Conventional Filtering Methods Research Articles

Hadamard transformation based frequency response mismatch calibration for a 4-channel TIADC

The time-interleaved analog-to-digital converter (TIADC) is one of the most effective solutions for improving the sampling rate of acquisition systems. However, the frequency response mismatch between channels can seriously degrade the performance of the system. In this paper, we propose a calibration method for frequency response mismatches in 4-channel TIADC systems. Following the construction of a four-channel TIADC model using a four-order Hadamard matrix, the error signals were extracted using three time-invariant filters and subsequently subtracted from the output to complete the calibration. It uses the Hadamard transform instead of the complex exponential modulator used in conventional time-varying filter methods, which can further simplify the signal processing without the necessity of complicated calculations. We have employed a stimulated four-channel TIADC system to validate the effectiveness of the proposed calibration technique. Moreover, in a commercial 12.5 GSPS four-channel TIADC system, it is also demonstrated that this approach is effective in improving the spurious-free dynamic range of the system.

Convolutional neural networks combined with conventional filtering to semantically segment plant roots in rapidly scanned X-ray computed tomography volumes with high noise levels

BackgroundX-ray computed tomography (CT) is a powerful tool for measuring plant root growth in soil. However, a rapid scan with larger pots, which is required for throughput-prioritized crop breeding, results in high noise levels, low resolution, and blurred root segments in the CT volumes. Moreover, while plant root segmentation is essential for root quantification, detailed conditional studies on segmenting noisy root segments are scarce. The present study aimed to investigate the effects of scanning time and deep learning-based restoration of image quality on semantic segmentation of blurry rice (Oryza sativa) root segments in CT volumes.ResultsVoxResNet, a convolutional neural network-based voxel-wise residual network, was used as the segmentation model. The training efficiency of the model was compared using CT volumes obtained at scan times of 33, 66, 150, 300, and 600 s. The learning efficiencies of the samples were similar, except for scan times of 33 and 66 s. In addition, The noise levels of predicted volumes differd among scanning conditions, indicating that the noise level of a scan time ≥ 150 s does not affect the model training efficiency. Conventional filtering methods, such as median filtering and edge detection, increased the training efficiency by approximately 10% under any conditions. However, the training efficiency of 33 and 66 s-scanned samples remained relatively low. We concluded that scan time must be at least 150 s to not affect segmentation. Finally, we constructed a semantic segmentation model for 150 s-scanned CT volumes, for which the Dice loss reached 0.093. This model could not predict the lateral roots, which were not included in the training data. This limitation will be addressed by preparing appropriate training data.ConclusionsA semantic segmentation model can be constructed even with rapidly scanned CT volumes with high noise levels. Given that scanning times ≥ 150 s did not affect the segmentation results, this technique holds promise for rapid and low-dose scanning. This study offers insights into images other than CT volumes with high noise levels that are challenging to determine when annotating.

Rapid, contamination-less, and efficient environmental DNA filtration system

Due to the sporadic distribution and trace amount of environmental DNA (eDNA) in deep-sea water, in the context of biodiversity monitoring, large volumes of filtration and multiple filtration replicates are required for eDNA metabarcoding. To address issues tied to the use of multiple filtration devices and large filtration volumes (e.g., contamination, time consumption, etc.), we have developed two systems for simple, rapid, and contamination-less filtration simultaneously that allow for the processing of multiple sample replicates from large volumes of water. First, the water from a Niskin bottle was filtered directly using a solenoid pump. Second, the pumped deep-sea water, using the siphon effect, was directly filtered by a filtration device driven by water pressure. This system can process 24 replicates simultaneously without the need for expensive equipment and active driving force. Compared with conventional filtering methods, e.g., peristaltic pumps, the proposed systems reduce filtration time, minimizing contamination, and enabling the simultaneous acquisition of multiple replicates. Overall, the systems presented here provide an effective approach for eDNA metabarcoding analysis, particularly for the filtration of large volumes of water containing small amounts of eDNA, such as deep-sea water.•The present systems reduce filtration time and contamination without water having to be transferred.•Simultaneous multiple replicates improve the efficiency and reliability of biodiversity assessments.

Cooperative Vehicle Localization in Multi-Sensor Multi-Vehicle Systems Based on an Interval Split Covariance Intersection Filter with Fault Detection and Exclusion

In the cooperative multi-sensor multi-vehicle (MSMV) localization domain, the data incest problem yields inconsistent data fusion results, thereby reducing the accuracy of vehicle localization. In order to address this problem, we propose the interval split covariance intersection filter (ISCIF). At first, the proposed ISCIF method is applied to the absolute positioning step. Then, we combine the interval constraint propagation (ICP) method and the proposed ISCIF method to realize relative positioning. Additionally, in order to enhance the robustness of the MSMV localization system, a Kullback–Leibler divergence (KLD)-based fault detection and exclusion (FDE) method is implemented in our system. Three simulations were carried out: Simulation scenarios 1 and 2 aimed to assess the accuracy of the proposed ISCIF with various capabilities of absolute vehicle positioning, while simulation scenario 3 was designed to evaluate the localization performance when faults were present. The simulation results of scenarios 1 and 2 demonstrated that our proposed vehicle localization method reduced the root mean square error (RMSE) by 8.9% and 15.5%, respectively, compared to the conventional split covariance intersection filter (SCIF) method. The simulation results of scenario 3 indicated that the implemented FDE method could effectively reduce the RMSE of vehicles (by about 55%) when faults were present in the system.

Image denoising in acoustic microscopy using block-matching and 4D filter

Scanning acoustic microscopy (SAM) is a label-free imaging technique used in biomedical imaging, non-destructive testing, and material research to visualize surface and sub-surface structures. In ultrasonic imaging, noises in images can reduce contrast, edge and texture details, and resolution, negatively impacting post-processing algorithms. To reduce the noises in the scanned image, we have employed a 4D block-matching (BM4D) filter that can be used to denoise acoustic volumetric signals. BM4D filter utilizes the transform domain filtering technique with hard thresholding and Wiener filtering stages. The proposed algorithm produces the most suitable denoised output compared to other conventional filtering methods (Gaussian filter, median filter, and Wiener filter) when applied to noisy images. The output from the BM4D-filtered images was compared to the noise level with different conventional filters. Filtered images were qualitatively analyzed using metrics such as structural similarity index matrix (SSIM) and peak signal-to-noise ratio (PSNR). The combined qualitative and quantitative analysis demonstrates that the BM4D technique is the most suitable method for denoising acoustic imaging from the SAM. The proposed block matching filter opens a new avenue in the field of acoustic or photoacoustic image denoising, particularly in scenarios with poor signal-to-noise ratios.

Feasibility Study of Block-Matching and 3D Filtering Algorithm in Low-Dose CT Image with Tin Filter

The purpose of this study was to confirm the utility of block-matching and 3D filtering (BM3D) algorithm in computed tomography (CT) images using a tin filter with a high pitch. We acquired phantom images and measured the coefficient of variation (COV) and contrast-to-noise ratio (CNR) for quantitative evaluation. The optimized results were obtained when the sigma value of the BM3D algorithm was nine. In addition, when the sigma value of the optimized BM3D algorithm was applied, superior results were obtained compared with conventional filtering methods. In particular, we confirmed that the COV and CNR of the images obtained using the BM3D algorithm improved 6.52 and 5.49 times, respectively. In conclusion, the utility of the optimized BM3D algorithm has been demonstrated in low-dose CT images using a tin filter.

Detection of Quantum Signals Free of Classical Noise via Quantum Correlation.

Extracting useful signals is key to both classical and quantum technologies. Conventional noise filtering methods rely on different patterns of signal and noise in frequency or time domains, thus limiting their scope of application, especially in quantum sensing. Here, we propose a signal-nature-based (not signal-pattern-based) approach which singles out a quantum signal from its classical noise background by employing the intrinsic quantum nature of the system. We design a novel protocol to extract the quantum correlation signal and use it to single out the signal of a remote nuclear spin from its overwhelming classical noise backgrounds, which is impossible to be accomplished by conventional filter methods. Our Letter demonstrates the quantum or classical nature as a new degree of freedom in quantum sensing. The further generalization of this quantum nature-based method opens a new direction in quantum research.

Using ground coffee particle size and distribution to remodel beverage properties

This study examined the influence of ground coffee granulometry and particle distribution on extraction parameters. They have been investigated the physicochemical properties, and the bioactive and volatile compound content in coffee obtained by a conventional filter method, the French Press, as a function of particle size and distribution. Some samples have been used for the extraction the directly the grinding machine, set at different grinding grade, and other samples have been seed before the usage in order to reproduce samples at different particle size class very homogeneous. The results showed that bioactive and volatile compounds are released differently in the beverages depending on the specific particle size. The results have been demonstrated that a homogeneous grind was more deficient in bioactive compounds and total dissolved solids than a classical, bimodal grind. Moreover, extraction from a very fine homogeneous grind was poorest with respect to these compounds, despite the greater surface in contact with the solvent. Conversely, bimodal grinds obtained conventional by the grinding machine, which were more heterogeneous from a granulometric point of view, were found to be richer in volatile organic and bioactive compounds. The study highlights that the grind plays a key role in producing well-extracted coffee and, therefore, in making the most of the potential inherent in the roasted bean.

Image restoration of luminous objects in dense fog

Images captured in dense fog are always severely degraded. For luminous objects, the luminance and contrast of the image are severely reduced due to the strong absorption of suspended particles. In this case, owing to the influence of forward scattered light and detector noise, the signal-to-noise ratio of the image is greatly reduced. Traditional image dehazing algorithms will enhance the noise while improving the image contrast. Here a dynamic equation-based filter is proposed, which can strongly suppress speckle and noise while improving target contrast. Based on this filter, we design an image restoration method for luminous targets in dense fog. The effectiveness of the proposed method is verified by experiments. The experimental results show that the proposed method has a good recovery effect on the luminous targets in dense fog with an optical thickness of 4.51. The proposed method has better performance than the traditional dehazing algorithms and conventional filtering method.

Optimization of block-matching and 3D filtering (BM3D) algorithm in brain SPECT imaging using fan beam collimator: Phantom study

The purpose of this study is to model and optimize the block-matching and 3D filtering (BM3D) algorithm and to evaluate its applicability in brain single-photon emission computed tomography (SPECT) images using a fan beam collimator. For quantitative evaluation of the noise level, the coefficient of variation (COV) and contrast-to-noise ratio (CNR) were used, and finally, a no-reference-based evaluation parameter was used for optimization of the BM3D algorithm in the brain SPECT images. As a result, optimized results were derived when the sigma values of the BM3D algorithm were 0.15, 0.2, and 0.25 in brain SPECT images acquired for 5, 10, and 15 s, respectively. In addition, when the sigma value of the optimized BM3D algorithm was applied, superior results were obtained compared with conventional filtering methods. In particular, we confirmed that the COV and CNR of the images obtained using the BM3D algorithm were improved by 2.40 and 2.33 times, respectively, compared with the original image. In conclusion, the usefulness of the optimized BM3D algorithm in brain SPECT images using a fan beam collimator has been proven, and based on the results, it is expected that its application in various nuclear medicine examinations will be possible.

Application feasibility of median modified Wiener filter (MMWF) algorithm for customized breast ACR phantom images using 3D printing technology in mammography

The median modified Wiener filter (MMWF) algorithm, which is a combination of median and Wiener filters, was applied to reduce the noise signal contained in digital image systems. In this study, we investigated the effect of MMWF algorithm with mask sizes of 3 × 3, 5 × 5, 7 × 7, and 9 × 9 and compared it to the median and Wiener filters with a breast American College of Radiology (ACR) phantom designed using 3D printing technology. To compare the similarity between the conventional and 3D printed breast ACR phantoms at various tube voltages and tube currents, the peak signal-to-noise (PSNR) ratio was recorded. Then, the noise level of the mammographic X-ray image applied with the MMWF algorithm using various mask sizes and the conventional filtering methods were compared and evaluated. According to the results, when the MMWF algorithm using a mask size of 9 × 9 was applied to the X-ray phantom image, superior signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and coefficient of variation (COV) values were derived. In addition, by comparing the average values obtained using the conventional noise reduction methods, we confirmed that the SNR, CNR, and COV values obtained from the MMWF algorithm, using a 9 × 9 mask size, improved by 1.91, 2.29, and 4.77 times, respectively. In conclusion, applicability of the mammographic X-ray field after ACR phantom production using 3D printing technology was proven in this study. An optimal mask size of 9 × 9 for the MMWF algorithm was proposed for improving the image quality of the acquired image.

Noise level and similarity evaluations of non-local means algorithm in chest digital tomosynthesis X-ray imaging system: An experimental study

As a method for diagnosing chest lesions, a chest digital tomosynthesis (CDT) X-ray imaging system based on projection data using a limited angle has been widely used in the medical field since its development. In these CDT X-ray images, noise reduction methods using software that reduce the accuracy of lesion detection are essential. The aim of this study is to model the non-local means (NLM)-based algorithm, which is known to be very effective in removing noise from X-ray images, and to confirm its applicability in the CDT system. CDT X-ray images using a human phantom were generated employing the filtered back-projection reconstruction method with a projection angle of ±36.15°. For quantitative evaluation, a reference image was obtained using the full radiation dose, and a noisy image was obtained using the minimum radiation dose to increase the amount of noise. The NLM noise reduction algorithm was modeled as a method for obtaining weights based on the similarity of the neighboring set of pixels. Conventional filtering methods were used in the comparison group to analyze the efficiency of the NLM algorithm. As a result, we confirmed that the coefficient of variation of the noise level was improved about 9.74 times compared with the noisy image when the NLM noise reduction algorithm was applied to the CDT X-ray image. When the similarity evaluation parameters were measured, we proved that 11%–53% better values were derived compared with the noisy image in the NLM algorithm. In particular, the performance of the NLM algorithm showed superior results to those obtained with the conventional filtering methods. In conclusion, the applicability of the NLM noise reduction algorithm to CDT X-ray images using limited projection data was demonstrated.

Low complexity state-of-charge estimation for lithium-ion battery pack considering cell inconsistency

The estimation accuracy of State-of-Charge (SOC) has great significance for cell charge/discharge management, balance control, and safety management. However, some inconsistencies among cells may lead to model mismatch, which will have a significant impact on the estimation results. Furthermore, it is difficult to achieve an accurate SOC estimation with less computational burden when the battery pack contains hundreds (or thousands) of cells. This paper constructs a battery pack model that consists of a mean-model and several difference-models. The mean-model represents the overall performance of the battery pack. The difference-model describes the inconsistencies of SOC, internal resistance, and coulombic efficiency among cells. In addition, a joint algorithm is proposed to estimate SOC with low complexity. It includes a dual time-scale adaptive extended Kalman filter (AEKF) and a SOC correction method. Comparing with the conventional filter method of SOC estimation, it can effectively simplify a large number of matrix operations, and greatly reduce the overall computational complexity. The experimental results show that the established model can well describe the dynamic characteristics of the battery pack, and the proposed method can track the changing of SOC at a low computational cost. ● The CE inconsistency among cells is further considered in modeling. ● The estimated SOC tracks the changing of actual value well. ● The method is proposed to estimate the SOCs of many cells rapidly. ● The method requires low computation cost. ● The method is suitable for the real-time application.

Characterization of Airborne Particulates and Worker Exposures in Electro-static Powder Coating Operations.

This study was to examine the airborne particulates/dusts and worker exposures in electro-static powder coating operations. Temporal variation and size distribution of dusts were assessed using direct-reading instrument, while the worker task-specific dust exposures (n = 41) were measured in three factories using conventional filter method. Additionally, toxic metal levels in the coating powder (n = 27) were analyzed to assess hazard potentials. Continuous monitoring of airborne respirable dust levels indicated large temporal variation across a normal working day. The particulate levels outside spraying booths were generally low (below 0.10 mg m-3) during normal spraying operation, but short-term level may reach as high as 21.8 mg m-3 (averaged at 4-5 mg m-3) when specific task such as cleaning the pipelines and spraying booth, was performed. Exploratory assessment on particle size distribution at four major working zones, administration, uploading, spraying, and baking, indicated no major difference among the zones, and between factories. The particle count median aerodynamic diameter was 0.88-1.69 µm, while the mean number concentrations were 10.97, 15.17, 18.49, and 27.03 (# cm-3) for zones administration, uploading, spraying, and baking, respectively. The worker task-specific dust exposures were found low during normal spraying, with the mean short-term total and respirable dust concentrations being 0.88 and 0.41 mg m-3, respectively. Both levels increased to 3.48 and 5.19 mg m-3, respectively (P < 0.05), if powder changing and system cleaning were done. Short-term total and respirable dust concentrations were 94.2 and 56.1 mg m-3, respectively, when complete cleaning process was performed inside spraying booth. Both levels exceeded the short-term exposure limits. The mean concentration of metals in the powder samples were 6.78, 9.52, 10 717, 2805, 1020, and 1361 µg g-1 for Pb, Cr (total), Al, Fe, Cu, and Sr, respectively. This study demonstrated that cleaning of spraying booth resulted in high dust exposures well exceeding the exposure limit. Toxic metals, particularly Pb, Cd, and Cr (total), levels measured in the powder samples were found low, and the resulting exposures were below the exposure limits, indicating relative low risks associated with the exposure at current dust levels.

Robust singular value decomposition filtering for low signal-to-noise ratio seismic data

Reduced-rank filtering is a common method for attenuating noise in seismic data. Because conventional reduced-rank filtering distinguishes signals from noises only according to singular values, it performs poorly when the signal-to-noise ratio (S/N) is very low or when data contain high levels of isolate or coherent noise. Therefore, we have developed a novel and robust reduced-rank filtering method based on singular value decomposition in the time-space domain. In this method, noise is recognized and attenuated according to the characteristics of the singular values and the singular vectors. The left and right singular vectors corresponding to large singular values are selected first. Then, the right singular vectors are classified into different categories according to their curve characteristics, such as jump, pulse, and smooth. Each kind of right singular vector is related to a type of noise or seismic event, and it is corrected by using a different filtering technology, such as mean filtering, edge-preserving smoothing, or edge-preserving median filtering. The left singular vectors are also corrected by using the filtering methods based on frequency attributes such as main frequency and frequency bandwidth. To process seismic data containing a variety of events, local data are extracted along the local dip of the event. The optimal local dip is identified according to the singular values and singular vectors of the data matrices that are extracted along different trial directions. This new filtering method has been applied to synthetic and field seismic data, and its performance is compared with that of several conventional filtering methods. The results indicate that the new method is more robust for data with a low S/N, strong isolated noise, or coherent noise. The new method also overcomes the difficulties associated with selecting an optimal rank.

Harmonic Inversion Based Posterirori Minimization in HPA for 5G Communication Framework

The Number of Android Mobile (Voice, Mobile Data, Internet) users getting increased year by year, But there is a major scarcity of operating frequency for the users. Even though many spectral efficient techniques like OFDMA and NOMA are introduced to manage the spectral scarcity. In 5G communication, various frameworks play a vital role in peer-to-peer communication and cloud-based network frameworks. It required errorless signal transmission to achieve the goal of a high-quality signal. The harmonic cancellation or reduction process is the critical task in the 5G communication application. Post error detection and correction is a highly complex process to reduce harmonic distortions. The real time interference problems due to harmonics still exist. With the introduction of 5G Mobile Communication there is an exponential increase of radiations from communication equipment (BTS, Mobiles) due to massive MIMO antennas, mixers, H-PA (High-Power-Amplifier). To reduce the above effects, a modified V-LMS linearizer has been introduced instead of the conventional non-linear filter methods (N-LMS, RLS). V-LMS not only estimates the gain error (priori) of the HPA, but also introduces very low additive error (posteriori). Look up table (LUT) is used to distribute the digital data to the arithmetic units of V-LMS. High speed operation can able to reduce the complexity of the overall system. Multilevel 2D-LUT can perform much faster in proposed V-LMS algorithm. Digital data process through LUT is used to perform accurate error estimation and it reduce the adaptive error in the proposed V-LMS. The proposed has been executed through the 64-QAM non-linear modulated signal with amplifier operating at 1.89–2.32 GHz.

A New Method of LCL Filter Parameter Design Based on Fish Swarm Algorithm

Aiming at the problem that LCL filter is better than L, LC filter in filtering performance. However, the selection of parameters is more complicated, and it is prone to resonant spikes in the filtering process, this study introduces passive damping to measure its resonant spikes suppression, and combined with the attenuation of the grid-connected harmonic current of the LCL filter to measure its excellent filtering performance. Based on this, the objective function model is established, the fish swarm algorithm is adopted to optimize its parameters, and the grid-connected simulation model is found in MATLAB/Simulink. Its filtering effect is compared with the traditional method. The simulation proves that the filter after parameter optimization can effectively suppress and filter the harmonic components of the inverter output voltage compared with the conventional filtering method, and provide positive theoretical guidance for the stable grid connection of new energy power generation.

Low-frequency noise suppressing of desert seismic data by improved nonlinear autoregressive with external input neural network

Low-frequency signal and noise are the main components of seismic data in Northwest China desert area. To remove noise and preserve data details, we introduce a method of nonlinear autoregressive with external input neural network (NARX) which is based on recent developments in nonlinear time series prediction and improve its performance according to signal detection theory. The input of NARX is a noisy seismic signal, the output is original predicted background noise, and then a threshold is set for the output to eliminate the waveform distortions. The residual error of the input and the output is filtered signal. We test the proposed method on synthetic and field seismic data and compare it with some conventional filtering methods (wavelet denoising method, f–x filter and complex diffusion filtering). The results prove that the proposed method can greatly attenuate low-frequency noise, and preserve data details as completely as possible.

Assignment of PM2.5 sources in western Japan by non-negative matrix factorization of concentration-weighted trajectories of GED-ICP-MS/MS element concentrations.

Rapid economic growth in Asian countries has raised concerns about the influence of air pollutants transported to Japan by westerly winds. We coupled a gas exchange device (GED) with a tandem inductively coupled plasma mass spectrometer (ICP-MS/MS) to enable direct introduction of PM2.5 to ICP and thus provide better data than could be obtained from samples collected by conventional filter methods. We used the GED-ICP-MS/MS system in Matsue City in western Japan to monitor in real time 29 elements in PM2.5 at 10-min intervals and to estimate the pollutant sources by non-negative matrix factorization (NMF) of concentration-weighted air-mass trajectories. The trajectory analysis identified high V, As, Sn, and Sb concentrations over the ocean from Taiwan to Tsushima Strait. NMF analysis revealed that these elements could be decomposed to multiple factors that indicated a large contribution from oceanic areas. The elemental contributions of these factors were high for metals/metalloids with low melting points as oxides, strongly suggesting that they were sourced from combustion of ship fuel. Our results demonstrate that both emissions from ships at sea and land-based emissions from Japan and continental Asia contribute to PM2.5 in Matsue City.

An unbiased carrier-phase anti-interference filter based on mirror frequency amplitude compensation

Anti-interference and high-precision measurement are two important indicators of the performance of a satellite navigation receiver. However, current receiver designs do not simultaneously satisfy these two criteria. While the carrier-phase ranging technique is necessary for high-precision receivers, frequency domain interference suppression (FDIS) results in tracking error biases for nonideal analog receiver channels. Importantly, as the FDIS filter is adaptive, the bias will vary with the jamming pattern, particularly when the frequency of interference varies. For precision navigation applications, this bias must be mitigated. Therefore, a new FDIS filter based on the mirror frequency amplitude compensation (MFAC) method is proposed in this paper. The amplitude at the symmetry position of the notch frequency is doubled in the MFAC method to mitigate this carrier-phase bias. The simulation results showed that the MFAC method can reduce the range of the carrier-phase bias by more than 60% for different interference bandwidths, which substantially exceeds that achieved using the conventional FDIS and calibration filter methods of 20 orders.