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.

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

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.

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.