Distributed Source Coding for Sensor Data Model and Estimation of Cluster Head Errors Using Bayesian and K-Near Neighborhood Classifiers in Deployment of Dense Wireless Sensor Networks

The lifetime calculation of large dense sensor networks with fixed energy resources and the remaining residual energy have shown that for a constant energy resource in a sensor network the fault rate at the cluster head is network size invariant when using the network layer with no MAC losses.Even after increasing the battery capacities in the nodes the total lifetime does not increase after a max limit of 8 times. As this is a serious limitation lots of research has been done at the MAC layer which allows to adapt to the specific connectivity, traffic and channel polling needs for sensor networks. There have been lots of MAC protocols which allow to control the channel polling of new radios which are available to sensor nodes to communicate. This further reduces the communication overhead by idling and sleep scheduling thus extending the lifetime of the monitoring application. We address the two issues which effects the distributed characteristics and performance of connected MAC nodes. (1) To determine the theoretical minimum rate based on joint coding for a correlated data source at the singlehop, (2a) to estimate cluster head errors using Bayesian rule for routing using persistence clustering when node densities are the same and stored using prior probability at the network layer, (2b) to estimate the upper bound of routing errors when using passive clustering were the node densities at the multi-hop MACS are unknown and not stored at the multi-hop nodes a priori. In this paper we evaluate many MAC based sensor network protocols and study the effects on sensor network lifetime. A renewable energy MAC routing protocol is designed when the probabilities of active nodes are not known a priori. From theoretical derivations we show that for a Bayesian rule with known class densities of omega1, omega2 with expected error P* is bounded by max error rate of P=2P* for single-hop. We study the effects of energy losses using cross-layer simulation of - large sensor network MACS setup, the error rate which effect finding sufficient node densities to have reliable multi-hop communications due to unknown node densities. The simulation results show that even though the lifetime is comparable the expected Bayesian posterior probability error bound is close or higher than Pges2P*.

Broadcast in Adhoc 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 hoc wireless networks. VCG based mechanisms have two critical limitations: (i) the network is required to be 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.

Bayesian framework and message passing for joint support and signal recovery of approximately sparse signals

In this paper, we develop a low-complexity message passing algorithm for joint support and signal recovery of approximately sparse signals. The problem of recovery of strictly sparse signals from noisy measurements can be viewed as a problem of recovery of approximately sparse signals from noiseless measurements, making the approach applicable to strictly sparse signal recovery from noisy measurements. The support recovery embedded in the approach makes it suitable for recovery of signals with same sparsity profiles, as in the problem of multiple measurement vectors (MMV). Simulation results show that the proposed algorithm, termed as JSSR-MP (joint support and signal recovery via message passing) algorithm, achieves performance comparable to that of sparse Bayesian learning (M-SBL) algorithm in the literature, at one order less complexity compared to the M-SBL algorithm.

Usefulness of in Situ Single Crystal to Single Crystal Transformation (SCSC) Studies in Understanding the Temperature-Dependent Dimensionality Cross-over and Structural Reorganization in Copper-Containing Metal-Organic Frameworks (MOFs)

Two copper-containing compounds [Cu(3)(mu(3)-OH)(2)-(H(2)O)(2){(SO(3))-C(6)H(3)-(COO)(2)}(CH(3)COO)] , I, and [Cu(5)(mu(3)-OH)(2)(H(2)O)(6){(NO(2))-C(6)H(3)-(COO)(2)}(4)]center dot 5H(2)O, II, were prepared using sulphoisophthalic and nitroisophthalic acids. The removal of the coordinated water molecules in the compounds was investigated using in situ single crystal to single crystal (SCSC) transformation studies, temperature-dependent powder X-ray diffraction (PXRD), and thermogravimetric analysis (TGA). The efficacy of SCSC transformation studies were established by the observation of dimensionality cross-over from a two-dimensional (I) to a three-dimensional structure, Cu(6)(mu(3)-OH)(4){(SO(3))-C(6)H(3)-(COO)(2)}(2)(CH(3)COO)(2), Ia, during the removal of the coordinated water molecules. Compound H exhibited a structural reorganization forming Cu(5)(mu(2)-OH)(2){(NO(2))C(6)H(3)-(COO)(2))(4)], Ha, possessing trimeric (Cu(3)O(12)) and dimeric (Cu(2)O(8)) copper clusters. The PXRD studies indicate that the three-dimensional structure (Ia) is transient and unstable, reverting back to the more stable two-dimensional structure (I) on cooling to room temperature. Compound Ha appears to be more stable at room temperature. The rehydration/dehydration studies using a modified TGA setup suggest complete rehydration of the water molecules, indicating that the water molecules in both compounds are labile. A possible model for the observed changes in the structures has been proposed. Magnetic studies indicate changes in the exchanges between the copper centers in Ha, whereas no such behavior was observed in Ia.

Hybrid Bayesian Classifier for Improved Classification Accuracy

The widely used Bayesian classifier is based on the assumption of equal prior probabilities for all the classes. However, inclusion of equal prior probabilities may not guarantee high classification accuracy for the individual classes. Here, we propose a novel technique-Hybrid Bayesian Classifier (HBC)-where the class prior probabilities are determined by unmixing a supplemental low spatial-high spectral resolution multispectral (MS) data that are assigned to every pixel in a high spatial-low spectral resolution MS data in Bayesian classification. This is demonstrated with two separate experiments-first, class abundances are estimated per pixel by unmixing Moderate Resolution Imaging Spectroradiometer data to be used as prior probabilities, while posterior probabilities are determined from the training data obtained from ground. These have been used for classifying the Indian Remote Sensing Satellite LISS-III MS data through Bayesian classifier. In the second experiment, abundances obtained by unmixing Landsat Enhanced Thematic Mapper Plus are used as priors, and posterior probabilities are determined from the ground data to classify IKONOS MS images through Bayesian classifier. The results indicated that HBC systematically exploited the information from two image sources, improving the overall accuracy of LISS-III MS classification by 6% and IKONOS MS classification by 9%. Inclusion of prior probabilities increased the average producer's and user's accuracies by 5.5% and 6.5% in case of LISS-III MS with six classes and 12.5% and 5.4% in IKONOS MS for five classes considered.

The relevance of metal organic frameworks (MOFs) in inorganic materials chemistry

The metal organic frameworks (MOFs) have evolved to be an important family and a corner stone for research in the area of inorganic chemistry. The progress made since 2000 has attracted researchers from other disciplines to actively engage themselves in this area. This cooperative synergy of different scientific believes have provided important edge and spread to the chemistry of metal-organic frameworks. The ease of synthesis coupled with the observation of properties in the areas of catalysis, sorption, separation, luminescence, bioactivity, magnetism, etc., are a proof of this synergism. In this article, we present the recent developments in this area.

Metal-Ion Metathesis in Metal-Organic Frameworks: A Synthetic Route to New Metal-Organic Frameworks

A porous metalorganic framework, Mn(H3O)(Mn4Cl)(3)(hmtt)(8)] (POST-65), was prepared by the reaction of 5,5',10,10',15,15'-hexamethyltruxene-2,7,12-tricarboxylic acid (H(3)hmtt) with MnCl2 under solvothermal conditions. POST-65(Mn) was subjected to post-synthetic modification with Fe, Co, Ni, and Cu according to an ion-exchange method that resulted in the formation of three isomorphous frameworks, POST-65(Co/Ni/Cu), as well as a new framework, POST-65(Fe). The ion-exchanged samples could not be prepared by regular solvothermal reactions. The complete exchange of the metal ions and retention of the framework structure were verified by inductively coupled plasmaatomic emission spectrometry (ICP-AES), powder X-ray diffraction (PXRD), and BrunauerEmmettTeller (BET) surface-area analysis. Single-crystal X-ray diffractions studies revealed a single-crystal-to-single-crystal (SCSC)-transformation nature of the ion-exchange process. Hydrogen-sorption and magnetization measurements showed metal-specific properties of POST-65.

Cramer-Rao-Type Bounds for Sparse Bayesian Learning

In this paper, we derive Hybrid, Bayesian and Marginalized Cramer-Rao lower bounds (HCRB, BCRB and MCRB) for the single and multiple measurement vector Sparse Bayesian Learning (SBL) problem of estimating compressible vectors and their prior distribution parameters. We assume the unknown vector to be drawn from a compressible Student-prior distribution. We derive CRBs that encompass the deterministic or random nature of the unknown parameters of the prior distribution and the regression noise variance. We extend the MCRB to the case where the compressible vector is distributed according to a general compressible prior distribution, of which the generalized Pareto distribution is a special case. We use the derived bounds to uncover the relationship between the compressibility and Mean Square Error (MSE) in the estimates. Further, we illustrate the tightness and utility of the bounds through simulations, by comparing them with the MSE performance of two popular SBL-based estimators. We find that the MCRB is generally the tightest among the bounds derived and that the MSE performance of the Expectation-Maximization (EM) algorithm coincides with the MCRB for the compressible vector. We also illustrate the dependence of the MSE performance of SBL based estimators on the compressibility of the vector for several values of the number of observations and at different signal powers.

Error exponent analysis of energy-based bayesian spectrum sensing under fading channels

This paper analyzes the error exponents in Bayesian decentralized spectrum sensing, i.e., the detection of occupancy of the primary spectrum by a cognitive radio, with probability of error as the performance metric. At the individual sensors, the error exponents of a Central Limit Theorem (CLT) based detection scheme are analyzed. At the fusion center, a K-out-of-N rule is employed to arrive at the overall decision. It is shown that, in the presence of fading, for a fixed number of sensors, the error exponents with respect to the number of observations at both the individual sensors as well as at the fusion center are zero. This motivates the development of the error exponent with a certain probability as a novel metric that can be used to compare different detection schemes in the presence of fading. The metric is useful, for example, in answering the question of whether to sense for a pilot tone in a narrow band (and suffer Rayleigh fading) or to sense the entire wide-band signal (and suffer log-normal shadowing), in terms of the error exponent performance. The error exponents with a certain probability at both the individual sensors and at the fusion center are derived, with both Rayleigh as well as log-normal shadow fading. Numerical results are used to illustrate and provide a visual feel for the theoretical expressions obtained.

Proton Conduction in Metal-Organic Frameworks and Related Modularly Built Porous Solids

Proton-conducting materials are an important component of fuel cells. Development of new types of proton-conducting materials is one of the most important issues in fuel-cell technology. Herein, we present newly developed proton-conducting materials, modularly built porous solids, including coordination polymers (CPs) or metalorganic frameworks (MOFs). The designable and tunable nature of the porous materials allows for fast development in this research field. Design and synthesis of the new types of proton-conducting materials and their unique proton-conduction properties are discussed.

Modification of Extended Open Frameworks with Fluorescent Tags for Sensing Explosives: Competition between Size Selectivity and Electron Deficiency

Three new electron-rich metal-organic frameworks (MOF-1-MOF-3) have been synthesized by employing ligands bearing aromatic tags. The key role of the chosen aromatic tags is to enhance the -electron density of the luminescent MOFs. Single-crystal X-ray structures have revealed that these MOFs form three-dimensional porous networks with the aromatic tags projecting inwardly into the pores. These highly luminescent electron-rich MOFs have been successfully utilized for the detection of explosive nitroaromatic compounds (NACs) on the basis of fluorescence quenching. Although all of the prepared MOFs can serve as sensors for NACs, MOF-1 and MOF-2 exhibit superior sensitivity towards 4-nitrotoluene (4-NT) and 2,4-dinitrotoluene (DNT) compared to 2,4,6-trinitrotoluene (TNT) and 1,3,5-trinitrobenzene (TNB). MOF-3, on the other hand, shows an order of sensitivity in accordance with the electron deficiencies of the substrates. To understand such anomalous behavior, we have thoroughly analyzed both the steady-state and time-resolved fluorescence quenching associated with these interactions. Determination of static Stern-Volmer constants (K-S) as well as collisional constants (K-C) has revealed that MOF-1 and MOF-2 have higher K-S values with 4-NT than with TNT, whereas for MOF-3 the reverse order is observed. This apparently anomalous phenomenon was well corroborated by theoretical calculations. Moreover, recyclability and sensitivity studies have revealed that these MOFs can be reused several times and that their sensitivities towards TNT solution are at the parts per billion (ppb) level.