Andrew Madaliev
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Andrew Madaliev has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Ali Alhasan
Saint Petersburg Electrotechnical University LETI (ETU), Russia
1 shared publication - Mohammed Gismelbari
Saint Petersburg Electrotechnical University LETI (ETU), Russia
1 shared publication - Ruslan Gataullin
Saint Petersburg Electrotechnical University LETI (ETU), Russia
1 shared publication - Sergei Chuprov
Department of Computer Science, The University of Texas Rio Grande Valley, Edinburg, TX 78539, USA
1 shared publication - Vladimir Mikhailovich Ivanov
Peter the Great St. Petersburg Polytechnic University
1 shared publication
Latest Publications
- Open Access
- 19 December 2024
- Published by MDPI AG in Preprints.org
Innovative Approach To Human-IoT Interaction Using Weak AI Models
The aim of this study is to explore and validate the potential of using brain-computer interfaces (BCIs) and electroencephalography (EEG) signals to control external devices, specifically unmanned aerial vehicles (UAVs). The main problem of research is to integrate between human cognitive functions with Internet of things (IoT) devices, enabling hands-free control based on mental commands. Such an approach could transform fields where rapid, precise, and intuitive control is critical, including healthcare, search-and-rescue missions, and industrial automation. By capturing raw EEG data associated with simple motor commands, we demonstrate the feasibility of translating specific brainwave patterns into UAV instructions like "hover" and "land." The next phase will involve analog-to-digital processing and filtering to refine these signals, preparing them for machine learning algorithms to enhance command accuracy. Our findings highlight the promise of neural interfaces as a bridge between human thought and machine action, paving the way for advancements in IoT-based human-technology interaction.
- 19 December 2024
- Published by MDPI AG in Preprints.org
Open AccessInnovative Approach To Human-IoT Interaction Using Weak AI Models
The aim of this study is to explore and validate the potential of using brain-computer interfaces (BCIs) and electroencephalography (EEG) signals to control external devices, specifically unmanned aerial vehicles (UAVs). The main problem of research is to integrate between human cognitive functions with Internet of things (IoT) devices, enabling hands-free control based on mental commands. Such an approach could transform fields where rapid, precise, and intuitive control is critical, including healthcare, search-and-rescue missions, and industrial automation. By capturing raw EEG data associated with simple motor commands, we demonstrate the feasibility of translating specific brainwave patterns into UAV instructions like "hover" and "land." The next phase will involve analog-to-digital processing and filtering to refine these signals, preparing them for machine learning algorithms to enhance command accuracy. Our findings highlight the promise of neural interfaces as a bridge between human thought and machine action, paving the way for advancements in IoT-based human-technology interaction.
- 1 January 2023
- Published by Keldysh Institute of Applied Mathematics
Mixed Reality Surgical Navigation Method
The aim of the study is to present the features of our own new mixed reality surgical navigation method. The paper describes the workflow from the stage of preoperative patient scanning to postoperative patient scanning. Potential sources of positioning errors are identified, design and software approaches to reduce their impact are described: the implementation of the unity of the location of markers, the execution of markers. The applied approach for estimating the cumulative positioning error is described. As a result of the implementation of these approaches in practice, the value of the total navigation error was less than 2 mm, and more than 80 successful operations were performed in this way. This value of navigation error is sufficient for most surgical operations, but is unacceptable in neurosurgery. The study can be useful for improving similar systems, as well as for designing mixed reality systems in other scientific fields.
- 1 January 2023
- Published by Keldysh Institute of Applied Mathematics
Mixed Reality Surgical Navigation Method
The aim of the study is to present the features of our own new mixed reality surgical navigation method. The paper describes the workflow from the stage of preoperative patient scanning to postoperative patient scanning. Potential sources of positioning errors are identified, design and software approaches to reduce their impact are described: the implementation of the unity of the location of markers, the execution of markers. The applied approach for estimating the cumulative positioning error is described. As a result of the implementation of these approaches in practice, the value of the total navigation error was less than 2 mm, and more than 80 successful operations were performed in this way. This value of navigation error is sufficient for most surgical operations, but is unacceptable in neurosurgery. The study can be useful for improving similar systems, as well as for designing mixed reality systems in other scientific fields.