Radio Waves Research Articles

A Machine Learning Approach for Path Loss Prediction Using Combination of Regression and Classification Models.

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 MHz, 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.

An Analysis of Routing Protocol in Mobile Adhoc Networks and its Applications

Each hub in a mobile ad-hoc network (MANET) sends parcels bound for different hubs in the network by means of remote (radio transmission) transmission on a wilful premise, making it a self-beginning, powerful network made up of mobile hubs. Multi-bounce transferring is the essential inspiration for the advancement of ad hoc networking. Remote Ad hoc networks, otherwise called foundation less networks, can be set up rapidly and effectively using radio waves as the network's transmission channel. There is no principal server or referee in an ad hoc network. Every hub in a MANET is fit for playing out its own networking errands, for example, routing and bundle sending, in a decentralized and independent style. Uses of routing protocols like Ad hoc On-request Distance Routing Protocol (AODV), Dynamic Source Routing (DSR), Transiently Requested Routing Calculation (TORA), and Enhanced Connection State Routing (OLSR) are fascinating in light of the fact that routing is the focal issue in MANETs. These routing protocols are simulated using OPNET, and their effectiveness is investigated using various metrics. With the use of metrics, the most efficient channel for data transfer can be determined. The results demonstrate the viability of AODV and TORA for topology-changing in large-scale networks using a variety of criteria.

Galactic Stellar Black Hole Binaries: Spin Effects on Jet Emissions of High-Energy Gamma-Rays

In the last few decades, galactic stellar black hole X-ray binary systems (BHXRBs) have aroused intense observational and theoretical research efforts specifically focusing on their multi-messenger emissions (radio waves, X-rays, γ-rays, neutrinos, etc.). In this work, we investigate jet emissions of high-energy neutrinos and gamma-rays created through several hadronic and leptonic processes taking place within the jets. We pay special attention to the effect of the black hole’s spin (Kerr black holes) on the differential fluxes of photons originating from synchrotron emission and inverse Compton scattering and specifically on their absorption due to the accretion disk’s black-body radiation. The black hole’s spin (dimensionless spin parameter a*) enters into the calculations through the radius of the innermost circular orbit around the black hole, the RISCO parameter, assumed to be the inner radius of the accretion disk, which determines its optical depth τdisk. In our results, the differential photon fluxes after the absorption effect are depicted as a function of the photon energy in the range 1GeV ≤E≤103GeV. It is worth noting that when the black holes’ spin (α*) increases, the differential photon flux becomes significantly lower.

Full-duplex two-way no-relay cross-media communication method based on acoustic wave conversion

Abstract Due to the difference in acoustic impedance between water and air, the water interface becomes a natural barrier. Underwater sound waves will bounce when they propagate to the water surface. Electromagnetic waves and light waves in the air will rapidly attenuate after passing through the water surface and going underwater, making it difficult to complete air-water cross-media information transmission. In this article, we first analyze the theoretical characteristics and vibration model of the water surface after medium collision, and develop a new full-duplex communication method across the air-ocean medium, which does not rely on relay equipment and only uses three transmission media in the air. Propagate signals in the sea channel physical field to achieve two-way communication, using the mutual conversion between laser, acoustic, and radio frequency signals to fill the gap in cross-media communication technology that can simultaneously achieve two-way, full-duplex and non-relay. It has been verified through experimental installations carried out in laboratory water tanks and large comprehensive anechoic pools that it is able to achieve full-duplex communication links across the air-sea surface, indicating that the combination of laser acousticization and radio frequency-acoustic wave conversion can be used across medium full-duplex wireless communication provides a feasible method for communication that breaks through the water interface.

Review article about Magnetic Resonance Imaging (MRI)

Magnetic resonance imaging (MRI) is a medical imaging technique that uses a magnetic field and computer-generated radio waves to create detailed images of the organs and tissues in your body. MRI is a noninvasive way for a medical professional to examine the organs, tissues and skeletal system. It produces high-resolution images of the inside of the body that help diagnose a variety of conditions. Because MRI doesn’t use X-rays or other radiation, it’s the imaging test of choice when people will need frequent imaging for diagnosis or treatment monitoring, especially of their brain.

Visualization Technology for Radio Wave Propagation Characteristics in Sub-Terahertz Band

Visualization Technology for Radio Wave Propagation Characteristics in Sub-Terahertz Band

Wireless transmission method of electromagnetic wave of cross screw signal

Abstract Based on the near-bit azimuthing gamma measuring instrument developed by Changqing Drilling Company, this paper uses comsol multiphysics(COMSOL) numerical simulation software to build the electromagnetic wave wireless transmission model of cross-bolt signal while drilling. By using finite element method and a large amount of calculation and analysis, the influence law of source distance, drill wire conductivity, borehole and mud conductivity and other parameters on the response characteristics is obtained. The simulation modeling method presented in this paper and the conclusions obtained have important guiding significance for further optimizing and upgrading the near-bit wireless short-pass device and better understanding and mastering the signal transmission law of this kind of instrument.

MEMPSEP‐III. A Machine Learning‐Oriented Multivariate Data Set for Forecasting the Occurrence and Properties of Solar Energetic Particle Events Using a Multivariate Ensemble Approach

AbstractWe introduce a new multivariate data set that utilizes multiple spacecraft collecting in‐situ and remote sensing heliospheric measurements shown to be linked to physical processes responsible for generating solar energetic particles (SEPs). Using the Geostationary Operational Environmental Satellites (GOES) flare event list from Solar Cycle (SC) 23 and part of SC 24 (1998–2013), we identify 252 solar events (>C‐class flares) that produce SEPs and 17,542 events that do not. For each identified event, we acquire the local plasma properties at 1 au, such as energetic proton and electron data, upstream solar wind conditions, and the interplanetary magnetic field vector quantities using various instruments onboard GOES and the Advanced Composition Explorer spacecraft. We also collect remote sensing data from instruments onboard the Solar Dynamic Observatory, Solar and Heliospheric Observatory, and the Wind solar radio instrument WAVES. The data set is designed to allow for variations of the inputs and feature sets for machine learning (ML) in heliophysics and has a specific purpose for forecasting the occurrence of SEP events and their subsequent properties. This paper describes a data set created from multiple publicly available observation sources that is validated, cleaned, and carefully curated for our ML pipeline. The data set has been used to drive the newly‐developed Multivariate Ensemble of Models for Probabilistic Forecast of SEPs (MEMPSEP; see MEMPSEP‐I (Chatterjee et al., 2024, https://doi.org/10.1029/2023SW003568) and MEMPSEP‐II (Dayeh et al., 2024, https://doi.org/10.1029/2023SW003697) for accompanying papers).

Integration of dual band radio waves and ensemble-based approach for rice moisture content determination and localisation

Maintaining optimal moisture content in grain storage is critical to ensuring adequate supply throughout the year, but it presents a significant challenge. Current moisture measurement methods often necessitate sophisticated and costly equipment. This paper introduces an approach employing real-time rice moisture content determination and detection of spoilage (specifically wet spots) within a storage facility achieved through the utilisation of radio waves operating at 2.4 GHz and 868 MHz, along with an ensemble-based machine learning algorithm. Experimental samples spanning from 12% to 30% moisture levels were collected, then subjected to pre-processing, and subsequently employed to train the Ensemble-based Rice Moisture Content and Localisation (eRMCL) algorithm. The eRMCL produced an effective prediction of both rice moisture content and the localisation of wet spots within the grain storage unit. The results show that compared to support vector machine, random forest, and machine learning methods, the eRMCL algorithm had the best performance metrics, with an accuracy of 94.8% in predicting the moisture content and location of spoilage in storage. The measurement of moisture content and the identification of wet spots in rice storage using the dual frequency wave approach were found to be more accurate than with a single frequency band. Thus, the dual frequency band is a novel method for the determination of the moisture content of stored rice and the localisation of the spoilage area.

The CornerGuard: Seeing around Corners to Prevent Broadside Collisions

Nearly 3700 people are killed in broadside collisions in the U.S. every year. To reduce broadside collisions, we created and tested the CornerGuard, a prototype system that senses around a corner to alert a car driver of an impending collision with a pedestrian or automobile that is not in the line of sight (LOS). The CornerGuard leverages a microwave-transceiving radar sensor mounted on the car and a curved radio wave reflector installed at the corner to sense around the corner and detect a broadside collision threat. The car’s speed is constantly read by an onboard diagnostics (OBD) system to allow the sensor to differentiate between static objects and objects approaching around the corner. Field testing demonstrated that the CornerGuard can effectively and consistently detect threats at a consistent range without blind spots under broad weather conditions. Our proof of concept study shows that the CornerGuard can be enhanced to be readily integrated into automobile construction and street infrastructure.

Whistler echo trains triggered by energetic winter lightning

Lightning generated electromagnetic impulses propagating in the magnetospheric plasma disperse into whistlers – several seconds long radio wave signals with decreasing frequency. Sometimes, multiple reflections form long echo trains containing many whistlers with increasing dispersion. On January 3, 2017, two necessary prerequisites – a pronounced lightning activity and a magnetospheric plasma duct – allowed for observations of a large number of whistler echo trains by the high-latitude station in Kannuslehto, Finland. Our investigation reveals that the duct existed for nearly eight hours. We show that causative lightning sferics arrived to the duct entry from three different winter thunderstorms: a small storm at the Norwegian coast, which produced energetic lightning capable to trigger echo trains in 50% of cases, and two large storms at unexpectedly distant locations in the Mediterranean region. Our results show that intense thunderstorms can repetitively feed electromagnetic energy into a magnetospheric duct and form whistler echo trains after subionospheric propagation over distances as large as 4000 km.

What Radio Waves Tell Us about Sleep!

The ability to assess sleep at home, capture sleep stages, and detect the occurrence of apnea (without on-body sensors) simply by analyzing the radio waves bouncing off people's bodies while they sleep is quite powerful. Such a capability would allow for longitudinal data collection in patients' homes, informing our understanding of sleep and its interaction with various diseases and their therapeutic responses, both in clinical trials and routine care. In this article, we develop an advanced machine learning algorithm for passively monitoring sleep and nocturnal breathing from radio waves reflected off people while asleep. Validation results in comparison with the gold standard (i.e., polysomnography) (n=880) demonstrate that the model captures the sleep hypnogram (with an accuracy of 80.5% for 30-second epochs categorized into Wake, Light Sleep, Deep Sleep, or REM), detects sleep apnea (AUROC = 0.89), and measures the patient's Apnea-Hypopnea Index (ICC=0.90; 95% CI = [0.88, 0.91]). Notably, the model exhibits equitable performance across race, sex, and age. Moreover, the model uncovers informative interactions between sleep stages and a range of diseases including neurological, psychiatric, cardiovascular, and immunological disorders. These findings not only hold promise for clinical practice and interventional trials but also underscore the significance of sleep as a fundamental component in understanding and managing various diseases.

Investigating Different Interpolation Methods for High-Accuracy VTEC Analysis in Ionospheric Research

The dynamic structure of the ionosphere and its changes play an important role in comprehending the natural cycle by linking earth sciences and space sciences. Ionosphere research includes a variety of fields like meteorology, radio wave reflection from the atmosphere, atmospheric anomaly detection, the impact on GNSS (Global Navigation Satellite Systems) signals, the exploration of earthquake precursors, and the formation of the northern lights. To gain further insight into this layer and to monitor variations in the total electron content (TEC), ionospheric maps are created using a variety of data sources, including satellite sensors, GNSS data, and ionosonde data. In these maps, data deficiencies are addressed by using interpolation methods. The objective of this study was to obtain high-accuracy VTEC (Vertical Total Electron Content) information to analyze TEC anomalies as precursors to earthquakes. We propose an innovative approach: employing alternative mathematical surfaces for VTEC calculations, leading to enhanced change analytical interpretation for anomaly detections. Within the scope of the application, the second-degree polynomial method, kriging (point and block model), the radial basis multiquadric, and the thin plate spline (TPS) methods were implemented as interpolation methods. During a 49-day period, the TEC values were computed at three different IGS stations, generating 1176 hourly grids for each interpolation model. As reference data, the ionospheric maps produced by the CODE (Center for Orbit Determination in Europe) Analysis Center were used. This study’s findings showed that, based on statistical values, the TPS model offered more accurate results than other methods. Additionally, it has been observed that the peak values in TEC calculations based on polynomial surfaces are eliminated in TPSs.

Digital technologies for signal radio vision and radio monitoring

Objectives. Radiophysical processes involving the electrodynamic formation of signal radio images diffusely scattered by the signature of small-sized objects or induced by the near field of radio devices are relevant for identifying radiogenomic (cumulant) features of objects in the microwave range in the development of neuroimaging ultra-short pulse (USP) signal radio vision systems, telemonitoring, and near-radio detection. The paper sets out to develop methods and algorithms for vector analysis of radio wave deformation of nonstationary fields forming a signal radio image based on radiophysical and topological characteristics of small-sized objects; to develop software and hardware for registration and neural network recognition of signal radio images, including methods for the synthesis and extraction of signal radiogenomes using digital twins of objects obtained through vector electrodynamic modeling; and to analyze signal radio images induced by elements of printed topology of electronic devices.Methods. The study is based on statistical radiophysics methods, time-frequency approaches for wavelet transformation of USP radio images, numerical electrodynamic methods for creating digital twins of small-sized objects, as well as neural network authentication algorithms based on the cumulant theory of pole-genetic and resonant physically unclonable functions used in identifying signal radio images.Results. The results of fundamental research on electrodynamic effects of vector-wave deformation of nonstationary fields of sub-nanosecond configuration are presented as a means of identifying and authenticating signal radio images. Neural network techniques for cumulant formation of radio genomes of signal radio images are proposed on the basis of pole-genetic and resonant functions.Conclusions. A radiogenome, representing the unique authenticator of a radio image, is shown to be formed on the basis of physically unclonable functions determined by the structure and set of radiophysical parameters of the image. Cumulant features of signal radio images identified on the basis of pole-genetic and physically unclonable resonant functions of small-sized objects are revealed.

Interplanetary Type IV Solar Radio Bursts: A Comprehensive Catalog and Statistical Results

Decameter hectometric (DH; 1–14 MHz) type IV radio bursts are produced by flare-accelerated electrons trapped in postflare loops or the moving magnetic structures associated with the coronal mass ejections (CMEs). From a space weather perspective, it is important to systematically compile these bursts, explore their spectrotemporal characteristics, and study the associated CMEs. We present a comprehensive catalog of DH type IV bursts observed by the Radio and Plasma Wave Investigation instruments on board the Wind and Solar TErrestrial RElations Observatory spacecraft covering the period of white-light CME observations by the Large Angle and Spectrometric Coronagraph on board the Solar and Heliospheric Observatory mission between 1996 November and 2023 May. The catalog has 139 bursts, of which 73% are associated with a fast (>900 km s−1) and wide (>60°) CME, with a mean CME speed of 1301 km s−1. All DH type IV bursts are white-light CME-associated, with 78% of the events associated with halo CMEs. The CME source latitudes are within ±45°. Seventy-seven events had multiple-vantage-point observations from different spacecraft, letting us explore the impact of the line of sight on the dynamic spectra. For 48 of the 77 events, there were good data from at least two spacecraft. We find that, unless occulted by nearby plasma structures, a type IV burst is best viewed when observed within a ±60° line of sight. Also, bursts with a duration above 120 minutes have source longitudes within ±60°. Our inferences confirm the inherent directivity in the type IV emission. Additionally, the catalog forms a Sun-as-a-star DH type IV burst database.

Stellar Flares, Superflares, and Coronal Mass Ejections—Entering the Big Data Era

Flares, sometimes accompanied by coronal mass ejections (CMEs), are the result of sudden changes in the magnetic field of stars with high energy release through magnetic reconnection, which can be observed across a wide range of the electromagnetic spectrum from radio waves to the optical range to X-rays. In our observational review, we attempt to collect some fundamental new results, which can largely be linked to the Big Data era that has arrived due to the expansion of space photometric observations over the last two decades. We list the different types of stars showing flare activity and their observation strategies and discuss how their main stellar properties relate to the characteristics of the flares (or even CMEs) they emit. Our goal is to focus, without claiming to be complete, on those results that may, in one way or another, challenge the “standard” flare model based on the solar paradigm.

Use of wireless technologies in IoT projects

When it comes to creating projects based on the use of the Internet of Things (IoT), wireless sensor networks are often used. Edge computing in IoT technology reduces system response delays to sensor output signals and increases the network throughput. At the same time, a short-range sensor network can work locally without access to the Internet, while long-range networks, as a rule, require access to the Internet and use both edge and cloud computing. The paper shows the features of the application of both peripheral and cloud computing in the practical implementation of IoT projects with various methods of wireless data transmission. Specific examples show the possibility of data transmission over a short distance using ESP-NOW technology, nRF24L01 radio modules and creating a local Wi-Fi access point. The range of sensor data transmission between microcontrollers is practically determined for each proposed option. The calculation of the range of the LoRa radio line is carried out for the actual sensitivity values of the receiver. The Okamura-Hata radio wave propagation model is proposed to estimate the LoRa radio line’s total loss. The operational range of LoRa modules with different types of signal modulation is practically determined. The essence of edge computing combined with digital and analogue sensors is shown. Peculiarities of peripheral computing during the implementation of IoT projects for remote control of blood saturation of patients using wireless technologies are considered. An example of data transmission of the air quality control system through a gateway based on an ESP8266 microcontroller with a graphical display of measurements in the IoT cloud service ThingSpeak and Blynk was provided.

Ionospheric contributions to the excess power in high-redshift 21-cm power-spectrum observations with LOFAR

ABSTRACT The turbulent ionosphere causes phase shifts to incoming radio waves on a broad range of temporal and spatial scales. When an interferometer is not sufficiently calibrated for the direction-dependent ionospheric effects, the time-varying phase shifts can cause the signal to decorrelate. The ionosphere’s influence over various spatiotemporal scales introduces a baseline-dependent effect on the interferometric array. We study the impact of baseline-dependent decorrelation on high-redshift observations with the Low Frequency Array (LOFAR). Data sets with a range of ionospheric corruptions are simulated using a thin-screen ionosphere model, and calibrated using the state-of-the-art LOFAR epoch of reionization pipeline. For the first time ever, we show the ionospheric impact on various stages of the calibration process including an analysis of the transfer of gain errors from longer to shorter baselines using realistic end-to-end simulations. We find that direction-dependent calibration for source subtraction leaves excess power of up to two orders of magnitude above the thermal noise at the largest spectral scales in the cylindrically averaged autopower spectrum under normal ionospheric conditions. However, we demonstrate that this excess power can be removed through Gaussian process regression, leaving no excess power above the 10 per cent level for a $5~$ km diffractive scale. We conclude that ionospheric errors, in the absence of interactions with other aggravating effects, do not constitute a dominant component in the excess power observed in LOFAR epoch of reionization observations of the North Celestial Pole. Future work should therefore focus on less spectrally smooth effects, such as beam modelling errors.

Demonstration of symbol timing synchronization for rolling shutter-based optical camera communication

Optical camera communication (OCC) provides license-free communication channels without radio waves. The rolling shutter mechanism is expected to enhance the capacity of wireless links in OCC. However, signal erasure occurs due to waiting times between frames in consecutive signal transmission. To address this issue, we propose a symbol timing synchronization for OCC using a rolling shutter camera. The receiver camera synchronizes to the transmitter with additional waiting time which is computed based on the defined preamble signals. We analyzed the performance to clarify the theoretical limit. The feasibility of the proposed idea was confirmed with experimental results.

Characterisation Free Space Path Loss: Sub-6ghz and Millimetre Wave Frequency

There is a free-space route loss in free-space propagation, which is the propagation path with no obstacles between the transmitter and the receiver. This is characterized as the radio wave signal’s loss during free space transit. In order to build communication systems that can function as efficiently as possible despite potential problems, it is imperative to determine the path loss. Path loss has also been utilized in wireless survey instruments and radio communications to determine the antennas’ signal strength. Given the growing significance of wireless devices, including software and survey instruments, it is now beneficial to comprehend the idea of radio path loss in its entirety. In order to gain a comprehensive understanding of the free space propagation path loss and the factors influencing it, the main objective of this paper is to simulate the phenomenon. MATLAB software is utilized in this process to generate graphs that provide a clear and easy-to-understand representation of the path loss. Four frequencies were chosen two from the sub-6 frequency range and another two from the milliwave frequency range, Results showed a trend of an increasing free space path loss with increase in distance and frequencies with a greater loss at milliwave, but loss can be mitigated with antenna gain and following other recommendations.