Noise In Original Signal Research Articles

Improving distortion correction for isotropic high-resolution 3D diffusion MRI by optimizing Jacobian modulation.

To improve distortion correction for isotropic high-resolution whole-brain 3D diffusion MRI when in a time-saving acquisition scenario. Data were acquired using simultaneous multi-slab (SMSlab) acquisitions, with a b = 0 image pair encoded by reversed polarity gradients (RPG) for phase encoding (PE) and diffusion weighted images encoded by a single PE direction. Eddy current-induced distortions were corrected first. During the following susceptibility distortion correction, image deformation was first corrected by the field map estimated from the b = 0 image pair. Intensity variation was subsequently corrected by Jacobian modulation. Two Jacobian modulation methods were compared. They calculated the Jacobian modulation map from the field map, or from the deformation corrected b = 0 image pair, termed as JField and JRPG , respectively. A modified version of the JRPG method, with proper smoothing, was further proposed for improved correction performance, termed as JRPG-smooth . Compared to JField modulation, less remaining distortions are observed when using the JRPG and JRPG-smooth methods, especially in areas with large B0 field inhomogeneity. The original JRPG method causes signal-to-noise ratio (SNR) deficiency problem, which manifests as degraded SNR of the diffusion weighted images, while the JRPG-smooth method maintains the original image SNR. Less estimation errors of diffusion metrics are observed when using the JRPG-smooth method. This study improves the distortion correction for isotropic high-resolution whole-brain 3D diffusion MRI by optimizing Jacobian modulation. The optimized method outperforms the conventional JField method regarding intensity variation correction and accuracy of diffusion metrics estimation, and outperforms the original JRPG method regarding SNR performance.

Laser Doppler Signal Denoising Based on Wavelet Packet Thresholding Method

In laser Doppler velocimeter (LDV), calculation precision of Doppler shift is affected by noise contained in Doppler signal. In order to restrain the noise interference and improve the precision of signal processing, wavelet packet threshold denoising methods are proposed. Based on the analysis of Doppler signal, appropriate threshold function and decomposition layer number are selected. Heursure, sqtwolog, rigrsure, and minimaxi rules are adopted to get the thresholds. Processing results indicate that signal-to-noise ratio (SNR) and root mean square error (RMSE) of simulated signals with original SNR of 0 dB, 5 dB, and 10 dB in both low- and high-frequency ranges are significantly improved by wavelet packet threshold denoising. A double-beam and double-scattering LDV system is built in our laboratory. For measured signals obtained from the experimental system, the minimum relative error of denoised signal is only 0.079% (using minimaxi rule). The denoised waveforms of simulated and experimental signals are much more smooth and clear than that of original signals. Generally speaking, denoising effects of minimaxi and saqtwolog rules are better than those of heursure and rigrsure rules. As shown in the processing and analysis of simulated and experimental signals, denoising methods based on wavelet packet threshold have ability to depress the noise in laser Doppler signal and improve the precision of signal processing. Owing to its effectiveness and practicability, wavelet packet threshold denoising is a practical method for LDV signal processing.

Boundary Layer Heights as Derived From Ground-Based Radar Wind Profiler in Beijing

The vertical structure of wind is a key factor in modulating air quality, from which the determination of boundary layer height (BLH) remains a major challenge. In this paper, we developed an improved threshold method to determine the BLH from radar wind profiler (RWP) measurements. The normalized signal-to-noise ratio (SNR) profiles were used instead of the original SNR profiles to avoid instrumental inconsistencies. Additionally, a peak filter was designed to indicate the BLH based on the maximum SNR by taking into account the multiple peaks in the SNR profile. This algorithm was then applied to the RWP measurements taken in the summer (June–July–August) of 2018 in Beijing to obtain the BLHs. Validation analyses suggested that the BLH retrievals from RWP exhibited high consistency with those from radiosondes, with an average correlation coefficient of 0.69 (0.66) and a root mean squared error of 0.39 (0.41) in the daytime (nighttime). Additionally, the major features of summertime BLHs in Beijing were examined. In particular, a distinct diurnal variation in BLH was observed with a peak (1630 ± 510 m) occurring at 0600 universal time coordinated (UTC) and a minimum (587 ± 343 m) at 2300 UTC. Therefore, the algorithm presented here has great potential to be applied to other regions to obtain reliable BLHs. The findings obtained here highlight the importance of vertical wind structure in air quality studies.

A Quantitative Intelligent Diagnosis Method for Early Weak Faults of Aviation High-speed Bearings

An intelligent diagnosis method based on support vector machine (SVM) is proposed to quantitatively diagnose early weak faults of high-speed aero-engine’s bearings. In order to achieve a better performance in contrast with conventional kernel function, a mixed kernel function is constructed and particle swarm optimization (PSO) is used to optimize kernel coefficients and other parameters. Experimental raw data is preprocessed by sparse decomposition and reconstruction method to remove noise in original signals, which can provide effective and reliable samples for SVM. In order to verify the validity of the proposed method, experiments on different fault types with different defect sizes of high-speed bearings working at 30000rpm are carried out. The results show that the accuracy of the proposed method is greatly improved compared with traditional SVM. The proposed method can not only distinguish different types of failure but also distinguish different degrees of the same fault pattern, which achieves a quantitative intelligent diagnosis of early weak faults in aviation’s high-speed bearings.

Compressive sensing method to leverage prior information for submerged target echoes.

Reducing data volume and improving signal-to-noise ratio (SNR) is of great importance for echoes from submerged targets, affected by serious marine environment noise. The echo from a target is made of its response to the incident wave with the superposition of highlights (sub-echoes from main constituents of the target). Each of these highlights can be seen as a block, and the echo therefore has a block-sparse feature. This paper proposes a compressive sensing method to leverage prior information (CSPI), in which knowledge of the incident wave and the block-sparse feature are leveraged into the dictionary structure and signal reconstruction. CSPI is illustrated with simulations and field measurements of backscattering for a 1:20 model of the Benchmark Target Strength Simulation Submarine. For simulated signals with different noise levels, CSPI can reconstruct an almost invisible signal (original SNR = 0 dB), and improve SNR by up to 13 dB (for an original SNR of 4 dB) down to a still significant SNR of 7 dB (for an original SNR of 0 dB). For field measurements, CSPI can obtain the same SNR as the original signal using only 13% of the data, increasing the SNR to 15 dB using 30% data, and increasing with the compression ratio.

Application of an Improved Multipoint Optimal Minimum Entropy Deconvolution Adjusted for Gearbox Composite Fault Diagnosis.

The fault feature extraction of gearbox is difficult to achieve under complex working conditions, and this paper presents a hybrid fault diagnosis method for gearbox based on the combining product function (CPF) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) methods. First, ensemble local mean decomposition (ELMD) is utilized to reduce the noise in original signal, and get a series of product functions (PFs), through the correlation coefficient method to remove false components and residual components. Then, multi-point kurtosis of the definition is achieved by calculating the multi-point kurtosis spectrum of each layer PF, and the fault feature period is extracted and the PFs without periodic impact are removed. After that, in order to maintain the integrity of the original signal, the PFs with the same period are recombined by the combined product function method. Finally, the different cycle interval is configured, reduce the noise through MOMEDA on the combined signal, to further extract the fault feature. The method is applied to the feature extraction of gear box composite fault to verify the feasibility of this method.

SNR Enhancement in Phase-Sensitive OTDR with Adaptive 2-D Bilateral Filtering Algorithm

An adaptive image restoration algorithm based on 2-D bilateral filtering has been proposed to enhance the signal-to-noise ratio (SNR) of the intrusion location for phase-sensitive optical time domain reflectometry (Ф-OTDR) system. By converting the spatial and time information of the Ф-OTDR traces into 2-D image, the proposed 2-D bilateral filtering algorithm can smooth the noise and preserve the useful signal efficiently. To simplify the algorithm, a Lorentz spatial function is adopted to replace the original Gaussian function, which has higher practicability. Furthermore, an adaptive parameter setting method is developed according to the relation between the optimal gray level standard deviation and noise standard deviation, which is much faster and more robust for different types of signals. In the experiment, the SNR of location information has been improved over 14 dB without spatial resolution loss for a signal with original SNR of 6.43 dB in 27.6 km sensing fiber. The proposed method has the potential to precisely extract intrusion location from a harsh environment with strong background noise.

Fuji apple storage time rapid determination method using Vis/NIR spectroscopy

Fuji apple storage time rapid determination method using visible/near-infrared (Vis/NIR) spectroscopy was studied in this paper. Vis/NIR diffuse reflection spectroscopy responses to samples were measured for 6 days. Spectroscopy data were processed by stochastic resonance (SR). Principal component analysis (PCA) was utilized to analyze original spectroscopy data and SNR eigen value. Results demonstrated that PCA could not totally discriminate Fuji apples using original spectroscopy data. Signal-to-noise ratio (SNR) spectrum clearly classified all apple samples. PCA using SNR spectrum successfully discriminated apple samples. Therefore, Vis/NIR spectroscopy was effective for Fuji apple storage time rapid discrimination. The proposed method is also promising in condition safety control and management for food and environmental laboratories.

Filtering multifocal VEP signals using Prony’s method

BackgroundThis paper describes use of Prony’s method as a filter applied to multifocal visual-evoked-potential (mfVEP) signals. Prony’s method can be viewed as an extension of Fourier analysis that allows a signal to be decomposed into a linear combination of functions with different amplitudes, damping factors, frequencies and phase angles. MethodBy selecting Prony method parameters, a frequency filter has been developed which improves signal-to-noise ratio (SNR). Three different criteria were applied to data recorded from control subjects to produce three separate datasets: unfiltered raw data, data filtered using the traditional method (fast Fourier transform: FFT), and data filtered using Prony’s method. ResultsFiltering using Prony’s method improved the signals’ original SNR by 44.52%, while the FFT filter improved the SNR by 33.56%. The extent to which signal can be separated from noise was analysed using receiver–operating–characteristic (ROC) curves. The area under the curve (AUC) was greater in the signals filtered using Prony’s method than in the original signals or in those filtered using the FFT. Conclusionfiltering using Prony’s method improves the quality of mfVEP signal pre-processing when compared with the original signals, or with those filtered using the FFT.

Double-sampling to improve signal-to-noise ratio (SNR) of dynamic spectrum (DS) in full spectral range

To balance the signal-to-noise ratio (SNR) of dynamic spectrum (DS) in the full spectral range due to the light intensity and the tissue absorption, we present a double-sampling method which is implemented by two spectral samplings at different integration times. In the first spectral sampling, we obtain a part of DS in the spectral range with high SNR. For the second, we increase the integration time to enhance the sensitivity of DS in original low SNR range. The combinative spectrum of two samplings is defined as the final DS for the continued model input variables. The experimental results demonstrated that the double-sampling method can widen the effective spectral range and significantly improve the SNR of DS.

A Method Based on Wavelet Transform for Mineral Spectral Characteristics Extraction

The basic principle of the wavelet analysis method to extract the mineral spectral absorption characteristics is summarized and different wavelet bases,wavelet decomposition scale extraction effect are analyzed in this paper. Then, the algorithm of mineral spectral absorption characteristics based on wavelet spectral analysis is provided and a large number of experiments are carried out using the spectrums of field rock mineral. Experimental result shows that such a method is less affected by original signal noise and depict the parameters of the mineral spectral absorption feature more precise, which is an ideal spectral feature extraction method compared with the traditional mineral spectral absorption feature extraction method.

Feasibility of similarity coefficient map for improving morphological evaluation of T2* weighted MRI for renal cancer

The purpose of this paper is to investigate the feasibility of using a similarity coefficient map (SCM) in improving the morphological evaluation of T2* weighted (T2*W) magnatic resonance imaging (MRI) for renal cancer. Simulation studies and in vivo 12-echo T2*W experiments for renal cancers were performed for this purpose. The results of the first simulation study suggest that an SCM can reveal small structures which are hard to distinguish from the background tissue in T2*W images and the corresponding T2* map. The capability of improving the morphological evaluation is likely due to the improvement in the signal-to-noise ratio (SNR) and the carrier-to-noise ratio (CNR) by using the SCM technique. Compared with T2*W images, an SCM can improve the SNR by a factor ranging from 1.87 to 2.47. Compared with T2* maps, an SCM can improve the SNR by a factor ranging from 3.85 to 33.31. Compared with T2*W images, an SCM can improve the CNR by a factor ranging from 2.09 to 2.43. Compared with T2* maps, an SCM can improve the CNR by a factor ranging from 1.94 to 8.14. For a given noise level, the improvements of the SNR and the CNR depend mainly on the original SNRs and CNRs in T2*W images, respectively. In vivo experiments confirmed the results of the first simulation study. The results of the second simulation study suggest that more echoes are used to generate the SCM, and higher SNRs and CNRs can be achieved in SCMs. In conclusion, an SCM can provide improved morphological evaluation of T2*W MR images for renal cancer by unveiling fine structures which are ambiguous or invisible in the corresponding T2*W MR images and T2* maps. Furthermore, in practical applications, for a fixed total sampling time, one should increase the number of echoes as much as possible to achieve SCMs with better SNRs and CNRs.

Application of Fractal Dimensions and Fuzzy Clustering to Tool Wear Monitoring

Monitoring of metal cutting tool wear states is a key technology for automatic, unmanned and adaptive machining. As tool wear increases, the vibration signals of cutting tool become more and more irregular in the turning processes. The degree of tool wear can be indirectly monitored according to these changes of vibration signals. In order to quantitatively describe these changes, fractal theory and fuzzy clustering method were introduced into the cutting tool wear monitoring area. Firstly, wavelet de-noising method was used to reduce the noise of original signals, and eliminate the effect of noise on fractal dimensions. Secondly, the fractal dimensions based on fractal theory were got from the de-noised signals, including box dimension, information dimension, and correlation dimension. Finally, the relationship between the fractal dimensions and tool wear states was studied; the affinities between the known and unknown states can be obtained through fuzzy c-mean clustering algorithm; tool wear states can be recognized by those affinities based on fractal dimensions. The experiment results demonstrate that wavelet de-noising method can efficiently eliminate the effect of noise on fractal dimensions, and tool wear states can be real-timely and accurately recognized through the fuzzy clustering analysis on fractal dimensions. DOI: http://dx.doi.org/10.11591/telkomnika.v11i1.1887

Denoising Algorithm Based on Correlation of Inter Scales Wavelet Coefficient for Damage Signal of Wire Rope

In order to remove noise in the broken wire signal of wire rope, a new denoising algorithm based on the correlation of inter scales wavelet coefficient was proposed. Because the wavelet coefficient of signal has strong correlation and the wavelet coefficient of noise does not has obvious correlation, in the process of calculating threshold, an inter scales correlation factor was constructed considering the part specialty of next scale on the same position after wavelet transform. The proposed factor can adaptively adjust threshold on the position of signal and noise, thereby otaining good denoising effect and holding the break information in original signal. The actual processing effect of simulation and damage signal of wire rope shows that the proposed method, compared with the classical threshold, not only effectively removes noise in original signal and improve the signal noise ratio(SNR),but also retains the break information of inspected damage signal of wire rope.

Algorithm Considering Signal Correlation for Denoising of the Magnetic Flux Leakage of Wire Rope

In order to suppress the noise in the magnetic flux leakage, the algorithm considering signal correlation, in which an optimal predictor and an optimal updater were chosen for every sample at all scales according to the magnitude of the correlation coefficient, was proposed. So the wavelets obtained through designing predictor and updater can perfectly fit the local features of signal. The actually processing effect of the simulation and the magnetic flux leakage signal of damage in wire rope show that the proposed method, compared with the classical wavelet, can effectively remove noise in original signal and principally retain the local features of the original signal.

무선 네트워크 환경에서 멀티미디어 서비스를 위한 AP 선정 기법

최근 무선 랜(WLAN : Wireless Local Area Network) 기술이 사용자에게 편리성과 유연성을 제공함에 따라 무선 분야에 대한 관심이 증가하고 있다. 무선 랜 어플리케이션의 웹 접근과 같은 표준화된 인터넷 서비스에서부터 멀티미디어 비디오 및 음성 서비스와 같은 분야에서 매우 엄격한 지연시간/처리율이 요구된다. 따라서, 무선 랜의 효율을 높이기 위해 AP(Access Point)들 간 트래픽의 공정하고 효율적인 부하의 분산이 중요한 문제가 된다. 본 논문에서는 보다 나은 로드 밸런스를 달성하고, 네트워크 리소스 사용률을 증가시키기 위한 AP 선정 기법을 제안한다. 제안된 기법은 부하 측정과 AP 선정의 주요 파라미터로서 활성화 검사 패턴과 네트워크 지연을 사용한다. 또한, 상향 링크/하향링크 지연을 관찰함으로써 AP 트래픽 부하를 예측하고, 로드 밸런스를 통해 링크 자원의 효율성을 극대화하기 위해 예측 결과를 사용한다. 제안하는 기법은 NS-2(Network Simulation-2)를 이용하여 SNR(Signal to Noise Ratio) 기법과 비교하였다. 제안한 기법은 전체 네트워크의 처리율이 12.5% 향상된 것으로 나타났고, 상향링크/하향링크 지연 시간은 36.84%, 60.42%씩 감소되었다. 또한 제안한 기법은 전체 네트워크 처리율을 증가시켰으며, 비디오와 음성서비스를 우수한 품질로 제공하면서 상향링크/하향링크의 지연 시간을 감소시킴을 확인하였다 Recently, there has been a growing interest in the use of WLAN technology due to its easy deployment, flexibility and so on. Examples of WLAN applications range from standard internet services such as Web access to real-time services with strict latency/throughput requirements such as multimedia video and voice over IP on wireless network environments. Fair and efficient distribution of the traffic loads among APs(Access Points) has become an important issue for improved utilization of WLAN. This paper focuses on an AP selection scheme for achieving better load balance, and hence increasing network resource utilization for each user on wireless network environments. This scheme makes use of active scan patterns and the network delay as main parameters of load measurement and AP selection. This scheme attempts to estimate the AP traffic loads by observing the up/down delay and utilize the results to maximize the link resource efficiency through load balancing. We compared the proposed scheme with the original SNR(Signal to Noise Ratio)-based scheme using the NS-2(Network Simulation.2). We found that the proposed scheme improves the throughput by 12.5% and lower the network up/down link delay by 36.84% and 60.42%, respectively. All in all, the new scheme can significantly increase overall network throughput and reduce up/down delay while providing excellent quality for voice and video services.

Feedback Stabilization Over Signal-to-Noise Ratio Constrained Channels

There has recently been significant interest in feedback stabilization problems with communication constraints including constraints on the available data rate. Signal-to-noise ratio (SNR) constraints are one way in which data-rate limits arise, and are the focus of this paper. In both continuous and discrete-time settings, we show that there are limitations on the ability to stabilize an unstable plant over a SNR constrained channel using finite-dimensional linear time invariant (LTI) feedback. In the case of state feedback, or output feedback with a delay-free, minimum phase plant, these limitations in fact match precisely those that might have been inferred by considering the associated ideal Shannon capacity data rate over the same channel. In the case of LTI output feedback, additional limitations are shown to apply if the plant is nonminimum phase. In this case, we show that for a continuous-time nonminimum phase plant, a periodic linear time varying feedback scheme with fast sampling may be used to recover the original SNR requirement at the cost of robustness properties. The proposed framework inherently captures channel noise effects in a simple formulation suited to conventional LTI control performance and robustness analysis, and has potential to handle time delays and bandwidth constraints in a variety of control over communication links problems.

The effect of energy weighting on the SNR under the influence of non-ideal detectors in mammographic applications

This work investigates the effect of the energy-weighting technique on the signal to noise ratio (SNR) response of X-ray imaging detectors. So far in the literature all scintillation-detector characteristics (detection efficiency, conversion efficiency, light-attenuation effects, etc) that degrade image quality have been ignored. A theoretical evaluation of the scintillator's SNR output was carried out. An algorithm was produced to describe the variation of the weighting factor, and SNR, with respect to the anode material (Mo or W), in a particular energy range (25–40 keV), typical for mammography, using two different phantoms. Results show that under non-ideal conditions the ratio of the weighted SNR to the original SNR appears to be increasing from values that are close to unity, and under specific conditions, can reach values up to 30. For the further investigation of this method, a more complex, simulated computed tomography breast imaging system was modeled and studied for various parameters such as breast software phantoms, scintillation materials and reconstruction filters.

A wavelet-based method for improving signal-to-noise ratio and contrast in MR images

Magnetic resonance (MR) images acquired with fast measurement often display poor signal-to-noise ratio (SNR) and contrast. With the advent of high temporal resolution imaging, there is a growing need to remove these noise artifacts. The noise in magnitude MR images is signal-dependent (Rician), whereas most de-noising algorithms assume additive Gaussian (white) noise. However, the Rician distribution only looks Gaussian at high SNR. Some recent work by Nowak employs a wavelet-based method for de-noising the square magnitude images, and explicitly takes into account the Rician nature of the noise distribution. In this article, we apply a wavelet de-noising algorithm directly to the complex image obtained as the Fourier transform of the raw k-space two-channel (real and imaginary) data. By retaining the complex image, we are able to de-noise not only magnitude images but also phase images. A multiscale (complex) wavelet-domain Wiener-type filter is derived. The algorithm preserves edges better when the Haar wavelet rather than smoother wavelets, such as those of Daubechies, are used. The algorithm was tested on a simulated image to which various levels of noise were added, on several EPI image sequences, each of different SNR, and on a pair of low SNR MR micro-images acquired using gradient echo and spin echo sequences. For the simulated data, the original image could be well recovered even for high values of noise (SNR ≈ 0 dB), suggesting that the present algorithm may provide better recovery of the contrast than Nowak’s method. The mean-square error, bias, and variance are computed for the simulated images. Over a range of amounts of added noise, the present method is shown to give smaller bias than when using a soft threshold, and smaller variance than a hard threshold; in general, it provides a better bias-variance balance than either hard or soft threshold methods. For the EPI (MR) images, contrast improvements of up to 8% (for SNR = 33 dB) were found. In general, the improvement in contrast was greater the lower the original SNR, for example, up to 50% contrast improvement for SNR of about 20 dB in micro-imaging. Applications of the algorithm to the segmentation of medical images, to micro-imaging and angiography (where the correct preservation of phase is important for flow encoding to be possible), as well as to de-noising time series of functional MR images, are discussed.

Application of the EM algorithm to radiographic images.

The expectation maximization (EM) algorithm has received considerable attention in the area of positron emitted tomography (PET) as a restoration and reconstruction technique. In this paper, the restoration capabilities of the EM algorithm when applied to radiographic images is investigated. This application does not involve reconstruction. The performance of the EM algorithm is quantitatively evaluated using a "perceived" signal-to-noise ratio (SNR) as the image quality metric. This perceived SNR is based on statistical decision theory and includes both the observer's visual response function and a noise component internal to the eye-brain system. For a variety of processing parameters, the relative SNR (ratio of the processed SNR to the original SNR) is calculated and used as a metric to compare quantitatively the effects of the EM algorithm with two other image enhancement techniques: global contrast enhancement (windowing) and unsharp mask filtering. The results suggest that the EM algorithm's performance is superior when compared to unsharp mask filtering and global contrast enhancement for radiographic images which contain objects smaller than 4 mm.