@conference {495, title = {CAN Coach: Vehicular Control through Human Cyber-Physical Systems}, booktitle = {Proceedings of the ACM/IEEE 12th International Conference on Cyber-Physical Systems}, year = {2021}, publisher = {Association for Computing Machinery}, organization = {Association for Computing Machinery}, address = {New York, NY, USA}, abstract = {

This work addresses whether a human-in-the-loop cyber-physical system (HCPS) can be effective in improving the longitudinal control of an individual vehicle in a traffic flow. We introduce the CAN Coach, which is a system that gives feedback to the human-in-the-loop using radar data (relative speed and position information to objects ahead) that is available on the controller area network (CAN). Using a cohort of six human subjects driving an instrumented vehicle, we compare the ability of the human-in-the-loop driver to achieve a constant time-gap control policy using only human-based visual perception to the car ahead, and by augmenting human perception with audible feedback from CAN sensor data. The addition of CAN-based feedback reduces the mean time-gap error by an average of 73\%, and also improves the consistency of the human by reducing the standard deviation of the time-gap error by 53\%. We remove human perception from the loop using a ghost mode in which the human-in-the-loop is coached to track a virtual vehicle on the road, rather than a physical one. The loss of visual perception of the vehicle ahead degrades the performance for most drivers, but by varying amounts. We show that human subjects can match the velocity of the lead vehicle ahead with and without CAN-based feedback, but velocity matching does not offer regulation of vehicle spacing. The viability of dynamic time-gap control is also demonstrated. We conclude that (1) it is possible to coach drivers to improve performance on driving tasks using CAN data, and (2) it is a true HCPS, since removing human perception from the control loop reduces performance at the given control objective.

}, keywords = {controller area network, cyber-physical systems, human-in-the-loop, vehicles}, isbn = {9781450383530}, doi = {10.1145/3450267.3450541}, url = {https://doi.org/10.1145/3450267.3450541}, author = {Nice, Matthew and Elmadani, Safwan and Bhadani, Rahul and Bunting, Matt and Sprinkle, Jonathan and Work, Dan} } @article {484, title = {Constellation Optimization for Phase-Shift Keying Coherent States With Displacement Receiver to Maximize Mutual Information}, journal = {IEEE Access}, volume = {8}, year = {2020}, month = {11/2020}, pages = {224409{\textendash}224419}, abstract = {

An important problem in quantum information theory is finding the best possible capacity of the optical communication channel employing suitable codewords, receiver design, and constellation optimization techniques. In this paper, we derive an alternative channel capacity, $C_G$ , of phase-shift keying coherent state with a realizable displacement receiver by maximizing mutual information over symbol priors and pre-detection displacement. We find that\ $C_G$ is higher than the capacity achieved by maximizing mutual information over symbol prior but with zero displacements. The overall scheme demonstrates designing an improved, yet easy-to-implement receiver for better communication performance by tuning it at different photon number regime. Further, we present a comparative analysis of\ CG\ with existing receiver designs. We extend our study to account for detector imperfections.

}, issn = {2169-3536}, doi = {10.1109/ACCESS.2020.3044086}, author = {Bhadani, Rahul and Djordjevic, Ivan B} } @conference {448, title = {{The CAT Vehicle Testbed: A Simulator with Hardware in the Loop for Autonomous Vehicle Applications}}, booktitle = {Proceedings 2nd International Workshop on Safe Control of Autonomous Vehicles (SCAV 2018), Porto, Portugal, Electronic Proceedings in Theoretical Computer Science }, volume = {269}, year = {2018}, month = {04/2018}, type = {Workshop}, keywords = {autonomous vehicles, simulation, testbed}, doi = {10.4204/EPTCS.269.4}, author = {Rahul Bhadani and Jonathan Sprinkle and Matthew Bunting} } @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 {Work:2017:CUE:3055378.3055380, title = {Controlling for Unsafe Events in Dense Traffic Through Autonomous Vehicles: Invited Talk Abstract}, booktitle = {Proceedings of the 1st International Workshop on Safe Control of Connected and Autonomous Vehicles}, year = {2017}, pages = {7{\textendash}7}, publisher = {ACM}, organization = {ACM}, address = {New York, NY, USA}, keywords = {Sugiyama experiment, Traffic flow}, isbn = {978-1-4503-4976-5}, doi = {10.1145/3055378.3055380}, url = {http://doi.acm.org/10.1145/3055378.3055380}, author = {Daniel B. Work and Raphael E Stern and Fangyu Wu and Miles Churchill and Shumo Cui and Hannah Pohlmann and Benjamin Seibold and Benedetto Piccoli and Rahul Bhadani and Matthew Bunting and Jonathan Sprinkle and Maria Laura Dell Monache and Nathaniel Hamilton and Haulcy, R.} } @article {sprinkle266, title = {Computationally-Aware Switching Criteria for Hybrid Model Predictive Control Of Cyber-Physical Systems}, journal = {IEEE Transactions on Automation Science and Engineering}, volume = {13}, year = {2016}, month = {04/2016}, pages = {479-490}, doi = {10.1109/TASE.2016.2523341}, url = {http://dx.doi.org/10.1109/TASE.2016.2523341}, author = {Kun Zhang and Jonathan Sprinkle and Ricardo G. Sanfelice} } @article {zhang2015auro, title = {Computationally-Aware Control of Autonomous Vehicles: A Hybrid Model Predictive Control Approach}, journal = {Autonomous Robots}, year = {2015}, pages = {503-517}, doi = {10.1007/s10514-015-9469-5}, url = {http://dx.doi.org/10.1007/s10514-015-9469-5}, author = {Kun Zhang and Jonathan Sprinkle and Ricardo G. Sanfelice} } @conference {sprinkle250, title = {A Closed-loop Model-based Design Approach Based On Automatic Verification and Transformation}, booktitle = {The 14th Workshop on Domain-Specific Modeling}, year = {2014}, month = {2014}, pages = {1-6}, abstract = {

Domain-specific modeling languages effectively constrain struc- tural concepts, but constraints that are not easily captured with structural constraints are still important to fix at design time. In practice these kinds of constraints are implicitly left to be carried out by the domain modelers. This paper explores the process of in- corporating system behavioral (not just structural) constraints into a DSML, and studies the way of generating feasible transformation solutions if those constraints fail, based on a transformation library constructed in advance. Our approach is to carry out the verifica- tion process through code generation, but utilize the results of veri- fication as an input to a model transformation generator. The output transformation then operates on the original model. As a case study, we applied the approach to finite state machine (FSM) models that control a cyber-physical system.

}, doi = {10.1145/2688447.2688448}, url = {http://dx.doi.org/10.1145/2688447.2688448}, author = {Kun Zhang and Jonathan Sprinkle} } @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} } @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} } @article {HCTCPS, title = {A Community Report of the 2008 High Confidence Transportation Cyber-Physical Systems {(HCTCPS)} Workshop}, year = {2009}, month = {July 22}, address = {High Confidence Transportation Cyber-Physical Systems (HCTCPS)}, url = {http://www.ee.washington.edu/research/nsl/aar-cps/}, author = {Radha Poovendran and Raj Rajkumar and David Corman and Jim Paunicka and William P. Milam and K Venkatesh Prasad and Shige Wang and Jim Barhorst and Christopher Gill and Sandeep Gupta and Krishna Sampigethaya and Jonathan Sprinkle and Doug Stuart and Wayne Wolf and Rahul Mangharam} } @conference {w:EklundCSB2005, title = {Computing Inverse {MEG} Signals in the Brain}, booktitle = {2005 IEEE Computational Systems Bioinformatics Conference, Controlling Complexity}, year = {2005}, month = {August}, pages = {332{\textendash}335}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1540637}, author = {J. Mikael Eklund and Ruzena Bajcsy and Jonathan Sprinkle and Gregory V. Simpson} } @article {tr:ANTSTechReport, title = {Computer-aided aircraft maintenance scheduling}, number = {ISIS-02-303}, year = {2002}, month = {November}, publisher = {Vanderbilt University}, type = {Tech. Rep.}, address = {2015 Terrace Place}, author = {Christopher P. van Buskirk and Benoit Dawant and G{\'a}bor Karsai and Jonathan Sprinkle and Gabor Szokoli and Karlkim Suwanmongkol and Russ Currer} } @article {j:LedecziComputer2001, title = {Composing Domain-Specific Design Environments}, journal = {IEEE Computer}, volume = {34}, number = {11}, year = {2001}, month = {November}, pages = {44{\textendash}51}, doi = {10.1109/2.963443}, url = {http://dx.doi.org/10.1109/2.963443}, author = {{\'A}kos L{\'e}deczi and {\'A}rpad Bakay and Miklos Maroti and P{\'e}ter Volgyesi and Greg Nordstrom and Jonathan Sprinkle and G{\'a}bor Karsai} }