scholarly journals Effects of Noise and Absorption on High Frequency Measurements of Acoustic-Backscatter from Fish

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Masahiko Furusawa
Keyword(s):  
Measurement Error ◽  
Environmental Conditions ◽  
High Frequency ◽  
Signal To Noise Ratio ◽  
Target Strength ◽  
Acoustic Backscatter ◽  
Signal To Noise ◽  
Absorption Coefficients ◽  
Frequency Measurements ◽  
Multiple Frequencies

Quantitative echosounders operating at multiple frequencies (e.g., 18, 38, 70, 120, 200, 333, and 710 kHz) are often used to observe fish and zooplankton and identify their species. At frequencies above 100 kHz, the absorption attenuation increases rapidly and decreases the signal-to-noise ratio (SNR). Also, incomplete compensation for the attenuation may result in measurement error. This paper addresses the effects of the attenuation and noise on high frequency measurements of acoustic backscatter from fish. It is shown that measurements of a fish with target strength of −40 dB at 200 m depth are limited by SNR to frequencies up to about 100 kHz. Above 100 kHz, absorption coefficients must be matched to local environmental conditions.

Denoising for full-waveform inversion with expanded prediction-error filters

Geophysics ◽  
2021 ◽  
pp. 1-54
Author(s):  
Milad Bader ◽  
Robert G. Clapp ◽  
Biondo Biondi
Keyword(s):  
High Frequency ◽  
Prediction Error ◽  
Signal To Noise Ratio ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Full Waveform Inversion ◽  
Frequency Data ◽  
Signal To Noise ◽  
Frequency Signal ◽  
Full Waveform

Low-frequency data below 5 Hz are essential to the convergence of full-waveform inversion towards a useful solution. They help build the velocity model low wavenumbers and reduce the risk of cycle-skipping. In marine environments, low-frequency data are characterized by a low signal-to-noise ratio and can lead to erroneous models when inverted, especially if the noise contains coherent components. Often field data are high-pass filtered before any processing step, sacrificing weak but essential signal for full-waveform inversion. We propose to denoise the low-frequency data using prediction-error filters that we estimate from a high-frequency component with a high signal-to-noise ratio. The constructed filter captures the multi-dimensional spectrum of the high-frequency signal. We expand the filter's axes in the time-space domain to compress its spectrum towards the low frequencies and wavenumbers. The expanded filter becomes a predictor of the target low-frequency signal, and we incorporate it in a minimization scheme to attenuate noise. To account for data non-stationarity while retaining the simplicity of stationary filters, we divide the data into non-overlapping patches and linearly interpolate stationary filters at each data sample. We apply our method to synthetic stationary and non-stationary data, and we show it improves the full-waveform inversion results initialized at 2.5 Hz using the Marmousi model. We also demonstrate that the denoising attenuates non-stationary shear energy recorded by the vertical component of ocean-bottom nodes.

scholarly journals 53 Piscium, an SPB Star with Activity

2002 ◽  
Vol 185 ◽  
pp. 236-237
Author(s):  
J.-M. Le Contel ◽  
P. Mathias ◽  
E. Chapellier ◽  
J.-C. Valtier
Keyword(s):  
High Resolution ◽  
False Alarm ◽  
High Frequency ◽  
Signal To Noise Ratio ◽  
High Signal ◽  
Correlation Technique ◽  
Signal To Noise ◽  
Spectroscopic Observations ◽  
Auto Correlation ◽  
Frequency Variations

The star 53 Psc (HD 3379, B2.5IV) has been observed as variable by several authors (Sareyan et al., 1979) with frequencies around 10 c d–1 and has been classified as a β Cephei star. Conversely, other authors (e.g. Percy, 1971) found it to be constant.New high resolution, high signal-to-noise ratio, Spectroscopic observations have been performed at the Observatoire de Haute-Provence in 1996 over 11 nights. The spectral domain covers around 200 Å and is centered on Hδ. Radial velocities were deduced from an auto-correlation technique with a scatter around 0.4kms−1.No high frequency variations are observed. Three frequencies have been detected with a false alarm detection above the 1 % level. A fourth one may be present but its amplitude is below this 1 % level. Results are displayed in Table 1.

scholarly journals Estimation and Correction of Sampling Time Offsets in Tiadcs using Optimization Algorithm

2020 ◽  
Vol 9 (4) ◽  
pp. 1217-1222
Keyword(s):  
Optimization Algorithm ◽  
High Frequency ◽  
Performance Metrics ◽  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
Sampling Time ◽  
Signal To Noise ◽  
High Sampling ◽  
Noise Ratio ◽  
Estimation And Correction

In recent communication technologies, very high sampling rates are required for rf signals particularly for signals coming under ultra high frequency (UHF), super high frequency (SHF) and extremely high frequency (EHF) ranges. The applications include global positioning system (GPS), satellite communication, radar, radio astronomy, 5G mobile phones etc. Such high sampling rates can be accomplished with time-interleaved analog to digital converters (TIADCs). However, sampling time offsets existing in TIADCs produce non-uniform samples. This poses a drawback in the reconstruction of the signal. The current paper addresses this drawback and offers a solution for improved signal reconstruction by estimation and correction of the offsets. A modified differential evolution (MDE) algorithm, which is an optimization algorithm, is used for estimating the sampling time offsets and the estimated offsets are used for correction. The estimation algorithm is implemented on an FPGA board and correction is implemented using MATLAB. The power consumption of FPGA for implementation is 57mW. IO utilization is 27% for 4-channel TIADCs and 13% for 2-channel TIADCs. The algorithm estimated the sampling time offsets precisely. For estimation the algorithm uses a sinusoidal signal as a test signal. Correction is performed with sinusoidal and speech signals as inputs for TIADCs. Performance metrics used for evaluating the algorithm are SNR (signal to noise ratio), SNDR (signal to noise and distortion ratio), SFDR (spurious-free dynamic range) and PSNR (peak signal to noise ratio). A noteworthy improvement is observed in the above mentioned parameters. Results are compared with the existing state of the art algorithms and superiority of the proposed algorithm is verified.

scholarly journals Across-subjects classification of stimulus modality from human MEG high frequency activity

2017 ◽  
Author(s):  
Britta U. Westner ◽  
Sarang S. Dalal ◽  
Simon Hanslmayr ◽  
Tobias Staudigl
Keyword(s):  
Random Forest ◽  
High Frequency ◽  
Signal To Noise Ratio ◽  
Source Reconstruction ◽  
Single Trial ◽  
Signal To Noise ◽  
Stimulus Modality ◽  
Random Forest Classification ◽  
Forest Classification ◽  
High Frequency Activity

AbstractSingle-trial analyses have the potential to uncover meaningful brain dynamics that are obscured when averaging across trials. However, low signal-to-noise ratio (SNR) can impede the use of single-trial analyses and decoding methods. In this study, we investigate the applicability of a single-trial approach to decode stimulus modality from magnetoencephalography (MEG) high frequency activity. In order to classify the auditory versus visual presentation of words, we combine beamformer source reconstruction with the random forest classification method. To enable group level inference, the classification is embedded in an across-subjects framework.We show that single-trial gamma SNR allows for good classification performance (accuracy across subjects: 66.44 %). This implies that the characteristics of high frequency activity have a high consistency across trials and subjects. The random forest classifier assigned informational value to activity in both auditory and visual cortex with high spatial specificity. Across time, gamma power was most informative during stimulus presentation. Among all frequency bands, the 75-95 Hz band was the most informative frequency band in visual as well as in auditory areas. Especially in visual areas, a broad range of gamma frequencies (55-125 Hz) contributed to the successful classification.Thus, we demonstrate the feasibility of single-trial approaches for decoding the stimulus modality across subjects from high frequency activity and describe the discriminative gamma activity in time, frequency, and space.Author SummaryAveraging brain activity across trials is a powerful way to increase signal-to-noise ratio in MEG data. This approach, however, potentially obscures meaningful brain dynamics that unfold on the single-trial level. Single-trial analyses have been successfully applied to time domain or low frequency oscillatory activity; its application to MEG high frequency activity is hindered by the low amplitude of these signals. In the present study, we show that stimulus modality (visual versus auditory presentation of words) can successfully be decoded from single-trial MEG high frequency activity by combining source reconstruction with a random forest classification algorithm. This approach reveals patterns of activity above 75 Hz in both visual and auditory cortex, highlighting the importance of high frequency activity for the processing of domain-specific stimuli. Thereby, our results extend prior findings by revealing high-frequency activity in auditory cortex related to auditory word stimuli in MEG data. The adopted across-subjects framework furthermore suggests a high inter-individual consistency in the high frequency activity patterns.

Correlation of waveforms from closely spaced regional events

1990 ◽  
Vol 80 (6B) ◽  
pp. 2177-2193 ◽  
Author(s):  
Hans Israelsson
Keyword(s):  
High Frequency ◽  
Signal To Noise Ratio ◽  
Covariance Matrices ◽  
Statistical Uncertainty ◽  
Signal To Noise ◽  
Cutoff Value ◽  
Large Group ◽  
Quarter Wavelength ◽  
Back Azimuth ◽  
Mining Districts

Abstract We studied the similarity of waveforms recorded at the high frequency element of the NORESS array from 137 events (ML = 0.8 to 2.1) in or near the mining districts of Central Sweden. Waveform correlations based on the covariance matrices of three component recordings and on maximum amplitudes were calculated from traces filtered between 2.5 and 4.0 Hz, in which band the signal-to-noise ratio consistently peaked for the waveforms. A cutoff value for the correlations to separate waveforms with poor and good correlation values could be defined from the statistical uncertainty in back azimuth estimates of NORESS. More than 80 per cent of the events could be grouped with hierarchical clustering into one large group of 98 events and five smaller groups. The NORESS epicenters of the events in the large group were scattered over an area of 20 × 75 km. An exponential decay of the waveform correlation with event separation (d in km) could be fitted to the data of this group (exp(−d/8), taking NORESS epicenter uncertainties into account. A procedure for location of close events based on correlation values is defined. When applied to the large event group it limits the event epicenters to be within an area of about 4 km. For one small event group, high correlation values were obtained above 15 Hz. These frequencies are significantly higher than those reported in other studies, and the so called quarter wavelength argument constrains the event epicenters to within 0.1 km.

scholarly journals Weights-Based Image Demosaicking Using Posteriori Gradients and the Correlation of R–B Channels in High Frequency

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 600
Author(s):  
Meidong Xia ◽  
Chengyou Wang ◽  
Wenhan Ge
Keyword(s):  
High Frequency ◽  
Signal To Noise Ratio ◽  
Experimental Results ◽  
Color Filter ◽  
Signal To Noise ◽  
Visual Effect ◽  
B Values ◽  
Bayer Pattern ◽  
Filter Array ◽  
Composite Peak

In this paper, we propose a weights-based image demosaicking algorithm which is based on the Bayer pattern color filter array (CFA). When reconstructing the missing G components, the proposed algorithm uses weights based on posteriori gradients to mitigate color artifacts and distortions. Furthermore, the proposed algorithm makes full use of the correlation of R–B channels in high frequency when interpolating R/B values at B/R positions. Experimental results show that the proposed algorithm is superior to previous similar algorithms in composite peak signal-to-noise ratio (CPSNR) and subjective visual effect. The biggest advantage of the proposed algorithm is the use of posteriori gradients and the correlation of R–B channels in high frequency.

scholarly journals GNSS (GPS) Monitoring of Dynamic Deflections of Bridges: Structural Constraints and Metrological Limitations

2021 ◽  
Vol 6 (2) ◽  
pp. 23
Author(s):  
Stathis C. Stiros
Keyword(s):  
Experimental Data ◽  
Experimental Evidence ◽  
High Frequency ◽  
Signal To Noise Ratio ◽  
Structural Models ◽  
Structural Constraints ◽  
Careful Monitoring ◽  
Signal To Noise ◽  
Representative Case ◽  
Satellite Signal

The advent of modern geodetic satellite techniques (GNSS, including GPS) permitted to observe dynamic deflections of bridges, initially of long flexible ones, and more recently of short, essentially stiff bridges with modal frequencies > 1 Hz, and with small SNR (signal-to-noise ratio), even SNR < 1. This was an enormous progress, but not without problems. Apart from monitoring results consistent with structural models, experimental data and serviceability criteria, there exist some apparently unexplained cases of stiff bridges for which there have been claimed apparent dynamic deflections too large for common healthy structures. Summarizing previous experience, this article: (i) discusses structural constraints, experimental evidence, and serviceability limits of bridges as constraints to GNSS monitoring; (ii) examines a representative case of careful monitoring of a reinforced concrete road bridge with reported excessive dynamic deflections; and (iii) explains such deflections as a result of a double process generated by large reflective surfaces of passing vehicles near the antenna; first corruption/distortion of the satellite signal because of high-frequency dynamic multipath, and second, shadowing of some satellites; this last effect leads to a modified observations system and to instantaneously changed coordinates and deflections. In order to recognize and avoid such bias in GNSS monitoring, a strategy based on practical rules and structural constraints is presented.

scholarly journals Audio Hiding based on Wavelet Transform and Linear Predictive Coding

2016 ◽  
Vol 42 (1) ◽  
pp. 30-37
Author(s):  
Jamal Hasoon ◽  
Saad Al-Saad
Keyword(s):  
Wavelet Transform ◽  
Efficient Method ◽  
High Frequency ◽  
Signal To Noise Ratio ◽  
Predictive Coding ◽  
Discrete Wavelet ◽  
Linear Predictive Coding ◽  
Signal To Noise ◽  
Log Likelihood ◽  
Objective Tests

In this work an efficient method for hiding a speech in audio is proposed. The features of secretspeech is extracted with LPC (Linear Predictive Coding), and these parameters embedded in audio inchaotic order. Discrete Wavelet Transform (DWT) is applied on audio frames to split the signal in high andlow frequencies. The embedding parameters are embedded in high frequency. The stego audio isperceptually indistinguishable from the equivalent cover audio. The proposed method allows hiding a sameduration of speech (secret) and audio (cover). The stego audio is subjected to objective tests such signal to noiseratio (SNR), signal to noise ratio segmental (SNRseg), Segmental Spectral SNR, Log Likelihood Ratio (LLR)and Correlation (Rxy) to determine the similarity with original audio.

scholarly journals An Investigation of Binary Coded Excitation Methods Using Golay Code Pairs for High Frequency Ultrasound Imaging

2021 ◽  
Author(s):  
Xuegang Su
Keyword(s):  
Ultrasound Imaging ◽  
High Frequency ◽  
Signal To Noise Ratio ◽  
High Frequency Ultrasound ◽  
Golay Code ◽  
Signal To Noise ◽  
Coded Excitation ◽  
High Frequency Ultrasound Imaging ◽  
Noise Ratio ◽  
Frequency Ultrasound

We are investigating the feasibility of binary coded excitation methods using Golay code pairs for high frequency ultrasound imaging as a way to increase the signal to noise ratio. I present some theoretical models used to simulate the coded excitation method and results generated from the models. A new coded excitation high frequency ultrasound prototype system was built to verify the simulation results. Both the simulation and the experimental results show that binary coded excitation can improve the signal to noise ratio in high frequency ultrasound backscatter signals. These results are confirmed in phantoms and excised bovine liver. If just white noise is considered, the encoding gain is 15dB for a Golay pair of length 4. We find the system to be very sensitive to motion (i.e. phase shift) and frequency dependent (FD) attenuation, creating sidelobes and degrading axial resolution and encoding gain. Methods to address these issues are discussed.

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