Ramzan, Qaseem

Syryamkin, Vladimir I.

English

Tomsk State University Repository

ФЕДЕРАЛЬНОЕ ГОСУДАРСТВЕННОЕ АВТОНОМНОЕ ОБРАЗОВАТЕЛЬНОЕ УЧРЕЖДЕНИЕ ВЫСШЕГО ОБРАЗОВАНИЯ «НАЦИОНАЛЬНЫЙ ИССЛЕДОВАТЕЛЬСКИЙ ТОМСКИЙ ГОСУДАРСТВЕННЫЙ УНИВЕРСИТЕТ»   III Международная конференция «Когнитивная Робототехника» в рамках Международного форума «Интеллектуальные системы  4-й промышленной революции»  СБОРНИК ТЕЗИСОВ КОНФЕРЕНЦИИ   21-23 ноября 2018 года  Томск Издательский Дом Томского государственного университета 2018 36 [5] Solinthone Ph., Rumyantseva T.B., Syryamkin V.I. Information computer technology Master Plan 2020 of the Association of Southeast Asian Nations (AIM 2020) // Инноватика-2016 : сб. материалов XII Междунар. школы-конф. студентов, аспирантов и молодых ученых / под ред. А.Н. Солдатова, С.Л. Минькова. Томск, 2016. С. 417–421. [6] Rumyantseva T.B., Solinthone Ph., Syryamkin V.I., Vaganova E.V., Zinov V.G. Technology management. Part 2: Strategic management of intellectual property / ed. by V.I. Syryamkin. Tomsk, 2016.  MERGING BRAIN COMPUTING INTERFACE (BCI) & NEURAL NETWORKS FOR BETTER  AUTHENTICATION & RECOGNITION Q. Ramzan, V.I. Syriamkin National Research Tomsk State University toggleTechs (a creativity platform) qaseemramzan0@gmail.com Brain Computing Interface (BCI) has been proved helpful for the different streams of technology, considering the sensitivity of data in the current era it is required to build new security protocols and authentication models. Just like other fields of technology Brain Computing Interface could also be useful for making the data security better by using BCI as an authentication method without any hard physical inputs. The focus of the issue shifts to ‘recognition’ of EEG signals pattern and making the authentication model self-learning to increase its efficiency. This leads us to involve Artificial Neural Networks in the authentication system to make it efficient and intelligent. A brain computer interface (BCI), sometimes called a mind-machine interface (MMI), direct neural interface (DNI), or brain–machine interface (BMI), is a direct communication pathway between an enhanced or wired brain and an external device. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions.  BCIs comprise an active area of research and could start to integrate advances from adjacent fields such as neuroscience, nanomaterials, electronics miniaturization, and machine learning. For example, one neuro-imaging research project is starting to make guesses as to what participants see during brain scans, purporting to be able to distinguish between a cat and a person. Merging this kind of functionality with BCIs might produce new applications. Other experimental BCI projects have been proposed. One is Neocortical Brain-Cloud Interfaces: autonomous nanorobots that could 37 connect to axons and neuronal synaptic clefts, or embed themselves into the peripheral calvaria and pericranium of the skull. Another project, Brainets, envisions linking multiple organic computing units (brains) to silicon computing networks. A third project is Neural Dust, in which thousands of 10-100 micron-sized free-floating sensor nodes would reside in the brain and provide a computing processing network.  Combining Brain Computing Interface (BCI) with Neural Networks. Presently ever device is under a threat of security breach and taking into ac-count the sensitivity of personal data and how human lives are half digital makes this issue an important one for humans. It is required to secure our devices with a more secure and innovative protocols instead of the main-stream methods. All the past authentication methods being used by us are breach-able and have a history of either hack attacks or simple security breach due to the weak type of authentication models. All of the authentica-tion models we have so far need an input from the user. This input method is the main loophole which makes it not good enough to protect our devices. If the authentication models will need a physical input from users then it is highly likely that the user is at risk because the input can be acquired either by trick or by force. An authentication model based on EEG signals was proposed to over-come this problem and it could be a method of thoughts without any hard physical input. BCI is capable of reading the Brain Signals and then the pro-posed model in previous paper was developed to authenticate a user based on brain signals and matching the patter with the one stored in a database. Authentication for BCI with Neural Networks. As we have developed  a consensus above that Neural Networks can perform recognition in a very impressive way which is not possible otherwise. So a better thought is to use Neural Networks on the EEG Based Authentication Model and make the Neural Network learn the patterns of user’s authentication routine. Here the Neural Network will not only match the pattern to authenticate the users but it will learn gradually the changes in EEG Signals pattern and become more intelligent in a way to recognize the user. The ultimate task will be to train the Neural Network to the point where it will not need to match the EEG Signals with the first every stored pattern but it will be able to recognize the user based on the later learnings which were learnt in result of trainings. This study was supported by the Russian Foundation for Basic Research (grant No. 16-29-04388). 38 References  [1] Brain Computer Interface. URL: https://en.wikipedia.org/wiki/Brain–computer_interface  [2] History of Brain Computer Interfaces. URL: http://www.brainvision.co.uk/blog/2014/04/the-brief-history-of-brain-computer-interfaces  [3] Qaseem Ramzan. Evolution of BCI and Proposed EEG Based Authentication Model.  [4] Melanie Swan. Future of Brain Computing Interfaces (Block-chaining Your Way into  a Cloud Mind). URL: http://jetpress.org/v26.2/swan.htm  [5] Internet of Things. URL: https://en.wikipedia.org/wiki/Internet_of_things  [6] Jerry J. Shih. Brain-Computer Interfaces in Medicine. URL: https://www.ncbi.nlm.nih.gov/ pmc/articles/PMC3497935/  [7] OpenBCI. URL: https://en.wikipedia.org/wiki/OpenBCI  [8] Artificial Neural Networks – Ahmad Aljebaly. URL: https://www.cs.wmich.edu/~elise/ courses/cs6800/Neural-Networks.ppt  [9] Artificial Neural Networks – Ahmad Aljebaly. URL: https://www.cs.wmich.edu/~elise/ courses/cs6800/Neural-Networks.ppt [10] Daniel L. Silver. Theory and Application of Artificial Neural Networks. URL: plto.acadiau.ca/courses/comp/dsilver/5013/Slides/ANN_ml.ppt [11] Ashraful Alam. Neural Networks A Brief Overview. URL: www.d.umn.edu/~alam0026/ NeuralNetwork.ppt [12] Igor V. Tetko. Associative Neural Networks. URL: https://link.springer.com/article/ 10.1023/A:1019903710291 [13] Rumyantseva T.B., Solinthone Ph., Syryamkin V.I., Vaganova E.V., Zinov V.G. Technolo-gy management. Part 2: Strategic management of intellectual property / ed. by V.I. Syryamkin. Tomsk, 2016. [14] Shumilov V.N., Syryamkin V.I., Syryamkin M.V. Modelling of pathologies of the nervous system by the example of computational and electronic models of elementary nervous systems // AIP Conference Proceedings 5. «New Operational Technologies, NEWOT 2015: Proceedings of the 5th International Scientific Conference “New Operational Technologies”» 2015. С. 040007.  РЕШЕНИЕ ЗАДАЧИ ПРЯМОЙ КИНЕМАТИКИ  ДЛЯ АНТРОПОМОРФНОГО МЕХАНИЗМА  С ИСПОЛЬЗОВАНИЕМ КВАТЕРНИОНОВ Д.С. Жданов, А.Ш. Буреев, Я.В. Костелей, Л.А. Хохлова Национальный исследовательский Томский государственный университет dim@diagnostic.tom.ru Роботизированные системы различного рода и назначения в совре-менном мире получили большое распространение и используются не только в промышленности, но и в сферах, где жизненно необходимо заменить человека (экстремальные температуры, радиация, пожары, боевые действия). В настоящее время помимо промышленных роботов, 

Merging brain computing interface (BCI) & neural networks for better authentication & recognition

 Федеральное государственное автономное образовательное учреждение высшего образования  «Национальный исследовательский  Томский государственный университет»   IX Международная научно-практическая конференция «Информационно-измерительная техника и технологии» в рамках Международного форума  «Интеллектуальные системы 4-й промышленной революции»  СБОРНИК ТЕЗИСОВ КОНФЕРЕНЦИИ    21-24 ноября 2018 года г. Томск  53  [6] R. Berwick. An Idiot’s guide to Support vector machines (SVMs). Village Idiot - Massachusetts Institute of Technology, USA. URL:  http://web.mit.edu/6.034/wwwbob/svm-notes-long-08.pdf  [7]. URL: https://en.wikipedia.org/wiki/Principal_component_analysis [8]. Alaa Tharwat, Tarek Gaber, Abdelhameed Ibrahim, Aboul Ella Hassanien.  Linear discriminant analysis: A detailed tutorial -  in AI Communications 30(2):169-190, with 23,752 Reads- DOI: 10.3233/AIC-170729, 2017. [9] Quan Wang. Kernel Principal Component Analysis and its Applications in Face Recognition and Active Shape Models. Rensselaer Polytechnic Institute, 110 Eighth Street, Troy, NY 12180 USA, URL: https://arxiv.org/pdf/1207.3538.pdf, 2014 [10] URL: https://docs.opencv.org/3.4.3/d7/d8b/tutorial_py_face_detection.html  Merging Brain Computing Interface (BCI) & Neural Networks for Better Authentication & Recognition  Qaseem Ramzan Vladimir Syryamkin National Research Tomsk State University; toggleTechs (a creativity platform)  qaseemramzan0@gmail.com Brain Computing Interface (BCI) has been proved helpful for the different streams of technology, considering the sensitivity of data in the current era it is required to build new security protocols and authentication models. Just like other fields of technology Brain Computing Interface could also be useful for making the data security better by using BCI as an authentication method without any hard physical inputs. The focus of the issue shifts to ‘recognition’ of EEG signals pattern and making the authentication model self-learning to increase its efficiency. This leads us to involve Artificial Neural Networks in the authentication system to make it efficient and intelligent. A brain computer interface (BCI), sometimes called a mind-machine interface (MMI), direct neural interface (DNI), or brain–machine interface (BMI), is a direct communication pathway between an enhanced or wired brain and an external device. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions.  BCIs comprise an active area of research and could start to integrate advances from adjacent fields such as neuroscience, nanomaterials, electronics miniaturization, and machine learning. For example, one neuro-imaging research project is starting to make guesses as to what participants see during brain scans, purporting to be able to distinguish between a cat and a person. Merging this kind of functionality with BCIs might produce new applications. Other experimental BCI projects have been proposed. One is Neocortical Brain-Cloud Interfaces: autonomous nanorobots that could connect to axons and neuronal synaptic clefts, or embed themselves into the peripheral calvaria and pericranium of the skull. Another project, Brainets, envisions linking multiple organic computing units (brains) to silicon computing networks. A third project is Neural Dust, in which thousands of 10-100 micron-sized free-floating sensor nodes would reside in the brain and provide a computing processing network.  Combining Brain Computing Interface (BCI) with Neural Networks. Presently ever device is under a threat of security breach and taking into account the sensitivity of personal data and how human lives are half digital makes this issue an important one for humans. It is required to secure our devices with a more secure and innovative protocols instead of the mainstream methods. All the past authentication methods being used by us are breach-able and have a history of either hack attacks or simple security breach due to the weak type of authentication models. All of the authentication models we have so far need an input from the user. This input method is the main loophole which makes it not good enough to protect our devices. If the authentication models will need a physical input from users then it is highly likely that the user is at risk because the input can be acquired either by trick or by force. An authentication model based on EEG signals was proposed to overcome this problem and it could be a method of thoughts without any hard physical input. BCI is capable of reading the Brain Signals and then the proposed model in previous paper was developed to authenticate a user based on brain signals and matching the patter with the one stored in a database. Authentication for BCI with Neural Networks. As we have developed a consensus above that Neural Networks can perform recognition in a very impressive way which is not possible otherwise. So a better thought is to use Neural Networks on the EEG Based Authentication Model and make the Neural Network learn the patterns of user’s authentication routine. Here the Neural Network will not only match the pattern to authenticate the users but it will learn gradually the changes in EEG Signals pattern and become more intelligent in a way to recognize the user. The ultimate task will be to train the Neural Network to the point where it will not need to match the EEG Signals with the first every stored pattern but it will be able to recognize the user based on the later learnings which were learnt in result of trainings. References: [1] Brain Computer Interface. [Electronic resource] URL: https://en.wikipedia.org/wiki/Brain–computer_interface  [2] History of Brain Computer Interfaces. [Electronic resource] URL: http://www.brainvision.co.uk/blog/2014/04/the-brief-history-of-brain-computer-interfaces  [3] Qaseem Ramzan. Evolution of BCI and Proposed EEG Based Authentication Model. [Electronic resource] URL: https://www.matec-conferences.org/articles/matecconf/abs/2018/14/matecconf_imet2018_01006/matecconf_imet2018_01006.html  54  [4] Melanie Swan. Future of Brain Computing Interfaces (Block-chaining Your Way into a Cloud Mind). [Electronic resource] URL: http://jetpress.org/v26.2/swan.htm  [5]     Internet of Things [Electronic resource] URL: https://en.wikipedia.org/wiki/Internet_of_things  [6]  Jerry J. Shih. Brain-Computer Interfaces in Medicine [Electronic resource] URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3497935/  [7] OpenBCI. [Electronic resource] URL: https://en.wikipedia.org/wiki/OpenBCI  [8] Artificial Neural Networks – Ahmad Aljebaly. [Electronic resource] URL: https://www.cs.wmich.edu/~elise/courses/cs6800/Neural-Networks.ppt  [9] Artificial Neural Networks – Ahmad Aljebaly. [Electronic resource] URL: https://www.cs.wmich.edu/~elise/courses/cs6800/Neural-Networks.ppt [10] Daniel L. Silver. Theory and Application of Artificial Neural Networks. [Electronic resource] URL: plato.acadiau.ca/courses/comp/dsilver/5013/Slides/ANN_ml.ppt [11] Ashraful Alam. Neural Networks A Brief Overview. [Electronic resource] URL: www.d.umn.edu/~alam0026/NeuralNetwork.ppt [12] Igor V. Tetko. Associative Neural Networks. [Electronic resource] URL: https://link.springer.com/article/10.1023/A:1019903710291  Генератор ультразвуковых сигналов с цифровой обратной связью Суханов Дмитрий Яковлевич,  Муксунов Тимур Рамильевич, Кузьменко Иван Юрьевич, Завьялова Ксения Владимировна Томский государственный университет sdy@mail.tsu.ru Для генерирования мощного ультразвукового сигнала амплитудой в сотни Вольт при питающем напряжении меньше 30 Вольт и мощности 200 Вт необходимо использовать эффект резонанса. При резонансе происходит накопление энергии в колебательной системе и увеличение амплитуды колебаний. Вследствие этого генератор должен подстраивать свою частоту под резонансную частоту управляемой системы. Резонансная частота будет зависеть от механических свойств ультразвукового хирургического инструмента, электрических свойств пьезоэлемента и согласующих электрических компонентов схемы генератора. Учесть все эти факторы с достаточной точностью заранее крайне сложно. Аналитические решения и численное моделирование могут позволить приблизительно оценить спектр резонансных частот, однако, такие оценки не будут точно совпадать характеристиками реального устройства. Необходимо предусмотреть систему обратной связи и управления частотой генератора. Предлагается осуществлять цифровую обратную связь и цифровое управление генератором, что позволит реализовать достаточно сложный алгоритм управления, не выполнимый схемотехническими средствами. Предлагается функциональная схема макета блока управления и генерирования мощного ультразвукового сигнала изображённая на рис. 1. Цифровой микроконтроллер генерирует периодические импульсы, запускающие генератор с соответствующей частотой, и управляет напряжением на выходе регулируемого источника питания. Уровень напряжения источника питания будет определять мощность генерируемого сигнала. Сигнал с генератора подаётся на пьезоэлементы ультразвукового инструмента. Значения тока и напряжения на входе инструмента измеряются с помощью аналого-цифровых преобразователей микроконтроллера через линии обратной связи. На основе сигналов обратной связи микроконтроллер может оценить амплитуду колебаний, и их спектральный состав, и отреагировать на изменения резонансной частоты изменением  выходных импульсов и питающего напряжения. Данная конструкция функциональной схемы выбрана по причине обеспечения цифровой обратной связи при наличии полного цифрового контроля частотой и фазой генерируемых сигналов, что позволит реализовать самые сложные алгоритмы управления.  

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Merging brain computing interface (BCI) & neural networks for better authentication & recognition

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