Highly Sensitive Carbon Nanotubes Coated Etched Fiber Bragg Grating Sensor for Humidity Sensing

The sensing of relative humidity (RH) at room temperature has potential applications in several areas ranging from biomedical to horticulture, paper, and textile industries. In this paper, a highly sensitive humidity sensor based on carbon nanotubes (CNTs) coated on the surface of an etched fiber Bragg grating (EFBG) sensor has been demonstrated, for detecting RH over a wide range of 20%-90% at room temperature. When water molecules interact with the CNT coated EFBG, the effective refractive index of the fiber core changes, resulting in a shift in the Bragg wavelength. It has been possible to achieve a high sensitivity of similar to 31 pm/% RH, which is the highest compared with many of the existing FBG-based humidity sensors. The limit of detection in the CNT coated EFBG has been found to be similar to 0.03 RH. The experimental data shows a linear response of Bragg wavelength shift with increase in humidity. This novel method of incorporating CNTs on to the FBG sensor for humidity sensing has not been reported before.

Fabrication and Experimentation of a Cantilever Beam Based Piezoelectric Actuator and Sensor for Vibration Energy Harvesting

In the immediate surroundings of our daily life, we can find a lot of places where the energy in the form of vibration is being wasted. Therefore, we have enormous opportunities to utilize the same. Piezoelectric character of matter enables us to convert this mechanical vibration energy into electrical energy which can be stored and used to power other device, instead of being wasted. This work is done to realize both actuator and sensor in a cantilever beam based on piezoelectricity. The sensor part is called vibration energy harvester. The numerical analyses were performed for the cantilever beam using the commercial package ANSYS and MATLAB. The cantilever beam is realized by taking a plate and fixing its one end between two massive plates. Two PZT patches were glued to the beam on its two faces. Experiments were performed using data acquisition system (DAQ) and LABVIEW software for actuating and sensing the vibration of the cantilever beam.

Energy Sharing for Multiple Sensor Nodes With Finite Buffers

We consider the problem of finding optimal energy sharing policies that maximize the network performance of a system comprising of multiple sensor nodes and a single energy harvesting (EH) source. Sensor nodes periodically sense the random field and generate data, which is stored in the corresponding data queues. The EH source harnesses energy from ambient energy sources and the generated energy is stored in an energy buffer. Sensor nodes receive energy for data transmission from the EH source. The EH source has to efficiently share the stored energy among the nodes to minimize the long-run average delay in data transmission. We formulate the problem of energy sharing between the nodes in the framework of average cost infinite-horizon Markov decision processes (MDPs). We develop efficient energy sharing algorithms, namely Q-learning algorithm with exploration mechanisms based on the epsilon-greedy method as well as upper confidence bound (UCB). We extend these algorithms by incorporating state and action space aggregation to tackle state-action space explosion in the MDP. We also develop a cross entropy based method that incorporates policy parameterization to find near optimal energy sharing policies. Through simulations, we show that our algorithms yield energy sharing policies that outperform the heuristic greedy method.

Action Recognition from Motion Capture Data using Meta-cognitive RBF Network Classifier

Action recognition plays an important role in various applications, including smart homes and personal assistive robotics. In this paper, we propose an algorithm for recognizing human actions using motion capture action data. Motion capture data provides accurate three dimensional positions of joints which constitute the human skeleton. We model the movement of the skeletal joints temporally in order to classify the action. The skeleton in each frame of an action sequence is represented as a 129 dimensional vector, of which each component is a 31) angle made by each joint with a fixed point on the skeleton. Finally, the video is represented as a histogram over a codebook obtained from all action sequences. Along with this, the temporal variance of the skeletal joints is used as additional feature. The actions are classified using Meta-Cognitive Radial Basis Function Network (McRBFN) and its Projection Based Learning (PBL) algorithm. We achieve over 97% recognition accuracy on the widely used Berkeley Multimodal Human Action Database (MHAD).

An approximation to the QoS aware throughput region of a tree network under IEEE 802.15.4 CSMA/CA with application to wireless sensor network design

In the context of wireless sensor networks, we are motivated by the design of a tree network spanning a set of source nodes that generate packets, a set of additional relay nodes that only forward packets from the sources, and a data sink. We assume that the paths from the sources to the sink have bounded hop count, that the nodes use the IEEE 802.15.4 CSMA/CA for medium access control, and that there are no hidden terminals. In this setting, starting with a set of simple fixed point equations, we derive explicit conditions on the packet generation rates at the sources, so that the tree network approximately provides certain quality of service (QoS) such as end-to-end delivery probability and mean delay. The structures of our conditions provide insight on the dependence of the network performance on the arrival rate vector, and the topological properties of the tree network. Our numerical experiments suggest that our approximations are able to capture a significant part of the QoS aware throughput region (of a tree network), that is adequate for many sensor network applications. Furthermore, for the special case of equal arrival rates, default backoff parameters, and for a range of values of target QoS, we show that among all path-length-bounded trees (spanning a given set of sources and the data sink) that meet the conditions derived in the paper, a shortest path tree achieves the maximum throughput. (C) 2015 Elsevier B.V. All rights reserved.

Shock tunnel measurements of surface pressures in shock induced separated flow field using MEMS sensor array

Characterized not just by high Mach numbers, but also high flow total enthalpies-often accompanied by dissociation and ionization of flowing gas itself-the experimental simulation of hypersonic flows requires impulse facilities like shock tunnels. However, shock tunnel simulation imposes challenges and restrictions on the flow diagnostics, not just because of the possible extreme flow conditions, but also the short run times-typically around 1 ms. The development, calibration and application of fast response MEMS sensors for surface pressure measurements in IISc hypersonic shock tunnel HST-2, with a typical test time of 600 mu s, for the complex flow field of strong (impinging) shock boundary layer interaction with separation close to the leading edge, is delineated in this paper. For Mach numbers 5.96 (total enthalpy 1.3 MJ kg(-1)) and 8.67 (total enthalpy 1.6 MJ kg(-1)), surface pressures ranging from around 200 Pa to 50 000 Pa, in various regions of the flow field, are measured using the MEMS sensors. The measurements are found to compare well with the measurements using commercial sensors. It was possible to resolve important regions of the flow field involving significant spatial gradients of pressure, with a resolution of 5 data points within 12 mm in each MEMS array, which cannot be achieved with the other commercial sensors. In particular, MEMS sensors enabled the measurement of separation pressure (at Mach 8.67) near the leading edge and the sharply varying pressure in the reattachment zone.

QoS Constrained Optimal Sink and Relay Placement in Planned Wireless Sensor Networks

We are given a set of sensors at given locations, a set of potential locations for placing base stations (BSs, or sinks), and another set of potential locations for placing wireless relay nodes. There is a cost for placing a BS and a cost for placing a relay. The problem we consider is to select a set of BS locations, a set of relay locations, and an association of sensor nodes with the selected BS locations, so that the number of hops in the path from each sensor to its BS is bounded by h(max), and among all such feasible networks, the cost of the selected network is the minimum. The hop count bound suffices to ensure a certain probability of the data being delivered to the BS within a given maximum delay under a light traffic model. We observe that the problem is NP-Hard, and is hard to even approximate within a constant factor. For this problem, we propose a polynomial time approximation algorithm (SmartSelect) based on a relay placement algorithm proposed in our earlier work, along with a modification of the greedy algorithm for weighted set cover. We have analyzed the worst case approximation guarantee for this algorithm. We have also proposed a polynomial time heuristic to improve upon the solution provided by SmartSelect. Our numerical results demonstrate that the algorithms provide good quality solutions using very little computation time in various randomly generated network scenarios.

Speaker verification based on the fusion of speech acoustics and inverted articulatory signals

We propose apractical, feature-level and score-level fusion approach by combining acoustic and estimated articulatory information for both text independent and text dependent speaker verification. From a practical point of view, we study how to improve speaker verification performance by combining dynamic articulatory information with the conventional acoustic features. On text independent speaker verification, we find that concatenating articulatory features obtained from measured speech production data with conventional Mel-frequency cepstral coefficients (MFCCs) improves the performance dramatically. However, since directly measuring articulatory data is not feasible in many real world applications, we also experiment with estimated articulatory features obtained through acoustic-to-articulatory inversion. We explore both feature level and score level fusion methods and find that the overall system performance is significantly enhanced even with estimated articulatory features. Such a performance boost could be due to the inter-speaker variation information embedded in the estimated articulatory features. Since the dynamics of articulation contain important information, we included inverted articulatory trajectories in text dependent speaker verification. We demonstrate that the articulatory constraints introduced by inverted articulatory features help to reject wrong password trials and improve the performance after score level fusion. We evaluate the proposed methods on the X-ray Microbeam database and the RSR 2015 database, respectively, for the aforementioned two tasks. Experimental results show that we achieve more than 15% relative equal error rate reduction for both speaker verification tasks. (C) 2015 Elsevier Ltd. All rights reserved.

Fiber Bragg Grating Sensor Package for Submicron Level Displacement Measurements

In this article, the design and development of a Fiber Bragg Grating (FBG) based displacement sensor package for submicron level displacement measurements are presented. A linear shift of 12.12 nm in Bragg wavelength of the FBG sensor is obtained for a displacement of 6 mm with a calibration factor of 0.495 mu m/pm. Field trials have also been conducted by comparing the FBG displacement sensor package against a conventional dial gauge, on a five block masonry prism specimen loaded using three-point bending technique. The responses from both the sensors are in good agreement, up to the failure of the masonry prism. Furthermore, from the real-time displacement data recorded using FBG, it is possible to detect the time at which early creaks generated inside the body of the specimen which then prorogate to the surface to develop visible surface cracks; the respective load from the load cell can be obtained from the inflection (stress release point) in the displacement curve. Thus the developed FBG displacement sensor package can be used to detect failures in structures much earlier and to provide an adequate time to exercise necessary action, thereby avoiding the possible disaster.

Status of the 98-125 GeV Higgs bosons scenario with updated LHC-8 data

In the context of the minimal supersymmetric standard model (MSSM), we discuss the possibility of the lightest Higgs boson with mass M-h = 98 GeV to be consistent with the 2.3 sigma excess observed at the LEP in the decay mode e(+)e(-) -> Zh, with h -> b (b) over bar. In the same region of the MSSM parameter space, the heavier Higgs boson (H) with mass M-H similar to 125 GeV is required to be consistent with the latest data on Higgs coupling measurements at the end of the 7 + 8 TeV LHC run with 25 fb(-1) of data. While scanning the MSSM parameter space, we impose constraints coming from flavor physics, relic density of the cold dark matter as well as direct dark matter searches. We study the possibility of observing this light Higgs boson in vector boson fusion process and associated production with W/Z-boson at the high luminosity (3000 fb(-1)) run of the 14 TeV LHC. Our analysis shows that this scenario can hardly be ruled out even at the high luminosity run of the LHC. However, the precise measurement of the Higgs signal strength ratios can play a major role to distinguish this scenario from the canonical MSSM one.

RPRDC: Reliable Proliferation Routing with low Duty-Cycle in Wireless Sensor Networks

Ensuring reliable energy efficient data communication in resource constrained Wireless Sensor Networks (WSNs) is of primary concern. Traditionally, two types of re-transmission have been proposed for the data-loss, namely, End-to-End loss recovery (E2E) and per hop. In these mechanisms, lost packets are re-transmitted from a source node or an intermediate node with a low success rate. The proliferation routing(1) for QoS provisioning in WSNs low End-to-End reliability, not energy efficient and works only for transmissions from sensors to sink. This paper proposes a Reliable Proliferation Routing with low Duty Cycle RPRDC] in WSNs that integrates three core concepts namely, (i) reliable path finder, (ii) a randomized dispersity, and (iii) forwarding. Simulation results demonstrates that packet successful delivery rate can be maintained upto 93% in RPRDC and outperform Proliferation Routing(1). (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

A novel, sensitive potentiometric hydrocarbon sensor for high-vacuum applications.

A potentiometric device based on interfacing a solid electrolyte oxygen ion conductor with a thin platinum film acts as a robust, reproducible sensor for the detection of hydrocarbons in high- or ultrahigh-vacuum environments. Sensitivities in the order of approximately 5 x 10(-10) mbar are achievable under open circuit conditions, with good selectivity for discrimination between n-butane on one hand and toluene, n-octane, n-hexane, and 1-butene on the other hand. The sensor's sensitivity may be tuned by operating under constant current (closed circuit) conditions; injection of anodic current is also a very effective means of restoring a clean sensing surface at any desired point. XPS data and potentiometric measurements confirm the proposed mode of sensing action: the steady-state coverage of Oa, which sets the potential of the Pt sensing electrode, is determined by the partial pressure and dissociative sticking probability of the impinging hydrocarbon. The principles established here provide the basis for a viable, inherently flexible, and promising means for the sensitive and selective detection of hydrocarbons under demanding conditions.

Streaming covariance selection with applications to adaptive querying in sensor networks

Sensor networks can be naturally represented as graphical models, where the edge set encodes the presence of sparsity in the correlation structure between sensors. Such graphical representations can be valuable for information mining purposes as well as for optimizing bandwidth and battery usage with minimal loss of estimation accuracy. We use a computationally efficient technique for estimating sparse graphical models which fits a sparse linear regression locally at each node of the graph via the Lasso estimator. Using a recently suggested online, temporally adaptive implementation of the Lasso, we propose an algorithm for streaming graphical model selection over sensor networks. With battery consumption minimization applications in mind, we use this algorithm as the basis of an adaptive querying scheme. We discuss implementation issues in the context of environmental monitoring using sensor networks, where the objective is short-term forecasting of local wind direction. The algorithm is tested against real UK weather data and conclusions are drawn about certain tradeoffs inherent in decentralized sensor networks data analysis. © 2010 The Author. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved.

Relay node placement for wireless sensor networks deployed in tunnels

Node placement plays a significant role in the effective and successful deployment of Wireless Sensor Networks (WSNs), i.e., meeting design goals such as cost effectiveness, coverage, connectivity, lifetime and data latency. In this paper, we propose a new strategy to assist in the placement of Relay Nodes (RNs) for a WSN monitoring underground tunnel infrastructure. By applying for the first time an accurate empirical mean path loss propagation model along with a well fitted fading distribution model specifically defined for the tunnel environment, we address the RN placement problem with guaranteed levels of radio link performance. The simulation results show that the choice of appropriate path loss model and fading distribution model for a typical environment is vital in the determination of the number and the positions of RNs. Furthermore, we adapt a two-tier clustering multi-hop framework in which the first tier of the RN placement is modelled as the minimum set cover problem, and the second tier placement is solved using the search-and-find algorithm. The implementation of the proposed scheme is evaluated by simulation, and it lays the foundations for further work in WSN planning for underground tunnel applications. © 2010 IEEE.