2024
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Документ Відкритий доступ Investigation of Electrical Signals Transmission through Light-Induced Conductive Channels on the Surface of CdS Single Crystal(КПІ ім. Ігоря Сікорського, 2024) Boikynia, A. O.; Tkachenko, N. S.; Didenko, Yu. V.; Oliinyk, O. O.; Tatarchuk, D. D.Further development of information technologies hinges on innovations in the electronic components sector, particularly in enhancing electronic communication devices. This involves creating dynamic interconnects—electrically conductive channels that can be configured on-demand within chip circuitry to overcome the "tyranny of interconnects," which limits electronic systems due to the fixed nature of conventional interconnects. This paper presents experimental verification of transmitting information through photoconductive channels formed on a photosensitive cadmium sulfide (CdS) semiconductor single crystal using optical irradiation. By directing a focused light beam to specific areas of the CdS crystal, localized conductivity is induced, allowing for the dynamic formation of conductive channels. This method's efficacy in real-time signal transmission validates the theoretical framework and suggests new possibilities for semiconductor technology. The integration of dynamic interconnects could revolutionize communication systems by enhancing device efficiency and processing capabilities. This technology could lead to more complex electronic architectures needed in high-speed computing and advanced telecommunications. Additionally, this approach has potential applications in optoelectronics, improving device interaction with light. Dynamic interconnects could enhance solar cell efficiency, increase light sensor sensitivity, and aid in developing innovative visual displays. The ability to control material conductivity through light not only advances existing device performance but also opens doors to new electronic designs and operations. This includes fully reconfigurable circuits that adapt in real-time, self-optimizing network components, and smart sensors that respond to environmental changes. In summary, this research not only confirms the practicality of using photoconductive channels for information transmission but also emphasizes the significant implications for electronic and communication system advancements. As this technology evolves, it promises to significantly impact the design and functionality of future electronic devices, paving the way for more adaptable and powerful systems.Документ Відкритий доступ Deep Learning for the Detection and Classification of Diabetic Retinopathy Stages(КПІ ім. Ігоря Сікорського, 2024) Basarab, M. R.; Ivanko, K. O.The incidence of diabetic retinopathy (DR), a complication of diabetes leading to severe vision impairment and potential blindness, has surged worldwide in recent years. This condition is considered one of the leading causes of vision loss. To improve diagnostic accuracy for DR and reduce the burden on healthcare professionals, artificial intelligence (AI) methods are increasingly implemented in medical institutions. AI-based models, in particular, are integrating more algorithms to enhance the performance of existing neural network architectures that are commercially used for DR detection. However, these neural network models still exhibit limitations, such as the need for high computational power and lower accuracy in detecting early DR stages. To overcome these challenges, developing more advanced machine learning models for precise DR detection and classification of DR stages is essential, as it would aid ophthalmologists in making accurate diagnoses. This article reviews current research on the use of deep learning in diagnosing and classifying DR and related diseases, as well as the challenges ophthalmologists face in detecting this condition and potential solutions for early-stage DR detection. This review provides information on modern approaches to DR detection using deep learning applications and discusses the issues and limitations in this area.Документ Відкритий доступ Soitware Support for the Higher Mathematics Course at the Technical University(КПІ ім. Ігоря Сікорського, 2024) Bogdanov, O. V.; Butsenko, Yu. P.; Balina, O. I.; Bezklubenko, I. S.The paper addresses the challenges of training modern engineers in mathematical disciplines. It emphasizes the need for graduates to possess theoretical knowledge and be capable of using modern software solutions for efficient mathematical calculations. The article discusses the importance of incorporating mathematical software into higher education to enhance the teaching of mathematical disciplines. Explores the potential benefits and challenges of using mathematical software in the learning process, such as the need for coordination among teachers and the importance of familiarizing students with the interfaces of mathematical packages. The paper recommends using specific mathematical software packages, such as Scilab, for scientific and engineering calculations. It also highlights the necessity of guiding students in understanding the practical applications of mathematical software in solving complex mathematical problems. Furthermore, the paper addresses the issue of preventing students from inappropriately using online calculators for tasks, emphasizing the importance of mastering theoretical foundations before utilizing mathematical software packages. The paper generally advocates for a balanced approach that incorporates both traditional theoretical exposition and practical application of mathematical programs in higher education to promote effective teaching and learning of mathematical disciplines.Документ Відкритий доступ A Review of Broadband Microfabricated Ultrasonic Systems for Biomedical Applications(КПІ ім. Ігоря Сікорського, 2024) Kostiuk, R. Yu.; Naidas, S. A.Starting from an overview of historical aspects of biomedical ultrasound development and its application areas, as well as the brief description of state-of-the art microfabrication technologies, used for capacitive and piezoelectrical micromachined ultrasonic transducers manufacturing, also outlining their modelling approaches, the reader will be further presented with an overview of existing methods for achieving broadband operation both at unit transducer and transducers array levels. Moreover, a generalized signal processing system is discussed, including description of known approaches for building blocks implementation in analog, digital and mixed-signal domains (such as drivers, amplifiers, ADCs, etc.).Документ Відкритий доступ Comparison of the Efficiency of a Neural Network for Image Recognition on Microcontrollers(КПІ ім. Ігоря Сікорського, 2024) Sharuiev, R. D.; Popovych, P. V.The paper is devoted to comparing two popular models of 32-bit microcontrollers for working with neural networks for object recognition. The target devices were the ESP32 and STM32 microcontrollers, on which an artificial neural network was deployed, written using the Python programming language and the TensorFlow library. Micropython was chosen as the operating system for the microcontrollers. The paper compares the performance of the ESP32 and STM32 microcontrollers for object detection using a neural network and their classification. The image recognition time and the percentage of correctly classified objects were compared depending on the number of neuron layers and the number of training epochs within these networks. The article shows that the number of layers and training epochs directly affects the accuracy of object classification in the image. The obtained results show that increasing the number of layers of the neural network increases the overall accuracy of object recognition using the studied neural network, increasing the number of training epochs logarithmically increases the accuracy of recognition and classification within the neural network, but at the same time, increasing the number of neuron layers leads to an increase in the total recognition time. The difference in the obtained results for the accuracy of image recognition of microcontrollers differs within 5%.Документ Відкритий доступ Formation of the Spectral Composition of the Output Voltage of Converters for Nuclear Magnetic Resonance(КПІ ім. Ігоря Сікорського, 2024) Zahranychnyi, A. V.In this paper, we consider the construction of an asynchronous control system for semiconductor converters for nuclear magnetic resonance. The relevance of research in this direction, main problems that arise during the construction of these systems are shown. Derived the mathematical model of the inverter, on the basis of which a control system with asynchronous pulse-width modulation is constructed. Proposed an equivalent converter circuit with a constant structure, constant parameters, and an equivalent EMF generator. Established correspondence between the equivalent circuit of the converter and its mathematical model in the form of differential equations. On the basis of mathematical equations, a structural diagram of the converter with a control system was developed and the principle of operation of the device was described according to it. To develop an algorithm for the control system, the dependence of the frequency change relative to the resonant frequency on the phase shift between the current through the filter and the voltage on the antenna circuit is determined. For which the model was built in Simulink and the corresponding simulation was carried out. Numerical dependences of reference signal frequency change and phase shift were obtained. The increase in efficiency of device for nuclear magnetic resonance is considered due to the use of multilevel inverters with tuning the frequency of operation to the frequency of the resonant circuit. Simulation of a three-level diode-clamped inverter showed that when the capacitance of the resonant circuit changes and, therefore, the resonant frequency of the circuit, the input current increases. The work obtained specific numerical dependencies. Time diagrams of voltages and currents on the main elements of the converter are given, which illustrate the implementation asynchronous pulse-width modulation in the control system. After working out the automatic frequency control algorithm, the increase in current consumption can be leveled off. The simulation results also show that it is possible to reduce the amplitude of the third harmonic. The disadvantages of the proposed system include the fact that the frequency of the converter is adjusted at each subsequent period of its operation. At the same time, the parameters of the filter can change again, which leads us to believe that adjusting the frequency will never give a 100% result, but will only allow to get as close as possible to the set parameters of the sounding signal. The work also indicates that it is possible to improve the spectral composition of the probing voltage generated by the converter by using more levels of a diode-clamped multi-level inverter. However, increasing the number of levels reduces the action speed of the system and complicates the control system itself. Therefore, the need to maintain a balance between the number of levels of the inverter and the complexity of the system is indicated.Документ Відкритий доступ Inductor-Less Broadband Energy-Efficient Active Balun up to 60GHz(КПІ ім. Ігоря Сікорського, 2024) Baluta, T. O.; Meyer, A.; Issakov, V.; Vountesmery, Yu. V.The paper presents a non-inductive broadband energy-efficient active balun based on a differential pair, intended for use in the input block of a frequency divider for 22 nm technology. The developed device can operate in the frequency range from 2 GHz to 60 GHz with a supply voltage of 0.8 V and consume less than 3 mA. The special feature of the developed active balun is that it does not have inductive components, which reduces its size and signal loss. Amplitude of the output signal in working frequency range is from 450 mV to 200 mV. Signal gain in the range from 1-60 GHz varies from -10 dB up to 4 dB. The size of the circuit on the chip is 48x34 um. The device allows you to receive a stable signal at high data transfer rates and provides energy savings due to low current consumption.Документ Відкритий доступ Evaluation of the Limitation of Operational Parameters of the IEEE 802.11 ac Network in the 20MHz Channel(КПІ ім. Ігоря Сікорського, 2024) Omelianets, O. O.; Lazebnyi, V. S.IEEE 802.11 wireless network technologies are widely used to create corporate and personal local networks for data exchange and access to Internet resources. The main principle of operation of IEEE 802.11 networks is the principle of competitive access, according to which all wireless network users have the same access rights to the information transmission environment. This method of access leads to the occurrence of collisions in networks with a large number of users, which complicates the process of network functioning and leads to the degradation of quality indicators. The purpose of the study is to estimate the limit values of the operational characteristics of the IEEE 802.11 ac wireless network in the mode with the highest transmission rate (MCS8) in a frequency channel of 20 MHz with one spatial stream, provided that the network has a significant number of active stations with a saturated load. An alternative model of processes in IEEE 802.11 networks based on the concept of a virtual competitive window is used for research. According to the concept of virtual contention window (VCW), the process of data transmission in a network with competitive access is considered as a quasistationary process. Numerical data were obtained and graphs of channel bandwidth, transmission delay, and delay nonuniformity were given in the presence of one to sixteen active stations with a saturated load in the network, in the case of transmission of frames with a data volume of 512 or 1500 bytes. The maximum possible bandwidth of the channel with a frequency band of 20 MHz (68.387 bit/s) was determined, in the case of using frames with the maximum load (11454 bytes) provided by the standard. Estimated data on the number of collisions occurring in a network with a saturated load and the number of frames transmitted at various stages of channel access are also provided. The frame transmission delay increases almost proportionally to the number of active stations and varies from 0.605 ms to 5.293 ms, in the case of loading all data frames of 512 bytes, and from 0.785 to 6.41 ms, in the case of a load of 1500 bytes, for changes in the number of active stations in the network from 2 to 16. The unevenness of the delay exceeds the average delay and grows non-linearly, in the case of an increase in the number of active stations from 1 to 6 (CWmin=15), and linearly — with a further increase in the number of stations (over 6). The obtained results are useful for reasonable planning of wireless networks and configuration of network equipment parameters.Документ Відкритий доступ Estimation of Acting Factor in Stress from Motorbike Sounds(КПІ ім. Ігоря Сікорського, 2024) Pareniuk, A. V.; Pareniuk, D. V.In the presented study, the search for the acting force in stressor acoustic signal and common everyday acoustic signal is presented. As stressors the signals of acoustic siren of air raid alert and other dangers in the different counties were used, and as everyday signals the sound of motorbikes passing by observers were used. In total five different signals of alert sirens were used. Numerical values presented in research were obtained via frequency analysis with Hann’s window and later – via spectrogram survey. This survey allowed us to find the presence of a steady frequency components in the observed signals, and, most importantly, the presence of rises and falls in said components. These changes in frequency had their speed of change calculated for sirens and motorbikes. For the rise of frequency mean speed in the siren group was calculated as 164 Hz/second, fall was 80 Hz/second. For the motorbike, the speed of frequency rise had a mean value calculated as 166 Hz/second and fall of frequency was estimated as 67 Hz/second. Possible sources for said effect in motorbike signals are Doppler effect and rise of RPM during acceleration. During the statistical analysis via implementation of the non-parametric method due to the character of data distribution in the studied group the lack of statistically meaningful differences between speeds of frequency rise in frequency components of the signals was found. Said rise is presumed to be the acting factor in stress from everyday sounds.Документ Відкритий доступ Determination of Signs of Sleep Apnea Using Machine Learning Methods in Combination with Reducing the Dimensionality of Heart Rate Variability Features(КПІ ім. Ігоря Сікорського, 2024) Samsonenko, A. S.; Popov, A. O.Obstructive Sleep Apnea Syndrome (OSAS) is a clinically significant disorder characterized by recurrent episodes of upper airway obstruction, manifesting as either apnea or hypopnea, predominantly occurring at the pharyngeal level. Despite the preservation of respiratory muscle function during these episodes, OSAS poses considerable health risks, including cardiovascular complications and cognitive impairment. In recent years, a growing body of literature has explored novel methodologies to discern and diagnose OSAS, with a particular focus on cardiac activity analysis through Heart Rate Variability (HRV). This study contributes to the existing literature by conducting a comprehensive HRV analysis aimed at identifying indicative patterns of sleep apnea. The analysis incorporates diverse parameters within both time and frequency domains, facilitating a nuanced understanding of the complex interplay between cardiac dynamics and respiratory disruptions during sleep. In an effort to enhance the interpretability of the data, various scaling and dimensionality reduction techniques, such as Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP), were applied. The dataset utilized in this investigation comprises records from 70 patients, sourced from the Apnea-ECG Database on the Physionet platform. To discern the optimal classification model, several machine learning algorithms were employed after the dimensionality reduction, including k-Nearest Neighbors (k-NN), logistic regression, Support Vector Machine (SVM), Decision Tree, Random Forest, and Gradient Boosting. Intriguingly, the results demonstrate a remarkable 100% accuracy across all classifiers when utilizing the UMAP dimensionality reduction method. A distinctive feature of the proposed methodology lies in its amalgamation of machine learning techniques with HRV parameters post-dimensionality reduction. This approach not only enhances the interpretability of the complex physiological data but also underscores the potential applicability of the developed model in real-world scenarios for the detection of OSAS. The robustness of the proposed approach, as evidenced by its high accuracy rates, positions it as a promising tool for advancing diagnostic capabilities in the realm of sleep medicine. Future research endeavors may further refine and validate this methodology, paving the way for its integration into clinical practice and contributing to the broader landscape of sleep disorder diagnostics.Документ Відкритий доступ Application of k-Nearest Neighbors Method for Drug Concentraiton and Cardiotoxicity Classification Using Extracellular Field Potentials and Reconstructed Action Potentials of Cardiac Cells(КПІ ім. Ігоря Сікорського, 2024) Shpotak, M. O.; Ivanushkina, N. H.Micro-electrode array (MEA) systems are important for measuring extracellular field potentials (FP) of cardiac cells, which is a crucial step in cardiotoxicity assessment. However, without modification, the MEA system is only capable of recording FPs. This limits the number of parameters for cardiotoxicity assessment only to FP parameters, while the action potential (AP) parameters remain unused. To address this issue the MEA systems are often modified to use electroor optoporation to record the local extracellular APs (LEAPs), which allows to reliably quantify the AP morphology. As an alternative to MEA modification and cell membrane stimulation the AP can be reconstructed mathematically.This study explores how using additional parameters from reconstructed action potentials (RAPs), derived from FPs, can improve the accuracy of k-NN machine learning models for drug concentration and potential cardiotoxicity classification. The k-NN classifier was trained using combinations of FP and RAP parameters. The k-NN models were evaluated using five-fold stratified cross-validation and cross-channel validation. Their performances were compared using error rate, macro precision, macro recall and macro F1 score accuracy metrics. The results indicated that ncorporating RAP parameters into the feature set increased the F1 score of k-NN model for DMSO concentration classification by up to 10.78% compared to the training set with only FP features.