Signal Behavior Research Articles

Physical and Upconversion Photoluminescence Properties in Nd<sup>3+</sup>-Yb<sup>3+ </sup>Doped Lead Borate Glasses at 980 nm Excitation

Frequency upconversion (UC) photoluminescence (PL) in PbO-B2O3 glass codoped with trivalent ions of neodymium (Nd3+) and ytterbium (Yb3+) is prepared by melt quench method. X-ray diffraction (XRD) and differential scanning carorimetry (DSC) done for structural and thermal studies. Physical properties as density, oxygen packing density, lanthanide ion concentration etc. calculated. Photoluminescence of the sample gives the information of spectral lines, corresponding to the Nd3+ transitions 4G9/2 4I9/2 (at 500 nm), 4G7/24I9/2 (at 550 nm), [4G5/2; 2G7/2] 4I11/2 (at 595 nm) and 4G7/24I13/2 (at 660 nm). The dependence of the UC intensity with the Yb3+ concentration and the time behavior of the UC signal indicated the presence of two energy transfer (ET) pathways involving Nd3+-Yb3+ pairs and Yb3+-Nd3+-Yb3+ triads. Rate-equations for the population densities of the lanthanide energy levels were used to describe the dynamics of the UC emission and to determine the ET rates.

Effects of repetitive transcranial magnetic stimulation combined with cognitive training for improving response inhibition: A proof-of-concept, single-blind randomised controlled study

BackgroundImpaired response inhibition is a common characteristic of various psychiatric disorders. Cognitive training (CT) can improve cognitive function, but the benefits may be limited. Repetitive transcranial magnetic stimulation (rTMS) is a promising tool to enhance neuroplasticity, and thereby augment the effects of CT. We aimed to investigate the augmentation effects of rTMS on CT for response inhibition in healthy participants. MethodsSixty healthy participants were randomly assigned to two experimental groups: one with prolonged intermittent theta burst stimulation (iTBS) + CT and the other with sham iTBS + CT over four experimental sessions. Prolonged iTBS (1800 pulses) was used to stimulate the right inferior frontal cortex (rIFC) and pre-supplementary motor area (pre-SMA) in a counterbalanced order. Participants completed a Stop Signal training task following iTBS over one brain region, followed by the Go/No-Go training task after iTBS over the other brain region. The Stroop task with concomitant electroencephalography was conducted before and immediately after the intervention. ResultsThere were no significant differences between groups in behavioural outcomes on the Stop Signal task, Go/No-Go task, Stroop task or Behavior Rating Inventory of Executive Functioning for Adults. Similarly, analysis of event-related potentials (ERPs) from the Stroop task (N200 and N400) and exploratory cluster-based permutation analysis did not reveal any significant differences between groups. Subgroup analyses revealed that individuals with higher baseline impulsivity exhibited better learning effects in the active group. ConclusionsThis first proof of concept study did not find evidence that four sessions of active rTMS + CT could induce cognitive or neurophysiological effects on response inhibition in healthy participants. However, subgroup analyses suggests that rTMS combined with CT could be useful in improving response inhibition in individuals with high impulsivity. It is recommended that future proof of concept studies examine its potential in this clinical population.

Flexible 3D nanofiber-based SERS biosensor for detection of miRNA-223-3p in early Laryngeal Cancer diagnosis

MicroRNAs (miRNAs) are small non-coding RNAs (18–22 nucleotides) that regulate gene expression and are associated with various diseases, including Laryngeal Cancer (LCa), which has a high mortality rate due to late diagnosis. Traditional methods for miRNA detection present several drawbacks (time-consuming steps, high cost and high false positive rate). Early-stage diagnosis and selective detection of miRNAs remain challenging.This study proposes a 3D flexible biosensor that combines nanofibers (NFs), gold nanoparticles (AuNPs), and an inverse molecular sentinel (iMS) for enzyme-free, SERS-based detection of miRNA-223-3p, evaluated as a potential LCa biomarker. The electrospun flexible nanofibers decorated with AuNPs enhance Raman signal. Selective detection of miRNA-223-3p is achieved by immobilizing an iMS-DNA probe labeled with a Raman reporter (Cyanine 3) on the AuNPs. The iMS distinctive stem-and-loop structure undergoes a conformational change upon interaction with the miRNA-223-3p, producing an “on to off” SERS signal. The proposed sensor demonstrated a linear detection range from 10 to 250 fM, with a limit of detection (LOD) of 19.50 ± 0.05 fM. The sensor selectivity was confirmed by analyzing the SERS signal behaviour in the presence of both Non-complementary miRNA and miRNA with three mismatched base pairs. This easily fabricable sensor requires no amplification and offers key advantages, including sensitivity, flexibility, and cost-effectiveness.

A method for signal components identification in acoustic signal with non-Gaussian background noise using clustering of data in time-frequency domain

This paper presents a novel method for fault detection in vibration/acoustic signals contaminated with non-Gaussian noise, specifically addressing the challenge of random impulsive and wideband disturbances in industrial measurements. While damage detection in Gaussian noise environments is well understood, high-amplitude non-cyclic impulsive disturbances arising from random aspects of industrial processes, such as non-uniform operations and random impacts, pose significant analytical challenges.The proposed method analyzes the distribution densities of spectral vectors derived from spectrograms. It considers a simple additive model consisting of the signal of interest (SOI) and Gaussian and non-Gaussian noise. Using the density-based spatial clustering algorithm (DBSCAN), the method isolates distinct classes of spectral vectors from the spectrogram, effectively separating different signal behaviors and extracting fault-related information. The effectiveness of the proposed method was validated using an envelope spectrum-based indicator (ENVSI) and successfully demonstrated on real signals from an industrial machine with a faulty bearing.

MR‐zero meets FLASH – controlling the transient signal decay in gradient‐ and RF‐spoiled gradient echo sequences

AbstractPurposeThe complex signal decay during the transient FLASH MRI readout can lead to artifacts in magnitude and phase images. We show that target‐driven optimization of individual RF flip angles and phases can realize near‐ideal signal behavior and mitigate artifacts.MethodsThe differentiable end‐to‐end optimization framework MR‐zero is used to optimize RF trains of the FLASH sequence. We focus herein on minimizing deviations from the ideally spoiled signal by using a mono‐exponential Look–Locker target. We first obtain the transient FLASH signal decay substructure, and then minimize the deviation to the Look–Locker decay by optimizing the individual (i) flip angles, (ii) RF phases, and (iii) flip angles and RF phases. Comparison between measurement and simulation is performed using Pulseq in 1D and 2D.ResultsWe were able to reproduce the complex substructure of the transient FLASH signal decay. All three optimization objectives can bring the real FLASH signal closer to the ideal case, with best results when both flip angles and RF phases are adjusted jointly. This solution outperformed all tested conventional quadratic RF cyclings in terms of (i) matching the Look–Locker target signal, (ii) phase stability, (iii) point spread functions ideality, (iv) robustness against parameter changes, and (v) magnitude and phase image quality. Other target functions for the signal could as well be realized, yet their response is not as general as for the Look–Locker target and needs to be optimized for a specific context.ConclusionIndividual flip angle and RF phase optimization improves the transient signal decay of FLASH MRI sequences.

Performance analysis of cyanoacrylate bonded optical fiber 2 × 2 acoustic couplers for sensing applications

In this paper the behavior of a cyanoacrylate (CA) bonded optical fiber acoustic signal coupler will be studied in detail. A stainless-steel mold was used to create a 2×2 CA coupler that transfers guided waves between two optical fibers. Four FBGs placed at each port were used to assess the repeatability, signal behavior, and performance of the coupler produced. Signal propagation through the network was mapped to determine how the initial acoustic signal traveled and subdivided through the fiber network. To examine the repeatability of the signal coupler interaction, the experiments were performed multiple times. Finally, the acoustic energy transferred through the coupler’s paths was calculated to assess the performance of the 2×2 coupler. The coupler successfully and repeatably transferred an acoustic signal from one optical fiber to another; however, the performance associated with the current coupler design was limited by relatively large excess and return losses. Improvements to the CA coupler design are then proposed from the results.

Wind tunnel and flight testing of a lamb wave-based ice accretion sensor

Abstract Severe icing conditions are still a risk in aviation. Heavy ice accretion on aircraft can affect mass and aerodynamic performance in a way, that a catastrophic loss of control may occur. Detecting icing conditions and the extent of ice accretion is therefore essential to react instantly and to assess the severity. This eases the decision to either activate ice protection systems and continue the flight or to leave the icing conditions immediately. These functions can be provided by an icing sensor. Within the EU funded SENS4ICE project, several ice sensors have been developed. One of them is the so-called local ice layer detector (LILD). It uses lamb waves which are guided by the aircraft structure. If an ice accretion happens, the wave guide behavior is affected changing amplitude and lag time of the lamb wave pulses. This can be measured. If the sensor is mounted to a surface on the aircraft which is prone to icing, e.g. the leading edge of the wing, ice can be detected where it is relevant for the aerodynamic performance. Within the project, the sensor hard- and software have been developed to create and measure lamb wave pulses in a wide frequency range. To investigate the influence of ice accretion on the lamb waves, wind tunnel tests were undertaken. Finally, the sensor was investigated in a flight test. In this paper, an overview about the function of the sensor as well as the general signal behavior are given. The results of the wind tunnel test and some preliminary results from the flight tests will be shown. In both tests, the sensor was able to detect even very thin ice layers with minimum delay from the beginning of the ice accretion.

Modified REL-Based Piecewise Path Loss Modeling Approach for Shore-to-Ship Communication at 5.6 GHz

The need for reliable and uninterrupted communication systems in the marine environment has become critically important with increasing maritime activities, environmental monitoring, and the spread of autonomous systems. However, the complex structure of electromagnetic wave propagation in a sea environment limits the accuracy of the existing propagation models. Thus, the Modified Round Earth Loss (REL) model was first developed in this study to estimate the path loss more accurately in shore-to-ship communication. Subsequently, a piecewise modeling approach based on the principle of two-segment data modeling was proposed. In the Modified REL model, unlike the traditional REL model, the paths and gains of the direct and reflected waves were not considered equal in the calculations. Moreover, unlike in the classical approach, the receiver height was not taken as a fixed value; the estimated best receiver height value for each measurement was included in the calculations as a representation of the effect of roughness in the sea environment. Thus, the model is better adapted to various environmental conditions. In addition, the proposed piecewise model divides the propagation medium into two regions using a break point calculated by Fresnel zone theory. The Modified REL model was used for the first region and the log-distance model was used for the second region. This method allows for more accurate modeling of signal behaviors, especially at different distances. Experimental measurements and performance evaluations conducted using four different shore-to-ship communication scenarios show that the Modified REL model shows an average improvement of up to 3% in Root Mean Square Error (RMSE) values compared to the classical REL model. Additionally, the proposed piecewise model improves the fitting error of the Modified REL model, which models the data as a single whole, by an average of 22.25%. These findings emphasize the necessity of propagation models that are sensitive and adaptable to environmental changes for maritime communication.

Characterizing EEG signal dynamics in healthy, seizure-free, and seizure states using the chaos decision tree algorithm

Abstract Epilepsy is a multifaceted neurological condition marked by repetitive seizures that arise from irregular electrical activity in the brain. To understand this condition, a thorough examination of brain signals captured in different states is needed. In order to examine the dynamic behavior of brain signals in three different conditions: healthy, seizure-free, and seizure periods, this study uses the chaos decision tree algorithm. The findings show notable variations in these situations’ dynamics. Chaos is evident during seizure moments, showing extremely chaotic activity. The signals mostly exhibit stochastic behavior in the healthy condition, which is consistent with typical brain dynamics. It is noteworthy that an intermediate state exhibiting a blend of stochastic and chaotic signal dynamics is exhibited throughout the seizure-free time. Furthermore, the research shows that the frequency of chaotic signals rises with increasing proximity to the epileptogenic zone. These discoveries clarify the complex nature of epilepsy and offer insightful information about the dynamic properties of brain signals in various stages, aiding in improved understanding and potential diagnostic approaches.

Spatial‐Temporal Analysis of Inter Tropical Discontinuity Influence on Radio Signal Behavior in Nigeria

AbstractThis study investigates the influence of the inter tropical discontinuity (ITD) on radio signal behavior over Nigeria using ERA5 data from the Copernicus Climate Change Services (C3S). Monthly variations of refractivity gradient values and ITD movements were analyzed through spatial distribution and wavelet coherence. Results indicate that the ITD position significantly affects refractivity gradient values. Below the ITD, values range from −5 to −110 N‐units/km, while above the ITD, values are less than −110 N‐units/km, indicating predominant ducting conditions. The ITD shifts latitudinally from a peak at 20° in August to a low at 5° in December. Correlation coefficients between ITD position and refractivity gradient values in different climatic regions (Am, Aw, BSh, BWh) range from −0.920 to 0.844, emphasizing the significant influence of ITD on atmospheric conditions in these regions. Statistical analysis using the wavelet coherence method demonstrates a strong connection between ITD and refractivity gradient, with coherence values indicating synchronization at specific frequency‐time pairs.

Asymmetric measures of polar Chebyshev chaotic map for discrete/dimensional emotion recognition using PPG

Human emotion recognition is a challenge in both medical and non-medical fields. While there are several physiological signals available for detecting emotions, the Photoplethysmogram (PPG) signal has been disregarded, despite its non-invasive nature and ease of recording using common devices like smartphones. This study addresses the research question: How can we effectively analyze PPG signals to enhance emotion recognition? To tackle this question, we employed novel nonlinear measures based on the polar form of the Chebyshev chaotic map to analyze the dynamic behavior of PPG signals. What makes this research unique is the use of the polar form of the Chebyshev chaotic map for biosignal analysis, specifically in quantifying the map’s asymmetry using multiple indices. Our main goal is to improve our understanding of the nonlinear characteristics of PPG signals and gain new insights into their dynamic properties. While previous research has explored PPG-based emotion recognition, our study stands out by utilizing the Chebyshev chaotic map to characterize PPG signals. We evaluated the effectiveness of these features in discrete and dimensional emotion recognition tasks, using the Database for Emotion Analysis using Physiological signals (DEAP) as a benchmark. Our experimental results showed a maximum accuracy of 94.49% for discrete emotion recognition and 85.59% for dimensional emotion recognition. Experiments were also conducted on another public dataset to further validate the efficacy of the proposed approach. The significance of our study lies in providing new insights into the nonlinear characteristics of PPG signals and enhancing emotion recognition capabilities, thereby contributing to both the fields of emotional analysis and biomedical engineering.

CONTINUOUS SCORING OF AUTISM SPECTRUM DISORDER PATIENTS BY ANALYZING THEIR EEG SIGNALS

Clinical manifestations and standard psychological tests have been widely used to diagnose autism spectrum disorder (ASD) patients and evaluate their severity level. The gold-standard criterion to diagnose ASD patients is the childhood autism rating scale (CARS), which is a qualitative questionnaire that is filled out through a systematic interview while no physiological test/record is performed to determine this score. To make the diagnosis process quantitative, electroencephalography (EEG) signals have been repeatedly analyzed to differentiate healthy subjects from ASD patients. However, the precise relationship between the abnormal behavior of EEG signals and different ASD severity levels is not well investigated. Here, we use CARS to qualitatively determine the severity level of 14 autistic children, who voluntarily enrolled in our study. We recorded their EEG signals from 19 scalp channels when they were awake in the idle state and elicited three informative features including approximation entropy, multiscale entropy and sample entropy in successive time frames. Among the three measures of entropy, the last one exhibits the highest sensitivity, where its correlation coefficient (CC) exceeds 0.7, on the electrode positions T6 (CC [Formula: see text] 0.74), P4 (CC [Formula: see text] 0.76) and Cz (CC [Formula: see text] 0.75). Results of sample entropy in channels Cz-versus-Pz and P4-versus-FP2 show that a simple K-nearest neighbor classifier can provide 93% classification accuracy among patients with mild, moderate and severe ASD levels. Comparing the proposed method to the conventional ones, we also extracted power spectral density features from the channels but they failed to identify the ASD severity level with an acceptable accuracy.

Adaptive nonlinear filtering algorithms for removal of non-stationary noise in electronystagmographic signals

Non-stationary physiological noise poses significant difficulties due to its time-varying and previously unknown characteristics. When processing electronystagmographic signals, linear filtering can distort diagnostically significant rapid changes caused by saccadic eye movements. In such cases, nonlinear filters based on robust estimators are more appropriate, whereas linear filtering effectively reduces white noise in parts of a signal with linear behaviour. Therefore, an adaptive signal processing approach that varies in nonlinearity from highly nonlinear robust estimation to linear averaging and combines their benefits appears promising. We have proposed a low-complexity adaptive method for switching filter sets and relevant filter parameter settings based on the estimated noise level and local signal behaviour. This method does not require time for filter parameter modification and does not need prior information on the signal model and noise variance. Based on this method, we have designed adaptive nonlinear filtering algorithms to exploit the advantages of both the nonlinear robust and linear averaging estimators. We have evaluated the filtering quality statistically using the minimum mean square error criteria and the maximum signal-to-noise ratio for a model signal with different levels of additive Gaussian noise. The proposed real-time adaptive filtering algorithms demonstrate a significant efficiency improvement compared to the commonly used filters. The proposed adaptive myriad algorithm achieves the highest efficiency by adjusting a sample myriad linearity parameter depending on the local estimate of a signal scale and changing the sliding window length and the coefficient affecting the linearity parameter.

Assessing tumor microstructure with time-dependent diffusion imaging: Considerations and feasibility on clinical MRI and MRI-Linac.

Quantitative imaging biomarkers (QIBs) can characterize tumor heterogeneity and provide information for biological guidance in radiotherapy (RT). Time-dependent diffusion MRI (TDD-MRI) derived parameters are promising QIBs, as they describe tissue microstructure with more specificity than traditional diffusion-weighted MRI (DW-MRI). Specifically, TDD-MRI can provide information about both restricted diffusion and diffusional exchange, which are the two time-dependent effects affecting diffusion in tissue, and relevant in tumors. However, exhaustive modeling of both effects can require long acquisitions and complex model fitting. Furthermore, several introduced TDD-MRI measurements can require high gradient strengths and/or complex gradient waveforms that are possibly not available in RT settings. In this study, we investigated the feasibility of a simple analysis framework for the detection of restricted diffusion and diffusional exchange effects in the TDD-MRI signal. To promote the clinical applicability, we use standard gradient waveforms on a conventional 1.5 T MRI system with moderate gradient strength (Gmax=45 mT/m), and on a hybrid 1.5 T MRI-Linac system with low gradient strength (Gmax=15 mT/m). Restricted diffusion and diffusional exchange were simulated in geometries mimicking tumor microstructure to investigate the DW-MRI signal behavior and to determine optimal experimental parameters. TDD-MRI was implemented using pulsed field gradient spin echo with the optimized parameters on a conventional MRI system and a MRI-Linac. Experiments in green asparagus and 10 patients with brain lesions were performed to evaluate the time-dependent diffusion (TDD) contrast in the source DW-images. Simulations demonstrated how the TDD contrast was able to differentiate only dominating diffusional exchange in smaller cells from dominating restricted diffusion in larger cells. The maximal TDD contrast in simulations with typical cancer cell sizes and in asparagus measurements exceeded 5% on the conventional MRI but remained below 5% on the MRI-Linac. In particular, the simulated TDD contrast in typical cancer cell sizes (r=5-10µm) remained below or around 2% with the MRI-Linac gradient strength. In patients measured with the conventional MRI, we found sub-regions reflecting either dominating restricted diffusion or dominating diffusional exchange in and around brain lesions compared to the noisy appearing white matter. On the conventional MRI system, the TDD contrast maps showed consistent tumor sub-regions indicating different dominating TDD effects, potentially providing information on the spatial tumor heterogeneity. On the MRI-Linac, the available TDD contrast measured in asparagus showed the same trends as with the conventional MRI but remained close to typical measurement noise levels when simulated in common cancer cell sizes. On conventional MRI systems with moderate gradient strengths, the TDD contrast could potentially be used as a tool to identify which time-dependent effects to include when choosing a biophysical model for more specific tumor characterization.

Time Frequency Analysis Based Fault Detection in PV Array Using Scaling Basis Chirplet Transform

ABSTRACTPhotovoltaic (PV) arrays have gained significant attention in recent years due to their potential for sustainable energy generation. However, the reliable operation of PV arrays is crucial for optimal performance and long‐term durability. The early detection of faults in PV arrays is vital to prevent further damage, improve maintenance strategies, and ensure uninterrupted energy production. In this study, we propose a novel fault detection method based on Time Frequency Analysis (TFA) using the Scaling Basis Chirplet Transform (SBCT). In this proposed fault detection method, PV array signal is decomposed into a set of chirplets using the SBCT. The chirplets represent localized time‐frequency components that can capture the dynamic behavior of the PV array signal. To evaluate the effectiveness of the proposed method, extensive simulations and experiments are conducted using real‐world PV array data. The SBCT with combination of various machine learning algorithms is proposed to detect faults in PV array. SBCT in combination with Support Vector Machine, Decision Tree, Random Forest, and ANN classifiers are able to detect faults in PV array with 99%, 98.5%, 99.2%, and 99.5% accuracies in no shading condition and 88%, 85%, 89%, and 89.5% accuracies in severe shading condition. The proposed method achieves high accuracy and robustness in detecting various types of faults in PV arrays, even in the presence of noise and uncertainties. The proposed fault detection method using TFA based on the SBCT offers a promising solution for efficient and reliable fault detection in PV arrays. It enables early fault detection, facilitating timely maintenance and minimizing energy losses. The proposed approach can contribute to enhancing the overall performance, reliability, and lifespan of PV arrays, thereby advancing the adoption of renewable energy sources and promoting sustainable development.

Performance Evaluation of Radio Frequency Visualisation Tool for Wireless Communications: A UNIUYO Campus Case Study

This study was motivated by the challenge of evaluating radio frequency (RF) signal behaviour concerning transmitter and receiver positioning prior to deployment and during troubleshooting. This paper presented the development and testing of a MATLAB-based signal visualisation application to enhance RF signal analysis across various frequencies and terrains, specifically focusing on the University of Uyo (UNIUYO) permanent site and selected locations in Uyo. The application featured three distinct tabs with specialised functions. The first tab integrated campus radio broadcasts at 144.5 MHz and visualised signals for mobile communication standards (2G, 3G, and 4G). The second tab offered a computation interface for selected radio propagation parameters and a tool for converting geographic coordinates from degrees, minutes, and seconds (DMS) to decimal degrees (DD). The third tab was dedicated to 5G testing and deployment. To determine the signal travel extent for each transmitter location, the Longley-Rice and Freespace propagation models were used to represent worst-case and best-case scenarios, respectively. The results, illustrated graphically, demonstrated the application’s effectiveness and versatility in RF signal analysis and 5G deployment planning. The study explored two primary 5G deployment strategies: the standalone (SA) approach and the non-standalone (NSA) technique. The SA strategy employed mid-band frequency (3.5 GHz) point-to-multipoint stations within clusters. The application utilised up to 19 transmitters (one main transmitter and 18 repeaters) per cluster. Conversely, the NSA technique integrated 5G technology with the existing 4G infrastructure to enhance coverage. The findings offer valuable insights into optimising 5G deployment in terrains similar to those at the UNIUYO permanent site and Uyo in general.

A Stochastic Dynamic Operator Framework That Improves the Precision of Analysis and Prediction Relative to the Classical Spike-Triggered Average Method, Extending the Toolkit.

Here we test the stochastic dynamic operator (SDO) as a new framework for describing physiological signal dynamics relative to spiking or stimulus events. The SDO is a natural extension of existing spike-triggered average (STA) or stimulus-triggered average techniques currently used in neural analysis. It extends the classic STA to cover state-dependent and probabilistic responses where STA may fail. In simulated data, SDO methods were more sensitive and specific than the STA for identifying state-dependent relationships. We have tested SDO analysis for interactions between electrophysiological recordings of spinal interneurons, single motor units, and aggregate muscle electromyograms (EMG) of major muscles in the spinal frog hindlimb. When predicting target signal behavior relative to spiking events, the SDO framework outperformed or matched classical spike-triggered averaging methods. SDO analysis permits more complicated spike-signal relationships to be captured, analyzed, and interpreted visually and intuitively. SDO methods can be applied at different scales of interest where spike-triggered averaging methods are currently employed, and beyond, from single neurons to gross motor behaviors. SDOs may be readily generated and analyzed using the provided SDO Analysis Toolkit We anticipate this method will be broadly useful for describing dynamical signal behavior and uncovering state-dependent relationships of stochastic signals relative to discrete event times.

Research on visible light communication channel model in underground mines

Abstract In order to effectively model the Visible Light Communication (VLC) channel in underground mines, this paper delves into the challenges of the underground environment and develops a mathematical model by analyzing the propagation characteristics of visible light. The objective is to deepen our understanding of light signal behavior in this unique setting. Most existing research focuses on visible light channel modeling in indoor environments, with limited studies on underground mines. Our model takes into account the irregular surfaces of mine walls, as well as the effects of factors such as high dust concentration, extreme temperatures and pressures, light absorption by mine walls, and obstruction caused by large machinery on the propagation of visible light signals. This paper introduces refined optical propagation models that incorporate both scattering and light absorption considerations, resulting in a maximum received power of -46.12 dBm, which is significantly lower than that in indoor environments. These models enhance our ability to analyze and improve channel performance, thereby optimizing communication efficiency.

Denoising methods for removal of Baseline Wander, AWGN and power line interface noises in ECG signal: a comparative analysis

ABSTRACT The electrocardiogram (ECG) is one of the most significant signals in the field of biomedicine for the diagnosis of cardiac arrhythmia (CA). Due to non-stationary behaviour of the ECG signal, it is often interrupted with various noises, which cause difficulty in diagnosis. To solve the issues, the image denoising process helps the physicians make the perfect decision. In this paper, Denoising Techniques using Empirical Mode Decomposition (EMD) and Wavelet Transform for the removal of various noises like Additive White Gaussian Noise (AWGN), Baseline Wander (BW) noise, Power Line Interface (PLI) noise at various concentrations. The proposed method has quality and good efficiency in Peak Signal-to-Noise Ratio (PSNR), Root Mean Square Error (RSME), Signal-to-Noise Ratio (SNR), Cross-Correlation (CC) and Percent Root Square Difference (PRD).

Analysis of interference effect in VL‐NOMA network considering signal power parameters performance

AbstractThis study analyses the interference effect in a visible light‐non‐orthogonal multiple access (VL‐NOMA) network that considers the signal power parameters performance for near and far users. The light‐emitting diode (LED) as a carrier transmits signals, and we investigate the interference effect. The interference effect challenge is a result of unaligned signal power parameters, thereby producing noise or echo during the signal transmission. The signal power parameters are successfully aligned, and NOMA techniques are deployed, which improves the signal performance in terms of bit‐error rate (BER), achieved data rate, and signal‐to‐interference plus noise ratio (SINR). Furthermore, the deployed NOMA techniques, such as power allocations (PA) to assign the signals appropriately, then superposition coding (SC) encodes the entire signal, and successive interference cancellation (SIC) cancels the interference within the signals. The signal behavior of the aligned and the unaligned signal power parameters performance are used to investigate the interference effect. We observed that unaligned signal power parameters reduce the signal performance of achieved data rate, BER, and SINR. Further, the aligned signal power parameter with NOMA techniques improves the signal performance. Moreover, in the aligned signal power scenario of NOMA, the near user performed better than the far user.