Energy efficient area monitoring using information coverage in wireless sensor networks

In this paper, we are concerned with energy efficient area monitoring using information coverage in wireless sensor networks, where collaboration among multiple sensors can enable accurate sensing of a point in a given area-to-monitor even if that point falls outside the physical coverage of all the sensors. We refer to any set of sensors that can collectively sense all points in the entire area-to-monitor as a full area information cover. We first propose a low-complexity heuristic algorithm to obtain full area information covers. Using these covers, we then obtain the optimum schedule for activating the sensing activity of various sensors that maximizes the sensing lifetime. The scheduling of sensor activity using the optimum schedules obtained using the proposed algorithm is shown to achieve significantly longer sensing lifetimes compared to those achieved using physical coverage. Relaxing the full area coverage requirement to a partial area coverage (e.g., 95% of area coverage as adequate instead of 100% area coverage) further enhances the lifetime.

Efficient scheduling of sensor activity for information coverage in wireless sensor networks

In this paper, we are concerned with algorithms for scheduling the sensing activity of sensor nodes that are deployed to sense/measure point-targets in wireless sensor networks using information coverage. Defining a set of sensors which collectively can sense a target accurately as an information cover, we propose an algorithm to obtain Disjoint Set of Information Covers (DSIC), which achieves longer network life compared to the set of covers obtained using an Exhaustive-Greedy-Equalized Heuristic (EGEH) algorithm proposed recently in the literature. We also present a detailed complexity comparison between the DSIC and EGEH algorithms.

Memory-efficient spatial prediction image compression scheme

In prediction phase, the hierarchical tree structure obtained from the test image is used to predict every central pixel of an image by its four neighboring pixels. The prediction scheme generates the predicted error image, to which the wavelet/sub-band coding algorithm can be applied to obtain efficient compression. In quantization phase, we used a modified SPIHT algorithm to achieve efficiency in memory requirements. The memory constraint plays a vital role in wireless and bandwidth-limited applications. A single reusable list is used instead of three continuously growing linked lists as in case of SPIHT. This method is error resilient. The performance is measured in terms of PSNR and memory requirements. The algorithm shows good compression performance and significant savings in memory. (C) 2006 Elsevier B.V. All rights reserved.

Synthesis of BaSO4 nanoparticles by precipitation method using sodium hexa metaphosphate as a stabilizer

We describe the synthesis and structure of Barium sulfate nanoparticles by precipitation method in the presence of water soluble inorganic stabilizing agent, sodium hexametaphosphate, (NaPO3)(6). The structural parameters were refined by the Rietveld refinement method using powder X-ray diffraction data. Barium sulfate nanoparticles were crystallized in the orthorhombic structure with space group Pbnm (No. 62) having the lattice parameters a = 7.215(1) (angstrom), b = 8.949(1) (angstrom) and c = 5.501 (1) (angstrom) respectively. Transmission electron microscopy study reveals that the nanoparticles are size range, 30-50 nm. Fourier transform infrared spectra showed distinct absorption due to the SO42- moiety at 1115 and 1084 cm(-1) indicating formation of barium sulfate nanoparticles free from the phosphate group from the stabilizer used in the synthesis. (C) 2009 Elsevier Ltd. All rights reserved.

Single atom (Pd/Pt) supported on graphitic carbon nitride as efficient photocatalyst for visible-light reduction of carbon dioxide

Reducing carbon dioxide (CO2) to hydrocarbon fuel with solar energy is significant for high-density solar energy storage and carbon balance. In this work, single palladium/platinum (Pd/Pt) atoms supported on graphitic carbon nitride (g-C3N4), i.e. Pd/g-C3N4 and Pt/g-C3N4, acting as photocatalysts for CO2 reduction were investigated by density function theory (DFT) calcu-lations for the first time. During CO2 reduction, the individual metal atoms function as the active sites, while g-C3N4 provides the source of hydrogen (H*) from hydrogen evolution reaction. The complete, as-designed photocatalysts exhibit excellent activity in CO2 reduction. HCOOH is the preferred product of CO2 reduction on the Pd/g-C3N4 catalyst with a rate-determining barrier of 0.66 eV, while the Pt/g-C3N4 catalyst prefers to reduce CO2 to CH4 with a rate-determining barrier of 1.16 eV. In addition, depositing atom catalysts on g-C3N4 significantly enhances the visible light absorption, rendering them ideal for visible light reduction of CO2. Our findings open a new avenue of CO2 reduction for renewable energy supply.

Efficient content-based image retrieval based on multi-feature fusion

The increased availability of image capturing devices has enabled collections of digital images to rapidly expand in both size and diversity. This has created a constantly growing need for efficient and effective image browsing, searching, and retrieval tools. Pseudo-relevance feedback (PRF) has proven to be an effective mechanism for improving retrieval accuracy. An original, simple yet effective rank-based PRF mechanism (RB-PRF) that takes into account the initial rank order of each image to improve retrieval accuracy is proposed. This RB-PRF mechanism innovates by making use of binary image signatures to improve retrieval precision by promoting images similar to highly ranked images and demoting images similar to lower ranked images. Empirical evaluations based on standard benchmarks, namely Wang, Oliva & Torralba, and Corel datasets demonstrate the effectiveness of the proposed RB-PRF mechanism in image retrieval.

A Computationally Efficient Generalized Poisson Solution for Independent Double-Gate Transistors

Previous techniques used for solving the 1-D Poisson equation ( PE) rigorously for long-channel asymmetric and independent double-gate (IDG) transistors result in potential models that involve multiple intercoupled implicit equations. As these equations need to be solved self-consistently, such potential models are clearly inefficient for compact modeling. This paper reports a different rigorous technique for solving the same PE by which one can obtain the potential profile of a generalized IDG transistor that involves a single implicit equation. The proposed Poisson solution is shown to be computationally more efficient for circuit simulation than the previous solutions.

Two-Phase Thermodynamic Model for Efficient and Accurate Absolute Entropy of Water from Molecular Dynamics Simulations

Presented here is the two-phase thermodynamic (2PT) model for the calculation of energy and entropy of molecular fluids from the trajectory of molecular dynamics (MD) simulations. In this method, the density of state (DoS) functions (including the normal modes of translation, rotation, and intramolecular vibration motions) are determined from the Fourier transform of the corresponding velocity autocorrelation functions. A fluidicity parameter (f), extracted from the thermodynamic state of the system derived from the same MD, is used to partition the translation and rotation modes into a diffusive, gas-like component (with 3Nf degrees of freedom) and a nondiffusive, solid-like component. The thermodynamic properties, including the absolute value of entropy, are then obtained by applying quantum statistics to the solid component and applying hard sphere/rigid rotor thermodynamics to the gas component. The 2PT method produces exact thermodynamic properties of the system in two limiting states: the nondiffusive solid state (where the fluidicity is zero) and the ideal gas state (where the fluidicity becomes unity). We examine the 2PT entropy for various water models (F3C, SPC, SPC/E, TIP3P, and TIP4P-Ew) at ambient conditions and find good agreement with literature results obtained based on other simulation techniques. We also validate the entropy of water in the liquid and vapor phases along the vapor-liquid equilibrium curve from the triple point to the critical point. We show that this method produces converged liquid phase entropy in tens of picoseconds, making it an efficient means for extracting thermodynamic properties from MD simulations.

An efficient pricing based protocol for broadcasting in wireless ad hoc networks

In many applications of wireless ad hoc networks, wireless nodes are owned by rational and intelligent users. In this paper, we call nodes selfish if they are owned by independent users and their only objective is to maximize their individual goals. In such situations, it may not be possible to use the existing protocols for wireless ad hoc networks as these protocols assume that nodes follow the prescribed protocol without deviation. Stimulating cooperation among these nodes is an interesting and challenging problem. Providing incentives and pricing the transactions are well known approaches to stimulate cooperation. In this paper, we present a game theoretic framework for truthful broadcast protocol and strategy proof pricing mechanism called Immediate Predecessor Node Pricing Mechanism (IPNPM). The phrase strategy proof here means that truth revelation of cost is a weakly dominant-strategy (in game theoretic terms) for each node. In order to steer our mechanism-design approach towards practical implementation, we compute the payments to nodes using a distributed algorithm. We also propose a new protocol for broadcast in wireless ad hoc network with selfish nodes based on IPNPM. The features of the proposed broadcast protocol are reliability and a significantly reduced number of packet forwards compared to the number of network nodes, which in turn leads to less system-wide power consumption to broadcast a single packet. Our simulation results show the efficacy of the proposed broadcast protocol.

An efficient and scalable checkpointing and recovery algorithm for distributed systems

In this paper, we describe an efficient coordinated-checkpointing and recovery algorithm which can work even when the channels are assumed to be non-FIFO, and messages may be lost. Nodes are assumed to be autonomous, and they do not block while taking checkpoints. Based on the local conditions, any process can request the previous coordinator for the 'permission' to initiate a new checkpoint. Allowing multiple initiators of checkpoints avoids the bottleneck associated with a single initiator, but the algorithm permits only a single instance of checkpointing process at any given time, thus reducing much of the overhead associated with multiple initiators of distributed algorithms.

A neuro-adaptive augmented optimal dynamic inversion approach for effective and efficient treatment of chronic myelogenous leukemia

Combining the advanced techniques of optimal dynamic inversion and model-following neuro-adaptive control design, an efficient technique is presented for effective treatment of chronic myelogenous leukemia (CML). A recently developed nonlinear mathematical model for cell dynamics is used for the control (medication) synthesis. First, taking a set of nominal parameters, a nominal controller is designed based on the principle of optimal dynamic inversion. This controller can treat nominal patients (patients having same nominal parameters as used for the control design) effectively. However, since the parameters of an actual patient can be different from that of the ideal patient, to make the treatment strategy more effective and efficient, a model-following neuro-adaptive controller is augmented to the nominal controller. In this approach, a neural network trained online (based on Lyapunov stability theory) facilitates a new adaptive controller, computed online. From the simulation studies, this adaptive control design approach (treatment strategy) is found to be very effective to treat the CML disease for actual patients. Sufficient generality is retained in the theoretical developments in this paper, so that the techniques presented can be applied to other similar problem as well. Note that the technique presented is computationally non-intensive and all computations can be carried out online.

Computationally efficient optical tomographic reconstructions through waveletizing the normalized quadratic perturbation equation - art. no.61640R

In this paper, we present a wavelet - based approach to solve the non-linear perturbation equation encountered in optical tomography. A particularly suitable data gathering geometry is used to gather a data set consisting of differential changes in intensity owing to the presence of the inhomogeneous regions. With this scheme, the unknown image, the data, as well as the weight matrix are all represented by wavelet expansions, thus yielding the representation of the original non - linear perturbation equation in the wavelet domain. The advantage in use of the non-linear perturbation equation is that there is no need to recompute the derivatives during the entire reconstruction process. Once the derivatives are computed, they are transformed into the wavelet domain. The purpose of going to the wavelet domain, is that, it has an inherent localization and de-noising property. The use of approximation coefficients, without the detail coefficients, is ideally suited for diffuse optical tomographic reconstructions, as the diffusion equation removes most of the high frequency information and the reconstruction appears low-pass filtered. We demonstrate through numerical simulations, that through solving merely the approximation coefficients one can reconstruct an image which has the same information content as the reconstruction from a non-waveletized procedure. In addition we demonstrate a better noise tolerance and much reduced computation time for reconstructions from this approach.

Isoselenocyanates derived from Boc/Z-amino acids: synthesis, isolation,characterization, and application to the efficient synthesis of unsymmetrical selenoureas and selenoureidopeptidomimetics

Isoselenocyanates derived from Boc/Z-amino acids are prepared by the reaction of the corresponding isonitriles with selenium powder in presence of triethylamine at reflux. The utility of these new classes of isoselenocyanates in the preparation of selenoureidodipeptidomimetics possessing both amino as well as carboxy termini has been accomplished. The H-1 NMR analysis confirmed that the protocol involving the conversion of isonitriles to isoselenocyanates and their use as coupling agents in assembling selenour-eido derivatives is free from racemization. (C) 2010 Elsevier Ltd. All rights reserved.

Efficient algorithms for domination and Hamilton circuit problems on permutation graphs

The domination and Hamilton circuit problems are of interest both in algorithm design and complexity theory. The domination problem has applications in facility location and the Hamilton circuit problem has applications in routing problems in communications and operations research.The problem of deciding if G has a dominating set of cardinality at most k, and the problem of determining if G has a Hamilton circuit are NP-Complete. Polynomial time algorithms are, however, available for a large number of restricted classes. A motivation for the study of these algorithms is that they not only give insight into the characterization of these classes but also require a variety of algorithmic techniques and data structures. So the search for efficient algorithms, for these problems in many classes still continues.A class of perfect graphs which is practically important and mathematically interesting is the class of permutation graphs. The domination problem is polynomial time solvable on permutation graphs. Algorithms that are already available are of time complexity O(n2) or more, and space complexity O(n2) on these graphs. The Hamilton circuit problem is open for this class.We present a simple O(n) time and O(n) space algorithm for the domination problem on permutation graphs. Unlike the existing algorithms, we use the concept of geometric representation of permutation graphs. Further, exploiting this geometric notion, we develop an O(n2) time and O(n) space algorithm for the Hamilton circuit problem.