CNS: a new energy efficient transmission scheme for wireless sensor networks

We present in this paper a new energy-efficient communication scheme called CNS (Compression with Null Symbol) that combines the power of data compression and communication through silent symbol. The concept of communication through silent symbol is borrowed from the energy efficient schemes proposed in Sinha (Proceedings of 6th IEEE consumer communications and networking conference (CCNC), Las Vegas, pp. 1–5, 2009), Ghosh et al. (Proceedings of 27th IEEE international performance computing and communications conference (IPCCC), USA, pp. 85–92, 2008), and Sinha and Sinha (Proceedings of international conference on distributed computing and internet technologies (ICDCIT), LNCS, pp. 139–144, 2008). We show that the average theoretical energy saving at the transmitter by CNS is 62.5%, assuming an ideal channel and for equal likelihood of all possible binary strings of a given length. Next, we propose a transceiver design that uses a hybrid modulation scheme utilizing FSK and ASK so as to keep the cost/complexity of the radio devices low. Considering an additive white gaussian noise (AWGN) channel and a non-coherent detection based receiver, CNS shows a saving in transmitter energy by 30% when compared to binary FSK, for equal likelihood of all possible binary strings of a given length. Simultaneously, there is a saving of 50% at the receiver for all types of data modulation due to halving of the transmitted data duration, compared to binary encoding. In contrast, RBNSiZeComm proposed in Sinha (Proceedings of 6th IEEE consumer communications and networking conference (CCNC), Las Vegas, pp. 1–5, 2009), TSS proposed in Ghosh et al. (Proceedings of 27th IEEE international performance computing and communications conference (IPCCC), USA, pp. 85–92, 2008) and RZE proposed in Sinha and Sinha (Proceedings of international conference on distributed computing and internet technologies (ICDCIT), LNCS, pp. 139–144, 2008) generate average transmitter energy savings of about 41, 20, and 35.2%, respectively. Also, at the receiver side, while RBNSiZeComm does not generate any saving, TSS and RZE produce about 36.9 and 12.5% savings on an average, respectively. Considering certain data types that may occur in the context of some wireless sensor networks (WSN) based applications (e.g., remote healthcare, agricultural WSNs, etc.), our simulation results demonstrate that for AWGN noisy channels, the transmitter side savings vary from about 33–50% on an average, while for RBNSiZeComm, this saving is about 33–61% on the same data set (Sinha in Proceedings of 6th IEEE consumer communications and networking conference (CCNC), Las Vegas, pp. 1–5, 2009). Thus, taking into account the low cost/complexity of the proposed transceiver, these results clearly establish that CNS can be a suitable candidate for communication in low power wireless sensor networks, such as in remote healthcare applications, body area networks, home automation, WSNs for agriculture and many others.     

Progressive cutting tool wear detection from machined surface images using Voronoi tessellation method

Tool condition monitoring by machine vision approach has been gaining popularity day by day since it is a low cost and flexible method. In this paper, a tool condition monitoring technique by analysing turned surface images has been presented. The aim of this work is to apply an image texture analysis technique on turned surface images for quantitative assessment of cutting tool flank wear, progressively. A novel method by the concept of Voronoi tessellation has been applied in this study to analyse the surface texture of machined surface after the creation of Voronoi diagram. Two texture features, namely, number of polygons with zero cross moment and total void area of Voronoi diagram of machined surface images have been extracted. A correlation study between measured flank wear and extracted texture features has been done for depicting the tool flank wear. It has been found that number of polygons with zero cross moment has better linear relationship with tool flank wear than that of total void area.     

Linked Simulation-Optimization based Dedicated Monitoring Network Design for Unknown Pollutant Source Identification using Dynamic Time Warping Distance

Implementation of monitoring strategy for increasing the efficiency of groundwater pollutant source characterization is often necessary, especially when only inadequate and arbitrary concentration measurement data are initially available. Two main parameters that need to be estimated for efficient and accurate characterization of groundwater pollution sources are: location of the source and the time when the source became active. Complexities involved with the explicit estimation of the time of start and source activity have not been addressed so far in previous studies. The main complexity arises due to the fact that the spatial location and time of activity are inter-related. Therefore, specifying one and solving for the other simplifies the source characterization problem. Hence, in this study, both the source location and time of initiation are treated as unknowns. The developed methodology uses dynamic time warping distance in the linked simulation-optimization model to address some complex issues in designing a monitoring network to efficiently estimate source characteristics including the time of first activity of unknown groundwater source. Performance of the developed methodology is evaluated on illustrative contaminated aquifer. These evaluation results demonstrate the potential use of the developed methodology.     

Reverse stream flow routing by using Muskingum models

Reverse stream flowrouting is a procedure that determines the upstream hydrograph given the downstream hydrograph. This paper presents the development of methodology for Muskingum models parameter estimation for reverse stream flow routing. The standard application of the Muskingum models involves calibration and prediction steps. The calibration step must be performed before the prediction step. The calibration step in a reverse stream flow routing system uses the outflow hydrograph and the inflow at the end period of the inflow hydrograph as the known inputs and Muskingum model parameters are determined by minimizing the error between the remaining portion of the predicted and observed inflow hydrographs. In the present study, methodology for parameter estimation is developed which is based on the concept of minimizing the sum of squares of normalized difference between observed and computed inflows subject to the satisfaction of the routing equation. The parameter estimation problems are formulated as constrained nonlinear optimization problem, and a computational scheme is developed to solve the resulting nonlinear problem. The performance evaluation tests indicate that a fresh calibration is necessary to use the Muskingum models for reverse stream flow routing.     

Optimal Dynamic Monitoring Network Design and Identification of Unknown Groundwater Pollution Sources

The identification of unknown pollution sources is a prerequisite for designing of a remediation strategy. In most of the real world situations, it is difficult to identify the pollution sources without a scientifically designed efficient monitoring network. The locations of the contaminant concentration measurement sites would determine the efficiency of the unknown source identification process to a large extent. Therefore coupled and iterative sequential source identification and dynamic monitoring network design framework is developed. The coupled approach provides a framework for necessary sequential exchange of information between monitoring network and source identification methodology. The preliminary identification of unknown sources, based on limited concentration data from existing arbitrarily located wells provides the initial rough estimate of the source fluxes. These identified source fluxes are then utilized for designing an optimal monitoring network for the first stage. Both the monitoring network and source identification process is repeated by sequential identification of sources and design of monitoring network which provides the feedback information. In the optimal source identification model, the Jacobian matrix which is the determinant for the search direction in the nonlinear optimization model links the groundwater flow-transport simulator and the optimization method. For the optimal monitoring network design, the integer programming based optimal design model requires as input, simulated sets of concentration data. In the proposed methodology, the concentration measurement data from the designed and implemented monitoring network are used as feedback information for sequential identification of unknown pollution sources. The potential applicability of the developed methodology is demonstrated for an illustrative study area.     

Simplified Impedance Model for Adhesively Bonded Piezo-Impedance Transducers

The electromechanical impedance technique employs surface-bonded lead zirconate titanate piezoelectric ceramic patches as impedance transducers for structural health monitoring and nondestructive evaluation. The patches are bonded to the monitored structures using finitely thick adhesive bond layer, which introduces shear lag effect, thus invariably influencing the electromechanical admittance signatures. This paper presents a new simplified impedance model to incorporate shear lag effect into electromechanical admittance formulations, both one-dimensional and two-dimensional. This provides a closed-form analytical solution of the inverse problem, i.e. to derive the true structural impedance from the measured conductance and susceptance signatures, thus an improvement over the existing models. The influence of various parameters (associated with the bond layer) on admittance signatures is investigated using the proposed model and the results compared with existing models. The results show that the new model, which is far simpler than the existing models, models the shear lag phenomenon reasonably well besides providing direct solution of a complex inverse problem.     

Modelling of a downdraft biomass gasifier with finite rate kinetics in the reduction zone

A model of a downdraft gasifier has been developed based on chemical equilibrium in the pyro‐oxidation zone and finite rate kinetic‐controlled chemical reactions in the reduction zone. The char reactivity factor (C_RF) in the reduction zone, representing the number of active sites on the char and its degree of burn out, has been optimized by comparing the model predictions against the experimental results from the literature. The model predictions agree well with the temperature distribution and exit gas composition obtained from the experiments at C_RF=100. A detailed parametric study has been performed at different equivalence ratios (between 2 and 3.4) and moisture content (in the range of 0–40%) in the fuel to obtain the composition of the producer gas as well as its heating value. It is observed that the heating value of the producer gas increases with the increase in the equivalence ratio and decrease in the biomass moisture content. The effect of divergence angle of the reduction zone geometry (in the range of 30–150°) on the temperature and species concentration distributions in the gasifier has been studied. An optimum divergence angle, giving the best quality of the producer gas, has been identified for a particular height of the reduction zone. Copyright © 2009 John Wiley & Sons, Ltd.     

Zincated Nanoclay Polymer Composites (ZNCPCs): Synthesis,Characterization, Biodegradation and Controlled Release Behaviour in Soil

Novel zincated nanoclay polymer composites (ZNCPCs) with variable percentage of commercial bentonite and nanobentonite (8%, 10% and 12% of monomer for each case) were synthesized. Polyacrylic acid-Polyacrylamide copolymer was synthesized using N, N-Methylene bisacrylamide as crosslinker and ammonium persulfate as initiator. Clays as well as ZNCPCs were characterized by XRD, SEM, TEM and FTIR. 12% nanoclay containing formulation showed slowest release rate. ZNCPCs containing 8% clay recorded highest Zn content as well as highest equilibrium water absorbency. Biodegradation study revealed that Aspergillus spp was more effective as compared with Trichoderma spp in degradation of ZNCPCs.     

Robust and minimum norm partial quadratic eigenvalue assignment in vibrating systems: A new optimization approach

The partial quadratic eigenvalue assignment problem (PQEVAP) concerns reassigning a few undesired eigenvalues of a quadratic matrix pencil to suitably chosen locations and keeping the other large number of eigenvalues and eigenvectors unchanged (no spill-over). The problem naturally arises in controlling dangerous vibrations in structures by means of active feedback control design. For practical viability, the design must be robust, which requires that the norms of the feedback matrices and the condition number of the closed-loop eigenvectors are as small as possible. The problem of computing feedback matrices that satisfy the above two practical requirements is known as the Robust Partial Quadratic Eigenvalue Assignment Problem (RPQEVAP). In this paper, we formulate the RPQEVAP as an unconstrained minimization problem with the cost function involving the condition number of the closed-loop eigenvector matrix and two feedback norms. Since only a small number of eigenvalues of the open-loop quadratic pencil are computable using the state-of-the-art matrix computational techniques and/or measurable in a vibration laboratory, it is imperative that the problem is solved using these small number of eigenvalues and the corresponding eigenvectors. To this end, a class of the feedback matrices are obtained in parametric form, parameterized by a single parametric matrix, and the cost function and the required gradient formulas for the optimization problem are developed in terms of the small number of eigenvalues that are reassigned and their corresponding eigenvectors. The problem is solved directly in quadratic setting without transforming it to a standard first-order control problem and most importantly, the significant “no spill-over property” of the closed-loop eigenvalues and eigenvectors is established by means of a mathematical result. These features make the proposed method practically applicable even for very large structures. Results on numerical experiments show that the proposed method considerably reduces both feedback norms and the sensitivity of the closed-loop eigenvalues. A study on robustness of the system responses of the method under small perturbations show that the responses of the perturbed closed-loop system are compatible with perturbations.