Change of Acoustic Emission Characteristics during Temperature Induced Transition from Twinning to Dislocation Slip under Compression in Polycrystalline SN

In this study, acoustic emission (AE) measurements on polycrystalline tin as a function of temperature at different driving rates under compression were carried out. It is shown that there is a definite difference between the acoustic emission characteristics belonging to twinning (low temperatures) as well as to dislocation slip (high temperatures). The stress averaged values of the exponents of the energy probability density functions decreased from = 1.45 ± 0.05 (−60 °C) to = 1.20 ± 0.15 (50 °C) at a driving rate of , and the total acoustic energy decreased by three orders of magnitude with increasing temperature. In addition, the exponent γ in the scaling relation SAE~DAEγ (SAE is the area and DAEis the duration) also shows similar temperature dependence (changing from γ = 1.78 ± 0.08 to γ = 1.35 ± 0.05), illustrating that the avalanche statistics belong to two different microscopic deformation mechanisms. The power law scaling relations were also analyzed, taking into account that the detected signal is always the convolution of the source signal and the transfer function of the system. It was obtained that approximate values of the power exponents can be obtained from the parts of the above functions, belonging to large values of parameters. At short duration times, the attenuation effect of the AE detection system dominates the time dependence, from which the characteristic attenuation time, τa, was determined as τa≅ 70 μs.

Leak location procedure based on the complex-valued FastICA blind deconvolution algorithm for water-filled branch pipe

Water pipe networks have a large number of branch joints. Branch joint shunting generates vortices in the fluid, which excite the pipe wall to produce a type of branch noise. The branch noise is coupled with the leak source signal through the pipe. Here, a novel leak location protocol based on the complex-valued FastICA method (C-FastICA) is proposed to address the leak location problem under the branch noise interference. The C-FastICA, a complex-value domain blind deconvolution algorithm, effectively extended the cost function, constraint function, and iteration rules of the instantaneous linear FastICA into the complex-valued domain. The C-FastICA method was used to realize the separation of branch noise and leak source signal. The experimental results showed that the separation efficiency of the C-FastICA was higher than that of time-domain blind convolution separation (T-BCS). Furthermore, the relative location error of the C-FastICA method to the leak point was less than 14.238%, which was significantly lower than in traditional T-BCS and direct cross-correlation (DCC) technology.

Physical Layer Secrecy by Power Splitting and Jamming in Cooperative Multiple Relay Based on Energy Harvesting in Full-Duplex Network

In this article, we investigated the secrecy performance of a three-hop relay network system with Power Splitting (PS) and Energy Harvesting (EH). In the presence of one eavesdropper, a signal is transferred from source to destination with the help of a relay. The source signal transmits in full-duplex (FD) mood, jamming the relay transfer signals to the destination. The relay and source employ Time Switching (TS) and Energy Harvesting (EH) techniques to obtain the power from the power beacon. In this study, we compared the Secrecy Rate of two Cooperative Schemes, Amplify and Forward (AF) and Decode and Forward (DF), for both designed systems with the established EH and PS system. The Secrecy Rate was improved by 50.5% in the AF scheme and by 44.2% in the DF scheme between the relay and eavesdropper at 40 m apart for the proposed system in EH and PS. This simulation was performed using the Monto Carlo method in MATLAB.

Radiometric Identification of Signals by Matched Whitening Transform

Radiometric identification is the problem of attributing a signal to a specific source. In this work, a radiometric identification algorithm is developed using the whitening transformation. The approach stands out from the more established methods in that it works directly on the raw IQ data and hence is featureless. As such, the commonly used dimensionality reduction algorithms do not apply. The premise of the idea is that a data set is “most white” when projected on its own whitening matrix than on any other. In practice, transformed data are never strictly white since the training and the test data differ. The Förstner-Moonen measure that quantifies the similarity of covariance matrices is used to establish the degree of whiteness. The whitening transform that produces a data set with the minimum Förstner-Moonen distance to a white noise process is the source signal. The source is determined by the output of the mode function operated on the Majority Vote Classifier decisions. Using the Förstner-Moonen measure presents a different perspective compared to maximum likelihood and Euclidean distance metrics. The whitening transform is also contrasted with the more recent deep learning approaches that are still dependent on feature vectors with large dimensions and lengthy training phases. It is shown that the proposed method is simpler to implement, requires no features vectors, needs minimal training and because of its non-iterative structure is faster than existing approaches.

Optimization of Kurtosis in the Extend-Infomax Blind Signal Separation Algorithm

A kurtosis optimization method is proposed to improve the blind separated signal qualities based on the extend-infomax algorithm. The kurtosis of the hypothetical source signal was optimized based on the probability density function of sub-Gaussian signals. Obtained parameters after kurtosis optimization were then utilized to validate the effectiveness of the algorithm, which showed that the running time of the algorithm was significantly reduced, and the qualities of the separated signals were enhanced. Methods. Using kurtosis as a control variable, a one-way analysis of variance (ANOVA) was carried out on the algorithm’s performance metrics, the number of iterations, and the signal-to-noise ratio of the separated signal. Results. The results showed that there were significant differences in the above metrics under different kurtosis levels. The curves of average metric values indicate that, with the increase in kurtosis of the hypothetical source signal, the performance of the algorithm was improved.

A Novel Underdetermined Blind Source Separation Method Based on OPTICS and Subspace Projection

In recent years, the problem of underdetermined blind source separation (UBSS) has become a research hotspot due to its practical potential. This paper presents a novel method to solve the problem of UBSS, which mainly includes the following three steps: Single source points (SSPs) are first screened out using the principal component analysis (PCA) approach, which is based on the statistical features of signal time-frequency (TF) points. Second, a mixing matrix estimation method is proposed that combines Ordering Points To Identify the Clustering Structure (OPTICS) with an improved potential function to directly detect the number of source signals, remove noise points, and accurately calculate the mixing matrix vector; it is independent of the input parameters and offers great accuracy and robustness. Finally, an improved subspace projection method is used for source signal recovery, and the upper limit for the number of active sources at each mixed signal is increased from m−1 to m. The unmixing process of the proposed algorithm is symmetrical to the actual signal mixing process, allowing it to accurately estimate the mixing matrix and perform well in noisy environments. When compared to previous methods, the source signal recovery accuracy is improved. The method’s effectiveness is demonstrated by both theoretical and experimental results.

Amplitude Recovery and Deconvolution of Chirp and Boomer Data for Marine Geology and Offshore Engineering

The processing of Chirp data is limited by the usual recording of the signal envelope, which enhances its immediate visibility but prevents applying methods based on wave equations. This is normally not the case for Boomer data. However, both systems are monochannel instruments, which cannot estimate properly the propagation velocity of the signal in the rocks. In this paper, we present two theorems: the first one links the Chirp or Boomer source spectrum with an expected amplitude decay curve; the second one defines conditions for the deconvolution stability of the enveloped Boomer signal when the full waveform of the source signal is known. In this way, we can jointly process and integrate heterogeneous surveys including both data types. We validated the proposed algorithms by applying them to synthetic and real data. The presented tools can improve the image resolution and the characterization of geological formations in marine surveys by reflectivity anomalies, which are distorted by standard equalization methods.

Novel Electrode Architecture for Subgaleal Electroencephalography: A Feasibility Study

Electroencephalography (EEG) has been widely used to understand the nervous system and as a clinical diagnostic tool. In the case of neurological conditions with intermittent episodes, such as epilepsy, long-term EEG monitoring outside the clinics and in the community setting is vital. Subgaleal EEG (sgEEG) has emerged as an essential tool for long-term monitoring over several years. Current sgEEG solutions share a need for at least a 10 cm long lead wire, resulting in a bulky and invasive device. This work introduces a novel electrode architecture for subgaleal EEG recording, which forgoes the need for lead wires. A back-to-back electrode configuration with an electrode spacing of less than 1~mm is proposed. Compared to the current side-by-side approaches with an electrode spacing of several cm, our proposed approach results in at least one order of magnitude reduction in volume. The efficacy of the proposed electrode architecture is investigated through finite element modeling, phantom measurements, and cadaver studies. Our results suggest that compared to the conventional side-by-side electrode configuration, the source signal can be recorded reliably. Lead wires have posed a significant challenge from a device reliability and measurement quality perspective. Moreover, lead wires and the associated feedthrough connectors are bulky. Our proposed lead-free EEG recording solution may lead to a less invasive surgical placement through volume reduction and improve EEG recording quality.

Seismic radiation from wind turbines: observations and analytical modeling of frequency-dependent amplitude decays

In this study, we determine spectral characteristics and amplitude decays of wind turbine induced seismic signals in the far field of a wind farm (WF) close to Uettingen, Germany. Average power spectral densities (PSDs) are calculated from 10 min time segments extracted from (up to) 6 months of continuous recordings at 19 seismic stations, positioned along an 8 km profile starting from the WF. We identify seven distinct PSD peaks in the frequency range between 1 and 8 Hz that can be observed to at least 4 km distance; lower-frequency peaks are detectable up to the end of the profile. At distances between 300 m and 4 km the PSD amplitude decay can be described by a power law with exponent b. The measured b values exhibit a linear frequency dependence and range from b=0.39 at 1.14 Hz to b=3.93 at 7.6 Hz. In a second step, the seismic radiation and amplitude decays are modeled using an analytical approach that approximates the surface wave field. Since we observe temporally varying phase differences between seismograms recorded directly at the base of the individual wind turbines (WTs), source signal phase information is included in the modeling approach. We show that phase differences between source signals have significant effects on the seismic radiation pattern and amplitude decays. Therefore, we develop a phase shift elimination method to handle the challenge of choosing representative source characteristics as an input for the modeling. To optimize the fitting of modeled and observed amplitude decay curves, we perform a grid search to constrain the two model parameters, i.e., the seismic shear wave velocity and quality factor. The comparison of modeled and observed amplitude decays for the seven prominent frequencies shows very good agreement and allows the constraint of shear velocities and quality factors for a two-layer model of the subsurface. The approach is generalized to predict amplitude decays and radiation patterns for WFs of arbitrary geometry.