Browsing by Subject "Distributed networks"

Now showing 1 - 6 of 6

Open Access
Category-selective top-down modulation in the fusiform face area of the human brain during visual search
(IEEE, 2017) Dar, Salman Ul Hassan; Çukur, Tolga
Several regions in the ventral-temporal cortex of the human brain are thought to have representations of specific categories of objects. Furthermore, a distributed network of frontal and parietal brain regions is implicated in attentional control. It is assumed that during visual search, attention-control regions send top-down signals to the target category-selective areas to bias the processing in favour of the attended object category. However, little is known about such causal interactions during naturalistic visual search. Here we assess the influence of attention-control brain regions on a well-known face selective area fusiform face area (FFA) during natural visual search using Granger causality analysis. Our results indicate that attending to humans enhances the influence of attention-control regions on the fusiform face area.
Open Access
Communication efficient channel estimation over distributed networks
(IEEE, 2014) Sayın, Muhammed O.; Vanlı, N. Denizcan; Göze, T.; Kozat, Süleyman Serdar
We study diffusion based channel estimation in distributed architectures suitable for various communication applications such as cognitive radios. Although the demand for distributed processing is steadily growing, these architectures require a substantial amount of communication among their nodes (or processing elements) causing significant energy consumption and increase in carbon footprint. Due to growing awareness of telecommunication industry's impact on the environment, the need to mitigate this problem is indisputable. To this end, we introduce algorithms significantly reducing the communication load between distributed nodes, which is the main cause in energy consumption, while providing outstanding performance. In this framework, after each node produces its local estimate of the communication channel, a single bit or a couple of bits of information is generated using certain random projections. This newly generated data is diffused and then used in neighboring nodes to recover the original full information, i.e., the channel estimate of the desired communication channel. We provide the complete state-space description of these algorithms and demonstrate the substantial gains through our experiments.
Open Access
Online learning over distributed networks
(2015) Sayın, Muhammed Ömer
We study online learning strategies over distributed networks. Here, we have a distributed collection of agents with learning and cooperation capabilities. These agents observe a noisy version of a desired state of the nature through a linear model. The agents seek to learn this state by also interacting with each other yet the communication load plays significant role. To this end, we propose compressive diffusion strategies that extract the compressed information from the diffused data. Agents can compress the information into a scalar or a single bit, i.e., a substantial reduction in the communication load. Importantly, we show that agents can achieve a comparable performance to the conventional diffusion strategies that require the direct diffusion of information without compression and with infinite precision. We also examine which information to disclose and how to utilize them optimally in the mean-square-error (MSE) sense. Note that all the well-known distributed learning strategies achieve suboptimal learning performance in the MSE sense. Hence, we provide algorithms that achieve distributed minimum MSE (MMSE) performance over an arbitrary network topology based on the aggregation of information at each agent. This approach differs from the diffusion of information across network, i.e., exchange of local estimates. Notably, exchange of local estimates is sufficient only over the certain network topologies. For these networks, we also propose strategies that achieve the distributed MMSE performance through the diffusion of information. Hence, we can substantially reduce the communication load while achieving the best possible MSE performance. Finally, for practical implementations we provide approaches to reduce the complexity of the algorithms through the time-windowing of the observations.
Open Access
Performance analysis of scalar diffusion strategy over distributed network
(IEEE, 2014) Sayın, Muhammed Ö.; Kozat, Süleyman Serdar
In this paper, we present a complete performance analysis of the scalar diffusion strategies over distributed networks. Scalar diffusion strategies are based on the diffusion implementation and adaptive extraction of the information from the diffusion data which is compressed into a scalar. This strategy require significantly less communication load while achieving similar performance with the full information exchange configuration. Here, we provide the transient and steady-state analysis of the scalar diffusion strategies for Gaussian regressors. Finally, in the numerical examples, we demonstrate that the theoretical results match with the simulation results.
Open Access
Single bit and reduced dimension diffusion strategies over distributed networks
(IEEE, 2013) Sayin, M. O.; Kozat, S. S.
We introduce novel diffusion based adaptive estimation strategies for distributed networks that have significantly less communication load and achieve comparable performance to the full information exchange configurations. After local estimates of the desired data is produced in each node, a single bit of information (or a reduced dimensional data vector) is generated using certain random projections of the local estimates. This newly generated data is diffused and then used in neighboring nodes to recover the original full information. We provide the complete state-space description and the mean stability analysis of our algorithms.
Open Access
Team-optimal distributed MMSE estimation in general and tree networks
(Elsevier Inc., 2017) Sayin, M. O.; Kozat, S. S.; Başar, T.
We construct team-optimal estimation algorithms over distributed networks for state estimation in the finite-horizon mean-square error (MSE) sense. Here, we have a distributed collection of agents with processing and cooperation capabilities. These agents observe noisy samples of a desired state through a linear model and seek to learn this state by interacting with each other. Although this problem has attracted significant attention and been studied extensively in fields including machine learning and signal processing, all the well-known strategies do not achieve team-optimal learning performance in the finite-horizon MSE sense. To this end, we formulate the finite-horizon distributed minimum MSE (MMSE) when there is no restriction on the size of the disclosed information, i.e., oracle performance, over an arbitrary network topology. Subsequently, we show that exchange of local estimates is sufficient to achieve the oracle performance only over certain network topologies. By inspecting these network structures, we propose recursive algorithms achieving the oracle performance through the disclosure of local estimates. For practical implementations we also provide approaches to reduce the complexity of the algorithms through the time-windowing of the observations. Finally, in the numerical examples, we demonstrate the superior performance of the introduced algorithms in the finite-horizon MSE sense due to optimal estimation.