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Journal articles on the topic 'Echo-optimization'

Author: Grafiati

Published: 11 March 2023

Last updated: 19 July 2025

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1

Zhang, Q. "Echo optimization of cardiac resynchronization therapy." International Journal of Cardiology 125 (February 2008): S15. http://dx.doi.org/10.1016/s0167-5273(08)70158-x.

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2

Johnson, G., and E. X. Wu. "Sensitivity Optimization of Echo Times and Data Sampling Times for Spin-Echo Spectroscopy." Journal of Magnetic Resonance, Series B 105, no. 3 (1994): 238–41. http://dx.doi.org/10.1006/jmrb.1994.1130.

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3

R., Vincy, and J. William. "Optimization of Acoustic Echo and Noise Reduction in Non Stationary Environment." International Journal of Advance Research and Innovation 3, no. 1 (2015): 104–8. http://dx.doi.org/10.51976/ijari.311519.

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Optimized speech enhancement method combines acoustic echo reduction and noise reduction in a unified framework for non stationary environment. Simultaneous optimization of noise and echo reduction is already done in stationary environment. In most of the times in transmission, signal properties change over time. We need to remove the artifacts of sound in those conditions. Recursive least square method proposed for noise and echo reduction. It gives little amount of mean square error and better results. Normally, partial optimization of acoustic echo reduction and noise reduction does not lead to total optimization. A cascade method of multiple functions causes mutual interference between these functions and degrades eventual speech enhancement performance. Unlike cascade methods, the proposed method combines all functions to optimize eventual speech enhancement performance based on a unified framework, which is also robust against the mutual interference problem. With the proposed method, in addition to time-invariant linear filters, time-varying filters are used to reduce residual acoustic echo signal, and background noise signal which cannot be reduced using time-invariant filters. These time-invariant filters and time-varying filters are also optimized based on a unified likelihood function to avoid the mutual interference problem. Under this, all the parameters are optimized simultaneously based on the expectation-maximization algorithm and calculates a minimum mean squared error estimate of a desired signal. The experimental results show that the proposed method is superior to the cascade methods.

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4

Lin, Ziyang, Jincheng Xu, Yuxuan Yang, and Yichen Zeng. "Echo cancellation using Artificial Intelligence technique." Applied and Computational Engineering 39, no. 1 (2024): 260–65. http://dx.doi.org/10.54254/2755-2721/39/20230611.

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To improve the audio quality in telecommunication, echo cancellation was proposed to prevent signals from re-appearing. Generally speaking, the echo canceler first recognizes the original transmitted signal that appears again due to delay in the transmitted or recieved signal. Then, the echo canceler subtracts the echo in the signals. Commonly, echo cancelers are calledacoustic echo cancellation (AEC) in general, whether they aim for acoustic echo, line echo, or both. However, in modern times, new problems have been emerging which require more innovative, more advanced methods to tackle the echo issue. Thus, the paper reviews methods regarding Artificial Intelligence (AI) to improve the current means for echo cancellation. In this paper, we mainly discuss the approaches to echo cancellation, the identification of echo using adaptive AI and the usage of adaptive filters and neural network for echo cancellation. For instance, deep learning approaches can facilitate the issues of AEC (active echo control) and ANC (active noise control),the optimization of LMS algorithm can be achieved by combining Swarm Intelligence (SI), combining adaptive digital filter and recurrent neural network for acoustic echo cancellation etc. Using AI in echo cancellation can help us achieve high-quality, full-duplex telecommunication in modern telephony system.

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5

Thiede, Luca Anthony, and Ulrich Parlitz. "Gradient based hyperparameter optimization in Echo State Networks." Neural Networks 115 (July 2019): 23–29. http://dx.doi.org/10.1016/j.neunet.2019.02.001.

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6

Gowland, P. A., and R. Bowtell. "Theoretical optimization of multi-echo fMRI data acquisition." Physics in Medicine and Biology 52, no. 7 (2007): 1801–13. http://dx.doi.org/10.1088/0031-9155/52/7/003.

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7

Vidarsson, Logi, Steven M. Conolly, Kelvin O. Lim, Garry E. Gold, and John M. Pauly. "Echo time optimization for linear combination myelin imaging." Magnetic Resonance in Medicine 53, no. 2 (2005): 398–407. http://dx.doi.org/10.1002/mrm.20360.

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8

Hu, Haofei, Chongyi Fan, and Xiaotao Huang. "Radar Target Detection Based on Waveform Design under Atmospheric Attenuation." Journal of Physics: Conference Series 2290, no. 1 (2022): 012071. http://dx.doi.org/10.1088/1742-6596/2290/1/012071.

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Abstract Under the condition of atmospheric attenuation, the mismatch of radar echo signals leads to the decrease of signal-to-noise ratio (SNR) and detection probability. A joint optimization algorithm for transmitting signal and receiving filter of MIMO radar with atmospheric system response is presented in this paper. Considering the energy constraint, similarity constraint and spectral coexistence constraint of the transmitting signal, and introducing the atmospheric channel response matrix, the echo model under the atmospheric response condition is constructed. By optimizing the transmitting signal and the receiving filter, the echo signal SNR and radar detection probability can be improved. On this basis, the ARMA stochastic model is used to simulate the atmospheric system response. Solve non-convex problems by matrix optimization, positive semi-definite relaxation, Charnes-Cooper transformation, and loop optimization methods. Finally, the rank-one matrix decomposition method is used to extract the optimal signal from the signal autocorrelation matrix. And we analyze the convergence of the algorithm, the degree of SNR improvement and the detection probability. Through numerical experiments, it is proved that after the atmospheric channel response is introduced and matched correctly, the SNR of the echo signal can be improved and the radar detection probability can be greatly increased.

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9

Xue, Yu, Qi Zhang, and Adam Slowik. "Automatic topology optimization of echo state network based on particle swarm optimization." Engineering Applications of Artificial Intelligence 117 (January 2023): 105574. http://dx.doi.org/10.1016/j.engappai.2022.105574.

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10

Liu, Wen-Jie, Yu-Ting Bai, Xue-Bo Jin, Jian-Lei Kong, and Ting-Li Su. "A Novel Broad Echo State Network for Time Series Prediction: Cascade of Mapping Nodes and Optimization of Enhancement Layer." Applied Sciences 12, no. 13 (2022): 6396. http://dx.doi.org/10.3390/app12136396.

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Time series prediction is crucial for advanced control and management of complex systems, while the actual data are usually highly nonlinear and nonstationary. A novel broad echo state network is proposed herein for the prediction problem of complex time series data. Firstly, the framework of the broad echo state network with cascade of mapping nodes (CMBESN) is designed by embedding the echo state network units into the broad learning system. Secondly, the number of enhancement layer nodes of the CMBESN is determined by proposing an incremental algorithm. It can obtain the optimal network structure parameters. Meanwhile, an optimization method is proposed based on the nonstationary statistic metrics to determine the enhancement layer. Finally, experiments are conducted both on the simulated and actual datasets. The results show that the proposed CMBESN and its optimization have good prediction capability for nonstationary time series data.

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11

Baravi, Talia, Ofer Feinerman, and Oren Raz. "Echo chambers in the Ising model and implications on the mean magnetization." Journal of Statistical Mechanics: Theory and Experiment 2022, no. 4 (2022): 043402. http://dx.doi.org/10.1088/1742-5468/ac5d42.

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Abstract The echo-chamber effect is a common term in opinion dynamic modeling to describe how a person’s opinion might be artificially enhanced as it is reflected back at her through social interactions. Here, we study the existence of this effect in statistical mechanics models, which are commonly used to study opinion dynamics. We show that the Ising model does not exhibit echo-chambers, but this result is a consequence of a special symmetry. We then distinguish between three types of models: (i) those with a strong echo-chamber symmetry, that have no echo-chambers at all; (ii) those with a weak echo-chamber symmetry that can exhibit echo-chambers but only if there are external fields in the system, and (iii) models without echo-chamber symmetry that generically have echo-chambers. We use these results to construct an efficient algorithm to efficiently and precisely calculate magnetization in arbitrary tree networks. Finally, we apply this algorithm to study two systems: phase transitions in the random field Ising model on a Bethe lattice and the influence optimization problem in social networks.

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12

Zhao, Wei, and Chun Peng Dong. "The Complex Blind Deflation Algorithm Based Particle Swarm Optimization with Survival of the Fittest Mechanism." Applied Mechanics and Materials 347-350 (August 2013): 2656–60. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.2656.

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For multi-constraint nonlinear optimization, this paper puts forward a complex blind deflation algorithm based particle swarm optimization with survival of the fittest mechanism (CBD-PSOSFM) which has faster convergence speed, and then gives a quantificational formula of the improved convergence speed, discusses implement method and the rule of parameters design; Because of the blind source separation (BSS) optimization characteristic in nature, the algorithm can be used to implement semi-BSS with nonlinear multi-constraint. For active object echo detection, the paper sets up fitness function with the multi-constraint like as kurtosis, energy and outline and forms the complex blind deflation algorithm. Finally, the simulation experiment of blind deflation to complex echo validates the algorithms validity and faster convergence capability.

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13

Liu, Wen-Jie, Yu-Ting Bai, Xue-Bo Jin, Ting-Li Su, and Jian-Lei Kong. "Adaptive Broad Echo State Network for Nonstationary Time Series Forecasting." Mathematics 10, no. 17 (2022): 3188. http://dx.doi.org/10.3390/math10173188.

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Time series forecasting provides a vital basis for the control and management of various systems. The time series data in the real world are usually strongly nonstationary and nonlinear, which increases the difficulty of reliable forecasting. To fully utilize the learning capability of machine learning in time series forecasting, an adaptive broad echo state network (ABESN) is proposed in this paper. Firstly, the broad learning system (BLS) is used as a framework, and the reservoir pools in the echo state network (ESN) are introduced to form the broad echo state network (BESN). Secondly, for the problem of information redundancy in the reservoir structure in BESN, an adaptive optimization algorithm for the BESN structure based on the pruning algorithm is proposed. Thirdly, an adaptive optimization algorithm of hyperparameters based on the nonstationary test index is proposed. In brief, the structure and hyperparameter optimization algorithms are studied to form the ABESN based on the proposed BESN model in this paper. The ABESN is applied to the data forecasting of air humidity and electric load. The experiments show that the proposed ABESN has a better learning ability for nonstationary time series data and can achieve higher forecasting accuracy.

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14

Li, Guangming, Jingye Yan, and Ailan Lan. "An Improved Meteor Echo Recognition Algorithm for SuperDARN HF Radar." Electronics 10, no. 16 (2021): 1971. http://dx.doi.org/10.3390/electronics10161971.

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The SuperDARN HF radars can be used for meteor observation and inversion of mid-upper atmosphere neutral wind using observed meteor echo Doppler velocities. Aiming at the problem that the extraction of meteor echo based on echo power, Doppler velocity and spectral width is rough and contains ionospheric echo, this paper optimizes the extraction algorithm of meteor echo. Based on the AgileDARN HF radar’s digital characteristics, the observation method of meteor echo was improved, and we designed a meteor observation mode without changing the hardware system: using a meteor observation with a 7.5 km range resolution and a 2 s integration time, we extracted the Doppler characteristics of different echo types at meteor echo ranges; according to these features, the extraction algorithm of meteor echo was optimized. By analyzing the measured data, the characteristics of diurnal variation, power distribution, Doppler velocity distribution and spectral width distribution of meteor echo extracted by the optimization algorithm were obtained. The meteor echo characteristics obtained by the improved algorithm are more consistent with the theoretical analysis; thus, the improved algorithm is better than the SuperDARN high frequency radar meteor echo extraction algorithm and has good performance. The meteor echo extraction algorithm presented in this paper can extract the meteor echo more accurately, so that the atmospheric neutral wind can be retrieved more accurately. At the same time, the proposed algorithm is not only applicable to AgileDARN HF radar meteor observation mode data, but also to AgileDARN and SuperDARN normal mode data, which is beneficial to expand the data application of SuperDARN radars.

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15

Armenio, Luca Bugliari, Lorenzo Fagiano, Enrico Terzi, Marcello Farina, and Riccardo Scattolini. "Optimal Training of Echo State Networks via Scenario Optimization." IFAC-PapersOnLine 53, no. 2 (2020): 5183–88. http://dx.doi.org/10.1016/j.ifacol.2020.12.1187.

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16

Zhang, Feihu, Tingfeng Tan, Xujia Hou, Liang Zhao, Chun Cao, and Zewen Wang. "Underwater Mapping and Optimization Based on Multibeam Echo Sounders." Journal of Marine Science and Engineering 12, no. 7 (2024): 1222. http://dx.doi.org/10.3390/jmse12071222.

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Multibeam echo sounders (MBESs) enable extensive underwater environment exploration. However, due to weak correlation between adjacent multibeam sonar data and difficulties in inter-frame feature matching, the resulting underwater mapping accuracy frequently falls short of the desired level. To address this issue, this study presents the development of a multibeam data processing system, which includes functionalities for sonar parameter configuration, data storage, and point cloud conversion. Subsequently, an Iterative Extended Kalman Filter (iEKF) algorithm is employed for odometry estimation, facilitating the initial construction of the point cloud map. To further enhance mapping accuracy, we utilize the Generalized Iterative Closest Point (GICP) algorithm for point cloud registration, effectively merging point cloud data collected at different times from the same location. Finally, real-world lake experiments demonstrate that our method achieves an Absolute Trajectory Error (ATE) of 15.10 m and an average local point cloud registration error of 0.97 m. Furthermore, we conduct measurements on various types of artificial targets. The experimental results indicate that the average location error of the targets calculated by our method is 4.62 m, which meets the accuracy requirements for underwater target exploration.

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17

Liu, Junxiu, Tiening Sun, Yuling Luo, Su Yang, Yi Cao, and Jia Zhai. "Echo state network optimization using binary grey wolf algorithm." Neurocomputing 385 (April 2020): 310–18. http://dx.doi.org/10.1016/j.neucom.2019.12.069.

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18

Li, Dingyuan, Fu Liu, Junfei Qiao, and Rong Li. "Structure optimization for echo state network based on contribution." Tsinghua Science and Technology 24, no. 1 (2019): 97–105. http://dx.doi.org/10.26599/tst.2018.9010049.

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19

Rekveldt, M. Theo, Chris P. Duif, Wicher H. Kraan, Jeroen Plomp, and Wim G. Bouwman. "Polarization optimization of spin-echo small angle scattering instruments." Review of Scientific Instruments 79, no. 1 (2008): 015113. http://dx.doi.org/10.1063/1.2832350.

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20

Perzanowski, Christian. "Is echo-guided optimization of biventricular devices time practical?" International Journal of Cardiology 148, no. 1 (2011): 114–15. http://dx.doi.org/10.1016/j.ijcard.2011.01.043.

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21

KIMOTO, MASANORI, and TAKUYA ASAMI. "Multichannel Acoustic Echo Canceler Based on Particle Swarm Optimization." Electronics and Communications in Japan 99, no. 6 (2016): 31–40. http://dx.doi.org/10.1002/ecj.11818.

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22

Lorusso, R., G. La Canna, M. Gargano, et al. "Wrapped muscle activation time optimization by echo-doppler assessment." Journal of Cardiothoracic and Vascular Anesthesia 8, no. 3 (1994): 50. http://dx.doi.org/10.1016/1053-0770(94)90453-7.

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23

Xue, Mingxi. "Optimization and Design of Integrated Space Target." Open Electrical & Electronic Engineering Journal 11, no. 1 (2017): 125–40. http://dx.doi.org/10.2174/1874129001711010125.

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Background & Objective: The design and optimization of laser detection system based on MEMS (Micro-electro Mechanical Systems) scanning mirror is presented in the paper. According to requirements of application, it adopts the laser module to design the laser driver circuit, and the designed modulation circuit can satisfy the actual needs. Method: The designed information processing module is composed of the echo receiving module, signal amplification and filter circuit module, and the synchronous circuit of measuring angle and distance, etc. Conclusion: It gives out the distance decoding method and explores the method of capturing echo signal, which can fast and accurately calculate the distance and azimuth information of scanning.

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24

Halder, Arjama, Chad T. Harris, Curtis N. Wiens, Andrea Soddu, and Blaine A. Chronik. "Optimization of Gradient-Echo Echo-Planar Imaging for T2* Contrast in the Brain at 0.5 T." Sensors 23, no. 20 (2023): 8428. http://dx.doi.org/10.3390/s23208428.

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Gradient-recalled echo (GRE) echo-planar imaging (EPI) is an efficient MRI pulse sequence that is commonly used for several enticing applications, including functional MRI (fMRI), susceptibility-weighted imaging (SWI), and proton resonance frequency (PRF) thermometry. These applications are typically not performed in the mid-field (<1 T) as longer T2* and lower polarization present significant challenges. However, recent developments of mid-field scanners equipped with high-performance gradient sets offer the possibility to re-evaluate the feasibility of these applications. The paper introduces a metric “T2* contrast efficiency” for this evaluation, which minimizes dead time in the EPI sequence while maximizing T2* contrast so that the temporal and pseudo signal-to-noise ratios (SNRs) can be attained, which could be used to quantify experimental parameters for future fMRI experiments in the mid-field. To guide the optimization, T2* measurements of the cortical gray matter are conducted, focusing on specific regions of interest (ROIs). Temporal and pseudo SNR are calculated with the measured time-series EPI data to observe the echo times at which the maximum T2* contrast efficiency is achieved. T2* for a specific cortical ROI is reported at 0.5 T. The results suggest the optimized echo time for the EPI protocols is shorter than the effective T2* of that region. The effective reduction of dead time prior to the echo train is feasible with an optimized EPI protocol, which will increase the overall scan efficiency for several EPI-based applications at 0.5 T.

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Cheng, Yunhan, Chenggao Luo, Heng Zhang, Chuanying Liang, Hongqiang Wang, and Qi Yang. "Untrained Metamaterial-Based Coded Aperture Imaging Optimization Model Based on Modified U-Net." Remote Sensing 16, no. 5 (2024): 795. http://dx.doi.org/10.3390/rs16050795.

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Metamaterial-based coded aperture imaging (MCAI) is a forward-looking radar imaging technique based on wavefront modulation. The scattering coefficients of the target can resolve as an ill-posed inverse problem. Data-based deep-learning methods provide an efficient, but expensive, way for target reconstruction. To address the difficulty in collecting paired training data, an untrained deep radar-echo-prior-based MCAI (DMCAI) optimization model is proposed. DMCAI combines the MCAI model with a modified U-Net for predicting radar echo. A joint loss function based on deep-radar echo prior and total variation is utilized to optimize network weights through back-propagation. A target reconstruction strategy by alternatively using the imaginary and real part of the radar echo signal (STAIR) is proposed to solve the DMCAI. It makes the target reconstruction task turn into an estimation from an input image by the U-Net. Then, the optimized weights serve as a parametrization that bridges the input image and the target. The simulation and experimental results demonstrate the effectiveness of the proposed approach under different SNRs or compression measurements.

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26

Wallmeier, Ludwig, and Lutz Wiegrebe. "Self-motion facilitates echo-acoustic orientation in humans." Royal Society Open Science 1, no. 3 (2014): 140185. http://dx.doi.org/10.1098/rsos.140185.

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The ability of blind humans to navigate complex environments through echolocation has received rapidly increasing scientific interest. However, technical limitations have precluded a formal quantification of the interplay between echolocation and self-motion. Here, we use a novel virtual echo-acoustic space technique to formally quantify the influence of self-motion on echo-acoustic orientation. We show that both the vestibular and proprioceptive components of self-motion contribute significantly to successful echo-acoustic orientation in humans: specifically, our results show that vestibular input induced by whole-body self-motion resolves orientation-dependent biases in echo-acoustic cues. Fast head motions, relative to the body, provide additional proprioceptive cues which allow subjects to effectively assess echo-acoustic space referenced against the body orientation. These psychophysical findings clearly demonstrate that human echolocation is well suited to drive precise locomotor adjustments. Our data shed new light on the sensory–motor interactions, and on possible optimization strategies underlying echolocation in humans.

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27

Zhang, Yu, Qiang Xing, Jiang-Hua Zhang, Wei-Feng Jiang, Mu Qin, and Xu Liu. "Long-Term Effect of Different Optimizing Methods for Cardiac Resynchronization Therapy in Patients with Heart Failure: A Randomized and Controlled Pilot Study." Cardiology 142, no. 3 (2019): 158–66. http://dx.doi.org/10.1159/000499502.

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Aim: During cardiac resynchronization therapy (CRT), optimized programming of the atrioventricular (AV) delay and ventricular-to-ventricular (VV) interval can lead to improved hemodynamics, symptomatic response, and left ventricular systolic function. Currently, however, there is no recommendation for the best optimization method. This study aimed to compare the long-term clinical outcomes of 4 different CRT optimization methods. Methods: One hundred and twenty-four consecutive CRT patients with severe heart failure and left bundle-branch block configuration were randomly assigned into four groups to undergo AV/VV delay optimization through echocardiogram (ECHO; n = 30), electrocardiogram (ECG; n = 32), QuickOpt algorithm (n = 28), and nominal AV/VV (n = 36) groups. Patients were followed up and underwent examinations, including New York Heart Association (NYHA) cardiac functional classification, 6-min walking distance (6MWD), and echocardiography, at 6, 12, 24, 36, and 48 months, respectively. The patients’ survival and clinical outcomes were compared among the four groups. Results: Kaplan-Meier survival analyses showed that the median survival was the same in the 4 groups: ECHO, 43 months; ECG, 44 months; QuickOpt, 44 months, and nominal, 41 months. At the 6-month follow-up, the reduction in left ventricular end diastolic diameter (LVEDD) was significantly less in the nominal group (–1.91 ± 2.58 mm) than that in the other three groups (ECHO: –3.70 ± 2.78 mm, p = 0.012; ECG: –3.53 ± 3.14 mm, p = 0.020; QuickOpt: –3.46 ± 2.65 mm, p = 0.032); 6MWD was significantly shorter in the nominal group (87.88 ± 34.76 m) than that in the other three groups (ECHO: 120.63 ± 56.93 m, p = 0.006; ECG: 114.97 ± 54.95 m, p = 0.020; QuickOpt: 114.57 ± 35.41 m, p = 0.027). Left ventricular ejection fraction (LVEF) significantly increased in ECHO (7.23 ± 2.76%, p = 0.010), ECG (8.50 ± 3.17%, p < 0.001), and QuickOpt (8.39 ± 2.90%, p < 0.001) compared with the nominal group (5.35 ± 2.59%). There were no significant differences among the groups in the aforementioned parameters at 24, 36, and 48 months, respectively. Conclusion: While LVEDD, LVEF, 6MWD, and NYHA were significantly improved in ECHO, ECG, and QuickOpt at 6 months, there were no significant improvements in any of the groups at 12, 24, and 48 months. These findings suggested that the long-term effect of the four CRT methods for heart failure was not significantly different.

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Liu, Z., X. Gao, G. Li, and J. Chen. "DECOMPOSITION TECHNIQUES FOR ICESAT/GLAS FULL-WAVEFORM DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 1179–82. http://dx.doi.org/10.5194/isprs-archives-xlii-3-1179-2018.

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The geoscience laser altimeter system (GLAS) on the board Ice, Cloud, and land Elevation Satellite (ICESat), is the first long-duration space borne full-waveform LiDAR for measuring the topography of the ice shelf and temporal variation, cloud and atmospheric characteristics. In order to extract the characteristic parameters of the waveform, the key step is to process the full waveform data. In this paper, the modified waveform decomposition method is proposed to extract the echo components from full-waveform. First, the initial parameter estimation is implemented through data preprocessing and waveform detection. Next, the waveform fitting is demonstrated using the Levenberg-Marquard (LM) optimization method. The results show that the modified waveform decomposition method can effectively extract the overlapped echo components and missing echo components compared with the results from GLA14 product. The echo components can also be extracted from the complex waveforms.

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Lun, Shu-xian, Hai-feng Hu, and Xian-shuang Yao. "The modified sufficient conditions for echo state property and parameter optimization of leaky integrator echo state network." Applied Soft Computing 77 (April 2019): 750–60. http://dx.doi.org/10.1016/j.asoc.2019.02.005.

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Lyon, Kenneth S., and Roger A. Sedjo. "Binary-Search SPOC: An Optimal Control Theory Version of ECHO." Forest Science 32, no. 3 (1986): 576–84. http://dx.doi.org/10.1093/forestscience/32.3.576.

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Abstract Two models for identifying the optimal harvest of old-growth forests—the supply potential optimal control model, SPOC, and the economic harvest optimization model, ECHO—are compared theoretically and numerically. Walker's ECHO restart procedure to solve convergence problems is examined and found to be theoretically sound. The primary difference between the models is that SPOC incorporates the effects of future rotations while ECHO does not. A new algorithm, binary-search SPOC, is identified which incorporates the effects of future rotations by including a difference equation for the shadow value of these rotations. This algorithm is the solution to a discrete time optimal control problem. The numerical analysis of Walker's original problem revealed that the ECHO solution was very close in the early years when the implicit land rental was low and rising, but generated the wrong long-run equilibrium. Forest Sci. 32:576-584.

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31

Nelasov, N. J., R. V. Sidorov, M. N. Morgunov, et al. "Echocardiographic Stress Test with Adenosine Triphosphate: Optimization of the Algorithm." Kardiologiia 59, no. 11 (2019): 39–47. http://dx.doi.org/10.18087/cardio.2019.11.2665.

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Purpose. To: 1) optimize algorithm of stress echocardiography (s-Echo) with intravenous adenosine triphosphate (ATP) infusion taking into account pharmacokinetics and pharmacodynamics of ATP in human body, 2) test new algorithm in patients with coronary and other heart diseases. Materials and methods. In order to determine spectrum of factors influencing the results of stress test with ATP we inspected main scientific data bases and found 48 publications on ATP application for diagnostic purposes. Analysis of these publications allowed us to optimize algorithm of ATP s-Echo. Optimized algorithm was tested on 26 subjects, who underwent ATP 4D strain-stress-echocardiography of the left ventricle. Results and discussion. Optimized algorithm has three stages: registration of Echo data sets before, at the time of ATP infusion, and after 5 min of ATP infusion termination. Registration of Echo parameters at the second stage must begin not earlier than 3 min after the onset of ATP infusion and only in the presence of signs of coronary vasodilation. We think that the main indirect criterion of submaximal coronary vasodilation is 5 mm Hg or more decrease in systolic blood pressure (SBP), but not below SBP level of 90 mm Hg. Initial dose of ATP is 140 µg/kg/min. If after 2 min of infusion SBP do not diminish we increase the infusion rate at first to 175 and then to 210 µg/kg/min. While testing new algorithm in all cases we have achieved criteria of effective vasodilation. Mean SBP decrease was 16.4±13.7 mm Hg, heart rate increase – 12.7±8.1 bpm. In all patients we obtained interpretable 4D LV Echo data sets for visual analysis of local contractility and automatic strain analysis. Conclusion. Optimization of ATP sEcho algorithm was performed. Safety and efficacy of optimized algorythm for registration of echo data was demonstrated. New ATP infusion algorithm can also be recommended for testing with other cardiac imaging modalities in evaluation of myocardial perfusion and contractility (SPECT, CT, MRI, PET).

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32

Pedrycz, Adam, Henri-Pierre Valero, Hiroshi Hori, Kojiro Nishimiya, Hitoshi Sugiyama, and Yoshino Sakata. "An ultrasonic echo characterization approach based on particle swarm optimization." Journal of the Acoustical Society of America 136, no. 4 (2014): 2213. http://dx.doi.org/10.1121/1.4900032.

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Kimoto, Masanori, and Takuya Asami. "Multi-channel Acoustic Echo Canceler Based on Particle Swarm Optimization." IEEJ Transactions on Electronics, Information and Systems 135, no. 1 (2015): 37–44. http://dx.doi.org/10.1541/ieejeiss.135.37.

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Amano, T., K. Tsuchiya, M. Kurita, S. Ooshimoda, Y. Kusakabe, and K. Ito. "Optimization of multi shot Echo Planar Imaging of the abdomen." Japanese Journal of Radiological Technology 53, no. 1 (1997): 190. http://dx.doi.org/10.6009/jjrt.kj00001355185.

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Chew, Jouh Yeong, Daisuke Kurabayashi, and Yudai Nakamura. "Echo state networks with Tikhonov regularization: optimization using integral gain." Advanced Robotics 29, no. 12 (2015): 801–14. http://dx.doi.org/10.1080/01691864.2015.1010576.

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Epstein, Frederick H., and Andrew E. Arai. "Optimization of fast cardiac imaging using an echo-train readout." Journal of Magnetic Resonance Imaging 11, no. 2 (2000): 75–80. http://dx.doi.org/10.1002/(sici)1522-2586(200002)11:2<75::aid-jmri1>3.0.co;2-p.

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Tenenbaum, R. A., L. T. Stutz, and K. M. Fernandes. "Damage Identification in Bars with a Wave Propagation Approach: Performance Comparison of Five Hybrid Optimization Methods." Shock and Vibration 20, no. 5 (2013): 863–78. http://dx.doi.org/10.1155/2013/467590.

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The formulation and solution of the inverse problem of damage identification based on an one-dimensional wave propagation approach are presented in this paper. Time history responses, obtained from pulse-echo synthetic experiments, are used to damage identification. The identification process is built on the minimization of the squared residue between the synthetic experimental echo, obtained by using a sequential algebraic algorithm, and the corresponding analytical one. Five different hybrid optimization methods are investigated. The hybridization is performed combining the deterministic Levenberg-Marquardt method and each one of the following stochastic techniques: The Particle Swarm Optimization; the Luus-Jaakola optimization method; the Simulated Annealing method; the Particle Collision method; and a Genetic Algorithm. A performance comparison of the five hybrid techniques is presented. Different damage scenarios are considered and, in order to account for noise corrupted data, signals with 10 dB of signal to noise ratio are also considered. It is shown that the damage identification procedure built on the Sequential Algebraic Algorithm yielded to very fast and successful solutions. In the performance comparison, it is also shown that the hybrid technique combining the Luus-Jaakola and the Levenberg-Marquardt optimization methods provides the faster damage recovery.

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Yin, Zhiping, Xinfei Lu, and Weidong Chen. "Echo Preprocessing to Enhance SNR for 2D CS-Based ISAR Imaging Method." Sensors 18, no. 12 (2018): 4409. http://dx.doi.org/10.3390/s18124409.

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A new CS-based inverse synthetic aperture radar (ISAR) imaging framework is proposed to enhance both the image performance and the robustness at a low SNR. An ISAR echo preprocessing method for enhancing the ISAR imaging quality of compressed sensing (CS) based algorithms is developed by implementing matched filtering, echo denoising and matrix optimization sequentially. After the preprocessing, the two-dimensional (2D) SL0 algorithm is applied to reconstruct an ISAR image in the range and cross-range plane through a series of 2D matrices using the 2D CS theory, rather than converting the 2D convex optimization problem to the one-dimensional (1D) problem in the image reconstruction process. The proposed preprocessing framework is verified by simulations and experiment. Simulations and experimental results show that the ISAR image obtained by the 2D sparse recovery algorithm with our proposed method has a better performance.

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Huang, Chuan, Zhongyu Li, Mingyue Lou, et al. "BeiDou-Based Passive Radar Vessel Target Detection: Method and Experiment via Long-Time Optimized Integration." Remote Sensing 13, no. 19 (2021): 3933. http://dx.doi.org/10.3390/rs13193933.

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The BeiDou navigation satellite system shows its potential for passive radar vessel target detection owing to its global-scale coverage. However, the restrained power budget from BeiDou satellite hampers the detection performance. To solve this limitation, this paper proposes a long-time optimized integration method to obtain an adequate signal-to-noise ratio (SNR). During the long observation time, the range migration, intricate Doppler migration, and noncoherence characteristic bring challenges to the integration processing. In this paper, first, the keystone transform is applied to correct the range walk. Then, considering the noncoherence of the entire echo, the hybrid integration strategy is adopted. To remove the Doppler migration and correct the residual range migration, the long-time integration is modeled as an optimization problem. Finally, the particle swarm optimization (PSO) algorithm is applied to solve the optimization problem, after which the target echo over the long observation time is well concentrated, providing a reliable detection performance for the BeiDou-based passive radar. Its effectiveness is shown by the simulated and experimental results.

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Alger, Jeffry R., Julie H. Harreld, Sloane Chen, Jan Mintorovitch, and David S. K. Lu. "Time-to-echo optimization for spin echo magnetic resonance imaging of liver metastasis using superparamagnetic iron oxide particles." Journal of Magnetic Resonance Imaging 14, no. 5 (2001): 586–94. http://dx.doi.org/10.1002/jmri.1223.

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Fang, Hong Yuan, Jian Li, Yue Meng Wang, and Jia Li. "Inverse Analysis of Pavement Thickness Based on Improved Particle Swarm Optimization." Advanced Materials Research 850-851 (December 2013): 813–16. http://dx.doi.org/10.4028/www.scientific.net/amr.850-851.813.

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Core-drilling is the traditional pavement thickness detection method. However, this way is low efficient and destructive. The Ground Penetrating Radar (GPR) is a continuous, high efficient, non-destructive pavement quality testing tool. The dielectric constant, thickness and other information of pavement structure layer are obtained by inverse analysis of GPR echo signal. In this paper, the improved Particle Swarm Optimization (IPSO) is developed to analyze the dielectric constant and thickness of the pavement structure. Compared with inverse analysis results of theoretical model, the inverse precision of IPSO is higher than that of PSO. In addition, the measured echo signal of GPR is analyzed by the IPSO. The errors between inverse results and the actual core-drilling measurements are less than 3%.

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Kirillova, V. V. "Echocardiography features in determining left atrial dilatation in patients with hypertension." Translational Medicine 8, no. 3 (2021): 5–13. http://dx.doi.org/10.18705/2311-4495-2021-8-3-5-13.

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Background. The correct echocardiographic (Echo) determination of left atrial (LA) dilatation is a criterion for diagnosing heart failure with the preserved ejection fraction.Objective. The aim is to detect probable reasons for underestimating the LA area by Echo in hypertension patients.Design and methods. In the prospective study, the LA area was measured echocardiographically in 89 ambulatory patients (P-wave duration in standard lead II was > 0.1 mm in 57 patients and ≤ 0.1 mm in 32 patients) from the apical four- and two-chamber views with and without focusing on the LA, and from the subcostal four-chamber view.Results. A strong positive correlation was detected between ECG P-wave duration and the Echo LA area (r = 0.74). The LA areas were revealed to be different in patients with P-wave duration over 0.1 mm from the apical fourchamber view with optimization of displaying the LA (22.33 ± 0.68 and 19.02 ± 0.56 cm2, respectively). The maximum LA area was detected from the subcostal fourchamber view (24.41 ± 0.78).Conclusion. Thus, using the correspondence between the LA area and Pwave duration in standard lead II, optimization of displaying the LA, and additionally measuring the LA dimensions from the subcostal view, sonologists can avoid underestimating LA dimensions.

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Pichardo, Eduardo, Esteban Anides, Angel Vazquez, et al. "A Compact and High-Performance Acoustic Echo Canceller Neural Processor Using Grey Wolf Optimizer along with Least Mean Square Algorithms." Mathematics 11, no. 6 (2023): 1421. http://dx.doi.org/10.3390/math11061421.

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Recently, the use of acoustic echo canceller (AEC) systems in portable devices has significantly increased. Therefore, the need for superior audio quality in resource-constrained devices opens new horizons in the creation of high-convergence speed adaptive algorithms and optimal digital designs. Nowadays, AEC systems mainly use the least mean square (LMS) algorithm, since its implementation in digital hardware architectures demands low area consumption. However, its performance in acoustic echo cancellation is limited. In addition, this algorithm presents local convergence optimization problems. Recently, new approaches, based on stochastic optimization algorithms, have emerged to increase the probability of encountering the global minimum. However, the simulation of these algorithms requires high-performance computational systems. As a consequence, these algorithms have only been conceived as theoretical approaches. Therefore, the creation of a low-complexity algorithm potentially allows the development of compact AEC hardware architectures. In this paper, we propose a new convex combination, based on grey wolf optimization and LMS algorithms, to save area and achieve high convergence speed by exploiting to the maximum the best features of each algorithm. In addition, the proposed convex combination algorithm shows superior tracking capabilities when compared with existing approaches. Furthermore, we present a new neuromorphic hardware architecture to simulate the proposed convex combination. Specifically, we present a customized time-multiplexing control scheme to dynamically vary the number of search agents. To demonstrate the high computational capabilities of this architecture, we performed exhaustive testing. In this way, we proved that it can be used in real-world acoustic echo cancellation scenarios.

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Cheng, Guoliang, Lele Liao, Kai Chen, Yuxiang Hu, Changbao Zhu, and Jing Lu. "Semi-blind source separation using convolutive transfer function for nonlinear acoustic echo cancellation." Journal of the Acoustical Society of America 153, no. 1 (2023): 88–95. http://dx.doi.org/10.1121/10.0016823.

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The recently proposed semi-blind source separation (SBSS) method for nonlinear acoustic echo cancellation (NAEC) outperforms adaptive NAEC in attenuating the nonlinear acoustic echo. However, the multiplicative transfer function (MTF) approximation makes it unsuitable for real-time applications, especially in highly reverberant environments, and the natural gradient makes it hard to balance well between fast convergence speed and stability. In this paper, two more effective SBSS methods based on auxiliary-function-based independent vector analysis (AuxIVA) and independent low-rank matrix analysis (ILRMA) are proposed. The convolutive transfer function approximation is used instead of the MTF so that a long impulse response can be modeled with a short latency. The optimization schemes used in AuxIVA and ILRMA are carefully regularized according to the constrained demixing matrix of NAEC. The experimental results validate significantly better echo cancellation performances of the proposed methods.

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Li, Yi Ning, Pei Lin Zhang, Chao Xu, Yun Qiang Zhang, and Long Yun Li. "An Improved Online De-Noising Method for Ultrasonic Echo Signal of Wear Debris in Oil." Applied Mechanics and Materials 631-632 (September 2014): 490–93. http://dx.doi.org/10.4028/www.scientific.net/amm.631-632.490.

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The ultrasonic echo signal of wear debris is influenced by many matters. It causes so much more noise. Therefore, it puts forward an improved online de-noising method for ultrasonic echo signal of wear debris in oil. In the dual-tree complex wavelet transform (DTCWT) field, a method, which combines the new non-linear threshold function with adaptive threshold, utilizes particle swarm optimization (PSO) for optimizing the parameter of non-linear threshold function to get the optimal solution. The result of de-noising method can be evaluated. Experimental results show that the proposed method has obvious effect on signal de-noising.

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Haitao Jia, Wei Zhang, Mei Xie, Ke Zhang, and Xixu He. "Based on Target Echo Model Netted Radar Coverage Fusion Performance Optimization." International Journal of Digital Content Technology and its Applications 7, no. 5 (2013): 424–33. http://dx.doi.org/10.4156/jdcta.vol7.issue5.51.

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Jaeger, Herbert, Mantas Lukoševičius, Dan Popovici, and Udo Siewert. "Optimization and applications of echo state networks with leaky- integrator neurons." Neural Networks 20, no. 3 (2007): 335–52. http://dx.doi.org/10.1016/j.neunet.2007.04.016.

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Duan, Haibin, and Xiaohua Wang. "Echo State Networks With Orthogonal Pigeon-Inspired Optimization for Image Restoration." IEEE Transactions on Neural Networks and Learning Systems 27, no. 11 (2016): 2413–25. http://dx.doi.org/10.1109/tnnls.2015.2479117.

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Chan, Kimberly L., Nicolaas A. J. Puts, Karim Snoussi, Ashley D. Harris, Peter B. Barker, and Richard A. E. Edden. "Echo time optimization for J-difference editing of glutathione at 3T." Magnetic Resonance in Medicine 77, no. 2 (2016): 498–504. http://dx.doi.org/10.1002/mrm.26122.

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Zhang, Heng, Hongqiang Wang, Chenggao Luo, Chuanying Liang, and Feng Lan. "Waveform Optimization for Enhancing the Performance of a Scanning Imaging Radar Utilizing a Terahertz Metamaterial Antenna." Remote Sensing 17, no. 9 (2025): 1595. https://doi.org/10.3390/rs17091595.

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A scanning radar based on terahertz metamaterial phased array (TMPA) is a novel system for forward-looking imaging. In this paper, a waveform optimization method based on random hopping frequency (RHF) and amplitude modulation is proposed to improve the performance of TMPA scanning imaging radars. The RHF signal waveform is employed to reduce the sidelobes of the range ambiguity function for improving the measurement accuracy in range, while the amplitude modulation is applied to optimize the convolution matrix composed of samples of the antenna pattern, thereby enhancing the azimuth super-resolution. Interestingly, amplitude modulation for waveform design is based on the criterion of minimizing the residual between the real echo and the reconstructed echo with the limited transmission power, without any assumptions about the statistical distribution of target scattering. The effectiveness of the proposed waveform optimization method for enhancing the performance of a TMPA scanning imaging radar is verified through simulations and experiments.

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