جست‌وجوی استاد و پژوهشگر

Alexey Evgenievich Osadchiyمشاهده پروفایل

استاد

• digital signal processing
• magnetoencephalography
• electroencephalography
• +۹ مورد دیگر

Alexey Evgenievich Osadchiy is a Professor at the National Research University Higher School of Economics (HSE University), where he serves as Director of the Center for Bioelectric Interfaces at the Institute of Cognitive Neuroscience. He has been working at HSE since 2013 with 21 years of scientific and teaching experience. His academic appointments include Professor at the Faculty of Computer Science in the Department of Data Analysis and Artificial Intelligence. 2023 - Doctor of Science: National Research University Higher School of Economics 2003 - PhD: University of Southern California, specialty "Physical and Mathematical Sciences" and "Neurobiology" 1997 - Specialty: Bauman Moscow State Technical University, major in Autonomous Information and Control Systems Professor Osadchiy's research focuses on digital signal processing, magnetoencephalography (MEG), electroencephalography, inverse problems, synchronization, non-invasive detection, and brain mapping. His work bridges neuroscience, computer science, and medical applications, with particular emphasis on brain-computer interfaces, neurofeedback systems, and precision medicine applications for neurological disorders. He has pioneered methods for real-time brain activity monitoring and developed novel approaches for functional connectivity estimation in neural networks. His recent publications demonstrate a strong trend toward developing hardware-enabled low-latency systems for brain-state dependent stimulation, improving MEG technology with optically pumped magnetometers, and advancing speech mapping techniques for neurosurgical applications. His work increasingly integrates AI and deep learning approaches with traditional neuroimaging techniques to create more precise and accessible brain measurement and modulation systems. Scientific Awards and Recognition HSE University "Recognition - 10 Years of Successful Work" Medal (July 2025) Letter of Gratitude from the Higher School of Economics (September 2021) Letter of Gratitude from the Faculty of Computer Science at HSE (August 2018) Allowance for defending a doctoral dissertation (2023–2026) Bonuses for publications in international peer-reviewed journals (2015–2029) Professor Osadchiy has successfully advised numerous graduate students and doctoral candidates, with eight dissertation research projects currently under his supervision. His research has been supported by significant grants including a Russian Ministry of Education and Science contract for "System for registration and decoding of human brain bioelectric activity" (2014-2017), RFBR grants for "New non-invasive experimental-mathematical paradigm for preoperative magnetoencephalographic mapping of speech cortex" (14-02-00917, 16-04-01863), and projects on "Endogenous enhancement of brain-computer interface efficiency." As Director of the Center for Bioelectric Interfaces at the Institute of Cognitive Neuroscience, Professor Osadchiy leads a multidisciplinary team working on cutting-edge neurotechnology. His center collaborates with the Federal Brain and Neural Technology Centre at the Federal Medical and Biological Agency, where they established the Laboratory of Medical Neural Interfaces and Artificial Intelligence for Clinical Applications. The center is actively involved in developing brain-computer interfaces for rehabilitation, particularly for stroke patients and those with locomotor function disorders, and has created Russia's first neurointerface for controlling exoskeletons using imagined lower limb movements.

• Dendrochronology
• Climate Change Research
• Tree Ring Analysis
• +۴ مورد دیگر

Evgeny Aleksandrovich Vaganov is a distinguished academic serving as the scientific director of Siberian Federal University (SFU) and a member of both the Academic Council of SFU and the Scientific and Technical Council. As an Academician of the Russian Academy of Sciences, he holds one of the highest scholarly honors in Russia. His primary research address is at Akademgorodok, 50A, building 2, office 511 in Krasnoyarsk. Dr. Vaganov earned his education at Krasnoyarsk State University, specializing in Biophysics in 1971. His academic journey has led him to become a leading expert in dendrochronology and environmental science. Vaganov's research focuses on the analysis of tree rings as indicators of climate change and environmental conditions, with significant contributions to ecological modeling. His work bridges the gap between traditional dendrochronology and modern climate science, developing innovative methods to extract climate signals from wood anatomy. He has pioneered approaches in quantitative wood anatomy and developed models like VS-Cambium-Developer to understand cambium functioning under environmental stress. His extensive publication record demonstrates a clear progression from traditional tree-ring width analysis to more sophisticated dendroanatomical and dendrochemical approaches. Recent work emphasizes high-resolution analysis of wood cells, soil carbon dynamics, and the development of process-based models that connect daily meteorological conditions with tree growth patterns. His research spans multiple Siberian ecosystems, from the Western Sayan Mountains to Central Siberian forests. Diploma "Golden Badge of Public Recognition" (Russia) Laureate of the Alexander von Humboldt Foundation Prize (Germany) Laureate of the V. N. Sukachev Prize of the Presidium of the Russian Academy of Sciences Honorary badge "Silver Sigma" for many years of creative work Medal of the Order "For Merit to the Fatherland", 2nd degree Order of Friendship Honorary citizen of Krasnoyarsk As scientific director of SFU, Vaganov oversees significant research initiatives, particularly those related to forest-climate interactions and carbon cycling. His work on Russian forests' contribution to climate change mitigation has informed national policy discussions related to the Paris Climate Agreement. He has led major projects examining the carbon budget of Siberian forests and the role of Russian forests in global carbon cycling. Vaganov's research group operates within Siberian Federal University's extensive research infrastructure, conducting field studies across Siberia's diverse forest ecosystems. His team utilizes advanced laboratory techniques for wood anatomy analysis and maintains long-term ecological monitoring sites throughout the region. Current work focuses on developing high-resolution climate proxies from wood anatomy and understanding forest responses to increasing climate variability.

• Neuroinformatics
• Neural Networks
• Semantic Networks
• +۶ مورد دیگر

Alexander Alexandrovich Kharlamov serves as a Professor at the National Research University Higher School of Economics (HSE), specifically within the Faculty of Computer Science and Department of Software Engineering. He joined HSE in 2015 and has accumulated 42 years of scientific and teaching experience. His academic profile includes prominent identifiers such as ORCID: 0000-0003-2942-5101, ResearcherID: E-3760-2014, and Scopus AuthorID: 57193909855. He maintains an office at the Pokrovsky Boulevard campus (AUK "Pokrovsky Boulevard", Pokrovsky blvd, 11, office S913) and serves as the Editor-in-chief of the journal "Speech Technologies" since 2008. Doctor of Technical Sciences (2010) from Moscow State Institute of Electronics and Mathematics Candidate of Technical Sciences (1983) from Moscow State Technical University named after N.E. Bauman Postgraduate study (1980) at Moscow State Technical University named after N.E. Bauman Mathematics degree (1976) from Lomonosov Moscow State University Thermal Physics degree (1970) from Moscow Power Engineering Institute Professor Kharlamov's research focuses primarily on neuroinformatics, with significant contributions to neural networks, semantic representations, and intelligent information systems. His work bridges computational approaches with cognitive modeling, exploring how semantic networks can be used for text analysis, speech recognition, and situation monitoring. His research interests span both theoretical foundations of neuroinformatics and practical applications in areas such as social media analysis, smart city development, and digital transformation. He has developed the TextAnalyst technology for automatic semantic analysis of text and has made significant contributions to understanding how humans process information through neural network models. His recent scholarly output demonstrates a clear trajectory toward multimodal systems, digital transformation in urban environments, and advanced semantic analysis techniques. The publications reveal a consistent focus on applying neuroinformatics principles to real-world problems, particularly in analyzing social dynamics through digital footprints, developing intelligent monitoring systems, and creating semantic frameworks for information processing. His work increasingly addresses contemporary challenges such as pandemic response, social stress analysis, and digital conflict zones, showing adaptability to emerging societal needs while maintaining theoretical rigor. Corresponding Member of the International Academy of Informatization (1994) Professor Kharlamov has secured significant research funding through grants from the Russian Foundation for Basic Research (No. 14-06-00363) and the Russian Humanitarian Science Foundation (No. 15-03-00860). His research leadership spans decades, with documented projects from 1996 to present, covering areas such as neural network modeling, speech recognition systems, semantic analysis technologies, and intelligent information systems. He has served as principal investigator for numerous projects, often collaborating with institutions like IITP RAS and Bauman Moscow State Technical University. His teaching portfolio includes advanced courses in neuroinformatics, neuromathematics, and neural network theory for both bachelor's and minor programs at HSE. As Editor-in-chief of the journal "Speech Technologies" since 2008, Professor Kharlamov has shaped discourse in his field while continuing his own research on semantic networks and neural modeling. His work on homogeneous semantic networks represents a significant contribution to the field, providing frameworks for text analysis, situation monitoring, and knowledge representation. The integration of his theoretical work on neural networks with practical applications in information systems demonstrates a career-long commitment to bridging theoretical computer science with real-world implementation challenges.

• Intelligent Transport Systems
• Location-Based Services
• Software and Hardware Integrated Navigation Systems
• +۷ مورد دیگر

Saleh Hadi Mohammed is an Associate Professor at the Faculty of Computer Science , National Research University Higher School of Economics (HSE), where he has worked since 2015. With over 10 years of academic experience, his expertise spans software engineering, blockchain systems, and intelligent transport technologies. Candidate of Technical Sciences (2013) Engineer in 'Computers, Complexes, Systems and Networks' (2008) Professional training in Moodle-based distance learning Research interests include: Intelligent transport systems and location-based services Software/hardware navigation integration and blockchain applications Cloud services, mobile app development, and MLOps platforms His recent publications analyze MLOps scalability, blockchain donation tracking, and AI-driven navigation for visually impaired users. Notable collaborations involve Springer, IEEE, and CEUR Workshop Proceedings. Scientific awards : Gratitude from HSE Faculty of Computer Science (2024) As a thesis supervisor , he has guided 15+ students in projects ranging from fraud detection to mobile education apps. He also leads SmartMLOps , HSE’s AI service deployment platform (2024), and participated in the Administrative Personnel Reserve Program (2024-2025).

• Computational Biology
• Bioinformatics
• Mass Spectrometry Data Analysis
• +۵ مورد دیگر

Attila Kertesz-Farkas is a Hungarian-born academic currently serving as Assistant Professor at the Faculty of Computer Science, Department of Data Analysis and Artificial Intelligence, at the National Research University Higher School of Economics (HSE) in Moscow. Since 2021, he has also been Head of the Research and Educational Laboratory of Artificial Intelligence for Computational Biology. He joined HSE in 2015 after completing postdoctoral positions at the University of Washington and the International Centre of Genetic Engineering and Biotechnology. His educational background includes: Doctor of Science (2022) from National Research University Higher School of Economics PhD (2010) from University of Szeged Master's degree in Computer Science (2004) from University of Szeged Kertesz-Farkas's research focuses on the intersection of artificial intelligence and computational biology, particularly in mass spectrometry data analysis. His work spans computational proteomics, human gait analysis, and medical applications of machine learning. He develops novel algorithms for peptide identification, protein classification, and single-cell analysis, with applications ranging from cancer research to environmental contamination analysis. His research group actively explores how deep learning can improve the analysis of biological data that is inherently non-human readable. His scientific contributions have been recognized with several awards including a Letter of thanks from the Rector of HSE (December 2022), a Letter of gratitude from the Faculty of Computer Science (September 2021), and the Best presentation award at ICMLC 2023 conference. As an academic supervisor, Kertesz-Farkas mentors multiple PhD students working on diverse projects including generative models for mass spectrometry data, human gait control systems for prosthetics, and deep learning applications for single-cell analysis. His research is supported by the allowance for defending a doctoral dissertation (2022-2025) and various research projects at HSE. He leads the Laboratory of Artificial Intelligence for Computational Biology, which focuses on developing AI methods specifically tailored for biological data analysis problems. The laboratory conducts research in computational proteomics, medical applications of machine learning, and the development of tools for mass spectrometry data interpretation.

• medical informatics
• image analysis and processing
• medical information systems
• +۶ مورد دیگر

Oleg Stanislavovich P'yanykh is a Professor in the Department of Data Analysis and Artificial Intelligence at the Faculty of Computer Science, National Research University Higher School of Economics (HSE) in Moscow. He has been working at HSE since 2012 with 13 years of scientific and teaching experience. P'yanykh maintains active roles as a Guest Editor for both the American Journal of Roentgenology (since 2011) and Pattern Recognition (since 2010), and serves as a permanent member of the International DICOM Committee working group. His educational background includes a PhD in Computer Science from Louisiana State University (1998), an MS with honors in Applied Mathematics and Physics from Moscow State University (1994), and a Diplôme d'Etudes et de Recherches in Philosophy from French University College, Moscow State University and Sorbonne University (1994). P'yanykh's research spans medical informatics, image analysis and processing, medical information systems and standards (particularly DICOM and PACS), computer-aided diagnostics, teleradiology, machine learning, and control theory. His work bridges computer science with practical medical applications, focusing on improving medical imaging systems and data analysis. His publication record shows consistent contributions from 1997 through 2024, with recent work emphasizing scheduling algorithms, machine learning interpretability, human knowledge modeling, and medical image quality assessment. His research demonstrates a clear trajectory from foundational signal processing work to increasingly applied medical informatics solutions. Bonus for publication in an international peer-reviewed scientific journal (2019-2021) Bonus for publication in an international peer-reviewed scientific journal (2017-2018) Bonus for an article in a foreign peer-reviewed journal (2013-2015) P'yanykh has supervised student research including Viktor Sergeevich Lopatin's bachelor's thesis on 3D medical image processing and Ksenia Dmitrievna Loginova's master's thesis on medical image quality assessment. He has taught courses including Big Data and Machine Learning in Healthcare across multiple academic years (2020-2026) and Medical Informatics (2020-2022), reflecting his focus on applying computational methods to healthcare challenges.

• Remote Sensing
• Geographic Information Systems (GIS)
• Forest Ecology
• +۵ مورد دیگر

Sergey Im is an Associate Professor in the Department of Geography at Siberian Federal University's School of Ecology and Geography. With 20 years of professional experience in his specialty, he holds a Candidate of Technical Sciences degree (equivalent to PhD) awarded in 2004 for his dissertation on Microwave and spectroradiometric surveys in forest area research . He graduated from Krasnoyarsk State Technical University in 2001 with a specialization in information systems in geoinformation systems. Dr. Im's research focuses on the application of remote sensing data and GIS technologies for forest area research, with particular emphasis on climate change impacts on Siberian forests, permafrost dynamics, and vegetation monitoring. His work spans several key areas including forest-tundra ecotone dynamics, larch forest responses to warming, and satellite monitoring of environmental changes across Siberia. His extensive publication record shows a clear trend toward increasingly sophisticated remote sensing techniques applied to pressing environmental questions, particularly the interaction between climate warming, permafrost degradation, and vegetation dynamics in Siberia. Recent work demonstrates growing integration of multiple satellite data sources and advanced analytical methods to monitor ecosystem changes at various spatial and temporal scales. Krasnoyarsk City Mayor's Prize for Young Talents (2007) Dr. Im teaches courses in Remote sensing of the Earth, Geographic information databases, Mathematical statistics, and GIS in geography. His advanced training includes multiple certifications in information and communication technologies, modern GIS applications, e-learning methodologies, and pedagogical approaches. His research collaborations span numerous Siberian institutions and involve extensive fieldwork across diverse Siberian ecosystems from the Arctic to mountainous southern regions.

• institutional economics
• agency theory
• economics of education
• +۲ مورد دیگر

Prakhov Ilya Arkadyevich is an Associate Professor at the Department of Applied Economics within the Faculty of Economic Sciences at the National Research University Higher School of Economics (HSE) in Moscow. He has been working at HSE since 2007, accumulating 16 years of scientific and teaching experience. His academic roles also include Senior Researcher at the International Laboratory for Institutional Analysis of Economic Reforms. Dr. Prakhov's educational background includes: Candidate of Economic Sciences (2013) from HSE, specializing in 'Economics and Management of the National Economy' with dissertation on 'The Impact of Investments in Additional Training on the Results of the Unified State Examination' Master's degree in Economics (2009) from State University - Higher School of Economics Bachelor's degree in Economics (2007) from National Research University 'Higher School of Economics' His primary research focuses on institutional economics, agency theory, and economics of education, with particular expertise in analyzing the Russian Unified State Examination system. Dr. Prakhov has conducted extensive research on educational accessibility, student mobility patterns, faculty compensation structures, and the socioeconomic determinants of educational outcomes. His work examines how family background, socioeconomic status, and preparatory investments affect educational trajectories and access to higher education in Russia. Dr. Prakhov's research output shows a consistent focus on educational equity, institutional analysis of higher education systems, and the economics of educational decision-making. His recent publications demonstrate increasing international recognition, with articles appearing in leading journals across education, economics, and sociology disciplines. His methodological approach combines institutional analysis with rigorous empirical economic research, often utilizing large-scale datasets to examine educational outcomes and mobility patterns. His academic achievements have been recognized through numerous awards: Medal 'Recognition - 15 years of successful work' (December 2023) 'Best Teacher' award (2011-2025) Multiple publication bonuses for research in international peer-reviewed journals Academic Work Allowance (2011-2019) Membership in the HSE Personnel Reserve (High Professional Potential Group) Dr. Prakhov actively contributes to academic service through supervising student research, participating in international conferences, and leading research projects. His current project 'Dynamics of human capital in the academic sector' continues his long-standing investigation into the Russian higher education system and its relationship with broader socioeconomic factors.

• Computer Science
• Video Coding
• Drone Technology
• +۵ مورد دیگر

Evgeny Belyaev is a Research Fellow at ITMO University's International Laboratory 'Computer Technologies'. Holding dual degrees as Candidate of Science in technical sciences (Russia, 2009) and Doctor of Technology (Tampere University of Technology, Finland), he specializes in video coding, drone-based infrastructure inspections, and vehicular communication systems. Education: Candidate of Science (Technical Sciences, 2009, Russia), Doctor of Technology (Tampere University of Technology, Finland) His research focuses on video coding , drone inspections , and real-time transmission systems , with applications in road surveillance and infrastructure monitoring. Key contributions include iterative aerial map construction techniques and robust video transmission protocols. Scientific achievements include the Grand Video Compression Challenge award (2013), multiple IEEE Exemplary Reviewer recognitions (2013, 2015, 2016), and a Best Paper Award (2011). He has authored over 40 publications and holds 4 patents.

• Adaptive Control
• Robust Control
• Nonlinear Systems
• +۳ مورد دیگر

Vladimir Nikiforov is the Vice Rector for Research at ITMO University and a Professor at the Faculty of Control Systems and Robotics. He serves as editor-in-chief of the Scientific and Technical Journal of Information Technologies, Mechanics and Optics (Scopus-cited) and chairs the Academic Degree Council. His career spans over three decades at ITMO University, transitioning from researcher to professor and administrative leadership. Education: BSc in Electrical Engineering (1986), Leningrad Institute of Fine Mechanics and Optics (now ITMO University) PhD (1991), thesis: 'Adaptive control of trajectory motion of electro-mechanical systems' DSc (2001), thesis: 'Adaptive and robust control with compensation of external disturbances' Nikiforov's research focuses on adaptive and robust control systems , particularly for nonlinear systems, spark ignition engines, and ship propulsion systems. His work includes high-precision control algorithms, disturbance compensation, and stability analysis in multivariable systems. He has contributed to theoretical advancements in backstepping design, high-order tuners, and regressor extension techniques. Part-time roles include: Senior Researcher at the Institute of Problems of Mechanical Engineering (1993-1994, 2000-2005), Associate Professor at St. Petersburg University of Printing Industry (1994-1997), and Technical Director at LOMO Company (2004-2013). His recent publications emphasize adaptive control frameworks with dynamic regressor extension and memory regressor techniques. Key projects include computer-based control systems for ship screw-propeller motors, adaptive compensation of external disturbances, and digital control for brushless DC motors in mechatronic systems.

• Automatic text analysis
• Information search
• IT project management
• +۲ مورد دیگر

Natalia Sergeevna Belova is an Associate Professor in the Department of Software Engineering at the Faculty of Computer Science, National Research University Higher School of Economics (HSE University) in Moscow. She has been working at HSE since 2012 with 13 years of scientific and teaching experience. Education: 2010: Candidate of Technical Sciences from Moscow State University of Instrument Engineering and Computer Science, specialty 05.13.11 "Mathematical and software support for computing machines, complexes and computer networks" 2009: Postgraduate study from Moscow State University of Instrument Engineering and Computer Science 2005: Engineer qualification in "Computers, complexes, systems and networks" from Moscow State Academy of Instrument Engineering and Computer Science Research Interests: Dr. Belova's research focuses on automatic text analysis, information search, IT project management, embedded databases, and project-based learning in engineering education. Her work bridges theoretical computer science with practical applications in software engineering education and industry. She has made significant contributions to pattern recognition, face identification systems, and the development of computationally efficient methods for intelligent systems processing multimedia information. Publications Trend: Dr. Belova's research has evolved from foundational work on embedded databases to more recent contributions in computer vision, affect recognition, and AI applications. Her early work (2009-2013) focused on database systems reliability and project-based learning methodologies. Since 2015, her research shifted toward computer vision, pattern recognition, and deep learning applications. Her most recent publications (2022-2025) demonstrate expertise in AI applications across various domains including transportation systems and video analysis. Scientific Achievements: Recipient of the Grant of the President of the Russian Federation for young doctors of science MD-306.2017.9 (2017-2018) Gratitude from the Vice-Rector of HSE University (February 2025) Gratitude from the Faculty of Computer Science, HSE University (July 2023) Multiple publication bonuses for journals in List A and international peer-reviewed publications (2016-2025) Best Teacher award for 2016-2017 Member of the High Professional Potential Group (HSE personnel reserve) Teaching and Mentoring: Dr. Belova has supervised over 25 bachelor's theses on diverse software engineering topics including mobile applications, web platforms, biometric identification systems, and educational software tools. She teaches courses on "Group Dynamics and Communications in Professional Software Engineering Practice" and "Economics of Software Engineering." Her commitment to project-based learning methodology is evident in both her research and teaching practice. Professional Development: Dr. Belova actively participates in advanced training programs, including recent courses on modernization of educational programs in software engineering (2025) and problems of training modern specialists in software engineering (2024).

• Probability Theory
• Mathematical Statistics
• Combinatorial Optimization
• +۲ مورد دیگر

Dr. Zhukova Galina Nikolaevna is an Associate Professor at the Department of Software Engineering , Faculty of Computer Science , National Research University Higher School of Economics (HSE), where she has worked since 2017. Her academic career spans 21 years, including prior roles at Moscow State University of Printing Arts and Moscow Polytechnic University. PhD in Physical and Mathematical Sciences (2004, Moscow State University of Printing Arts) Specialist in Applied Mathematics (1999, Moscow State University) Advanced training in software engineering pedagogy (2018–2025) Research Focus : Dr. Zhukova specializes in Probability Theory , Mathematical Statistics , and Combinatorial Optimization . Her work analyzes algorithm complexity distributions, develops hybrid exact methods for the Traveling Salesman Problem (TSP), and investigates symbolic sequence reconstruction under noise. She contributes to probabilistic modeling of computational efficiency and quantile-based complexity metrics . Publication Trends : Her 15 most recent articles focus on TSP complexity prediction , noise-resistant sequence reconstruction , and hybrid algorithm design for asymmetric problems. These combine probabilistic modeling , statistical analysis , and computational efficiency studies , with applications in optimization and data processing. Gratitude from HSE Software Engineering Department (2023) Gratitude from HSE Faculty of Computer Science (2019) Selected for HSE Personnel Reserve (High Professional Potential Group) Recognized under 'New Teachers' category (2018) Teaching Contributions : Dr. Zhukova delivers courses on Operations Research , Mathematical Analysis , and Algebra in Python . She designs practical Python-based curricula for software engineering students and teaches minors in Applied Linear Algebra for the Faculty of Economic Sciences.

Baranov Pavel Fedorovichمشاهده پروفایل

دانشیار

• Non-Destructive Testing
• Metrology
• Electrical Measurement
• +۳ مورد دیگر

Baranov Pavel Fedorovich is an Associate Professor and current Director of the Engineering School of Non-Destructive Testing and Safety at Tomsk Polytechnic University, holding a Candidate of Technical Sciences degree and affiliated with the Department of Electronic Engineering. His educational background includes: 2004: Entered Tomsk Polytechnic University's Faculty of Electrophysics in the "Metrology, standardization and certification" program 2010: Master's degree completion 2010-2013: Postgraduate studies 2013: PhD defense in specialty "05.11.01 - Instruments and methods of measurement (measurement of electrical and magnetic quantities)" Dr. Baranov's research centers on precision measurement technologies with emphasis on electrical and magnetic phenomena. His work involves developing specialized instrumentation to overcome measurement challenges in high-interference environments, including: Synchronous amplifier design with differential inputs for detecting minute physical deviations amid strong common-mode noise Advanced fluxgate magnetometer development and analysis Complex waveform synthesis for specialized measurement scenarios His expertise spans non-destructive testing methodologies, metrology fundamentals, and signal processing techniques for industrial applications. No scientific awards were documented in the source material. Academic advising activities and research grant information were not specified in the available records. His teaching responsibilities include the course "Measuring technologies of the Internet of Things". As Director of the Engineering School of Non-Destructive Testing and Safety, he leads institutional efforts in developing non-destructive evaluation standards, safety protocols, and measurement education programs, overseeing faculty and research initiatives within this specialized engineering domain.

• Signal Processing
• Satellite Navigation Systems
• Radio Engineering
• +۲ مورد دیگر

Зандер Феликс Викторович is an Associate Professor and Head of the Department of Radioelectronic Systems at the Institute of Engineering Physics and Radio Electronics (IIFIRE), Siberian Federal University. He holds a Candidate of Technical Sciences degree (2002) from Krasnoyarsk Polytechnic Institute, specializing in Radio Engineering. His dissertation focused on advanced methods for radio signal parameter estimation. Research Interests: His work spans signal reception/processing, satellite navigation (particularly GLONASS systems), digital communications, and radio device optimization. Specific areas include: High-precision synchronization for distributed systems Radio signal parameter algorithms Digital television/radio networks Mobile communication infrastructure Publications: His research emphasizes practical applications in satellite navigation and signal measurement, with recent work (2012) addressing cost-effective GLONASS synchronization and earlier contributions (2003) developing algorithms for non-standard signal intervals. Teaching: He instructs courses including Radio Receiving Devices , Digital Signal Processing Devices , Mobile Communication Systems , and Digital Broadcasting Networks .

• Broadband radio navigation systems
• Noise immunity of reception of spectrally effective noise-like signals
• Synchronization in navigation and control systems

Bondarenko Valery Nikolaevich serves as an Associate Professor at the Department of Radioelectronic Systems, Siberian Federal University (SFU), holding the academic title of Doctor of Technical Sciences. His work integrates advanced research in radio navigation systems with core engineering education. His academic credentials include a Radio Engineering degree from Krasnoyarsk Polytechnic Institute, followed by a Candidate of Technical Sciences (1977) and Doctor of Technical Sciences (2009), both specializing in Radar and Radio Navigation (05.12.14). The dissertations addressed signal processing fundamentals and noise immunity enhancement in broadband navigation systems. Research focuses on three critical domains: broadband radio navigation architectures, noise immunity optimization for spectrally efficient signals, and synchronization mechanisms in navigation/control systems. His methodologies specifically target performance enhancement under limited frequency resources, addressing real-world constraints in modern radio systems. Publication trends reveal sustained expertise in noise-like signal processing since 2007, with emphasis on receiver design (correlation and quasi-optimal systems), phase/frequency synchronization, and noise immunity techniques. Key subfields consistently include Minimum Frequency Shift Keying implementations and robust signal acquisition strategies across navigation applications. No scientific awards or student advisement activities are documented in available materials. Teaching responsibilities include Automation and Control, Radio Automation, and Radio Navigation Systems theory, reflecting his dual expertise in control engineering and navigation technologies.