@conference {481,
	title = {Programming the Kennedy Receiver for Capacity Maximization versus Minimizing One-shot Error Probability},
	booktitle = {Frontiers in Optics 2020},
	year = {2020},
	month = {09/2020},
	publisher = {The Optical Society of America},
	organization = {The Optical Society of America},
	address = {Online},
	abstract = {<p>We find the capacity attained by the Kennedy receiver for coherent-state BPSK when the symbol prior $p$ and pre-detection displacement $\beta$ are optimized. The optimal $\beta$ is different than what minimizes error probability for single-shot BPSK state discrimination.</p>
},
	keywords = {Photonics, Photonics Information Theory, Quantum Optics},
	isbn = {978-1-943580-80-4},
	url = {https://www.osapublishing.org/abstract.cfm?uri=FiO-2020-JM6B.29},
	author = {Rahul Bhadani and Ivan B. Djordjevic and Jonathan Sprinkle and Saikat Guha},
	editor = {Michael Grace}
}
@conference {457,
	title = {A Pseudo-Derivative Method for Sliding Window Path Mapping in Robotics-Based Image Processing},
	year = {2019},
	publisher = {CAT Vehicle Research Experience for Undergraduates},
	organization = {CAT Vehicle Research Experience for Undergraduates},
	type = {Undergraduate Research},
	address = {Accepted at IEEE International Conference on Robotic Computing},
	abstract = {<p>A sliding window technique in robotics-based image processing applications is a common approach to path mapping from extracted features. Mapping a path inside an image requires finding a series of points representing the path. Previous approaches find these points by sliding a window along the path in fixed increments across one image dimension. After each slide, the center of the window in the other dimension is adjusted so that the window maximally covers the path in that area. This approach, however, fails to map paths that experience sharp curvature since the windows slide along only one dimension. The method proposed herein uses a pseudo-derivative approach to sliding windows that improves upon the traditional technique by dynamically adjusting the windows along both image dimensions during each slide. In this method, the directional components of a vector representing the previous slide are used as a naive estimation to perform the current slide. If this fails to map the path, the vector direction is used to enlarge the window dimensions. The method was tested in the domain of autonomous vehicles as an approach for detecting road lane markings. The algorithm proved more successful than previous sliding window approaches on perspective mapped lane images.</p>
},
	keywords = {autonomous vehicles, image processing},
	doi = {10.13140/RG.2.2.35628.10885},
	author = {Landon Bentley and Joe MacInnes and Rahul Bhadani and Tamal Bose}
}
@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 { c:whitsitt2012cdc,
	title = {A Passenger Comfort Controller for an Autonomous Ground Vehicle},
	booktitle = {51st IEEE Conference on Decision and Control},
	year = {2012},
	pages = {3380-3385},
	doi = {10.1109/CDC.2012.6426049},
	url = {http://dx.doi.org/10.1109/CDC.2012.6426049},
	author = {Sean Whitsitt and Jonathan Sprinkle}
}
@proceedings {225,
	title = {Proceedings of the 12th Workshop on Domain-specific Modeling},
	year = {2012},
	month = {10/2012},
	publisher = {ACM},
	address = {New York, NY, USA},
	author = {Juka-Pekka Tolvanen and Jonathan Sprinkle and Matti Rossi and Jeffrey Gray}
}
@proceedings {223,
	title = {Proceedings of the 11th Workshop on Domain-Specific Modeling},
	year = {2011},
	publisher = {ACM},
	address = {Portland, OR},
	author = {Juka-Pekka Tolvanen and Jonathan Sprinkle and Matti Rossi and Jeffrey Gray}
}
@proceedings {229,
	title = {Proceedings of the 18th IEEE International Conference and Workshops on Engineering of Computer-Based Systems},
	year = {2011},
	month = {2011},
	pages = {292},
	publisher = {IEEE Computer Society},
	address = {Las Vegas, NV},
	author = {Jonathan Sprinkle and Sterritt, Roy and Breitman, Karin}
}
@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: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:OOPSLA_EdSym04,
	title = {A Paradigm for Teaching Modeling Environment Design},
	booktitle = {OOPSLA{\textquoteright}04 Educators Symposium (Poster Session)},
	year = {2004},
	month = {October},
	publisher = {ACM},
	organization = {ACM},
	url = {http://www.eecs.berkeley.edu/~sprinkle/work/publications/rep/TeachingModelingWithGME.pdf},
	author = {Jonathan Sprinkle and James Davis and Greg Nordstrom}
}
@article {p:sprinkleNASAGSFC,
	title = {Pursuit/Evasion of Fixed-wing Aircraft through Model-Predictive Control},
	year = {2004},
	note = {The NASA/Goddard Space Flight Centers Information Science and Technology (IS\&T) Colloquium Series},
	month = {October},
	author = {Jonathan Sprinkle and J. Mikael Eklund and H. Jin Kim and S. Shankar Sastry}
}
