Current Issue : October-December Volume : 2025 Issue Number : 4 Articles : 5 Articles
The purpose of this study was to investigate and compare the results of calculating the parameters of the LoRa network obtained by computer modelling with the results of experimental measurements. To fulfil this purpose, the methods of computer modelling of signal loss were used. Specifically, the study described a modification of the FLoRa simulator to estimate signal losses during propagation, perform computer simulations in FLoRa, and compare the findings obtained with the data obtained during the experiment. In addition, the RSSI values obtained in the simulation were compared with the experimental values. The functionality of the FLoRa software simulator was extended by adding signal power loss values to the simulation results table. Using the FLoRa software, the study simulated the signal power loss along the propagation path at frequencies of 433 MHz, 868 MHz, and 2.4 GHz. A comparative analysis revealed that the simulation results for different spreading factors and different signal frequencies correspond to the experimental data. It was found that the received signal power values are represented in the software as RSSI values. The signal power at the input does not correspond to the RSSI values and depends on the concrete type of receiver chip, and therefore the RSSI calculation methodology should be adjusted. It was confirmed that the results table should display both the signal strength at the receiver input and the RSSI value. To improve the accuracy of the FLoRa computer model, specifically, the calculation of RSSI values, it was proposed to consider the specific features of measuring these values by different types of LoRa receiver chips. The obtained findings can be used to improve the accuracy of modelling and, accordingly, the quality of designing networks based on LoRa technology...
Serial communication enables communication between devices by providing high speed and efficiency in data transfer within modern communication systems. It has a wide range of applications, including business, healthcare, education, industry, and consumer electronics. This method offers an economical and efficient solution as it requires fewer cables and consumes fewer resources compared to parallel communication. It is particularly preferred in scenarios requiring remote communication and high-speed data transmission. Although serial communication offers speed and efficiency in data transfer, it also has certain disadvantages and limitations. Various techniques have been developed over time to enhance the speed and efficiency of data transmission. Line coding techniques have also evolved within this context. In this paper, a new technique (multiplier technique) has been developed, offering a new perspective on the line coding techniques used in serial data transmission by processing repetitive data to transmit multiple data units at once. This technique, like other line coding techniques, aims to increase the data transmission rate and overcome bandwidth limitations. In multi-level line coding techniques, instead of symbol data corresponding to each level, the coded data is derived by transmitting the number of repetitions of the logic value as a multiplier. In multi-level line coding techniques, instead of symbol data corresponding to each level, the encoded data is derived by transmitting the number of repetitions of the logic value as a multiplier. In the application example, within the logic voltage level range, a microcontroller, logic gates, and analog switches were used to analyze, deduplicate, and replicate the data. In addition, an interface design and a basic-level protocol were developed for data transmission and analyzed for high data transmission efficiency based on various parameters included in the technique....
This research study proposes an indoor temperature regulation predictive optimal control system that entails the use of both deep reinforcement learning and the Modbus TCP communication protocol. The designed architecture comprises distributed sub-parts, namely, distributed room-level units as well as a centralized main-part AI controller for maximizing efficient HVAC management in single-family residences as well as small-sized buildings. The system utilizes an LSTM model for forecasting temperature trends as well as an optimized control action using an envisaged DQN with predicted states, sensors, as well as user preferences. InfluxDB is utilized for gathering real-time environmental data such as temperature and humidity, as well as consumed power, and storing it. The AI controller processes these data to infer control commands for energy efficiency as well as thermal comfort. Experimentation on an NVIDIA Jetson Orin Nano as well as on a Raspberry Pi 4 proved the efficacy of the system, utilizing 8761 data points gathered hourly over 2023 in Cheonan, Korea. An added hysteresis-based mechanism for controlling power was incorporated to limit device wear resulting from repeated switching. Results indicate that the AI-based control system closely maintains target temperature setpoints with negligible deviations, affirming that it is a scalable, cost-efficient solution for intelligent climate management in buildings....
A brain-computer interface (BCI) system enables direct communication between the brain and external devices, offering significant potential for assistive technologies and advanced human-computer interaction. Despite progress, BCI systems face persistent challenges, including signal variability, classification inefficiency, and difficulty adapting to individual users in real time. In this study, we propose a novel hybrid quantum learning model, termed QSVM-QNN, which integrates a Quantum Support Vector Machine (QSVM) with a Quantum Neural Network (QNN), to improve classification accuracy and robustness in EEG-based BCI tasks. Unlike existing models, QSVM-QNN combines the decision boundary capabilities of QSVM with the expressive learning power of QNN, leading to superior generalization performance. The proposed model is evaluated on two benchmark EEG datasets, achieving high accuracies of 0.990 and 0.950, outperforming both classical and standalone quantum models. To demonstrate real-world viability, we further validated the robustness of QNN, QSVM, and QSVM-QNN against six realistic quantum noise models, including bit flip and phase damping. These experiments reveal that QSVM-QNN maintains stable performance under noisy conditions, establishing its applicability for deployment in practical, noisy quantum environments. Beyond BCI, the proposed hybrid quantum architecture is generalizable to other biomedical and time-series classification tasks, offering a scalable and noise-resilient solution for nextgeneration neurotechnological systems. Impact Statement—Integrating quantum computing and machine learning techniques into brain-computer interface (BCI) systems represents a transformative step towards overcoming existing challenges in signal processing and classification accuracy. By harnessing the capabilities of quantum parallelism and entanglement, these innovative approaches have the potential to significantly enhance the efficiency and accuracy of BCI systems, ultimately enabling more seamless interaction between individuals and external devices. The promising results obtained from applying the proposed quantum algorithm, which integrates the quantum neural network (QNN) and the quantum support vector machine (QSVM), to EEG-based BCI datasets demonstrate their superiority over classical methods in terms of accuracy and robustness. Index Terms—Brain-Computer Interface (BCI), ElectroEncephaloGram (EEG), Quantum Computing (QC), Quantum Machine Learning (QML), Quantum Neural Network (QNN), Quantum Support Vector Machine (QSVM)...
This study aims to identify and analyze the various security challenges faced in modern computer networks and operating systems. The research method used is descriptive qualitative, with data obtained from various sources, including journal articles related to computer networks and operating systems. Journal articles on network management, protocols, security, and performance, as well as research reports and case studies that address security challenges in modern computer networks and operating systems. The results show that the main challenge in maintaining the security of networks and operating systems is increasingly sophisticated and complex cyber attacks, which often go beyond the capabilities of existing security technologies. In addition, the low level of security awareness among users is a significant factor that aggravates the situation, as many users do not follow basic security practices. The study also found that effective implementation of security policies is still an unresolved issue in many organizations. Many existing policies are not implemented consistently or are not updated according to the development of new threats. By identifying these challenges, the study makes an important contribution to the development of better strategies and policies to improve the security of networks and operating systems in the future....
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