Tailoring the visible photoluminescence of mass-produced ZnO nanowires

We have grown defect-rich ZnO nanowires on a large scale by the vapour phase reaction method without using any metal catalyst and vacuum system. The defects, including zinc vacancies, oxygen interstitials and oxygen antisites, are related to the excess of oxygen in ZnO nanowires and are controllable. The nanowires having high excess of oxygen exhibit a brown-colour photoluminescence, due to the dominant emission band composed by violet, blue and green emissions. Those having more balanced Zn and O show a dominant green emission, giving rise to a green colour under UV light illumination. By O2-annealing treatment the violet luminescence after the band-edge emission UV peak can be enhanced for as-grown nanowires. However, the green emission shows different changing trends under O2-annealing treatment, associated with the excess of oxygen in the nanowires.

Three carrier ambiguity resolution : distance-independent performance demonstrated using semi-generated triple frequency GPS signals

In spite of significant research in the development of efficient algorithms for three carrier ambiguity resolution, full performance potential of the additional frequency signals cannot be demonstrated effectively without actual triple frequency data. In addition, all the proposed algorithms showed their difficulties in reliable resolution of the medium-lane and narrow-lane ambiguities in different long-range scenarios. In this contribution, we will investigate the effects of various distance-dependent biases, identifying the tropospheric delay to be the key limitation for long-range three carrier ambiguity resolution. In order to achieve reliable ambiguity resolution in regional networks with the inter-station distances of hundreds of kilometers, a new geometry-free and ionosphere-free model is proposed to fix the integer ambiguities of the medium-lane or narrow-lane observables over just several minutes without distance constraint. Finally, the semi-simulation method is introduced to generate the third frequency signals from dual-frequency GPS data and experimentally demonstrate the research findings of this paper.

Separation of acoustic emission signals from small size multi-cylinder diesel engine

This paper presents techniques which can lead to diagnosis of faults in a small size multi-cylinder diesel engine. Preliminary analysis of the acoustic emission (AE) signals is outline, including time-frequency analysis and selection of optimum frequency band.The results of applying mean field independent component analysis (MFICA) to separate the AE root mean square (RMS) signals and the effects of changing parameter values are also outlined. The results on separation of RMS signals show thsi technique has the potential of increasing the probability to successfully identify the AE events associated with the various mechanical events within the combustion process of multi-cylinder diesel engines.

A Bias in ML Estimates of Branch Lengths in the Presence of Multiple Signals

Sequence data often have competing signals that are detected by network programs or Lento plots. Such data can be formed by generating sequences on more than one tree, and combining the results, a mixture model. We report that with such mixture models, the estimates of edge (branch) lengths from maximum likelihood (ML) methods that assume a single tree are biased. Based on the observed number of competing signals in real data, such a bias of ML is expected to occur frequently. Because network methods can recover competing signals more accurately, there is a need for ML methods allowing a network. A fundamental problem is that mixture models can have more parameters than can be recovered from the data, so that some mixtures are not, in principle, identifiable. We recommend that network programs be incorporated into best practice analysis, along with ML and Bayesian trees.

Diesel engine problems, acoustic emission signals and simulated misfire faults

This paper discusses commonly encountered diesel engine problems and the underlying combustion related faults. Also discussed are the methods used in previous studies to simulate diesel engine faults and the initial results of an experimental simulation of a common combustion related diesel engine fault, namely diesel engine misfire. This experimental fault simulation represents the first step towards a comprehensive investigation and analysis into the characteristics of acoustic emission signals arising from combustion related diesel engine faults. Data corresponding to different engine running conditions was captured using in-cylinder pressure, vibration and acoustic emission transducers along with both crank-angle encoder and top-dead centre signals. Using these signals, it was possible to characterise the diesel engine in-cylinder pressure profiles and the effect of different combustion conditions on both vibration and acoustic emission signals.

Effective discrimination of acoustic emission source signals for structural health monitoring

Acoustic emission (AE) is the phenomenon where stress waves are generated due to rapid release of energy within a material caused by sources such as crack initiation or growth. AE technique involves recording the stress waves by means of sensors and subsequent analysis of the recorded signals to gather information about the nature of the source. Though AE technique is one of the popular non destructive evaluation (NDE) techniques for structural health monitoring of mechanical, aerospace and civil structures; several challenges still exist in successful application of this technique. Presence of spurious noise signals can mask genuine damage‐related AE signals; hence a major challenge identified is finding ways to discriminate signals from different sources. Analysis of parameters of recorded AE signals, comparison of amplitudes of AE wave modes and investigation of uniqueness of recorded AE signals have been mentioned as possible criteria for source differentiation. This paper reviews common approaches currently in use for source discrimination, particularly focusing on structural health monitoring of civil engineering structural components such as beams; and further investigates the applications of some of these methods by analyzing AE data from laboratory tests.

Spectrum sensing, detection and optimisation in cognitive radio for non-stationary primary user signals

Cognitive radio is an emerging technology proposing the concept of dynamic spec- trum access as a solution to the looming problem of spectrum scarcity caused by the growth in wireless communication systems. Under the proposed concept, non- licensed, secondary users (SU) can access spectrum owned by licensed, primary users (PU) so long as interference to PU are kept minimal. Spectrum sensing is a crucial task in cognitive radio whereby the SU senses the spectrum to detect the presence or absence of any PU signal. Conventional spectrum sensing assumes the PU signal as ‘stationary’ and remains in the same activity state during the sensing cycle, while an emerging trend models PU as ‘non-stationary’ and undergoes state changes. Existing studies have focused on non-stationary PU during the transmission period, however very little research considered the impact on spectrum sensing when the PU is non-stationary during the sensing period. The concept of PU duty cycle is developed as a tool to analyse the performance of spectrum sensing detectors when detecting non-stationary PU signals. New detectors are also proposed to optimise detection with respect to duty cycle ex- hibited by the PU. This research consists of two major investigations. The first stage investigates the impact of duty cycle on the performance of existing detec- tors and the extent of the problem in existing studies. The second stage develops new detection models and frameworks to ensure the integrity of spectrum sensing when detecting non-stationary PU signals. The first investigation demonstrates that conventional signal model formulated for stationary PU does not accurately reflect the behaviour of a non-stationary PU. Therefore the performance calculated and assumed to be achievable by the conventional detector does not reflect actual performance achieved. Through analysing the statistical properties of duty cycle, performance degradation is proved to be a problem that cannot be easily neglected in existing sensing studies when PU is modelled as non-stationary. The second investigation presents detectors that are aware of the duty cycle ex- hibited by a non-stationary PU. A two stage detection model is proposed to improve the detection performance and robustness to changes in duty cycle. This detector is most suitable for applications that require long sensing periods. A second detector, the duty cycle based energy detector is formulated by integrat- ing the distribution of duty cycle into the test statistic of the energy detector and suitable for short sensing periods. The decision threshold is optimised with respect to the traffic model of the PU, hence the proposed detector can calculate average detection performance that reflect realistic results. A detection framework for the application of spectrum sensing optimisation is proposed to provide clear guidance on the constraints on sensing and detection model. Following this framework will ensure the signal model accurately reflects practical behaviour while the detection model implemented is also suitable for the desired detection assumption. Based on this framework, a spectrum sensing optimisation algorithm is further developed to maximise the sensing efficiency for non-stationary PU. New optimisation constraints are derived to account for any PU state changes within the sensing cycle while implementing the proposed duty cycle based detector.

Detection of excessive diesel engine piston slap using acoustic emission signals

This paper presents a study whereby a series of tests was undertaken using a naturally aspirated 4 cylinder, 2.216 litre, Perkins Diesel engine fitted with a piston having an undersized skirt. This experimental simulation resulted in engine running conditions that included abnormally high levels of piston slap occurring in one of the cylinders. The detectability of the resultant Diesel engine piston slap was investigated using acoustic emission signals. Data corresponding to both normal and piston slap engine running conditions was captured using acoustic emission transducers along with both; in-cylinder pressure and top-dead centre reference signals. Using these signals it was possible to demonstrate that the increased piston slap running conditions were distinguishable by monitoring the piston slap events occurring near the piston mid-stroke positions. However, when monitoring the piston slap events occurring near the TDC/BDC piston stroke positions, the normal and excessive piston slap engine running condition were not clearly distinguishable.

Determination of vertical hydraulic conductivity of aquitards in a multilayered leaky system using water-level signals in adjacent aquifers

This paper presents a methodology for determining the vertical hydraulic conductivity (Kv) of an aquitard, in a multilayered leaky system, based on the harmonic analysis of arbitrary water-level fluctuations in aquifers. As a result, Kv of the aquitard is expressed as a function of the phase-shift of water-level signals measured in the two adjacent aquifers. Based on this expression, we propose a robust method to calculate Kv by employing linear regression analysis of logarithm transformed frequencies and phases. The frequencies, where the Kv are calculated, are identified by coherence analysis. The proposed methods are validated by a synthetic case study and are then applied to the Westbourne and Birkhead aquitards, which form part of a five-layered leaky system in the Eromanga Basin, Australia.

Cone signals and activity in myopia and emmetropia

Myopia (short-sightedness) is a common ocular disorder of children and young adults. Studies primarily using animal models have shown that the retina controls eye growth and the outer retina is likely to have a key role. One theory is that the proportion of L (long-wavelength-sensitive) and M (medium-wavelength-sensitive) cones is related to myopia development; with a high L/M cone ratio predisposing individuals to myopia. However, not all dichromats (persons with red-green colour vision deficiency) with extreme L/M cone ratios have high refractive errors. We predict that the L/M cone ratio will vary in individuals with normal trichromatic colour vision but not show a systematic difference simply due to refractive error. The aim of this study was to determine if L/M cone ratios in the central 30° are different between myopic and emmetropic young, colour normal adults. Information about L/M cone ratios was determined using the multifocal visual evoked potential (mfVEP). The mfVEP can be used to measure the response of visual cortex to different visual stimuli. The visual stimuli were generated and measurements performed using the Visual Evoked Response Imaging System (VERIS 5.1). The mfVEP was measured when the L and M cone systems were separately stimulated using the method of silent substitution. The method of silent substitution alters the output of three primary lights, each with physically different spectral distributions to control the excitation of one or more photoreceptor classes without changing the excitation of the unmodulated photoreceptor classes. The stimulus was a dartboard array subtending 30° horizontally and 30° vertically on a calibrated LCD screen. The m-sequence of the stimulus was 215-1. The N1-P1 amplitude ratio of the mfVEP was used to estimate the L/M cone ratio. Data were collected for 30 young adults (22 to 33 years of age), consisting of 10 emmetropes (+0.3±0.4 D) and 20 myopes (–3.4±1.7 D). The stimulus and analysis techniques were confirmed using responses of two dichromats. For the entire participant group, the estimated central L/M cone ratios ranged from 0.56 to 1.80 in the central 3°-13° diameter ring and from 0.94 to 1.91 in the more peripheral 13°-30° diameter ring. Within 3°-13°, the mean L/M cone ratio of the emmetropic group was 1.20±0.33 and the mean was similar, 1.20±0.26, for the myopic group. For the 13°-30° ring, the mean L/M cone ratio of the emmetropic group was 1.48±0.27 and it was slightly lower in the myopic group, 1.30±0.27. Independent-samples t-test indicated no significant difference between the L/M cone ratios of the emmetropic and myopic group for either the central 3°-13° ring (p=0.986) or the more peripheral 13°-30° ring (p=0.108). The similar distributions of estimated L/M cone ratios in the sample of emmetropes and myopes indicates that there is likely to be no association between the L/M cone ratio and refractive error in humans.

Utilizing Wide-Area Signals for off-center SVCs to damp interarea oscillations

Wide-Area Measurement Systems (WAMS) provide the opportunity of utilizing remote signals from different locations for the enhancement of power system stability. This paper focuses on the implementation of remote measurements as supplementary signals for off-center Static Var Compensators (SVCs) to damp inter-area oscillations. Combination of participation factor and residue method is used for the selection of most effective stabilizing signal. Speed difference of two generators from separate areas is identified as the best stabilizing signal and used as a supplementary signal for lead-lag controller of SVCs. Time delays of remote measurements and control signals is considered. Wide-Area Damping Controller (WADC) is deployed in Matlab Simulink framework and is tested under different operating conditions. Simulation results reveal that the proposed WADC improve the dynamic characteristic of the system significantly.

Testing second order cyclostationarity in the squared envelope spectrum of non-white vibration signals

Cyclostationary models for the diagnostic signals measured on faulty rotating machineries have proved to be successful in many laboratory tests and industrial applications. The squared envelope spectrum has been pointed out as the most efficient indicator for the assessment of second order cyclostationary symptoms of damages, which are typical, for instance, of rolling element bearing faults. In an attempt to foster the spread of rotating machinery diagnostics, the current trend in the field is to reach higher levels of automation of the condition monitoring systems. For this purpose, statistical tests for the presence of cyclostationarity have been proposed during the last years. The statistical thresholds proposed in the past for the identification of cyclostationary components have been obtained under the hypothesis of having a white noise signal when the component is healthy. This need, coupled with the non-white nature of the real signals implies the necessity of pre-whitening or filtering the signal in optimal narrow-bands, increasing the complexity of the algorithm and the risk of losing diagnostic information or introducing biases on the result. In this paper, the authors introduce an original analytical derivation of the statistical tests for cyclostationarity in the squared envelope spectrum, dropping the hypothesis of white noise from the beginning. The effect of first order and second order cyclostationary components on the distribution of the squared envelope spectrum will be quantified and the effectiveness of the newly proposed threshold verified, providing a sound theoretical basis and a practical starting point for efficient automated diagnostics of machine components such as rolling element bearings. The analytical results will be verified by means of numerical simulations and by using experimental vibration data of rolling element bearings.

Development of a new signal processing diagnostic tool for vibration signals acquired in transient conditions

The diagnostics of mechanical components operating in transient conditions is still an open issue, in both research and industrial field. Indeed, the signal processing techniques developed to analyse stationary data are not applicable or are affected by a loss of effectiveness when applied to signal acquired in transient conditions. In this paper, a suitable and original signal processing tool (named EEMED), which can be used for mechanical component diagnostics in whatever operating condition and noise level, is developed exploiting some data-adaptive techniques such as Empirical Mode Decomposition (EMD), Minimum Entropy Deconvolution (MED) and the analytical approach of the Hilbert transform. The proposed tool is able to supply diagnostic information on the basis of experimental vibrations measured in transient conditions. The tool has been originally developed in order to detect localized faults on bearings installed in high speed train traction equipments and it is more effective to detect a fault in non-stationary conditions than signal processing tools based on spectral kurtosis or envelope analysis, which represent until now the landmark for bearings diagnostics.

Estimation of vibration amplitude in Fourier domain optical coherence tomography interferometric signals from Doppler spectrum

In presented method combination of Fourier and Time domain detection enables to broaden the effective bandwidth for time dependent Doppler Signal that allows for using higher-order Bessel functions to calculate unambiguously the vibration amplitudes.