Готові списки джерел за темами / Echo-optimization

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Добірка наукової літератури з теми "Echo-optimization"

Автор: Grafiati

Опубліковано: 11 березня 2023

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Статті в журналах з теми "Echo-optimization"

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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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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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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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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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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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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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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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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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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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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Більше джерел

Дисертації з теми "Echo-optimization"

RUEDA, CAMILO VELASCO. "ESNPREDICTOR: TIME SERIES FORECASTING APPLICATION BASED ON ECHO STATE NETWORKS OPTIMIZED BY GENETICS ALGORITHMS AND PARTICLE SWARM OPTIMIZATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2014. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=24785@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO PROGRAMA DE EXCELENCIA ACADEMICA A previsão de séries temporais é fundamental na tomada de decisões de curto, médio e longo prazo, em diversas áreas como o setor elétrico, a bolsa de valores, a meteorologia, entre outros. Tem-se na atualidade uma diversidade de técnicas e modelos para realizar essas previsões, mas as ferramentas estatísticas são as mais utilizadas principalmente por apresentarem um maior grau de interpretabilidade. No entanto, as técnicas de inteligência computacional têm sido cada vez mais aplicadas em previsão de séries temporais, destacando-se as Redes Neurais Artificiais (RNA) e os Sistemas de Inferência Fuzzy (SIF). Recentemente foi criado um novo tipo de RNA, denominada Echo State Networks (ESN), as quais diferem das RNA clássicas por apresentarem uma camada escondida com conexões aleatórias, denominada de Reservoir (Reservatório). Este Reservoir é ativado pelas entradas da rede e pelos seus estados anteriores, gerando o efeito de Echo State (Eco), fornecendo assim um dinamismo e um desempenho melhor para tarefas de natureza temporal. Uma dificuldade dessas redes ESN é a presença de diversos parâmetros, tais como Raio Espectral, Tamanho do Reservoir e a Percentual de Conexão, que precisam ser calibrados para que a ESN forneça bons resultados. Portanto, este trabalho tem como principal objetivo o desenvolvimento de uma ferramenta computacional capaz de realizar previsões de séries temporais, baseada nas ESN, com ajuste automático de seus parâmetros por Particle Swarm Optimization (PSO) e Algoritmos Genéticos (GA), facilitando a sua utilização pelo usuário. A ferramenta computacional desenvolvida oferece uma interface gráfica intuitiva e amigável, tanto em termos da modelagem da ESN, quanto em termos de realização de eventuais pré-processamentos na série a ser prevista. The time series forecasting is critical to decision making in the short, medium and long term in several areas such as electrical, stock market, weather and industry. Today exist different techniques to model this forecast, but statistics are more used, because they have a bigger interpretability, due by the mathematic models created. However, intelligent techniques are being more applied in time series forecasting, where the principal models are the Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS). A new type of ANN called Echo State Networks (ESN) was created recently, which differs from the classic ANN in a randomly connected hidden layer called Reservoir. This Reservoir is activated by the network inputs, and the historic of the reservoir activations generating so, the Echo State and giving to the network more dynamism and a better performance in temporal nature tasks. One problem with these networks is the presence of some parameters as, Spectral Radius, Reservoir Size and Connection Percent, which require calibration to make the network provide positive results. Therefore the aim of this work is to develop a computational application capable to do time series forecasting, based on ESN, with automatic parameters adjustment by Particle Swarm Optimization (PSO) and Genetic Algorithms (GA), facilitating its use by the user. The developed computational tool offers an intuitive and friendly interface, both in terms of modeling the ESN, and in terms of achievement of possible pre-process on the series to be forecasted.

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Prokudaylo, Sergey. "Calculations for neutron spin echo optimization of the magnetic field geometries and preparations and analysis of experiments on crystal lattice dynamics /." [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=97258840X.

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Ailleres, Norbert. "Etude comparative des séquences d'échos de spins, d'échos stimulés et d'échos de gradients en I. R. M : optimisation du contraste." Toulouse 3, 1992. http://www.theses.fr/1992TOU30254.

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Анотація:

A partir des expressions du signal en imagerie par resonance magnetique nucleaire (i. R. M. ), une methode mathematique est proposee pour effectuer une etude comparative du contraste de l'image obtenue a partir d'une sequence quelconque d'impulsions. Dans ce but, nous proposons l'utilisation de fonctions de contraste qui permettent d'evaluer la ponderation des differents parametres intrinseques de l'objet (densite de protons rho, temps de relaxation longitudinale t1 et transversale t2) en fonction des valeurs choisies pour les parametres instrumentaux. La methode proposee, qui s'applique a une sequence d'impulsions quelconque, est tout d'abord verifiee pour les sequences classiques d'echos de spins et d'inversion recuperation. Les modeles theoriques correspondants sont valides a l'aide d'images reelles qui sont par ailleurs comparees a celle obtenues au moyen d'un programme de simulation d'image developpe selon le formalisme matriciel introduit par hinshaw. Les sequences d'imagerie rapide telles que steam (stimulated echo acquisition mode) et f. F. E. (fast field echo) ont ensuite ete etudiees

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Charles, Adam Shabti. "Dynamics and correlations in sparse signal acquisition." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53592.

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One of the most important parts of engineered and biological systems is the ability to acquire and interpret information from the surrounding world accurately and in time-scales relevant to the tasks critical to system performance. This classical concept of efficient signal acquisition has been a cornerstone of signal processing research, spawning traditional sampling theorems (e.g. Shannon-Nyquist sampling), efficient filter designs (e.g. the Parks-McClellan algorithm), novel VLSI chipsets for embedded systems, and optimal tracking algorithms (e.g. Kalman filtering). Traditional techniques have made minimal assumptions on the actual signals that were being measured and interpreted, essentially only assuming a limited bandwidth. While these assumptions have provided the foundational works in signal processing, recently the ability to collect and analyze large datasets have allowed researchers to see that many important signal classes have much more regularity than having finite bandwidth. One of the major advances of modern signal processing is to greatly improve on classical signal processing results by leveraging more specific signal statistics. By assuming even very broad classes of signals, signal acquisition and recovery can be greatly improved in regimes where classical techniques are extremely pessimistic. One of the most successful signal assumptions that has gained popularity in recet hears is notion of sparsity. Under the sparsity assumption, the signal is assumed to be composed of a small number of atomic signals from a potentially large dictionary. This limit in the underlying degrees of freedom (the number of atoms used) as opposed to the ambient dimension of the signal has allowed for improved signal acquisition, in particular when the number of measurements is severely limited. While techniques for leveraging sparsity have been explored extensively in many contexts, typically works in this regime concentrate on exploring static measurement systems which result in static measurements of static signals. Many systems, however, have non-trivial dynamic components, either in the measurement system's operation or in the nature of the signal being observed. Due to the promising prior work leveraging sparsity for signal acquisition and the large number of dynamical systems and signals in many important applications, it is critical to understand whether sparsity assumptions are compatible with dynamical systems. Therefore, this work seeks to understand how dynamics and sparsity can be used jointly in various aspects of signal measurement and inference. Specifically, this work looks at three different ways that dynamical systems and sparsity assumptions can interact. In terms of measurement systems, we analyze a dynamical neural network that accumulates signal information over time. We prove a series of bounds on the length of the input signal that drives the network that can be recovered from the values at the network nodes~[1--9]. We also analyze sparse signals that are generated via a dynamical system (i.e. a series of correlated, temporally ordered, sparse signals). For this class of signals, we present a series of inference algorithms that leverage both dynamics and sparsity information, improving the potential for signal recovery in a host of applications~[10--19]. As an extension of dynamical filtering, we show how these dynamic filtering ideas can be expanded to the broader class of spatially correlated signals. Specifically, explore how sparsity and spatial correlations can improve inference of material distributions and spectral super-resolution in hyperspectral imagery~[20--25]. Finally, we analyze dynamical systems that perform optimization routines for sparsity-based inference. We analyze a networked system driven by a continuous-time differential equation and show that such a system is capable of recovering a large variety of different sparse signal classes~[26--30].

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Laule, Cornelia. "Optimization of a 48 echo magnetic resonance imaging sequence using variable TR data acquisition." Thesis, 2000. http://hdl.handle.net/2429/11310.

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Magnetic resonance imaging (MRI) is a very valuable tool for studying the brain. Currently, MRI is the only non-invasive method for investigating myelin. A unique MRI pulse sequence which is used to investigate myelin is a 48 echo CPMG experiment with TR = 3800ms and TE 10 and 50ms. Unfortunately, this experiment takes over 33 minutes to complete, making clinically less feasible to use. By collecting higher order regions of k-space at shorter TR times, the experiment can be shortened, but at a cost of increasing image blurrines and at a potential loss of data. The purpose of this thesis was to investigate collecting different regions of k-space at different TR times in order to try and optimize a new 48 echo variable TR pulse sequence. Simulations were first performed using five spin-echo images with different TR. By creating simulated variable TR images, we were able to qualitatively investigate the resulting blurriness of the images. Visual assesment of the created images and the difference images allowed us to determine what degree of resolution deterioration would still allow us to differentiate between important structures. It was decided that the simulation for 60 out of 128 lines collected at a shorter TR had the optimal decrease in scan time, without too great a compromise in image quality. The variable TR CPMG experiment was then run on 9 phantoms with different T1 and T2 relaxation times. By studying samples with known T1 and T2 relaxation times, we were able to investigate the reliability of the variable TR pulse sequence. Comparing decay curves showed no difference between 0 and 100 lines of k-space collected at a shorter TR - it was only when all 128 lines of kspace were collected at the shorter TR that a decrease in amplitude of the decay curve occurred. Experiments showed that proton density, GMT2 and chi squared of the T2 decay curve fit for the phantoms were unaffected up to and including 100 lines of k-space collected at TR of 2120ms. Finally, in-vivo studies were performed on five volunteers. Comparing the difference in decay curves, proton density and geometric mean T2 showed only very minor differences between data collected using the constant TR sequence and data collected using the variable TR program in which 60 out of 128 k-space lines were collected at a shorter TR of 2120ms. Experiments showed small differences in myelin water fraction, which could be explained by ROI's being drawn slightly different on the constant and variable images. The chi squared was less for the variable TR, which could be caused by smoothing introduced when collecting different k-space lines at different TR's.

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Danagoulian, Giovanna Selvaggi. "Quantum mechanical analysis and numerical optimization of a "zero-field" spin echo small angle neutron scattering instrument /." 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3301123.

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Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007. Source: Dissertation Abstracts International, Volume: 69-02, Section: B, page: 1301. Adviser: Brent J. Heuser. Includes bibliographical references (leaves 105-106) Available on microfilm from Pro Quest Information and Learning.

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Prokudaylo, Sergey [Verfasser]. "Calculations for neutron spin echo : optimization of the magnetic field geometries and preparations and analysis of experiments on crystal lattice dynamics / Sergey Prokudaylo." 2004. http://d-nb.info/97258840X/34.

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Santanni, Fabio. "Molecular approaches for the optimization of electron spin-based quantum bits and quantum logic gates." Doctoral thesis, 2022. http://hdl.handle.net/2158/1262928.

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This work highlights the different possibilities given by a rational chemical approach to obtain molecular-based quantum bits and quantum logic gates as an appealing and alternative platform for implementing quantum computation. This work resumes the three years of the author's work on this subject, mainly presenting and focusing on experimental results obtained for some new potential hydrogen-free molecular qubits, multi-qubit structures, and state-of-the-art EPR-based (Electron Paramagnetic Resonance) experiments on an archetypical vanadyl-based qubit.

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Частини книг з теми "Echo-optimization"

Zhang, Jinwei, Hang Zhang, Chao Li, et al. "Temporal Feature Fusion with Sampling Pattern Optimization for Multi-echo Gradient Echo Acquisition and Image Reconstruction." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87231-1_23.

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Wang, Yan, Chao Wang, and Jie Li. "The Optimization of Radar Echo Pulse Compression Algorithm Based on DSP." In Lecture Notes in Electrical Engineering. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3229-5_27.

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Pedersen, Peder C., and Li Wan. "Optimization of Pulse-Echo Array Transducer System for Identification of Specified Targets." In Acoustical Imaging. Springer US, 2002. http://dx.doi.org/10.1007/978-1-4419-8606-1_47.

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Song, Ke. "Optimization of Echo Parameter in Intelligent Instrument Under the Condition of Numerical Stability." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15235-2_165.

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Abadlia, I., L. Hassaine, F. Abdoune, and A. Beddar. "Energy Management Strategy and Optimization of a MicroGrid System Based on Echo State Networks." In Artificial Intelligence and Renewables Towards an Energy Transition. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63846-7_17.

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Song, Qingsong, Zuren Feng, and Yonggang Wang. "Stable Training Method for Echo State Networks Running in Closed-Loop Based on Particle Swarm Optimization Algorithm." In Neural Information Processing. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10684-2_28.

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Stavroulakis, Georgios E. "Global optimization for crack identification: impact-echo experiments." In Combinatorial and Global Optimization. WORLD SCIENTIFIC, 2002. http://dx.doi.org/10.1142/9789812778215_0021.

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"Joint Optimization of Acoustic Echo Cancellation and Adaptive Beamforming." In Topics in Acoustic Echo and Noise Control. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-33213-8_2.

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Vieillard-Baron, Antoine. "Haemodynamic assessments in mechanically ventilated patients." In Oxford Textbook of Advanced Critical Care Echocardiography, edited by Anthony McLean, Stephen Huang, and Andrew Hilton. Oxford University Press, 2020. http://dx.doi.org/10.1093/med/9780198749288.003.0025.

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Knowledge of heart–lung interactions is key to manage haemodynamics in mechanically ventilated patients (see also Chapter 5). It allows intensivists to understand the meaning of blood and pulse pressure respiratory variations (PPV). Unlike spontaneous breathing, positive pressure ventilation increases blood pressure and pulse pressure during inspiration following by a decrease during expiration. This is called reverse pulsus paradoxus and includes a ‘d-down’ and a ‘d-up’ effect. No variation means no effect of mechanical ventilation on the heart and especially on the right heart. In case of significant PPV, tidal volume usually reduces right ventricular stroke volume by way of reducing preload where systemic venous return is decreased (fluid expansion is useful to restore haemodynamics, when impaired) or increasing afterload (obstruction of pulmonary capillaries due to alveolar inflation and, in this case, fluid expansion is useless or even sometimes deleterious). Clinical examination as well as evaluation of respiratory variations of superior vena cava by echo, helps to distinguish between these two situations. By studying the beat-by-beat changes in echo parameters induced by positive pressure ventilation heartbeat by heartbeat, echocardiography is perfectly suited to study heart–lung interactions and then to propose an appropriate optimization in case of haemodynamic impairment.

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Balik, Martin. "Acute respiratory failure." In Oxford Textbook of Advanced Critical Care Echocardiography, edited by Anthony McLean, Stephen Huang, and Andrew Hilton. Oxford University Press, 2020. http://dx.doi.org/10.1093/med/9780198749288.003.0021.

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Acute respiratory failure is a common reason for admission to the intensive care ward and it is frequently accompanied by haemodynamic instability. Obligatory assessments in every patient should include left ventricular function, left atrial and left ventricular filling pressures in addition to an assessment of right ventricular function and the pulmonary circulation. A systematic echo protocol is warranted to judiciously decide on treatment strategy, including optimization of the patient’s preload, contractility, heart rate, and afterload. This allows for a more effective management of the respiratory disequilibrium, which can continue to be monitored by ultrasound examination. Monitoring of lung parenchyma and pleural space adds to the echo derived information and assist the physician in deciding on an optimal ventilation strategy, need for bronchoscopy, pleural drainage, and patient positioning including proning. The appropriateness of prescribed therapy for the acute respiratory failure can then be monitored by echocardiography and lung ultrasonography to optimize pulmonary gas exchange without haemodynamic deterioration and conversely improve the patient’s haemodynamic status without adding an unnecessary burden onto the respiratory system. After respiratory failure responds to treatment, echocardiography can then assist with the weaning and subsequent withdrawal of mechanical ventilatory support. Where respiratory failure does not respond to conventional measures, a rapid assessment with echocardiography and chest ultrasound helps to decide whether to proceed to extracorporeal life support and, if adopted, its optimal configuration.

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Тези доповідей конференцій з теми "Echo-optimization"

Lin, Zhenliang, Qikang Li, Rui Wang, Guobin Li, and Jie Luo. "Optimization of Acoustic Noise for Single-Shot Echo-Planar Imaging by Varying Echo Spacing." In ICBBE '20: 2020 7th International Conference on Biomedical and Bioinformatics Engineering. ACM, 2020. http://dx.doi.org/10.1145/3444884.3444895.

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Cai, Mao, Xingming Fan, Chao Wang, Linlin Gao, and Xin Zhang. "Research for Parameters Optimization of Echo State Network." In 2018 11th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2018. http://dx.doi.org/10.1109/iscid.2018.10142.

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Fang, Wenxing, and Yiping P. Du. "Optimization of 3D time-of-flight MR angiography with shortened echo time and dual-echo acquisition." In 2013 6th International Conference on Biomedical Engineering and Informatics (BMEI). IEEE, 2013. http://dx.doi.org/10.1109/bmei.2013.6746906.

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Zhang, Zhaozhao, Xiaohui Wang, and Yingqin Zhu. "Echo State Network Optimization Based on Improved Fireworks Algorithm." In 2022 7th International Conference on Image, Vision and Computing (ICIVC). IEEE, 2022. http://dx.doi.org/10.1109/icivc55077.2022.9886609.

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Wenting Deng, S. Reeves, and D. B. Twieg. "Fast magnetic resonance spectroscopic imaging using echo-time optimization." In 2010 IEEE Nuclear Science Symposium and Medical Imaging Conference (2010 NSS/MIC). IEEE, 2010. http://dx.doi.org/10.1109/nssmic.2010.5874152.

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"Evolutionary Optimization of Echo State Networks: Multiple Motor Pattern Learning." In 6th International Workshop on Artificial Neural Networks and Intelligent Information Processing. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0003027500630071.

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Merabti, Hocine, and Daniel Massicotte. "Robust nonlinear acoustic echo cancellation using a metaheuristic optimization approach." In 2015 IEEE International Conference on Digital Signal Processing (DSP). IEEE, 2015. http://dx.doi.org/10.1109/icdsp.2015.7251879.

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Romoli, Laura, Stefania Cecchi, Francesco Piazza, Danilo Comminiello, Michele Scarpiniti, and Aurelio Uncini. "An interactive optimization procedure for stereophonic acoustic echo cancellation systems." In 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 2015. http://dx.doi.org/10.1109/ijcnn.2015.7280444.

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Fan, Hsiao-Tien, Wei Wang, and Zhanpeng Jin. "Performance optimization of echo state networks through principal neuron reinforcement." In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017. http://dx.doi.org/10.1109/ijcnn.2017.7966058.

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Maat, Jacob Reinier, Nikos Gianniotis, and Pavlos Protopapas. "Efficient Optimization of Echo State Networks for Time Series Datasets." In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018. http://dx.doi.org/10.1109/ijcnn.2018.8489094.

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