Phase synchronization in brain networks derived from correlation between probabilities of recurrences in functional MRI data

It is increasingly being recognized that resting state brain connectivity derived from functional magnetic resonance imaging (fMRI) data is an important marker of brain function both in healthy and clinical populations. Though linear correlation has been extensively used to characterize brain connectivity, it is limited to detecting first order dependencies. In this study, we propose a framework where in phase synchronization (PS) between brain regions is characterized using a new metric ``correlation between probabilities of recurrence'' (CPR) and subsequent graph-theoretic analysis of the ensuing networks. We applied this method to resting state fMRI data obtained from human subjects with and without administration of propofol anesthetic. Our results showed decreased PS during anesthesia and a biologically more plausible community structure using CPR rather than linear correlation. We conclude that CPR provides an attractive nonparametric method for modeling interactions in brain networks as compared to standard correlation for obtaining physiologically meaningful insights about brain function.

Continuously equivalent networks in state space

Schoeffler has derived continuously equivalent networks in the nodal-admittance domain. The letter derives a corresponding result in state space that combines the usefulness of Schoeffler's result and the power of the state-variable approach.

A Robust Three Phase State Estimation for Distribution Networks

Electric power utilities are installing distribution automation systems (DAS) for better management and control of the distribution networks during the recent past. The success of DAS, largely depends on the availability of reliable database of the control centre and thus requires an efficient state estimation (SE) solution technique. This paper presents an efficient and robust three-phase SE algorithm for application to radial distribution networks. This method exploits the radial nature of the network and uses forward and backward propagation scheme to estimate the line flows, node voltage and loads at each node, based on the measured quantities. The SE cannot be executed without adequate number of measurements. The extension of the method to the network observability analysis and bad data detection is also discussed. The proposed method has been tested to analyze several practical distribution networks of various voltage levels and also having high R:X ratio of lines. The results for a typical network are presented for illustration purposes. © 2000 Elsevier Science S.A. All rights reserved.

Solid State and Solution Mediated Multistep Sequential Transformations in Metal-Organic Coordination Networks

Sequential transformation in a family of metal-organic framework compounds has been investigated employing both a solid-state as well as a solution mediated route. The compounds, cobalt oxy-bis(benzoate) and manganese oxybis(benzoate) having a two-dimensional structure, were reacted with bipyridine forming cobalt oxy-bis(benzoate)-4,4'-bipyridine and manganese oxy-bis(benzoate)-4,4'-bipyridine, respectively. The bipyridine containing compounds appear to form sequentially through stable intermediates. For the cobalt system, the transformation from a two-dimensional compound, Co(H2O)(2)(OBA)] (OBA = 4,4'-oxy-bis(benzoate)), I, to two different three-dimensional compounds, Co(bpy)(OBA)]center dot bpy, II, (bpy = 4,4'-bipyridine) and Co(bpy)(0.5)(OBA)], III, and reversibility between II and III have been investigated. In the manganese system, transformation from a two-dimensional compound, Mn(H2O)(2)(OBA)], Ia, to two different three-dimensional compounds, Mn (bpy)(OBA)]center dot bpy, Ha and Ha to Mn(bpy)(0.5)(OBA)], Ilia, has been investigated. It has also been possible to identify intermediate products during these transformation reactions. The possible pathways for the formation of the compounds were postulated.

Energy-Efficient Decentralized Cooperative Routing in Wireless Networks

Wireless adhoc networks transmit information from a source to a destination via multiple hops in order to save energy and, thus, increase the lifetime of battery-operated nodes. The energy savings can be especially significant in cooperative transmission schemes, where several nodes cooperate during one hop to forward the information to the next node along a route to the destination. Finding the best multi-hop transmission policy in such a network which determines nodes that are involved in each hop, is a very important problem, but also a very difficult one especially when the physical wireless channel behavior is to be accounted for and exploited. We model the above optimization problem for randomly fading channels as a decentralized control problem - the channel observations available at each node define the information structure, while the control policy is defined by the power and phase of the signal transmitted by each node. In particular, we consider the problem of computing an energy-optimal cooperative transmission scheme in a wireless network for two different channel fading models: (i) slow fading channels, where the channel gains of the links remain the same for a large number of transmissions, and (ii) fast fading channels, where the channel gains of the links change quickly from one transmission to another. For slow fading, we consider a factored class of policies (corresponding to local cooperation between nodes), and show that the computation of an optimal policy in this class is equivalent to a shortest path computation on an induced graph, whose edge costs can be computed in a decentralized manner using only locally available channel state information (CSI). For fast fading, both CSI acquisition and data transmission consume energy. Hence, we need to jointly optimize over both these; we cast this optimization problem as a large stochastic optimization problem. We then jointly optimize over a set of CSI functions of the local channel states, and a c- - orresponding factored class of control poli.

Single-Symbol ML Decodable Distributed STBCs for Partially-Coherent Cooperative Networks

A relay network with N relays and a single source-destination pair is called a partially-coherent relay channel (PCRC) if the destination has perfect channel state information (CSI) of all the channels and the relays have only the phase information of the source-to-relay channels. In this paper, first, a new set of necessary and sufficient conditions for a space-time block code (STBC) to be single-symbol decodable (SSD) for colocated multiple antenna communication is obtained. Then, this is extended to a set of necessary and sufficient conditions for a distributed STBC (DSTBC) to be SSD for. a PCRC. Using this, several SSD DSTBCs for PCRC are identified. It is proved that even if a SSD STBC for a co-located MIMO channel does not satisfy the additional conditions for the code to be SSD for a PCRC, single-symbol decoding of it in a PCRC gives full-diversity and only coding gain is lost. It is shown that when a DSTBC is SSD for a PCRC, then arbitrary coordinate interleaving of the in-phase and quadrature-phase components of the variables does not disturb its SSD property for PCRC. Finally, it is shown that the possibility of channel phase compensation operation at the relay nodes using partial CSI at the relays increases the possible rate of SSD DSTBCs from (2)/(N) when the relays do not have CSI to(1)/(2), which is independent of N.

State Dependent Attempt Rate Modeling of Single Cell IEEE 802.11 WLANs with Homogeneous Nodes and Poisson Arrivals

Analytical models of IEEE 802.11-based WLANs are invariably based on approximations, such as the well-known mean-field approximations proposed by Bianchi for saturated nodes. In this paper, we provide a new approach for modeling the situation when the nodes are not saturated. We study a State Dependent Attempt Rate (SDAR) approximation to model M queues (one queue per node) served by the CSMA/CA protocol as standardized in the IEEE 802.11 DCF. The approximation is that, when n of the M queues are non-empty, the attempt probability of the n non-empty nodes is given by the long-term attempt probability of n saturated nodes as provided by Bianchi's model. This yields a coupled queue system. When packets arrive to the M queues according to independent Poisson processes, we provide an exact model for the coupled queue system with SDAR service. The main contribution of this paper is to provide an analysis of the coupled queue process by studying a lower dimensional process and by introducing a certain conditional independence approximation. We show that the numerical results obtained from our finite buffer analysis are in excellent agreement with the corresponding results obtained from ns-2 simulations. We replace the CSMA/CA protocol as implemented in the ns-2 simulator with the SDAR service model to show that the SDAR approximation provides an accurate model for the CSMA/CA protocol. We also report the simulation speed-ups thus obtained by our model-based simulation.

Distributed space-time codes for cooperative networks with partial CSI

Design criteria and full-diversity Distributed Space Time Codes (DSTCs) for the two phase transmission based cooperative diversity protocol of Jing-Hassibi and the Generalized Nonorthogonal Amplify and Forward (GNAF) protocol are reported, when the relay nodes are assumed to have knowledge of the phase component of the source to relay channel gains. It is shown that this under this partial channel state information (CSI), several well known space time codes for the colocated MIMO (Multiple Input Multiple Output) channel become amenable for use as DSTCs. In particular, the well known complex orthogonal designs, generalized coordinate interleaved orthogonal designs (GCIODs) and unitary weight single symbol decodable (UW-SSD) codes are shown to satisfy the required design constraints for DSTCs. Exploiting the relaxed code design constraints, we propose DSTCs obtained from Clifford Algebras which have low ML decoding complexity.

High-rate, Single-Symbol Decodable Distributed STBCs for Partially-Coherent Cooperative Networks

Space-time block codes (STBCs) that are single-symbol decodable (SSD) in a co-located multiple antenna setting need not be SSD in a distributed cooperative communication setting. A relay network with N relays and a single source-destination pair is called a partially-coherent relay channel (PCRC) if the destination has perfect channel state information (CSI) of an the channels and the relays have only the phase information of the source-to-relay channels. In our earlier work, we had derived a set of necessary and sufficient conditions for a distributed STBC (DSTBC) to be SSD for a PCRC. Using these conditions, in this paper we show that the possibility of channel phase compensation operation at the relay nodes using partial CSI at the relays increases the possible rate of SSD DSTBCs from 2/N when the relays do not have CSI to 1/2, which is independent of N. We also show that when a DSTBC is SSD for a PCRC, then arbitrary coordinate interleaving of the in-phase and quadrature-phase components of the variables does not disturb its SSD property. Using this property we are able to construct codes that are SSD and have higher rate than 2/N but giving full diversity only for signal constellations satisfying certain conditions.

Optimal sleep-wake scheduling for quickest intrusion detection using sensor networks

We consider the problem of quickest detection of an intrusion using a sensor network, keeping only a minimal number of sensors active. By using a minimal number of sensor devices, we ensure that the energy expenditure for sensing, computation and communication is minimized (and the lifetime of the network is maximized). We model the intrusion detection (or change detection) problem as a Markov decision process (MDP). Based on the theory of MDP, we develop the following closed loop sleep/wake scheduling algorithms: (1) optimal control of Mk+1, the number of sensors in the wake state in time slot k + 1, (2) optimal control of qk+1, the probability of a sensor in the wake state in time slot k + 1, and an open loop sleep/wake scheduling algorithm which (3) computes q, the optimal probability of a sensor in the wake state (which does not vary with time), based on the sensor observations obtained until time slot k. Our results show that an optimum closed loop control on Mk+1 significantly decreases the cost compared to keeping any number of sensors active all the time. Also, among the three algorithms described, we observe that the total cost is minimum for the optimum control on Mk+1 and is maximum for the optimum open loop control on q.

PVA-PSSA Membrane with Interpenetrating Networks and its Methanol Crossover Mitigating Effect in DMFCs

A membrane with interpenetrating networks between poly(vinyl alcohol) (PVA) and poly(styrene sulfonic acid) (PSSA) coupled with a high proton conductivity is realized and evaluated as a proton exchange membrane electrolyte for a direct methanol fuel cell (DMFC). Its reduced methanol permeability and improved performance in DMFCs suggest the new blend as an alternative membrane to Nafion membranes. The membrane has been characterized by powder X-ray diffraction, scanning electron microscopy, time-modulated differential scanning calorimetry, and thermogravimetric analysis in conjunction with its mechanical strength. The maximum proton conductivity of 3.3×10−2 S/cm for the PVA–PSSA blend membrane is observed at 373 K. From nuclear magnetic resonance imaging and volume localized spectroscopy experiments, the PVA–PSSA membrane has been found to exhibit a promising methanol impermeability, in DMFCs. On evaluating its utility in a DMFC, it has been found that a peak power density of 90 mW/cm2 at a load current density of 320 mA/cm2 is achieved with the PVA–PSSA membrane compared to a peak power density of 75 mW/cm2 at a load current density of 250 mA/cm2 achievable for a DMFC employing Nafion membrane electrolyte while operating under identical conditions; this is attributed primarily to the methanol crossover mitigating property of the PVA–PSSA membrane.

Three-dimensional hybrid networks based on aspartic acid

Three-dimensional achiral coordination polymers of the general formula M2(D, l-NHCH (COO)CH2COO)2·C4H4N2 where M = Ni and Co and pyrazine acts as the linker molecule have been prepared under hydrothermal conditions starting with [M(L-NHCH(COO)CH2COO)·3H2O] possessing a helical chain structure. A three-dimensional hybrid compound of the formula Pb2.5[N{CH(COO) CH2COO}22H2O] has also been prepared hydrothermally starting with aspartic acid and Pb(NO3)2. In this lead compound, where a secondary amine formed by the dimerisation of aspartic acid acts as the ligand, there is two-dimensional inorganic connectivity and one-dimensional organic connectivity.

A novel approach for large-scale polypeptide folding based on elastic networks using continuous optimization

We present a new computationally efficient method for large-scale polypeptide folding using coarse-grained elastic networks and gradient-based continuous optimization techniques. The folding is governed by minimization of energy based on Miyazawa–Jernigan contact potentials. Using this method we are able to substantially reduce the computation time on ordinary desktop computers for simulation of polypeptide folding starting from a fully unfolded state. We compare our results with available native state structures from Protein Data Bank (PDB) for a few de-novo proteins and two natural proteins, Ubiquitin and Lysozyme. Based on our simulations we are able to draw the energy landscape for a small de-novo protein, Chignolin. We also use two well known protein structure prediction software, MODELLER and GROMACS to compare our results. In the end, we show how a modification of normal elastic network model can lead to higher accuracy and lower time required for simulation.

Design and analysis of rate aware ad hoc 802.11 networks

802.11 WLANs are characterized by high bit error rate and frequent changes in network topology. The key feature that distinguishes WLANs from wired networks is the multi-rate transmission capability, which helps to accommodate a wide range of channel conditions. This has a significant impact on higher layers such as routing and transport levels. While many WLAN products provide rate control at the hardware level to adapt to the channel conditions, some chipsets like Atheros do not have support for automatic rate control. We first present a design and implementation of an FER-based automatic rate control state machine, which utilizes the statistics available at the device driver to find the optimal rate. The results show that the proposed rate switching mechanism adapts quite fast to the channel conditions. The hop count metric used by current routing protocols has proven itself for single rate networks. But it fails to take into account other important factors in a multi-rate network environment. We propose transmission time as a better path quality metric to guide routing decisions. It incorporates the effects of contention for the channel, the air time to send the data and the asymmetry of links. In this paper, we present a new design for a multi-rate mechanism as well as a new routing metric that is responsive to the rate. We address the issues involved in using transmission time as a metric and presents a comparison of the performance of different metrics for dynamic routing.

Composite MR image reconstruction and unaliasing for general trajectories using neural networks

In rapid parallel magnetic resonance imaging, the problem of image reconstruction is challenging. Here, a novel image reconstruction technique for data acquired along any general trajectory in neural network framework, called ``Composite Reconstruction And Unaliasing using Neural Networks'' (CRAUNN), is proposed. CRAUNN is based on the observation that the nature of aliasing remains unchanged whether the undersampled acquisition contains only low frequencies or includes high frequencies too. Here, the transformation needed to reconstruct the alias-free image from the aliased coil images is learnt, using acquisitions consisting of densely sampled low frequencies. Neural networks are made use of as machine learning tools to learn the transformation, in order to obtain the desired alias-free image for actual acquisitions containing sparsely sampled low as well as high frequencies. CRAUNN operates in the image domain and does not require explicit coil sensitivity estimation. It is also independent of the sampling trajectory used, and could be applied to arbitrary trajectories as well. As a pilot trial, the technique is first applied to Cartesian trajectory-sampled data. Experiments performed using radial and spiral trajectories on real and synthetic data, illustrate the performance of the method. The reconstruction errors depend on the acceleration factor as well as the sampling trajectory. It is found that higher acceleration factors can be obtained when radial trajectories are used. Comparisons against existing techniques are presented. CRAUNN has been found to perform on par with the state-of-the-art techniques. Acceleration factors of up to 4, 6 and 4 are achieved in Cartesian, radial and spiral cases, respectively. (C) 2010 Elsevier Inc. All rights reserved.