Decomposition Of Echoes Research Articles

Full-waveform LiDAR echo decomposition method based on deep learning and sparrow search algorithm

For the full-waveform LiDAR echo, the conventional decomposition method is to judge the number of waveform components in the echo through the IP (inflection point) method or RL (Richardson-Lucy) deconvolution method after filtering and obtain the initial estimation of their parameters, and then use the intelligent optimization algorithm to fit the waveform. However, this preprocessing method has poor accuracy in judging the echo components when the distance between targets is small. In this paper, a highly accurate decomposition method is proposed based on LSTM with SSA (Sparrow Search Algorithm). Firstly, the LSTM network trained by the simulation data sets under three kinds of background noise is used to judge the number of Gaussian components in the full-waveform LiDAR echo, and then SSA is used for waveform fitting. The accuracy rate of each LSTM network is more than 95%. This method is compared with IP, RL and MGD (multi-Gaussian decomposition) method. Within the accuracy of 0.1 m, the minimum decomposition distance of LiDAR echo is shorten from 0.7 m to 0.45 m compared with IP method and from 0.55 m to 0.45 m compared with RL and MGD method. The minimum ranging distance of LiDAR echo is shorten from 0.75 m to 0.65 m compared with IP and RL method and from 0.85 m to 0.65 m compared with MGD method. With proposed method, the accuracy of full-waveform LiDAR echo decomposition is improved and distance limit of decomposition is reduced.

LiDAR Echo Gaussian Decomposition Algorithm for FPGA Implementation.

As the existing processing algorithms for LiDAR echo decomposition are time-consuming, this paper proposes an FPGA-based improved Gaussian full-waveform decomposition method. The proposed FPGA architecture consists of three modules: (i) a pre-processing module, which is used to pipeline data reading and Gaussian filtering, (ii) the inflection point coordinate solution module, applied to the second-order differential operation and to calculate inflection point coordinates, and (iii) the Gaussian component parameter solution and echo component positioning module, which is utilized to calculate the Gaussian component and echo time parameters. Finally, two LiDAR datasets, covering the Congo and Antarctic regions, are used to verify the accuracy and speed of the proposed method. The experimental results show that (i) the accuracy of the FPGA-based processing is equivalent to that of PC-based processing, and (ii) the processing speed of the FPGA-based processing is 292 times faster than that of PC-based processing.

Full-waveform LiDAR echo decomposition based on dense and residual neural networks.

For full-waveform (FW) LiDAR signals, conventional echo decomposition methods use complicated filtering or de-noising algorithms for signal pre-processing. However, the speed and accuracy of these algorithms are limited. In this paper, we study a highly efficient and accurate decomposition method based on the FW dense connection network (FDCN) or FW deep residual network (FDRN). FDCN is a lightweight and efficient network for SNR higher than 24 dB, while FDRN is a deeper neural network with multiple residual blocks and works well for low SNR such as 12 dB. We compare FDCN and FDRN with other conventional methods. With FDCN and FDRN, the mean error for estimating an echo peak location is under 0.2 ns, while the amplitude error is under 5 mV when the dynamic range is 0∼100mV. Both errors are much lower than the values using conventional methods.

End-to-End Full-Waveform Echo Decomposition Based on Self-Attention Classification and U-Net Decomposition

Different from conventional decomposition methods which utilize several steps to obtain the final result, a self-attention based neural network, Attention Full-waveform Decomposition Network (AFD-Net), is discussed in this paper for end-to-end full-waveform LiDAR signal decomposition. In existing LiDAR waveform decomposition methods, complicate functional models are used to fit echo components. Thus, the echo decomposition problem can be translated into a function approximation task. Recent studies present great progress in estimating the parameters of fitting models, hence in the final decomposition results. However, the shape of received echo components are always irregular. None of the parametric functional models can fit the received echo components perfectly, which leads to unavoidable errors in the initial step of echo decomposition. In this paper, we propose an end-to-end net work AFD-Net to solve the echo decomposition problem without assuming any parametric functional models. AFD-Net is consisted with two modules, the classification module and the decomposition module. The former module is used to determine the number of echo components in a received waveform. Then the decomposition module is used to output the echo components. By experiments, we have a classification accuracy 96 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> using the first module. The average <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula> coefficient for the decomposed echo components is 0.9799. In addition, there are no public datasets for the waveform decomposition task available. Thus, another contribution of our work is to develop a tool to generate synthetic full-waveform LiDAR signals, which can help researchers to construct their own dataset for related works. All of our source codes are available in the following site: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ZedFm/AFD-Net</uri>

Minimal Damage to the Supinator Muscle After the Double-Incision Technique for Distal Biceps Tendon Repair.

Background:The effect of the double-incision technique on the supinator muscle is unclear.Purpose:The aim of this study was to quantify fatty atrophy of the supinator muscle and map the area of muscle damage.Study Design:Case series; Level of evidence, 4.Methods:A total of 19 male patients (median age, 43 years) who underwent distal biceps tendon repair were included in the analysis. Patients with a minimum of 12 months of follow-up were included. The following variables were analyzed: range of motion; shortened version of Disabilities of the Arm, Shoulder and Hand (QuickDASH) score; Summary Outcome Determination (SOD) score; and isokinetic peak force and endurance in supination. Quantitative analysis and mapping of fatty infiltration of the supinator muscle were based on the calculation of proton density fat fraction on magnetic resonance imaging scans of both elbows using the IDEAL (Iterative Decomposition of Echoes of Asymmetrical Length) sequence.Results:At an average follow-up of 24 months (range, 12-64 months), the median SOD score was 9.0 (95% CI, 7.8-9.4), and the mean QuickDASH score was 6.7 (95% CI, 0.0-14.1). A difference of 17% in peak torque was measured between repaired and nonrepaired elbows (repaired elbow: 9.7 N·m; nonrepaired elbow: 11.7 N·m; P = .11). Endurance was better in the repaired elbow than the nonrepaired elbow (8.4% vs 14.9% work fatigue, respectively; P = .02). The average fat fraction of the supinator muscle was 19% (95% CI, 16%-21%) in repaired elbows and 14% (95% CI, 13%-16%) in contralateral elbows (P = .04). The increase in fat fraction was located in a limited area between the radius and ulna at the level of the bicipital tuberosity.Conclusion:The assessment of the supinator muscle showed a limited increase in fat fraction between the radius and ulna at the level of the bicipital tuberosity. No significant effect on supination strength was highlighted.

Surgical rotator cuff muscle biopsies: are they representative of overall muscle quality?

Current research on human rotator cuff pathology relies on superficial biopsy specimens. It is unclear whether these biopsies are representative of overall muscle quality. The purpose of this study is to use magnetic resonance imaging with iterative decomposition of echoes of asymmetric length sequencing to investigate variability of fatty infiltration within the supraspinatus and infraspinatus muscle. We retrospectively identified 45 patients who underwent arthroscopic rotator cuff repair with preoperative iterative decomposition of echoes of asymmetric length imaging completed. The supraspinatus and infraspinatus were segmented on 4 consecutive slices, including the scapular Y, 2 slices medial, and 1 slice lateral. Intramuscular fat was measured in multiple regions for both supraspinatus (whole muscle, anterior, posterior, superficial band, anterior band, and posterior band) and infraspinatus (whole muscle, superior, inferior, superficial band, superior band, and inferior band). Comparisons of intramuscular fat were determined with Wilcoxon sign-rank tests. Analysis of variance was used to compare between the 4 consecutive slices. Significance was defined as P < .05. Magnetic resonance imaging showed 31 full-thickness supraspinatus tears, 10 partial-thickness supraspinatus tears, and 4 intact supraspinatus tendons and 3 full-thickness infraspinatus tears, 2 partial-thickness infraspinatus tears, and 40 intact infraspinatus tendons. The anterior supraspinatus contained significantly higher fat content than the posterior supraspinatus (7.4% ± 7.4% vs. 5.4% ± 5.7%, P = .003). The superior and inferior halves of the infraspinatus were not different from each other (P = .11). The superficial band did not differ from the whole muscle in both supraspinatus (P = .14) and infraspinatus (P = .20). However, the anterior band of the supraspinatus had significantly more fat than the posterior band (8.2% ± 9.3% vs. 5.0% ± 5.7%, respectively, P < .0001), and the superior band of the infraspinatus had significantly more fat than the inferior band (5.2% ± 4.8% vs. 4.2% ± 5.3%, respectively, P = .03). There was no difference between all 4 medial and lateral slices in the supraspinatus (P = .92) and infraspinatus (P = .90). Fat fractions within the supraspinatus and infraspinatus demonstrate significant spatial variability that may influence interpretation of local biopsy samples. Future biopsy studies may benefit from multiple samples between different specific locations.

Echo decomposition of full-waveform LiDAR based on a digital implicit model and a particle swarm optimization.

In the waveform decomposition of full-waveform LiDAR, the Gaussian model (GSM) and the generalized Gaussian model (GGSM) are widely used to extract echoes from return waveforms. However, those models have explicit functions that follow specific distribution shapes, so they are suitable only for decomposing echo waveforms with similar shapes. This paper introduces a digital implicit model (DIM) and presents a universal decomposition method for the full-waveform LiDAR. In this method, the decomposition model is considered to be an implicit function, associated with a digital template waveform library, whose optimization is implemented by a modified particle swarm algorithm. The template waveform library is a customized fingerprint for any special full-waveform LiDAR, so the DIM can deal effectively with infinite echoes with arbitrary shapes. A full-waveform LiDAR system with asymmetric echo distribution is designed to compare the decomposition performances among the GSM, GGSM, and DIM. Experimental results show that, when decomposing the return waveform containing a single echo, the normalized sum of squares due to fitting error (SSE) of the DIM can be 60 times lower than the GSM and the GGSM. By comparing the estimation accuracies of the amplitude, the FWHM and the location of the echo component, the DIM has the best decomposition performance and the best ranging accuracy (subcentimeter level) among the three models; when decomposing the return waveform containing three overlapping echoes, the normalized SSE of the DIM can be 28 times lower than the GSM and 12 times lower than the GGSM. By comparing the estimation accuracies of the amplitude, FWHM, and location of echoes components, the DIM has the best decomposition performance and best ranging accuracy (centimeter level) among the three models.

Continuous wavelet transform and iterative decrement algorithm for the Lidar full-waveform echo decomposition.

In this paper, we propose a continuous wavelet transform and iterative decrement algorithm to decompose the light detection and ranging (LiDAR) full-waveform echoes into a series of components, each of which can be assumed as Gaussian essentially. We calculate the scale of continuous wavelet transform in real time according to the relationship between the center frequency of the mother wavelet and the approximate frequency of the transmitted laser pulse. The approximated frequency is calculated according to the half-width of the effective part of transmitted laser pulse. The positions of the Gaussian model components in the echoes can be precisely predicted according to the positions of the maxima of the continuous wavelet transform coefficient. And the boundary points which locate at the left and right sides of the position of the detected components can be detected. Then, the effective sections can be clipped according to the positions of the boundary points. In order to detect the hidden components which are obscured by the high responses from their adjacent components and estimate the initial parameters, the iterative decrement algorithm is carried out. The initial parameters are fitted by the Levenberg-Marquardt algorithm. In order to verify the proposed method, the simulations and experiments whose data is recorded by our coding LiDAR have been done. The simulations and experiments results indicate that the proposed method exhibits excellent performances, and it is valid for the complex full-waveform echo, which includes serious overlapping components.

Lidar full-waveform decomposition based on empirical mode decomposition and local-Levenberg-Marquard fitting.

The light detection and ranging (LIDAR) full-waveform echo decomposition method based on empirical mode decomposition (EMD) and the local-Levenberg-Marquard (LM) algorithm is proposed in this paper. The proposed method can decompose the full-waveform echo into a series of components, each of which can be assumed as essentially Gaussian. The original full-waveform echo is decomposed into the intrinsic mode functions (IMFs) and a final residual by using the EMD first. Then, the average period (Tm¯) and corresponding energy densities (EDs) of all IMFs are calculated. A suitable IMF is selected based on the relationship between the EDs of IMFs and the white-noise theoretical spread lines of the 99% confidence-limit level. The components in the full-waveform echo can be detected according to the positions of the maxima of the selected IMF. The initial parameters are estimated by using local-LM fitting. The initial parameters are fitted by global-LM fitting. Compared to the traditional (zero-crossing) ZC method, the proposed method has strong anti-noise performance. It can precisely detect the components and estimate the initial parameters of the components. The proposed method is verified by using the synthetic data; coding LIDAR recorded data; and Land, Vegetation, and Ice Sensor data.

Preoperative IDEAL (Iterative Decomposition of Echoes of Asymmetrical Length) magnetic resonance imaging rotator cuff muscle fat fractions are associated with rotator cuff repair outcomes

IDEAL (Iterative Decomposition of Echoes of Asymmetrical Length) imaging is amagnetic resonance imaging sequence that precisely measures rotator cuff muscle fatty infiltration. The influence of lower levels of fatty infiltration on outcomes after rotator cuff repair remains unclear. We hypothesized that increased preoperative fat fractions would be associated with inferior clinical outcomes after rotator cuff repair. We retrospectively identified patients who underwent arthroscopic rotator cuff repair with preoperative IDEAL imaging. Patients completed the Patient-Reported Outcomes Measurement Information System (PROMIS) Upper Extremity computer adaptive survey at a minimum of 2 years after repair. Muscle segmentation was performed on 4 consecutive slices. Correlations between intramuscular fat fractions and PROMIS scores were determined. Patients were grouped by PROMIS scores of 50 or greater and scores lower than 50 for comparison of fat fractions. Multivariate linear regression was performed to model PROMIS scores as a function of demographic characteristics, tear size, and fat fractions. Significance was defined as P < .05. Eighty patients were included (mean follow-up, 42.5 ± 10.7 months). Postoperative PROMIS scores were significantly inversely correlated with the infraspinatus (ρ = -0.25, P = .02) and subscapularis (ρ = -0.29, P = .009) fat fractions. The infraspinatus (7.2% ± 4.9% vs. 5.2% ± 3.0%, P = .046) and subscapularis (10.4% ± 5.1% vs. 8.2% ± 5.0%, P = .001) fat fractions were significantly higher for patients with low PROMIS scores vs. those with PROMIS scores of 50 or greater. In multivariate modeling, the infraspinatus fat fraction (β = -0.68, P = .029) was the only significant independent predictor of postoperative PROMIS score. Intramuscular fat as determined by quantitative magnetic resonance imaging is an important factor in postoperative outcomes even in patients with lower levels of preoperative fatty infiltration.

Full-waveform LiDAR echo decomposition method

Full-waveform LiDAR echo decomposition method

A Prospective, Quantitative Evaluation of Fatty Infiltration Before and After Rotator Cuff Repair.

Background:Current evaluation of muscle fatty infiltration has been limited by subjective classifications. Quantitative fat evaluation through magnetic resonance imaging (MRI) may allow for an improved longitudinal evaluation of the effect of surgical repair on the progression of fatty infiltration.Hypotheses:We hypothesized that (1) patients with isolated full-thickness supraspinatus tendon tears would have less progression in fatty infiltration compared with patients with full-thickness tears of multiple tendons and (2) patients with eventual failed repair would have higher baseline levels of fatty infiltration.Study Design:Cohort study; Level of evidence, 2.Methods:Thirty-five patients with full-thickness rotator cuff tears were followed longitudinally. All patients received a shoulder MRI, including the iterative decomposition of echoes of asymmetric length (IDEAL) sequence for fat measurement, prior to surgical treatment and at 6 months after surgical repair. Fat fractions were recorded for all 4 rotator cuff muscles from measurements on 4 sagittal slices centered at the scapular-Y. Demographics and tear characteristics were recorded. Baseline and follow-up fat fractions were compared for patients with isolated supraspinatus tears versus multitendon tears and for patients with intact repairs versus failed repairs. Statistical significance was set at P < .05.Results:The mean fat fractions were significantly higher at follow-up than at baseline for the supraspinatus (9.8% ± 7.0% vs 8.3% ± 5.7%; P = .025) and infraspinatus (7.4% ± 6.1% vs 5.7% ± 4.4%; P = .027) muscles. Patients with multitendon tears showed no significant change for any rotator cuff muscle after repair. Patients with isolated supraspinatus tears showed a significant progression in the supraspinatus fat fraction from baseline to follow-up (from 6.8% ± 4.9% to 8.6% ± 6.8%; P = .0083). Baseline supraspinatus fat fractions were significantly higher in patients with eventual failed repairs compared with those with intact repairs (11.7% ± 6.8% vs 7.1% ± 4.8%; P = .037).Conclusion:Contrary to our initial hypothesis, patients with isolated supraspinatus tears showed a significant progression of fatty infiltration. Patients with eventual repair failure had higher baseline fat fractions in the supraspinatus.

Full-waveform LiDAR echo decomposition based on wavelet decomposition and particle swarm optimization

To measure the distances and properties of the objects within a laser footprint, a decomposition method for full-waveform light detection and ranging (LiDAR) echoes is proposed. In this method, firstly, wavelet decomposition is used to filter the noise and estimate the noise level in a full-waveform echo. Secondly, peak and inflection points of the filtered full-waveform echo are used to detect the echo components in the filtered full-waveform echo. Lastly, particle swarm optimization (PSO) is used to remove the noise-caused echo components and optimize the parameters of the most probable echo components. Simulation results show that the wavelet-decomposition-based filter is of the best improvement of SNR and decomposition success rates than Wiener and Gaussian smoothing filters. In addition, the noise level estimated using wavelet-decomposition-based filter is more accurate than those estimated using other two commonly used methods. Experiments were carried out to evaluate the proposed method that was compared with our previous method (called GS-LM for short). In experiments, a lab-build full-waveform LiDAR system was utilized to provide eight types of full-waveform echoes scattered from three objects at different distances. Experimental results show that the proposed method has higher success rates for decomposition of full-waveform echoes and more accurate parameters estimation for echo components than those of GS-LM. The proposed method based on wavelet decomposition and PSO is valid to decompose the more complicated full-waveform echoes for estimating the multi-level distances of the objects and measuring the properties of the objects in a laser footprint.

A high success rate full-waveform lidar echo decomposition method

A full-waveform Light detection and ranging (LiDAR) echo decomposition method is proposed in this paper. In this method, the peak points are used to detect the separated echo components, while the inflection points are combined with corresponding peak points to detect the overlapping echo components. The detected echo components are then sorted according to their energies in a descending order. The sorted echo components are one by one added into the decomposition model according to their orders. For each addition, the parameters of all echo components already added into the decomposition model are iteratively renewed. After renewing, the amplitudes and full width at half maximums of the echo components are compared with pre-set thresholds to determine and remove the false echo components. Both simulation and experiment were carried out to evaluate the proposed method. In simulation, 4000 full-waveform echoes with different numbers and parameters of echo components were generated and decomposed using the proposed and three other commonly used methods. Results show that the proposed method is of the highest success rate, 91.43%. In experiment, 9549 Geoscience Laser Altimeter System (GLAS) echoes for Shennongjia forest district in south China were employed as test echoes. The test echoes were first decomposed using the four methods and the decomposition results were also compared with those provided by the National Snow and Ice Data Center. Comparison results show that the determination coefficient () of the proposed method is of the largest mean, 0.6838, and the smallest standard deviation, 0.3588, and the distribution of the number of the echo components decomposed from the GLAS echoes is the most satisfied with the situation of full-waveform echoes from the forest area, implying that the superposition of the echo components decomposed from a full-waveform echo by using the proposed method can best approximate the full-waveform echo.

Inverse Synthetic Aperture Radar Echo Decomposition of Ballistic Target Based on Chirplet

摘要: 尾附体回波分量的分离是改善弹道目标主体ISAR成像质量的关键。该文建立了主体和尾附体的LFM回波模型，构建了慢时间域单频信号回波，分析了主体和尾附体的瞬时多普勒差异，提出了一种基于chirplet的慢时间域分解方法，将主体回波分解为若干线性调频信号，并重构了主体及尾附体回波，给出了具体实现步骤，仿真实验结果表明，ISAR成像质量较分离前有显著提高。 关键词: ISAR / Chirplet / 进动 / 自旋 / 多普勒差异