Anti-jamming approach based on radar transmitted waveform matching

This study proposes a novel approach to suppress noise jamming and smart jamming. The traditional method of using auxiliary channels to cancel interference requires pure interference samples to calculate weights, which is almost impossible for pulsed interference signals. In this work, to avoid the difficulty of choosing suitable interference samples, we construct the parameterized expected signal according to the time-delay relation between target reflecting echo and transmitted signal. The objective function is established in the form of the minimum mean square error between the recovered signal and the expected signal. The optimization problem is solved by an alternating iteration method. Simulation results demonstrate that the proposed method achieves excellent performance for suppressing noise jamming and smart jamming and is not sensitive to signal-to-noise ratio and jamming-to-noise ratio. The processing results of the measured data show that the method has a certain practical application value.

An iterative reconstruction method for sparse-projection data for low-dose CT

Reducing X-ray radiation is beneficial for reducing the risk of cancer in patients. There are two main approaches for achieving this goal namely, one is to reduce the X-ray current, and another is to apply sparse-view protocols to do image scanning and projections. However, these techniques usually lead to degradation of the reconstructed image quality, resulting in excessive noise and severe edge artifacts, which seriously affect the diagnosis result. In order to overcome such limitation, this study proposes and tests an algorithm based on guided kernel filtering. The algorithm combines the characteristics of anisotropic edges between adjacent image voxels, expresses the relevant weights with an exponential function, and adjusts the weights adaptively through local gray gradients to better preserve the image structure while suppressing noise information. Experiments show that the proposed method can effectively suppress noise and preserve the image structure. Comparing with similar algorithms, the proposed algorithm greatly improves the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and root mean square error (RMSE) of the reconstructed image. The proposed algorithm has the best effect in quantitative analysis, which verifies the effectiveness of the proposed method and good image reconstruction performance. Overall, this study demonstrates that the proposed method can reduce the number of projections required for repeated CT scans and has potential for medical applications in reducing radiation doses.

Feedback autoregulation of gene expression always amplifies noise in timing of threshold crossing

Many cellular events occur when their corresponding regulatory proteins attain critical thresholds. Can cells schedule such events with precision by controlling the dynamics of the proteins? We investigate this question by considering a simple gene expression model that consists of switching the gene between OFF and ON states and degradation of the protein. Because feedback regulation is a pervasive method of control in biological systems, we analyzed three feedback mechanisms (protein regulates either its own transcription, or the rate at which gene turns ON, or the rate at which gene turns OFF) for their abilities to suppress noise in timing. We show that in the limiting case where the protein does not degrade, feedbacks always amplify noise in event timing.

A hybrid CAI/IMU solution for higher navigation performance

<p>Atom Interferometers as inertial sensors were getting quite some interest in the last decade. Several attempts have been made to combine the two sensors (i.e. classical inertial measurement units IMU and cold atom interferometers), mainly with the goal to use the atom interferometer as main sensor, and support it with different conventional sensors in order to suppress noise and achieve maximum sensitivity and long-term stability.<br>We present a quite promising combination of both sensors in an error state extended Kalman Filter framework aimed especially on further improving the performance of a conventional high end IMU. While the full potential of the cold atom interferometer is not yet entirely exploited in this combination, first simulations in terrestrial applications with small and even larger change of inertial forces show an increase of the navigation solution precision by a factor of 20 and more.</p>

Propeller feature extraction of UUVs study based on CEEMD combined with symmetric correlation

In this paper, in view of the characteristic that UUV radiation noise is low and easily interfered by strong noise, the complementary Ensemble Empirical Mode Decomposition (CEEMD) combined with symmetric correlation processing is proposed, which can improve the extraction performance of UUV’s propeller features. First, the CEEMD decomposition combined with symmetric correlation processing was used to reduce the radiated noise of the target, then the signals after the noise reduction were demodulated and computed to obtain the DEMON spectrum, and finally features such as the rotational speed of the UUV’s propeller were extracted from the DEMON spectrum. The Sea trials signal processing results prove that the method has better noise suppression performance under low SNR conditions, and can clearly and comprehensively extract the DEMON information of the radiated noise, and then accurately extract the propeller features of the UUV. Compared to conventional demodulation techniques, this technique has a greater ability to suppress noise and does not require manual selection of the modulated frequency band.

Analysis of the Wiener filters application to the spectral fluctuation patterns segmentation

The Wiener filter is an adaptive filter which able to produce the desired estimates. Besides, this filter can also suppress noise in digital signal processing. This study aims to segment the fluctuation pattern, which results from data acquisition from a capacitive sensor with the object H2O. The fluctuation pattern to be processed is the High Fluctuation (HF) pattern by dividing the pattern into several segments according to the input frequency. It aims to see in more detail and clearly the state of each segmentation of the pattern. The results will show noise attenuation and suppression after filtering with a Wiener filter. The Signal to Noise Ratio (SNR) value will also be analyzed, which shows that the signal quality is getting better after applying the Wiener filter. Then, the analysis of the Mean Square Error (MSE) results can provide more consistent results with a smaller average error.

Neighborhood Singular Value Decomposition Filter and Application in Adaptive Beamforming for Coherent Plane-Wave Compounding

Coherent plane-wave compounding (CPWC) is widely used in medical ultrasound imaging, in which plane-waves tilted at multiple angles are used to reconstruct ultrasound images. CPWC helps to achieve a balance between frame rate and image quality. However, the image quality of CPWC is limited due to sidelobes and noise interferences. Filtering techniques and adaptive beamforming methods are commonly used to suppress noise and sidelobes. Here, we propose a neighborhood singular value decomposition (NSVD) filter to obtain high-quality images in CPWC. The NSVD filter is applied to adaptive beamforming by combining with adaptive weighting factors. The NSVD filter is advantageous because of its singular value decomposition (SVD) and smoothing filters, performing the SVD processing in neighboring regions while using a sliding rectangular window to filter the entire imaging region. We also tested the application of NSVD in adaptive beamforming. The NSVD filter was combined with short-lag spatial coherence (SLSC), coherence factor (CF), and generalized coherence factor (GCF) to enhance performances of adaptive beamforming methods. The proposed methods were evaluated using simulated and experimental datasets. We found that NSVD can suppress noise and achieve improved contrast (contrast ratio (CR), contrast-to-noise ratio (CNR) and generalized CNR (gCNR)) compared to CPWC. When the NSVD filter is used, adaptive weighting methods provide higher CR, CNR, gCNR and speckle signal-to-noise ratio (sSNR), indicating that NSVD is able to improve the imaging performance of adaptive beamforming in noise suppression and speckle pattern preservation.

Mapping dark matter and finding filaments: calibration of lensing analysis techniques on simulated data

ABSTRACT We quantify the performance of mass mapping techniques on mock imaging and gravitational lensing data of galaxy clusters. The optimum method depends upon the scientific goal. We assess measurements of clusters’ radial density profiles, departures from sphericity, and their filamentary attachment to the cosmic web. We find that mass maps produced by direct (KS93) inversion of shear measurements are unbiased, and that their noise can be suppressed via filtering with mrlens. Forward-fitting techniques, such as lenstool, suppress noise further, but at a cost of biased ellipticity in the cluster core and overestimation of mass at large radii. Interestingly, current searches for filaments are noise-limited by the intrinsic shapes of weakly lensed galaxies, rather than by the projection of line-of-sight structures. Therefore, space-based or balloon-based imaging surveys that resolve a high density of lensed galaxies could soon detect one or two filaments around most clusters.