@conference {
	title = {Event-triggered Feedback Control for Signal Temporal Logic Tasks},
	author = {Lindemann, Lars., Maity, Dipankar., Baras, John S., Dimarogonas,Dimos.},
	year = {2018},
	month = {12/2018},
	address = {Miami, Florida},
	booktitle = { Conference on Decision and Control (CDC)2018}
}
@conference {
	title = {"State-dependent Data Queuing in Shared-resource Networked Control Systems"},
	author = {Mamduhi, Mohammad H., Baras, John S., Johansson, Karl Henrik., Hirche, Sandra.},
	year = {2018},
	month = {12/2018},
	abstract = {In the design of shared resource networked control systems (NCSs), resource managers play an important role to
appropriately allocate limited resources across the distributedsystem. They are often used to fairly distribute the limited bandwidth among the medium-sharing entities at the expense of delaying or discarding unnecessary data samples. Considering the rapidly growing volume of information that need to be exchanged, a relevant scenario for efficient resource management is state-dependent data buffering through network queues. In this paper, we propose state-dependent data buffering for shared-resource NCSs, such that both buffer input and output can be adjusted depending on the real-time conditions of the control systems and the communication network. We consider that the transmission decisions at the sensor side are taken by event-based schedulers, and that those data eventually sent for transmission are queued and processed, depending on the available communication resource. To improve the buffer service, we propose a state-dependent buffer discharge method, based on real-time feedback from the communication network. We derive sufficient stability conditions under which the overall NCS with the proposed cross-layer transmission scheme is stable in mean-square sense. Moreover, we show performance improvements resulting from our proposed design in comparison with its state-independent counterpart.},
	address = {Miami Beach, Florida},
	booktitle = {Conference on Decision and Control (CDC)2018}
}
@conference {
	title = {Non-Commutative Probability Models in Human Decision Making: Binary Hypothesis Testing},
	author = {Raghavan, Aneesh., Baras, John S.},
	year = {2018},
	month = {12/2018},
	abstract = {In this paper, we consider the binary hypothesis testing problem, as the simplest human
decision making problem, using a von-Neumann non-commutative probability framework.
We present two approaches to this decision making problem. In the rst approach, we represent
the available data as coming from measurements modeled via projection valued measures (PVM)
and retrieve the results of the underlying detection problem solved using classical probability
models. In the second approach, we represent the measurements using positive operator valued
measures (POVM). We prove that the minimum probability of error achieved in the second
approach is the same as in the rst approach.},
	address = {Miami, Florida},
	booktitle = {2nd IFAc Conference on Cyber-Physical \& Human-Systems (CPHS)2018}
}
@conference {
	title = {Optimal Decision Fusion Under Order Effects},
	author = {Ehsan Nekouei, Wei, Jieqiang., John S. Baras, Mikael Skoglund, Johansson, Karl Henrik.},
	year = {2018},
	month = {12/2018},
	abstract = {This paper studies an optimal decision fusion problem with a group of human decision makers when an order effect is present. The order effect refers to situations wherein the process of decision making by a human is affected by the order of decisions. In our set-up, all human decision makers, called observers, receive the same data which is generated by a common but unknown hypothesis. Then, each observer independently generates a sequence of decisions which are modeled by employing noncommutative probabilistic models of the data and their relation to the unknown hypothesis. The use of non-commutative probability models is motivated by recent psychological studies which indicate that these non-commutative probability models are more suitable for capturing the order effect in human decision making, compared with the classical probability model. A central decision maker (CDM) receives (possibly a subset of) the observers{\textquoteright} decisions and decides on the true hypothesis. The considered problem becomes an optimal decision fusion problem with observations modeled using a non-commutative (Von Neumann) probability model. The structure of the optimal decision rule at the CDM is studied under two scenarios. In the first scenario, the CDM receives the entire history of the observers{\textquoteright} decisions whereas in the second scenario, the CDM receives only the last decision of each observer. The perfromance of the optimal fusion rule is numerically evaluated and compared with the optimal fusion rule derived when using a classical probability model.},
	address = {Miami, Florida},
	booktitle = {2nd IFAc Conference on Cyber-Physical \& Human-Systems (CPHS)2018}
}
