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Journal articles on the topic 'Multi-response signal-to-noise ratio'

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1

Pal, Surajit, and Susanta Kumar Gauri. "Multi-Response Optimization Using Multiple Regression–Based Weighted Signal-to-Noise Ratio (MRWSN)." Quality Engineering 22, no. 4 (2010): 336–50. http://dx.doi.org/10.1080/08982112.2010.495368.

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Liao, Kefei, Zerui Yu, Ningbo Xie, and Junzheng Jiang. "Joint Estimation of Azimuth and Distance for Far-Field Multi Targets Based on Graph Signal Processing." Remote Sensing 14, no. 5 (2022): 1110. http://dx.doi.org/10.3390/rs14051110.

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Target position estimation is one of the important research directions in array signal processing. In recent years, the research of target azimuth estimation based on graph signal processing (GSP) has sprung up, which provides new ideas for the Direction of Arrival (DoA) application. In this article, by extending GSP-based DOA to joint azimuth and distance estimation and constructing a fully connected graph signal model, a multi-target joint azimuth and distance estimation method based on GSP is proposed. Firstly, the fully connection graph model is established related to the phase information of a linear array. For the fully connection graph, the Fourier transform method is used to solve the estimated response function, and the one-dimensional estimation of azimuth and distance is completed, respectively. Finally, the azimuth and distance estimation information are combined, and the false points in the merging process are removed by using CLEAN algorithm to complete the two-dimensional estimation of targets. The simulation results show that the proposed method has a smaller mean square error than the Multiple Signal Classification (MUSIC) algorithm in azimuth estimation under the condition of a low signal-to-noise ratio and more accurate response values than the MUSIC algorithm in distance estimation under any signal-to-noise ratio in multi-target estimation.
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Pal, Surajit, and Susanta Kumar Gauri. "Optimization of multi-response dynamic systems integrating multiple regression and Taguchi’s dynamic signal-to-noise ratio concept." International Journal of Engineering, Science and Technology 9, no. 1 (2017): 16. http://dx.doi.org/10.4314/ijest.v9i1.2.

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Bao, Yuan, Yan Wang, Panyun Li, et al. "A novel crystal-analyzer phase retrieval algorithm and its noise property." Journal of Synchrotron Radiation 22, no. 3 (2015): 786–95. http://dx.doi.org/10.1107/s1600577515003616.

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A description of the rocking curve in diffraction enhanced imaging (DEI) is presented in terms of the angular signal response function and a simple multi-information retrieval algorithm based on the cosine function fitting. A comprehensive analysis of noise properties of DEI is also given considering the noise transfer characteristic of the X-ray source. The validation has been performed with synchrotron radiation experimental data and Monte Carlo simulations based on theGeant4toolkit combined with the refractive process of X-rays, which show good agreement with each other. Moreover, results indicate that the signal-to-noise ratios of the refraction and scattering images are about one order of magnitude better than that of the absorption image at the edges of low-Zsamples. The noise penalty is drastically reduced with the increasing photon flux and visibility. Finally, this work demonstrates that the analytical method can build an interesting connection between DEI and GDPCI (grating-based differential phase contrast imaging) and is widely suitable for a variety of measurement noise in the angular signal response imaging prototype. The analysis significantly contributes to the understanding of noise characteristics of DEI images and may allow improvements to the signal-to-noise ratio in biomedical and material science imaging.
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Wang, Weijie, Xuben Wang, Xiaodong Yu, et al. "BA-ATEMNet: Bayesian Learning and Multi-Head Self-Attention for Theoretical Denoising of Airborne Transient Electromagnetic Signals." Sensors 25, no. 1 (2024): 77. https://doi.org/10.3390/s25010077.

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Airborne transient electromagnetic (ATEM) surveys provide a fast, flexible approach for identifying conductive metal deposits across a variety of intricate terrains. Nonetheless, the secondary electromagnetic response signals captured by ATEM systems frequently suffer from numerous noise interferences, which impede effective data processing and interpretation. Traditional denoising methods often fall short in addressing these complex noise backgrounds, leading to less-than-optimal signal extraction. To tackle this issue, a deep learning-based denoising network, called BA-ATEMNet, is introduced, using Bayesian learning alongside a multi-head self-attention mechanism to effectively denoise ATEM signals. The incorporation of a multi-head self-attention mechanism significantly enhances the feature extraction capabilities of the convolutional neural network, allowing for improved differentiation between signal and noise. Moreover, the combination of Bayesian learning with a weighted integration of prior knowledge and SNR enhances the model’s performance across varying noise levels, thereby increasing its adaptability to complex noise environments. Our experimental findings indicate that BA-ATEMNet surpasses other denoising models in both single and multiple noise conditions, achieving an average signal-to-noise ratio of 37.21 dB in multiple noise scenarios. This notable enhancement in SNR, compared to the next best model, which achieves an average SNR of 36.10 dB, holds substantial implications for ATEM-based mineral exploration and geological surveys.
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Ahmad, Naseer, Shahid Kamal, Zulfiqar Ali Raza, et al. "Comparative Analysis of the Performances of Six Taguchi-Based Multi-Response Optimisation Techniques for Product Development in Textiles." Fibres and Textiles in Eastern Europe 29, no. 5(149) (2021): 100–108. http://dx.doi.org/10.5604/01.3001.0014.9312.

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Researchers are using different statistical techniques for process optimisation and product development both in academia and industries. Similarly, several statistical tools are being employed in the textile industry for process optimisation during the manufacturing of different products. The purpose of this study was to analyse different Taguchi-based techniques in the multi-response optimisation of selected industrial processes and then to generalise the outcomes. Herein, six different Taguchi-based multi-response optimisation techniques, including grey relational analysis (GRA), the weighted signal-to-noise (WSN) ratio, principal component analysis, VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje), the multiple response signal-to-noise ratio, and Fuzzy logic were compared against three data sets of industrial processes. The researchers herein optimised cotton dyeing, the finishing of textile to make them oleo-hydrophobic, and the production of rhamnolipids (bio-surfactants). The results demonstrated that the Fuzzy logic-based Taguchi method gave the best optimisation amongst all the other approaches, followed by GRA and WSN for all the selected processes. The said statistical techniques were applied to specific textile and biotechnological processes. The outcomes of this study can help researchers in practical implementation in industrial sectors. In this study, a comparative analysis of the performances of six Taguchi-based multi-response optimisation techniques was conducted for potential industrial processes, particularly textile processing .
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Li, Jiangqiao, Li Jiang, Fujian Yu, Ye Zhang, and Kun Gao. "Research on improving measurement accuracy of acoustic transfer function of underwater vehicle." MATEC Web of Conferences 336 (2021): 01006. http://dx.doi.org/10.1051/matecconf/202133601006.

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To address the problem that acoustic transfer functions with underwater platforms cannot be measured accurately, this paper presents a method based on phase compensation to improve the accuracy of acoustic transfer function measurements on underwater platforms. The time-domain impulse response signals with multiple cycles are first collected and intercepted, and then their phase differences are estimated using the least-squares method, and phase compensation is used to align the phases of all the signals, and then the impulse response signals are weighted and averaged over all the impulse response signals to cancel out the random noise. The water pool test proves that this method reduces the measurement random noise while obtaining a high-fidelity time domain transfer function, which effectively improves the signal-to-noise ratio of the measurement. The method adopts only one measurement signal, and without changing the measurement system, the random noise is cancelled out by the in-phase superposition of the multi-cycle impulse response signals to avoid the nonlinear distortion of the measurement results.
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Zuo, Yun, Gebiao Hu, Fan Gan, et al. "Multi-Modal Joint Pulsed Eddy Current Sensor Signal Denoising Method Integrating Inductive Disturbance Mechanism." Sensors 25, no. 12 (2025): 3830. https://doi.org/10.3390/s25123830.

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Pulsed eddy current (PEC) testing technology has been widely used in the field of non-destructive testing of metal grounding structures due to its wide-band excitation and response characteristics. However, multi-source noise in industrial environments can significantly degrade the performance of PEC sensors, thereby limiting their detection accuracy. This study proposes a multi-modal joint pulsed eddy current signal sensor denoising method that integrates the inductive disturbance mechanism. This method constructs the Improved Whale Optimization -Variational Mode Decomposition-Singular Value Decomposition-Wavelet Threshold Denoising (IWOA-VMD-SVD-WTD) fourth-order processing architecture: IWOA adaptively optimizes the VMD essential variables (K, α) and employs the optimized VMD to decompose the perception coefficient (IMF) of the PEC signal. It utilizes the correlation coefficient criterion to filter and identify the primary noise components within the signal, and the SVD-WTD joint denoising model is established to reconstruct each component to remove the noise signal received by the PEC sensor. To ascertain the efficacy of this approach, we compared the IWOA-VMD-SVD-WTD method with other denoising methods under three different noise levels through experiments. The test results show that compared with other VMD-based denoising techniques, the average signal-to-noise ratio (SNR) of the PEC signal received by the receiving coil for 200 noise signals in different noise environments is 24.31 dB, 29.72 dB and 29.64 dB, respectively. The average SNR of the other two denoising techniques in different noise environments is 15.48 dB, 18.87 dB, 18.46 dB and 19.32 dB, 27.13 dB, 26.78 dB, respectively, which is significantly better than other denoising methods. In addition, in practical applications, this method is better than other technologies in denoising PEC signals and successfully achieves noise reduction and signal feature extraction. This study provides a new technical solution for extracting pure and impurity-free PEC signals in complex electromagnetic environments.
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Tanash, Moayad, Raed Al Al Athamneh, Dania Bani Bani Hani, Mumen Rababah, and Zaid Albataineh. "A PDCA Framework towards a Multi-Response Optimization of Process Parameters Based on Taguchi-Fuzzy Model." Processes 10, no. 9 (2022): 1894. http://dx.doi.org/10.3390/pr10091894.

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Multi-response optimization problems investigation is a crucial element in initiatives designed to enhance quality and overall productivity for manufacturing processes. Since no particular algorithm can be employed for all multi-response problems, defining the method that is utilized as a problem-solving technique is a vital step in the process factors optimization. Identifying a formal procedure of implementing the improvement approach in a multi-criteria decision-making problem is a critical need to ensure the consistency and sustainability of the enhancement methods. In this study, a Plan–Do–Check–Act (PDCA) framework is implemented for a case study in the food industry under which a multi-response optimization problem is investigated. The design of experiment (DOE) is used to examine the effect of process parameters on the quality responses by using the Taguchi method to find the optimal setting for each parameter. An orthogonal array (OA) and signal-to-noise (SNR) ratio is employed to investigate the performance characteristics. Each performance characteristic is then converted into a signal-to-noise ratio, and all the ratios are then fed into a fuzzy model to produce a single comprehensive output measure (COM). The average COM values for various factor levels are calculated, and the level that maximizes the COM value for each factor is identified as the optimal level. Results indicated the effectiveness of the applied method to find the optimal factor levels for the multi-response optimization problem under study. The global optimal factor levels that are driven from the fuzzy logic for the studied parameters are 1250, 40, 7.5, and 1:2, for the speed, frying time, cooking time, and the coating ratio, respectively. Means of all the studied quality characteristics were closer to the target values when compared with the initial factors’ settings.
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Rodenacker, Karsten, Klaus Hahn, Gerhard Winkler, and Dorothea P. Auer. "SPATIO-TEMPORAL DATA ANALYSIS WITH NON-LINEAR FILTERS: BRAIN MAPPING WITH fMRI DATA." Image Analysis & Stereology 19, no. 3 (2011): 189. http://dx.doi.org/10.5566/ias.v19.p189-194.

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Spatio-temporal digital data from fMRI (functional Magnetic Resonance Imaging) are used to analyse and to model brain activation. To map brain functions, a well-defined sensory activation is offered to a test person and the hemodynamic response to neuronal activity is studied. This so-called BOLD effect in fMRI is typically small and characterised by a very low signal to noise ratio. Hence the activation is repeated and the three dimensional signal (multi-slice 2D) is gathered during relatively long time ranges (3-5 min). From the noisy and distorted spatio-temporal signal the expected response has to be filtered out. Presented methods of spatio-temporal signal processing base on non-linear concepts of data reconstruction and filters of mathematical morphology (e.g. alternating sequential morphological filters). Filters applied are compared by classifications of activations.
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11

Buchner, Andre, Stefan Hadrath, Roman Burkard, et al. "Analytical Evaluation of Signal-to-Noise Ratios for Avalanche- and Single-Photon Avalanche Diodes." Sensors 21, no. 8 (2021): 2887. http://dx.doi.org/10.3390/s21082887.

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Performance of systems for optical detection depends on the choice of the right detector for the right application. Designers of optical systems for ranging applications can choose from a variety of highly sensitive photodetectors, of which the two most prominent ones are linear mode avalanche photodiodes (LM-APDs or APDs) and Geiger-mode APDs or single-photon avalanche diodes (SPADs). Both achieve high responsivity and fast optical response, while maintaining low noise characteristics, which is crucial in low-light applications such as fluorescence lifetime measurements or high intensity measurements, for example, Light Detection and Ranging (LiDAR), in outdoor scenarios. The signal-to-noise ratio (SNR) of detectors is used as an analytical, scenario-dependent tool to simplify detector choice for optical system designers depending on technologically achievable photodiode parameters. In this article, analytical methods are used to obtain a universal SNR comparison of APDs and SPADs for the first time. Different signal and ambient light power levels are evaluated. The low noise characteristic of a typical SPAD leads to high SNR in scenarios with overall low signal power, but high background illumination can saturate the detector. LM-APDs achieve higher SNR in systems with higher signal and noise power but compromise signals with low power because of the noise characteristic of the diode and its readout electronics. Besides pure differentiation of signal levels without time information, ranging performance in LiDAR with time-dependent signals is discussed for a reference distance of 100 m. This evaluation should support LiDAR system designers in choosing a matching photodiode and allows for further discussion regarding future technological development and multi pixel detector designs in a common framework.
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Zhou, Xiang, Ru Wu, Wen Chen, Meiling Dai, Peibin Zhu, and Xiaomei Xu. "Thresholding Dolphin Whistles Based on Signal Correlation and Impulsive Noise Features Under Stationary Wavelet Transform." Journal of Marine Science and Engineering 13, no. 2 (2025): 312. https://doi.org/10.3390/jmse13020312.

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The time–frequency characteristics of dolphin whistle signals under diverse ecological conditions and during environmental changes are key research topics that focus on the adaptive and response mechanisms of dolphins to the marine environment. To enhance the quality and utilization of passive acoustic monitoring (PAM) recorded dolphin whistles, the challenges faced by current wavelet thresholding methods in achieving precise threshold denoising under low signal-to-noise ratio (SNR) are confronted. This paper presents a thresholding denoising method based on stationary wavelet transform (SWT), utilizing suppression impulsive and autocorrelation function (SI-ACF) to select precise thresholds. This method introduces a denoising metric ρ, based on the correlation of whistle signals, which facilitates precise threshold estimation under low SNR without requiring prior information. Additionally, it exploits the high amplitude and broadband characteristics of impulsive noise, and utilizes the multi-resolution information of the wavelet domain to remove impulsive noise through a multi-level sliding window approach. The SI-ACF method was validated using both simulated and real whistle datasets. Simulated signals were employed to evaluate the method’s denoising performance under three types of typical underwater noise. Real whistles were used to confirm its applicability in real scenarios. The test results show the SI-ACF method effectively eliminates noise, improves whistle signal spectrogram visualization, and enhances the accuracy of automated whistle detection, highlighting its potential for whistle signal preprocessing under low SNR.
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Du, Jinzhi, Weijia Cui, Bin Ba, Chunxiao Jian, and Liye Zhang. "Joint Estimation for Time Delay and Direction of Arrival in Reconfigurable Intelligent Surface with OFDM." Sensors 22, no. 18 (2022): 7083. http://dx.doi.org/10.3390/s22187083.

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Recently, the joint estimation for time delay (TD) and direction of arrival (DOA) has suffered from the high complexity of processing multi-dimensional signal models and the ineffectiveness of correlated/coherent signals. In order to improve this situation, a joint estimation method using orthogonal frequency division multiplexing (OFDM) and a uniform planar array composed of reconfigurable intelligent surface (RIS) is proposed. First, the time-domain coding function of the RIS is combined with the multi-carrier characteristic of the OFDM signal to construct the coded channel frequency response in tensor form. Then, the coded channel frequency response covariance matrix is decomposed by CANDECOMP/PARAFAC (CPD) to separate the signal subspaces of TD and DOA. Finally, we perform a one-dimensional (1D) spectral search for TD values and a two-dimensional (2D) spectral search for DOA values. Compared to previous efforts, this algorithm not only enhances the adaptability of coherent signals, but also greatly decreases the complexity. Simulation results indicate the robustness and effectiveness for the proposed algorithm in independent, coherent, and mixed multipath environments and low signal-to-noise ratio (SNR) conditions.
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Xiaoyu, X., C. Lian, Q. Weiwei, and J. Ge. "Study on a micro radiation probe with high SNR based on CdZnTe detector." Journal of Instrumentation 17, no. 06 (2022): P06001. http://dx.doi.org/10.1088/1748-0221/17/06/p06001.

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Abstract In this paper, we develop a micro radiation probe with a CdZnTe detector. To obtain a high signal-to-noise ratio(SNR) performance, circuit simulation and analysis were performed before circuit design, to evaluate the signal characters and frequency response of the electronic design. We adopted a method to evaluate the electronic noise level of specific readout circuits design and determined a better timing parameter of the shaping circuit. Our results confirmed that using a proper design of charge sensitive amplifier(CSA) circuit and the filter system on the readout circuit improved the energy resolution of the probe. Energy spectrum measurement using a 137Cs source showed that the energy resolution of 662 keV γ-ray can reach 2.6%. This enables this microprobe for multi-nuclides recognition or nuclides to monitor in nuclear medicine applications.
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Liu, Yifan, Junqi Yang, Bingyan Wu та ін. "High SNR Φ-OTDR with Multi-Transverse Modes Heterodyne Matched-Filtering Technology". Sensors 21, № 22 (2021): 7460. http://dx.doi.org/10.3390/s21227460.

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Phase-sensitive optical time domain reflectometer (Φ-OTDR) has attracted attention in scientific research and industry because of its distributed dynamic linear response to external disturbances. However, the signal-to-noise ratio (SNR) of Φ-OTDR is still a limited factor by the weak Rayleigh Backscattering coefficient. Here, the multi-transverse modes heterodyne matched-filtering technology is proposed to improve the system SNR. The capture efficiency and nonlinear threshold are increased with multiple transverse modes in few-mode fibers; the incident light energy is permitted to be enlarged by a wider probe pulse by using heterodyne matched-filtering without spatial resolution being deteriorated. As far as we know, this is the first time that both multi-transverse modes integration method and digital heterodyne matched filtering method have been used to improve the SNR of Φ-OTDR simultaneously. Experimental results show that the noise floor is reduced by 11.4 dB, while the target signal is kept. We believe that this proposed method will help DAS find important applications in marine acoustic detection and seismic detection.
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Elnemr, Youssef Ezzat, Aya Abu-Libdeh, Gian Carlo Antony Raj, et al. "Multi-Transduction-Mechanism Technology, an Emerging Approach to Enhance Sensor Performance." Sensors 23, no. 9 (2023): 4457. http://dx.doi.org/10.3390/s23094457.

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Conventional sensor systems employ single-transduction technology where they respond to an input stimulus and transduce the measured parameter into a readable output signal. As such, the technology can only provide limited corresponding data of the detected parameters due to relying on a single transformed output signal for information acquisition. This limitation commonly results in the need for utilizing sensor array technology to detect targeted parameters in complex environments. Multi-transduction-mechanism technology, on the other hand, may combine more than one transduction mechanism into a single structure. By employing this technology, sensors can be designed to simultaneously distinguish between different input signals from complex environments for greater degrees of freedom. This allows a multi-parameter response, which results in an increased range of detection and improved signal-to-noise ratio. In addition, utilizing a multi-transduction-mechanism approach can achieve miniaturization by reducing the number of required sensors in an array, providing further miniaturization and enhanced performance. This paper introduces the concept of multi-transduction-mechanism technology by exploring different candidate combinations of fundamental transduction mechanisms such as piezoresistive, piezoelectric, triboelectric, capacitive, and inductive mechanisms.
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Bhone, Nitin, Nilesh Diwakar, and S. S. Chinchanikar. "Multi-response optimization for AISI M7 Hard Turning Using the utility concept." Scientific Temper 14, no. 01 (2023): 142–49. http://dx.doi.org/10.58414/scientifictemper.2023.14.1.16.

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The utility idea is used to optimize AISI M7 hard turning in the present study. This study uses the Taguchi optimization approach to examine the effects of insert nose radius and machining parameters such as cutting speed, feed rate, and depth of cut on surface roughness (Ra) and material removal rate (MRR) in a turning operation. The signal-to-noise (S/N) ratio is used to analyze the performance characteristics in the turning of AISI M7 employing nose radius of 0.4, 0.8, and 1.2 mm carbide inserts on CNC turning centre in a three-level, four-parameter design of experiment using L9 orthogonal array using MINITAB 17. Every trial is held in a dry setting. According to the results of the current investigation, feed rate and nose radius are the most important variables affecting surface roughness and material removal rate.
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Dang, Bo, Ling Yang, Changzan Liu, et al. "A Uniform Linear Multi-Coil Array-Based Borehole Transient Electromagnetic System for Non-Destructive Evaluations of Downhole Casings." Sensors 18, no. 8 (2018): 2707. http://dx.doi.org/10.3390/s18082707.

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Borehole transient electromagnetic (TEM) techniques have been proven to be efficient for nondestructive evaluations (NDEs) of metal casings using eddy-current properties. However, physical limitations and bad borehole conditions restrict the use of eddy-current sensors, which makes downhole casing inspections very different from those of conventional NDE systems. In this paper, we present a uniform linear multi-coil array-based borehole TEM system for NDEs of downhole casings. On the basis of the borehole TEM signal model, a numerical multi-coil array approach using the Gauss–Legendre quadrature is derived. The TEM response can be divided into two independent parts related to the transmitting-receiving distance (TRD) and the observation time and casing thickness. Using this property, the signal received by the multi-coil array is weighted to cancel the influence of the TRDs of the different array elements to obtain the optimal response according to the linearly constrained minimum variance criterion, which can be shown to be identical to that of achieving the maximum signal-to-noise ratio. The effectiveness of the proposed method was verified by applying the uniform linear multi-coil array to a borehole TEM system for NDEs of oil-well casings. Field experiments were conducted, and the results demonstrate the effectiveness of the proposed method.
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De Araújo, Rodrigo Rossi, Débora Marcondes Bastos Pereira Milori, Milene Corso Mitsuyuki, Célia Regina Montes, and Adolpho José Melfi. "Laser-Induced Breakdown Spectroscopy Optimization Using Response Surface Methodology." Brazilian Journal of Experimental Design, Data Analysis and Inferential Statistics 3, no. 1 (2024): 71–84. http://dx.doi.org/10.55747/bjedis.v3i1.57177.

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Laser-induced breakdown spectroscopy (LIBS) is a modern analytical technique that is capable of fast, multi-elemental, lowcost and environmental friendly analysis, which does not require complex sample preparation. Albeit its potential, LIBS analysis still presents many limitations in terms of sensitivity and reproducibility, especially when analyzing complex matrices such as of sediments and soil samples. In order to reduce these matrix related effects, it is highly recommended that the system temporal parameters, which are responsible for controlling plasma evolution and signal collection, are optimized beforehand. In this work, we proposed the design of experiments (DOE) tool – specifically, the response surface methodology (RSM) – as an approach to optimize LIBS’s most important parameters (delay-time, interpulse delay, gate width and accumulated pulse). Signal-to-noise ratio (SNR) of the emission lines for Zn, Cd, Mg, Al, Ni, Cu, Ca, Cr, Sr, Fe were the response variable assessed during the procedure. The results showed that the RSM was an effective optimization tool for LIBS parameters and the final condition improved SNR ratios by up to a 48 ratio, when comparing to the not-optimal conditions.
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Ghazali, Farizah Adliza, Yupiter HP Manurung, and Mohamed Ackiel Mohamed. "Multi-Response Optimization Using Taguchi Method of Resistance Spot Welding Parameters." Applied Mechanics and Materials 660 (October 2014): 120–24. http://dx.doi.org/10.4028/www.scientific.net/amm.660.120.

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This paper presents the effects of spot welding parameters on multi-response weld quality and optimizes the governing parameters of Resistance Spot Welding (RSW) towards the tensile shear strength and nugget size using Taguchi method. The main affecting welding parameters such as electrode force, weld time and weld current were determined as the basis for quality evaluation. The selected quality features were classified into shear strength and nugget size which were required for application with highly dynamic loading. The optimum welding parameter was obtained using multi signal-to-noise ratio (MSNR) and the significant level of welding parameters was further analysed using analysis of variance (ANOVA). Based on the results, electrode force was found to be most effective parameter affecting shear strength and nugget size. The experiment was conducted at optimum welding parameter and validated the used of multi-objective Taguchi method for enhancing performance and optimizing the welding parameters in RSW process.
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Peng, Xiaoye, Zhiyu Wang, Jiongjiong Mo, Chenge Wang, Jiarui Liu, and Faxin Yu. "A Blind Calibration Model for I/Q Imbalances of Wideband Zero-IF Receivers." Electronics 9, no. 11 (2020): 1868. http://dx.doi.org/10.3390/electronics9111868.

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Frequency-dependent I/Q imbalance and frequency-independent I/Q imbalance are the major impairments in wideband zero-IF receivers, and they both cannot be ignored. In this paper, a blind calibration model is designed for compensating these I/Q imbalances. In order to accurately estimate the imbalance parameters with low cost, a classification rule is proposed according to the frequency-domain statistical characteristics of the received signal. The calibration points in the frequency-domain are divided into two groups. Then, the amplitude imbalance and the frequency-dependent phase imbalance are derived from the group of signal points and, separately, the frequency-independent phase imbalance is calculated from the group of noise points. In the derivation of the frequency-dependent phase imbalance, a general fitting model suitable for all signal points is proposed, which does not require special calculations for either DC point or fs/2 point. Then, a finite impulse response (FIR) real-valued filter is designed to correct the impairments of received signal. The performances of the proposed calibration model are evaluated through both simulations and experiments. The simulation results show the image rejection ratio (IRR) improvement to around 35–45 dBc at high signal-to-noise ratio (SNR). Based on the mismatched data of the ADRV9009 evaluation board, the experimental results exhibit the IRR improvement of both multi-tone and wideband signals to about 30 dBc.
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Mohamed, Mohamed Ackiel, Yupiter HP Manurung, Mohammad Ridzwan Abdul Rahim, Norasiah Muhammad, and Farizah Adliza Ghazali. "Optimization of Friction Stir Welding Parameters with Simultaneous Multiple Response Consideration Using Multi-Objective Taguchi Method." Advanced Materials Research 974 (June 2014): 408–12. http://dx.doi.org/10.4028/www.scientific.net/amr.974.408.

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This paper presents an unconventional method to optimize the governing process parameters of Friction Stir Welding (FSW) towards the mechanical properties and weld quality. The optimization approach attempts to consider simultaneously the multiple quality characteristics namely tensile strength, nugget zone hardness and weld quality class using Multi-objective Taguchi Method (MTM). The experimental study was conducted for plate thickness of 6.0 mm under different rotational and traverse speed. The optimum welding parameters were investigated using Taguchi method with L9 orthogonal array. The significant level of the welding parameters is to be investigated by using analysis of variance (ANOVA). Furthermore, the optimum value was analyzed by means of MTM which involved the calculation of total normalized quality loss (TNQL) and multi signal to noise ratio (MSNR).
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Liu, Yi, Liye Zhao, and Xukai Ding. "A Low-Frequency Multi-Band Piezoelectric MEMS Acoustic Sensor Inspired by Ormia ochracea." Micromachines 16, no. 4 (2025): 451. https://doi.org/10.3390/mi16040451.

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Since the discovery of the unique auditory system of the Ormia ochracea fly, researchers have designed various directional acoustic sensors inspired by its principles. However, most of these sensors operate only within a single- or dual-frequency band and typically exhibit high eigenfrequencies, making them unsuitable for low-frequency applications. This paper proposes a low-frequency, multi-band piezoelectric MEMS acoustic sensor that incorporates an improved coupling structure within the inner diaphragm to enable low-frequency signal detection in a compact design. Additionally, an asymmetric wing and coupled structure are introduced in both the inner and outer diaphragms to achieve multi-band frequency response. Aluminum nitride (AlN), a material with low dielectric and acoustic losses, is selected as the piezoelectric material. The sensor operates in the d₃₃ mode and employs a branched comb-like interdigitated electrode design to enhance the signal-to-noise ratio (SNR). Simulation results demonstrate that the four eigenfrequencies of the sensor are evenly distributed below 2000 Hz, and at all eigenfrequencies, the sensor exhibits a consistent cosine response to variations in the incident elevation angle of the sound source.
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Skoe, Erika, and Nina Kraus. "Neural Delays in Processing Speech in Background Noise Minimized after Short-Term Auditory Training." Biology 13, no. 7 (2024): 509. http://dx.doi.org/10.3390/biology13070509.

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Background noise disrupts the neural processing of sound, resulting in delayed and diminished far-field auditory-evoked responses. In young adults, we previously provided evidence that cognitively based short-term auditory training can ameliorate the impact of background noise on the frequency-following response (FFR), leading to greater neural synchrony to the speech fundamental frequency(F0) in noisy listening conditions. In this same dataset (55 healthy young adults), we now examine whether training-related changes extend to the latency of the FFR, with the prediction of faster neural timing after training. FFRs were measured on two days separated by ~8 weeks. FFRs were elicited by the syllable “da” presented at a signal-to-noise ratio (SNR) of +10 dB SPL relative to a background of multi-talker noise. Half of the participants participated in 20 sessions of computerized training (Listening and Communication Enhancement Program, LACE) between test sessions, while the other half served as Controls. In both groups, half of the participants were non-native speakers of English. In the Control Group, response latencies were unchanged at retest, but for the training group, response latencies were earlier. Findings suggest that auditory training can improve how the adult nervous system responds in noisy listening conditions, as demonstrated by decreased response latencies.
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25

Medvetskyi, M., M. Beshley, and H. Beshley. "Enhancing video quality in wireless software-defined networks using multi-connection." Information and communication technologies, electronic engineering 3, no. 2 (2023): 1–11. http://dx.doi.org/10.23939/ictee2023.02.001.

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This article presents a method to enhance the quality of video streaming using multi-connection in software-defined wireless networks. The method utilizes bonding technology to combine various physical network adapters into a single integrated device, effectively utilizing network resources and ensuring high-quality video transmission. The study also includes an analysis of the use of the Peak Signal-to-Noise Ratio (PSNR) as a quality perception metric and the method's response to deteriorating communication channel parameters. The results indicate that an increase in packet loss leads to a decrease in PSNR values. This is explained by the fact that packet loss introduces errors or noise into the compressed signal, reducing its accuracy compared to the original. Specifically, packet loss can be caused by insufficient network bandwidth when the volume of data attempting to pass through the network exceeds its capacity. In such cases, packet loss can lead to a deterioration in the quality of the video stream. On the other hand, packet loss due to radio signal instability may result from external factors such as obstacles or interference, which can lead to packet loss even with sufficient network bandwidth. These losses can also impact the quality of the video stream and reduce PSNR values. During the study, it was observed that an equal percentage of packet losses caused by insufficient network bandwidth and losses due to radio signal instability have different effects on image perception quality. In general, the overall research results confirm the potential for significant improvement in the quality of service for wireless network users using the proposed method, without requiring additional network resource expenditures. This can have significant practical implications for advancing network technologies and ensuring high-quality video data transmission in modern wireless networks.
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26

Shen, Ronghe, Zixing Zhou, Guidong Xu, et al. "Adaptive Beamforming Damage Imaging of Lamb Wave Based on CNN." Applied Sciences 15, no. 7 (2025): 3801. https://doi.org/10.3390/app15073801.

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Among damage imaging methods based on Lamb waves, the Minimum Variance Distortionless Response (MVDR) method adaptively calculates channel weights to suppress interference signals, improving imaging resolution and the signal-to-noise ratio (SNR). However, the MVDR method involves matrix inversion, which introduces a high computational burden to the implementation process and makes real-time damage detection challenging. We propose constructing a Convolutional-Neural-Network (CNN)-based network architecture based on the Delay-and-Sum (DAS) beamforming method. This architecture replaces the MVDR’s adaptive weight calculation by establishing a nonlinear mapping from multi-channel data to weighting factors, enabling efficient high-resolution Lamb wave damage imaging with an enhanced SNR. To verify the effectiveness and imaging performance of the CNN-based method, damage in an aluminum plate is imaged using both simulation and experimental methods. The imaging results are compared and analyzed against those of the DAS and MVDR methods. The results show that the proposed CNN-based adaptive Lamb wave beamforming method, which combines the advantages of a high resolution and signal-to-noise ratio, as well as rapid imaging, can provide reference and support for real-time Lamb-wave-based Structural Health Monitoring (SHM).
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27

Fan, Liming, Shuhai Yu, Xing Zhong, et al. "A General Relative Radiometric Correction Method for Vignetting Noise Drift." Remote Sensing 15, no. 21 (2023): 5129. http://dx.doi.org/10.3390/rs15215129.

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Due to the limitation of the number of sensor pixels, optical splicing is commonly used to improve the imaging width of remote sensing satellites, and this optical stitching can cause vignetting in the image data of adjacent sensors. The weak energy, low signal-to-noise ratio, and poor response stability of vignetting are key factors that restrict the relative radiometric correction of optical splicing remote satellites. This paper proposes a stability analysis method and a relative radiometric correction method for vignetting. First, we analyzed the stability of the response and the noise impact of vignetting. Massive data from the Jilin-1 GF03D satellites was used to analyze the stability of the response using the vignetting stability analysis method. Secondly, the data on the deep sea during nighttime (DDSN) of Jilin-1 GF03D satellites was used to obtain the characteristics of the sensors’ noise. Thirdly, by building a noise drift model, we calculated the coefficient of the noise drift according to its characteristics. Using the coefficient to eliminate the noise drift of each pixel in vignetting can improve the response stability of vignetting. The average response stability increased by 37.64% by this method. Finally, the automatic relative radiometric correction method was completed through histogram matching. Furthermore, we proposed color aberration metrics (CAMs) to evaluate the multi-spectral images after relative radiometric correction, and massive data from the 16 satellites of Jilin-1 GF03D was used to verify the effectiveness and generality. The experimental results show that the average CAM of the images increased by 15.97% using the proposed method compared to the traditional method.
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28

Ai, Lingmei, Yunfan Shi, Ruoxia Yao, and Liangfu Li. "MFCA-MICNN: a convolutional neural network with multiscale fast channel attention and multibranch irregular convolution for noise removal in dMRI." Physics in Medicine & Biology 69, no. 21 (2024): 215003. http://dx.doi.org/10.1088/1361-6560/ad8294.

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Abstract Diffusion magnetic resonance imaging (dMRI) currently stands as the foremost noninvasive method for quantifying brain tissue microstructure and reconstructing white matter fiber pathways. However, the inherent free diffusion motion of water molecules in dMRI results in signal decay, diminishing the signal-to-noise ratio (SNR) and adversely affecting the accuracy and precision of microstructural data. In response to this challenge, we propose a novel method known as the Multiscale Fast Attention-Multibranch Irregular Convolutional Neural Network for dMRI image denoising. In this work, we introduce Multiscale Fast Channel Attention, a novel approach for efficient multiscale feature extraction with attention weight computation across feature channels. This enhances the model’s capability to capture complex features and improves overall performance. Furthermore, we propose a multi-branch irregular convolutional architecture that effectively disrupts spatial noise correlation and captures noise features, thereby further enhancing the denoising performance of the model. Lastly, we design a novel loss function, which ensures excellent performance in both edge and flat regions. Experimental results demonstrate that the proposed method outperforms other state-of-the-art deep learning denoising methods in both quantitative and qualitative aspects for dMRI image denoising with fewer parameters and faster operational speed.
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29

Yin, Fan, Chao Li, Haibin Wang, and Fan Yang. "Automatic Tracking of Weak Acoustic Targets within Jamming Environment by Using Image Processing Methods." Applied Sciences 12, no. 13 (2022): 6698. http://dx.doi.org/10.3390/app12136698.

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Bear time records, which are the accumulations of spatial spectrum estimates on the time axis, are often employed for passive sonar information processing. Multi-target jamming is a common difficulty in this approach due to the constraints of Rayleigh limit, and neither the conventional beamforming (CBF) nor minimum variance distortionless response (MVDR) technique can handle it well. This work presents a post-processing tracking framework based on visual pattern recognition algorithms to track weak acoustic targets within jamming environments, which includes target motion analysis, matched filtering, and principal component analysis-based denoising, and we call this ‘P-Gabor’ algorithm. The simulations and sea-trial experiments show that the proposed method can track a weak target successfully under −23 dB (signal-to-interference ratio) SIR, which is more effective than the references, especially in terms of using real-world data from sea trials. We further demonstrate that the method also has stable tracking performance at even −25 dB SNR (signal-to-noise ratio) circumstances.
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30

Ji, Yingying, Jun Gao, Xing Shao, and Cuixiang Wang. "Research on multi-path quadratic convolutional neural network-based bearing fault diagnosis." Insight - Non-Destructive Testing and Condition Monitoring 66, no. 12 (2024): 758–66. https://doi.org/10.1784/insi.2024.66.12.758.

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In real-world complex situations, high levels of noise from the surroundings and other component resonances frequently distort collected vibration signals, giving the collected data non-linear features. This research presents a multi-path quadratic convolutional neural network (MPQCNN) for bearing fault diagnosis in response to the issue of the low generalisation performance of traditional deep learning-based bearing fault diagnosis methods and their limited diagnostic capabilities in noisy situations. The proposed MPQCNN combines an attention mechanism and a residual structure, utilising the potent feature representation capability of quadratic neurons to process the input in noisy situations. By using dilated convolutions with different dilation rates, the receptive field of the MPQCNN is expanded and the multi-scale features obtained are fused to enhance the fault diagnosis capability. Moreover, a dynamic balance adaptive threshold residual block is used to enhance the robustness of the model. To perform pertinent experiments, the MPQCNN uses bearing datasets from the Southeast University and Case Western Reserve University (CWRU). The results show that the suggested approach has strong noise immunity. The diagnostic accuracy of the MPQCNN for the CWRU and Southeast University bearing datasets can reach up to 100% when the signal-to-noise ratio (SNR) is 6.
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31

Ding, Dan, Ye Li, Peng Zhao, Kaitai Li, Sheng Jiang, and Yanxiu Liu. "Single Infrared Image Stripe Removal via Residual Attention Network." Sensors 22, no. 22 (2022): 8734. http://dx.doi.org/10.3390/s22228734.

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The non-uniformity of the readout circuit response in the infrared focal plane array unit detector can result in fixed pattern noise with stripe, which seriously affects the quality of the infrared images. Considering the problems of existing non-uniformity correction, such as the loss of image detail and edge blurring, a multi-scale residual network with attention mechanism is proposed for single infrared image stripe noise removal. A multi-scale feature representation module is designed to decompose the original image into varying scales to obtain more image information. The product of the direction structure similarity parameter and the Gaussian weighted Mahalanobis distance is used as the similarity metric; a channel spatial attention mechanism based on similarity (CSAS) ensures the extraction of a more discriminative channel and spatial feature. The method is employed to eliminate the stripe noise in the vertical and horizontal directions, respectively, while preserving the edge texture information of the image. The experimental results show that the proposed method outperforms four state-of-the-art methods by a large margin in terms of the qualitative and quantitative assessments. One hundred infrared images with different simulated noise intensities are applied to verify the performance of our method, and the result shows that the average peak signal-to-noise ratio and average structural similarity of the corrected image exceed 40.08 dB and 0.98, respectively.
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32

Dewantoro, Gunawan. "Multi-objective Optimization Scheme for PID-Controlled DC Motor." International Journal of Power Electronics and Drive Systems (IJPEDS) 7, no. 3 (2016): 734. http://dx.doi.org/10.11591/ijpeds.v7.i3.pp734-742.

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DC Motor is the most basic electro-mechanical equipment and well-known for its merit and simplicity. The performance of DC motor is assessed based on several qualities that are most-likely contradictory each other, i.e. settling time and overshoot percentage. Most of controllers optimization problems are multi-objective in nature since they normally have several conflicting objectives that must be met simultaneously. In this study, the grey relational analysis (GRA) was combined with Taguchi method to search the optimum PID parameter for multi-objective problem. First, a L<sub>9 </sub>(3<sup>3</sup>) orthogonal array was used to plan out the processing parameters that would affect the DC motor’s speed. Then GRA was applied to overcome the drawback of single quality characteristics in the Taguchi method, and then the optimized PID parameter combination was obtained for multiple quality characteristics from the response table and the response graph from GRA. Signal-to-noise ratio (S/N ratio) calculation and analysis of variance (ANOVA) would be performed to find out the significant factors. Lastly, the reliability and reproducibility of the experiment was verified by confirming a confidence interval (CI) of 95%.
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33

Majumder, Himadri, and Kalipada Maity. "Optimization of Machining Condition in WEDM for Titanium Grade 6 Using MOORA Coupled with PCA — A Multivariate Hybrid Approach." Journal of Advanced Manufacturing Systems 16, no. 02 (2017): 81–99. http://dx.doi.org/10.1142/s0219686717500068.

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This paper represents a multivariate hybrid approach, combining Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) and Principal Component Analysis (PCA) to optimize different correlated responses during Wire Electrical Discharge Machining (WEDM) process of titanium grade 6. The response parameters selected are the average cutting speed, average Kerf width and average surface roughness (Ra). All of them have been studied in terms of pulse-ON time, pulse-OFF time, wire feed and wire tension. As indicated by Taguchi’s signal-to-noise ratio, the optimum process parameters were achieved for the desired average cutting speed, average Kerf width and average surface roughness, respectively. At last, the optimum combination of process parameters was validated by affirmation test which gave considerably improved various quality characteristics. Confirmation test outcome revealed that multivariate hybrid approach MOORA coupled with PCA was a competent strategy to decide available cutting parameters for a desired response quality for WEDM of titanium grade 6.
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34

Kuo, Chung-Feng Jeffrey, and Wen-Tsung Lin. "A study of multi-quality processing parameter optimization for sueded fabric." Textile Research Journal 87, no. 4 (2016): 389–98. http://dx.doi.org/10.1177/0040517516631314.

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Sueded fabric quality control depends on the processing parameter settings. The quality characteristics considered in this study are surface softness and color difference. The Taguchi method was combined with gray relational analysis (GRA) to optimize the multi-quality sueding processing parameter combinations. First, an orthogonal array is designed by using the design of experiments of the Taguchi method for the major processing parameters of the sueding machine. The signal/noise ratio and analysis of variance are calculated from the measured fabric surface softness and color difference data, significant factors influencing the quality characteristics obtained, and GRA used to remedy the deficiency in the Taguchi method, which is only applicable to single-quality characteristics. The optimum processing parameters of multiple-quality characteristics are obtained from the response table and response diagram of GRA. The quality of suede fabric can be controlled effectively by using the optimum processing parameters to set the processing parameters, and the 95% confidence interval validates the reliability and reproducibility of the experiment.
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35

Fedai, Yusuf, and Hediye Kirli Akin. "Optimization of machining parameters in face milling using multi-objective Taguchi technique." Tehnički glasnik 12, no. 2 (2018): 104–8. http://dx.doi.org/10.31803/tg-20180201125123.

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In this research, the effect of machining parameters on the various surface roughness characteristics (arithmetic average roughness (Ra), root mean square average roughness (Rq) and average maximum height of the profile (Rz)) in the milling of AISI 4140 steel were experimentally investigated. Depth of cut, feed rate, cutting speed and the number of insert were considered as control factors; Ra, Rz and Rq were considered as response factors. Experiments were designed considering Taguchi L9 orthogonal array. Multi signal-to-noise ratio was calculated for the response variables simultaneously. Analysis of variance was conducted to detect the significance of control factors on responses. Moreover, the percent contributions of the control factors on the surface roughness were obtained to be the number of insert (71.89 %), feed (19.74 %), cutting speed (5.08%) and depth of cut (3.29 %). Minimum surface roughness values for Ra, Rz and Rq were obtained at 325 m/min cutting speed, 0.08 mm/rev feed rate, 1 number of insert and 1 mm depth of cut by using multi-objective Taguchi technique.
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36

Li, Yuan, Song Gao, Saimin Zhang, Hu He, Pengfei Xian, and Chunmei Yuan. "The baseline wander correction based on the improved ensemble empirical mode decomposition (EEMD) algorithm for grounded electrical source airborne transient electromagnetic signals." Geoscientific Instrumentation, Methods and Data Systems 9, no. 2 (2020): 443–50. http://dx.doi.org/10.5194/gi-9-443-2020.

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Abstract. The grounded electrical source airborne transient electromagnetic (GREATEM) system is an important method for obtaining subsurface conductivity distribution as well as outstanding detection efficiency and easy flight control. However, there are the superposition of desired signals and various noises for the GREATEM signal. The baseline wander caused by the receiving coil motion always exists in the process of data acquisition and affects measurement results. The baseline wander is one of the main noise sources, which has its own characteristics such as being low frequency, large amplitude, non-periodic, and non-stationary and so on. Consequently, it is important to correct the GREATEM signal for an inversion explanation. In this paper, we propose improving the method of ensemble empirical mode decomposition (EEMD) by adaptive filtering (EEMD-AF) based on EEMD to suppress baseline wander. Firstly, the EEMD-AF method will decompose the electromagnetic signal into multi-stage intrinsic mode function (IMF) components. Subsequently, the adaptive filter will process higher-index IMF components containing the baseline wander. Lastly, the de-noised signal will be reconstructed. To examine the performance of our introduced method, we processed the simulated and field signal containing the baseline wander by different methods. Through the evaluation of the signal-to-noise ratio (SNR) and mean-square error (MSE), the result indicates that the signal using the EEMD-AF method can get a higher SNR and lower MSE. Comparing correctional data using the EEMD-AF and the wavelet-based method in the anomaly curve profile images of the response signal, it is proved that the EEMD-AF method is practical and effective for the suppression of the baseline wander in the GREATEM signal.
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37

Chen, Yan Xiang, Yu He Li, Yong Rong Qiu, Kai Sen Guan, and Da Peng Zhao. "Dynamic Characteristics Test of a Bearing Micro Workbench Used for Fiber Measurement." Applied Mechanics and Materials 333-335 (July 2013): 117–21. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.117.

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Dynamic characteristics of a bearing micro workbench are very important in the optical fiber measurement. A quantitative analysis method used for measuring the modal frequency and damping rate of the micro workbench is studied. A parameter identification method based on modal analysis is proposed. On the basis of it, a test system used for measuring the micro workbench impulse response is put up. By making use of the impulse excitation that is generated by the instantaneous change of the surface airflow, the micro bench impulse response curve is obtained. In order to improve the signal-to-noise ratio, a series of methods are utilized, such as precise trigger sampling and superposed average of multi-period data. Experimental results show that the micro workbench has a high modal frequency and a suitable damping rate, which meets the needs of the fiber measurement.
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38

Meng, David, Angela Wong, Jennifer Clemesha, and Jorge Mejia. "Dissociated neural tracking as an index of speech understanding in background noise." Journal of the Acoustical Society of America 154, no. 4_supplement (2023): A333. http://dx.doi.org/10.1121/10.0023711.

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Human brain activity has been shown to track hierarchical linguistic units embedded in connected speech and these responses can be directly modulated by changes in speech intelligibility caused by spectral degradation or prior knowledge. In this study, we introduce a background noise and manipulate its level relative to the target speech to test the hypothesis that the tracking responses are modulated differently by the variations in Signal-to-Noise Ratio (SNR) and changes in speech intelligibility. Electroencephalography (EEG) responses to speech in quiet and in noise (multi-talker babble) at three different levels were measured from 19 normal hearing participants. Driven by the reduction in intelligibility, cortical coherence to “abstract” linguistic units with no accompanying acoustic cues was reduced relative to the “speech only” condition, and lateralized to single cerebral hemispheres. In contrast, brain responses coherent to words, aligned with acoustic onsets, were bilateral and reduced systematically as noise level increased. Strength of the tracking response correlated with subjective ratings from each participant on how much they can understand the speech sentences at all different linguistic levels. These results provide an objective and sensitive neural marker of speech intelligibility which can be further developed into clinical applications for objective assessment of speech-in-noise understanding.
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39

Jia, Li’An, Shang Gao, Jian Jiang, and Meng Zhang. "Research on damage monitoring of sandwich armor composite structure based on time reversal principle." Journal of Physics: Conference Series 2262, no. 1 (2022): 012009. http://dx.doi.org/10.1088/1742-6596/2262/1/012009.

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Abstract With the wide application of lightweight armor composite, it is of great significance to monitor delamination damage in laminated materials. Ultrasonic testing often needs to be immersed in liquid for monitoring, which is inconvenient for large-size specimens and cannot be monitored in real time. In structural health monitoring, the corresponding relationship between damage and response signal characteristics (such as amplitude and energy) is established, which is greatly affected by various environmental factors (such as temperature and load). Due to the particularity of the multi-layer composite structure with large thickness, the problem of Lamb wave multi-mode and frequency dispersion affects the accuracy of damage monitoring, which brings great difficulty to the accuracy of damage monitoring. Four piezoelectric sensors were arranged on the surface of the 5 mm sandwich armor composite composed of alumina ceramic, T700 carbon fiber and TC4 titanium alloy. The signal-to-noise ratio of the obtained damage scattering signal was improved by time reversal method and Gabor wavelet transform. Monitoring and imaging of single damage and multiple damage on the reverse side of armor composite were studied. The experimental results show that the radial error of single damage location is 4.24 mm, and the radial error of multiple damage location is 5.39 mm.
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40

Sriprateep, Keartisak. "Optimization for Turning Metal Matrix Composites with Multiple Performance Characteristics." Applied Mechanics and Materials 121-126 (October 2011): 1479–83. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.1479.

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In this paper, an optimization technique for turning metal matrix composites (MMCs) is proposed using the Taguchi method with multiple performance characteristics. The orthogonal array, multi-response signal to noise (S/N) ratio and analysis of variance (ANOVA) are employed to study the performance characteristics. The cutting parameters (cutting velocity and feed) and cutting time are optimized with consideration of multiple performance characteristics including tool wear, surface roughness and the power required to perform the machining operation. The results showed that cutting time was the dominant variable on multiple cutting performance characteristics. Confirmation tests of the optimal levels with the initial cutting parameters are carried out in order to illustrate the effectiveness of this method in turning metal matrix composite.
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41

Ogunwede, O. I., M. S. Abolarin, A. S. Abdulrahman, et al. "Process optimization of the mechanical properties of AISI 1020 steel quenched using maize-stover ash potash solution." Nigerian Journal of Technology 39, no. 3 (2020): 821–29. http://dx.doi.org/10.4314/njt.v39i3.23.

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The process parameter optimization of maize-stover ash potash (MSAP) as a quenching medium for heat treatment of AISI-1020 steel was conducted in this study to improve the mechanical properties of steel after carburization and quenching. The optimization process utilized Taguchi L9(32) orthogonal array method to determine the individual Signal to Noise (S/N) ratio and Analysis of Variance (ANOVA). A multi-response weighted analysis technique was applied to derive combined quality responses of the heat treated test pieces. The result shows that the optimal factor level of MSAP solution strength was achieved at AM1BM1, which offered 57.6 HRC hardness, 39 J toughness and 1971 N/mm2 tensile strength as improved mechanical properties for the heat treated steel.
 Keywords: Optimization, MSAP, quenching, Taguchi, ANOVA
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42

Zu, Guangyue, Ye Kuang, Jingjin Dong, et al. "Gadolinium(III)-based Polymeric Magnetic Resonance Imaging Agents for Tumor Imaging." Current Medicinal Chemistry 25, no. 25 (2018): 2910–37. http://dx.doi.org/10.2174/0929867324666170314121946.

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Contrast agents (CAs) are widely used to improve the signal-noise ratio in the magnetic resonance imaging (MRI) examinations. The majority of MRI CAs used in clinic are gadolinium( III) (Gd(III)) chelates with low molecular weight. Compared with these small-molecule CAs, Gd(III)-based polymeric magnetic resonance imaging agents (i.e. macromolecular contrast agents, mCAs), prepared by conjugating small-molecule Gd(III) chelates onto macromolecules, possess high relaxivity and relative long blood circulation time, which are favorable for MRI examinations. In last decades, increasing attention was paid to the design of mCAs with various structures, and further evaluation of the MRI performance both in vitro and in vivo. Herein, we focus on the recent progress of mCAs, including structures, properties and applications. Meanwhile, this review also highlights the emerging MRI mCAs with smart response and multi-function: tumor microenvironment- stimulated MRI, multi-mode imaging and MRI-based theranostics.
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43

Ferreira, Edilene C., Jesús M. Anzano, Débora M. B. P. Milori, et al. "Multiple Response Optimization of Laser-Induced Breakdown Spectroscopy Parameters for Multi-Element Analysis of Soil Samples." Applied Spectroscopy 63, no. 9 (2009): 1081–88. http://dx.doi.org/10.1366/000370209789379394.

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Laser-induced breakdown spectroscopy (LIBS) is an emerging analytical technique to perform elemental analysis in natural samples independent of their physical state (solid, liquid, or gaseous). Due to its instrumental features, LIBS shows promising potential to perform analysis in situ and in environments at risk. Since the analytical performance of LIBS strongly depends on the choice of experimental conditions, each particular application needs a specific instrumental adjustment. The present study evaluated three LIBS instrumental parameters regarding their influences on signal-to-noise ratio (SNR) of seven elements in soil samples: laser pulse energy, delay time, and integration time gate. A multivariate technique was used due to the significant interaction among the evaluated parameters. Subsequently, to optimize LIBS parameters for each individual element response, a method for multiple response optimization was used. With only one simple screening design, it was possible to obtain a good combination among the studied parameters in order to simultaneously increase the SNR for all analytes. Moreover, the analysis of individual response for elements is helpful to understand their physical behavior in the plasma and also how they are embedded in the sample matrix.
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Rupajati, Pathya, M. Kurniadi Rasyid, and Ali Nurdin. "Multi objective optimization for kerf and material removal rate in wire electrical discharge machining using Taguchi method combined grey relational analysis." MATEC Web of Conferences 204 (2018): 06006. http://dx.doi.org/10.1051/matecconf/201820406006.

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This paper investigated the multi performance characteristics of wire electrical discharge machining for an optimal machining parameters to get low kerf and high material removal rate at the same time. The machining parameters i.e arc on time, on time, servo voltage and wire feed were used in this experiment. Based on L9 orthogonal array, the signal-to-noise (S/N) ratio and the analysis of variance (ANOVA) was used to study the machining parameters of DIN 1.2510 tool steel. Multi response characteristics were solved by Taguchi method combined grey relational analysis. Experimental results are provided to demonstrate the effectiveness of this method i.e. kerf decreased from 354 μm to 345 μm, while material removal rate (MRR) increased from 9,313 mm3/min to 13,989 mm3/min. From the optimization result validated in the confirmation experiment the machining parameters combination that could produce the optimum responses are arc on time of 2 A, pulse on time of 8 μs, servo voltage 80 V and wire feed 60 mm/min.
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Chaturvedi, Vedansh, and Diksha Singh. "Multi Response Optimization of Process Parameters of Abrasive Water Jet Machining for Stainless Steel AISI 304 Using VIKOR Approach Coupled with Signal to Noise Ratio Methodology." Journal of Advanced Manufacturing Systems 14, no. 02 (2015): 107–21. http://dx.doi.org/10.1142/s0219686715500080.

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As the population of the world is continuously increasing, demand of the mechanical manufactured products is also increasing. Machining is the most important process in any mechanical manufacturing, and in machining two factors, i.e. material removal rate (MRR) and surface roughness (SR) are the most important responses. If the MRR is high, the product will get desired shape in minimum time so the production rate will be high, but we could not scarify with the surface finishing also because in close tolerance limit parts like in automobile industry, if the surface is rough exact fit cannot take place. The term optimization is intensively related to the field of quality engineering. Abrasive water jet machining is an important unconventional machining, in order to obtain better response, i.e. material removal rate and surface roughness. Various process parameters of AWJM need to be observed and selected to improve machining characteristics. Better machining characteristics can be achieved by optimizing various process parameters of AWJM. This study considers four process control parameters such as transverse speed, standoff distance, abrasive flow rate and water pressure. The response is taken to be material removal rate and surface roughness. The work piece for stainless steel AISI 304 material of size 15 cm × 10 cm × 2 cm is selected for experiments. Sixteen experimental runs (two trials for each experimental runs) were carried out for calculating MRR and SR and average value of these two trials have been taken for analysis. MRR is normalized according to higher-is-better and SR is normalized according to lower is better. The experiment data analysis is done and VIKOR index is found. Finally, the analysis of VIKOR index using S/N ratio is done and found the most significant factor for AWJM and predicted optimal parameters setting for higher material removal rate and lower surface roughness. Verification of the improvement in quality characteristics has been made through confirmation test with the predicted optimal parameters setting. It is found that the determined optimum combination of AWJM parameters gives the lowest VIKOR INDEX which shows the successful implementation of VIKOR Method coupled with S/N ratio in AWJM.
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46

Charitha, Mali. "Deep Causal Speech Enhancement and Recognition Using Efficient Long-Short Term Memory Recurrent Neural Network." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49326.

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Abstract - In this work, we propose an attention-based beamforming framework for multi-channel speech enhancement that dynamically adapts spatial filtering to complex acoustic environments. Traditional beamforming methods rely on fixed or heuristically derived spatial filters, limiting their robustness in the presence of non-stationary noise and reverberation. Our approach leverages a self-attention mechanism to learn context-aware representations of spatial cues across multiple microphone channels, enabling the model to emphasize target speech while suppressing interfering sources. By integrating the attention mechanism within a neural beamformer architecture, we enable end-to-end optimization of both spatial filtering and spectral enhancement. Experiments conducted on benchmark multi-channel datasets demonstrate significant improvements in signal-to-noise ratio (SNR), perceptual quality (PESQ), and speech intelligibility (STOI), outperforming conventional and deep learning-based baselines. This method offers a promising direction for robust speech enhancement in real-world far-field and noisy scenarios.Multi-channel speech enhancement aims to extract clean speech from noisy and reverberant environments using spatial information captured by multiple microphones. Conventional beamforming methods, such as Minimum Variance Distortionless Response (MVDR) and Generalized Eigenvalue (GEV) beamformers, typically rely on accurate estimation of spatial covariance matrices (SCMs) and steering vectors, which are challenging to compute in dynamic or real-world conditions. Key Words: Multi-channel speech enhancement Attention mechanism Beamforming Neural beamformer Self-attention
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47

Yang, Yifan, Rihui Zeng, Qingqing Zhu, Weijin Fang, Biyun Ma, and Yide Wang. "Coherent DOA Estimation of Multi-Beam Frequency Beam-Scanning LWAs Based on Maximum Likelihood Algorithm." Sensors 25, no. 12 (2025): 3791. https://doi.org/10.3390/s25123791.

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Multi-Beam frequency scanning leaky-wave antennas (FBS-LWAs) offer a viable solution for hardware miniaturization in direction-of-arrival (DOA) estimation systems. However, the presence of multiple spatial harmonics results in responses in multiple directions for a given incident source, introducing estimation ambiguity and significantly challenging accurate DOA estimation. Moreover, due to the nonlinear frequency response of the FBS-LWA, its response matrix does not satisfy the Vandermonde structure, which renders common rank-recovery techniques ineffective for processing coherent signals. As a result, the DOA estimation of coherent sources using multi-beam FBS-LWAs remains an open and challenging problem. To address this issue, this paper proposes a novel DOA estimation method for coherent signals based on multi-beam frequency scanning leaky-wave antennas. First, the received signals are transformed into the frequency domain via fast Fourier transform (FFT) to construct the signal data matrix from which the covariance matrix is computed.Then, conventional beamforming (CBF) is employed to obtain an initial estimate of the angle set, which will be further refined by a smaller grid to form a candidate angle set. Finally, a maximum likelihood algorithm based on the stochastic principle (Sto-ML) is used to suppress the interference of the parasitic directions and select the final DOA estimates from the candidate angle set. Simulation results show that the proposed method effectively mitigates the impact of parasitic directions and achieves an accurate DOA estimation of multiple coherent sources, even under both low and medium-to-high signal-to-noise ratio (SNR) conditions.
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48

Abdul Rahim, Mohammad Ridzwan, Yupiter H. P. Manurung, Roseleena Jaafar, et al. "Optimizing Robotic Welding Parameter of Single Passed Butt Joint under Simultaneous Consideration of Multiple Response Using Multi Objective Taguchi Method." Advanced Materials Research 576 (October 2012): 177–80. http://dx.doi.org/10.4028/www.scientific.net/amr.576.177.

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This paper presents an alternative method to optimize parameters on single V butt welding. The effect of single-passed welding parameters such as current, voltage, welding speed and width of weaving movement on major welding defects by using a one-sided clamping method was investigated. The optimum parameter values were analyzed using Multi Objective Taguchi Methods (MTM) which started with the application of the common Taguchi methods (L8) Orthogonal Array (OA) and Total Normalized Quality Loss (TNQL) followed by ANOVA under simultaneous consideration of response weighting factors. Further, the value was analyzed by employing Multi Signal to Noise Ratio (MSNR). For the experimental study, a robotic welding system ABB IRB 2400/16 with digital welding power source KEMPPI Pro Evolution ProMIG 540 MXE with shielding gas Argon 80% and Carbon Dioxide 20% were applied. The material used is low carbon steel with 4 mm plate thickness. Based on the verification test result, it is found out that MTM can be used as an alternative method to investigate the optimum value of single passed welding parameter with multiple quality features.
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49

Neupane, Sneha, Gurneesh Singh Jatana, Timothy P. Lutz, and William P. Partridge. "Development of A Multi-Spectral Pyrometry Sensor for High-Speed Transient Surface-Temperature Measurements in Combustion-Relevant Harsh Environments." Sensors 23, no. 1 (2022): 105. http://dx.doi.org/10.3390/s23010105.

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Accurate and high-speed transient surface-temperature measurements of combustion devices including internal combustion (IC) engines, gas turbines, etc., provide validation targets and boundary conditions for computational fluid dynamics models, and are broadly relevant to technology advancements such as performance improvement and emissions reduction. Development and demonstration of a multi-infrared-channel pyrometry-based optical instrument for high-speed surface-temperature measurement is described. The measurement principle is based on multi-spectral radiation thermometry (MRT) and uses surface thermal radiation at four discrete spectral regions and a corresponding emissivity model to obtain surface temperature via non-linear least squares (NLLS) optimization. Rules of thumb for specifying the spectral regions and considerations to avoid interference with common combustion products are developed; the impact of these along with linear and non-linear MRT analysis are assessed as a function of temperature and signal-to-noise ratio. A multi-start method to determine the MRT-solution global optimum is described and demonstrated. The resulting multi-channel transient pyrometry instrument is described along with practical considerations including optical-alignment drift, matching intra-channel transient response, and solution-confidence indicators. The instrument demonstrated excellent >97% accuracy and >99% 2-sigma precision over the 400–800 °C range, with ~20 µs (50 kHz, equivalent to 0.2 cad at 2000 RPM IC-engine operation) transient response in the bench validation.
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50

Liu, Gan, Yingwei Tian, Biyang Wen, and Chen Liu. "Combined Coherent and Non-Coherent Long-Time Integration Method for High-Speed Target Detection Using High-Frequency Radar." Remote Sensing 16, no. 12 (2024): 2139. http://dx.doi.org/10.3390/rs16122139.

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High-frequency (HF) radar plays a crucial role in the detection of far-range, stealth, and high-speed targets. Nevertheless, the echo signal of such targets typically exhibits a low signal-to-noise ratio (SNR) and significant amplitude fluctuations because their radar cross-section (RCS) accounting for the HF band is in the resonance region. While enhancing detection performance often requires long-time integration, existing algorithms inadequately consider the impact of amplitude fluctuation. In response to this challenge, this article introduces an improved approach based on coherent and non-coherent integration. Initially, coherent integration, employing the generalized Radon Fourier transform (GRFT), is utilized to derive a candidate detection set of targets’ range–time trajectories. This involves a joint solution for range migration (RM) and Doppler frequency migration (DFM) through a multi-parameter motion model search. Subsequently, the removal of low SNR pulses, followed by non-coherent integration, is implemented to mitigate amplitude fluctuation, referred to as Amplitude Fluctuation Suppression (AFS), and refine the detection outcomes. Both simulation and experiment results are provided to prove the effectiveness of the proposed AFS-GRFT algorithm.
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