Algorithm for Autonomous Reorganization of Mobile Wireless Camera Sensor Networks to Improve Coverage

In this paper, sensing coverage by wireless camera-embedded sensor networks (WCSNs), a class of directional sensors is studied. The proposed work facilitates the autonomous tuning of orientation parameters and displacement of camera-sensor nodes in the bounded field of interest (FoI), where the network coverage in terms of every point in the FoI is important. The proposed work is first of its kind to study the problem of maximizing coverage of randomly deployed mobile WCSNs which exploits their mobility. We propose an algorithm uncovered region exploration algorithm (UREA-CS) that can be executed in centralized and distributed modes. Further, the work is extended for two special scenarios: 1) to suit autonomous combing operations after initial random WCSN deployments and 2) to improve the network coverage with occlusions in the FoI. The extensive simulation results show that the performance of UREA-CS is consistent, robust, and versatile to achieve maximum coverage, both in centralized and distributed modes. The centralized and distributed modes are further analyzed with respect to the computational and communicational overheads.

Developing acetylcholinesterase-based inhibition assay by modulated synthesis of silver nanoparticles: applications for sensing of organophosphorus pesticides

A novel and highly sensitive sensing strategy for the detection of organophosphorus compounds (OPs) based on the catalytic reaction of acetylcholinesterase (AChE) and acetylcholine (ATCh) during the modulated synthesis of silver nanoparticles (AgNPs) has been developed. The enzymatic hydrolysis of ATCh by AChE yields thiocholine (TCh), which induces the aggregation of AgNPs during synthesis, and the absorption peak at 382 nm corresponding to AgNPs decreases. The enzymatic reaction can be regulated by OPs, which can covalently bind to the active site of AChE and decrease the TCh formation, thereby decreasing the aggregation and significantly enhancing the absorption peak at 382 nm. The proposed system achieved good linearity and limits of detection of 0.078 nM and 2.402 nM for trichlorfon and malathion, respectively, by UV-visible spectroscopy. Further, the sensitivity of the proposed system was demonstrated through the determination of OPs in different spiked real samples. The described work shows the potential application for further development of a colorimetric sensor for other OP pesticide detection during the synthesis of AgNPs using enzyme-based assays.

Physical Layer Data Fusion Via Distributed Co-Phasing With General Signal Constellations

This paper studies a pilot-assisted physical layer data fusion technique known as Distributed Co-Phasing (DCP). In this two-phase scheme, the sensors first estimate the channel to the fusion center (FC) using pilots sent by the latter; and then they simultaneously transmit their common data by pre-rotating them by the estimated channel phase, thereby achieving physical layer data fusion. First, by analyzing the symmetric mutual information of the system, it is shown that the use of higher order constellations (HOC) can improve the throughput of DCP compared to the binary signaling considered heretofore. Using an HOC in the DCP setting requires the estimation of the composite DCP channel at the FC for data decoding. To this end, two blind algorithms are proposed: 1) power method, and 2) modified K-means algorithm. The latter algorithm is shown to be computationally efficient and converges significantly faster than the conventional K-means algorithm. Analytical expressions for the probability of error are derived, and it is found that even at moderate to low SNRs, the modified K-means algorithm achieves a probability of error comparable to that achievable with a perfect channel estimate at the FC, while requiring no pilot symbols to be transmitted from the sensor nodes. Also, the problem of signal corruption due to imperfect DCP is investigated, and constellation shaping to minimize the probability of signal corruption is proposed and analyzed. The analysis is validated, and the promising performance of DCP for energy-efficient physical layer data fusion is illustrated, using Monte Carlo simulations.

A Fast and Fault-Tolerant Distributed Algorithm for Near-Optimal TDMA Scheduling in WSNs

The time division multiple access (TDMA) based channel access mechanisms perform better than the contention based channel access mechanisms, in terms of channel utilization, reliability and power consumption, specially for high data rate applications in wireless sensor networks (WSNs). Most of the existing distributed TDMA scheduling techniques can be classified as either static or dynamic. The primary purpose of static TDMA scheduling algorithms is to improve the channel utilization by generating a schedule of smaller length. But, they usually take longer time to schedule, and hence, are not suitable for WSNs, in which the network topology changes dynamically. On the other hand, dynamic TDMA scheduling algorithms generate a schedule quickly, but they are not efficient in terms of generated schedule length. In this paper, we propose a novel scheme for TDMA scheduling in WSNs, which can generate a compact schedule similar to static scheduling algorithms, while its runtime performance can be matched with those of dynamic scheduling algorithms. Furthermore, the proposed distributed TDMA scheduling algorithm has the capability to trade-off schedule length with the time required to generate the schedule. This would allow the developers of WSNs, to tune the performance, as per the requirement of prevalent WSN applications, and the requirement to perform re-scheduling. Finally, the proposed TDMA scheduling is fault-tolerant to packet loss due to erroneous wireless channel. The algorithm has been simulated using the Castalia simulator to compare its performance with those of others in terms of generated schedule length and the time required to generate the TDMA schedule. Simulation results show that the proposed algorithm generates a compact schedule in a very less time.

Co-Exploration of NLA Kernels and Specification of Compute Elements in Distributed Memory CGRAs

Coarse Grained Reconfigurable Architectures (CGRA) are emerging as embedded application processing units in computing platforms for Exascale computing. Such CGRAs are distributed memory multi- core compute elements on a chip that communicate over a Network-on-chip (NoC). Numerical Linear Algebra (NLA) kernels are key to several high performance computing applications. In this paper we propose a systematic methodology to obtain the specification of Compute Elements (CE) for such CGRAs. We analyze block Matrix Multiplication and block LU Decomposition algorithms in the context of a CGRA, and obtain theoretical bounds on communication requirements, and memory sizes for a CE. Support for high performance custom computations common to NLA kernels are met through custom function units (CFUs) in the CEs. We present results to justify the merits of such CFUs.

Application of principal component analysis to gas sensing characteristics of Cr0.8Fe0.2NbO4 thick film array

The transient changes in resistances of Cr0.8Fe0.2NbO4 thick film sensors towards specified concentrations of H-2, NH3, acetonitrile, acetone, alcohol, cyclohexane and petroleum gas at different operating temperatures were recorded. The analyte-specific characteristics such as slopes of the response and retrace curves, area under the curve and sensitivity deduced from the transient curve of the respective analyte gas have been used to construct a data matrix. Principal component analysis (PCA) was applied to this data and the score plot was obtained. Distinguishing one reducing gas from the other is demonstrated based on this approach, which otherwise is not possible by measuring relative changes in conductivity. This methodology is extended for three Cr0.8Fe0.2NbO4 thick film sensor array operated at different temperatures. (C) 2015 Elsevier B.V. All rights reserved.

Enantio sensing property of helicin, the derivative of a natural product: Discrimination of amines and amino alcohols

The chiral sensing property of helicin (the derivative of natural product obtained by partial oxidation of salicin, extracted from willow tree (Salix helix)) is reported. The use of helicin as a chiral derivatizing agent for the discrimination of amines and amino alcohols is convincingly established using H-1 NMR spectroscopy. The large chemical shift separation achieved between the discriminated peaks facilitated the accurate quantification of enantiomeric composition. The consistent trend observed in the shifting of imine proton peak (Delta delta) of helicin in all the derivatized molecules might aid the determination of spatial configuration. (C) 2015 Elsevier B.V. All rights reserved.

Enantio sensing property of helicin, the derivative of a natural product: Discrimination of amines and amino alcohols

The chiral sensing property of helicin (the derivative of natural product obtained by partial oxidation of salicin, extracted from willow tree (Salix helix)) is reported. The use of helicin as a chiral derivatizing agent for the discrimination of amines and amino alcohols is convincingly established using H-1 NMR spectroscopy. The large chemical shift separation achieved between the discriminated peaks facilitated the accurate quantification of enantiomeric composition. The consistent trend observed in the shifting of imine proton peak (Delta delta) of helicin in all the derivatized molecules might aid the determination of spatial configuration. (C) 2015 Elsevier B.V. All rights reserved.

Enantio sensing property of helicin, the derivative of a natural product: Discrimination of amines and amino alcohols

The chiral sensing property of helicin (the derivative of natural product obtained by partial oxidation of salicin, extracted from willow tree (Salix helix)) is reported. The use of helicin as a chiral derivatizing agent for the discrimination of amines and amino alcohols is convincingly established using H-1 NMR spectroscopy. The large chemical shift separation achieved between the discriminated peaks facilitated the accurate quantification of enantiomeric composition. The consistent trend observed in the shifting of imine proton peak (Delta delta) of helicin in all the derivatized molecules might aid the determination of spatial configuration. (C) 2015 Elsevier B.V. All rights reserved.

Nickel substitution induced effects on gas sensing properties of cobalt ferrite nanoparticles

Ni2+ ion induced unusual conductivity reversal and an enhancement in the gas sensing properties of ferrites based gas sensors, is reported. The Co1-xNixFe2O4 (for x = 0, 0.5 and 1) nanoparticles were synthesized by wet chemical co-precipitation method and gas sensing properties were studied as a function of composition and temperature. The structural, morphological and microstructural characterization revealed crystallite size of in the range 10-20 nm with porous morphology consisting of nano-sized grains. The Energy Dispersive X-ray (EDX) mapping confirms homogeneous distribution of Co, Ni, Fe and O elements in the ferrites. The non-stoichiometry of the inverse spinel type ferrites and the relative concentration of Ni3+/Co3+ defects were studied using X-ray photoelectron spectroscopy. It is found that the addition of Ni2+ ions into cobalt ferrite shows preferred selectivity towards CO gas at high temperature (325 degrees C) and ethanol gas at low temperature (250 degrees C), unlike undoped cobalt ferrite or undoped nickel ferrite, which show similar response for both these gases. Moreover, an unusual conductivity reversal is observed, except cobalt ferrite due to the difference in reactivity of the gases as well as characteristic non-stoichiometry of ferrites. This behavior is highly gas ambient dependent and hence can be well-exploited for selective detection of gases. (C) 2015 Elsevier B.V. All rights reserved.

Tailored nitrogen dioxide sensing response of three-dimensional graphene foam

Ultralight and macroporous three-dimensional reduced graphene oxide (rGO) foams are prepared by lyophilization (freeze-drying) technique to avoid a conventional template method. This method allows tailoring the porosity of the foams by varying the weight percentages of graphene oxide dispersions in water. Three different rGO foams of 0.2, 0.5 and 1.0 wt% are used for NO2 sensing. Sensing response from the tailored structure of rGO is found to be directly related to the density. A maximum of 20% sensing response is observed for a higher porosity of the structure, better than the known results so far on graphene foams in the literature. (C) 2015 Elsevier B.V. All rights reserved.

Load Balancing with Availability Checker and Load Reporters (LB-ACLRs) for improved Performance in Distributed Systems

Distributed system has quite a lot of servers to attain increased availability of service and for fault tolerance. Balancing the load among these servers is an important task to achieve better performance. There are various hardware and software based load balancing solutions available. However there is always an overhead on Servers and the Load Balancer while communicating with each other and sharing their availability and the current load status information. Load balancer is always busy in listening to clients' request and redirecting them. It also needs to collect the servers' availability status frequently, to keep itself up-to-date. Servers are busy in not only providing service to clients but also sharing their current load information with load balancing algorithms. In this paper we have proposed and discussed the concept and system model for software based load balancer along with Availability-Checker and Load Reporters (LB-ACLRs) which reduces the overhead on server and the load balancer. We have also described the architectural components with their roles and responsibilities. We have presented a detailed analysis to show how our proposed Availability Checker significantly increases the performance of the system.

Diffractive Optical Analysis for Refractive Index Sensing using Transparent Phase Gratings

We report the implementation of a micro-patterned, glass-based photonic sensing element that is capable of label-free biosensing. The diffractive optical analyzer is based on the differential response of diffracted orders to bulk as well as surface refractive index changes. The differential read-out suppresses signal drifts and enables time-resolved determination of refractive index changes in the sample cell. A remarkable feature of this device is that under appropriate conditions, the measurement sensitivity of the sensor can be enhanced by more than two orders of magnitude due to interference between multiply reflected diffracted orders. A noise-equivalent limit of detection (LoD) of 6 x 10(-7) was achieved with this technique with scope for further improvement.

Near-Optimal Detection Thresholds for Bayesian Spectrum Sensing Under Fading

This paper considers the problem of energy-based, Bayesian spectrum sensing in cognitive radios under various fading environments. Under the well-known central limit theorem based model for energy detection, we derive analytically tractable expressions for near-optimal detection thresholds that minimize the probability of error under lognormal, Nakagami-m, and Weibull fading. For the Suzuki fading case, a generalized gamma approximation is provided, which saves on the computation of an integral. In each case, the accuracy of the theoretical expressions as compared to the optimal thresholds are illustrated through simulations.

Multi-dimensional Goodness-of-Fit Tests for Spectrum Sensing Based on Stochastic Distances

In this paper, we study two multi-dimensional Goodness-of-Fit tests for spectrum sensing in cognitive radios. The multi-dimensional scenario refers to multiple CR nodes, each with multiple antennas, that record multiple observations from multiple primary users for spectrum sensing. These tests, viz., the Interpoint Distance (ID) based test and the h, f distance based tests are constructed based on the properties of stochastic distances. The ID test is studied in detail for a single CR node case, and a possible extension to handle multiple nodes is discussed. On the other hand, the h, f test is applicable in a multi-node setup. A robustness feature of the KL distance based test is discussed, which has connections with Middleton's class A model. Through Monte-Carlo simulations, the proposed tests are shown to outperform the existing techniques such as the eigenvalue ratio based test, John's test, and the sphericity test, in several scenarios.