Residual Noise Identification for PIM Measurement Systems using Variable Low-PIM Termination

<p>In this letter, we describe the design for the variable low-PIM termination composed of the voltage-controlled IM-source, a fixed attenuator, and a linear attenuator.</p> <p>The design method for evaluating PIM-level of the fixed attenuator is presented in order not to limit the variable range of entire termination.</p> <p>It is possible to maintain low-PIM performance in spite of using active voltage-controlled IM-source, whose IM level is extremely high. </p> <p>This termination is used for a dynamic residual noise identification for PIM measurement systems by observing the saturation value for voltage sweep.</p> <p>The validity is confirmed by experiments in 2GHz band.</p>

Residual Noise Identification for PIM Measurement Systems using Variable Low-PIM Termination

<p>In this letter, we describe the design for the variable low-PIM termination composed of the voltage-controlled IM-source, a fixed attenuator, and a linear attenuator.</p> <p>The design method for evaluating PIM-level of the fixed attenuator is presented in order not to limit the variable range of entire termination.</p> <p>It is possible to maintain low-PIM performance in spite of using active voltage-controlled IM-source, whose IM level is extremely high. </p> <p>This termination is used for a dynamic residual noise identification for PIM measurement systems by observing the saturation value for voltage sweep.</p> <p>The validity is confirmed by experiments in 2GHz band.</p>

Role of Well Logs and Seismic Data Conditioning in Improving the Imaging of Complex Reservoirs from Abu Dhabi

Rock physics/seismic inversion is a powerful tool that deliver information about intra-wells rocks elastic attributes and reservoir properties such as porosity, saturation and rock lithology classification. In principle, inversion is like an engine that should be fueled by proper input quality of both seismic and well data. As for the well data, sonic and density logs measure the rock properties a few inches from the borehole. Reliability of sonic transit-time and bulk density logs can be affected by large and rapid variation in the diameter and shape of the borehole cross-section, as well as the process of drilling fluid invasion. The basic assumption for acoustic well logs editing and conditioning is to use other recorded logs (not affected by bad-hole conditions) in a Multivariate-Regression Algorithm. In addition, Fluid Substitution was implemented to correct for the mud invasion that affects the acoustic and elastic properties based on the PVT data for fluid properties computation. The logs were then quality checked by multiple cross-plotting comparisons to the standard Rock-physics trends templates. As for seismic data, there are several factors affecting the quality of surface seismic data including the presence of residual noise and multiples contamination that caused improper amplitude balancing. Optimizing the seismic data processing for the inversion studies require reviewing and conditioning the seismic gathers and pre-stack volumes, guided by a deterministic seismic-to-well tie analysis after every major stage of the processing sequence. The applied processes are mainly consisting of Curvelet domain noise attenuation to attenuate residual noise. This was followed by high resolution Radon anti-multiple to attenuate residual surface multiples and Extended interbed multiple prediction to attenuate interbed multiples. In addition, Offset dependent amplitude and spectral balancing were applied to maintain the seismic amplitudes fidelity. This paper will illustrate a case from Abu Dhabi where data conditioning results improved the Hydrocarbon saturated carbonates vs brine saturated carbonate and the lithology predictions, leading to optimizing field development plans and drilling operations.

Estimation of Phase Noise Transfer Function

Quantifying frequency converters’ residual phase noise is essential in various applications, including radar systems, high-speed digital communication, or particle accelerators. Multi-input signal source analyzers can perform such measurements out of the box, but the high cost limits their accessibility. Based on an analysis of phase noise transmission theory and the capabilities of popular instrumentation, we propose a technique extending the functionality of single-input devices. The method supplements absolute noise measurements with estimates of the phase noise transfer function (also called the jitter transfer function), allowing the calculation of residual noise. The details of the hardware setup used for the method verification are presented. The injection of single-tone and pseudo-random modulations to the test signal is examined. Optional employment of a spectrum analyzer can reduce the time and number of data needed for characterization. A wideband synthesizer with an integrated voltage-controlled oscillator was investigated using the method. The estimated transfer function matches a white-box model based on synthesizer’s structure and values of loop components. The first results confirm the validity of the proposed technique.

PlanetEvidence: Planet or Noise?

The work presented here attempts at answering the following question: how do we decide when a given detection is a planet or just residual noise in exoplanet direct imaging data? To this end we implement a metric meant to replace the empirical frequentist-based thresholds for detection. Our method, implemented within a Bayesian framework, introduces an “evidence-based” approach to help decide whether a given detection is a true planet or just noise. We apply this metric jointly with a postprocessing technique and Karhunen–Loeve Image Processing (KLIP), which models and subtracts the stellar PSF from the image. As a proof of concept we implemented a new routine named PlanetEvidence that integrates the nested sampling technique (Multinest) with the KLIP algorithm. This is a first step to recast such a postprocessing method into a fully Bayesian perspective. We test our approach on real direct imaging data, specifically using GPI data of β Pictoris b, and on synthetic data. We find that for the former the method strongly favors the presence of a planet (as expected) and recovers the true parameter posterior distributions. For the latter case our approach allows us to detect (true) dim sources invisible to the naked eye as real planets, rather than background noise, and set a new lower threshold for detection at ∼2.5σ level. Further it allows us to quantify our confidence that a given detection is a real planet and not just residual noise.

Peculiarity of the use of pseudonoise signals in electrical prospecting equipment

The article provides a practical assessment of the effectiveness of the use of electrical prospecting equipment with pseudonoise sounding signals based on the results of field experiments with a new electrical prospecting measuring complex developed at the Scientific Station of the Russian Academy of Sciences. Two main sources of interference are considered, limiting the possibilities of effective use of pseudonoise signals in electrical prospecting equipment: “structural disturbances”, manifested in the process of correlation processing of recorded signals and interference, the source of which is an industrial power network with a frequency of 50 Hz. Methods of reducing the influence of the above noises on the sounding curve obtained during data processing are considered using specially developed algorithms for eliminating “structural disturbances” and suppressing residual noise and interference.

A new denoising strategy and the X-shaped supplement denoising operator targeting time-reversed mirror imaging technique

Imaging of vertical structures is a challenge in the seismic imaging field. The conventional imaging methods for vertical structures are highly dependent on the reference model or boreholes. Time-reversed mirror imaging can effectively image the vertical structures based on the multiples and a smoothed velocity model without the need of accurate seismic wavelet estimation. Although the Laplacian operator is applied in time-reversed mirror imaging, there still exists severe residual noise. In this study, we developed a new imaging denoising strategy and an X-shaped supplement denoising operator for time-reversed mirror imaging based on the geometric features of the image and the causes of imaging noise. Synthetic results for the single- and double-staircase model prove the powerful denoising capacity of the X-shaped supplement denoising operator. In addition, the results of a Marmousi model prove that the X-shaped denoising operator can also effectively suppress the noise when applying time-reversed mirror imaging method to image complex inclined structures. However, the X-shaped denoising operator still contains some limitations, such as non-amplitude-preserving.

An Improved Channel Estimation Algorithm Based on WD-DDA in OFDM System

Channel estimation is the key technology to ensure reliable transmission in orthogonal frequency division multiplexing (OFDM) system. In order to improve the accuracy of the channel estimation algorithm in a low signal-to-noise ratio (SNR) channel environment, in this paper, we proposed an improved channel estimation algorithm based on the transform domain. The improved algorithm with wavelet denoising (WD) and distance decision analysis (DDA) to perform secondary denoising on the channel estimation algorithm based on the transform domain is proposed. First, after the least-squares (LS) algorithm, WD is used to denoise for the first time, then the DDA is used to further suppress the residual noise in the transform domain, and the important channel taps are screened out. Simulation results show that the proposed algorithm can improve the detection performance of existing channel estimation algorithms based on transform domain in low SNR.

Speech Enhancement for Hearing Impaired Based on Bandpass Filters and a Compound Deep Denoising Autoencoder

Deep neural networks have been applied for speech enhancements efficiently. However, for large variations of speech patterns and noisy environments, an individual neural network with a fixed number of hidden layers causes strong interference, which can lead to a slow learning process, poor generalisation in an unknown signal-to-noise ratio in new inputs, and some residual noise in the enhanced output. In this paper, we present a new approach for the hearing impaired based on combining two stages: (1) a set of bandpass filters that split up the signal into eight separate bands each performing a frequency analysis of the speech signal; (2) multiple deep denoising autoencoder networks, with each working for a small specific enhancement task and learning to handle a subset of the whole training set. To evaluate the performance of the approach, the hearing-aid speech perception index, the hearing aid sound quality index, and the perceptual evaluation of speech quality were used. Improvements in speech quality and intelligibility were evaluated using seven subjects of sensorineural hearing loss audiogram. We compared the performance of the proposed approach with individual denoising autoencoder networks with three and five hidden layers. The experimental results showed that the proposed approach yielded higher quality and was more intelligible compared with three and five layers.

Low-complexity artificial noise suppression methods for deep learning-based speech enhancement algorithms

Deep learning-based speech enhancement algorithms have shown their powerful ability in removing both stationary and non-stationary noise components from noisy speech observations. But they often introduce artificial residual noise, especially when the training target does not contain the phase information, e.g., ideal ratio mask, or the clean speech magnitude and its variations. It is well-known that once the power of the residual noise components exceeds the noise masking threshold of the human auditory system, the perceptual speech quality may degrade. One intuitive way is to further suppress the residual noise components by a postprocessing scheme. However, the highly non-stationary nature of this kind of residual noise makes the noise power spectral density (PSD) estimation a challenging problem. To solve this problem, the paper proposes three strategies to estimate the noise PSD frame by frame, and then the residual noise can be removed effectively by applying a gain function based on the decision-directed approach. The objective measurement results show that the proposed postfiltering strategies outperform the conventional postfilter in terms of segmental signal-to-noise ratio (SNR) as well as speech quality improvement. Moreover, the AB subjective listening test shows that the preference percentages of the proposed strategies are over 60%.