Indoor localization of wireless nodes is a relevant task for wireless sensor networks with mobile nodes using mobile robots. Despite the fact that outdoor localization is successfully performed by Global Positioning System (GPS) technology, indoor environments face several challenges due to multipath signal propagation, reflections from walls and objects, along with noise and interference. This results in the need for the development of new localization techniques. In this paper, Long-Range Wide-Area Network (LoRaWAN) technology is employed to address localization problems. A novel approach is proposed, based on the preliminary division of the room into sectors using a Received Signal Strength Indicator (RSSI) fingerprinting technique combined with machine learning (ML). Among various ML methods, the Gated Recurrent Unit (GRU) model reached the most accurate results, achieving localization accuracies of 94.54%, 91.02%, and 85.12% across three scenarios with a division into 256 sectors. Analysis of the cumulative error distribution function revealed the average localization error of 0.384 m, while the mean absolute error reached 0.246 m. These results demonstrate that the proposed sectorization method effectively mitigates the effects of noise and nonlinear signal propagation, ensuring precise localization of mobile nodes indoors.

Wireless communication technologies (WSN) are pivotal for the successful deployment of the Internet of Things (IoT). Among them, long-range (LoRa) and long-range wide-area network (LoRaWAN) technologies have been widely adopted due to their ability to provide long-distance communication, low energy consumption (EC), and cost-effectiveness. One of the critical issues in the implementation of wireless networks is the selection of optimal transmission parameters to minimize EC while maximizing the packet delivery ratio (PDR). This study introduces a reinforcement learning (RL) algorithm, Double Deep Q-Network with Prioritized Experience Replay (DDQN-PER), designed to optimize network transmission parameter selection, particularly the spreading factor (SF) and transmission power (TP). This research explores a variety of network scenarios, characterized by different device numbers and simulation times. The proposed approach demonstrates the best performance, achieving a 17.2% increase in the packet delivery ratio compared to the traditional Adaptive Data Rate (ADR) algorithm. The proposed DDQN-PER algorithm showed PDR improvement in the range of 6.2–8.11% compared to other existing RL and machine-learning-based works.

As information systems and technologies continue to evolve, there remains a noticeable gap in the efficiency and practicality of data processing algorithms, especially in the field of emotion recognition. This study explores several neural network models designed to classify emotions based on thermal images (thermograms). The dataset used for training included 1,642 images, some of which were generated through augmentation, with all images captured while participants viewed emotionally charged videos. The goal was to recognize six basic emotions: joy, sadness, fear, disgust, anger, and surprise. To identify the most effective architecture, the performance of five models were compared: a standard convolutional neural network (CNN), Quadruplet Network, U-Net, Inception, and SqueezeNet. Each model was trained on the same dataset under consistent conditions. Classification accuracy and validation loss were the main evaluation metrics. In addition, data augmentation and early stopping were applied to improve generalization and prevent overfitting. Among the tested architectures, the Inception model achieved the highest test accuracy of 97.5%, while the Quadruplet Network achieved 96.85% accuracy with a lower validation loss of 0.571, indicating stronger generalization. These results suggest that both models are well-suited for real-time emotion recognition using thermal imaging. The findings highlight the potential of combining infrared data with modern neural architectures to advance emotion detection systems beyond traditional RGB-based methods.

Повышение эффективности фотоэлектрических систем является важной задачей, особенно в условиях частичного затенения, которое снижает производительность солнечных панелей. Традиционные методы отслеживания точки максимальной мощности (ОТММ) часто сталкиваются с проблемой фиксации на локальных максимумах, что ограничивает их эффективность. Решение этой проблемы имеет критическое значение для увеличения энергетической отдачи солнечных установок, особенно в условиях переменной освещенности. В данной работе исследуется применение метаэвристического алгоритма «Оптимизация социальных пауков» (ОСП) для отслеживания ТММ в фотоэлектрических системах. Основная задача работы заключалась в нахождении глобальной точки максимальной мощности в условиях частичного затенения. Экспериментальные результаты показали, что алгоритм обеспечивает высокую точность отслеживания ТММ с эффективностью до 98,72 % при различных уровнях освещенности. Применение SEPIC-преобразователя позволило стабилизировать выходное напряжение и минимизировать пульсации, что улучшило общую производительность системы. Алгоритм продемонстрировал устойчивую работу при частичном затенении панелей, что особенно важно для реальных условий эксплуатации. Внедрение алгоритма может значительно повысить энергоэффективность и надежность фотоэлектрических установок, открывая перспективы для дальнейшего развития методов оптимизации в солнечной энергетике. Ключевые слова: солнечная энергетика, точка максимальной мощности, глобальная оптимизация, SEPIC-преобразователь, энергоэффективность.

Thermal imaging offers a non-invasive and robust approach to emotion recognition by capturing facial temperature patterns that correlate with psychophysiological states. This study investigates the application of deep neural networks to classify six basic human emotions – happiness, sadness, fear, disgust, anger, and surprise – using facial thermograms. A balanced dataset was collected under controlled experimental conditions, and four deep learning architectures were evaluated: Convolutional Neural Network (CNN), Fully Convolutional Network (FCN), EfficientNet, and MobileNet. The models were trained and tested on a curated set of preprocessed thermal facial images. Among the evaluated architectures, FCN achieved the highest classification accuracy of 90.04%. The results demonstrate that deep learning models, particularly FCNs, are well-suited for emotion recognition from thermal data, with potential applications in psychophysiological monitoring, healthcare, and real-time humancomputer interaction systems.