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1
Feuer, Arie, and Nadav Berman. "Performance analysis of the smoothed least mean square (SLMS) algorithm." Signal Processing 11, no. 3 (October 1986): 265–76. http://dx.doi.org/10.1016/0165-1684(86)90005-8.
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Song, Aiguo, Lizheng Pan, Guozheng Xu, and Huijun Li. "Adaptive motion control of arm rehabilitation robot based on impedance identification." Robotica 33, no. 9 (May 1, 2014): 1795–812. http://dx.doi.org/10.1017/s026357471400099x.
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SUMMARYThere is increasing interest in using rehabilitation robots to assist post-stroke patients during rehabilitation therapy. The motion control of the robot plays an important role in the process of functional recovery training. Due to the change of the arm impedance of the post-stroke patient in the passive recovery training, the conventional motion control based on a proportional-integral (PI) controller is difficult to produce smooth movement of the robot to track the designed trajectory set by the rehabilitation therapist. In this paper, we model the dynamics of post-stroke patient arm as an impedance model, and propose an adaptive control scheme, which consists of an adaptive PI control algorithm and an adaptive damping control algorithm, to control the rehabilitation robot moving along predefined trajectories stably and smoothly. An equivalent two-port circuit of the rehabilitation robot and human arm is built, and the passivity theory of circuits is used to analyze the stability and smoothness performance of the robot. A slide Least Mean Square with adaptive window (SLMS-AW) method is presented for on-line estimation of the parameters of the arm impedance model, which is used for adjusting the gains of the PI-damping controller. In this paper, the Barrett WAM Arm manipulator is used as the main hardware platform for the functional recovery training of the post-stroke patient. Passive recovery training has been implemented on the WAM Arm, and the experimental results demonstrate the effectiveness and potential of the proposed adaptive control strategies.
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Samantaray, Barsa, Kunal Kumar Das, and Jibendu Sekhar Roy. "Beamforming in Smart Antenna using Some Variants of Least Mean Square Algorithm." Circulation in Computer Science MCSP2017, no. 01 (September 24, 2017): 23–26. http://dx.doi.org/10.22632/ccs-2017-mcsp034.
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Beamforming and side lobe level reduction of smart antenna are important tasks in mobile network. Adaptive signal processing algorithms are used for beam forming in smart antenna. In this paper, variable step-size sign least mean square (VS-SLMS) algorithm is used for beam forming of smart antenna with linear antenna array. The results are compared with the results obtained using sign least mean square (SLMS) algorithm. Variable step-size algorithm shows good results for beam forming compared to ordinary constant step-size algorithm.
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Pichardo, Eduardo, Ángel Vázquez, Esteban R. Anides, Juan C. Sánchez, Hector Perez, Juan G. Avalos, and Giovanny Sánchez. "A Dual Adaptive Filter Spike-Based Hardware Architecture for Implementation of a New Active Noise Control Structure." Electronics 10, no. 16 (August 12, 2021): 1945. http://dx.doi.org/10.3390/electronics10161945.
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Presently, the technology development trend of active noise control (ANC) systems is focused on implementing advanced adaptive filters in resource-constrained electronic appliances. Recently, several authors have proved that the use of two adaptive filter algorithms significantly improves the overall adaptive filter performance. However, the computational cost of these approaches is significantly increased since they use two filters simultaneously. Consequently, these filters cannot be implemented in these devices. To solve this problem, we propose a new ANC structure with switching selection based on filtered-x normalized least mean square (FxNLMS) and filtered-x sign least mean square (FxSLMS) algorithms to reduce the computational cost of the ANC system. The improvement of this factor has allowed us to introduce for the first time an advanced spike-based architecture, which can perform dual filter operations using dynamic routing, to be used in real ANC applications. The results have demonstrated that the computational cost of the proposed dual D-FxNLMS/SLMS algorithm is lower compared with previously reported solutions.
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Zhang, Kun, Minrui Fei, Xin Li, and Huiyu Zhou. "Adaptive Bacteria Colony Picking in Unstructured Environments Using Intensity Histogram and Unascertained LS-SVM Classifier." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/928395.
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Features analysis is an important task which can significantly affect the performance of automatic bacteria colony picking. Unstructured environments also affect the automatic colony screening. This paper presents a novel approach for adaptive colony segmentation in unstructured environments by treating the detected peaks of intensity histograms as a morphological feature of images. In order to avoid disturbing peaks, an entropy based mean shift filter is introduced to smooth images as a preprocessing step. The relevance and importance of these features can be determined in an improved support vector machine classifier using unascertained least square estimation. Experimental results show that the proposed unascertained least square support vector machine (ULSSVM) has better recognition accuracy than the other state-of-the-art techniques, and its training process takes less time than most of the traditional approaches presented in this paper.
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Pratama, Jovian Dian, Ratna Herdiana, and Susilo Hariyanto. "Elliptical Orbits Mode Application for Approximation of Fuel Volume Change." CAUCHY 7, no. 2 (March 11, 2022): 316–31. http://dx.doi.org/10.18860/ca.v7i2.14407.
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This article discusses the Elliptical Orbits Mode (EOM) as a method of approximating the function of changing the volume of fuel in the Underground Yank (UT). This research was conducted at the 45.507.21 Candirejo Tuntang Pertamina Gas Station. The calculation of the approximation method will be applied to the measuring book data from the Semarang Metrology Regency specifically for the Pertalite (Fuel Product of Pertamina) buried tank, because the calculation of the gas station is not smooth, it is necessary for a smoother data fitting by considering Residual Square Error (RSS) and Mean Square Error (MSE). The result of this research is the application of EOM(θ) measuring book with elliptical height control produces smaller RSS and MSE compared to using COM, EOM, Least Square degree two and three.
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SCHULTZ, M. P., and K. A. FLACK. "The rough-wall turbulent boundary layer from the hydraulically smooth to the fully rough regime." Journal of Fluid Mechanics 580 (May 21, 2007): 381–405. http://dx.doi.org/10.1017/s0022112007005502.
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Turbulence measurements for rough-wall boundary layers are presented and compared to those for a smooth wall. The rough-wall experiments were made on a three-dimensional rough surface geometrically similar to the honed pipe roughness used by Shockling, Allen & Smits (J. Fluid Mech. vol. 564, 2006, p. 267). The present work covers a wide Reynolds-number range (Reθ = 2180–27 100), spanning the hydraulically smooth to the fully rough flow regimes for a single surface, while maintaining a roughness height that is a small fraction of the boundary-layer thickness. In this investigation, the root-mean-square roughness height was at least three orders of magnitude smaller than the boundary-layer thickness, and the Kármán number (δ+), typifying the ratio of the largest to the smallest turbulent scales in the flow, was as high as 10100. The mean velocity profiles for the rough and smooth walls show remarkable similarity in the outer layer using velocity-defect scaling. The Reynolds stresses and higher-order turbulence statistics also show excellent agreement in the outer layer. The results lend strong support to the concept of outer layer similarity for rough walls in which there is a large separation between the roughness length scale and the largest turbulence scales in the flow.
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Pei, Tianqi, Caoyang Yu, Yiming Zhong, Junjun Cao, and Lian Lian. "Advanced Marine Craft Model Identification via Multi-Kernel Weighted Least Square Support Vector Machine and Characteristic Model Techniques." Journal of Marine Science and Engineering 11, no. 5 (May 22, 2023): 1091. http://dx.doi.org/10.3390/jmse11051091.
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This paper combines the piecewise Cubic Hermite (CH) interpolation algorithm and the weighted least square support vector machine (WLS-SVM) to improve identification accuracy for marine crafts built based on the characteristic model. The characteristic model is first used to describe the heading dynamics of marine crafts and is a superior model to the traditional response model in both accuracy and complexity. Especially in order to improve identification accuracy, a CH-based data preprocessing strategy is utilized to densify and smooth data for further accurate identification. Subsequently, the combination of the linear kernel function and the Gaussian kernel function is introduced in the conventional WLS-SVM method, which renders global and local performance improvements compared with the conventional WLS-SVM method. Finally, informative maneuvers composed of Zigzag and Sine are carried out to test the performance of the improved identification method. Compared to the conventional LS-SVM method based on the response model, the root mean square error of the proposed CH-MK-WLS-SVM method based on the characteristic model is reduced by an order of magnitude in the presence of sensor noise.
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Zhou, Zhu, Sohrab Rahimi, Stavros Avramidis, and Yiming Fang. "Species- and moisture-based sorting of green timber mix with near infrared spectroscopy." BioResources 15, no. 1 (November 18, 2019): 317–30. http://dx.doi.org/10.15376/biores.15.1.317-330.
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Methods suitable for the determination and classification of green timber mix (western hemlock and amabilis fir), with respect to species and moisture content, were developed and tested using near infrared spectroscopy and chemometrics. One thousand two hundred samples were distributed into a calibration set (720 samples) and a prediction set (480 samples). Partial least squares (PLS) and least squares-support vector machines (LS-SVM) for both regression (PLSR and LS-SVR) and classification (PLS-DA and LS-SVC) with different spectral preprocessing methods were implemented. LS-SVM outperformed PLS models for both regression and classification. The coefficient of determination (R2p) and root mean square error (RMSEP) of prediction for the best LS-SVR model with spectra pretreated by smooth and first derivative were 0.9824 and 8.7%, respectively, for wood moisture content prediction in the range of 30% to 253%. The best classification model was LS-SVC with spectra pretreated by smooth and second derivative, with overall accuracies of 99.8% in the prediction set, when the samples were divided into four classes. NIRS combined with LS-SVM can be used as a rapid alternative method for qualitative and quantitative analysis of green hem-fir mix before kiln drying. The results could be helpful for sorting green hem-fir mixes with an on-line application.
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Elnady, A., and M. AlShabi. "Operation of Parallel Inverters in Microgrid Using New Adaptive PI Controllers Based on Least Mean Fourth Technique." Mathematical Problems in Engineering 2019 (June 19, 2019): 1–19. http://dx.doi.org/10.1155/2019/4854803.
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This paper shows the operation of the microgrid using a new adaptive PI controller based operational (control) scheme. The core of the proposed control scheme is the suggested adaptive PI controller. The parameters of the PI controller are adaptively tuned using a variable step-size least mean fourth algorithm with no need for any system model to operate this adaptive controller. The main merit of the proposed scheme is that it stabilizes the magnitude and frequency of the voltage at any loading condition such as variable balanced loads, variable unbalanced loads, and nonlinear loads. The proposed scheme has a simple structure and accurate performance. In addition, the structure of proposed scheme provides a seamless transition toward any loss or reconnection of any inverter in the microgrid. Furthermore, the suggested operational scheme is flexible enough to enable the microgrid to be operative in a grid-connected mode and to transfer from the voltage control mode to power control mode with a smooth transitional procedure. To validate the meritorious performance of the suggested scheme, its performance is compared to similar schemes based on a linear controller (regular PI controller), single-neuron PI controller (adaptive PI controller), recursive least square-support vector machine based PI controller (another adaptive PI controller), and nonlinear controller (sliding mode controller) for different operations of the microgrid.
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Haider, Hassaan, Jawad Ali Shah, Kushsairy Kadir, and Najeeb Khan. "Sparse Reconstruction Using Hyperbolic Tangent as Smooth l1-Norm Approximation." Computation 11, no. 1 (January 4, 2023): 7. http://dx.doi.org/10.3390/computation11010007.
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In the Compressed Sensing (CS) framework, the underdetermined system of linear equation (USLE) can have infinitely many possible solutions. However, we intend to find the sparsest possible solution, which is -norm minimization. However, finding an norm solution out of infinitely many possible solutions is NP-hard problem that becomes non-convex optimization problem. It has been a practically proven fact that norm penalty can be adequately estimated by norm, which recasts a non-convex minimization problem to a convex problem. However, norm non-differentiable and gradient-based minimization algorithms are not applicable, due to this very reason there is a need to approximate norm by its smooth approximation. Iterative shrinkage algorithms provide an efficient method to numerically minimize -regularized least square optimization problem. These algorithms are required to induce sparsity in their solutions to meet the CS recovery requirement. In this research article, we have developed a novel recovery method that uses hyperbolic tangent function to recover undersampled signal/images in CS framework. In our work, norm and soft thresholding are both approximated with the hyperbolic tangent functions. We have also proposed the criteria to tune optimization parameters to get optimal results. The error bounds for the proposed norm approximation are evaluated. To evaluate performance of our proposed method, we have utilized a dataset comprised of 1-D sparse signal, compressively sampled MR image and cardiac cine MRI. The MRI is an important imaging modality for assessing cardiac vascular function. It provides the ejection fraction and cardiac output of the heart. However, this advantage comes at the cost of a slow acquisition process. Hence, it is essential to speed up the acquisition process to take the full benefits of cardiac cine MRI. Numerical results based on performance metrics, such as Structural Similarity (SSIM), Peak Signal to Noise Ratio (PSNR) and Root Mean Square Error (RMSE) show that the proposed tangent hyperbolic based CS recovery offers a much better performance as compared to the traditional Iterative Soft Thresholding (IST) recovery methods.
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DelSole, Timothy, Liwei Jia, and Michael K. Tippett. "Scale-Selective Ridge Regression for Multimodel Forecasting." Journal of Climate 26, no. 20 (October 4, 2013): 7957–65. http://dx.doi.org/10.1175/jcli-d-13-00030.1.
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Abstract This paper proposes a new approach to linearly combining multimodel forecasts, called scale-selective ridge regression, which ensures that the weighting coefficients satisfy certain smoothness constraints. The smoothness constraint reflects the “prior assumption” that seasonally predictable patterns tend to be large scale. In the absence of a smoothness constraint, regression methods typically produce noisy weights and hence noisy predictions. Constraining the weights to be smooth ensures that the multimodel combination is no less smooth than the individual model forecasts. The proposed method is equivalent to minimizing a cost function comprising the familiar mean square error plus a “penalty function” that penalizes weights with large spatial gradients. The method reduces to pointwise ridge regression for a suitable choice of constraint. The method is tested using the Ensemble-Based Predictions of Climate Changes and Their Impacts (ENSEMBLES) hindcast dataset during 1960–2005. The cross-validated skill of the proposed forecast method is shown to be larger than the skill of either ordinary least squares or pointwise ridge regression, although the significance of this difference is difficult to test owing to the small sample size. The model weights derived from the method are much smoother than those obtained from ordinary least squares or pointwise ridge regression. Interestingly, regressions in which the weights are completely independent of space give comparable overall skill. The scale-selective ridge is numerically more intensive than pointwise methods since the solution requires solving equations that couple all grid points together.
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Wang, Bin, Cai Liu, Xue Li Wu, and Lei Liu. "RBF Neural Network Based Adaptive Tracking Control for a Class of Nonlinear Plant Using Stochastic U-Model." Key Engineering Materials 474-476 (April 2011): 1209–14. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.1209.
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In this paper an adaptive tracking control algorithm and its step by step implementation procedure are developed for a class of nonlinear plants within a U-model framework with unknown parameters. With the author’s previous justification, not only the control oriented model represents a wide range of smooth (polynomial) nonlinear dynamic plants (without using linearisation approximation at all), but also make almost all linear control system design techniques directly applicable (with a root solver bridging the linear design and calculation of controller output). A new technique is proposed to design an online control algorithm using the Radial Basis Functions Neural Network (RBFNN). The plant parameters are estimated online and are used to update the weights of the RBFNN. The weights update equations are derived based on the well known LMS (least mean square). A number of simulated case studies are conducted to illustrate the efficiency of the claimed insight and design procedure.
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Zhang, Lianjun, Jeffrey H. Gove, Chuangmin Liu, and William B. Leak. "A finite mixture of two Weibull distributions for modeling the diameter distributions of rotated-sigmoid, uneven-aged stands." Canadian Journal of Forest Research 31, no. 9 (September 1, 2001): 1654–59. http://dx.doi.org/10.1139/x01-086.
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The rotated-sigmoid form is a characteristic of old-growth, uneven-aged forest stands caused by past disturbances such as cutting, fire, disease, and insect attacks. The diameter frequency distribution of the rotated-sigmoid form is bimodal with the second rounded peak in the midsized classes, rather than a smooth, steeply descending, monotonic curve. In this study a finite mixture of two Weibull distributions is used to describe the diameter distributions of the rotated-sigmoid, uneven-aged forest stands. Four example stands are selected to demonstrate model fitting and comparison. Compared with a single Weibull or negative exponential function, the finite finite mixture model is the only one that fits the diameter distributions well and produces root mean square error at least four times smaller than the other two. The results show that the finite mixture distribution is a better alternative method for modeling the diameter distribution of the rotated-sigmoid, uneven-aged forest stands.
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Fan, Zhao, Xu, Liang, Yang, Feng, Yang, Wang, Chen, and Wei. "Hyperspectral-based Estimation of Leaf Nitrogen Content in Corn Using Optimal Selection of Multiple Spectral Variables." Sensors 19, no. 13 (June 30, 2019): 2898. http://dx.doi.org/10.3390/s19132898.
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Accurate and dynamic monitoring of crop nitrogen status is the basis of scientific decisions regarding fertilization. In this study, we compared and analyzed three types of spectral variables: Sensitive spectral bands, the position of spectral features, and typical hyperspectral vegetation indices. First, the Savitzky-Golay technique was used to smooth the original spectrum, following which three types of spectral parameters describing crop spectral characteristics were extracted. Next, the successive projections algorithm (SPA) was adopted to screen out the sensitive variable set from each type of parameters. Finally, partial least squares (PLS) regression and random forest (RF) algorithms were used to comprehensively compare and analyze the performance of different types of spectral variables for estimating corn leaf nitrogen content (LNC). The results show that the integrated variable set composed of the optimal ones screened by SPA from three types of variables had the best performance for LNC estimation by the validation data set, with the values of R2, root means square error (RMSE), and normalized root mean square error (NRMSE) of 0.77, 0.31, and 17.1%, and 0.55, 0.43, and 23.9% from PLS and RF, respectively. It indicates that the PLS model with optimally multitype spectral variables can provide better fits and be a more effective tool for evaluating corn LNC.
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Yu, Zihao, Jin Liu, Haima Yang, Bo Huang, and Yumei Jian. "A Multifrequency Heterodyne Phase Error Compensation Method for 3D Reconstruction." Journal of Sensors 2020 (August 8, 2020): 1–10. http://dx.doi.org/10.1155/2020/8833305.
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In view of the problem of “jumping points” in the phase unwrapping of the multifrequency heterodyne principle, this paper proposes a novel method to improve the multifrequency heterodyne. By solving the root-mean-square error of the original frequency function, it includes the relationship between the error and the adjacent phase in the condition of constrained phase unwrapping, and it compensates phase ±2π of the skip point. To ensure the accuracy of phase unwrapping, the function with a “jump point” after each phase unwrapping and the absolute phase curve of the principal value function are used to establish the threshold judgment model of the least square method, and the initial phase unwrapping of the principal value function with different frequencies is carried out continuously. The simulation analysis of phase compensation with the four-step phase-shifting method shows that the error is reduced to 36% under the set environment. The experimental result of 3D reconstruction by measuring the flatness of the plate shows that the error decreases by 41% after phase compensation compared with before phase compensation. The three-dimensional reconstruction experiment of pitch measurement with a nut shows that the nut after phase compensation is smooth without noise, and the pitch error is 0.033 mm, which verified the method is workable and effective.
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Shirokanev, Aleksandr, Nataly Ilyasova, Nikita Andriyanov, Evgeniy Zamytskiy, Andrey Zolotarev, and Dmitriy Kirsh. "Modeling of Fundus Laser Exposure for Estimating Safe Laser Coagulation Parameters in the Treatment of Diabetic Retinopathy." Mathematics 9, no. 9 (April 26, 2021): 967. http://dx.doi.org/10.3390/math9090967.
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A personalized medical approach can make diabetic retinopathy treatment more effective. To select effective methods of treatment, deep analysis and diagnostic data of a patient’s fundus are required. For this purpose, flat optical coherence tomography images are used to restore the three-dimensional structure of the fundus. Heat propagation through this structure is simulated via numerical methods. The article proposes algorithms for smooth segmentation of the retina for 3D model reconstruction and mathematical modeling of laser exposure while considering various parameters. The experiment was based on a two-fold improvement in the number of intervals and the calculation of the root mean square deviation between the modeled temperature values and the corresponding coordinates shown for the convergence of the integro-interpolation method (balance method). By doubling the number of intervals for a specific spatial or temporal coordinate, a decrease in the root mean square deviation takes place between the simulated temperature values by a factor of 1.7–5.9. This modeling allows us to estimate the basic parameters required for the actual practice of diabetic retinopathy treatment while optimizing for efficiency and safety. Mathematical modeling is used to estimate retina heating caused by the spread of heat from the vascular layer, where the temperature rose to 45 °C in 0.2 ms. It was identified that the formation of two coagulates is possible when they are located at least 180 μm from each other. Moreover, the distance can be reduced to 160 μm with a 15 ms delay between imaging.
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Zhang, Jingqi, Yugang Chen, Ning Li, Jingyu Zhai, Qingkai Han, and Zengxuan Hou. "A Denoising Method of Micro-Turbine Acoustic Pressure Signal Based on CEEMDAN and Improved Variable Step-Size NLMS Algorithm." Machines 10, no. 6 (June 4, 2022): 444. http://dx.doi.org/10.3390/machines10060444.
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The acoustic pressure signal generated by blades is one of the key indicators for condition monitoring and fault diagnosis in the field of turbines. Generally, the working conditions of the turbine are harsh, resulting in a large amount of interference and noise in the measured acoustic pressure signal. Therefore, denoising the acoustic pressure signal is the basis of the subsequent research. In this paper, a denoising method of micro-turbine acoustic pressure signal based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variable step-size Normalized Least Mean Square (VSS-NLMS) algorithms is proposed. Firstly, the CEEMDAN algorithm is used to decompose the original signal into multiple intrinsic mode functions (IMFs), based on the cross-correlation coefficient and continuous mean square error (CMSE) criterion; the obtained IMFs are divided into clear IMFs, noise-dominated IMFs, and noise IMFs. Finally, the improved VSS-NLMS algorithm is adopted to denoise the noise-dominated IMFs and combined with the clear IMF for reconstruction to obtain the final denoised signal. Adopting the above principles, the acoustic pressure signals generated by a micro-turbine with different rotation speeds and different states (normal turbine and fractured turbine) are denoised, respectively, and the results are compared with the axial flow fan test (ideal interference-free signal). The results show that the denoising method proposed in this paper has a good denoising effect, and the denoised signal is smooth and the important features are well preserved, which is conducive to the extraction of acoustic pressure signal characteristics.
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Hadi Mohammad, Alaa, Azura Che Soh, Noor Faezah Ismail, Ribhan Zafira Abdul Rahman, and Mohd Amran Mohd Radzi. "Improvement of LMS adaptive noise canceller using uniform Poly-phase digital filter bank." Indonesian Journal of Electrical Engineering and Computer Science 17, no. 3 (March 1, 2020): 1258. http://dx.doi.org/10.11591/ijeecs.v17.i3.pp1258-1265.
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<span>This paper presents the Least Mean Square (LMS) noise canceller using uniform poly-phase digital filter bank to improve the noise can-cellation process. Analysis filter bank is used to decompose the full-band distorted input signal into sub-band signals. Decomposition the full-band input distorted signal into sub-band signals based on the fact that the signal to noise ratio (S/N) is inversely proportional to the signal bandwidth. Each sub-band signal is fed to individual LMS algorithm to produce the optimal sub-band output. Synthesis filter bank is used to compose the optimal sub-band outputs to produce the final optimal full-band output. In this paper, m-band uniform Discrete Fourier Transform (DFT) digital filter bank has been used because its computational complexity is much smaller than the direct implementation of digital filter bank. The simulation results show that the proposed method provides the efficient performance with less and smooth error signal as compared to conventional LMS noise canceller.</span>
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Abou Mandour, Mohsen Abdou, Mohamed Mohamed El-Affify, Mohamed Hassan Hassan, and Amir Kamel Alramady. "Linear vs. nonlinear porosity estimation of NMR oil reservoir data." IIUM Engineering Journal 8, no. 1 (September 29, 2010): 19–33. http://dx.doi.org/10.31436/iiumej.v8i1.85.
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Nuclear magnetic resonance is widely used to assess oil reservoir properties especially those that can not be evaluated using conventional techniques. In this regard, porosity determination and the related estimation of the oil present play a very important role in assessing the eco1nomic value of the oil wells. Nuclear Magnetic Resonance data is usually fit to the sum of decaying exponentials. The resulting distribution; i.e. T2 distribution; is directly related to porosity determination. In this work, three reservoir core samples (Tight Sandstone and two Carbonate samples) were analyzed. Linear Least Square method (LLS) and non-linear least square fitting using Levenberg-Marquardt method were used to calculate the T2 distribution and the resulting incremental porosity. Parametric analysis for the two methods was performed to evaluate the impact of number of exponentials, and effect of the regularization parameter (?) on the smoothing of the solution. Effect of the type of solution on porosity determination was carried out. It was found that 12 exponentials is the optimum number of exponentials for both the linear and nonlinear solutions. In the mean time, it was shown that the linear solution begins to be smooth at α = 0.5 which corresponds to the standard industrial value for the regularization parameter. The order of magnitude of time needed for the linear solution is in the range of few minutes while it is in the range of few hours for the nonlinear solution. Regardless of the fact that small differences exist between the linear and nonlinear solutions, these small values make an appreciable difference in porosity. The nonlinear solution predicts 12% less porosity for the tight sandstone sample and 4.5 % and 13 % more porosity in the two carbonate samples respectively.
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Zhao, Shuaijie, Jinjie Zhou, Yao Liu, Jitang Zhang, and Jie Cui. "Application of Adaptive Filtering Based on Variational Mode Decomposition for High-Temperature Electromagnetic Acoustic Transducer Denoising." Sensors 22, no. 18 (September 17, 2022): 7042. http://dx.doi.org/10.3390/s22187042.
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In high-temperature environments, the signal-to-noise ratio (SNR) of the signal measured by electromagnetic acoustic transducers (EMAT) is low, and the signal characteristics are difficult to extract, which greatly affects their application in practical industry. Aiming at this problem, this paper proposes the least mean square adaptive filtering interpolation denoising method based on variational modal decomposition (AFIV). Firstly, the high-temperature EMAT signal was decomposed by variational modal decomposition (VMD). Then the high-frequency and low-frequency noises in the signal were filtered according to the excitation center frequency. Following the wavelet threshold denoising (WTD) for the noise component after VMD decomposition was carried out. Afterward, the noise component and signal component were connected by an adaptive filtering process to achieve further noise reduction. Finally, cubic spline interpolation was used to smooth the noise reduction curve and obtain the time information. To verify the effectiveness of the proposed method, it was applied to two kinds of ultrasonic signals from 25 to 700 °C. Compared with VMD, WTD, and empirical mode decomposition denoising, the SNR was increased by 2 times. The results show that this method can better extract the effective information of echo signals and realize the online thickness measurement at high temperature.
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Guerrero, M. K. M. R., J. A. M. Vivar, R. V. Ramos, and A. M. Tamondong. "ASSESSMENT OF SEAGRASS PERCENT COVER AND WATER QUALITY USING UAV IMAGES AND FIELD MEASUREMENTS IN BOLINAO, PANGASINAN." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W19 (December 23, 2019): 233–40. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w19-233-2019.
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Abstract. The sensitivity to changes in water quality inherent to seagrass communities makes them vital for determining the overall health of the coastal ecosystem. Numerous efforts including community-based coastal resource management, conservation and rehabilitation plans are currently undertaken to protect these marine species. In this study, the relationship of water quality parameters, specifically chlorophyll-a (chl-a) and turbidity, with seagrass percent cover is assessed quantitatively. Support Vector Machine, a pixel-based image classification method, is applied to determine seagrass and non-seagrass areas from the orthomosaic which yielded a 91.0369% accuracy. In-situ measurements of chl-a and turbidity are acquired using an infinity-CLW water quality sensor. Geostatistical techniques are utilized in this study to determine accurate surfaces for chl-a and turbidity. In two hundred interpolation tests for both chl-a and turbidity, Simple Kriging (Gaussian-model type and Smooth- neighborhood type) performs best with Mean Prediction equal to −0.1371 FTU and 0.0061 μg/L, Root Mean Square Standardized error equal to −0.0688 FTU and −0.0048 μg/L, RMS error of 8.7699 FTU and 1.8006 μg/L and Average Standard Error equal to 10.8360 FTU and 1.6726 μg/L. Zones are determined using fishnet tool and Moran’s I to calculate for the seagrass percent cover. Ordinary Least Squares (OLS) is used as a regression analysis to quantify the relationship of seagrass percent cover and water quality parameters. The regression analysis result indicates that turbidity has an inverse relationship while chlorophyll-a has a direct relationship with seagrass percent cover.
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Mondal, N. "Thinness as Major Underlying Problem Among Adolescents of Northeast India." Journal of Nepal Paediatric Society 34, no. 1 (March 24, 2014): 39–47. http://dx.doi.org/10.3126/jnps.v34i1.8922.
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Background: Undernutrition is a major public health concern in many of the developing countries of Asia. Due to immense population size, socioeconomic disparities, illiteracy and inadequate access to health facilities prevalence is very high in India. The objective of this study was to determine the prevalence of age-sex specific thinness (low BMI-for-age) among adolescents residing in rural regions in India. Materials and Methods: This cross-sectional study has conducted among 1165 adolescent (602 boys; 563 girls) aged 10-18 years of Darjeeling district, West Bengal, India. Anthropometric measurements were height and weight obtained and BMI (weight/height2, kg/ m2) was calculated. The prevalence of thinness was assessed using newly proposed age-sex specific cut-offs of Cole et al. The data were analyzed using chi-square, ANOVA and Least Mean and Square (L,M and S) model approach. Results: Prevalence of overall thinness is 49.10% (51.16% boys, 46.89% girls) among rural adolescents. The boys are found to be more sufferer than girls in the different thinness grades include mild (grade I; 27.41% vs. 27.11%), moderate (grade II; 14.62% vs. 12.08%) and severe (grade III; 9.14% vs. 8.70%) (p>0.05). The age and sex specific smooth percentile curves of BMI were derived using L,M and S model approach for further evaluation of nutritional status. Conclusion: The existence of high prevalence of thinness among adolescents indicates nutritional deprivation among rural Indian adolescents. There is an urgent need of appropriate nutritional intervention program to address the public health problem related to undernutrition especially among nutritionally vulnerable segments of adolescents residing in rural regions to ameliorate the nutritional status DOI: http://dx.doi.org/10.3126/jnps.v34i1.8922 J Nepal Paediatr Soc 2014;34(1):39-47
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Arora, Yashika, Pushpinder Walia, Mitsuhiro Hayashibe, Makii Muthalib, Shubhajit Roy Chowdhury, Stephane Perrey, and Anirban Dutta. "Grey-box modeling and hypothesis testing of functional near-infrared spectroscopy-based cerebrovascular reactivity to anodal high-definition tDCS in healthy humans." PLOS Computational Biology 17, no. 10 (October 6, 2021): e1009386. http://dx.doi.org/10.1371/journal.pcbi.1009386.
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Transcranial direct current stimulation (tDCS) has been shown to evoke hemodynamics response; however, the mechanisms have not been investigated systematically using systems biology approaches. Our study presents a grey-box linear model that was developed from a physiologically detailed multi-compartmental neurovascular unit model consisting of the vascular smooth muscle, perivascular space, synaptic space, and astrocyte glial cell. Then, model linearization was performed on the physiologically detailed nonlinear model to find appropriate complexity (Akaike information criterion) to fit functional near-infrared spectroscopy (fNIRS) based measure of blood volume changes, called cerebrovascular reactivity (CVR), to high-definition (HD) tDCS. The grey-box linear model was applied on the fNIRS-based CVR during the first 150 seconds of anodal HD-tDCS in eleven healthy humans. The grey-box linear models for each of the four nested pathways starting from tDCS scalp current density that perturbed synaptic potassium released from active neurons for Pathway 1, astrocytic transmembrane current for Pathway 2, perivascular potassium concentration for Pathway 3, and voltage-gated ion channel current on the smooth muscle cell for Pathway 4 were fitted to the total hemoglobin concentration (tHb) changes from optodes in the vicinity of 4x1 HD-tDCS electrodes as well as on the contralateral sensorimotor cortex. We found that the tDCS perturbation Pathway 3 presented the least mean square error (MSE, median <2.5%) and the lowest Akaike information criterion (AIC, median -1.726) from the individual grey-box linear model fitting at the targeted-region. Then, minimal realization transfer function with reduced-order approximations of the grey-box model pathways was fitted to the ensemble average tHb time series. Again, Pathway 3 with nine poles and two zeros (all free parameters), provided the best Goodness of Fit of 0.0078 for Chi-Square difference test of nested pathways. Therefore, our study provided a systems biology approach to investigate the initial transient hemodynamic response to tDCS based on fNIRS tHb data. Future studies need to investigate the steady-state responses, including steady-state oscillations found to be driven by calcium dynamics, where transcranial alternating current stimulation may provide frequency-dependent physiological entrainment for system identification. We postulate that such a mechanistic understanding from system identification of the hemodynamics response to transcranial electrical stimulation can facilitate adequate delivery of the current density to the neurovascular tissue under simultaneous portable imaging in various cerebrovascular diseases.
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Ilin, A. E., A. G. Butkevich, M. S. Chubey, D. I. Gorshanov, I. I. Kanayev, T. R. Kirian, I. M. Kopylov, and V. N. Yershov. "Struve - Space Astrometry and Photometry Project." Highlights of Astronomy 11, no. 1 (1998): 585. http://dx.doi.org/10.1017/s1539299600022449.
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The Space Astrometry and Photometry Project Struve is being designed at the Pulkovo Observatory in cooperation with other Russian space institutes. It is expected to be launched before 2010 with a duration of mission of at least 36 months.The main objectives are to extend at milliarcsecond accuracy the Hipparcos satellite reference system to fainter objects including quasars and to get a second epoch for Hipparcos stars. The project Struve, along with the recently suggested project DIVA, will fill the intermediate place between Hipparcos and microarcsecond astrometry. Unlike DIVA, we propose far more extensive astrometric and photometric surveys.We expect an Output Catalogue of 20 million stars (density of about 500 stars per square degree). A sky survey will be complete down to V = 14 (about 15 million stars), and selected objects down to V = 19.5 will be observed within a special program. The proper motions of the Hipparcos stars will be determined with an accuracy of about 0.1 mas/yr. The mean accuracy of star positions in the output catalogue is expected to be 0.6 mas which could be achieved by proper design of the satellite (symmetry, smooth rotation etc.), optics and the micrometer. A properly designed micrometer (with CCD arrays, special processors for image processing and the compression of the data flux to the ground station) will give the possibility of observing all objects of the sky down to a definite limiting magnitude.
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Liang, Xing, Yuanxing Luo, Fei Deng, and Yan Li. "Investigation on Vibration Signal Characteristics in a Centrifugal Pump Using EMD-LS-MFDFA." Processes 10, no. 6 (June 10, 2022): 1169. http://dx.doi.org/10.3390/pr10061169.
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Vibration signals from centrifugal pumps are nonlinear, non-smooth, and possess implied trend terms, which makes it difficult for traditional signal processing methods to accurately extract their fault characteristics and details. With a view to rectifying this, we introduced empirical mode decomposition (EMD) to extract the trend term signals. These were then refit using the least squares (LS) method. The result (EMD-LS) was then combined with multi-fractal theory to form a new signal identification method (EMD-LS-MFDFA), whose accuracy was verified with a binomial multi-fractal sequence (BMS). Then, based on the centrifugal pump test platform, the vibration signals of shell failures under different degrees of cavitation and separate states of loosened foot bolts were collected. The signals’ multi-fractal spectra parameters were analyzed using the EMD-LS-MFDFA method, from which five spectral parameters (Δα, Δf, α0, αmax, and αmin) were extracted for comparison and analysis. The results showed EMD-LS-MFDFA’s performance was closer to the BMS theoretical value than that of MFDFA, displayed high accuracy, and was fully capable of revealing the multiple fractal characteristics of the centrifugal pump fault vibration signal. Additionally, the mean values of the five types of multi-fractal spectral characteristic parameters it extracted were much greater than the normal state values. This indicates that the parameters could effectively distinguish the normal state and fault state of the centrifugal pump. Moreover, α0 and αmax had a smaller mean square than Δα, Δf and αmin, and their stability was higher. Thus, compared to the feature parameters extracted by MFDFA, our method could better realize the separation between the normal state, cavitation (whether slight, moderate, or severe), and when the anchor bolt was loose. This can be used to characterize centrifugal pump failure, quantify and characterize a pump’s different working states, and provide a meaningful reference for the diagnosis and study of pump faults.
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Lopez-Gordo, M. A., D. Sánchez-Morillo, and Marcel A. J. Van Gerven. "Spreading Codes Enables the Blind Estimation of the Hemodynamic Response with Short-Events Sequences." International Journal of Neural Systems 25, no. 01 (January 6, 2015): 1450035. http://dx.doi.org/10.1142/s012906571450035x.
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Finite impulse response (FIR) filters are considered the least constrained option for the blind estimation of the hemodynamic response function (HRF). However, they have a tendency to yield unstable solutions in the case of short-events sequences. There are solutions based on regularization, e.g. smooth FIR (sFIR), but at the cost of a regularization penalty and prior knowledge, thus breaking the blind principle. In this study, we show that spreading codes (scFIR) outperforms FIR and sFIR in short-events sequences, thus enabling the blind and dynamic estimation of the HRF without numerical instabilities and the regularization penalty. The scFIR approach was applied in short-events sequences of simulated and experimental functional magnetic resonance imaging (fMRI) data. In general terms, scFIR performed the best with both simulated and experimental data. While FIR was unable to compute the blind estimation of two simulated target HRFs for the shortest sequences (15 and 31 events) and sFIR yielded shapes barely correlated with the targets, scFIR achieved a normalized correlation coefficient above 0.9. Furthermore, scFIR was able to estimate in a responsive way dynamic changes of the amplitude of a simulated target HRF more accurately than FIR and sFIR. With experimental fMRI data, the ability of scFIR to estimate the real HRF obtained from a training data set was superior in terms of correlation and mean-square error. The use of short-events sequences for the blind estimation of the HRF could benefit patients in terms of scanning time or intensity of magnetic field in clinical tests. Furthermore, short-events sequences could be used, for instance, on the online detection of rapid shifts of visual attention that, according to literature, entails rapid changes in the amplitude of the HRF.
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Gomez-Pelaez, Angel J., Ramon Ramos, Emilio Cuevas, Vanessa Gomez-Trueba, and Enrique Reyes. "Atmospheric CO<sub>2</sub>, CH<sub>4</sub>, and CO with the CRDS technique at the Izaña Global GAW station: instrumental tests, developments, and first measurement results." Atmospheric Measurement Techniques 12, no. 4 (April 3, 2019): 2043–66. http://dx.doi.org/10.5194/amt-12-2043-2019.
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Abstract. At the end of 2015, a CO2/CH4/CO cavity ring-down spectrometer (CRDS) was installed at the Izaña Global Atmosphere Watch (GAW) station (Tenerife, Spain) to improve the Izaña Greenhouse Gases GAW Measurement Programme, and to guarantee the renewal of the instrumentation and the long-term maintenance of this program. We present the results of the CRDS acceptance tests, the raw data processing scheme applied, and the response functions used. Also, the calibration results, the implemented water vapor correction, the target gas injection statistics, the ambient measurements performed from December 2015 to July 2017, and their comparison with other continuous in situ measurements are described. The agreement with other in situ continuous measurements is good most of the time for CO2 and CH4, but for CO it is just outside the GAW 2 ppb objective. It seems the disagreement is not produced by significant drifts in the CRDS CO World Meteorological Organization (WMO) tertiary standards. The more relevant contributions of the present article are (1) determination of linear relationships between flow rate, CRDS inlet pressure, and CRDS outlet valve aperture; (2) determination of a slight CO2 correction that takes into account changes in the inlet pressure/flow rate (as well as its stability over the years), and attributing it to the existence of a small spatial inhomogeneity in the pressure field inside the CRDS cavity due to the gas dynamics; (3) drift rate determination for the pressure and temperature sensors located inside the CRDS cavity from the CO2 and CH4 response function drift trends; (4) the determination of the H2O correction for CO has been performed using raw spectral peak data instead of the raw CO provided by the CRDS and using a running mean to smooth random noise in a long water-droplet test (12 h) before performing the least square fit; and (5) the existence of a small H2O dependence in the CRDS flow and of a small spatial inhomogeneity in the temperature field inside the CRDS cavity are pointed out and their origin discussed.
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Liu, Guanghui, Qiang Li, Lijin Fang, Bing Han, and Hualiang Zhang. "A new joint friction model for parameter identification and sensor-less hand guiding in industrial robots." Industrial Robot: the international journal of robotics research and application 47, no. 6 (July 22, 2020): 847–57. http://dx.doi.org/10.1108/ir-03-2020-0053.
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Purpose The purpose of this paper is to propose a new joint friction model, which can accurately model the real friction, especially in cases with sudden changes in the motion direction. The identification and sensor-less control algorithm are investigated to verify the validity of this model. Design/methodology/approach The proposed friction model is nonlinear and it considers the angular displacement and angular velocity of the joint as a secondary compensation for identification. In the present study, the authors design a pipeline – including a manually designed excitation trajectory, a weighted least squares algorithm for identifying the dynamic parameters and a hand guiding controller for the arm’s direct teaching. Findings Compared with the conventional joint friction model, the proposed method can effectively predict friction factors during the dynamic motion of the arm. Then friction parameters are quantitatively obtained and compared with the proposed friction model and the conventional friction model indirectly. It is found that the average root mean square error of predicted six joints in the proposed method decreases by more than 54%. The arm’s force control with the full torque using the estimated dynamic parameters is qualitatively studied. It is concluded that a light-weight industrial robot can be dragged smoothly by the hand guiding. Practical implications In the present study, a systematic pipeline is proposed for identifying and controlling an industrial arm. The whole procedure has been verified in a commercial six DOF industrial arm. Based on the conducted experiment, it is found that the proposed approach is more accurate in comparison with conventional methods. A hand-guiding demo also illustrates that the proposed approach can provide the industrial arm with the full torque compensation. This essential functionality is widely required in many industrial arms such as kinaesthetic teaching. Originality/value First, a new friction model is proposed. Based on this model, identifying the dynamic parameter is carried out to obtain a set of model parameters of an industrial arm. Finally, a smooth hand guiding control is demonstrated based on the proposed dynamic model.
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"Real Time Implementation of SIGN LMS Adaptive Filters using Xilinx System Generator." International Journal of Mathematics and Computers in Simulation 14 (May 4, 2020). http://dx.doi.org/10.46300/9102.2020.14.2.
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Sign Least Mean Square (SLMS) adaptive filter can adapt dynamically based on corresponding filter output. One of the major applications of adaptive filter is Noise cancellation. In real time applications like medical computing, speed of the process developing hardware is essential hence the hardware realization of SLMS adaptive filter using Xilinx System generator is proposed in this work. The propose architecture aims to reduce convergence rate, path delay and increasing speed. In this work (i) Modified architecture is designed for a 8-tap SLMS adaptive filter and (ii) multiplier less structure for Modified DLMS Filter. The designed architecture tested for ECG signal. The functionality of the algorithm is verified in MATLAB with various ECG data from the MIT-BIH database as input. Both LMS and SLMS are designed, simulated, synthesized and implemented in Virtex-5 FPGA using Xilnix ISE 14.3 . The result shows 5% decrease in total real time router completion and also decrease in the number of adders and subtractors, the maximum combinational path delay has been reduced by 48.84% in Systolic Sign LMS Filter when compared to LMS Filter.
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Ning, Xiaoling, Keyi Zhang, and Lei Zhu. "Cosine function variable step-size transform domain least mean square algorithm based on matrix rotation." AIP Advances 13, no. 9 (September 1, 2023). http://dx.doi.org/10.1063/5.0161492.
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A novel cosine function variable step-size transform domain least mean square (LMS) algorithm based on matrix rotation (COS-ROTDCT-LMS-DFE) was put forward. Based on the discrete cosine transform domain LMS (DCT-LMS) algorithm, it was first processed by matrix rotation in an anticlockwise direction to resolve the problem of self-interference between signals, which effectively sped up the convergence of the algorithm. Subsequently, a cosine function variable step-size algorithm was developed considering that its curve was characterized by a smooth top and fast rising and falling speed so as to further improve the convergence speed of the algorithm. Subsequently, the decision feedback equalizer (DFE) was introduced into the algorithm to compensate signal distortion and noise. The convergence performance of the algorithm was simulated in typical multipath underwater acoustic channels. The simulation results showed that the convergence performance of the new algorithm was equivalent to that of recursive least squares (RLS). The convergence speed of COS-ROTDCT-LMS-DFE was even better than that of the recursive least square decision feedback equalizer (RLS-DFE) algorithm. The new algorithm performed well in terms of both convergence speed and steady-state error and could be easily implemented.
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Zhang, Xiandong, Zhengdao Wang, Hui Yang, Zuchao Zhu, and Yikun Wei. "Performance optimization of the active noise control algorithm based on logarithmic function and its application in secondary channel online identification ANC system." Measurement and Control, November 4, 2022, 002029402211050. http://dx.doi.org/10.1177/00202940221105088.
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In this paper, several methods about the active noise control (ANC) system are studied. The common adaptive recognition system based on the variable step filter x least mean square (FxLMS) algorithm are elaborated. By introducing the momentum term into the variable step algorithm, the value range of the variable step is increased, so as to speed up the convergence of the algorithm and improve the effect of noise reduction. The weight of the control filter based on the improved logarithmic function variable step size least mean square (VSSLMS) algorithm is updated in the improved second channel online recognition algorithm. The initial smooth noise signal, the noise signal of increasing amplitude, and the abrupt noise signal are utilized to verify the convergence speed and the noise reduction effect of the modified ANC method. Numerical results show that the improved algorithm structure could improve the convergence speed and reduce the noises of the whole system at three different primary noises. The initial smooth noise signal, the noise signal of increasing amplitude, and the abrupt noise signal are utilized to verify the convergence speed and the noise reduction effect of the modified ANC method. Numerical results under three different primary noises show that the improved algorithm structure could improve the convergence speed and reduce the noise of the whole system.
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ESTRADA-DÍAZ, JORGE A., OSCAR MARTÍNEZ-ROMERO, DANIEL OLVERA-TREJO, and ALEX ELÍAS-ZÚÑIGA. "ELUCIDATING THE FRACTAL NATURE OF POWDER BED IN SELECTIVE LASER MELTING OF METALLIC COMPONENTS." Fractals, March 24, 2022. http://dx.doi.org/10.1142/s0218348x22500621.
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In this work, the fractal nature of Selective Laser Melting (SLM) additive manufacturing process (AM) is elucidated. Fractal dimension and lacunarity of metallic powders are calculated from Scanning Electron Microscopy (SEM) images adapted from literature. The complexity and homogeneity of the textures of the powder beds are also studied through the comparison of fractal dimension and lacunarity. It is found that better densification results are obtained when the powder bed’s fractal dimension is closer to the golden mean number of 1.618. Furthermore, this finding is extended to expressions for predicting the component’s bulk density produced via SLM by setting the [Formula: see text] exponent equal to the golden mean value and finding the proportionality constant, [Formula: see text], using a nonlinear least squares method. The proposed approach works well since theoretical prediction and experimental data compare well with root-mean-square-error (RMSE) values that do not exceed [Formula: see text]. This work sheds new light on enhancing additive manufacturing technologies considering the fractal nature of SLM since its process mathematical models are constructed around Euclidian space-time with continuous smooth assumptions that should be adapted to include the fractal nature of the manufacturing process aiming to improve their precision. The underlying interweaving of SLM, as a fractal process, and the golden mean number is revealed.
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Maheswari, K. Uma, and S. Sathiyamoorthy. "Fixed grid wavelet network segmentation on diffuse optical tomography image to detect sarcoma." Journal of Applied Research and Technology 16, no. 2 (June 20, 2018). http://dx.doi.org/10.22201/icat.16656423.2018.16.2.706.
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Objective: To detect and explore the boundary of the sarcoma in Diffuse Optical Tomography (DOT) images, we need to extract the scattering and absorption property of the tissue at the cellular level. The DOT images suffer with lower optical resolution; therefore to improve the resolution in non-invasive imaging technique we apply Fixed Grid Wavelet Network (FGWN) image segmentation. Methods: We have subjected the reconstructed optical image to Vignette Correction to enhance the corners so that it traces the smooth boundary of tumor region. Fixed Grid Wavelet Network segmentation applied to reduce the training with the significant ortho-normal property. R, G and B values of optical image were considered as network inputs which lead to the formation of Wavelet network. Effective wavelet selection was based on Orthogonal Least Squares Algorithm and the network weights were calculated to optimize the network structure. The Mexican hat wavelet chosen facilitates the diffusion operator for image restoration, hence well-suited for Diffuse Optical Tomography (DOT) images.Results: Analysis made on data base of 30 DOT images and the 6 criteria results was evaluated. The boundary of the tumor region was traced on grayscale and the following Image Metrics were measured namely Mean Square Error, Root Mean Square Error, Peak Signal to Noise Ratio, Pearson Correlation Coefficient and Mean absolute error. The Receiver Operating Characteristics (ROC) was estimated at 99.527%, 88.73% and 93.8% with respect to sensitivity, specificity and overall accuracy. Conclusions: FGWN was compared with genetic algorithm and graph cut segmentation based on image metrics which exhibited 5.2% improvement and it was evaluated such that FGWN based image segmentation was superior to other methodologies.
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Luna, Aderval, Alexandre Torres, Camilla Cunha, Igor Lima, and Luis Nonato. "Employing Auto-Machine Learning Algorithms for Predicting the Cold Filter Plugging and Kinematic Viscosity at 40 ºC in Biodiesel Blends using Vibrational Spectroscopy Data." Brazilian Journal of Analytical Chemistry, August 31, 2022. http://dx.doi.org/10.30744/brjac.2179-3425.ar-30-2022.
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This work aims to develop an auto-machine learning method using Mid-Infrared (MIR) spectroscopy data to determine the cold filter plugging point (CFPP) and kinematic viscosity at 40 ºC of biodiesel, diesel, and mixtures samples. The biodiesel was obtained by the transesterification reaction and later purified. The first dataset was composed of 108 blends (biodiesel obtained from different biomass such as soy, corn, sunflower, and canola) with binary, ternary and quaternary mixtures. The second dataset was composed of 227 blends of diesel-biodiesel and diesel-biodiesel-ethanol, respectively. The physical properties of the samples were obtained according to ABNT NBR 14747 and ABNT NBR 10441, respectively. The MIR Spectroscopy data were acquired from 7,800 to 450 cm-1, with a 4 cm-1 resolution and 20 scans. The spectra' baseline alignment was carried out using the asymmetric least squares method. A Savitzky–Golay filter was applied to a set of digital data points to smooth the data. This work used a first-order polynomial and a zero derivative function to smooth the spectra. The dataset was split into training and test sets using the function CreateDataPartition from the caret package. It was adopted 70% for training and 30% for test sets. In this work, the model training process was carried out using the open-source Python library LazyPredict. The LazyPredict returns the trained models and their performance metrics. The kinematic viscosity at 40 ºC of the biodiesel samples and their blends could be modeled using the MIR Spectroscopy dataset using different auto-machine learning algorithms. The RMSEP (Root Mean Square Error of Prediction) (≤ 0.02 mm2 s-1) was similar to the experimental error obtained after log transformation. The CFPP of the biodiesel samples and their blends could be modeled using the MIR Spectroscopy dataset by different auto-machine learning algorithms with an RMSEP (≤ 1.6 ºC) similar to the experimental error obtained by traditional methodology. Based on the lower computational time and the same performance observed by the RMSEP and R2 (coefficient of determination) values from different algorithms, it is recommended to use Ridge or Ridge Cross-Validation Regression models for both physical properties using MIR Spectroscopy data.
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Borlinghaus, Moritz, Christian Neyers, and Jan Martin Brockmann. "Development of a continuous spatiotemporal finite element-based representation of the mean sea surface." Journal of Geodesy 97, no. 2 (February 2023). http://dx.doi.org/10.1007/s00190-023-01709-1.
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AbstractThe mean sea surface (MSS) has an important role, both, in the calculation of the mean dynamic topography and in the area of sea-level change as a reference surface. This paper presents a new approach to estimate a continuous spatiotemporal MSS from along-track altimetric sea surface height measurements. A parametric function continuously defined in the spatial as well as temporal domain is constructed from a $$C^1$$ C 1 -smooth finite element space to represent the MSS. Least-squares observation equations are set up, to estimate the unknown scaling coefficients from the sea surface height measurements as collected by altimetric exact repeat missions and geodetic missions. An advantage of the proposed method is that the surface is represented by an analytic model and the unknown parameters can be physically interpreted. Whereas the static component of the function represents the MSS, the temporal component is used to absorb the ocean variability. Within this initial study, 10 years of satellite altimetry over the period 2010–2019 are analyzed in a small study region around the Agulhas Current. To obtain the best possible data coverage, all missions available via AVISO, which provide data in the study region and period, are included, i.e., geodetic missions and mission phases as well as exact repeat missions. Besides the static MSS, the temporal component, which is co-estimated to absorb the dominating ocean variability, is modeled with different basis functions to study their performance. On the one hand, global basis functions considering a linear trend and periodic functions are compared with B-Spline basis functions. The comparison of the static component to the global CNES_CLS15 MSS shows a reasonable agreement with a root-mean-square error in the range of 1–4 cm for the well-suited model configurations. To validate the modeling approach and the different analyzed configurations, the temporal model component is compared to gridded sea-level anomaly products. Although it is not (yet) a target quantity, the analysis can serve as a quality check of the MSS and the proposed modeling approach as well. It is shown that in regions with relatively low ocean variability the combination of a linear trend with an annual period is well suited to model the dominant temporal signal, whereas it is not sufficient in regions with strong ocean variability, e.g., close to the Agulhas Current. In those regions, the scenario which utilizes B-Splines in the temporal domain performs significantly better. In general, it is demonstrated that the proposed approach can be an alternative to the well-established MSS estimation procedures.
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Feng, Zi-Heng, Lu-Yuan Wang, Zhe-Qing Yang, Yan-Yan Zhang, Xiao Li, Li Song, Li He, Jian-Zhao Duan, and Wei Feng. "Hyperspectral Monitoring of Powdery Mildew Disease Severity in Wheat Based on Machine Learning." Frontiers in Plant Science 13 (March 21, 2022). http://dx.doi.org/10.3389/fpls.2022.828454.
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Powdery mildew has a negative impact on wheat growth and restricts yield formation. Therefore, accurate monitoring of the disease is of great significance for the prevention and control of powdery mildew to protect world food security. The canopy spectral reflectance was obtained using a ground feature hyperspectrometer during the flowering and filling periods of wheat, and then the Savitzky–Golay method was used to smooth the measured spectral data, and as original reflectivity (OR). Firstly, the OR was spectrally transformed using the mean centralization (MC), multivariate scattering correction (MSC), and standard normal variate transform (SNV) methods. Secondly, the feature bands of above four transformed spectral data were extracted through a combination of the Competitive Adaptive Reweighted Sampling (CARS) and Successive Projections Algorithm (SPA) algorithms. Finally, partial least square regression (PLSR), support vector regression (SVR), and random forest regression (RFR) were used to construct an optimal monitoring model for wheat powdery mildew disease index (mean disease index, mDI). The results showed that after Pearson correlation, two-band optimization combinations and machine learning method modeling comparisons, the comprehensive performance of the MC spectrum data was the best, and it was a better method for pretreating disease spectrum data. The transformed spectral data combined with the CARS–SPA algorithm was able to extract the characteristic bands more effectively. The number of bands screened was more than the number of bands extracted by the OR data, and the band positions were more evenly distributed. In comparison of different machine learning modeling methods, the RFR model performed the best (coefficient of determination, R2 = 0.741–0.852), while the SVR and PLSR models performed similarly (R2 = 0.733–0.836). Taken together, the estimation accuracy of spectral data transformation using the MC method combined with the RFR model (MC-RFR) was the highest, the model R2 was 0.849–0.852, and the root mean square error (RMSE) and the mean absolute error (MAE) ranged from 2.084 to 2.177 and 1.684 to 1.777, respectively. Compared with the OR combined with the RFR model (OR-RFR), the R2 increased by 14.39%, and the R2 of RMSE and MAE decreased by 23.9 and 27.87%. Also, the monitoring accuracy of flowering stage is better than that of grain filling stage, which is due to the relative stability of canopy structure in flowering stage. It can be seen that without changing the shape of the spectral curve, and that the use of MC to preprocess spectral data, the use of CARS and SPA algorithms to extract characteristic bands, and the use of RFR modeling methods to enhance the synergy between multiple variables, and the established model (MC-CARS-SPA-RFR) can better extract the covariant relationship between the canopy spectrum and the disease, thereby improving the monitoring accuracy of wheat powdery mildew. The research results of this study provide ideas and methods for realizing high-precision remote sensing monitoring of crop disease status.
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Langlie, J. A., B. O. Omontese, A. D. DiCostanzo, R. B. Cox, and M. J. Webb. "Influence of Cattle Backgrounding Systems on Animal and Carcass Characteristics." Meat and Muscle Biology 3, no. 2 (December 1, 2019). http://dx.doi.org/10.22175/mmb.10696.
Full textAbstract:
ObjectivesCattle can be managed differently during the backgrounding segment, which may alter long-term animal and carcass characteristics. Therefore, the objectives of this study were to 1) measure carcass composition over time, and 2) determine the effect of different backgrounding diets on animal growth and carcass characteristics.Materials and MethodsAngus and Angus × Simmental crossed calves (n = 65) were stratified by dam age, birth date, weaning weight, breed, and sex post weaning in a completely randomized design to one of three treatments: (1) perennial pasture (PP; grazing quack grass, orchard grass; smooth brome grass, red clover, and alfalfa); (2) summer annual cover crop (CC; grazing cereal oats, purple top turnips, hunter forage brassica, and graza forage radish); and (3) dry lot (DL; bunk fed a haylage ration consisting of 28 NEm Mcal/50.8 kg DM) during backgrounding for 55 d. Concluding backgrounding, the CC and PP treatments were transported to pens where all treatments were sorted by gender and acclimated to a finishing ration over a period of 14 d and continued receiving 3 step-up diets over the next 25 d. Two pens during the finishing segment were utilized to house heifers and steers, respectively. The heifers were top dressed with melengestral acetate till harvest, which was determined by targeting a common backfat thickness per treatment. From backgrounding to harvest, cattle were weighed to determine body weight (BW), average daily gain (ADG) and hip height (HH) measurements were recorded every 28 d. Five periodic carcass ultrasound measures were recorded to evaluate ultrasound rib eye area (uREA), rib fat thickness (uRFT), and percent intramuscular fat (uIMF). At harvest, carcass measurements included hot carcass weight (HCW), LMA, 12th rib backfat (FT), kidney, pelvic and heart fat (KPH), marbling and maturity score and objective color (L*, a*, b*). Statistical analyses were conducted using mixed model procedures and animal weaning weight was used as a covariate. Least square means were computed and separated using least significant differences when treatment effects were significant at α ≤ 0.05.ResultsCattle ADG, uREA, uRFT, and HH did not differ (P ≥ 0.05) among treatments from backgrounding to harvest (Table 2). Cattle in DL were heavier (P ≤ 0.05) in BW than CC and PP, which were similar (P ≥ 0.05). Percent uIMF was greater (P ≤ 0.05) for DL and similar (P ≥ 0.05) to PP though CC was lower (P ≤ 0.05) and similar (P ≤ 0.05) to PP.ConclusionTreatments utilizing different backgrounding diets influence average body weights and ultrasound intramuscular adipose. Cattle grazing forages have lighter body weights and lower ultrasound intramuscular adipose though, cattle grazing perennial pastures were most variable in carcass ultrasound intramuscular adipose.Table 2Least squares mean performance responses and ultrasound-measured composition (averaged across all periodic measurements) according to the backgrounding treatment1