A stochastic time-domain model for burst data aggregation in IEEE 802.15.4 wireless sensor networks

In many network applications, the nature of traffic is of burst type. Often, the transient response of network to such traffics is the result of a series of interdependant events whose occurrence prediction is not a trivial task. The previous efforts in IEEE 802.15.4 networks often followed top-down approaches to model those sequences of events, i.e., through making top-view models of the whole network, they tried to track the transient response of network to burst packet arrivals. The problem with such approaches was that they were unable to give station-level views of network response and were usually complex. In this paper, we propose a non-stationary analytical model for the IEEE 802.15.4 slotted CSMA/CA medium access control (MAC) protocol under burst traffic arrival assumption and without the optional acknowledgements. We develop a station-level stochastic time-domain method from which the network-level metrics are extracted. Our bottom-up approach makes finding station-level details such as delay, collision and failure distributions possible. Moreover, network-level metrics like the average packet loss or transmission success rate can be extracted from the model. Compared to the previous models, our model is proven to be of lower memory and computational complexity order and also supports contention window sizes of greater than one. We have carried out extensive and comparative simulations to show the high accuracy of our model.

FEM calibrated ARMAX model updating method for time domain damage identification

Due to environmental loads, mechanical damages, structural aging and human factors, civil infrastructure inevitably deteriorate during their service lives. Since their damage may claim human lives and cause significant economic losses, how to identify damages and assess structural conditions timely and accurately has drawn increasingly more attentions from structural engineering community worldwide. In this study, a fast and sensitive time domain damage identification method will be developed. To do this, a finite element model of a steel pipe laid on the soil is built and the structural responses are simulated under different damage scenarios. Based on the simulated data, an Auto Regressive Moving Average Exogenous (ARMAX) model is then built and calibrated. The calibrated ARMAX model is used to identify different damage scenarios through model updating process using clonal selection algorithm (CSA). The results demonstrate the application potential of the proposed method in identifying the pipeline conditions. To further verify its performance, laboratory tests of a steel pipe laid on the soil with and without soil support (free span damage) are carried out. The identification results of pipe-soil system show that the proposed method is capable of identifying damagein a complex structural system. Therefore, it can be applied to identifying pipeline conditions.

Damage identification of civil infrastructure in the time domain using FEM calibrated ARMAX model

Due to environmental loads, mechanical damages, structural aging and human factors, civil infrastructure inevitably deteriorate during their service lives. Since their damage may claim human lives and cause significant economic losses, how to identify damages and assess structural conditions timely and accurately has drawn increasingly more attentions from structural engineering community worldwide. In this study, a fast and sensitive time domain damage identification method will be developed. First, a high quality finite element model is built and the structural responses are simulated under different damage scenarios. Based on the simulated data, an Auto Regressive Moving Average Exogenous (ARMAX) model is then developed and calibrated. The calibrated ARMAX model can be used to identify damage in different scenarios through model updating process using clonal selection algorithm (CSA). The identification results demonstrate the performance of the proposed methodology, which has the potential to be used for damage identification in practices.

Effects of increasing neuromuscular electrical stimulation current intensity on cortical sensorimotor network activation: a time domain fNIRS study

Neuroimaging studies have shown neuromuscular electrical stimulation (NMES)-evoked movements activate regions of the cortical sensorimotor network, including the primary sensorimotor cortex (SMC), premotor cortex (PMC), supplementary motor area (SMA), and secondary somatosensory area (S2), as well as regions of the prefrontal cortex (PFC) known to be involved in pain processing. The aim of this study, on nine healthy subjects, was to compare the cortical network activation profile and pain ratings during NMES of the right forearm wrist extensor muscles at increasing current intensities up to and slightly over the individual maximal tolerated intensity (MTI), and with reference to voluntary (VOL) wrist extension movements. By exploiting the capability of the multi-channel time domain functional near-infrared spectroscopy technique to relate depth information to the photon time-of-flight, the cortical and superficial oxygenated (O2Hb) and deoxygenated (HHb) hemoglobin concentrations were estimated. The O2Hb and HHb maps obtained using the General Linear Model (NIRS-SPM) analysis method, showed that the VOL and NMES-evoked movements significantly increased activation (i.e., increase in O2Hb and corresponding decrease in HHb) in the cortical layer of the contralateral sensorimotor network (SMC, PMC/SMA, and S2). However, the level and area of contralateral sensorimotor network (including PFC) activation was significantly greater for NMES than VOL. Furthermore, there was greater bilateral sensorimotor network activation with the high NMES current intensities which corresponded with increased pain ratings. In conclusion, our findings suggest that greater bilateral sensorimotor network activation profile with high NMES current intensities could be in part attributable to increased attentional/pain processing and to increased bilateral sensorimotor integration in these cortical regions.

A study of guided wave propagation in timber pole using spectral finite element method

Timber is one of the most widely used structural material all over the world. Round timbers can be seen as a structural component in historical buildings, jetties, short span bridges and also as piles for foundation and poles for electrical and power distribution. To evaluate the current condition of these cylindrical type timber structures, guided wave has a great potential. However, the difficulties associated with the guided wave propagation in timber materials includes orthotropic behaviour of wood, moisture contents, temperature, grain direction, etc. In addition, the effect of fully or partially filled surrounding media, such as soil, water, etc. causes attenuation on the generated stress wave. In order to investigate the effects of these parameters on guided wave propagation, extensive numerical simulation is required to conduct parametric studies. Moreover, due to the presence of multi modes in guided wave propagation, dispersion curves are of great importance. Even though conventional finite element method (FEM) can determine dispersion curves along with wave propagation in time domain, it is highly computationally expensive. Furthermore, incorporating orthotropic behaviour and surrounding media to model a thick cylindrical wave (large diameter cylindrical structures) make conventional FEM inefficient for this purpose. In contrast, spectral finite element method (SFEM) is a semi analytical method to model the guided wave propagation which does not need fine meshes compared to the other methods, such as FEM or finite difference method (FDM). Also, even distribution of mass and stiffness of structures can be obtained with very few elements using SFEM. In this paper, the suitability of SFEM is investigated to model guided wave propagation through an orthotropic cylindrical waveguide with the presence of surrounding soil. Both the frequency domain analysis (dispersion curves) and time domain reconstruction for a multi-mode generated input signal are presented under different loading location. The dispersion curves obtained from SFEM are compared against analytical solution to verify its accuracy. Lastly, different numerical issues to solve for the dispersion curves and time domain results using SFEM are also discussed.

Acoustic emission study on corona discharge using FDTD model

This paper attempts to study the propagating characteristics of acoustic signals emitted from the breakdown of air using time domain numerical model. Acoustic emissions are produced by high voltage faults such as partial discharge and surface discharge. Study of such emissions has become popular among researchers because of the promising correlation between partial and surface discharges and its byproduct, acoustic signal emission. In this paper, propagation characteristics of acoustic signals are studied using finite difference time domain (FDTD) method. Multiple monitoring points are placed within a designated computation space at different distance away from a source.

Spectral element modelling of wave propagation with boundary and structural discontinuity reflections

Spectral element method is very efficient in modelling high-frequency stress wave propagation because it works in the frequency domain. It does not need to use very fine meshes in order to capture high frequency wave energy as the time domain methods do, such as finite element method. However, the conventional spectral element method requires a throw-off element to be added to the structural boundaries to act as a conduit for energy to transmit out of the system. This makes the method difficult to model wave reflection at boundaries. To overcome this limitation, imaginary spectral elements are proposed in this study, which are combined with the real structural elements to model wave reflections at structural boundaries. The efficiency and accuracy of this proposed approach is verified by comparing the numerical simulation results with measured results of one dimensional stress wave propagation in a steel bar. The method is also applied to model wave propagation in a steel bar with not only boundary reflection, but also reflections from single and multiple cracks. The reflection and transmission coefficients, which are obtained from the discrete spring model, are adopted to quantify the discontinuities. Experimental tests of wave propagation in a steel bar with one crack of different depths are also carried out. Numerical simulations and experimental results show that the proposed method is effective and reliable in modelling wave propagation in one-dimensional waveguides with reflections from boundary and structural discontinuities. The proposed method can be applied to effectively model stress wave propagation for structural damage detection.

Parametric investigation and measurement of near end and far end crosstalk in multiconductor microstrip transmission lines

This paper investigates and measures the near end and far end crosstalk in the multiconductor transmission line (MTL) mounted on the PCB by varying the parameters which are associated with physical dimension, characteristic of the substrate and the nature of input signal. With the variation of these factors, the coupling inductance and capacitance vary causing crosstalk. By using the method of moment (MoM), the per unit length parameters are calculated for microstrip lines. Subcircuit model is used to investigate the time domain and frequency domain analysis of near field and far field crosstalk. This parametric investigation is very useful for designing high speed interconnectors on PCB substrates. Some experimental results are presented to validate the analytical findings.

Vibration based damage identification of a scale-model steel frame structure subjected to bolt connection failures

Large-span steel frame structures prove to be an ideal choice for their speed of construction, relatively low cost, strength, durability and structural design flexibility. For this type of structure, the beam-column connections are critical for its structural integrity and overall stability. This is because a steel frame generally fails first at its connectors, due to the change in stress redistribution with adjacent members and material related failures, caused by various factors such as fire, seismic activity or material deterioration. Since particular attention is required at a steel frame’s connection points, this study explores the applicability of a comprehensive structural health monitoring (SHM) method to identify early damage and prolong the lifespan of connection points of steel frames. An impact hammer test was performed on a scale-model steel frame structure, recording its dynamic response to the hammer strike via an accelerometer. The testing procedure included an intact scenario and two damage scenarios by unfastening four bolt connections in an accumulating order. Based entirely on time-domain experimental data for its calibration, an Auto Regressive Average Exogenous (ARMAX) model is used to create a simple and accurate model for vibration simulation. The calibrated ARMAX model is then used to identify various bolt-connection related damage scenarios via R² value. The findings in this study suggest that the proposed time-domain approach is capable of identifying structural damage in a parsimonious manner and can be used as a quick or initial solution.

Small-signal stability assessment of active distribution networks with dynamic loads

This paper investigates small-signal stability of a distribution system with distributed generator and induction motor load, as a dynamic element. The analysis is carried out over a distribution test system with different types of induction motor loads. The system is linearised by the perturbation method. Eigenvalues and participation factors are calculated to see the modal interaction of the system. The study indicates that load voltage dynamics significantly influence the damping of a newly identified voltage mode. This mode has frequency of oscillation between the electromechanical and subsynchronous oscillation of power systems. To justify the validity of the modal analysis time domain simulation is also carried out. Finally, significant parameters of the system that affect the damping and frequency of the oscillation are identified.

Voltage oscillations in power distribution networks in the presence of DFIGs and induction motor loads

This paper investigates the oscillatory behavior of power distribution systems in the presence of distributed generation. The analysis is carried out over a distribution test system with two doubly fed induction type wind generators and different types of induction motor loads. The system is linearized by the perturbation method. Eigenvalues are calculated to see the modal interaction within the system. The study indicates that interactions between closely placed converter controllers and induction motor loads significantly influence the damping of the oscillatory modes of the system. The critical modes have a frequency of oscillation between the electromechanical and subsynchronous oscillations of power systems. Time-domain simulations are carried out to verify the validity of the modal analysis and to provide a physical feel for the types of oscillations that occur in distribution systems. Finally, significant parameters of the system that affect the damping and frequency of the oscillation are identified.

Design and fabrication of nano-sinusoid LSPR devices

Applications of LSPR nano-particles in various areas of solar cells, LSPR biosensors, and SERS biosensors, based on interaction of light with noble metal nano-particles is increasing. Therefore, design and nano-fabrication of the LSPR devices is a key step in developing such applications. Design of nano-structures with desirable spectral properties using numerical techniques such as finite difference time domain (FDTD) is the first step in this work. A new structure called nano-sinusoid, satisfying the some desirable LSPR characteristics, is designed and simulated using the FDTD method. In the next stage, analytical method of electro static eigen mode method is used to validate the simulation results. The, nano-fabrications method of electron beam lithography (EBL) is implemented to fabricate the proposed profile with high precision. Finally, atomic force microscopy (AFM) is used to investigate the shape of the fabricated nano-particles, and the dark field microscopy is employed to demonstrate the particular spectral characteristics of the proposed nano-sinusoids.

Detection of respiratory arousals using photoplethysmography (PPG) signal in sleep apnea patients

Respiratory events during sleep induce cortical arousals and manifest changes in autonomic markers in sleep disorder breathing (SDB). Finger photoplethysmography (PPG) has been shown to be a reliable method of determining sympathetic activation. We hypothesize that changes in PPG signals are sufficient to predict the occurrence of respiratory-event-related cortical arousal. In this study, we develop a respiratory arousal detection model in SDB subjects by using PPG features. PPG signals from 10 SDB subjects (9 male, 1 female) with age range 43-75 years were used in this study. Time domain features of PPG signals, such as 1) PWA--pulse wave amplitude, 2) PPI--peak-to-peak interval, and 3) Area--area under peak, were used to detect arousal events. In this study, PWA and Area have shown better performance (higher accuracy and lower false rate) compared to PPI features. After investigating possible groupings of these features, combination of PWA and Area (PWA + Area) was shown to provide better accuracy with a lower false detection rate in arousal detection. PPG-based arousal indexes agreed well across a wide range of decision thresholds, resulting in a receiver operating characteristic with an area under the curve of 0.91. For the decision threshold (PC(thresh) = 25%) chosen for the final analyses, a sensitivity of 68.1% and a specificity of 95.2% were obtained. The results showed an accuracy of 84.68%, 85.15%, 86.93%, and 50.79% with a false rate of 21.80%, 55.41%, 64.78%, and 50.79% at PC(thresh) = 25% or PPI, PWA, Area , and PWA + Area features, respectively. This indicates that combining PWA and Area features reduced the false positive rate without much affecting the sensitivity of the arousal detection system. In conclusion, the PPG-based respiratory arousal detection model is a simple and promising alternative to the conventional electroencephalogram (EEG)-based respiratory arousal detection system.

Damage identification scheme based on compressive sensing

Civil infrastructures are critical to every nation, due to their substantial investment, long service period, and enormous negative impacts after failure. However, they inevitably deteriorate during their service lives. Therefore, methods capable of assessing conditions and identifying damage in a structure timely and accurately have drawn increasing attention. Recently, compressive sensing (CS), a significant breakthrough in signal processing, has been proposed to capture and represent compressible signals at a rate significantly below the traditional Nyquist rate. Due to its sound theoretical background and notable influence, this methodology has been successfully applied in many research areas. In order to explore its application in structural damage identification, a new CS-based damage identification scheme is proposed in this paper, by regarding damage identification problems as pattern classification problems. The time domain structural responses are transferred to the frequency domain as sparse representation, and then the numerical simulated data under various damage scenarios will be used to train a feature matrix as input information. This matrix can be used for damage identification through an optimization process. This will be one of the first few applications of this advanced technique to structural engineering areas. In order to demonstrate its effectiveness, numerical simulation results on a complex pipe soil interaction model are used to train the parameters and then to identify the simulated pipe degradation damage and free-spanning damage. To further demonstrate the method, vibration tests of a steel pipe laid on the ground are carried out. The measured acceleration time histories are used for damage identification. Both numerical and experimental verification results confirm that the proposed damage identification scheme will be a promising tool for structural health monitoring.