Renyi Entropy based Target Tracking in Mobile Sensor Networks

This paper proposes an entropy based target tracking approach for mobile sensor networks. The proposed tracking algorithm runs a target state estimation stage and a motion control stage alternatively. A distributed particle filter is developed to estimate the target position in the first stage. This distributed particle filter does not require to transmit the weighted particles from one sensor node to another. Instead, a Gaussian mixture model is formulated to approximate the posterior distribution represented by the weighted particles via an EM algorithm. The EM algorithm is developed in a distributed form to compute the parameters of Gaussian mixture model via local communication, which leads to the distributed implementation of the particle filter. A flocking controller is developed to control the mobile sensor nodes to track the target in the second stage. The flocking control algorithm includes three components. Collision avoidance component is based on the design of a separation potential function. Alignment component is based on a consensus algorithm. Navigation component is based on the minimization of an quadratic Renyi entropy. The quadratic Renyi entropy of Gaussian mixture model has an analytical expression so that its optimization is feasible in mobile sensor networks. The proposed active tracking algorithm is tested in simulation. © 2011 IFAC

Changes to the chemical state of the Northern Hemisphere atmosphere during the second half of the twentieth century

The NOx (NO and NO2) and HOx (OH and HO2) budgets of the atmosphere exert a major influence on atmospheric composition, controlling removal of primary pollutants and formation of a wide range of secondary products, including ozone, that can influence human health and climate. However, there remain large uncertainties in the changes to these budgets over recent decades. Due to their short atmospheric lifetimes, NOx and HOx are highly variable in space and time, and so the measurements of these species are of limited value for examining long-term, large-scale changes to their budgets. Here, we take an alternative approach by examining long-term atmospheric trends of alkyl nitrates, the production efficiency of which is dependent on the atmospheric [NO] ∕ [HO2] ratio. We derive long-term trends in the alkyl nitrates from measurements in firn air from the NEEM site, Greenland. Their mixing ratios increased by a factor of 3–5 between the 1970s and 1990s. This was followed by a steep decline to the sampling date of 2008. Moreover, we examine how the trends in the alkyl nitrates compare to similarly derived trends in their parent alkanes (i.e. the alkanes which, when oxidised in the presence of NOx, lead to the formation of the alkyl nitrates). The ratios of the alkyl nitrates to their parent alkanes increased from around 1970 to the late 1990s. This is consistent with large changes to the [NO] ∕ [HO2] ratio in the Northern Hemisphere atmosphere during this period. Alternatively, they could represent changes to concentrations of the hydroxyl radical, OH, or to the transport time of the air masses from source regions to the Arctic

Extreme 13C depletion of CCl2F2 in firn air samples from NEEM, Greenland

A series of 12 high volume air samples collected from the S2 firn core during the North Greenland Eemian Ice Drilling (NEEM) 2009 campaign have been measured for mixing ratio and stable carbon isotope composition of the chlorofluorocarbon CFC-12 (CCl2F2). While the mixing ratio measurements compare favorably to other firn air studies, the isotope results show extreme 13C depletion at the deepest measurable depth (65 m), to values lower than d13C = -80‰ vs. VPDB (the international stable carbon isotope scale), compared to present day surface tropospheric measurements near -40‰. Firn air modeling was used to interpret these measurements. Reconstructed atmospheric time series indicate even larger depletions (to -120‰) near 1950 AD, with subsequent rapid enrichment of the atmospheric reservoir of the compound to the present day value. Mass-balance calculations show that this change is likely to have been caused by a large change in the isotopic composition of anthropogenic CFC-12 emissions, probably due to technological advances in the CFC production process over the last 80 yr, though direct evidence is lacking

Anatomical Connectivity Influences both Intra- and Inter-Brain Synchronizations

Recent development in diffusion spectrum brain imaging combined to functional simulation has the potential to further our understanding of how structure and dynamics are intertwined in the human brain. At the intra-individual scale, neurocomputational models have already started to uncover how the human connectome constrains the coordination of brain activity across distributed brain regions. In parallel, at the inter-individual scale, nascent social neuroscience provides a new dynamical vista of the coupling between two embodied cognitive agents. Using EEG hyperscanning to record simultaneously the brain activities of subjects during their ongoing interaction, we have previously demonstrated that behavioral synchrony correlates with the emergence of inter-brain synchronization. However, the functional meaning of such synchronization remains to be specified. Here, we use a biophysical model to quantify to what extent inter-brain synchronizations are related to the anatomical and functional similarity of the two brains in interaction. Pairs of interacting brains were numerically simulated and compared to real data. Results show a potential dynamical property of the human connectome to facilitate inter-individual synchronizations and thus may partly account for our propensity to generate dynamical couplings with others

Complex networks: new trends for the analysis of brain connectivity

Today, the human brain can be studied as a whole. Electroencephalography, magnetoencephalography, or functional magnetic resonance imaging techniques provide functional connectivity patterns between different brain areas, and during different pathological and cognitive neuro-dynamical states. In this Tutorial we review novel complex networks approaches to unveil how brain networks can efficiently manage local processing and global integration for the transfer of information, while being at the same time capable of adapting to satisfy changing neural demands.Comment: Tutorial paper to appear in the Int. J. Bif. Chao

Inter-Brain Synchronization during Social Interaction

During social interaction, both participants are continuously active, each modifying their own actions in response to the continuously changing actions of the partner. This continuous mutual adaptation results in interactional synchrony to which both members contribute. Freely exchanging the role of imitator and model is a well-framed example of interactional synchrony resulting from a mutual behavioral negotiation. How the participants' brain activity underlies this process is currently a question that hyperscanning recordings allow us to explore. In particular, it remains largely unknown to what extent oscillatory synchronization could emerge between two brains during social interaction. To explore this issue, 18 participants paired as 9 dyads were recorded with dual-video and dual-EEG setups while they were engaged in spontaneous imitation of hand movements. We measured interactional synchrony and the turn-taking between model and imitator. We discovered by the use of nonlinear techniques that states of interactional synchrony correlate with the emergence of an interbrain synchronizing network in the alpha-mu band between the right centroparietal regions. These regions have been suggested to play a pivotal role in social interaction. Here, they acted symmetrically as key functional hubs in the interindividual brainweb. Additionally, neural synchronization became asymmetrical in the higher frequency bands possibly reflecting a top-down modulation of the roles of model and imitator in the ongoing interaction