One of the key parameters in radio link planning is the propagation path loss. Most of the existing methods for its prediction are not characterized by a good balance between accuracy, generality, and low computational complexity. To address this problem, a machine learning approach for path loss prediction is presented in this study. The novelty is the proposal of a compound model, which consists of two regression models and one classifier. The first regression model is adequate when a line-of-sight scenario is fulfilled in radio wave propagation, whereas the second one is appropriate for non-line-of-sight conditions. The classification model is intended to provide a probabilistic output, through which the outputs of the regression models are combined. The number of used input parameters is only five. They are related to the distance, the antenna heights, and the statistics of the terrain profile and line-of-sight obstacles. The proposed approach allows creation of a generalized model that is valid for various types of areas and terrains, different antenna heights, and line-of-sight and non line-of-sight propagation conditions. An experimental dataset is provided by measurements for a variety of relief types (flat, hilly, mountain, and foothill) and for rural, urban, and suburban areas. The experimental results show an excellent performances in terms of a root mean square error of a prediction as low as $7.3$ dB and a coefficient of determination as high as 0.702. Although the study covers only one operating frequency of 433 $\mathrm{M}$$\mathrm{Hz}$, the proposed model can be trained and applied for any frequency in the decimeter wavelength range. The main reason for the choice of such an operating frequency is because it falls within the range in which many wireless systems of different types are operating. These include Internet of Things (IoT), machine-to-machine (M2M) mesh radio networks, power efficient communication over long distances such as Low-Power Wide-Area Network (LPWAN)—LoRa, etc.

Face and ECG Based Multi-Modal Biometric Authentication | InTechOpen, Published on: 2011-08-09. Authors: Ognian Boumbarov, Yuliyan Velchev, Krasimir Tonchev, et

This work presents the development and testing of an experimental web-based SDR (software-defined radio) monitoring system for indirect solar activity detection, which has the ability to estimate and potentially predict various events in space and on earth, including solar flares, coronal mass ejections, and geomagnetic storms. The proposed system can be used to investigate the effect of solar activity on the propagation of very-low-frequency (VLF) signals. The advantages and benefits of the given approach are as follows: increasing measurement accuracy and eventual solar activity identification by combining measurements from multiple spatially distributed SDRs. The verification process involves carrying out several experiments comparing data from the GOES satellite system and the Dunksin SuperSID system with information received by the SDR monitoring system. Then, utilizing Pearson correlation coefficients, the measured data from the SDRs, along with those from the GOES satellite system and the Dunsing monitoring station, are investigated. At the time of a solar flare, the correlation value is above 90% for most of the stations used. Combining the signal-to-noise ratio via summation also shows an improvement in the results, with a correlation above 98%.

In this paper we present a hybrid convolutional neural network–hidden Markov model framework for detecting solar flare events of intensity greater than or equal to M1.0 from very low frequency signals via their induced sudden ionospheric disturbances. The convolutional neural network processes fixed-length windows of raw very low frequency signals and their temporal derivatives to produce probabilistic flare estimates, which serve as emission probabilities for a two-state hidden Markov model. Viterbi decoding enforces temporal consistency, suppressing spurious fluctuations and yielding physically plausible event sequences. The approach is specifically designed to detect the onset-to-peak interval of flare events and, with further development, could operate in real time for early flare warning. The model was trained and evaluated on very low frequency data from the DHO38 transmitter in Germany to a receiver near Birr, Ireland. Sample-level evaluation achieved a balanced accuracy of 0.819 and a Matthews correlation coefficient of 0.529, while event-level detection reached a peak F1-score of 0.558 for moderate-to-strong flares of intensity greater than or equal to C6.0. These results demonstrate automated, physically consistent detection of solar flares based on sudden ionospheric disturbances, indicating the potential of the proposed approach, when combined across multiple receivers, to act as a low-cost complement to satellite-based monitoring.

The collection of solar radio burst spectrograms through the spatial distribution of survey sites reveals heterogeneity and differences in frequency sampling, temporal resolution, signal intensity, and local observing conditions. Because of this heterogeneity, it is difficult to incorporate observations of diverse stations directly into a single dataset, which restricts the generalizability of machine learning models that were trained using records from isolated stations. Thus, it is well established that for reliable automated analysis of solar radio bursts, a standard representation of spectrograms is necessary. This paper proposes a framework for harmonizing heterogeneous multi-site spectrograms of solar radio bursts, which transforms them into a unified representation capable of accommodating station-specific observational configurations suitable for machine learning applications. The proposed methodology combines adaptive frequency interval selection, frequency sampling consistency checking, logarithmic intensity transformation with baseline correction, time–frequency harmonization, and explicit handling of missing frequency channels using binary validity masks. To overcome the heterogeneous nature of the observations taken from different instruments and stations, nearest-neighbor interpolation in both time and frequency domains is applied. The unified frequency grid is constructed considering the statistics of the frequency channels used in spectrometers that form the e-CALLISTO network, a space weather instrument array. Two one-dimensional convolutional autoencoders with two latent space dimensions (32 and 64) are proposed and trained on more than 17,000 spectrograms to evaluate the quality of latent representations of the harmonized data. The best performance was achieved by the autoencoder with simpler architecture and with 64-dimensional latent space, reaching root-mean-square error of $0.88$$\mathrm{dB}$ and a mean absolute error of $0.61$$\mathrm{dB}$. The experimental results show that models with 64-dimensional latent space outperform their 32-dimensional counterparts, while increased architectural complexity provided no substantial improvement. The proposed methodology provides a standardized basis for constructing harmonized datasets suitable for a wide range of subsequent machine learning applications in solar radio astronomy, including automated burst detection, classification, representation learning, anomaly detection, and predictive modeling.