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Particle swarm optimization assisted B-spline neural network based predistorter design to enable transmit precoding for nonlinear MIMO downlink. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.06.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Mata G, Radojević M, Fernandez-Lozano C, Smal I, Werij N, Morales M, Meijering E, Rubio J. Automated Neuron Detection in High-Content Fluorescence Microscopy Images Using Machine Learning. Neuroinformatics 2019; 17:253-269. [PMID: 30215167 DOI: 10.1007/s12021-018-9399-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
The study of neuronal morphology in relation to function, and the development of effective medicines to positively impact this relationship in patients suffering from neurodegenerative diseases, increasingly involves image-based high-content screening and analysis. The first critical step toward fully automated high-content image analyses in such studies is to detect all neuronal cells and distinguish them from possible non-neuronal cells or artifacts in the images. Here we investigate the performance of well-established machine learning techniques for this purpose. These include support vector machines, random forests, k-nearest neighbors, and generalized linear model classifiers, operating on an extensive set of image features extracted using the compound hierarchy of algorithms representing morphology, and the scale-invariant feature transform. We present experiments on a dataset of rat hippocampal neurons from our own studies to find the most suitable classifier(s) and subset(s) of features in the common practical setting where there is very limited annotated data for training. The results indicate that a random forests classifier using the right feature subset ranks best for the considered task, although its performance is not statistically significantly better than some support vector machine based classification models.
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Affiliation(s)
- Gadea Mata
- Department of Mathematics and Computer Science, University of La Rioja, Logroño, Spain.
| | - Miroslav Radojević
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Carlos Fernandez-Lozano
- Department of Computer Science, University of A Coruña, A Coruña, Spain.,Instituto de Investigación Biomédica de A Coruña, Complexo Hospitalario Universitario de A Coruña, A Coruña, Spain
| | - Ihor Smal
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Niels Werij
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Miguel Morales
- Molecular Cognition Laboratory, Biophysics Institute, CSIC-UPV/EHU, Campus Universidad del País Vasco, Leioa, Spain
| | - Erik Meijering
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Julio Rubio
- Department of Mathematics and Computer Science, University of La Rioja, Logroño, Spain
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Yu J, Wu Z, Wang M, Tan M. CPG Network Optimization for a Biomimetic Robotic Fish via PSO. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2016; 27:1962-1968. [PMID: 26259223 DOI: 10.1109/tnnls.2015.2459913] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
In this brief, we investigate the parameter optimization issue of a central pattern generator (CPG) network governed forward and backward swimming for a fully untethered, multijoint biomimetic robotic fish. Considering that the CPG parameters are tightly linked to the propulsive performance of the robotic fish, we propose a method for determination of relatively optimized control parameters. Within the framework of evolutionary computation, we use a combination of dynamic model and particle swarm optimization (PSO) algorithm to seek the CPG characteristic parameters for an enhanced performance. The PSO-based optimization scheme is validated with extensive experiments conducted on the actual robotic fish. Noticeably, the optimized results are shown to be superior to previously reported forward and backward swimming speeds.
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