing extensive spatial coverage and direct support for global ecosystem models. These measurements however contain large uncertainties and errors. A new suite of mobile devices, equipped with geo-location, image capture, and transmission capabilities could aid with vegetation phenology observations and documentation. The iPhone, with its wide distribution and array of sensors, can contribute significantly to the field of citizen science. In this project we are developing an end-to-end system for the collection, processing, and visualization of land surface vegetation phenology. The system consists of a client-server application and a Google Earth based visualization model. The client side (an iPhone app) intuitively guides the observer to capture up to three images per location: a close-up image of leaves, flowers, or fruits, an individual plant image, and a panoramic landscape image. The iPhone automatically embeds location, orientation, date/time, and other metadata with the images and allows the observer to add text comments. The images are then transmitted to the server, where they are validated, post-processed, archived, and made available to the interactive visualization system. The images are separated into primary colors and processed into a greenness index comparable to the classical VI. These measurements are then plotted against satellite based VI time series to aid in their validation and the characterization of the location phenology. With this effort we hope to recruit global observers into contributing to the field of land surface vegetation change detection and characterization.

}, author = {Sean Whitsitt and Armando Barreto and Sundaresh Ram and Hussain Al-Helal and Maribel Hudson and Diyang Chu and Jonathan Sprinkle and Kamel Didan} } @conference { c:alhelal-ecbs-2010, title = {{UAV} Search : Maximizing Target Acquisition}, booktitle = {17th IEEE Conference on the Engineering of Computer Based Systems}, year = {2010}, month = {March}, pages = {9{\textendash}19}, publisher = {IEEE}, organization = {IEEE}, doi = {10.1109/ECBS.2010.9}, url = {http://dx.doi.org/10.1109/ECBS.2010.9}, author = {Hussain Al-Helal and Jonathan Sprinkle} } @article {tr:AlHelal-IEEE-StudentPaper-2009, title = {UAV Search: Maximising Target Acquisition}, year = {2009}, month = {April}, publisher = {University of Arizona}, type = {IEEE Student Paper Competition}, address = {1230 E. Speedway Blvd., Bldg. 104}, url = {http://www.ece.arizona.edu/~sprinkjm/wiki/uploads/Main/UAVSearch-MaximisingTargetAcquisition-AlHelal.pdf}, author = {Hussain Al-Helal} } @conference { c:gulotta-ecbs-2009, title = {Using Integrative Models in an Advanced Heterogeneous System Simulation}, booktitle = {IEEE International Conference on the Engineering of Computer-Based Systems}, year = {2009}, pages = {3-10}, publisher = {IEEE Computer Society}, organization = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, isbn = {978-0-7695-3602-6}, doi = {10.1109/ECBS.2009.42}, url = {http://doi.ieeecomputersociety.org/10.1109/ECBS.2009.42}, author = {Jacob Gulotta and Diyang Chu and Ximing Yu and Hussain Al-Helal and Tapasya Patki and Jason Hansen and Maribel Hudson and Jonathan Sprinkle} } @conference { w:patki-oopsla-2008, title = {Using Integrative Modeling for Advanced Heterogeneous System Simulation}, booktitle = {The 8th OOPSLA Workshop on Domain-Specific Modeling}, year = {2008}, month = {October 19-20}, pages = {80-85}, url = {http://www.dsmforum.org/events/DSM08/Papers/14-Patki.pdf}, author = {Tapasya Patki and Hussain Al-Helal and Jacob Gulotta and Jason Hansen and Jonathan Sprinkle} } @article {tr:sbdt2007, title = {DARPA Urban Challenge Technical Paper: Sydney-Berkeley Driving Team}, year = {2007}, month = {June}, publisher = {University of Sydney; University of Technology, Sydney; University of California, Berkeley}, url = {http://chess.eecs.berkeley.edu/pubs/379.html}, author = {Ben Upcroft and Michael Moser and Alexi Makarenko and David Johnson and Ashod Donikan and Alen Alempijevic and Robert Fitch and William Uther and Jan Biermeyer and Humberto Gonzalez and Esten Ingar Gr{\o}tli and Todd Templeton and Vason P. Srini and Jonathan Sprinkle} } @article {j:sprinkle-ISSE-2005, title = {Online Safety Calculations for Glideslope Recapture}, journal = {Innovations in Systems and Software Engineering}, volume = {1}, number = {2}, year = {2005}, month = {September}, pages = {157{\textendash}175}, doi = {10.1007/s11334-005-0017-x}, url = {http://dx.doi.org/10.1007/s11334-005-0017-x}, author = {Jonathan Sprinkle and Aaron D. Ames and J. Mikael Eklund and Ian Mitchell and S. Shankar Sastry} } @conference {c:SprinkleCDC2005, title = {On the Partitioning of Syntax and Semantics For Hybrid Systems Tools}, booktitle = {44th IEEE Conference on Decision and Control and European Control Conference ECC 2005 (CDC-ECC{\textquoteright}05)}, year = {2005}, month = {December}, pages = {4694{\textendash}4699}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1582903}, author = {Jonathan Sprinkle and Aaron D. Ames and Alessandro Pinto and Haiyang Zheng and S. Shankar Sastry} } @conference {c:SprinkleECBS2003, title = {Domain Translation Using Graph Transformations}, booktitle = {Tenth IEEE International Conference and Workshop on the Engineering of Computer-Based Systems}, year = {2003}, month = {April}, pages = {159{\textendash}168}, doi = {10.1109/ECBS.2003.1194795}, url = {http://dx.doi.org/10.1109/ECBS.2003.1194795}, author = {Jonathan Sprinkle and Aditya Agrawal and T{\'\i}hamer Levendovszky and Feng Shi and G{\'a}bor Karsai} } @article {j:KarsaiJUCS, title = {On the Use of Graph Transformation in the Formal Specification of Model Interpreters}, journal = {Journal of Universal Computer Science}, volume = {9}, number = {11}, year = {2003}, month = {November}, pages = {1296{\textendash}1321}, url = {http://www.jucs.org/jucs_9_11/on_the_use_of/Karsai_G.pdf}, author = {G{\'a}bor Karsai and Aditya Agrawal and Feng Shi and Jonathan Sprinkle} } @conference {w:SprinkleOOPSLA2002, title = {Domain Evolution in Visual Languages Using Graph Transformations}, booktitle = {OOPSLA, 2nd ACM OOPSLA Workshop on Domain-Specific Languages}, year = {2002}, month = {November}, url = {http://www.isis.vanderbilt.edu/publications/archive/Sprinkle_JM_11_4_2002_Domain_Evo.doc}, author = {Jonathan Sprinkle and Aditya Agrawal and T{\'\i}hamer Levendovszky and Feng Shi and G{\'a}bor Karsai} } @conference {w:Agrawal2002, title = {Generative Programming via Graph Transformations in the Model-Driven Architecture}, booktitle = {OOPSLA, Workshop on Generative Techniques in the Context of Model Driven Architecture}, year = {2002}, month = {November}, url = {http://www.isis.vanderbilt.edu/publications/archive/Agrawal_A_11_5_2002_Generative.pdf}, author = {Aditya Agrawal and T{\'\i}hamer Levendovszky and Jonathan Sprinkle and Feng Shi and G{\'a}bor Karsai} }