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.

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.

Design and analysis of an antenna for wireless energy harvesting in head-mountable DBS device

This paper presents design and simulation of a circular meander dipole antenna at the industrial, scientific, and medical band of 915 MHz for energy scavenging in a passive head-mountable deep brain stimulation device. The interaction of the proposed antenna with a rat body is modeled and discussed. In the antenna, the radiating layer is meandered, and a FR-4 substrate is used to limit the radius and height of the antenna to 14 mm and 1.60 mm, respectively. The resonance frequency of the designed antenna is 915 MHz and the bandwidth of 15 MHz at a return loss of -10 dB in free space. To model the interaction of the antenna with a rat body, two aspects including functional and biological are considered. The functional aspect includes input impedance, resonance frequency, gain pattern, radiation efficiency of the antenna, and the biological aspect involves electric field distribution, and SAR value. A complete rat model is used in the finite difference time domain based EM simulation software XFdtd. The simulated results demonstrate that the specific absorption rate distributions occur within the skull in the rat model, and their values are higher than the standard regulated values for the antenna receiving power of 1W.

Electromagnetic energy harvesting in a head-mountable DBS device using a circular PIFA

A circular planar inverted-F antenna (PIFA) is designed and simulated at the industrial, scientific, and medical (ISM) band of 915 MHz for energy harvesting in a head-mountable deep brain stimulation device. Moreover, a rectifier is designed, and also the interaction of the PIFA with a rat head model is investigated. In the proposed PIFA, the top radiating layer is meandered, and a substrate of FR-4 is used. The radius and the height of the antenna are 10 mm and 1.8 mm, respectively. The bottom conductive layer works as a ground plate, and a superstrate of polyethylene reduces the electromagnetic penetration into the rat head. The resonance frequency of the designed antenna is 915 MHz with a bandwidth of 18 MHz at the return loss of -10 dB in free space. The antenna parameters (e.g. reflection coefficient, gain, radiation efficiency), electric field distribution, and SAR value are evaluated within a seven-layer rat head model by using the finite difference time domain EM simulation software XFdtd. The interactions of the antenna and the rat head model are studied in both functional and biological aspects.

Design and analysis of an antenna for batteryless transcranial direct current stimulation devices

The purpose of this study is to design a low-cost planar Archimedean dipole antenna for batteryless transcranial direct current stimulation devices. The antenna parameters including resonance frequency, radiation efficiency, radiation pattern, and gain are simulated using finite difference time domain based electromagnetic simulation software XFdtd. The proposed antenna is simulated with low-cost FR4 PCB substrate of thickness of 1.6 mm. The antenna is designed with half wavelength of resonant frequency and fed with a matching line. The target frequency band is the industrial, scientific and medical (ISM) band of 915 MHz which is in the simulated band width of 31 MHz (903-934MHz). Moreover, since the bio-effect of specific absorption rate by radio frequency electromagnetic wave for power harvesting is an important concern, we try to find out the safety limit. Thus a quantitative analysis of distributions of electric field and power absorption in anatomical human head model by the far field radio frequency energy received by our designed antenna has been presented.

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.

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.

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.

Use of artificial neural networks as explicit finite difference operators

Results of a numerical exercise, substituting a numerical operator by an artificial neural network (ANN) are presented in this paper. The numerical operator used is the explicit form of the finite difference (FD) scheme. The FD scheme was used to discretize the one-dimensional transport equation, which included both the advection and dispersion terms. Inputs to the ANN are the FD representation of the transport equation, and the concentration was designated as the output. Concentration values used for training the ANN were obtained from analytical solutions. The numerical operator was reconstructed from a back calculation of the weights of the ANN. Linear transfer functions were used for this purpose. The ANN was able to accurately recover the velocity used in the training data, but not the dispersion coefficient. This capability was improved when numerical dispersion was taken into account; however, it is limited to the condition: C/P<0.5 , where C is the Courant number and P , the Peclet number (i.e., the restriction imposed by the Neumann stability condition).

Women's preferences for selective estrogen reuptake modulators: an investigation using the time trade off technique

PurposeSelective Estrogen Receptor Modulators (SERMs) reduce the risk of breast cancer for women at increased risk by 38%. However, uptake is extremely low and the reasons for this are not completely understood. The aims of this study were to utilize time trade-off methods to determine the degree of risk reduction required to make taking SERMs worthwhile to women, and the factors associated with requiring greater risk reduction to take SERMs. MethodsWomen at increased risk of breast cancer (N = 107) were recruited from two familial cancer clinics in Australia. Participants completed a questionnaire either online or in pen and paper format. Hierarchical multiple linear regression analysis was used to analyze the data. ResultsOverall, there was considerable heterogeneity in the degree of risk reduction required to make taking SERMs worthwhile. Women with higher perceived breast cancer risk and those with stronger intentions to undergo (or who had undergone) an oophorectomy required a smaller degree of risk reduction to consider taking SERMs worthwhile. ConclusionWomen at increased familial risk appear motivated to consider SERMs for prevention. A tailored approach to communicating about medical prevention is essential. Health professionals could usefully highlight the absolute (rather than relative) probability of side effects and take into account an individual’s perceived (rather than objective) risk of breast cancer.

A dynamic time warped clustering technique for discrete event simulation-based system analysis

Abstract This paper introduces a novel approach for discrete event simulation output analysis. The approach combines dynamic time warping and clustering to enable the identification of system behaviours contributing to overall system performance, by linking the clustering cases to specific causal events within the system. Simulation model event logs have been analysed to group entity flows based on the path taken and travel time through the system. The proposed approach is investigated for a discrete event simulation of an international airport baggage handling system. Results show that the method is able to automatically identify key factors that influence the overall dwell time of system entities, such as bags that fail primary screening. The novel analysis methodology provides insight into system performance, beyond that achievable through traditional analysis techniques. This technique also has potential application to agent-based modelling paradigms and also business event logs traditionally studied using process mining techniques.

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.