Background Noise Model Research Articles

The dissociative role of bursting and non-bursting neural activity in the oscillatory nature of functional brain networks

Abstract The oscillatory nature of intrinsic brain networks is largely taken for granted in the systems neuroscience community. However, the hypothesis that brain rhythms—and by extension transient bursting oscillations—underlie functional networks has not been demonstrated per se. Electrophysiological measures of functional connectivity are indeed affected by the power bias, which may lead to artefactual observations of spectrally specific network couplings not genuinely driven by neural oscillations, bursting or not. We investigate this crucial question by introducing a unique combination of a rigorous mathematical analysis of the power bias in frequency-dependent amplitude connectivity with a neurobiologically informed model of cerebral background noise based on hidden Markov modeling of resting-state magnetoencephalography (MEG). We demonstrate that the power bias may be corrected by a suitable renormalization depending nonlinearly on the signal-to-noise ratio, with noise identified as non-bursting oscillations. Applying this correction preserves the spectral content of amplitude connectivity, definitely proving the importance of brain rhythms in intrinsic functional networks. Our demonstration highlights a dichotomy between spontaneous oscillatory bursts underlying network couplings and non-bursting oscillations acting as background noise but whose function remains unsettled.

Background noise model of spaceborne photon-counting lidars over oceans and aerosol optical depth retrieval from ICESat-2 noise data

The ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) photon-counting lidar is a revolutionary active remote sensing device and is extremely sensitive to both laser signal and solar background induced noise photons. The weak laser signals, such as aerosol and water subsurface backscattered laser photons, may be buried in noise in the daytime. A noise model for spaceborne photon-counting lidars is of vital importance in understanding and evaluating the performance of a lidar system, as well as in data processing and applications. From the reciprocity perspective, the main noise source for photon-counting lidars is solar background radiation, which in turn is the signal source of ocean color sensors. In the absence of clouds, it is possible to estimate the aerosol information using background noise data of ICESat-2 if the background noise is accurately modeled. In this study, a background noise model of photon-counting lidars over oceans is proposed that carefully considers the contributions of gas molecules, aerosol, water surface, subsurface, and detector dark noise. As the background rate from clouds are much larger than the sum of other factors, in this study, we only consider the cloudless atmosphere by using a cloud screening process. By inputting the system and environmental parameters, the theoretical predictions of noise level in six study areas have an average MAPE of ∼5% compared with ICESat-2 measured noise data. The results also indicate that without clouds, the variation of aerosols dominates the variation of ICESat-2 total background noise in open ocean areas. Then, based on this noise model, a method to estimate aerosol optical depths (AODs) over oceans is proposed solely on the ICESat-2 product including background noise rate and environmental data. Validation against MODIS (Moderate Resolution Imaging Spectroradiometer) daily AODs indicates that the average MAPE is <20% and the average correlation coefficient is 0.95 in six study areas. This method may provide a new AOD retrieval way over oceans in the daytime, when it is difficult to directly calculate AODs using lidar backscattered signal under strong background noise. In addition, the background noise of the ICESat-2 photon-counting lidar in its six channels (or pixels) has the potential to be regarded as the signal of an “ocean color camera” with only a single narrow green band. Based on this consideration, future works may be conducted to combine ocean color remote sensing with ICESat-2 photon-counting lidar.

Seismic background noise levels in the Italian strong-motion network

Abstract. The Italian strong-motion network monitors the seismic activity in the region, with more than 585 stations with continuous data acquisition. In this study, we determine the background seismic noise characteristics of the network by using the data collected in 2022. We analyse the spatial and temporal characteristics of the background noise. It is found that most of the stations suffer from anthropogenic noises, since the strong-motion network is designed to capture the peak ground motions in populated areas. Hence, human activities enrich the low periods of noise. Therefore, land usage of the area where the stations are located affects the background noise levels. Stations can be noisier during the day, up to 12 dB, and during the weekday, up to 5 dB, in short periods. In long periods (≥ 5 s), accelerometric stations converge to similar noise levels and there are no significant daily or weekly changes. It is found that more than half of the stations exceed the background noise model designed for strong-motion stations in Switzerland by Cauzzi and Clinton (2013) in at least one of the calculated periods. We also develop an accelerometric seismic background noise model for periods between 0.0124 and 100 s for Italy by using the power spectral densities of the network. The model is in agreement with the background noise model developed by D’Alessandro et al. (2021) using broadband data for Italy in short periods, but in long periods there is no correlation among studies.

Quantifying the effect of non-seasonal non-tidal loadings on background noise properties of GPS vertical displacements in mainland China

Global positioning system (GPS) vertical displacements measuring the elastic response of Earth’s crust to tectonic motion have been used to produce vertical velocity estimates. These accurate estimates require minimal contamination by non-tectonic deformations, mainly including atmospheric-oceanic-hydrologic mass loadings. However, the variability of background noise properties in GPS time-series caused by the removal of these loading deformations remains unclear. In this study, we investigate how the non-seasonal parts of two types of combined non-tidal loading deformations (i.e., atmospheric plus non-tidal oceanic loadings (AO) or AO plus hydrological loadings (AOH)) affect the background noise properties. The study region is focused on the mainland China, and the data are 258 GPS vertical land motion time-series from the Crustal Movement Observation Network of China (CMONOC). Before and after the loading correction, we found that the more satisfactory background noise model is white plus power-law noise. The AO correction achieves significant improvement in white noise estimates and drastically reduces power-law noise content. Meanwhile, the estimated spectral index shows the worse stationarity of background noise properties but can be alleviated by additional hydrological loading correction. Besides, the exploited GPS imaging method reveals the spatial heterogeneity of the background noise amplitude, spectral index, and velocity uncertainty.

Abstract 244: A computation method for noise reduction based on ultra-deep targeted sequencing data

Abstract Detection of circulating tumor DNA is a promising approach for the diagnosis and monitor of solid tumors. However, sensitive detection of ctDNA remains challenging when minor allele frequency (MAF) is ultra-low. Numerous noise reduction data algorithms have been developed to reduce next-generation sequencing (NGS) errors, but variant callers that can take advantage of ultra-deep sequencing data are lacking. Here we develop a bin-based genotyping (BinGo) method for canceling sequencing noise using ultra-deep (&amp;gt; 100,000 X depth) targeted sequencing data. A mutation reference sample with 44 mutations was diluted to different MAFs at 1% and 0.5%. One blood sample for proficiency test was included. We constructed targeted sequencing libraries by anchored multiplex enrichment for a panel of targets that cover the 44 mutations, and sequenced to a very high depth (Mean X ranged: 71K to 350K).After raw NGS reads were mapped by BWA MEM, BinGo tags unique molecular index (UMI) to each alignment (BAM) to preserve alignment signal of all bases in all raw reads. BinGo then bins all reads with the same UMI into one bin, generating millions of bins. Millions of parallel Samtools mpileup was enabled by the GNU parallel tool. Typical mpileup analyses use the human genome (requires ~4GB RAM) as a reference, making millions of such analyses essentially impossible in a regular computing server. To overcome this hurdle, BinGo uses tailor-made reference sequences comprising the targets only (&amp;lt; 100KB), drastically reduces computation memory consumption and enables millions of mpileup analyses. BinGo recompiles Samtools to increase its default max depth from 8,000 to 8,000,000 to accommodate the ultra-deep data. Variants derived from the same bin are effectively phased, thereby enabling calling of di-nuclei mutations such as BRAF V600K (GT&amp;gt;TT) and V600R (GT&amp;gt;AG). This binning also allows for sensitive calling of insertions and deletions even down to a single read. To profile noise patterns, BinGo uses Samtools tview to acquire every base in every alignment position of the target sequences in all raw reads. Finally, BinGo uses multiple statistical testing to call variants, including calling nucleotide changes over the background noise model that is defined by nucleotide change and sequence context; filtering out strand imbalance likely due to DNA template damage, and a low number of supporting ligation starting sites. With 10 ng DNA as input and at an expected MAF of 0.5%, BinGo demonstrates a 100% (44/44) analytical sensitivity and 100% specificity. When using 1 ng DNA as input and at an expected MAF of 0.5%, only one mutation was missed, leading to a sensitivity of 97.73% (43/44) and 100% specificity. Furthermore, BinGo detected in the blood DNA sample a 15-bp EGFR exon 19 deletion at 0.05%.Bingo integrates UMI-bin derived variants and raw sequencing background noise modeling, enabling analysis of ultra-deep targeted NGS data for rare variant detection. Citation Format: Weiwei Bian, Firaol Kebede, Zongli Zheng. A computation method for noise reduction based on ultra-deep targeted sequencing data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 244.

Background noise reduction for airborne bathymetric full waveforms by creating trend models using Optech CZMIL in the Yellow Sea of China.

Raw full waveforms of green lasers used in airborne LiDAR bathymetry (ALB) are contaminated by background and random noise related to the environment and ALB devices. Traditional thresholding methods have been widely used to reduce background noise in raw full waveforms on the basis of the assumption of constant background noise. However, background noise that is mainly related to background solar radiation and detector dark current changes over time. Thresholding methods perform poorly on the full waveforms with a wide variation range of background noise. A background noise reduction method considering its wide variation is proposed to decrease the background noise by creating trend models. First, each green full waveform is divided into two parts: pulse- and non-pulse-return waveforms. Second, a linear interpolation is conducted on the non-pulse-return waveform to impute the missing noise. Third, a low-pass filter is used to filter the random noise with high frequency in the imputed non-pulse-return waveform and obtain the trend model of background noise of the full waveform. Finally, the derived background noise model is used to decrease the background noise in the pulse-return waveform. The proposed method is applied to decrease the background noise in raw green full waveforms collected by the Optech coastal zone mapping and imaging LiDAR (CZMIL). The mean and standard deviation of residual noise in the CZMIL waveform reduced by the trend model of background noise are -0.03 and 3.5 digitizer counts, respectively. The proposed background noise reduction method is easy to apply and can reduce the background noise to a significantly low level. This method is recommended for preprocessing the raw full waveforms of green lasers collected by Optech CZMIL for ALB.

Hyperbolic tangent cyclic correlation and its application to the joint estimation of time delay and doppler shift

Gaussian distribution is a classic model for additive background noise in scientific research. By assuming and applying Gaussian noise, scholars in different fields can apply themselves to fundamental research and propose many innovative theories based upon Gaussianity. As the scope of engineering applications continues to expand, a single model of background noise cannot meet the more stringent conditions, and the errors caused by inappropriate noise models cannot be ignored. In this paper, alpha-stable distribution is utilized to model additive background noise because of the generality of this distribution. Correspondingly, a novel similarity measurement named hyperbolic tangent correlation (HTC) is proposed to replace the conventional correlation. Then, hyperbolic tangent cyclic correlation (HTCC) used in cyclostationary signal processing is proposed and thoroughly studied. HTC and HTCC emphasize preserving the signal of interest (SOI) rather than suppressing the noise, as traditionally practiced. In particular, they effectively process both the amplitude and phase information in the SOI. To demonstrate the superiority of HTCC in processing amplitude and phase information against background noise, a representative application in time-frequency analysis is presented, and the resulting performance is compared to that of the existing methods. Considering the beneficial mathematical properties of the hyperbolic tangent function, we hope that HTC and HTCC will have more applications in signal processing.

Special Issue No. – 10, June, 2020 Journal &gt; Special Issue &gt; Special Issue No. – 10, June, 2020 &gt; Page 5 “Quantative Methods in Modern Science” organized by Academic Paper Ltd, Russia MORPHOLOGICAL AND ANATOMICAL FEATURES OF THE GENUS GAGEA SALISB., GROWING IN THE EAST KAZAKHSTAN REGION Authors: Zhamal T. Igissinova,Almash A. Kitapbayeva,Anargul S. Sharipkhanova,Alexander L. Vorobyev,Svetlana F. Kolosova,Zhanat

Special Issue No. – 10, June, 2020 Journal &gt; Special Issue &gt; Special Issue No. – 10, June, 2020 &gt; Page 5 “Quantative Methods in Modern Science” organized by Academic Paper Ltd, Russia MORPHOLOGICAL AND ANATOMICAL FEATURES OF THE GENUS GAGEA SALISB., GROWING IN THE EAST KAZAKHSTAN REGION Authors: Zhamal T. Igissinova,Almash A. Kitapbayeva,Anargul S. Sharipkhanova,Alexander L. Vorobyev,Svetlana F. Kolosova,Zhanat

A Computational Pipeline for the Extraction of Actionable Biological Information From NGS-Phage Display Experiments.

Phage Display is a powerful method for the identification of peptide binding to targets of variable complexities and tissues, from unique molecules to the internal surfaces of vessels of living organisms. Particularly for in vivo screenings, the resulting repertoires can be very complex and difficult to study with traditional approaches. Next Generation Sequencing (NGS) opened the possibility to acquire high resolution overviews of such repertoires and thus facilitates the identification of binders of interest. Additionally, the ever-increasing amount of available genome/proteome information became satisfactory regarding the identification of putative mimicked proteins, due to the large scale on which partial sequence homology is assessed. However, the subsequent production of massive data stresses the need for high-performance computational approaches in order to perform standardized and insightful molecular network analysis. Systems-level analysis is essential for efficient resolution of the underlying molecular complexity and the extraction of actionable interpretation, in terms of systemic biological processes and pathways that are systematically perturbed. In this work we introduce PepSimili, an integrated workflow tool, which performs mapping of massive peptide repertoires on whole proteomes and delivers a streamlined, systems-level biological interpretation. The tool employs modules for modeling and filtering of background noise due to random mappings and amplifies the biologically meaningful signal through coupling with BioInfoMiner, a systems interpretation tool that employs graph-theoretic methods for prioritization of systemic processes and corresponding driver genes. The current implementation exploits the Galaxy environment and is available online. A case study using public data is presented, with and without a control selection.

Theoretical background noise rate over water surface for a photon-counting lidar and its application in land and sea cover classification.

For photon-counting lidars, the classical theoretical rate of the noise photons reflected by the Earth's surface is under the assumption that the Earth's surface is a Lambert reflector, which is obviously not suitable for the water surface. In this paper, the specular reflection theorem is introduced to derive an analytical expression of noise photons arising from the water surface reflection. The verification uses the mean noise rate over water surface, calculated by the raw data photons measured by the Multiple Altimeter Beam Experiment Lidar (MABEL) near the East Coast in the North Carolina, USA. The measured result coincides well with the theoretical noise rate, as both of them equal to 8.4 kHz. In addition, the background noise model also indicates that the background noise rate over the land surface is one order of magnitude larger than that over the water surface, in certain conditions. Hence, a new method, based on the noise rates, is proposed for the Earth's surface type classification and it performs well in distinguishing all water surfaces from land surfaces in the coastal area. For space-borne or airborne photon-counting lidars, this paper not only fills the gap of theoretical rate of noise photons from the water surface but also provides a fast and effective method to classify the Earth's surface types. This method is also suitable for distinguishing ice and water in high-latitude sea-ice covered regions, which is the area of most interest of the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) mission.

Resolving fine electromechanical structure of collagen fibrils via sequential excitation piezoresponse force microscopy

Collagen is the main protein in extracellular matrix that is found in many connective tissues, and it exhibits piezoelectricity that is expected to correlate with its hierarchical microstructure. Resolving fine electromechanical structure of collagen, however, is challenging, due to its weak piezoresponse, rough topography, and microstructural hierarchy. Here we adopt the newly developed sequential excitation strategy in combination with piezoresponse force microscopy to overcome these difficulties. It excites the local electromechanical response of collagen via a sequence of distinct frequencies, minimizing crosstalk with topography, followed by principal component analysis to remove the background noise and simple harmonic oscillator model for physical analysis and data reconstruction. These enable us to acquire high fidelity mappings of fine electromechanical response at the nanoscale that correlate with the gap and overlap domains of collagen fibrils, which show substantial improvement over conventional piezoresponse force microscopy techniques. It also embodies the spirit of big data atomic force microscopy that can be readily extended into other applications with targeted data acquisition.

Improving Soft Decoding by Spectral Leakage Reduction in Presence of Narrow Band Interference in PLC

Communications over power lines (PLC) is a promising technology for a variety of applications. The use of a single network for power and data transmission with PLC provides weight, space, and cost savings for vehicular communications. Current work focuses on broadband PLC (BPL) systems compliant with the IEEE 1901 standard. Given that transmissions occur on a physical medium not designed for data communication, the channel and noise characteristics are mostly unfavorable. To deal with these conditions, turbo codes have been widely employed due to their high performance. However, their performance strongly depends on the accuracy of the noise estimation. This work addresses the estimation of the noise power spectral density (PSD) to perform soft-decoding. A general background noise (GBN) model and a narrowband interference (NBI) model is considered. To the best of our knowledge, this is the first work that analyzes the effects of the NBI leakage on noise estimation when it is performed during the reception of the data frame. We propose the use of the frame control (FC) symbol to reduce interference leakage on noise estimation. In addition, we investigate the use of payload symbols to further reduce spectral leakage. Finally, we show that system performance considerably improves by using the FC-based estimation in soft-decoding procedures.

A general theory on frequency and time–frequency analysis of irregularly sampled time series based on projection methods – Part 2: Extension to time–frequency analysis

Abstract. Geophysical time series are sometimes sampled irregularly along the time axis. The situation is particularly frequent in palaeoclimatology. Yet, there is so far no general framework for handling the continuous wavelet transform when the time sampling is irregular. Here we provide such a framework. To this end, we define the scalogram as the continuous-wavelet-transform equivalent of the extended Lomb–Scargle periodogram defined in Part 1 of this study (Lenoir and Crucifix, 2018). The signal being analysed is modelled as the sum of a locally periodic component in the time–frequency plane, a polynomial trend, and a background noise. The mother wavelet adopted here is the Morlet wavelet classically used in geophysical applications. The background noise model is a stationary Gaussian continuous autoregressive-moving-average (CARMA) process, which is more general than the traditional Gaussian white and red noise processes. The scalogram is smoothed by averaging over neighbouring times in order to reduce its variance. The Shannon–Nyquist exclusion zone is however defined as the area corrupted by local aliasing issues. The local amplitude in the time–frequency plane is then estimated with least-squares methods. We also derive an approximate formula linking the squared amplitude and the scalogram. Based on this property, we define a new analysis tool: the weighted smoothed scalogram, which we recommend for most analyses. The estimated signal amplitude also gives access to band and ridge filtering. Finally, we design a test of significance for the weighted smoothed scalogram against the stationary Gaussian CARMA background noise, and provide algorithms for computing confidence levels, either analytically or with Monte Carlo Markov chain methods. All the analysis tools presented in this article are available to the reader in the Python package WAVEPAL.

Characteristics Analysis and Modeling of Fault Sound and Background Noise of Large Central Air Conditioner

Performance of time delay estimation (TDE) algorithm directly affects effect of electromechanical equipment fault sound source positioning based on TDE. Designing more accurate algorithm based proper models of background noise and fault sound can improve accuracy of localization. Firstly, this paper analyzes the characteristics of noise and fault sound of external central air conditioners, then, models environmental noise using Alpha distribution. Finally, Analyzes measured data and preprocess them, realize the more appropriate modeling of environmental noise and eliminate operating noise sources interference. This study has a guiding significance in large-scale mechanical and electrical equipment failure sound source signal modeling, and is helpful to research on TDE algorithm.

Above the Noise: The Search for Periodicities in the Inner Heliosphere

Remote sensing of coronal and heliospheric periodicities can provide vital insight into the local conditions and dynamics of the solar atmosphere. We seek to trace long (one hour or longer) periodic oscillatory signatures (previously identified above the limb in the corona by, e.g., Telloni et al. in Astrophys. J.767, 138, 2013) from their origin at the solar surface out into the heliosphere. To do this, we combined on-disk measurements taken by the Atmospheric Imaging Assembly (AIA) onboard the Solar Dynamics Observatory (SDO) and concurrent extreme ultra-violet (EUV) and coronagraph data from one of the Solar Terrestrial Relations Observatory (STEREO) spacecraft to study the evolution of two active regions in the vicinity of an equatorial coronal hole over several days in early 2011. Fourier and wavelet analysis of signals were performed. Applying white-noise-based confidence levels to the power spectra associated with detrended intensity time series yields detections of oscillatory signatures with periods from 6 – 13 hours in both AIA and STEREO data. As was found by Telloni et al. (2013), these signatures are aligned with local magnetic structures. However, typical spectral power densities all vary substantially as a function of period, indicating spectra dominated by red (rather than white) noise. Contrary to the white-noise-based results, applying global confidence levels based on a generic background-noise model (allowing a combination of white noise, red noise, and transients following Auchère et al. in Astrophys. J.825, 110, 2016) without detrending the time series uncovers only sporadic, spatially uncorrelated evidence of periodic signatures in either instrument. Automating this method to individual pixels in the STEREO/COR coronagraph field of view is non-trivial. Efforts to identify and implement a more robust automatic background noise model fitting procedure are needed.

A Preliminary Study of Development of Standard Background Noise Model for the KMA Infrasound Stations

The infrasound is transmitted through the air, so the characteristics are changed by temperatures, humidity, and wind of the air. It is difficult to obtain high quality infrasound signals due to the background noise. The main sources of the background noise are human activities and wind. The development of a background noise model that quantifies the frequency band is important in the design of filters to improve the signal-to-noise ratio.In order to secure the quality of the infrasound signals. In this paper, we obtain the power spectral densities for the year of the 2015 infrasound recordings of Yanggu and Cheolwon station, and develop the model that obtained by maximum density per frequency in the upper 95% and lower 5% regions. Power spectral density analysis showed that the Yanggu station had relatively higher noise levels than the Cheolwon station and exceeded the international standard background noise model in certain frequency bands. The analysis of the meteorological data for the year of 2015 shows that the wind interfered the signal due to the high altitude of the station. The results of this paper are expected to be used as a background noise model to obtain high quality infrasound data. Keywords: Power Spectral Density, Infrasound Station of KMA, Background Noise

Noise characterization for the FID signal from proton precession magnetometer

Proton precession magnetometer is a high-precision device for weak magnetostatic field measurement. The measurement accuracy depends on the frequency measurement of free induction decay (FID) signal, while the signal to noise ratio (SNR) is an important factor affecting the results. Many signal processing methods have been proposed to improve the SNR of FID signal. However, the theoretical analysis of different types of noises for FID signal has not be conducted yet. In addition, the relationship between the frequency measurement accuracy and SNR has not been explicitly established and quantified. This paper first proposes a background noise model based on the extracted features from the FID signal. With this model, background noises, such as white noise, narrow-band noise, and phase noise etc., can be calculated and estimated. Secondly, the relationship between the frequency measurement accuracy and SNR is identified. We also built a prototype proton magnetometer for field tests and validation purpose. Experiments were conducted to investigate this relation through simulation. Different values for frequency accuracy were obtained with different SNRs from the acquired FID signals from field tests. The consistence between the measurement and computational results is observed. When SNR is larger than 30 dB, the absolute frequency accuracy becomes constant which is about 0.04 Hz. With the stability taken into account, the accuracy can be better even when the SNR is higher than 30 dB. This study provides a reference to optimize the design of proton precession magnetometer and the frequency calculation for FID signal.

An Anisotropic A Contrario Framework for the Detection of Convergences in Images

The a contrario framework for the detection of convergences in an image consists in counting, for each tested point, the number of elementary linear structures that converge to it (up to a given precision), and when this number is high enough, the point is declared to be a meaningful point of convergence. This is so far analogous to a Hough transform, and the main contribution of the a contrario framework is to provide a statistical definition of what "high enough" means: it means large enough to ensure that in an image where all elementary structures are distributed according to a background noise model, there is, in expectation, less than 1 detection. Our aim in this paper is to discuss, from a methodological viewpoint, the choice and the influence of the background noise model. This model is generally taken as the uniform independent distribution on elementary linear structures, and here, we discuss the case of images that have a natural anisotropic distribution of structures. Our motivating example is the one of mammograms in which we would like to detect stellate patterns (that appear as local convergences of spicules), and in which the linear structures are naturally oriented towards the nipple. In this paper, we show how to tackle the two problems of (a) defining and estimating an anisotropic "normal" distribution from an image, and of (b) computing the probability that a random structure, following an anisotropic distribution, converges to any given convex region. We illustrate the whole approach with several examples.

Analysis of the Characteristics of the Background Ambient Noise Origin of Infrasound Station

Meteorological elements are the important factors influencing the background noise model of the infrasound station. In order to determine the specific impact of meteorological elements on the model and find out the background ambient noise root of infrasound station, the infrasound and meteorological data from international infrasound station I34 is processed and analyzed through root mean square research and power spectral density analysis. The results show that the wind speed, wind direction, temperature and pressure is the main factors influencing the infrasound station environment background noise, and the wind speed exercises the greatest influence on background noise. Analysis results can provide theory basis for the modeling of background noise for the infrasound station.

Mining the VVV: star formation and embedded clusters

The aim of this study is to locate previously unknown stellar clusters from the VISTA variables in the V\'ia L\'actea Survey (VVV) catalogue data. The method, fitting a mixture model of Gaussian densities and background noise using the expectation maximization algorithm to a pre-filtered NIR survey stellar catalogue data, was developed by the authors for the UKIDSS Galactic Plane Survey (GPS). The search located 88 previously unknown mainly embedded stellar cluster candidates and 39 previously unknown sites of star formation in the 562 deg2 covered by VVV in the Galactic bulge and the southern disk.