Level based sampling techniques for energy conservation in large scale wireless sensor networks

As the size and node density of wireless sensor networks (WSN) increase,the energy conservation problem becomes more critical and the conventional methods become inadequate. This dissertation addresses two different problems in large scale WSNs where all sensors are involved in monitoring,but the traditional practice of periodic transmissions of observations from all sensors would drain excessive amount of energy. In the first problem,monitoring of the spatial distribution of a two dimensional correlated signal is considered using a large scale WSN. It is assumed that sensor observations are heavily affected by noise. We present an approach that is based on detecting contour lines of the signal distribution to estimate the spatial distribution of the signal without involving all sensors in the network. Energy efficient algorithms are proposed for detecting and tracking the temporal variation of the contours. Optimal contour levels that minimize the estimation error and a practical approach for selection of contour levels are explored. Performance of the proposed algorithm is explored with different types of contour levels and detection parameters. In the second problem,a WSN is considered that performs health monitoring of equipment from a power substation. The monitoring applications require transmissions of sensor observations from all sensor nodes on a regular basis to the base station,which is very costly in terms of communication cost. To address this problem,an efficient sampling technique using level-crossings (LCS) is proposed. This technique saves communication cost by suppressing transmissions of data samples that do not convey much information. The performance and cost of LCS for several different level-selection schemes are investigated. The number of required levels and the maximum sampling period for practical implementation of LCS are studied. Finally,in an experimental implementation of LCS with MICAzmote,the performance and cost of LCS for temperature sensing with uniform,logarithmic and a combined version of uniform and logarithmically spaced levels are compared with that using periodic sampling

The application of black box models to combustion processes in the internal combustion engine

The internal combustion engine has been under considerable pressure during the last few years. The publics growing sensitivity for emissions and resource wastage have led to increasingly stringent legislation. Engine manufacturers need to invest significant monetary funds and engineering resources in order to meet the designated regulations. In recent years, reductions in emissions and fuel consumption could be achieved with advanced engine technologies such as exhaust gas recirculation (EGR), variable geometry turbines (VGT), variable valve trains (VVT), variable compression ratios (VCR) or extended aftertreatment systems such as diesel particulate filters (DPF) or NOx traps or selective catalytic reduction (SCR) implementations. These approaches are characterised by a highly non-linear behaviour with an increasing demand for close-loop control. In consequence, successful controller design becomes an important part of meeting legislation requirements and acceptable standards. At the same time, the close-loop control requires additional monitoring information and, especially in the field of combustion control, this is a challenging task. Existing sensors in heavy-duty diesel applications for incylinder pressure detection enable the feedback of combustion conditions. However, high maintenance costs and reliability issues currently cancel this method out for mass-production vehicles. Methods of in-cylinder condition reconstruction for real-time applications have been presented over the last few decades. The methodical restrictions of these approaches are proving problematic. Hence, this work presents a method utilising artificial neural networks for the prediction of combustion-related engine parameters. The application of networks for the prediction of parameters such as emission formations of NOx and Particulate Matters will be shown initially. This thesis shows the importance of correct training and validation data choice together with a comprehensive network input set. In addition, an application of an efficient and accurate plant model as a support tool for an engine fuel-path controller is presented together with an efficient test data generation method. From these findings, an artificial neural network structure is developed for the prediction of in-cylinder combustion conditions. In-cylinder pressure and temperature provide valuable information about the combustion efficiency and quality. This work presents a structure that can predict these parameters from other more simple measurable variables within the engine auxiliaries. The structure is tested on data generated from a GT-Power simulation model and with a Caterpillar C6.6 heavy-duty diesel engine

Design, analysis and evaluation of sigma-delta based beamformers for medical ultrasound imaging applications

The inherent analogue nature of medical ultrasound signals in conjunction with the abundant merits provided by digital image acquisition, together with the increasing use of relatively simple front-end circuitries, have created considerable demand for single-bit beamformers in digital ultrasound imaging systems. Furthermore, the increasing need to design lightweight ultrasound systems with low power consumption and low noise, provide ample justification for development and innovation in the use of single-bit beamformers in ultrasound imaging systems. The overall aim of this research program is to investigate, establish, develop and confirm through a combination of theoretical analysis and detailed simulations, that utilize raw phantom data sets, suitable techniques for the design of simple-to-implement hardware efficient digital ultrasound beamformers to address the requirements for 3D scanners with large channel counts, as well as portable and lightweight ultrasound scanners for point-of-care applications and intravascular imaging systems. In addition, the stability boundaries of higher-order High-Pass (HP) and Band-Pass (BP) Σ−Δ modulators for single- and dual- sinusoidal inputs are determined using quasi-linear modeling together with the describing-function method, to more accurately model the modulator quantizer. The theoretical results are shown to be in good agreement with the simulation results for a variety of input amplitudes, bandwidths, and modulator orders. The proposed mathematical models of the quantizer will immensely help speed up the design of higher order HP and BP Σ−Δ modulators to be applicable for digital ultrasound beamformers. Finally, a user friendly design and performance evaluation tool for LP, BP and HP modulators is developed. This toolbox, which uses various design methodologies and covers an assortment of modulators topologies, is intended to accelerate the design process and evaluation of modulators. This design tool is further developed to enable the design, analysis and evaluation of beamformer structures including the noise analyses of the final B-scan images. Thus, this tool will allow researchers and practitioners to design and verify different reconstruction filters and analyze the results directly on the B-scan ultrasound images thereby saving considerable time and effort

Mitigation of Wide Angle Signal Interference in Terahertz Imaging Systems

abstract: The objective of this work is to design a low-profile compact Terahertz (THz) imaging system that can be installed in portable devices, unmanned aerial vehicles (UAVs), or CubeSats. Taking advantage of the rotational motion of these platforms, one can use linear antennas, such as leaky-wave antennas or linear phased arrays, to achieve fast image acquisition using simple RF front-end topologies. The proposed system relies on a novel image reconstructing technique that uses the principles of computerized tomography (Fourier-slice theorem). It can be implemented using a rotating antenna that produces a highly astigmatic fan-beam. In this work, the imaging system is composed of a linear phased antenna array with a highly directive beam pattern in the E-plane allowing for high spatial resolution imaging. However, the pattern is almost omnidirectional in the H-plane and extends beyond the required field-of-view (FOV). This is a major drawback as the scattered signals from any interferer outside the FOV will still be received by the imaging aperture and cause distortion in the reconstructed image. Also, fan beams exhibit significant distortion (curvature) when tilted at large angles, thus introducing errors in the final image due to its failure to achieve the assumed reconstructing algorithm. Therefore, a new design is proposed to alleviate these disadvantages. A 14×64 elements non-uniform array with an optimal flat-top pattern is designed with an iterative process using linear perturbation of a close starting pattern until the desired pattern is acquired. The principal advantage of this design is that it restricts the radiated/received power into the required FOV. As a result, a significant enhancement in the quality of images is achieved especially in the mitigation of the effect of any interferer outside the FOV. In this report, these two designs are presented and compared in terms of their imaging efficiency along with a series of numerical results verifying the proof of concept.Dissertation/ThesisMasters Thesis Electrical Engineering 201

Optimum energy allocation for detection in wireless sensor networks

The problem of binary hypothesis testing in a wireless sensor network is studied in the presence of noisy channels and for non-identical sensors. We have designed a mathematically tractable fusion rule for which optimal energy allocation for individual sensors can be achieved. In this thesis we considered two methods for transmitting the sensor observations; binary modulation and M-ary modulation. In binary modulation we are able to allocate the energy among the sensors and protect the individual quantized bits where as the M-ary modulation provides optimum energy allocation only among the sensors. The goal is to design a fusion rule and an energy allocation for the nodes subject to a limit on the total energy of all the nodes so as to optimize a cost function. Two cost functions were considered; the probability of error and the J-divergence distance measure. Probability of error is the most natural criteria used for binary hypothesis testing problem. Distance measure is applied when it is difficult to obtain a closed form for the error probability. Results of optimal energy allocation and the resulting probability of error are presented for the two cost functions. Comparisons are drawn between the two cost functions regarding the fusion rule, energy allocations and the error probability

Collaborative Estimation in Distributed Sensor Networks

Networks of smart ultra-portable devices are already indispensable in our lives, augmenting our senses and connecting our lives through real time processing and communication of sensory (e.g., audio, video, location) inputs. Though usually hidden from the user\u27s sight, the engineering of these devices involves fierce tradeoffs between energy availability (battery sizes impact portability) and signal processing / communication capability (which impacts the smartness of the devices). The goal of this dissertation is to provide a fundamental understanding and characterization of these tradeoffs in the context of a sensor network, where the goal is to estimate a common signal by coordinating a multitude of battery-powered sensor nodes. Most of the research so far has been based on two key assumptions -- distributed processing and temporal independence -- that lend analytical tractability to the problem but otherwise are often found lacking in practice. This dissertation introduces novel techniques to relax these assumptions -- leading to vastly efficient energy usage in typical networks (up to 20% savings) and new insights on the quality of inference. For example, the phenomenon of sensor drift is ubiquitous in applications such as air-quality monitoring, oceanography and bridge monitoring, where calibration is often difficult and costly. This dissertation provides an analytical framework linking the state of calibration to the overall uncertainty of the inferred parameters. In distributed estimation, sensor nodes locally process their observed data and send the resulting messages to a sink, which combines the received messages to produce a final estimate of the unknown parameter. In this dissertation, this problem is generalized and called collaborative estimation , where some sensors can potentially have access to the observations from neighboring sensors and use that information to enhance the quality of their messages sent to the sink, while using the same (or lower) energy resources. This is motivated by the fact that inter-sensor communication may be possible if sensors are geographically close. As demonstrated in this dissertation, collaborative estimation is particularly effective in energy-skewed and information-skewed networks, where some nodes may have larger batteries than others and similarly some nodes may be more informative (less noisy) compared to others. Since the node with the largest battery is not necessarily also the most informative, the proposed inter-sensor collaboration provides a natural framework to route the relevant information from low-energy-high-quality nodes to high-energy-low-quality nodes in a manner that enhances the overall power-distortion tradeoff. This dissertation also analyzes how time-correlated measurement noise affects the uncertainties of inferred parameters. Imperfections such as baseline drift in sensors result in a time-correlated additive component in the measurement noise. Though some models of drift have been reported in the literature earlier, none of the studies have considered the effect of drifting sensors on an estimation application. In this dissertation, approximate measures of estimation accuracy (Cramer-Rao bounds) are derived as a function of physical properties of sensors -- namely the drift strength, correlation (Markov) factor and the time-elapsed since last calibration. For stationary drift (Markov factor less than one), it is demonstrated that the first order effect of drift is asymptotically equivalent to scaling the measurement noise by an appropriate factor. When the drift is non-stationary (Markov factor equal to one), it is established that the constant part of a signal can only be estimated inconsistently (with non-zero asymptotic variance). The results help quantify the notions that measurements taken sooner after calibration result in more accurate inference

Investigation of wireless power transfer-based eddy current non-destructive testing and evaluation

PhD ThesisEddy current testing (ECT) is a non-contact inspection widely used as non-destructive testing and evaluation (NDT&E) of pipeline and rail lines due to its high sensitivity to surface and subsurface defects, cheap operating cost, tolerance to harsh environments, and capability of a customisable probe for complex geometric surfaces. However, the remote field of transmitter-receiver (Tx-Rx) ECT depends on the Tx-Rx coils gap, orientation, and lift-off distance, despite each coil responding to the effect of sample parameters according to its liftoff distance. They bring challenges to accurate defect detection and characterisation by weakening the ECT probe’s transfer response, affecting sensitivity to the defect, distorting the amplitude of the extracted features, and responding with fewer feature points at non-efficient energy transfer. Therefore, this study proposed a magnetically-coupled resonant wireless power transfer (WPT)-based ECT (WPTECT) concept to build the relationship between Tx-Rx coil at maximum energy transfer response, including shifting and splitting (resonance) frequency behaviour. The proposed WPTECT system was investigated in three different studies viz., (1) investigated the multiple resonance point features for detection and characterisation of slots on two different aluminium samples using a series-series (SS) topology of WPTECT; (2) mapped and scanned pipeline with a natural dent defect using a flexible printed coil (FPC) array probe based on the parallel-parallel (PP) topology of WPTECT; and (3) evaluated five different WPTECT topologies for optimal response and extracted features and characterised entire parameters of inclined angular Rolling Contact Fatigue (RCF) cracks in a rail-line material via an optimised topology. Multiple feature extraction, selection, and fusion were evaluated for the defect profile and compared in the study, unattainable by other ECT methods. The first study's contribution investigated multiple resonances and principal component analysis (PCA) features of the transfer response from scanning (eight) slots on two aluminium samples. The results have shown the potential of the multiple features for slot depth and width characterisation and demonstrated that the eddy-current density is highest at two points proportionate to the slot width. The second study's contribution provided a larger area scanning capability in a single probe amenable to complex geometrical structures like curvature surfaces. Among the extracted individual and fused features for defect reconstruction, the multi-layer feed-forward Deep learning-based multiple feature fusion has better 3D defect reconstruction, whilst the second resonances feature provided better local information than the first one for investigating pipeline dent area. The third study's contribution optimised WPTECT topology for multiple feature points capability and its optimal features extraction at the desired lift-off conditions. The PP and combined PP and SS (PS-PS) WPTECT topologies responded with multiple resonances compared to the other three topologies, with single resonance, under the same experimental situation. However, the extracted features from PS-PS topology provided the lowest sensitivity to lift-off distances and reconstructed depth, width, and inclined angle of RCF cracks with a maximum correlation, R2 -value of 96.4%, 93.1%, and 79.1%, respectively, and root-mean-square-error of 0.05mm, 0.08mm, and 6.60 , respectively. The demonstrated magnetically-coupled resonant WPTECT Tx-Rx probe characterised defects in oil and gas pipelines and rail lines through multiple features for multiple parameters information. Further work can investigate the phase of the transfer response as expected to offer robust features for material characterisation. The WPTECT system can be miniaturised using WPT IC chips as portable systems to characterise multiple layers parameters. It can further evaluate the thickness and gap between two concentric conductive tubes; pressure tube encircled by calandria tube in nuclear reactor fuel channels.PTDF Nigeri

Algorithms for propagation-aware underwater ranging and localization

Mención Internacional en el título de doctorWhile oceans occupy most of our planet, their exploration and conservation are one of the crucial research problems of modern time. Underwater localization stands among the key issues on the way to the proper inspection and monitoring of this significant part of our world. In this thesis, we investigate and tackle different challenges related to underwater ranging and localization. In particular, we focus on algorithms that consider underwater acoustic channel properties. This group of algorithms utilizes additional information about the environment and its impact on acoustic signal propagation, in order to improve the accuracy of location estimates, or to achieve a reduced complexity, or a reduced amount of resources (e.g., anchor nodes) compared to traditional algorithms. First, we tackle the problem of passive range estimation using the differences in the times of arrival of multipath replicas of a transmitted acoustic signal. This is a costand energy- effective algorithm that can be used for the localization of autonomous underwater vehicles (AUVs), and utilizes information about signal propagation. We study the accuracy of this method in the simplified case of constant sound speed profile (SSP) and compare it to a more realistic case with various non-constant SSP. We also propose an auxiliary quantity called effective sound speed. This quantity, when modeling acoustic propagation via ray models, takes into account the difference between rectilinear and non-rectilinear sound ray paths. According to our evaluation, this offers improved range estimation results with respect to standard algorithms that consider the actual value of the speed of sound. We then propose an algorithm suitable for the non-invasive tracking of AUVs or vocalizing marine animals, using only a single receiver. This algorithm evaluates the underwater acoustic channel impulse response differences induced by a diverse sea bottom profile, and proposes a computationally- and energy-efficient solution for passive localization. Finally, we propose another algorithm to solve the issue of 3D acoustic localization and tracking of marine fauna. To reach the expected degree of accuracy, more sensors are often required than are available in typical commercial off-the-shelf (COTS) phased arrays found, e.g., in ultra short baseline (USBL) systems. Direct combination of multiple COTS arrays may be constrained by array body elements, and lead to breaking the optimal array element spacing, or the desired array layout. Thus, the application of state-of-the-art direction of arrival (DoA) estimation algorithms may not be possible. We propose a solution for passive 3D localization and tracking using a wideband acoustic array of arbitrary shape, and validate the algorithm in multiple experiments, involving both active and passive targets.Part of the research in this thesis has been supported by the EU H2020 program under project SYMBIOSIS (G.A. no. 773753).This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Paul Daniel Mitchell.- Secretario: Antonio Fernández Anta.- Vocal: Santiago Zazo Bell

The TOTEM Experiment at the CERN Large Hadron Collider

The TOTEM Experiment will measure the total pp cross-section with the luminosity independent method and study elastic and diffractive scattering at the LHC. To achieve optimum forward coverage for charged particles emitted by the pp collisions in the interaction point IP5, two tracking telescopes, T1 and T2, will be installed on each side in the pseudorapidity region 3,1 <h< 6,5, and Roman Pot stations will be placed at distances of 147m and 220m from IP5. Being an independent experiment but technically integrated into CMS, TOTEM will first operate in standalone mode to pursue its own physics programme and at a later stage together with CMS for a common physics programme. This article gives a description of the TOTEM apparatus and its performance