Jean-François Chamberland

Learn More
In this paper, we investigate a binary decentralized detection problem in which a network of wireless sensors provides relevant information about the state of nature to a fusion center. Each sensor transmits its data over a multiple access channel. Upon reception of the information, the fusion center attempts to accurately reconstruct the state of nature.(More)
In this paper, we study a binary decentralized detection problem in which a set of sensor nodes provides partial information about the state of nature to a fusion center. Sensor nodes have access to conditionally independent and identically distributed observations, given the state of nature, and transmit their data over a wireless channel. Upon reception(More)
A detection problem in sensor networks is considered, where the sensor nodes are placed on a line and receive partial information about their environment. The nodes transmit a summary of their observations over a noisy communication channel to a fusion center for the purpose of detection. The observations at the sensors are samples of a spatial stochastic(More)
An approximate representation for the state space of a context-sensitive probabilistic Boolean network has previously been proposed and utilized to devise therapeutic intervention strategies. Whereas the full state of a context-sensitive probabilistic Boolean network is specified by an ordered pair composed of a network context and a gene-activity profile,(More)
Wireless systems offer a unique mixture of connectivity, flexibility, and freedom. It is therefore not surprising that wireless technology is being embraced with increasing vigor. For real-time applications, user satisfaction is closely linked to quantities such as queue length, packet loss probability, and delay. System performance is therefore related to,(More)
The concept of effective capacity offers a novel methodology to investigate the impact that design decisions at the physical layer may have on system performance at the link layer. Assuming a constant flow of incoming data, the effective capacity characterizes the maximum arrival rate that a wireless system can support as a function of its service(More)
A prime objective of modeling genetic regulatory networks is the identification of potential targets for therapeutic intervention. To date, optimal stochastic intervention has been studied in the context of probabilistic Boolean networks, with the control policy based on the transition probability matrix of the associated Markov chain and dynamic(More)
There is an ongoing effort to design optimal intervention strategies for discrete state-space synchronous genetic regulatory networks in the context of probabilistic Boolean networks; however, to date, there has been no corresponding effort for asynchronous networks. This paper addresses this issue by postulating two asynchronous extensions of probabilistic(More)
A key objective of gene network modeling is to develop intervention strategies to alter regulatory dynamics in such a way as to reduce the likelihood of undesirable phenotypes. Optimal stationary intervention policies have been developed for gene regulation in the framework of probabilistic Boolean networks in a number of settings. To mitigate the(More)
External control of a genetic regulatory network is used for the purpose of avoiding undesirable states such as those associated with a disease. Certain types of cancer therapies, such as chemotherapy, are given in cycles with each treatment being followed by a recovery period. During the recovery period, the side effects tend to gradually subside. In this(More)