Linear description of electrical networks for computer analysis

A parentheses-free code is suggested for the description of two-terminal electrical networks for computer analysis.

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.

Effect of amplifier imperfections on active networks

The deviation in the performance of active networks due to practical operational amplifiers (OA) is mainly because of the finite gain bandwidth productBand nonzero output resistanceR_0. The effect ofBandR_0on two OA impedances and single and multi-OA filters are discussed. In filters, the effect ofR_0is to add zeros to the transfer function often making it nonminimum phase. A simple method of analysis has been suggested for 3-OA biquad and coupled biquad circuits. A general method of noise minimization of the generalized impedance converter (GIC), while operating OA's within the prescribed voltage and current limits, is also discussed. The 3-OA biquadratic sections analyzed also exhibit noise behavior and signal handling capacity similar to the GIC. The GIC based structures are found to be better than other configurations both in biquadratic sections and direct realizations of higher order transfer functions.

Ligand dependent intra and inter subunit communication in human tryptophanyl tRNA synthetase as deduced from the dynamics of structure networks

Homodimeric protein tryptophanyl tRNA synthetase (TrpRS) has a Rossmann fold domain and belongs to the 1c subclass of aminoacyl tRNA synthetases. This enzyme performs the function of acylating the cognate tRNA. This process involves a number of molecules (2 protein subunits, 2 tRNAs and 2 activated Trps) and thus it is difficult to follow the complex steps in this process. Structures of human TrpRS complexed with certain ligands are available. Based on structural and biochemical data, mechanism of activation of Trp has been speculated. However, no structure has yet been solved in the presence of both the tRNA(Trp) and the activated Trp (TrpAMP). In this study, we have modeled the structure of human TrpRS bound to the activated ligand and the cognate tRNA. In addition, we have performed molecular dynamics (MD) simulations on these models as well as other complexes to capture the dynamical process of ligand induced conformational changes. We have analyzed both the local and global changes in the protein conformation from the protein structure network (PSN) of MD snapshots, by a method which was recently developed in our laboratory in the context of the functionally monomeric protein, methionyl tRNA synthetase. From these investigations, we obtain important information such as the ligand induced correlation between different residues of this protein, asymmetric binding of the ligands to the two subunits of the protein as seen in the crystal structure analysis, and the path of communication between the anticodon region and the aminoacylation site. Here we are able to elucidate the role of dimer interface at a level of detail, which has not been captured so far.

Greedy Scheduling in Multihop Wireless Networks

We study the performance of greedy scheduling in multihop wireless networks where the objective is aggregate utility maximization. Following standard approaches, we consider the dual of the original optimization problem. Optimal scheduling requires selecting independent sets of maximum aggregate price, but this problem is known to be NP-hard. We propose and evaluate a simple greedy heuristic. Analytical bounds on performance are provided and simulations indicate that the greedy heuristic performs well in practice.

Designing Dense Radar Networks for Weather Surveillance in Tropical Regions.

Design considerations are presented for a dense weather radar network to support multiple services including aviation. Conflicts, tradeoffs and optimization issues in the context of operation in a tropical region are brought out. The upcoming Indian radar network is used as a case study. Algorithms for data mosaicing are briefly outlined.

Novel 3-dimensional sixfold interpenetrating diamondoid networks of copper(I) coordination polymers of polypyridyl ligands - Syntheses,characterization and crystal structures

Two new coordination polymers [Cu(L-1)(2)](n)(ClO4)(n)center dot 2nH(2)O (1), [Cu(L-2)(2)](n)(ClO4)(n)center dot 2nH(2)O (2) of polydentate imine/pyridyl ligands, L-1 and L-2 with Cu(I) ion have been synthesized and characterized by single crystal X-ray diffraction studies, elemental analyses, IR' UV-vis and NMR spectroscopy. They represent 3-dimensional, sixfold interpenetrating diamondoid network structures having large pores of dimension, 35 x 21 angstrom(2) in 1 and 38 x 19 angstrom(2) in 2, respectively.

Allostery and conformational free energy changes in human tryptophanyl-tRNA synthetase from essential dynamics and structure networks

The interdependence of the concept of allostery and enzymatic catalysis, and they being guided by conformational mobility is gaining increased prominence. However, to gain a molecular level understanding of llostery and hence of enzymatic catalysis, it is of utter importance that the networks of amino acids participating in allostery be deciphered. Our lab has been exploring the methods of network analysis combined with molecular dynamics simulations to understand allostery at molecular level. Earlier we had outlined methods to obtain communication paths and then to map the rigid/flexible regions of proteins through network parameters like the shortest correlated paths, cliques, and communities. In this article, we advance the methodology to estimate the conformational populations in terms of cliques/communities formed by interactions including the side-chains and then to compute the ligand-induced population shift. Finally, we obtain the free-energy landscape of the protein in equilibrium, characterizing the free-energy minima accessed by the protein complexes. We have chosen human tryptophanyl-tRNA synthetase (hTrpRS), a protein esponsible for charging tryptophan to its cognate tRNA during protein biosynthesis for this investigation. This is a multidomain protein exhibiting excellent allosteric communication. Our approach has provided valuable structural as well as functional insights into the protein. The methodology adopted here is highly generalized to illuminate the linkage between protein structure networks and conformational mobility involved in the allosteric mechanism in any protein with known structure.

An adaptive drug delivery design using neural networks for effective treatment of infectious diseases: A simulation study

An adaptive drug delivery design is presented in this paper using neural networks for effective treatment of infectious diseases. The generic mathematical model used describes the coupled evolution of concentration of pathogens, plasma cells, antibodies and a numerical value that indicates the relative characteristic of a damaged organ due to the disease under the influence of external drugs. From a system theoretic point of view, the external drugs can be interpreted as control inputs, which can be designed based on control theoretic concepts. In this study, assuming a set of nominal parameters in the mathematical model, first a nonlinear controller (drug administration) is designed based on the principle of dynamic inversion. This nominal drug administration plan was found to be effective in curing "nominal model patients" (patients whose immunological dynamics conform to the mathematical model used for the control design exactly. However, it was found to be ineffective in curing "realistic model patients" (patients whose immunological dynamics may have off-nominal parameter values and possibly unwanted inputs) in general. Hence, to make the drug delivery dosage design more effective for realistic model patients, a model-following adaptive control design is carried out next by taking the help of neural networks, that are trained online. Simulation studies indicate that the adaptive controller proposed in this paper holds promise in killing the invading pathogens and healing the damaged organ even in the presence of parameter uncertainties and continued pathogen attack. Note that the computational requirements for computing the control are very minimal and all associated computations (including the training of neural networks) can be carried out online. However it assumes that the required diagnosis process can be carried out at a sufficient faster rate so that all the states are available for control computation.

A Low-Complexity Near-ML Performance Achieving Algorithm for Large MIMO Detection

In this paper, we present a low-complexity, near maximum-likelihood (ML) performance achieving detector for large MIMO systems having tens of transmit and receive antennas. Such large MIMO systems are of interest because of the high spectral efficiencies possible in such systems. The proposed detection algorithm, termed as multistage likelihood-ascent search (M-LAS) algorithm, is rooted in Hopfield neural networks, and is shown to possess excellent performance as well as complexity attributes. In terms of performance, in a 64 x 64 V-BLAST system with 4-QAM, the proposed algorithm achieves an uncoded BER of 10(-3) at an SNR of just about 1 dB away from AWGN-only SISO performance given by Q(root SNR). In terms of coded BER, with a rate-3/4 turbo code at a spectral efficiency of 96 bps/Hz the algorithm performs close to within about 4.5 dB from theoretical capacity, which is remarkable in terms of both high spectral efficiency as well as nearness to theoretical capacity. Our simulation results show that the above performance is achieved with a complexity of just O(NtNt) per symbol, where N-t and N-tau denote the number of transmit and receive antennas.

Analyzing information flow in brain networks with nonparametric Granger causality

Multielectrode neurophysiological recording and high-resolution neuroimaging generate multivariate data that are the basis for understanding the patterns of neural interactions. How to extract directions of information flow in brain networks from these data remains a key challenge. Research over the last few years has identified Granger causality as a statistically principled technique to furnish this capability. The estimation of Granger causality currently requires autoregressive modeling of neural data. Here, we propose a nonparametric approach based on widely used Fourier and wavelet transforms to estimate both pairwise and conditional measures of Granger causality, eliminating the need of explicit autoregressive data modeling. We demonstrate the effectiveness of this approach by applying it to synthetic data generated by network models with known connectivity and to local field potentials recorded from monkeys performing a sensorimotor task.

Correlation, Coding, and Cooperation in Wireless Sensor Networks

We consider a single-hop data-gathering sensor network, consisting of a set of sensor nodes that transmit data periodically to a base-station. We are interested in maximizing the lifetime of this network. With our definition of network lifetime and the assumption that the radio transmission energy consumption forms the most significant portion of the total energy consumption at a sensor node, we attempt to enhance the network lifetime by reducing the transmission energy budget of sensor nodes by exploiting three system-level opportunities. We pose the problem of maximizing lifetime as a max-min optimization problem subject to the constraint of successful data collection and limited energy supply at each node. This turns out to be an extremely difficult optimization to solve. To reduce the complexity of this problem, we allow the sensor nodes and the base-station to interactively communicate with each other and employ instantaneous decoding at the base-station. The chief contribution of the paper is to show that the computational complexity of our problem is determined by the complex interplay of various system-level opportunities and challenges.

Deep Packet Inspection Using Message Passing Networks

We propose a solution based on message passing bipartite networks, for deep packet inspection, which addresses both speed and memory issues, which are limiting factors in current solutions. We report on a preliminary implementation and propose a parallel architecture.

Broadcast in Ad hoc Wireless Networks with Selfish Nodes: A Bayesian Incentive Compatibility Approach

We consider the incentive compatible broadcast (ICB) problem in ad hoc wireless networks with selfish nodes. We design a Bayesian incentive compatible Broadcast (BIC-B) protocol to address this problem. VCG mechanism based schemes have been popularly used in the literature to design dominant strategy incentive compatible (DSIC) protocols for ad hoe wireless networks. VCG based mechanisms have two critical limitations: (i) the network is required to he bi-connected, (ii) the resulting protocol is not budget balanced. Our proposed BIC-B protocol overcomes these difficulties. We also prove the optimality of the proposed scheme.

Deterministic schemes for key distribution in wireless sensor networks

In this paper, we propose a new security metric for measuring resilience of a symmetric key distribution scheme in wireless sensor network. A polynomial-based and a novel complete connectivity schemes are proposed and an analytical comparison, in terms of security and connectivity, between the schemes is shown. Motivated by the schemes, we derive general expressions for security and connectivity. A number of conclusions are made using these general expressions.