Borısenok, Sergey
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Borisenok, Sergey
Borisenok, S.
Borisenok, S. V.
Borisenok, Sergey
Borisenok, Sergey V.
Sergey Borisenok
Borisenok, S.
Borisenok, S. V.
Borisenok, Sergey
Borisenok, Sergey V.
Sergey Borisenok
Job Title
Doç. Dr.
Email Address
sergey.borisenok@agu.edu.tr
Main Affiliation
02.05. Elektrik & Elektronik Mühendisliği
Status
Current Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Sustainable Development Goals
SDG data is not available
Documents
30
Citations
91
h-index
5
Documents
17
Citations
36
Scholarly Output
21
Articles
14
Views / Downloads
1168/759
Supervised MSc Theses
1
Supervised PhD Theses
0
WoS Citation Count
36
Scopus Citation Count
66
WoS h-index
3
Scopus h-index
4
Patents
0
Projects
5
WoS Citations per Publication
1.71
Scopus Citations per Publication
3.14
Open Access Source
16
Supervised Theses
1
| Journal | Count |
|---|---|
| Cybernetics and Physics | 7 |
| International Journal of Psychophysiology | 2 |
| Chaos Solitons & Fractals | 1 |
| Communications Faculty of Sciences University of Ankara Series A2-A3: Physical Sciences and Engineering | 1 |
| European Signal Processing Conference -- 26th European Signal Processing Conference, EUSIPCO 2018 -- Rome -- 143333 | 1 |
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21 results
Scholarly Output Search Results
Now showing 1 - 10 of 21
Conference Object Citation - WoS: 1EEG Informational Code Dependence on the Functional State: General Trends and Characteristic Period(Elsevier Science Bv, 2014) Mekler, Alexey A.; Borisenok, Sergey V.Research Project Hodgkin-Huxley Nöronlarında Ani Yükseliş ve Fırlama Dinamiklerinin Kontrolü(TUBİTAK, 2018) Borisenok, SergeyAni yükselen nöronları içeren ağlar, pek çok örüntü tanıma ve hesaplamalı nörobilim_x000D_ uygulamalarında önemli bir rol oynamaktadır. Modern deneysel bilim, biyolojik nöronların_x000D_ dinamiklerinin manipülasyonunda büyük bir ilerleme göstermektir. Fakat tek hücrenin ve_x000D_ kollektif ani yükseliş ve fırlama ile ilgili doğrusal olmayan davranışlarının kontrolünün_x000D_ matematiksel modellemesindeki teoretik algoritmaların geliştirilmesine ihtiyaç duymaktadır._x000D_ Projenin amacı, biyolojik nöronları modelleyen dört boyutlu dinamik sistemlerin ani yükseliş_x000D_ ve fırlama dinamiklerini dizayn etmek için etkili matematiksel kontrol algoritmaları_x000D_ geliştirmektir._x000D_ Bu amaç için, deneysel olarak en çok kabul edilen ve nöronların matematiksel modellemesi_x000D_ için gerçekçi olan dört boyutlu Hodgkin-Huxley (HH) doğrusal olmayan dinamik sistemi_x000D_ seçilmiştir. Membran aksiyon potansiyelleri sistem çıkışı olması rağmen, nöronal kümelerde_x000D_ dolaşan elektrik akımları kontrol sinyali olarak hizmet etmektir. HH modelindeki ani yükseliş_x000D_ rejimlerini tasarlamak ve sistemin dinamik davranışını üzerine yüklemek için, iki alternatif_x000D_ kontrol metodu kullanılır: hız gradyanı (HG) ve hedef çekicisi (HÇ) geribeslemeli kontrol. Son_x000D_ zamanlarda ispat ettiğimiz gibi, her iki metot dayankı-ve-yangın nöronların basitleştirilmiş iki_x000D_ boyutlu modellerinde dinamik davranışlarını kontrol etmek için yüksek verimlilik ve_x000D_ dayanıklılık göstermektedir._x000D_ Bu projede teorik kontrol algoritmasının HG ve HÇ iki farklı formu, Hodgkin-Huxley nöron_x000D_ ağının aksiyon potansiyelini izlemek için tasarlanmıştır. Metot, tek nöron üzerinde aktif_x000D_ kontrol uygulayarak, seçilmiş nöron kümesi düzeni (doğrusal ve halka şeklinde nöron zinciri)_x000D_ için isteğe bağlı aniyükseliş (spike), ani yükseliş dizisi (spike train) ve fırlama (burst)_x000D_ şekillerinin üretilmesine izin verir._x000D_ Projede geliştirilen algoritma küçük bir Hodgkin-Huxley nöron kümesi için epileptik yapıdaki_x000D_ toplu fırlamaları baskılamak için kullanılmaktadır._x000D_ Böylece, proje biyolojik nöronların matematiksel modelleri için uygulanan kontrol teorisinde_x000D_ uygun bir yer edinebilir ve Hodgkin - Huxley nöronal ağlarının temel küme yapılarındaki_x000D_ isteğe bağlı ani yükseliş veya fırlama rejiminin etkin nesili için özgün bir algoritma_x000D_ geliştirebilir.Article Citation - Scopus: 4Speed Gradient Control Over Qubit States(Institute for Problems in Mechanical Engineering, Russian Academy of Sciences, 2024) Borisenok, S.; Gogoleva, ElenaWe discuss the model of a quantum bit driven by an external classical field without decay in the rotating wave approximation. In such a model, the whole evolution of the qubit states takes place on the Bloch sphere. We reformulate the model as a unitless set of real ordinary differential equations and use the normalized external field as a feedback control parameter. The closed-loop algorithm is designed in the form of the speed gradient, driving the dynamical system towards minimizing a given nonnegative goal function expressed via the qubit variables. We investigate the achievability of the control goal, and focus on the most important features of the speed gradient algorithm applied to a quantum system in comparison with classical systems. Our approach is valid for the control over the ground and excited population levels, and over the qubit phase variables. The paper was presented at PhysCon2024. © 2024 Elsevier B.V., All rights reserved.Article Citation - Scopus: 1Control Over Performance of Qubit-Based Sensors(Institute for Problems in Mechanical Engineering, Russian Academy of Sciences dvv@msa.ipme.ru, 2018) Borisenok, S.The extreme sensitivity of quantum systems towards the external perturbations and in the same time their ability to be strongly coupled to the measured target field makes them to be stable under the environmental noise. A high quality quantum sensor can be engineered even on the platform of a single trapped qubit. There is a variety of optimal and sub-optimal algorithms for effective control over the quantum system states. Here we discuss the opportunity to improve the efficiency of the external field quantum sensor based on a single qubit via its feedback tracking. © 2020 Elsevier B.V., All rights reserved.Article Open-Loop Control on the Efficiency of Quantum Battery With Reservoir Engineering(Institute for Problems in Mechanical Engineering, Russian Academy of Sciences, 2025) Borisenok, S.We discuss the case of a harmonic oscillator-based quantum battery strongly coupled to a highly nonMarkovian thermal reservoir via the quantum charger described by the Caldeira–Leggett model. The coupling between the reservoir and the battery serves as a control parameter for the system. We consider the system to stay in the strongly underdamped regime. Within the framework of the open-loop approach, we determine the optimal shape of control for the battery charging work and then restore the control coupling characteristics of QB in the Hamiltonian for the alternative cases of low and high temperatures. Ultimately, we discuss some possible ways to develop our model for the feedback algorithms. © 2025 Elsevier B.V., All rights reserved.Article On Feedback Control Algorithms for Nitrogen-Vacancy Quantum Sensing(Institute for Problems in Mechanical Engineering, Russian Academy of Sciences, 2024) Borisenok, S.Ultrasensitive quantum detection of external weak signals at the nanoscale levels can be implemented in a variety of forms. Here we discuss different feedback control algorithms for the sensing scenario based on the semiclassical Tavis-Cummings model for nitrogen-vacancy (NV) centers located in the diamond. In the frame of this model, the sensing elements are considered as non-interacting two-level quantum systems, distributed in-homogeneously due to heterogeneous local magnetic and strain environments. The dynamical system of ordinary differential equations corresponding to the model contains the set of control parameters: the detunings between the drive frequency and the cavity frequency and between the drive frequency and NV transition frequency, as well as the relaxation coefficients. Correspondingly, it opens a gate for developing feedback control algorithms for tracking the cavity field, the income signal, and the reflection signal in the model sensing system. To study the principal features of algorithmic feedback we formulate the simplified ’toy model’ for the TavisCummings system and investigate alternative schemes of feedback (gradient methods, target attractor methods) to compare their pros and cons for effective control for nitrogen-vacancy-cavity quantum sensing based on different choices of the control parameter set. This work was supported by the Research Fund of Abdullah Gül University; Project Number: BAP FBA-2023-176 ’Geribesleme kontrol algoritmaları ile kubit tabanlı sensörlerin verimliliğinin artırılması’. The paper was presented at PhysCon2024. © 2024 Elsevier B.V., All rights reserved.Article Suppressing Epileptiform Dynamics in Small Hodgkin-Huxley Neuron Clusters via Target Repeller-Attractor Feedback(IOSR Journal of Mathematics (IOSR-JM), 2020) Sergey BorisenokModel: Quantum battery (QB) is a device that is capable to be charged efficiently and store the energy for a long period of time to be transferred to consumption centers. There are many different physical types of such devices and different charging schemes. Here we discuss the single-qubit based QB in the form of quantum oscillator in a Markovian bath environment. The charging of QB is performed via so-called 'coherent' control u(t) in the Hamiltonian and time dependent spectral density n(t) as an 'incoherent' control (number of excitations in the bath). Our goal is to drive the ergotropy of the stored qubit via the certain control algorithm. Methods: For the effective control we apply here Kolesnikov’s ‘target attractor’ (TA) feedback algorithm. In the frame of this approach we form an attractor set targeting the evolution of the basic characteristics of quantum battery. TA method makes the effective design of the control fields charging the battery; the corresponding control signals could be restored explicitly from the dynamical equations. Interestingly, the proposed algorithm applied to our single qubit model of QB has an analytical solution. Results and Discussion: As a result for the control goal, we obtain an exponentially converting behavior for driving the quantum battery ergotopic characteristics. Our algorithm can be extended to the multi-qubit model of QB (for the parallel or collective charging scheme). It could be applied also for different physical realizations of QBs: Dicke QB, spin QB, harmoniс oscillator QB; and for all working stages of the QB (charging, long time storage and the energy transfer to a consumption center or engine). Conclusion: Feedback algorithms, particularly in the form of target attractor approach, can be applied efficiently to control the set of fundamental characteristics of quantum batteries, including the ergotropy, charging power and others. The analytical study of the proposed model and its numerical simulations demonstrate the possibility to imply the developed mathematical algorithm experimentally for a single qubit system and the set of few qubits as well.Article Citation - Scopus: 2Detection and Control of Epileptiform Regime in the Hodgkin–Huxley Artificial Neural Networks via Quantum Algorithms(Institute for Problems in Mechanical Engineering, Russian Academy of Sciences, 2022) Borisenok, S.The problem of detection and the following suppression of epileptiform dynamics in artificial neural networks (ANN) still is a hot topic in modern theoretical and applied neuroscience. For the purpose of such modeling, the Hodgkin–Huxley (HH) elements are important due to the variety of their behavior such as resting, singular spikes, and spike trains and bursts. This dynamical spectrum of individual HH neurons can cause an epileptiform regime originated in the hyper-synchronization of the cell outcomes. Our model covers the detection and suppression of ictal behavior in a small ANN consisting of HH cells. The model follows our approach [Borisenok et al., 2018] for the HH neurons as a classical dynamical system driving the collective neural bursting, but here we use a quantum paradigm-based algorithm emulated with the pair of HH neurons. Such emulation becomes possible due to the complexity of the individual 4d HH dynamics. The linear chain of two HH neurons is connected to the rest of ANN and works autonomously. The first neuron plays a role of the detecting element for the hyper-synchronization in the ANN and the quantum algorithm emulator; while the second one works as a measuring element (emulation of the quantum measurement converting the signals into the classical domain) and the trigger for the feedback suppressing the epileptiform regime. We use here the speed gradient algorithm for controling the emulating neuron and discuss its pros and cons to compare with our classical model of epileptiform suppression. © 2022 Elsevier B.V., All rights reserved.Conference Object Citation - WoS: 7Citation - Scopus: 12Use of Topological Data Analysis in Motor Intention Based Brain-Computer Interfaces(European Signal Processing Conference, EUSIPCO, 2018) Altindis, Fatih; Yilmaz, Bulent; İçöz, Kutay; Borisenok, S.This study aims to investigate the use of topological data analysis in electroencephalography (EEG) based on brain-computer interface (BCI) applications. Our study focused on extracting topological features of EEG signals obtained from the motor cortex area of the brain. EEG signals from 8 subjects were used for forming data point clouds with a real-time simulation scenario and then each cloud was processed with JPlex toolbox in order to find out corresponding Betti numbers. These numbers represent the topological structure of the point data cloud related to the persistent homologies, which differ for different motor activity tasks. The estimated Betti numbers has been used as features in k-NN classifier to discriminate left or right hand motor intentions. © 2019 Elsevier B.V., All rights reserved.Article Ergotropy of Quantum Battery Controlled via Target Attractor Feedback(IOSR Journal Of Applied Physics (IOSR-JAP), 2020) Sergey BorisenokModel: Quantum battery (QB) is a device that is capable to be charged efficiently and store the energy for a long period of time to be transferred to consumption centers. There are many different physical types of such devices and different charging schemes. Here we discuss the single-qubit based QB in the form of quantum oscillator in a Markovian bath environment. The charging of QB is performed via so-called 'coherent' control u(t) in the Hamiltonian and time dependent spectral density n(t) as an 'incoherent' control (number of excitations in the bath). Our goal is to drive the ergotropy of the stored qubit via the certain control algorithm. Methods: For the effective control we apply here Kolesnikov’s ‘target attractor’ (TA) feedback algorithm. In the frame of this approach we form an attractor set targeting the evolution of the basic characteristics of quantum battery. TA method makes the effective design of the control fields charging the battery; the corresponding control signals could be restored explicitly from the dynamical equations. Interestingly, the proposed algorithm applied to our single qubit model of QB has an analytical solution. Results and Discussion: As a result for the control goal, we obtain an exponentially converting behavior for driving the quantum battery ergotopic characteristics. Our algorithm can be extended to the multi-qubit model of QB (for the parallel or collective charging scheme). It could be applied also for different physical realizations of QBs: Dicke QB, spin QB, harmoniс oscillator QB; and for all working stages of the QB (charging, long time storage and the energy transfer to a consumption center or engine). Conclusion: Feedback algorithms, particularly in the form of target attractor approach, can be applied efficiently to control the set of fundamental characteristics of quantum batteries, including the ergotropy, charging power and others. The analytical study of the proposed model and its numerical simulations demonstrate the possibility to imply the developed mathematical algorithm experimentally for a single qubit system and the set of few qubits as well.
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