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Biomarkers of brain activity for the performance evaluation in the process of solving cognitive tasks 5

Biomarkers of brain activity for the performance evaluation ...

引用

5th Scientific School on Dynamics of Complex Networks and their Applications, DCNA 2021

作者： Badarin, Artem Smirnov, Nikita Kurkin, Semen Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University Kaliningrad Russia

ISBN: (纸本)9781665442824

In this paper, we analyze the brain activity during the execution by the subject of simple cognitive tasks associated with visual attention and symbol perception. We obtain biomarkers of brain activity in the process of solving cognitive tasks. These biomarkers make it possible to relate the quality and speed of execution of the tasks offered to the subject with the brain’s activity of the cortical network. We use a combination of time-frequency and statistical analysis to calculate activity characteristics. The results obtained can be used to develop neural interfaces for training attention and adjusting the learning process. © 2021 IEEE

关键词： Biomarkers

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On Choosing Structure for a Machine Learning-based Reaction Force Predictor for Walking Robots

On Choosing Structure for a Machine Learning-based Reaction ...

引用

2020 International Conference on robotics and Artificial Intelligence, RoAI 2020

作者： Zalyaev, E. Savin, S. Salikhzyanov, A. Golousov, S. Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis420500 Russia Edge Vision Ltd. Innopolis420500 Russia Technology Arrival Ltd Banbury London United Kingdom

This paper focuses on the topic of contact reaction prediction for walking robots, namely on the analysis of performances on different structures of the machine-learning-based predictors. Predicting reaction forces is important due to the fact that it can allow us to retrieve a simplified model of the contact scenario, as was proposed in the literature preciously. This allows to turn the problem of contact model identification into a data collection and processing problem. In order to do it effectively, both a data compression (feature extraction) and regression strategies might be used. This research provides an analysis of both with respect to the discussed problem. © Published under licence by IOP Publishing Ltd.

关键词： Machine learning

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Application of CycleGAN-based Augmentation for Autonomous Driving at Night

Application of CycleGAN-based Augmentation for Autonomous Dr...

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2020 International Conference Nonlinearity, Information and robotics, NIR 2020

作者： Ostankovich, Vladislav Yagfarov, Rauf Rassabin, Maksim Gafurov, Salimzhan Innopolis University Center for Technologies in Robotics and Mechatronics Components Innopolis Russia

ISBN: (纸本)9781728187631

Self-driving vehicles contain a number of modules allowing them to autonomously navigate in uncertain environment. The robust, efficient, safe and accurate autonomous navigation are heavily depend on parameters of a perception module. In this paper, we consider perception module as a combination of object detection and road segmentation submodules. As a matter of fact, all of them are based on Deep learning technique. It leads to liability of a big training datasets to provide the accuracy, efficiency and robustness of a perception module for a self-driving car operating in a wide range of scenarios. This paper presents the GAN-based augmentation as a key factor allowing to improve the performances of perception. The provided research shows the comparison between classical augmentation method and CycleGAN-based method. The main focus is made on detection and segmentation problems at nights. The initial training data includes BDD100K dataset and our own one collected in winter time by means of front-view camera of a self-driving car developed in innopolis university. The obtained results show the improvement of segmentation task in case of application of CycleGAN augmentation. However, the chosen method of GAN-based augmentation has not shown the positive influence on object detection due to appeared visual artifacts. © 2020 IEEE.

关键词： Autonomous vehicles

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Traffic Sign Classification Using Embedding Learning Approach for Self-driving Cars 2nd

Traffic Sign Classification Using Embedding Learning Approac...

引用

2nd International Conference on Human Interaction and Emerging technologies: Future Applications, IHIET-AI 2020

作者： Yagfarov, Rauf Ostankovich, Vladislav Akhmetzyanov, Aydar Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia

ISBN: (纸本)9783030442668

Image classification is one of the most popular and important problems in computer vision. In self-driving cars image classification is used to classify detected traffic signs. Modern state-of-the-art algorithms based on deep neural networks use softmax function to interpret the output of the network as the probability that the input data belongs to a certain class. This approach works well, however it has several disadvantages. More precisely, it is necessary to know the number of classes in advance, and if one wants to add a new class, then it is necessary to retrain the network. Moreover, a large number of images of each class are required. In the case of road signs, datasets may contain only the most frequent signs while ignoring rarely used ones. Thus, the traffic signs recognition module in autonomous cars will not recognize traffic signs not included into training dataset, which can lead to accidents. In this paper we put forward another approach that does not have disadvantages of networks with softmax. The approach is based on learning image embeddings in which models are trained to bring closer objects of one class and to move away objects of other classes in embeddings space. Therefore, having even a small number of images of rare classes it becomes possible to create a working classification system. In this work, we test the applicability of these algorithms in the traffic signs classification problem, and also compare its accuracy with neural networks with softmax and with networks pre-trained on softmax. We developed publicly available toolbox for training and testing embedding networks with different loss functions, backbone models, training strategies and other configuration parameters and embedding space visualization tools. All our experiments were carried out on the russian road signs dataset. To simplify the process of conducting training experiments, a framework for embedding learning based neural networks making was created. The framework can b

关键词： Embeddings

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Artificial neural network predicts inter-areal functional connectivity

Artificial neural network predicts inter-areal functional co...

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Saratov Fall Meeting 2020 - Computations and Data Analysis: From Molecular Processes to Brain Functions

作者： Pitsik, Elena Frolov, Nikita Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Saratov State Medical University Saratov Russia

ISBN: (数字)9781510645332

ISBN: (纸本)9781510645325

In the present paper, we introduce an extended machine-learning-based approach to detect inter-areal functional connectivity based on an artificial neural network (ANN). Using the concept of generalized synchronization, we show that the proposed approach is relevant to infer functional dependencies between remote brain areas of interest from multivariate EEG recordings. We verify the ANN-based method to capture the reconfiguration of functional connectivity during motor execution. The proposed model showed good ability to approximate functional relations between the electrical activity of parietal and frontal areas and motor cortex at different stages of motor execution, providing an adequate pattern of functional connectivity network. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Multilayer neural networks

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Comparison on brain activity for mechanical imagery and execution: a brief review 5

Comparison on brain activity for mechanical imagery and exec...

引用

5th Scientific School on Dynamics of Complex Networks and their Applications, DCNA 2021

作者： Chepurova, Alla Kurkin, Semen Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University Kaliningrad Russia

ISBN: (纸本)9781665442824

With this review we summarize the current state of scientific studies in the field of MI (motor imagery) and ME (motor execution). We composed brain map and description which correlate different brain areas with type of movements it is responsible for. That gives a more complete and systematic picture of human brain functionality in a case of ME and MI. Also we found the research gap and possible improvements for the further researches in this field. © 2021 IEEE

关键词： Brain

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Quasi-movements as an intermediate type of motion 5

Quasi-movements as an intermediate type of motion

引用

5th Scientific School on Dynamics of Complex Networks and their Applications, DCNA 2021

作者： Smirnov, Nikita M. Kurkin, Semen Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University Kaliningrad Russia

ISBN: (纸本)9781665442824

This paper discusses the three distinct types of motor activity, namely quasi, real, and imagery. Quasi-motion is voluntary movements that are minimized to the point that finally become undetectable by objective measures. This type of motion is particular from the point of difficulty of classifying it as real or imaginary. In this paper, we are aiming to show it from the sensor- and source-level perspectives. © 2021 IEEE

关键词： Dynamics Complex networks

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Accuracy Analysis of a Ground Reaction Predictor-based Feedforward Control

Accuracy Analysis of a Ground Reaction Predictor-based Feedf...

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2020 International Conference Nonlinearity, Information and robotics, NIR 2020

作者： Savin, Sergei Innopolis University Center for Technologies in Robotics and Mechatronics Components Innopolis420500 Russia

ISBN: (数字)9781728187631

ISBN: (纸本)9781728187631

Robots with contact interactions can use machine learning-based reaction predictors to build local linear reaction models and use them in feedback and feedforward control. In this paper, a study of effects of noisy and biased reaction predictors on the feedforward control is considered. The paper presents a model of the predictor with exact accuracy, and a simplified model of a noisy and biased predictor. Distributions of the control errors and prediction errors based on the linearized contact model are demonstrated for two robots: a bipedal walking robot and an in-pipe four legged robot. The proposed procedure can be used to find stopping criteria for the training process of the predictors. © 2020 IEEE.

关键词： Feedforward control

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Unmanned Aerial Vehicle Path Planning for Exploration Mapping

Unmanned Aerial Vehicle Path Planning for Exploration Mappin...

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2020 International Conference Nonlinearity, Information and robotics, NIR 2020

作者： Respall, Victor Massague Devitt, Dmitry Fedorenko, Roman Innopolis University Center for Technologies in Robotics and Mechatronics Components Innopolis420500 Russia

ISBN: (纸本)9781728187631

This paper presents a new path planner for the exploration of previously unknown environments. The proposed algorithm uses a Next-Best-View (NBV) fashion to decide the movements ahead. A Rapidly-Exploring Random Tree (RRT) is built and the node with the highest gain is the next configuration to execute, based on the amount of unmapped volume in the field of view (FoV) of the sensor employed. The proposed planner is able to send online a 3D occupancy map of the volume and work with a variety of different depth sensors. The performance is analyzed in various challenging simulations, such as exploration of a city street or a narrow tunnel. Moreover, real experiments indoor and outdoor were conducted to further validate the proposed approach results. In cases with large scale volumes, it can cover a wide range in affordable flight time. © 2020 IEEE.

关键词： Motion planning

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Workspace Analysis of Tensegrity Structures with an Iterative Energy-based Algorithm

Workspace Analysis of Tensegrity Structures with an Iterativ...

引用

2020 International Conference Nonlinearity, Information and robotics, NIR 2020

作者： Savin, Sergei Balakhnov, Oleg Klimchik, Alexandr Innopolis University Center for Technologies in Robotics and Mechatronics Components Innopolis420500 Russia

ISBN: (纸本)9781728187631

The paper studies the use of energy-based forward and inverse kinematics procedures for calculation of operational space of tensegrity robotics structures. Tensegrity structures are compliant and highly dynamical, which makes them a favorable robotics component for a number of difficult tasks (such as collaborative robotics, space exploration and others), but at the same times make trajectory planning and control highly *** the paper it is demonstrated that the studied forward and inverse kinematics methods can be used to find workspaces of tensegrity structures, using six bar structure as an example. It is shown that the method can adjust to the choice of actuated and passive elastic elements, making it a design tool which maps the actuation pattern to the resulting workspace or to the law of change of the rest lengths of the actuated elastic elements. © 2020 IEEE.

关键词： Computational efficiency

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