The influence of observation environment on background noise level of gPhone gravimeter

Based on the observations of 36 gPhone gravimeters in 2015, the background noise levels in the seismic frequency band（200-600 s） and sub-seismic band（1-6 h） are calculated. The differences in the PSD（power spectrum density） of each band of gPhone gravimetric gauges in different surrounding environments were analyzed and compared with Peterson＇s NLNM（new low-noise model） which is derived from the envelope at the power spectrum density of 75 seismograph stations around the world. The results showed that： the influence of station type on the noise magnitude of gPhone gravimeter is very small; The seismic band noise magnitude（hereinafter referred to as SNM） and the sub-seismic band noise magnitude（hereinafter referred to as SSNM） in the coastal gPhone gravimeter are higher than those of inland stations. Although the local hydrological change has a great influence on the gravity observation, the rainfall is not directly relative to the noise magnitude of the instrument. Except 3 coastal stations, the eight stations which had the highest amplitudes in the SNM were located near the seismic belt. This indicates that the SNM of the gPhone Gravimeter may reflect some seismic information.Compared with the NLNM model, the PSD of the gPhone gravimeter is lower than the NLNM model in the long period band（〈3×10^（-5）Hz）, indicating that the gPhone gravimeter is more suitable for detecting long-period signals（〉10 h） than the seismometer.

Nonparametric VSS-APA based on precise background noise power estimate

The adaptive algorithm used for echo cancellation(EC) system needs to provide 1) low misadjustment and 2) high convergence rate. The affine projection algorithm(APA) is a better alternative than normalized least mean square(NLMS) algorithm in EC applications where the input signal is highly correlated. Since the APA with a constant step-size has to make compromise between the performance criteria 1) and 2), a variable step-size APA(VSS-APA) provides a more reliable solution. A nonparametric VSS-APA(NPVSS-APA) is proposed by recovering the background noise within the error signal instead of cancelling the a posteriori errors. The most problematic term of its variable step-size formula is the value of background noise power(BNP). The power difference between the desired signal and output signal, which equals the power of error signal statistically, has been considered the BNP estimate in a rough manner. Considering that the error signal consists of background noise and misalignment noise, a precise BNP estimate is achieved by multiplying the rough estimate with a corrective factor. After the analysis on the power ratio of misalignment noise to background noise of APA, the corrective factor is formulated depending on the projection order and the latest value of variable step-size. The new algorithm which does not require any a priori knowledge of EC environment has the advantage of easier controllability in practical application. The simulation results in the EC context indicate the accuracy of the proposed BNP estimate and the more effective behavior of the proposed algorithm compared with other versions of APA class.

Hybrid In-Vehicle Background Noise Reduction for Robust Speech Recognition:The Possibilities of Next Generation 5G Data Networks

This pilot study focuses on employment of hybrid LMS-ICA system for in-vehicle background noise reduction.Modern vehicles are nowadays increasingly supporting voice commands,which are one of the pillars of autonomous and SMART vehicles.Robust speaker recognition for context-aware in-vehicle applications is limited to a certain extent by in-vehicle back-ground noise.This article presents the new concept of a hybrid system which is implemented as a virtual instrument.The highly modular concept of the virtual car used in combination with real recordings of various driving scenarios enables effective testing of the investigated methods of in-vehicle background noise reduction.The study also presents a unique concept of an adaptive system using intelligent clusters of distributed next generation 5G data networks,which allows the exchange of interference information and/or optimal hybrid algorithm settings between individual vehicles.On average,the unfiltered voice commands were successfully recognized in 29.34%of all scenarios,while the LMS reached up to 71.81%,and LMS-ICA hybrid improved the performance further to 73.03%.

State Estimation for Sound and Vibration Emitted from a Machine Based on Sound Measurement Under Existenceof Background Noise

Many studies on the diagnosis for machines have become important recently because of increased use of various complex industrial systems.The correlation information between sound and vibration is very important for machine diagnosis.Usually,vibration pickups are attached directly to the machine in order to measure vibration data.However,in some cases,the sensors can not be attached directly on highly precise devices.In this study,a method to estimate the fluctuation of sound and vibration is proposed based on the measurement data of sound emitted from the machine under existence of background noise.The effectiveness of the proposed theory is experimentally confirmed by applying it to the observed data emitted from a rotational machine driven by an electric motor.

Applying nonlinear filtering in reverberation time measurement under strong background noise

Nonlinear filtering of impulse response obtained by M-sequence correlation method under strong background noise is presented. The research shows that the new method works very efficiently without the need to cut off impulse response data. Even if the ratio of signal to noise is below -15 dB, the same decay curve ranges can still be obtained as when S/N > 40 dB

Extraction of the key infrared radiation temperature features concerning stress and crack evolution of loaded rocks

The infrared radiation temperature(IRT)variation concerning stress and crack evolution of rocks is a critical focus in rock mechanics domain and engineering disaster warning.In this paper,a methodology to extract the key IRT features related to stress and crack evolution of loaded rocks is proposed.Specifically,the wavelet denoising and reconstruction in thermal image sequence(WDRTIS)method is employed to eliminate temporal noise in thermal image sequences.Subsequently,the adaptive partition temperature drift correction(APTDC)method is introduced to alleviate temperature drift.On this basis,the spatial noise correction method based on threshold segmentation and adaptive median filtering(OTSU-AMF)is proposed to extract the key IRT features associated with microcracks of loaded rocks.Following temperature drift correction,IRT provides an estimation of the thermoelastic factor in rocks,typically around 5.29×10^(-5) MPa^(-1) for sandstones.Results reveal that the high-temperature concentrated region in cumulative thermal images of crack evolution(TICE)can elucidate the spatiotemporal evolution of localized damage.Additionally,heat dissipation of crack evolution(HDCE)acquired from TICE quantifies the progressive failure process of rocks.The proposed methodology enhances the reliability of IRT monitoring results and provides an innovative approach for conducting research in rock mechanics and monitoring engineering disasters.

GPR data noise attenuation on the curvelet transform

Signal extraction is critical in GRP data processing and noise attenuation. When the target depth is shallow, its refl ection echo signal will overlap with the background noise, affecting the detection of arrival time and localization of the target. Thus, we propose a noise attenuation method based on the curvelet transform. First, the original signal is transformed into the curvelet domain, and then the curvelet coefficients of the background noise are extracted according to the distribution features that differ from the effective signal. In the curvelet domain, the coarse-scale curvelet atom is isotropic. Hence, a two-dimensional directional filter is designed to estimate the high-energy background noise in the coarsescale domain, and then, attenuate the background noise and highlight the effective signal. In this process, we also use a subscale threshold value of the curvelet domain to fi lter out random noise. Finally, we compare the proposed method with the average elimination and 2D continuous wavelet transform methods. The results show that the proposed method not only removes the background noise but also eliminates the coherent interference and random noise. The numerical simulation and the real data application suggest and verify the feasibility and effectiveness of the proposed method.

Rayleigh Wave Tomography of Ningxia and Its Adjacent Areas Based on Ambient Noise

In this article,the vertical components of the continuous waveform data of 90 seismic stations in Ningxia and its adjacent regions recorded from January 2012 to December 2013 are used to obtain the Rayleigh surface wave group velocity dispersion images in the study area( 101°- 112°E,31°-42°N) according to the method of noise imaging,with period between 6s - 50s and resolution of 0.5°. The Yinchuan basin in the 6s - 26 s period obviously shows a low velocity anomaly,which is not uniform and has a tendency to gradually weaken; the Guanzhong Basin in 6 s-22s shows a strip of low velocity anomaly and demonstrates a transverse inhomogeneity,where velocity in the southeast is slightly faster than that in the northwest. In the 30s - 50s period it shows that in the Yinchuan graben basin and its southern area,there is a large low velocity anomaly area,which moves from northeast to southwest. It shows that between the main active tectonic zones,like mountains and basins,there are obvious geomorphologic boundaries. For example,the deep fault near Liupan Mountain is the dividing line between two large tectonic units of eastern and western of China. The inversion results have good correlation with the geological structure and the stratigraphic landform. The results are consistent with the results of artificial seismic section tomography across the basin. It provides an important basis for the dynamics of active tectonic zones and the mechanism of earthquake occurrence in this area.

Performance analysis of a new baseline-based SS-Y extensometer

In order to solve the problems in the observation with the SS-Y extensometer, such as background noise and discontinuity of earth tide curve, this paper proposed to improve the stability of the instrument and the smoothness of the earth tide curve by improving the baseline structure of the instrument. In this study, a new φ20*1.2 invar tube was introduced in replacement of the existing φ6 invar rod as the baseline of the instrument and compared with the existing φ6 invar rod on the instrument’s linearity,sensitivity and other indexes. Firstly, the SS-Y extensometers using φ6 invar rod and cp20*1.2 invar tube were tested. Test results were then calculated, and accuracy errors of the instruments based on the two baselines were obtained. Finally, their accuracy errors and earth tide curves were compared. Results showed that, compared with the extensometer based on the existing φ6 invar rod, the extensometer based on the new φ20*1.2 invar tube was superior in performance, with linearity error reducing to 0.495% from 0.937%, sensitivity increasing to 68.65 mV/μm from 65.46 mV/μm, and earth tide curve growing more stable and continuous.

On Data-Independent Properties for Density-Based Dissimilarity Measures in Hybrid Clustering

Hybrid clustering combines partitional and hierarchical clustering for computational effectiveness and versatility in cluster shape. In such clustering, a dissimilarity measure plays a crucial role in the hierarchical merging. The dissimilarity measure has great impact on the final clustering, and data-independent properties are needed to choose the right dissimilarity measure for the problem at hand. Properties for distance-based dissimilarity measures have been studied for decades, but properties for density-based dissimilarity measures have so far received little attention. Here, we propose six data-independent properties to evaluate density-based dissimilarity measures associated with hybrid clustering, regarding equality, orthogonality, symmetry, outlier and noise observations, and light-tailed models for heavy-tailed clusters. The significance of the properties is investigated, and we study some well-known dissimilarity measures based on Shannon entropy, misclassification rate, Bhattacharyya distance and Kullback-Leibler divergence with respect to the proposed properties. As none of them satisfy all the proposed properties, we introduce a new dissimilarity measure based on the Kullback-Leibler information and show that it satisfies all proposed properties. The effect of the proposed properties is also illustrated on several real and simulated data sets.

Gender Differences in Auditory Perception and Computational Divided Attention Tasks

Women have been stereotyped as better multitaskers when compared to their male counterparts. The purpose of this study is to investigate whether there are differences in gender performance when performing cognitive combined tasks. Twenty-four graduate students （twelve females and twelve males） volunteered to participate in the study. The task requires participants to indicate when they perceive a change in the intensity of an auditory signal while simultaneously solving algebraic problems. Multivariate Analysis of Variance （MANOVA） results reveal no significant differences between genders when performing the combined tasks （p = 0.1831 and 2 = 0.7891） although the average number of false alarms made during the combined tasks by males is nearly 11% higher than the average number of false alarms made by females. However, （Multivariate Analysis of Variance） ANOVA results for the combined tasks show that males outperform females on the computational task while listening for changes in the auditory signal F（1, 22） - 5.09, p 〈 0.03, but there are no significant differences in their ability to detect noise intensity variation or in the number of false alarms made while multitasking. For the single task analysis the ANOVAs indicate no significant differences in signal detection task performance, computational task performance, or the number of false alarms made by males and females.

An interferometric phase shift fiber Bragg grating sensing system with greatly reduced background phase noise

We presented an interferometric phase shift fiber Bragg grating （FBG） sensor, which inherited the advantages of FBG sensors, and, at the same time, the greatly reduced full-width-at-half-maximum bandwidth brought longer coherent length, higher sensitivity, and lower phase noise. Experiments show that at least a 7 dB reduction of phase noise can be achieved compared to FBG sensors interrogated by interferometer with the same optical path difference.

Trigger and noise filtering algorithms for the LHAASO water Cherenkov detector array

Introduction The Large High Altitude Air Shower Observatory(LHAASO)will be constructed at Mt.Haizishan,Sichuan Province,China.Among several detector components of the LHAASO,the Water Cherenkov Detector Array(WCDA)is of great importance for gamma-ray astronomy from 100 GeV to 30 TeV.The single-channel counting rate of a photo-multiplier tube can reach as high as 30-35 kHz,most of them are background noise hits from the low energy cosmic ray showers,bringing a big challenge on data transferring,data storage and event reconstruction.Methods In this paper,a dedicated trigger scheme and a fast noise filtering method aiming to deal with these high rate background noise hits are introduced.These methods are tested with some Monte Carlo simulation data,showing a fair efficiency in filtering background noise hits,while most of the real shower signals are kept.Conclusion This method is proposed to be applied in a stage of the online processing just after the data are acquired in LHAASO-WCDA.

Distortion-free PCA on sample space for highly variable gene detection from single-cell RNA-seq data

Single-cell RNA-seq (scRNA-seq) allows the analysis of gene expression in each cell, which enables the detection of highly variable genes (HVG) that contribute to cell-to-cell variation within a homogeneous cell population. HVG detection is necessary for clustering analysis to improve the clustering result. scRNA-seq includes some genes that are expressed with a certain probability in all cells which make the cells indistinguishable. These genes are referred to as background noise. To remove the background noise and select the informative genes for clustering analysis, in this paper, we propose an effective HVG detection method based on principal component analysis (PCA). The proposed method utilizes PCA to evaluate the genes (features) on the sample space. The distortion-free principal components are selected to calculate the distance from the origin to gene as the weight of each gene. The genes that have the greatest distances to the origin are selected for clustering analysis. Experimental results on both synthetic and gene expression datasets show that the proposed method not only removes the background noise to select the informative genes for clustering analysis, but also outperforms the existing HVG detection methods.

Design of an experimental platform to investigate the effects of audible sounds on plant growth

An experimental platform was developed to investigate the effects of audible sound(20 Hz to 20 MHz)on plant growth promotion,which included a microcontroller-based embedded system for audible sound adjustment and analysis.The direct digital frequency synthesis(DDFS)method was used to generate various waveforms of sound in the platform.Soundproof glass and mufflers were used to reduce background noise.The developed system was tested on various plants,including hydroponic tomatoes,celery and mung bean.The testing results showed that the developed platform could produce pure tone and mixing audible sound with high stability and accuracy,make octave analysis of the sound under experimental environments,and the background noise in the testing chamber of the platform was lower than 55 dB(A)when the compression engine was working.The developed experimental platform has a great potential on facilitating scientific research on acoustic biology effects on plants and collecting real-time experimental data.

Acoustic improvement on two lecture auditoria： Simulation and experiment

Based on the commonly used indicators for speech intelligibility, this work acoustically evaluates the two largest auditoria in the Faculty of Engineering, Helwan University, Cairo, Egypt, using experimental and digital simulation techniques. Design treatments were also suggested to improve the acoustic performance of the auditoria, where the impact of these treatments was checked using the simutation as well. The models that were analysed using the CATT-software were first validated utilizing the results of the fietd work in the unoccupied rooms. The results showed that the acoustic quality of the two auditoria are far from the optimal conditions due to their improper acoustic characteristics and the high noise revers as weft. The results of improvement proposals showed that altering the ceiling shape and adding efficient absorptive materials to the rear surfaces successfully reduced the excessive reverberation time to the optimal values, increased the earty reflections and eliminated the shadow zones. In addition, decreasing the noise Levels by 20 dB due to improving the window insulation noticeably improved the speech intelligibitity at all receivers.

New progress on acoustic communication in the concave-eared torrent frog and its revelation

Animals have special solution to the problem of communication in high levels of background noise. A small group of vertebrates (bats,dolphins and whales,and some rodents) that use ultrasound for communication.Our research first demonstrated that the concave-eared torrent frog is the first non- mammalian vertebrate found to be capable of pro- ducing and detecting ultrasounds for communica- tion.This study may provide a clue for understand- ing why humans have ear canals and how animals auditory systems have evolved,and inspire in de- veloping bionic tecnology for improving hearing in noise.