Separation Algorithm Research Articles

Leak Location for Urban Elbowed Water Pipe Based on Complex-Optimized FastICA Blind Deconvolution

Previous research has primarily focused on leak diagnosis algorithms under the interference of outside environment noise. However, the former algorithms in leak detection are easily affected by noise from the pipe, especially the one that contains elbow noise. This article was mainly concerned with the effective and reliable approach to precisely locate leaks in the buried elbow pipeline system. To overcome the drawback of elbow noise interference and extract the leak source from the mixing signal in the greatest extent, this article proposes a novel frequency-domain-independent component analysis (ICA) blind deconvolution algorithm, which is based on a popular optimized FastICA (O-FICA) called complex-optimized FastICA (CO-FICA) algorithm. In comparison, CO-FICA differs from the time-domain blind convolution separation (T-BCS) algorithm, which transforms complicated time-domain convolution into a simple frequency-domain product problem. Compared with the conventional complex FastICA (C-FICA), the proposed CO-FICA method does not need to estimate signal statistics at each step, but it displays more excellent performance in convergent speed and separated precision. Altogether, the results from this study demonstrate that the localization precision of the proposed method is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx 87\%$ </tex-math></inline-formula> under the interference of elbow noise.

Algorithms for separating diffuse and beam irradiance from data over the East Asia-Pacific region: A multi-temporal-scale evaluation based on minute-level ground observations

Diffuse irradiance and direct normal irradiance are two components that are critical to both energy and ecological systems; however, these are rarely available in most ground observations, which only provide the global horizontal irradiance (GHI), leaving the separation of them from the GHI an inevitable process. Although many separation models have been developed to provide solutions, a large proportion of these has been constructed based on site-specific datasets at different time resolutions, limiting their possibilities to be adopted to other sites without previously being extensively evaluated. In this study, we evaluated a total of 80 separation algorithms with specific attention to their time resolutions, based on the finest minute-scale observations from 13 irradiation sites across six Köppen-Geiger-Photovoltaic climate classification zones in East Asia-Pacific. We found that there was a dramatic difference in the performance of hourly-scale models and that the Starke3 model gave the best performance of the minute-scale models. Further residual analyses revealed that the clearness index, solar zenith angle, and atmospheric parameters played important roles. Since data from the geostationary Himawari-8 satellite are available to provide minute-scale atmospheric parameters, there is the possibility of reducing the residuals by incorporating the information with high temporal resolutions into separation models. The results of this study will provide a more comprehensive understanding of the utilization and improvement of separation models.

An efficient method for parameter estimation and separation of multi-component LFM signals

In this paper, an efficient and robust algorithm for fast parameter estimation and separation of multi-component LFM signals is proposed, which is suitable for processing overlapping and intersect LFM signals in the time-frequency plane. First, the key innovative idea of the algorithm is to use the fourth-order origin moment of fractional Fourier transform spectrum to formulate a reasonable search strategy for the optimal rotation order to achieve parameter estimation, and propose a step-by-step filtering technique for signals in the fractional Fourier domain achieve the signal separation we are interested in. Then, the proposed algorithm overcomes the problems of low estimation accuracy and large computational of traditional two-dimensional search and time-frequency analysis methods by virtue of good impulse characteristics and anti-noise performance. Different from existing solutions, the method avoid interference problems between multiple signals with close time-frequency distance, overlapping and cross components. Moreover, the error analysis of the linear frequency modulation signal is carried out, and the Cramer-Rao lower bound for the unbiased estimation of the parameters is derived. Finally, the simulation results demonstrate the accuracy and effectiveness of the proposed method in noisy environments by comparing with traditional methods and existing methods.

Estimating the neural spike train from an unfused tetanic signal of low-threshold motor units using convolutive blind source separation

BackgroundIndividual motor units have been imaged using ultrafast ultrasound based on separating ultrasound images into motor unit twitches (unfused tetanus) evoked by the motoneuronal spike train. Currently, the spike train is estimated from the unfused tetanic signal using a Haar wavelet method (HWM). Although this ultrasound technique has great potential to provide comprehensive access to the neural drive to muscles for a large population of motor units simultaneously, the method has a limited identification rate of the active motor units. The estimation of spikes partly explains the limitation. Since the HWM may be sensitive to noise and unfused tetanic signals often are noisy, we must consider alternative methods with at least similar performance and robust against noise, among other factors.ResultsThis study aimed to estimate spike trains from simulated and experimental unfused tetani using a convolutive blind source separation (CBSS) algorithm and compare it against HWM. We evaluated the parameters of CBSS using simulations and compared the performance of CBSS against the HWM using simulated and experimental unfused tetanic signals from voluntary contractions of humans and evoked contraction of rats. We found that CBSS had a higher performance than HWM with respect to the simulated firings than HWM (97.5 ± 2.7 vs 96.9 ± 3.3, p < 0.001). In addition, we found that the estimated spike trains from CBSS and HWM highly agreed with the experimental spike trains (98.0% and 96.4%).ConclusionsThis result implies that CBSS can be used to estimate the spike train of an unfused tetanic signal and can be used directly within the current ultrasound-based motor unit identification pipeline. Extending this approach to decomposing ultrasound images into spike trains directly is promising. However, it remains to be investigated in future studies where spatial information is inevitable as a discriminating factor.

The Deterioration Risk Index: Developing and Piloting a Machine Learning Algorithm to Reduce Pediatric Inpatient Deterioration.

Develop and deploy a disease cohort-based machine learning algorithm for timely identification of hospitalized pediatric patients at risk for clinical deterioration that outperforms our existing situational awareness program. Retrospective cohort study. Nationwide Children's Hospital, a freestanding, quaternary-care, academic children's hospital in Columbus, OH. All patients admitted to inpatient units participating in the preexisting situational awareness program from October 20, 2015, to December 31, 2019, excluding patients over 18 years old at admission and those with a neonatal ICU stay during their hospitalization. We developed separate algorithms for cardiac, malignancy, and general cohorts via lasso-regularized logistic regression. Candidate model predictors included vital signs, supplemental oxygen, nursing assessments, early warning scores, diagnoses, lab results, and situational awareness criteria. Model performance was characterized in clinical terms and compared with our previous situational awareness program based on a novel retrospective validation approach. Simulations with frontline staff, prior to clinical implementation, informed user experience and refined interdisciplinary workflows. Model implementation was piloted on cardiology and hospital medicine units in early 2021. The Deterioration Risk Index (DRI) was 2.4 times as sensitive as our existing situational awareness program (sensitivities of 53% and 22%, respectively; p < 0.001) and required 2.3 times fewer alarms per detected event (121 DRI alarms per detected event vs 276 for existing program). Notable improvements were a four-fold sensitivity gain for the cardiac diagnostic cohort (73% vs 18%; p < 0.001) and a three-fold gain (81% vs 27%; p < 0.001) for the malignancy diagnostic cohort. Postimplementation pilot results over 18 months revealed a 77% reduction in deterioration events (three events observed vs 13.1 expected, p = 0.001). The etiology of pediatric inpatient deterioration requires acknowledgement of the unique pathophysiology among cardiology and oncology patients. Selection and weighting of diverse candidate risk factors via machine learning can produce a more sensitive early warning system for clinical deterioration. Leveraging preexisting situational awareness platforms and accounting for operational impacts of model implementation are key aspects to successful bedside translation.

Joint Estimation of Metal Density and Attenuation Maps with Pencil Beam XFET.

X-ray fluorescence emission tomography (XFET) is an emerging imaging modality that images the spatial distribution of metal without requiring biochemical modification or radioactivity. This work investigates the joint estimation of metal and attenuation maps with a pencil-beam XFET system that allows for direct metal measurement in the absence of attenuation. Using singular value decomposition on a simplified imaging model, we show that reconstructing metal and attenuation voxels far from the detector is an ill-conditioned problem. Using simulated data, we develop and compare two image reconstruction methods for joint estimation. The first method alternates between updating the attenuation map with a separable paraboloidal surrogates algorithm and updating the metal map with a closed-form solution. The second method performs simultaneous joint estimation with conjugate gradients based on a linearized imaging model. The alternating approach outperforms the linearized approach for iron and gold numerical phantom reconstructions. Reconstructing an (8 cm)3 object containing gold concentrations of 5 mg/cm3 and an unknown beam attenuation map using the alternating approach yields an accurate gold map (NRMSE = 0.19) and attenuation map (NRMSE = 0.14). This simulation demonstrates an accurate joint reconstruction of metal and attenuation maps, from emission data, without previous knowledge of any attenuation map.

Proximal humerus fracture management and outcomes are distinctly different for individuals 60 years of age or younger: a systematic review

Proximal humerus fractures (PHFs) occur most commonly in an elderly and osteoporotic population, but a considerable proportion of these injuries occur in relatively younger individuals. Differences in treatment principles and outcomes in this younger population remain poorly understood. The purpose of this review was to characterize the treatment algorithms and outcomes for patients less than or equal to 60 years of age with PHFs. A comprehensive search of the Medline, Pubmed, Embase, and Cochrane databases for articles published between January 2005 and December 2020 was performed in January 2021. Levels of evidence I-IV analyzing outcomes (patient reported outcomes and/or complications) following PHFs in adult patients less than or equal to 60 years of age were included. The search was carried out in accordance with the preferred reported items for systematic reviews and meta-analyses guidelines. The risk of bias 2 tool and methodological index for nonrandomized studies score were utilized to evaluate included studies. Fourteen studies met the inclusion criteria (open reduction internal fixation: 5, intramedullary nail: 4, hemiarthroplasty: 2, nonoperative: 1, and reverse total shoulder arthroplasty (RTSA): 1). Seven studies reported differences in outcomes between younger and older patient populations, with three studies noting separate management algorithms for those 60 years of age or younger. There were no studies comparing different treatments modalities in those less than 60 years of age, and the lone study on RTSA did not include patient-reported outcomes. Treatment algorithms and outcomes following PHFs in patients less than or equal to 60 years of age are distinctly different from that of a more elderly population. However, evidence-based treatment recommendations for this younger population are limited by the lack of studies comparing treatment modalities and the absence of patient-reported outcomes for individuals undergoing RTSA.

Learning to Harness Bandwidth With Multipath Congestion Control and Scheduling

Multipath TCP (MPTCP) has emerged as a facilitator for harnessing and pooling available bandwidth in wireless/wireline communication networks and in data centers. Existing implementations of MPTCP such as, Linked Increase Algorithm (LIA), Opportunistic LIA (OLIA) and BAlanced LInked Adaptation (BALIA) include separate algorithms for congestion control and packet scheduling, with pre-selected control parameters. We propose a Deep Q-Learning (DQL) based framework for joint congestion control and packet scheduling for MPTCP. At the heart of the solution is an intelligent agent for interface, learning and actuation, which learns from experience optimal congestion control and scheduling mechanism using DQL techniques with policy gradients. We provide a rigorous stability analysis of system dynamics which provides important practical design insights. In addition, the proposed DQL-MPTCP algorithm utilizes the ‘recurrent neural network’ and integrates it with ‘long short-term memory’ for continuously i) learning dynamic behavior of subflows (paths) and ii) responding promptly to their behavior using prioritized experience replay. With extensive emulations, we show that the proposed DQL-based MPTCP algorithm outperforms MPTCP LIA, OLIA and BALIA algorithms. Moreover, the DQL-MPTCP algorithm is robust to time-varying network characteristics, and provides dynamic exploration and exploitation of paths.

Minimum hydrogen consumption‐based energy management strategy for hybrid fuel cell unmanned aerial vehicles using direction prediction optimal foraging algorithm

AbstractIn hybrid energy storage systems of fuel cell unmanned aerial vehicles (UAVs), achieving energy management while minimizing hydrogen consumption is the main goal for economic aspects and endurance enhancement. The external energy maximization strategy (EEMS) and the equivalent consumption minimization strategy (ECMS) are commonly used energy management strategies. However, they use a gradient descent approach, which converges slowly and does not guarantee the optimal solution. Thus, this paper proposes an optimization method based on a direction prediction optimal foraging algorithm (OFA/DP), which has the advantages of high optimization capability and simple parameter definition. In this study, the hybrid energy storage system comprises fuel cells and lithium‐ion batteries for powering UAVs. To verify the validity of the proposed strategy, it is compared with rule‐based and optimized methods of state machine control, fuzzy logic control based on frequency separation, ECMS, EEMS, and genetic algorithm. The obtained results confirm the superiority of the proposed OFA/DP‐based EEMS method with an efficiency of 88.65% and a minimum hydrogen consumption of 19.06 g. Furthermore, it achieves optimal power distribution and leads to 38.62% minimization in hydrogen consumption.

Features detection of Al alloy porosity during GTAW process based on arc spectrum and improved porosity-focus decision tree

As one of the common internal defects in the aluminum welding process, porosity defect is still challenging for real-time control due to the complexity and diversity of the welding process. Arc spectrum contains large amount of welding process information and is effective for the detection of internal defects. This paper proposed a real-time porosity defect detection method for aluminum alloy in gas tungsten arc welding (GTAW) based on the improved gradient boosting decision tree and arc spectrum. The line and continuum spectrum are separated, and the arc blackbody radiation energy and electron temperature are extracted based on the new spectral separation algorithm. Considering the long-tail distribution characteristic of the training samples, a porosity-focus loss function and a parallel training structure were proposed. More targeted line spectrum features were extracted. The defect recall rate increased by 8.6 %, and the area under the receiver operating characteristic curve (AUC) increased by 5 %, without affecting the detection accuracy and response speed. The confidence level of the prediction results was improved. The model shows better robustness through testing experiments in the face of complex welding conditions and is of great importance to the internal quality monitoring of GTAW.

Attention‐based neural network for end‐to‐end music separation

AbstractThe end‐to‐end separation algorithm with superior performance in the field of speech separation has not been effectively used in music separation. Moreover, since music signals are often dual channel data with a high sampling rate, how to model long‐sequence data and make rational use of the relevant information between channels is also an urgent problem to be solved. In order to solve the above problems, the performance of the end‐to‐end music separation algorithm is enhanced by improving the network structure. Our main contributions include the following: (1) A more reasonable densely connected U‐Net is designed to capture the long‐term characteristics of music, such as main melody, tone and so on. (2) On this basis, the multi‐head attention and dual‐path transformer are introduced in the separation module. Channel attention units are applied recursively on the feature map of each layer of the network, enabling the network to perform long‐sequence separation. Experimental results show that after the introduction of the channel attention, the performance of the proposed algorithm has a stable improvement compared with the baseline system. On the MUSDB18 dataset, the average score of the separated audio exceeds that of the current best‐performing music separation algorithm based on the time‐frequency domain (T‐F domain).

Using general triangle inequalities within quadratic convex reformulation method

We consider the exact solution of Problem (P) which consists in minimizing a quadratic function subject to quadratic constraints. We start with an explicit description of new general triangle inequalities that are derived from the ranges of the variables of (P). We show that they extend the triangle inequalities, introduced for the binary case, to variables that belong to a generic interval. We also prove that these inequalities cut feasible solutions of McCormick envelopes, and we relate them to the literature. We then introduce (SDP), a strong semidefinite relaxation of (P), that we call ‘Shor's plus RLT plus Triangle’, which includes both the McCormick envelopes and the general triangle inequalities. We further show how to compute a convex relaxation whose optimal value reaches the value of (SDP). In order to handle these inequalities in the solution of (SDP), we solve it by a heuristic that also serves as a separation algorithm. We then solve (P) to global optimality with a branch-and-bound based on . Finally, we show that our method outperforms the compared solvers.

A speech separation algorithm based on the comb-filter effect

In the field of speech separation, the traditional single-channel and multi-channel speech separation methods have made great progress. However, the accuracy of separation and automatic speech recognition(ASR) rate are not yet satisfactory. With the development of neural networks, some scholars began to use deep learning to achieve speech separation. Although this kind of method improves the accuracy of speech separation, it also leads to the need for pre-training the model, higher computational complexity and reduced separation performance when the model does not match the mixed signal. This paper has conducted an in-depth study on the scene of multi-speaker separation, and proposed a new dual-channel speech separation algorithm based on the Comb-Filter Effect (CFE). The CFE is an effect that occurs when a signal passes through a first-order differential microphone(FDM) array. And this effect is discovered and exploited for the first time. By using this effect, this paper designed a new signal spectrum estimation method that can realize accurate estimation of speech signal, and combined this method with traditional spectral subtraction to achieve the purpose of speech separation. Finally, this paper compared the proposed algorithm with the traditional FastICA-based algorithm and the fully-convolutional time-domain audio separation network(Conv-TasNet)-based algorithm. The results of simulation and comparison experiments show that the algorithm can effectively separate two-way speech signals while greatly reducing the computational complexity and has excellent robustness. In various situations, the proposed algorithm can obtain the Scale-Invariant Source-to-Noise Ratio improvement (SI-SNRi) of 9.19 dB on average. In addition, the Short-Time Objective Intelligibility (STOI) and Perceptual Evaluation of Speech Quality (PESQ) of the speech signal can be improved by an average of 33% and 70% or more respectively.

A Synthetic Data Generation Technique for Enhancement of Prediction Accuracy of Electric Vehicles Demand

In terms of electric vehicles (EVs), electric kickboards are crucial elements of smart transportation networks for short-distance travel that is risk-free, economical, and environmentally friendly. Forecasting the daily demand can improve the local service provider's access to information and help them manage their short-term supply more effectively. This study developed the forecasting model using real-time data and weather information from Jeju Island, South Korea. Cluster analysis under the rental pattern of the electric kickboard is a component of the forecasting processes. We cannot achieve noticeable results at first because of the low amount of training data. We require a lot of data to produce a solid prediction result. For the sake of the subsequent experimental procedure, we created synthetic time-series data using a generative adversarial networks (GAN) approach and combined the synthetic data with the original data. The outcomes have shown how the GAN-based synthetic data generation approach has the potential to enhance prediction accuracy. We employ an ensemble model to improve prediction results that cannot be achieved using a single regressor model. It is a weighted combination of several base regression models to one meta-regressor. To anticipate the daily demand in this study, we create an ensemble model by merging three separate base machine learning algorithms, namely CatBoost, Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The effectiveness of the suggested strategies was assessed using some evaluation indicators. The forecasting outcomes demonstrate that mixing synthetic data with original data improves the robustness of daily demand forecasting and outperforms other models by generating more agreeable values for suggested assessment measures. The outcomes further show that applying ensemble techniques can reasonably increase the forecasting model's accuracy for daily electric kickboard demand.

Graphs with mixed metric dimension three and related algorithms

&lt;abstract&gt;&lt;p&gt;Let $ G = (V, E) $ be a simple connected graph. A vertex $ x\in V(G) $ resolves the elements $ u, v\in E(G)\cup V(G) $ if $ d_G(x, u)\neq d_G(x, v) $. A subset $ S\subseteq V(G) $ is a mixed metric resolving set for $ G $ if every two elements of $ G $ are resolved by some vertex of $ S $. A set of smallest cardinality of mixed metric generator for $ G $ is called the mixed metric dimension. In this paper trees and unicyclic graphs having mixed dimension three are classified. The main aim is to investigate the structure of a simple connected graph having mixed dimension three with respect to the order of graph, maximum degree of basis elements and distance partite sets of basis elements. In particular to find necessary and sufficient conditions for a graph to have mixed metric dimension 3. Moreover three separate algorithms are developed for trees, unicyclic graphs and in general for simple connected graph $ J_{n}\ncong P_{n} $ with $ n\geq 3 $ to determine "whether these graphs have mixed dimension three or not?". If these graphs have mixed dimension three, then these algorithms provide a mixed basis of an input graph.&lt;/p&gt;&lt;/abstract&gt;

A linear-time certifying algorithm for recognizing generalized series–parallel graphs

The problems of recognizing series–parallel graphs, outerplanar graphs, and generalized series–parallel graphs have been studied separately in the past. Efficient algorithms have been presented. However, none of the algorithms are certifying. A certifying algorithm generates, in addition to its answer, a certificate that can be used by a checker (a separate algorithm) to verify the correctness of the answer. The certificate is positive if the answer is ‘yes’, and is negative if the answer is ‘no’. In this paper, an O ( | E | + | V | ) -time certifying algorithm that simultaneously determines if a graph G = ( V , E ) is series–parallel, outerplanar, or generalized series–parallel is presented. The positive certificates are a construction sequence for constructing G if G is series–parallel, a generalized construction sequence for constructing G if G is generalized series–parallel but not series–parallel, and the edge set of the exterior boundary of an outerplanar embedding of G if G is outerplanar. The negative certificates are forbidden subgraphs or forbidden structures of G . All these certificates are generated by making only one pass over G after a preprocessing step decomposing G into its biconnected components.

Frequency-Domain Convolutive Bounded Component Analysis Algorithm for the Blind Separation of Dependent Sources

Frequency-Domain Convolutive Bounded Component Analysis Algorithm for the Blind Separation of Dependent Sources

SwISS: A scalable Markov chain Monte Carlo divide‐and‐conquer strategy

Divide‐and‐conquer strategies for Monte Carlo algorithms are an increasingly popular approach to making Bayesian inference scalable to large data sets. In its simplest form, the data are partitioned across multiple computing cores and a separate Markov chain Monte Carlo algorithm on each core targets the associated partial posterior distribution, which we refer to as a sub‐posterior, that is, the posterior given only the data from the segment of the partition associated with that core. Divide‐and‐conquer techniques reduce computational, memory and disk bottle necks but make it difficult to recombine the sub‐posterior samples. We propose SwISS: Sub‐posteriors with Inflation, Scaling and Shifting; a new approach for recombining the sub‐posterior samples, which is simple to apply, scales to high‐dimensional parameter spaces and accurately approximates the original posterior distribution through affine transformations of the sub‐posterior samples. We prove that our transformation is asymptotically optimal across a natural set of affine transformations and illustrate the efficacy of SwISS against competing algorithms on synthetic and real‐world data sets.

Early Screening of Autism Spectrum Disorder Diagnoses of Children Using Artificial Intelligence

In today’s society, with fast-growing case rates, medical expenditures, social implications, and lengthy waiting periods after the first screening, there is a need for early screening that is both simple and effective for children who may be at risk for autism spectrum disorder (ASD). This is of utmost significance in light of the significant rise in the case rates of ASDs, as well as the associated medical expenses and social effects, in the contemporary world. In this study, utilizing methods from machine learning, a system was constructed, which was effective in obtaining high performance for identifying the early indicators of ASD in children. The study was carried out by the authors of this paper. The purpose of this research is to categorize ASD data in order to give a fast, easily available, and simple method for supporting the early identification of ASD. It was suggested to use machine learning methods, such as k-nearest neighbors, linear discriminant analysis, the support vector machine (SVM) method, and the random forests (RF) method, to divide populations into those who have ASD and those who do not have it. These machine learning algorithms were examined and tested using standard data collected from the machine learning repository, which contains two classes: normal and autism. The dataset was split into a training portion of 80% and a testing portion of 20%. In their separate testing, both the SVM and RF algorithms achieved a level of accuracy that was exceptional (100%). In addition, the sensitivity analysis method was used to estimate the amount of inaccuracy that would be present between the values that were intended to be achieved and the values that were predicted. The findings of the sensitivity analysis revealed that both SVM and RF had an R 2 = 100% in both the phases. When the results obtained were compared with those of the current systems, it was found that the suggested algorithms performed better than that of existing systems. It is very important to diagnose ASD as early as possible. The machine learning algorithms obtained a high level of accuracy in the diagnosis of ASD. When it comes to the categorization of ASD data, the SVM and RF approaches exhibit the best results among the two different classification approaches.

A Novel Two-Stage Destriping Algorithm Based on MWIR Energy Separation and Image Guidance (MES-IG)

Long-wave infrared (LWIR) bands in multispectral datasets are extremely useful in many applications. However, the LWIR bands usually suffer from undesirable stripe noise, which impedes their further application. Compared with emission-dominated LWIR, the mid-wave infrared (MWIR) bands containing both emitted and reflected radiation usually exhibit higher image quality. In this paper, we propose a novel two-stage MWIR energy separation and image guidance (MES-IG) algorithm to destripe the LWIR images with the assistance of the MWIR bands. In the first stage, we decompose the MWIR image into the emitted and reflected components by solving a constrained optimization problem. Specifically, we impose the low-rank penalty to enforce the similarities between MWIR and LWIR, and we use the total variation (TV) regularization to exploit the similarities between MWIR and visible and near-infrared (VNIR) images. In the second stage, the obtained emitted component of MWIR is considered as the guidance image to remove the stripes in the LWIR images by adopting the 1-D guided filter algorithm. Numerical experiments on Chinese Gaofen-5 satellite and MODIS data demonstrate the utility of the proposed method in providing improved LWIR image destriping performance over the state-of-the-art algorithms.